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"userDefinedMetadata" : { + + }, + "method" : "predict" + } +] \ No newline at end of file diff --git a/qwen3_tts/speech_decoder/12hz-0.6b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/model.mil b/qwen3_tts/speech_decoder/12hz-0.6b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..4b4d5825c720e3ade79ddd7427c79a8b24ae9ac3 --- /dev/null +++ b/qwen3_tts/speech_decoder/12hz-0.6b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/model.mil @@ -0,0 +1,5458 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})] +{ + func latency(tensor audio_codes, tensor cache_length, tensor hidden_context, tensor hidden_context_mask, tensor key_cache, tensor key_padding_mask, tensor kv_cache_update_mask, tensor pre_conv_context, tensor value_cache) { + tensor codes_1_begin_0 = const()[name = string("codes_1_begin_0"), val = tensor([0, 0, 0])]; + tensor codes_1_end_0 = const()[name = string("codes_1_end_0"), val = tensor([1, 1, 1])]; + tensor codes_1_end_mask_0 = const()[name = string("codes_1_end_mask_0"), val = tensor([true, false, true])]; + tensor codes_1 = slice_by_index(begin = codes_1_begin_0, end = codes_1_end_0, end_mask = codes_1_end_mask_0, x = audio_codes)[name = string("codes_1")]; + tensor var_44 = const()[name = string("op_44"), val = tensor([1, 0, 2])]; + tensor input_1_begin_0 = const()[name = string("input_1_begin_0"), val = tensor([0, 0, 0])]; + tensor input_1_end_0 = const()[name = string("input_1_end_0"), val = tensor([1, 1, 1])]; + tensor input_1_end_mask_0 = const()[name = string("input_1_end_mask_0"), val = tensor([false, true, true])]; + tensor input_1_squeeze_mask_0 = const()[name = string("input_1_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor codes_3 = transpose(perm = var_44, x = codes_1)[name = string("transpose_27")]; + tensor input_1 = slice_by_index(begin = input_1_begin_0, end = input_1_end_0, end_mask = input_1_end_mask_0, squeeze_mask = input_1_squeeze_mask_0, x = codes_3)[name = string("input_1")]; + int32 quantized_1_axis_0 = const()[name = string("quantized_1_axis_0"), val = int32(0)]; + int32 quantized_1_batch_dims_0 = const()[name = string("quantized_1_batch_dims_0"), val = int32(0)]; + bool quantized_1_validate_indices_0 = const()[name = string("quantized_1_validate_indices_0"), val = bool(false)]; + tensor weight_1_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("weight_1_to_fp16_palettized")]; + string input_1_to_uint16_dtype_0 = const()[name = string("input_1_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_1_to_uint16 = cast(dtype = input_1_to_uint16_dtype_0, x = input_1)[name = string("cast_16")]; + tensor quantized_1_cast_fp16_cast_uint16 = gather(axis = quantized_1_axis_0, batch_dims = quantized_1_batch_dims_0, indices = input_1_to_uint16, validate_indices = quantized_1_validate_indices_0, x = weight_1_to_fp16_palettized)[name = string("quantized_1_cast_fp16_cast_uint16")]; + tensor var_57 = const()[name = string("op_57"), val = tensor([0, 2, 1])]; + tensor input_3_axes_0 = const()[name = string("input_3_axes_0"), val = tensor([2])]; + tensor var_58_cast_fp16 = transpose(perm = var_57, x = quantized_1_cast_fp16_cast_uint16)[name = string("transpose_26")]; + tensor input_3_cast_fp16 = expand_dims(axes = input_3_axes_0, x = var_58_cast_fp16)[name = string("input_3_cast_fp16")]; + string quantized_pad_type_0 = const()[name = string("quantized_pad_type_0"), val = string("valid")]; + tensor quantized_strides_0 = const()[name = string("quantized_strides_0"), val = tensor([1, 1])]; + tensor quantized_pad_0 = const()[name = string("quantized_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor quantized_dilations_0 = const()[name = string("quantized_dilations_0"), val = tensor([1, 1])]; + int32 quantized_groups_0 = const()[name = string("quantized_groups_0"), val = int32(1)]; + tensor quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656128))))[name = string("quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized")]; + tensor quantized_cast_fp16 = conv(dilations = quantized_dilations_0, groups = quantized_groups_0, pad = quantized_pad_0, pad_type = quantized_pad_type_0, strides = quantized_strides_0, weight = quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("quantized_cast_fp16")]; + tensor codes_5_begin_0 = const()[name = string("codes_5_begin_0"), val = tensor([0, 1, 0])]; + tensor codes_5_end_0 = const()[name = string("codes_5_end_0"), val = tensor([1, 16, 1])]; + tensor codes_5_end_mask_0 = const()[name = string("codes_5_end_mask_0"), val = tensor([true, true, true])]; + tensor codes_5 = slice_by_index(begin = codes_5_begin_0, end = codes_5_end_0, end_mask = codes_5_end_mask_0, x = audio_codes)[name = string("codes_5")]; + tensor var_70 = const()[name = string("op_70"), val = tensor([1, 0, 2])]; + tensor input_5_begin_0 = const()[name = string("input_5_begin_0"), val = tensor([0, 0, 0])]; + tensor input_5_end_0 = const()[name = string("input_5_end_0"), val = tensor([1, 1, 1])]; + tensor input_5_end_mask_0 = const()[name = string("input_5_end_mask_0"), val = tensor([false, true, true])]; + tensor input_5_squeeze_mask_0 = const()[name = string("input_5_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor codes = transpose(perm = var_70, x = codes_5)[name = string("transpose_25")]; + tensor input_5 = slice_by_index(begin = input_5_begin_0, end = input_5_end_0, end_mask = input_5_end_mask_0, squeeze_mask = input_5_squeeze_mask_0, x = codes)[name = string("input_5")]; + int32 quantized_3_axis_0 = const()[name = string("quantized_3_axis_0"), val = int32(0)]; + int32 quantized_3_batch_dims_0 = const()[name = string("quantized_3_batch_dims_0"), val = int32(0)]; + bool quantized_3_validate_indices_0 = const()[name = string("quantized_3_validate_indices_0"), val = bool(false)]; + tensor weight_5_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1181056))))[name = string("weight_5_to_fp16_palettized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_15")]; + tensor quantized_3_cast_fp16_cast_uint16 = gather(axis = quantized_3_axis_0, batch_dims = quantized_3_batch_dims_0, indices = input_5_to_uint16, validate_indices = quantized_3_validate_indices_0, x = weight_5_to_fp16_palettized)[name = string("quantized_3_cast_fp16_cast_uint16")]; + tensor var_111 = const()[name = string("op_111"), val = tensor([0, 2, 1])]; + tensor quantized_7_axes_0 = const()[name = string("quantized_7_axes_0"), val = tensor([2])]; + tensor var_112_cast_fp16 = transpose(perm = var_111, x = quantized_3_cast_fp16_cast_uint16)[name = string("transpose_24")]; + tensor quantized_7_cast_fp16 = expand_dims(axes = quantized_7_axes_0, x = var_112_cast_fp16)[name = string("quantized_7_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([1, 0, 0])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 1])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([false, true, true])]; + tensor input_7_squeeze_mask_0 = const()[name = string("input_7_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_7 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, squeeze_mask = input_7_squeeze_mask_0, x = codes)[name = string("input_7")]; + int32 quantized_5_axis_0 = const()[name = string("quantized_5_axis_0"), val = int32(0)]; + int32 quantized_5_batch_dims_0 = const()[name = string("quantized_5_batch_dims_0"), val = int32(0)]; + bool quantized_5_validate_indices_0 = const()[name = string("quantized_5_validate_indices_0"), val = bool(false)]; + tensor weight_7_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1181632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705984))))[name = string("weight_7_to_fp16_palettized")]; + string input_7_to_uint16_dtype_0 = const()[name = string("input_7_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_7_to_uint16 = cast(dtype = input_7_to_uint16_dtype_0, x = input_7)[name = string("cast_14")]; + tensor quantized_5_cast_fp16_cast_uint16 = gather(axis = quantized_5_axis_0, batch_dims = quantized_5_batch_dims_0, indices = input_7_to_uint16, validate_indices = quantized_5_validate_indices_0, x = weight_7_to_fp16_palettized)[name = string("quantized_5_cast_fp16_cast_uint16")]; + tensor var_123 = const()[name = string("op_123"), val = tensor([0, 2, 1])]; + tensor layer_out_1_axes_0 = const()[name = string("layer_out_1_axes_0"), val = tensor([2])]; + tensor var_124_cast_fp16 = transpose(perm = var_123, x = quantized_5_cast_fp16_cast_uint16)[name = string("transpose_23")]; + tensor layer_out_1_cast_fp16 = expand_dims(axes = layer_out_1_axes_0, x = var_124_cast_fp16)[name = string("layer_out_1_cast_fp16")]; + tensor quantized_11_cast_fp16 = add(x = quantized_7_cast_fp16, y = layer_out_1_cast_fp16)[name = string("quantized_11_cast_fp16")]; + tensor input_9_begin_0 = const()[name = string("input_9_begin_0"), val = tensor([2, 0, 0])]; + tensor input_9_end_0 = const()[name = string("input_9_end_0"), val = tensor([3, 1, 1])]; + tensor input_9_end_mask_0 = const()[name = string("input_9_end_mask_0"), val = tensor([false, true, true])]; + tensor input_9_squeeze_mask_0 = const()[name = string("input_9_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_9 = slice_by_index(begin = input_9_begin_0, end = input_9_end_0, end_mask = input_9_end_mask_0, squeeze_mask = input_9_squeeze_mask_0, x = codes)[name = string("input_9")]; + int32 quantized_9_axis_0 = const()[name = string("quantized_9_axis_0"), val = int32(0)]; + int32 quantized_9_batch_dims_0 = const()[name = string("quantized_9_batch_dims_0"), val = int32(0)]; + bool quantized_9_validate_indices_0 = const()[name = string("quantized_9_validate_indices_0"), val = bool(false)]; + tensor weight_9_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1706560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2230912))))[name = string("weight_9_to_fp16_palettized")]; + string input_9_to_uint16_dtype_0 = const()[name = string("input_9_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_9_to_uint16 = cast(dtype = input_9_to_uint16_dtype_0, x = input_9)[name = string("cast_13")]; + tensor quantized_9_cast_fp16_cast_uint16 = gather(axis = quantized_9_axis_0, batch_dims = quantized_9_batch_dims_0, indices = input_9_to_uint16, validate_indices = quantized_9_validate_indices_0, x = weight_9_to_fp16_palettized)[name = string("quantized_9_cast_fp16_cast_uint16")]; + tensor var_136 = const()[name = string("op_136"), val = tensor([0, 2, 1])]; + tensor layer_out_3_axes_0 = const()[name = string("layer_out_3_axes_0"), val = tensor([2])]; + tensor var_137_cast_fp16 = transpose(perm = var_136, x = quantized_9_cast_fp16_cast_uint16)[name = string("transpose_22")]; + tensor layer_out_3_cast_fp16 = expand_dims(axes = layer_out_3_axes_0, x = var_137_cast_fp16)[name = string("layer_out_3_cast_fp16")]; + tensor quantized_15_cast_fp16 = add(x = quantized_11_cast_fp16, y = layer_out_3_cast_fp16)[name = string("quantized_15_cast_fp16")]; + tensor input_11_begin_0 = const()[name = string("input_11_begin_0"), val = tensor([3, 0, 0])]; + tensor input_11_end_0 = const()[name = string("input_11_end_0"), val = tensor([4, 1, 1])]; + tensor input_11_end_mask_0 = const()[name = string("input_11_end_mask_0"), val = tensor([false, true, true])]; + tensor input_11_squeeze_mask_0 = const()[name = string("input_11_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_11 = slice_by_index(begin = input_11_begin_0, end = input_11_end_0, end_mask = input_11_end_mask_0, squeeze_mask = input_11_squeeze_mask_0, x = codes)[name = string("input_11")]; + int32 quantized_13_axis_0 = const()[name = string("quantized_13_axis_0"), val = int32(0)]; + int32 quantized_13_batch_dims_0 = const()[name = string("quantized_13_batch_dims_0"), val = int32(0)]; + bool quantized_13_validate_indices_0 = const()[name = string("quantized_13_validate_indices_0"), val = bool(false)]; + tensor weight_11_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2231488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2755840))))[name = string("weight_11_to_fp16_palettized")]; + string input_11_to_uint16_dtype_0 = const()[name = string("input_11_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_11_to_uint16 = cast(dtype = input_11_to_uint16_dtype_0, x = input_11)[name = string("cast_12")]; + tensor quantized_13_cast_fp16_cast_uint16 = gather(axis = quantized_13_axis_0, batch_dims = quantized_13_batch_dims_0, indices = input_11_to_uint16, validate_indices = quantized_13_validate_indices_0, x = weight_11_to_fp16_palettized)[name = string("quantized_13_cast_fp16_cast_uint16")]; + tensor var_149 = const()[name = string("op_149"), val = tensor([0, 2, 1])]; + tensor layer_out_5_axes_0 = const()[name = string("layer_out_5_axes_0"), val = tensor([2])]; + tensor var_150_cast_fp16 = transpose(perm = var_149, x = quantized_13_cast_fp16_cast_uint16)[name = string("transpose_21")]; + tensor layer_out_5_cast_fp16 = expand_dims(axes = layer_out_5_axes_0, x = var_150_cast_fp16)[name = string("layer_out_5_cast_fp16")]; + tensor quantized_19_cast_fp16 = add(x = quantized_15_cast_fp16, y = layer_out_5_cast_fp16)[name = string("quantized_19_cast_fp16")]; + tensor input_13_begin_0 = const()[name = string("input_13_begin_0"), val = tensor([4, 0, 0])]; + tensor input_13_end_0 = const()[name = string("input_13_end_0"), val = tensor([5, 1, 1])]; + tensor input_13_end_mask_0 = const()[name = string("input_13_end_mask_0"), val = tensor([false, true, true])]; + tensor input_13_squeeze_mask_0 = const()[name = string("input_13_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_13 = slice_by_index(begin = input_13_begin_0, end = input_13_end_0, end_mask = input_13_end_mask_0, squeeze_mask = input_13_squeeze_mask_0, x = codes)[name = string("input_13")]; + int32 quantized_17_axis_0 = const()[name = string("quantized_17_axis_0"), val = int32(0)]; + int32 quantized_17_batch_dims_0 = const()[name = string("quantized_17_batch_dims_0"), val = int32(0)]; + bool quantized_17_validate_indices_0 = const()[name = string("quantized_17_validate_indices_0"), val = bool(false)]; + tensor weight_13_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2756416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3280768))))[name = string("weight_13_to_fp16_palettized")]; + string input_13_to_uint16_dtype_0 = const()[name = string("input_13_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_13_to_uint16 = cast(dtype = input_13_to_uint16_dtype_0, x = input_13)[name = string("cast_11")]; + tensor quantized_17_cast_fp16_cast_uint16 = gather(axis = quantized_17_axis_0, batch_dims = quantized_17_batch_dims_0, indices = input_13_to_uint16, validate_indices = quantized_17_validate_indices_0, x = weight_13_to_fp16_palettized)[name = string("quantized_17_cast_fp16_cast_uint16")]; + tensor var_162 = const()[name = string("op_162"), val = tensor([0, 2, 1])]; + tensor layer_out_7_axes_0 = const()[name = string("layer_out_7_axes_0"), val = tensor([2])]; + tensor var_163_cast_fp16 = transpose(perm = var_162, x = quantized_17_cast_fp16_cast_uint16)[name = string("transpose_20")]; + tensor layer_out_7_cast_fp16 = expand_dims(axes = layer_out_7_axes_0, x = var_163_cast_fp16)[name = string("layer_out_7_cast_fp16")]; + tensor quantized_23_cast_fp16 = add(x = quantized_19_cast_fp16, y = layer_out_7_cast_fp16)[name = string("quantized_23_cast_fp16")]; + tensor input_15_begin_0 = const()[name = string("input_15_begin_0"), val = tensor([5, 0, 0])]; + tensor input_15_end_0 = const()[name = string("input_15_end_0"), val = tensor([6, 1, 1])]; + tensor input_15_end_mask_0 = const()[name = string("input_15_end_mask_0"), val = tensor([false, true, true])]; + tensor input_15_squeeze_mask_0 = const()[name = string("input_15_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_15 = slice_by_index(begin = input_15_begin_0, end = input_15_end_0, end_mask = input_15_end_mask_0, squeeze_mask = input_15_squeeze_mask_0, x = codes)[name = string("input_15")]; + int32 quantized_21_axis_0 = const()[name = string("quantized_21_axis_0"), val = int32(0)]; + int32 quantized_21_batch_dims_0 = const()[name = string("quantized_21_batch_dims_0"), val = int32(0)]; + bool quantized_21_validate_indices_0 = const()[name = string("quantized_21_validate_indices_0"), val = bool(false)]; + tensor weight_15_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3281344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3805696))))[name = string("weight_15_to_fp16_palettized")]; + string input_15_to_uint16_dtype_0 = const()[name = string("input_15_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_15_to_uint16 = cast(dtype = input_15_to_uint16_dtype_0, x = input_15)[name = string("cast_10")]; + tensor quantized_21_cast_fp16_cast_uint16 = gather(axis = quantized_21_axis_0, batch_dims = quantized_21_batch_dims_0, indices = input_15_to_uint16, validate_indices = quantized_21_validate_indices_0, x = weight_15_to_fp16_palettized)[name = string("quantized_21_cast_fp16_cast_uint16")]; + tensor var_175 = const()[name = string("op_175"), val = tensor([0, 2, 1])]; + tensor layer_out_9_axes_0 = const()[name = string("layer_out_9_axes_0"), val = tensor([2])]; + tensor var_176_cast_fp16 = transpose(perm = var_175, x = quantized_21_cast_fp16_cast_uint16)[name = string("transpose_19")]; + tensor layer_out_9_cast_fp16 = expand_dims(axes = layer_out_9_axes_0, x = var_176_cast_fp16)[name = string("layer_out_9_cast_fp16")]; + tensor quantized_27_cast_fp16 = add(x = quantized_23_cast_fp16, y = layer_out_9_cast_fp16)[name = string("quantized_27_cast_fp16")]; + tensor input_17_begin_0 = const()[name = string("input_17_begin_0"), val = tensor([6, 0, 0])]; + tensor input_17_end_0 = const()[name = string("input_17_end_0"), val = tensor([7, 1, 1])]; + tensor input_17_end_mask_0 = const()[name = string("input_17_end_mask_0"), val = tensor([false, true, true])]; + tensor input_17_squeeze_mask_0 = const()[name = string("input_17_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_17 = slice_by_index(begin = input_17_begin_0, end = input_17_end_0, end_mask = input_17_end_mask_0, squeeze_mask = input_17_squeeze_mask_0, x = codes)[name = string("input_17")]; + int32 quantized_25_axis_0 = const()[name = string("quantized_25_axis_0"), val = int32(0)]; + int32 quantized_25_batch_dims_0 = const()[name = string("quantized_25_batch_dims_0"), val = int32(0)]; + bool quantized_25_validate_indices_0 = const()[name = string("quantized_25_validate_indices_0"), val = bool(false)]; + tensor weight_17_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3806272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330624))))[name = string("weight_17_to_fp16_palettized")]; + string input_17_to_uint16_dtype_0 = const()[name = string("input_17_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_17_to_uint16 = cast(dtype = input_17_to_uint16_dtype_0, x = input_17)[name = string("cast_9")]; + tensor quantized_25_cast_fp16_cast_uint16 = gather(axis = quantized_25_axis_0, batch_dims = quantized_25_batch_dims_0, indices = input_17_to_uint16, validate_indices = quantized_25_validate_indices_0, x = weight_17_to_fp16_palettized)[name = string("quantized_25_cast_fp16_cast_uint16")]; + tensor var_188 = const()[name = string("op_188"), val = tensor([0, 2, 1])]; + tensor layer_out_11_axes_0 = const()[name = string("layer_out_11_axes_0"), val = tensor([2])]; + tensor var_189_cast_fp16 = transpose(perm = var_188, x = quantized_25_cast_fp16_cast_uint16)[name = string("transpose_18")]; + tensor layer_out_11_cast_fp16 = expand_dims(axes = layer_out_11_axes_0, x = var_189_cast_fp16)[name = string("layer_out_11_cast_fp16")]; + tensor quantized_31_cast_fp16 = add(x = quantized_27_cast_fp16, y = layer_out_11_cast_fp16)[name = string("quantized_31_cast_fp16")]; + tensor input_19_begin_0 = const()[name = string("input_19_begin_0"), val = tensor([7, 0, 0])]; + tensor input_19_end_0 = const()[name = string("input_19_end_0"), val = tensor([8, 1, 1])]; + tensor input_19_end_mask_0 = const()[name = string("input_19_end_mask_0"), val = tensor([false, true, true])]; + tensor input_19_squeeze_mask_0 = const()[name = string("input_19_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_19 = slice_by_index(begin = input_19_begin_0, end = input_19_end_0, end_mask = input_19_end_mask_0, squeeze_mask = input_19_squeeze_mask_0, x = codes)[name = string("input_19")]; + int32 quantized_29_axis_0 = const()[name = string("quantized_29_axis_0"), val = int32(0)]; + int32 quantized_29_batch_dims_0 = const()[name = string("quantized_29_batch_dims_0"), val = int32(0)]; + bool quantized_29_validate_indices_0 = const()[name = string("quantized_29_validate_indices_0"), val = bool(false)]; + tensor weight_19_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4331200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4855552))))[name = string("weight_19_to_fp16_palettized")]; + string input_19_to_uint16_dtype_0 = const()[name = string("input_19_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_19_to_uint16 = cast(dtype = input_19_to_uint16_dtype_0, x = input_19)[name = string("cast_8")]; + tensor quantized_29_cast_fp16_cast_uint16 = gather(axis = quantized_29_axis_0, batch_dims = quantized_29_batch_dims_0, indices = input_19_to_uint16, validate_indices = quantized_29_validate_indices_0, x = weight_19_to_fp16_palettized)[name = string("quantized_29_cast_fp16_cast_uint16")]; + tensor var_201 = const()[name = string("op_201"), val = tensor([0, 2, 1])]; + tensor layer_out_13_axes_0 = const()[name = string("layer_out_13_axes_0"), val = tensor([2])]; + tensor var_202_cast_fp16 = transpose(perm = var_201, x = quantized_29_cast_fp16_cast_uint16)[name = string("transpose_17")]; + tensor layer_out_13_cast_fp16 = expand_dims(axes = layer_out_13_axes_0, x = var_202_cast_fp16)[name = string("layer_out_13_cast_fp16")]; + tensor quantized_35_cast_fp16 = add(x = quantized_31_cast_fp16, y = layer_out_13_cast_fp16)[name = string("quantized_35_cast_fp16")]; + tensor input_21_begin_0 = const()[name = string("input_21_begin_0"), val = tensor([8, 0, 0])]; + tensor input_21_end_0 = const()[name = string("input_21_end_0"), val = tensor([9, 1, 1])]; + tensor input_21_end_mask_0 = const()[name = string("input_21_end_mask_0"), val = tensor([false, true, true])]; + tensor input_21_squeeze_mask_0 = const()[name = string("input_21_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_21 = slice_by_index(begin = input_21_begin_0, end = input_21_end_0, end_mask = input_21_end_mask_0, squeeze_mask = input_21_squeeze_mask_0, x = codes)[name = string("input_21")]; + int32 quantized_33_axis_0 = const()[name = string("quantized_33_axis_0"), val = int32(0)]; + int32 quantized_33_batch_dims_0 = const()[name = string("quantized_33_batch_dims_0"), val = int32(0)]; + bool quantized_33_validate_indices_0 = const()[name = string("quantized_33_validate_indices_0"), val = bool(false)]; + tensor weight_21_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4856128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5380480))))[name = string("weight_21_to_fp16_palettized")]; + string input_21_to_uint16_dtype_0 = const()[name = string("input_21_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_21_to_uint16 = cast(dtype = input_21_to_uint16_dtype_0, x = input_21)[name = string("cast_7")]; + tensor quantized_33_cast_fp16_cast_uint16 = gather(axis = quantized_33_axis_0, batch_dims = quantized_33_batch_dims_0, indices = input_21_to_uint16, validate_indices = quantized_33_validate_indices_0, x = weight_21_to_fp16_palettized)[name = string("quantized_33_cast_fp16_cast_uint16")]; + tensor var_214 = const()[name = string("op_214"), val = tensor([0, 2, 1])]; + tensor layer_out_15_axes_0 = const()[name = string("layer_out_15_axes_0"), val = tensor([2])]; + tensor var_215_cast_fp16 = transpose(perm = var_214, x = quantized_33_cast_fp16_cast_uint16)[name = string("transpose_16")]; + tensor layer_out_15_cast_fp16 = expand_dims(axes = layer_out_15_axes_0, x = var_215_cast_fp16)[name = string("layer_out_15_cast_fp16")]; + tensor quantized_39_cast_fp16 = add(x = quantized_35_cast_fp16, y = layer_out_15_cast_fp16)[name = string("quantized_39_cast_fp16")]; + tensor input_23_begin_0 = const()[name = string("input_23_begin_0"), val = tensor([9, 0, 0])]; + tensor input_23_end_0 = const()[name = string("input_23_end_0"), val = tensor([10, 1, 1])]; + tensor input_23_end_mask_0 = const()[name = string("input_23_end_mask_0"), val = tensor([false, true, true])]; + tensor input_23_squeeze_mask_0 = const()[name = string("input_23_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_23 = slice_by_index(begin = input_23_begin_0, end = input_23_end_0, end_mask = input_23_end_mask_0, squeeze_mask = input_23_squeeze_mask_0, x = codes)[name = string("input_23")]; + int32 quantized_37_axis_0 = const()[name = string("quantized_37_axis_0"), val = int32(0)]; + int32 quantized_37_batch_dims_0 = const()[name = string("quantized_37_batch_dims_0"), val = int32(0)]; + bool quantized_37_validate_indices_0 = const()[name = string("quantized_37_validate_indices_0"), val = bool(false)]; + tensor weight_23_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5381056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5905408))))[name = string("weight_23_to_fp16_palettized")]; + string input_23_to_uint16_dtype_0 = const()[name = string("input_23_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_23_to_uint16 = cast(dtype = input_23_to_uint16_dtype_0, x = input_23)[name = string("cast_6")]; + tensor quantized_37_cast_fp16_cast_uint16 = gather(axis = quantized_37_axis_0, batch_dims = quantized_37_batch_dims_0, indices = input_23_to_uint16, validate_indices = quantized_37_validate_indices_0, x = weight_23_to_fp16_palettized)[name = string("quantized_37_cast_fp16_cast_uint16")]; + tensor var_227 = const()[name = string("op_227"), val = tensor([0, 2, 1])]; + tensor layer_out_17_axes_0 = const()[name = string("layer_out_17_axes_0"), val = tensor([2])]; + tensor var_228_cast_fp16 = transpose(perm = var_227, x = quantized_37_cast_fp16_cast_uint16)[name = string("transpose_15")]; + tensor layer_out_17_cast_fp16 = expand_dims(axes = layer_out_17_axes_0, x = var_228_cast_fp16)[name = string("layer_out_17_cast_fp16")]; + tensor quantized_43_cast_fp16 = add(x = quantized_39_cast_fp16, y = layer_out_17_cast_fp16)[name = string("quantized_43_cast_fp16")]; + tensor input_25_begin_0 = const()[name = string("input_25_begin_0"), val = tensor([10, 0, 0])]; + tensor input_25_end_0 = const()[name = string("input_25_end_0"), val = tensor([11, 1, 1])]; + tensor input_25_end_mask_0 = const()[name = string("input_25_end_mask_0"), val = tensor([false, true, true])]; + tensor input_25_squeeze_mask_0 = const()[name = string("input_25_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_25 = slice_by_index(begin = input_25_begin_0, end = input_25_end_0, end_mask = input_25_end_mask_0, squeeze_mask = input_25_squeeze_mask_0, x = codes)[name = string("input_25")]; + int32 quantized_41_axis_0 = const()[name = string("quantized_41_axis_0"), val = int32(0)]; + int32 quantized_41_batch_dims_0 = const()[name = string("quantized_41_batch_dims_0"), val = int32(0)]; + bool quantized_41_validate_indices_0 = const()[name = string("quantized_41_validate_indices_0"), val = bool(false)]; + tensor weight_25_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5905984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6430336))))[name = string("weight_25_to_fp16_palettized")]; + string input_25_to_uint16_dtype_0 = const()[name = string("input_25_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_25_to_uint16 = cast(dtype = input_25_to_uint16_dtype_0, x = input_25)[name = string("cast_5")]; + tensor quantized_41_cast_fp16_cast_uint16 = gather(axis = quantized_41_axis_0, batch_dims = quantized_41_batch_dims_0, indices = input_25_to_uint16, validate_indices = quantized_41_validate_indices_0, x = weight_25_to_fp16_palettized)[name = string("quantized_41_cast_fp16_cast_uint16")]; + tensor var_240 = const()[name = string("op_240"), val = tensor([0, 2, 1])]; + tensor layer_out_19_axes_0 = const()[name = string("layer_out_19_axes_0"), val = tensor([2])]; + tensor var_241_cast_fp16 = transpose(perm = var_240, x = quantized_41_cast_fp16_cast_uint16)[name = string("transpose_14")]; + tensor layer_out_19_cast_fp16 = expand_dims(axes = layer_out_19_axes_0, x = var_241_cast_fp16)[name = string("layer_out_19_cast_fp16")]; + tensor quantized_47_cast_fp16 = add(x = quantized_43_cast_fp16, y = layer_out_19_cast_fp16)[name = string("quantized_47_cast_fp16")]; + tensor input_27_begin_0 = const()[name = string("input_27_begin_0"), val = tensor([11, 0, 0])]; + tensor input_27_end_0 = const()[name = string("input_27_end_0"), val = tensor([12, 1, 1])]; + tensor input_27_end_mask_0 = const()[name = string("input_27_end_mask_0"), val = tensor([false, true, true])]; + tensor input_27_squeeze_mask_0 = const()[name = string("input_27_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_27 = slice_by_index(begin = input_27_begin_0, end = input_27_end_0, end_mask = input_27_end_mask_0, squeeze_mask = input_27_squeeze_mask_0, x = codes)[name = string("input_27")]; + int32 quantized_45_axis_0 = const()[name = string("quantized_45_axis_0"), val = int32(0)]; + int32 quantized_45_batch_dims_0 = const()[name = string("quantized_45_batch_dims_0"), val = int32(0)]; + bool quantized_45_validate_indices_0 = const()[name = string("quantized_45_validate_indices_0"), val = bool(false)]; + tensor weight_27_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6430912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6955264))))[name = string("weight_27_to_fp16_palettized")]; + string input_27_to_uint16_dtype_0 = const()[name = string("input_27_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_27_to_uint16 = cast(dtype = input_27_to_uint16_dtype_0, x = input_27)[name = string("cast_4")]; + tensor quantized_45_cast_fp16_cast_uint16 = gather(axis = quantized_45_axis_0, batch_dims = quantized_45_batch_dims_0, indices = input_27_to_uint16, validate_indices = quantized_45_validate_indices_0, x = weight_27_to_fp16_palettized)[name = string("quantized_45_cast_fp16_cast_uint16")]; + tensor var_253 = const()[name = string("op_253"), val = tensor([0, 2, 1])]; + tensor layer_out_21_axes_0 = const()[name = string("layer_out_21_axes_0"), val = tensor([2])]; + tensor var_254_cast_fp16 = transpose(perm = var_253, x = quantized_45_cast_fp16_cast_uint16)[name = string("transpose_13")]; + tensor layer_out_21_cast_fp16 = expand_dims(axes = layer_out_21_axes_0, x = var_254_cast_fp16)[name = string("layer_out_21_cast_fp16")]; + tensor quantized_51_cast_fp16 = add(x = quantized_47_cast_fp16, y = layer_out_21_cast_fp16)[name = string("quantized_51_cast_fp16")]; + tensor input_29_begin_0 = const()[name = string("input_29_begin_0"), val = tensor([12, 0, 0])]; + tensor input_29_end_0 = const()[name = string("input_29_end_0"), val = tensor([13, 1, 1])]; + tensor input_29_end_mask_0 = const()[name = string("input_29_end_mask_0"), val = tensor([false, true, true])]; + tensor input_29_squeeze_mask_0 = const()[name = string("input_29_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_29 = slice_by_index(begin = input_29_begin_0, end = input_29_end_0, end_mask = input_29_end_mask_0, squeeze_mask = input_29_squeeze_mask_0, x = codes)[name = string("input_29")]; + int32 quantized_49_axis_0 = const()[name = string("quantized_49_axis_0"), val = int32(0)]; + int32 quantized_49_batch_dims_0 = const()[name = string("quantized_49_batch_dims_0"), val = int32(0)]; + bool quantized_49_validate_indices_0 = const()[name = string("quantized_49_validate_indices_0"), val = bool(false)]; + tensor weight_29_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6955840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480192))))[name = string("weight_29_to_fp16_palettized")]; + string input_29_to_uint16_dtype_0 = const()[name = string("input_29_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_29_to_uint16 = cast(dtype = input_29_to_uint16_dtype_0, x = input_29)[name = string("cast_3")]; + tensor quantized_49_cast_fp16_cast_uint16 = gather(axis = quantized_49_axis_0, batch_dims = quantized_49_batch_dims_0, indices = input_29_to_uint16, validate_indices = quantized_49_validate_indices_0, x = weight_29_to_fp16_palettized)[name = string("quantized_49_cast_fp16_cast_uint16")]; + tensor var_266 = const()[name = string("op_266"), val = tensor([0, 2, 1])]; + tensor layer_out_23_axes_0 = const()[name = string("layer_out_23_axes_0"), val = tensor([2])]; + tensor var_267_cast_fp16 = transpose(perm = var_266, x = quantized_49_cast_fp16_cast_uint16)[name = string("transpose_12")]; + tensor layer_out_23_cast_fp16 = expand_dims(axes = layer_out_23_axes_0, x = var_267_cast_fp16)[name = string("layer_out_23_cast_fp16")]; + tensor quantized_55_cast_fp16 = add(x = quantized_51_cast_fp16, y = layer_out_23_cast_fp16)[name = string("quantized_55_cast_fp16")]; + tensor input_31_begin_0 = const()[name = string("input_31_begin_0"), val = tensor([13, 0, 0])]; + tensor input_31_end_0 = const()[name = string("input_31_end_0"), val = tensor([14, 1, 1])]; + tensor input_31_end_mask_0 = const()[name = string("input_31_end_mask_0"), val = tensor([false, true, true])]; + tensor input_31_squeeze_mask_0 = const()[name = string("input_31_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_31 = slice_by_index(begin = input_31_begin_0, end = input_31_end_0, end_mask = input_31_end_mask_0, squeeze_mask = input_31_squeeze_mask_0, x = codes)[name = string("input_31")]; + int32 quantized_53_axis_0 = const()[name = string("quantized_53_axis_0"), val = int32(0)]; + int32 quantized_53_batch_dims_0 = const()[name = string("quantized_53_batch_dims_0"), val = int32(0)]; + bool quantized_53_validate_indices_0 = const()[name = string("quantized_53_validate_indices_0"), val = bool(false)]; + tensor weight_31_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8005120))))[name = string("weight_31_to_fp16_palettized")]; + string input_31_to_uint16_dtype_0 = const()[name = string("input_31_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_31_to_uint16 = cast(dtype = input_31_to_uint16_dtype_0, x = input_31)[name = string("cast_2")]; + tensor quantized_53_cast_fp16_cast_uint16 = gather(axis = quantized_53_axis_0, batch_dims = quantized_53_batch_dims_0, indices = input_31_to_uint16, validate_indices = quantized_53_validate_indices_0, x = weight_31_to_fp16_palettized)[name = string("quantized_53_cast_fp16_cast_uint16")]; + tensor var_279 = const()[name = string("op_279"), val = tensor([0, 2, 1])]; + tensor layer_out_25_axes_0 = const()[name = string("layer_out_25_axes_0"), val = tensor([2])]; + tensor var_280_cast_fp16 = transpose(perm = var_279, x = quantized_53_cast_fp16_cast_uint16)[name = string("transpose_11")]; + tensor layer_out_25_cast_fp16 = expand_dims(axes = layer_out_25_axes_0, x = var_280_cast_fp16)[name = string("layer_out_25_cast_fp16")]; + tensor quantized_59_cast_fp16 = add(x = quantized_55_cast_fp16, y = layer_out_25_cast_fp16)[name = string("quantized_59_cast_fp16")]; + tensor input_33_begin_0 = const()[name = string("input_33_begin_0"), val = tensor([14, 0, 0])]; + tensor input_33_end_0 = const()[name = string("input_33_end_0"), val = tensor([15, 1, 1])]; + tensor input_33_end_mask_0 = const()[name = string("input_33_end_mask_0"), val = tensor([false, true, true])]; + tensor input_33_squeeze_mask_0 = const()[name = string("input_33_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_33 = slice_by_index(begin = input_33_begin_0, end = input_33_end_0, end_mask = input_33_end_mask_0, squeeze_mask = input_33_squeeze_mask_0, x = codes)[name = string("input_33")]; + int32 quantized_57_axis_0 = const()[name = string("quantized_57_axis_0"), val = int32(0)]; + int32 quantized_57_batch_dims_0 = const()[name = string("quantized_57_batch_dims_0"), val = int32(0)]; + bool quantized_57_validate_indices_0 = const()[name = string("quantized_57_validate_indices_0"), val = bool(false)]; + tensor weight_33_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8005696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8530048))))[name = string("weight_33_to_fp16_palettized")]; + string input_33_to_uint16_dtype_0 = const()[name = string("input_33_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_33_to_uint16 = cast(dtype = input_33_to_uint16_dtype_0, x = input_33)[name = string("cast_1")]; + tensor quantized_57_cast_fp16_cast_uint16 = gather(axis = quantized_57_axis_0, batch_dims = quantized_57_batch_dims_0, indices = input_33_to_uint16, validate_indices = quantized_57_validate_indices_0, x = weight_33_to_fp16_palettized)[name = string("quantized_57_cast_fp16_cast_uint16")]; + tensor var_292 = const()[name = string("op_292"), val = tensor([0, 2, 1])]; + tensor layer_out_axes_0 = const()[name = string("layer_out_axes_0"), val = tensor([2])]; + tensor var_293_cast_fp16 = transpose(perm = var_292, x = quantized_57_cast_fp16_cast_uint16)[name = string("transpose_10")]; + tensor layer_out_cast_fp16 = expand_dims(axes = layer_out_axes_0, x = var_293_cast_fp16)[name = string("layer_out_cast_fp16")]; + tensor input_35_cast_fp16 = add(x = quantized_59_cast_fp16, y = layer_out_cast_fp16)[name = string("input_35_cast_fp16")]; + string var_301_pad_type_0 = const()[name = string("op_301_pad_type_0"), val = string("valid")]; + tensor var_301_strides_0 = const()[name = string("op_301_strides_0"), val = tensor([1, 1])]; + tensor var_301_pad_0 = const()[name = string("op_301_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_301_dilations_0 = const()[name = string("op_301_dilations_0"), val = tensor([1, 1])]; + int32 var_301_groups_0 = const()[name = string("op_301_groups_0"), val = int32(1)]; + tensor quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8530624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8661760))))[name = string("quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized")]; + tensor var_301_cast_fp16 = conv(dilations = var_301_dilations_0, groups = var_301_groups_0, pad = var_301_pad_0, pad_type = var_301_pad_type_0, strides = var_301_strides_0, weight = quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("op_301_cast_fp16")]; + tensor pre_conv_context_update = add(x = quantized_cast_fp16, y = var_301_cast_fp16)[name = string("hidden_in_cast_fp16")]; + int32 var_311_axis_0 = const()[name = string("op_311_axis_0"), val = int32(0)]; + int32 var_311_batch_dims_0 = const()[name = string("op_311_batch_dims_0"), val = int32(0)]; + bool var_311_validate_indices_0 = const()[name = string("op_311_validate_indices_0"), val = bool(false)]; + tensor rope_rope_cos_to_fp16 = const()[name = string("rope_rope_cos_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8662336)))]; + string cache_length_to_uint16_dtype_0 = const()[name = string("cache_length_to_uint16_dtype_0"), val = string("uint16")]; + tensor cache_length_to_uint16 = cast(dtype = cache_length_to_uint16_dtype_0, x = cache_length)[name = string("cast_0")]; + tensor var_311_cast_fp16_cast_uint16 = gather(axis = var_311_axis_0, batch_dims = var_311_batch_dims_0, indices = cache_length_to_uint16, validate_indices = var_311_validate_indices_0, x = rope_rope_cos_to_fp16)[name = string("op_311_cast_fp16_cast_uint16")]; + tensor obj_9_axes_0 = const()[name = string("obj_9_axes_0"), val = tensor([2])]; + tensor obj_9_cast_fp16 = expand_dims(axes = obj_9_axes_0, x = var_311_cast_fp16_cast_uint16)[name = string("obj_9_cast_fp16")]; + int32 var_315_axis_0 = const()[name = string("op_315_axis_0"), val = int32(0)]; + int32 var_315_batch_dims_0 = const()[name = string("op_315_batch_dims_0"), val = int32(0)]; + bool var_315_validate_indices_0 = const()[name = string("op_315_validate_indices_0"), val = bool(false)]; + tensor rope_rope_sin_to_fp16 = const()[name = string("rope_rope_sin_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9186688)))]; + tensor var_315_cast_fp16_cast_uint16 = gather(axis = var_315_axis_0, batch_dims = var_315_batch_dims_0, indices = cache_length_to_uint16, validate_indices = var_315_validate_indices_0, x = rope_rope_sin_to_fp16)[name = string("op_315_cast_fp16_cast_uint16")]; + tensor obj_11_axes_0 = const()[name = string("obj_11_axes_0"), val = tensor([2])]; + tensor obj_11_cast_fp16 = expand_dims(axes = obj_11_axes_0, x = var_315_cast_fp16_cast_uint16)[name = string("obj_11_cast_fp16")]; + int32 var_327 = const()[name = string("op_327"), val = int32(-2)]; + int32 var_332 = const()[name = string("op_332"), val = int32(3)]; + int32 var_337 = const()[name = string("op_337"), val = int32(-1)]; + int32 var_338 = const()[name = string("op_338"), val = int32(1)]; + tensor tile_0 = const()[name = string("tile_0"), val = tensor([1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024])]; + int32 var_361_axis_0 = const()[name = string("op_361_axis_0"), val = int32(1)]; + tensor var_361_cast_fp16_0, tensor var_361_cast_fp16_1, tensor var_361_cast_fp16_2, tensor var_361_cast_fp16_3, tensor var_361_cast_fp16_4, tensor var_361_cast_fp16_5, tensor var_361_cast_fp16_6, tensor var_361_cast_fp16_7 = split(axis = var_361_axis_0, split_sizes = tile_0, x = key_cache)[name = string("op_361_cast_fp16")]; + tensor tile_1 = const()[name = string("tile_1"), val = tensor([1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024])]; + int32 var_370_axis_0 = const()[name = string("op_370_axis_0"), val = int32(1)]; + tensor var_370_cast_fp16_0, tensor var_370_cast_fp16_1, tensor var_370_cast_fp16_2, tensor var_370_cast_fp16_3, tensor var_370_cast_fp16_4, tensor var_370_cast_fp16_5, tensor var_370_cast_fp16_6, tensor var_370_cast_fp16_7 = split(axis = var_370_axis_0, split_sizes = tile_1, x = value_cache)[name = string("op_370_cast_fp16")]; + bool input_37_interleave_0 = const()[name = string("input_37_interleave_0"), val = bool(false)]; + tensor input_37_cast_fp16 = concat(axis = var_337, interleave = input_37_interleave_0, values = (pre_conv_context, pre_conv_context_update))[name = string("input_37_cast_fp16")]; + string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")]; + tensor input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor([1, 1])]; + int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)]; + tensor pre_transformer_pre_conv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9711040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11283968))))[name = string("pre_transformer_pre_conv_conv_weight_to_fp16_palettized")]; + tensor pre_transformer_pre_conv_conv_bias_to_fp16 = const()[name = string("pre_transformer_pre_conv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11284544)))]; + tensor input_39_cast_fp16 = conv(bias = pre_transformer_pre_conv_conv_bias_to_fp16, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = pre_transformer_pre_conv_conv_weight_to_fp16_palettized, x = input_37_cast_fp16)[name = string("input_39_cast_fp16")]; + string inputs_1_pad_type_0 = const()[name = string("inputs_1_pad_type_0"), val = string("valid")]; + tensor inputs_1_strides_0 = const()[name = string("inputs_1_strides_0"), val = tensor([1, 1])]; + tensor inputs_1_pad_0 = const()[name = string("inputs_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor inputs_1_dilations_0 = const()[name = string("inputs_1_dilations_0"), val = tensor([1, 1])]; + int32 inputs_1_groups_0 = const()[name = string("inputs_1_groups_0"), val = int32(1)]; + tensor pre_transformer_input_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11286656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11811008))))[name = string("pre_transformer_input_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_input_proj_bias_to_fp16 = const()[name = string("pre_transformer_input_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11811584)))]; + tensor inputs_1_cast_fp16 = conv(bias = pre_transformer_input_proj_bias_to_fp16, dilations = inputs_1_dilations_0, groups = inputs_1_groups_0, pad = inputs_1_pad_0, pad_type = inputs_1_pad_type_0, strides = inputs_1_strides_0, weight = pre_transformer_input_proj_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("inputs_1_cast_fp16")]; + tensor inputs_sq_1_cast_fp16 = mul(x = inputs_1_cast_fp16, y = inputs_1_cast_fp16)[name = string("inputs_sq_1_cast_fp16")]; + tensor variance_1_axes_0 = const()[name = string("variance_1_axes_0"), val = tensor([1])]; + bool variance_1_keep_dims_0 = const()[name = string("variance_1_keep_dims_0"), val = bool(true)]; + tensor variance_1_cast_fp16 = reduce_mean(axes = variance_1_axes_0, keep_dims = variance_1_keep_dims_0, x = inputs_sq_1_cast_fp16)[name = string("variance_1_cast_fp16")]; + fp16 var_405_to_fp16 = const()[name = string("op_405_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_406_cast_fp16 = add(x = variance_1_cast_fp16, y = var_405_to_fp16)[name = string("op_406_cast_fp16")]; + fp32 var_407_epsilon_0 = const()[name = string("op_407_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_407_cast_fp16 = rsqrt(epsilon = var_407_epsilon_0, x = var_406_cast_fp16)[name = string("op_407_cast_fp16")]; + tensor hidden_states_1_cast_fp16 = mul(x = inputs_1_cast_fp16, y = var_407_cast_fp16)[name = string("hidden_states_1_cast_fp16")]; + tensor w_1_to_fp16 = const()[name = string("w_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11812672)))]; + tensor obj_1_cast_fp16 = mul(x = w_1_to_fp16, y = hidden_states_1_cast_fp16)[name = string("obj_1_cast_fp16")]; + string query_1_pad_type_0 = const()[name = string("query_1_pad_type_0"), val = string("valid")]; + tensor query_1_strides_0 = const()[name = string("query_1_strides_0"), val = tensor([1, 1])]; + tensor query_1_pad_0 = const()[name = string("query_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_1_dilations_0 = const()[name = string("query_1_dilations_0"), val = tensor([1, 1])]; + int32 query_1_groups_0 = const()[name = string("query_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11813760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12338112))))[name = string("pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = string("pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12338688)))]; + tensor query_1_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_1_dilations_0, groups = query_1_groups_0, pad = query_1_pad_0, pad_type = query_1_pad_type_0, strides = query_1_strides_0, weight = pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("query_1_cast_fp16")]; + string key_1_pad_type_0 = const()[name = string("key_1_pad_type_0"), val = string("valid")]; + tensor key_1_strides_0 = const()[name = string("key_1_strides_0"), val = tensor([1, 1])]; + tensor key_1_pad_0 = const()[name = string("key_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_1_dilations_0 = const()[name = string("key_1_dilations_0"), val = tensor([1, 1])]; + int32 key_1_groups_0 = const()[name = string("key_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12340800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12865152))))[name = string("pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_1_cast_fp16 = conv(dilations = key_1_dilations_0, groups = key_1_groups_0, pad = key_1_pad_0, pad_type = key_1_pad_type_0, strides = key_1_strides_0, weight = pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("key_1_cast_fp16")]; + string current_value_1_pad_type_0 = const()[name = string("current_value_1_pad_type_0"), val = string("valid")]; + tensor current_value_1_strides_0 = const()[name = string("current_value_1_strides_0"), val = tensor([1, 1])]; + tensor current_value_1_pad_0 = const()[name = string("current_value_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_1_dilations_0 = const()[name = string("current_value_1_dilations_0"), val = tensor([1, 1])]; + int32 current_value_1_groups_0 = const()[name = string("current_value_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12865728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13390080))))[name = string("pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_1_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_1_dilations_0, groups = current_value_1_groups_0, pad = current_value_1_pad_0, pad_type = current_value_1_pad_type_0, strides = current_value_1_strides_0, weight = pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("current_value_1_cast_fp16")]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_1_cast_fp16 = reshape(shape = var_445, x = query_1_cast_fp16)[name = string("mh_q_1_cast_fp16")]; + tensor var_447 = const()[name = string("op_447"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_1_cast_fp16 = reshape(shape = var_447, x = key_1_cast_fp16)[name = string("mh_k_1_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = obj_9_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = obj_11_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = mh_q_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor var_456_begin_0 = const()[name = string("op_456_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_456_end_0 = const()[name = string("op_456_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_456_end_mask_0 = const()[name = string("op_456_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_456_cast_fp16 = slice_by_index(begin = var_456_begin_0, end = var_456_end_0, end_mask = var_456_end_mask_0, x = mh_q_1_cast_fp16)[name = string("op_456_cast_fp16")]; + tensor var_462_begin_0 = const()[name = string("op_462_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_462_end_0 = const()[name = string("op_462_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_462_end_mask_0 = const()[name = string("op_462_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_462_cast_fp16 = slice_by_index(begin = var_462_begin_0, end = var_462_end_0, end_mask = var_462_end_mask_0, x = mh_q_1_cast_fp16)[name = string("op_462_cast_fp16")]; + fp16 const_31_promoted_to_fp16 = const()[name = string("const_31_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_464_cast_fp16 = mul(x = var_462_cast_fp16, y = const_31_promoted_to_fp16)[name = string("op_464_cast_fp16")]; + bool var_466_interleave_0 = const()[name = string("op_466_interleave_0"), val = bool(false)]; + tensor var_466_cast_fp16 = concat(axis = var_327, interleave = var_466_interleave_0, values = (var_464_cast_fp16, var_456_cast_fp16))[name = string("op_466_cast_fp16")]; + tensor var_467_cast_fp16 = mul(x = var_466_cast_fp16, y = sin_1_cast_fp16)[name = string("op_467_cast_fp16")]; + tensor mh_q_3_cast_fp16 = add(x = var_451_cast_fp16, y = var_467_cast_fp16)[name = string("mh_q_3_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = mh_k_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_474_begin_0 = const()[name = string("op_474_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_474_end_0 = const()[name = string("op_474_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_474_end_mask_0 = const()[name = string("op_474_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_474_cast_fp16 = slice_by_index(begin = var_474_begin_0, end = var_474_end_0, end_mask = var_474_end_mask_0, x = mh_k_1_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_480_begin_0 = const()[name = string("op_480_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_480_end_0 = const()[name = string("op_480_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_480_end_mask_0 = const()[name = string("op_480_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_480_cast_fp16 = slice_by_index(begin = var_480_begin_0, end = var_480_end_0, end_mask = var_480_end_mask_0, x = mh_k_1_cast_fp16)[name = string("op_480_cast_fp16")]; + fp16 const_34_promoted_to_fp16 = const()[name = string("const_34_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_482_cast_fp16 = mul(x = var_480_cast_fp16, y = const_34_promoted_to_fp16)[name = string("op_482_cast_fp16")]; + bool var_484_interleave_0 = const()[name = string("op_484_interleave_0"), val = bool(false)]; + tensor var_484_cast_fp16 = concat(axis = var_327, interleave = var_484_interleave_0, values = (var_482_cast_fp16, var_474_cast_fp16))[name = string("op_484_cast_fp16")]; + tensor var_485_cast_fp16 = mul(x = var_484_cast_fp16, y = sin_1_cast_fp16)[name = string("op_485_cast_fp16")]; + tensor mh_k_3_cast_fp16 = add(x = var_469_cast_fp16, y = var_485_cast_fp16)[name = string("mh_k_3_cast_fp16")]; + tensor var_489 = const()[name = string("op_489"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_1_cast_fp16 = reshape(shape = var_489, x = mh_k_3_cast_fp16)[name = string("current_key_1_cast_fp16")]; + tensor var_493_axes_0 = const()[name = string("op_493_axes_0"), val = tensor([2])]; + tensor var_493_cast_fp16 = expand_dims(axes = var_493_axes_0, x = kv_cache_update_mask)[name = string("op_493_cast_fp16")]; + fp16 var_326_to_fp16 = const()[name = string("op_326_to_fp16"), val = fp16(0x1p+0)]; + tensor var_495_cast_fp16 = sub(x = var_326_to_fp16, y = var_493_cast_fp16)[name = string("op_495_cast_fp16")]; + tensor var_496_cast_fp16 = mul(x = var_361_cast_fp16_0, y = var_495_cast_fp16)[name = string("op_496_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = current_key_1_cast_fp16, y = var_493_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor key_3_cast_fp16 = add(x = var_496_cast_fp16, y = var_497_cast_fp16)[name = string("key_3_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = var_370_cast_fp16_0, y = var_495_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = current_value_1_cast_fp16, y = var_493_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor value_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("value_1_cast_fp16")]; + fp16 var_507_to_fp16 = const()[name = string("op_507_to_fp16"), val = fp16(0x1p-3)]; + tensor var_508_cast_fp16 = mul(x = mh_q_3_cast_fp16, y = var_507_to_fp16)[name = string("op_508_cast_fp16")]; + tensor var_511 = const()[name = string("op_511"), val = tensor([1, 16, 64, 80])]; + tensor var_512_cast_fp16 = reshape(shape = var_511, x = key_3_cast_fp16)[name = string("op_512_cast_fp16")]; + bool mh_w_1_transpose_x_0 = const()[name = string("mh_w_1_transpose_x_0"), val = bool(true)]; + bool mh_w_1_transpose_y_0 = const()[name = string("mh_w_1_transpose_y_0"), val = bool(false)]; + tensor mh_w_1_cast_fp16 = matmul(transpose_x = mh_w_1_transpose_x_0, transpose_y = mh_w_1_transpose_y_0, x = var_508_cast_fp16, y = var_512_cast_fp16)[name = string("mh_w_1_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([1])]; + tensor var_516_cast_fp16 = expand_dims(axes = var_516_axes_0, x = key_padding_mask)[name = string("op_516_cast_fp16")]; + tensor var_517_axes_0 = const()[name = string("op_517_axes_0"), val = tensor([2])]; + tensor var_517_cast_fp16 = expand_dims(axes = var_517_axes_0, x = var_516_cast_fp16)[name = string("op_517_cast_fp16")]; + tensor mh_w_3_cast_fp16 = add(x = mh_w_1_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_3_cast_fp16")]; + tensor var_520_cast_fp16 = softmax(axis = var_332, x = mh_w_3_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor var_521 = const()[name = string("op_521"), val = tensor([1, 16, 64, 80])]; + tensor var_522_cast_fp16 = reshape(shape = var_521, x = value_1_cast_fp16)[name = string("op_522_cast_fp16")]; + bool attn_1_transpose_x_0 = const()[name = string("attn_1_transpose_x_0"), val = bool(false)]; + bool attn_1_transpose_y_0 = const()[name = string("attn_1_transpose_y_0"), val = bool(true)]; + tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_522_cast_fp16, y = var_520_cast_fp16)[name = string("attn_1_cast_fp16")]; + tensor var_525 = const()[name = string("op_525"), val = tensor([1, -1, 1, 1])]; + tensor input_41_cast_fp16 = reshape(shape = var_525, x = attn_1_cast_fp16)[name = string("input_41_cast_fp16")]; + string obj_13_pad_type_0 = const()[name = string("obj_13_pad_type_0"), val = string("valid")]; + tensor obj_13_strides_0 = const()[name = string("obj_13_strides_0"), val = tensor([1, 1])]; + tensor obj_13_pad_0 = const()[name = string("obj_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_13_dilations_0 = const()[name = string("obj_13_dilations_0"), val = tensor([1, 1])]; + int32 obj_13_groups_0 = const()[name = string("obj_13_groups_0"), val = int32(1)]; + tensor op_541_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13390656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13915008))))[name = string("op_541_weight_0_to_fp16_palettized")]; + tensor var_541_bias_0_to_fp16 = const()[name = string("op_541_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13915584)))]; + tensor var_541_cast_fp16 = conv(bias = var_541_bias_0_to_fp16, dilations = obj_13_dilations_0, groups = obj_13_groups_0, pad = obj_13_pad_0, pad_type = obj_13_pad_type_0, strides = obj_13_strides_0, weight = op_541_weight_0_to_fp16_palettized, x = input_41_cast_fp16)[name = string("op_541_cast_fp16")]; + tensor inputs_3_cast_fp16 = add(x = inputs_1_cast_fp16, y = var_541_cast_fp16)[name = string("inputs_3_cast_fp16")]; + tensor inputs_sq_3_cast_fp16 = mul(x = inputs_3_cast_fp16, y = inputs_3_cast_fp16)[name = string("inputs_sq_3_cast_fp16")]; + tensor variance_3_axes_0 = const()[name = string("variance_3_axes_0"), val = tensor([1])]; + bool variance_3_keep_dims_0 = const()[name = string("variance_3_keep_dims_0"), val = bool(true)]; + tensor variance_3_cast_fp16 = reduce_mean(axes = variance_3_axes_0, keep_dims = variance_3_keep_dims_0, x = inputs_sq_3_cast_fp16)[name = string("variance_3_cast_fp16")]; + fp16 var_547_to_fp16 = const()[name = string("op_547_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_548_cast_fp16 = add(x = variance_3_cast_fp16, y = var_547_to_fp16)[name = string("op_548_cast_fp16")]; + fp32 var_549_epsilon_0 = const()[name = string("op_549_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_549_cast_fp16 = rsqrt(epsilon = var_549_epsilon_0, x = var_548_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor hidden_states_3_cast_fp16 = mul(x = inputs_3_cast_fp16, y = var_549_cast_fp16)[name = string("hidden_states_3_cast_fp16")]; + tensor w_3_to_fp16 = const()[name = string("w_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13916672)))]; + tensor input_43_cast_fp16 = mul(x = w_3_to_fp16, y = hidden_states_3_cast_fp16)[name = string("input_43_cast_fp16")]; + string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")]; + tensor input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor([1, 1])]; + tensor input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor([1, 1])]; + int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13917760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14442112))))[name = string("pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_45_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor gate_1_cast_fp16 = silu(x = input_45_cast_fp16)[name = string("gate_1_cast_fp16")]; + string up_1_pad_type_0 = const()[name = string("up_1_pad_type_0"), val = string("valid")]; + tensor up_1_strides_0 = const()[name = string("up_1_strides_0"), val = tensor([1, 1])]; + tensor up_1_pad_0 = const()[name = string("up_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_1_dilations_0 = const()[name = string("up_1_dilations_0"), val = tensor([1, 1])]; + int32 up_1_groups_0 = const()[name = string("up_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14442688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14967040))))[name = string("pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_1_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_1_dilations_0, groups = up_1_groups_0, pad = up_1_pad_0, pad_type = up_1_pad_type_0, strides = up_1_strides_0, weight = pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = string("up_1_cast_fp16")]; + tensor input_47_cast_fp16 = mul(x = gate_1_cast_fp16, y = up_1_cast_fp16)[name = string("input_47_cast_fp16")]; + string hidden_states_5_pad_type_0 = const()[name = string("hidden_states_5_pad_type_0"), val = string("valid")]; + tensor hidden_states_5_strides_0 = const()[name = string("hidden_states_5_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_0 = const()[name = string("hidden_states_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_5_dilations_0 = const()[name = string("hidden_states_5_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_5_groups_0 = const()[name = string("hidden_states_5_groups_0"), val = int32(1)]; + tensor op_583_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14967616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15491968))))[name = string("op_583_weight_0_to_fp16_palettized")]; + tensor var_583_bias_0_to_fp16 = const()[name = string("op_583_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15492544)))]; + tensor var_583_cast_fp16 = conv(bias = var_583_bias_0_to_fp16, dilations = hidden_states_5_dilations_0, groups = hidden_states_5_groups_0, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = hidden_states_5_strides_0, weight = op_583_weight_0_to_fp16_palettized, x = input_47_cast_fp16)[name = string("op_583_cast_fp16")]; + tensor inputs_5_cast_fp16 = add(x = inputs_3_cast_fp16, y = var_583_cast_fp16)[name = string("inputs_5_cast_fp16")]; + tensor inputs_sq_5_cast_fp16 = mul(x = inputs_5_cast_fp16, y = inputs_5_cast_fp16)[name = string("inputs_sq_5_cast_fp16")]; + tensor variance_5_axes_0 = const()[name = string("variance_5_axes_0"), val = tensor([1])]; + bool variance_5_keep_dims_0 = const()[name = string("variance_5_keep_dims_0"), val = bool(true)]; + tensor variance_5_cast_fp16 = reduce_mean(axes = variance_5_axes_0, keep_dims = variance_5_keep_dims_0, x = inputs_sq_5_cast_fp16)[name = string("variance_5_cast_fp16")]; + fp16 var_599_to_fp16 = const()[name = string("op_599_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_600_cast_fp16 = add(x = variance_5_cast_fp16, y = var_599_to_fp16)[name = string("op_600_cast_fp16")]; + fp32 var_601_epsilon_0 = const()[name = string("op_601_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_601_cast_fp16 = rsqrt(epsilon = var_601_epsilon_0, x = var_600_cast_fp16)[name = string("op_601_cast_fp16")]; + tensor hidden_states_7_cast_fp16 = mul(x = inputs_5_cast_fp16, y = var_601_cast_fp16)[name = string("hidden_states_7_cast_fp16")]; + tensor w_5_to_fp16 = const()[name = string("w_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15493632)))]; + tensor obj_15_cast_fp16 = mul(x = w_5_to_fp16, y = hidden_states_7_cast_fp16)[name = string("obj_15_cast_fp16")]; + string query_5_pad_type_0 = const()[name = string("query_5_pad_type_0"), val = string("valid")]; + tensor query_5_strides_0 = const()[name = string("query_5_strides_0"), val = tensor([1, 1])]; + tensor query_5_pad_0 = const()[name = string("query_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_5_dilations_0 = const()[name = string("query_5_dilations_0"), val = tensor([1, 1])]; + int32 query_5_groups_0 = const()[name = string("query_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15494720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16019072))))[name = string("pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_5_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_5_dilations_0, groups = query_5_groups_0, pad = query_5_pad_0, pad_type = query_5_pad_type_0, strides = query_5_strides_0, weight = pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized, x = obj_15_cast_fp16)[name = string("query_5_cast_fp16")]; + string key_5_pad_type_0 = const()[name = string("key_5_pad_type_0"), val = string("valid")]; + tensor key_5_strides_0 = const()[name = string("key_5_strides_0"), val = tensor([1, 1])]; + tensor key_5_pad_0 = const()[name = string("key_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_5_dilations_0 = const()[name = string("key_5_dilations_0"), val = tensor([1, 1])]; + int32 key_5_groups_0 = const()[name = string("key_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16019648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16544000))))[name = string("pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_5_cast_fp16 = conv(dilations = key_5_dilations_0, groups = key_5_groups_0, pad = key_5_pad_0, pad_type = key_5_pad_type_0, strides = key_5_strides_0, weight = pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized, x = obj_15_cast_fp16)[name = string("key_5_cast_fp16")]; + string current_value_3_pad_type_0 = const()[name = string("current_value_3_pad_type_0"), val = string("valid")]; + tensor current_value_3_strides_0 = const()[name = string("current_value_3_strides_0"), val = tensor([1, 1])]; + tensor current_value_3_pad_0 = const()[name = string("current_value_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_3_dilations_0 = const()[name = string("current_value_3_dilations_0"), val = tensor([1, 1])]; + int32 current_value_3_groups_0 = const()[name = string("current_value_3_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16544576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17068928))))[name = string("pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_3_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_3_dilations_0, groups = current_value_3_groups_0, pad = current_value_3_pad_0, pad_type = current_value_3_pad_type_0, strides = current_value_3_strides_0, weight = pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized, x = obj_15_cast_fp16)[name = string("current_value_3_cast_fp16")]; + tensor var_639 = const()[name = string("op_639"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_7_cast_fp16 = reshape(shape = var_639, x = query_5_cast_fp16)[name = string("mh_q_7_cast_fp16")]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_5_cast_fp16 = reshape(shape = var_641, x = key_5_cast_fp16)[name = string("mh_k_5_cast_fp16")]; + tensor var_645_cast_fp16 = mul(x = mh_q_7_cast_fp16, y = cos_1_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_650_begin_0 = const()[name = string("op_650_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_650_end_0 = const()[name = string("op_650_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_650_end_mask_0 = const()[name = string("op_650_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_650_cast_fp16 = slice_by_index(begin = var_650_begin_0, end = var_650_end_0, end_mask = var_650_end_mask_0, x = mh_q_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor var_656_begin_0 = const()[name = string("op_656_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_656_end_0 = const()[name = string("op_656_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_656_end_mask_0 = const()[name = string("op_656_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_656_cast_fp16 = slice_by_index(begin = var_656_begin_0, end = var_656_end_0, end_mask = var_656_end_mask_0, x = mh_q_7_cast_fp16)[name = string("op_656_cast_fp16")]; + fp16 const_50_promoted_to_fp16 = const()[name = string("const_50_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_658_cast_fp16 = mul(x = var_656_cast_fp16, y = const_50_promoted_to_fp16)[name = string("op_658_cast_fp16")]; + bool var_660_interleave_0 = const()[name = string("op_660_interleave_0"), val = bool(false)]; + tensor var_660_cast_fp16 = concat(axis = var_327, interleave = var_660_interleave_0, values = (var_658_cast_fp16, var_650_cast_fp16))[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = var_660_cast_fp16, y = sin_1_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor mh_q_9_cast_fp16 = add(x = var_645_cast_fp16, y = var_661_cast_fp16)[name = string("mh_q_9_cast_fp16")]; + tensor var_663_cast_fp16 = mul(x = mh_k_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor var_668_begin_0 = const()[name = string("op_668_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_668_end_0 = const()[name = string("op_668_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_668_end_mask_0 = const()[name = string("op_668_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_668_cast_fp16 = slice_by_index(begin = var_668_begin_0, end = var_668_end_0, end_mask = var_668_end_mask_0, x = mh_k_5_cast_fp16)[name = string("op_668_cast_fp16")]; + tensor var_674_begin_0 = const()[name = string("op_674_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_674_end_0 = const()[name = string("op_674_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_674_end_mask_0 = const()[name = string("op_674_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_674_cast_fp16 = slice_by_index(begin = var_674_begin_0, end = var_674_end_0, end_mask = var_674_end_mask_0, x = mh_k_5_cast_fp16)[name = string("op_674_cast_fp16")]; + fp16 const_53_promoted_to_fp16 = const()[name = string("const_53_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_676_cast_fp16 = mul(x = var_674_cast_fp16, y = const_53_promoted_to_fp16)[name = string("op_676_cast_fp16")]; + bool var_678_interleave_0 = const()[name = string("op_678_interleave_0"), val = bool(false)]; + tensor var_678_cast_fp16 = concat(axis = var_327, interleave = var_678_interleave_0, values = (var_676_cast_fp16, var_668_cast_fp16))[name = string("op_678_cast_fp16")]; + tensor var_679_cast_fp16 = mul(x = var_678_cast_fp16, y = sin_1_cast_fp16)[name = string("op_679_cast_fp16")]; + tensor mh_k_7_cast_fp16 = add(x = var_663_cast_fp16, y = var_679_cast_fp16)[name = string("mh_k_7_cast_fp16")]; + tensor var_683 = const()[name = string("op_683"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_3_cast_fp16 = reshape(shape = var_683, x = mh_k_7_cast_fp16)[name = string("current_key_3_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_361_cast_fp16_1, y = var_495_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_cast_fp16 = mul(x = current_key_3_cast_fp16, y = var_493_cast_fp16)[name = string("op_691_cast_fp16")]; + tensor key_7_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("key_7_cast_fp16")]; + tensor var_694_cast_fp16 = mul(x = var_370_cast_fp16_1, y = var_495_cast_fp16)[name = string("op_694_cast_fp16")]; + tensor var_695_cast_fp16 = mul(x = current_value_3_cast_fp16, y = var_493_cast_fp16)[name = string("op_695_cast_fp16")]; + tensor value_3_cast_fp16 = add(x = var_694_cast_fp16, y = var_695_cast_fp16)[name = string("value_3_cast_fp16")]; + fp16 var_701_to_fp16 = const()[name = string("op_701_to_fp16"), val = fp16(0x1p-3)]; + tensor var_702_cast_fp16 = mul(x = mh_q_9_cast_fp16, y = var_701_to_fp16)[name = string("op_702_cast_fp16")]; + tensor var_705 = const()[name = string("op_705"), val = tensor([1, 16, 64, 80])]; + tensor var_706_cast_fp16 = reshape(shape = var_705, x = key_7_cast_fp16)[name = string("op_706_cast_fp16")]; + bool mh_w_5_transpose_x_0 = const()[name = string("mh_w_5_transpose_x_0"), val = bool(true)]; + bool mh_w_5_transpose_y_0 = const()[name = string("mh_w_5_transpose_y_0"), val = bool(false)]; + tensor mh_w_5_cast_fp16 = matmul(transpose_x = mh_w_5_transpose_x_0, transpose_y = mh_w_5_transpose_y_0, x = var_702_cast_fp16, y = var_706_cast_fp16)[name = string("mh_w_5_cast_fp16")]; + tensor mh_w_7_cast_fp16 = add(x = mh_w_5_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_7_cast_fp16")]; + tensor var_714_cast_fp16 = softmax(axis = var_332, x = mh_w_7_cast_fp16)[name = string("op_714_cast_fp16")]; + tensor var_715 = const()[name = string("op_715"), val = tensor([1, 16, 64, 80])]; + tensor var_716_cast_fp16 = reshape(shape = var_715, x = value_3_cast_fp16)[name = string("op_716_cast_fp16")]; + bool attn_3_transpose_x_0 = const()[name = string("attn_3_transpose_x_0"), val = bool(false)]; + bool attn_3_transpose_y_0 = const()[name = string("attn_3_transpose_y_0"), val = bool(true)]; + tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_716_cast_fp16, y = var_714_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_719 = const()[name = string("op_719"), val = tensor([1, -1, 1, 1])]; + tensor input_49_cast_fp16 = reshape(shape = var_719, x = attn_3_cast_fp16)[name = string("input_49_cast_fp16")]; + string obj_21_pad_type_0 = const()[name = string("obj_21_pad_type_0"), val = string("valid")]; + tensor obj_21_strides_0 = const()[name = string("obj_21_strides_0"), val = tensor([1, 1])]; + tensor obj_21_pad_0 = const()[name = string("obj_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_21_dilations_0 = const()[name = string("obj_21_dilations_0"), val = tensor([1, 1])]; + int32 obj_21_groups_0 = const()[name = string("obj_21_groups_0"), val = int32(1)]; + tensor op_735_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17069504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17593856))))[name = string("op_735_weight_0_to_fp16_palettized")]; + tensor var_735_bias_0_to_fp16 = const()[name = string("op_735_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17594432)))]; + tensor var_735_cast_fp16 = conv(bias = var_735_bias_0_to_fp16, dilations = obj_21_dilations_0, groups = obj_21_groups_0, pad = obj_21_pad_0, pad_type = obj_21_pad_type_0, strides = obj_21_strides_0, weight = op_735_weight_0_to_fp16_palettized, x = input_49_cast_fp16)[name = string("op_735_cast_fp16")]; + tensor inputs_7_cast_fp16 = add(x = inputs_5_cast_fp16, y = var_735_cast_fp16)[name = string("inputs_7_cast_fp16")]; + tensor inputs_sq_7_cast_fp16 = mul(x = inputs_7_cast_fp16, y = inputs_7_cast_fp16)[name = string("inputs_sq_7_cast_fp16")]; + tensor variance_7_axes_0 = const()[name = string("variance_7_axes_0"), val = tensor([1])]; + bool variance_7_keep_dims_0 = const()[name = string("variance_7_keep_dims_0"), val = bool(true)]; + tensor variance_7_cast_fp16 = reduce_mean(axes = variance_7_axes_0, keep_dims = variance_7_keep_dims_0, x = inputs_sq_7_cast_fp16)[name = string("variance_7_cast_fp16")]; + fp16 var_741_to_fp16 = const()[name = string("op_741_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_742_cast_fp16 = add(x = variance_7_cast_fp16, y = var_741_to_fp16)[name = string("op_742_cast_fp16")]; + fp32 var_743_epsilon_0 = const()[name = string("op_743_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_743_cast_fp16 = rsqrt(epsilon = var_743_epsilon_0, x = var_742_cast_fp16)[name = string("op_743_cast_fp16")]; + tensor hidden_states_9_cast_fp16 = mul(x = inputs_7_cast_fp16, y = var_743_cast_fp16)[name = string("hidden_states_9_cast_fp16")]; + tensor w_7_to_fp16 = const()[name = string("w_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17595520)))]; + tensor input_51_cast_fp16 = mul(x = w_7_to_fp16, y = hidden_states_9_cast_fp16)[name = string("input_51_cast_fp16")]; + string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")]; + tensor input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor([1, 1])]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor([1, 1])]; + int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17596608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18120960))))[name = string("pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_53_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor gate_3_cast_fp16 = silu(x = input_53_cast_fp16)[name = string("gate_3_cast_fp16")]; + string up_3_pad_type_0 = const()[name = string("up_3_pad_type_0"), val = string("valid")]; + tensor up_3_strides_0 = const()[name = string("up_3_strides_0"), val = tensor([1, 1])]; + tensor up_3_pad_0 = const()[name = string("up_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_3_dilations_0 = const()[name = string("up_3_dilations_0"), val = tensor([1, 1])]; + int32 up_3_groups_0 = const()[name = string("up_3_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18121536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18645888))))[name = string("pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_3_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_3_dilations_0, groups = up_3_groups_0, pad = up_3_pad_0, pad_type = up_3_pad_type_0, strides = up_3_strides_0, weight = pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("up_3_cast_fp16")]; + tensor input_55_cast_fp16 = mul(x = gate_3_cast_fp16, y = up_3_cast_fp16)[name = string("input_55_cast_fp16")]; + string hidden_states_11_pad_type_0 = const()[name = string("hidden_states_11_pad_type_0"), val = string("valid")]; + tensor hidden_states_11_strides_0 = const()[name = string("hidden_states_11_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_0 = const()[name = string("hidden_states_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_11_dilations_0 = const()[name = string("hidden_states_11_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_11_groups_0 = const()[name = string("hidden_states_11_groups_0"), val = int32(1)]; + tensor op_777_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18646464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19170816))))[name = string("op_777_weight_0_to_fp16_palettized")]; + tensor var_777_bias_0_to_fp16 = const()[name = string("op_777_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19171392)))]; + tensor var_777_cast_fp16 = conv(bias = var_777_bias_0_to_fp16, dilations = hidden_states_11_dilations_0, groups = hidden_states_11_groups_0, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = hidden_states_11_strides_0, weight = op_777_weight_0_to_fp16_palettized, x = input_55_cast_fp16)[name = string("op_777_cast_fp16")]; + tensor inputs_9_cast_fp16 = add(x = inputs_7_cast_fp16, y = var_777_cast_fp16)[name = string("inputs_9_cast_fp16")]; + tensor inputs_sq_9_cast_fp16 = mul(x = inputs_9_cast_fp16, y = inputs_9_cast_fp16)[name = string("inputs_sq_9_cast_fp16")]; + tensor variance_9_axes_0 = const()[name = string("variance_9_axes_0"), val = tensor([1])]; + bool variance_9_keep_dims_0 = const()[name = string("variance_9_keep_dims_0"), val = bool(true)]; + tensor variance_9_cast_fp16 = reduce_mean(axes = variance_9_axes_0, keep_dims = variance_9_keep_dims_0, x = inputs_sq_9_cast_fp16)[name = string("variance_9_cast_fp16")]; + fp16 var_793_to_fp16 = const()[name = string("op_793_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_794_cast_fp16 = add(x = variance_9_cast_fp16, y = var_793_to_fp16)[name = string("op_794_cast_fp16")]; + fp32 var_795_epsilon_0 = const()[name = string("op_795_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_795_cast_fp16 = rsqrt(epsilon = var_795_epsilon_0, x = var_794_cast_fp16)[name = string("op_795_cast_fp16")]; + tensor hidden_states_13_cast_fp16 = mul(x = inputs_9_cast_fp16, y = var_795_cast_fp16)[name = string("hidden_states_13_cast_fp16")]; + tensor w_9_to_fp16 = const()[name = string("w_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19172480)))]; + tensor obj_23_cast_fp16 = mul(x = w_9_to_fp16, y = hidden_states_13_cast_fp16)[name = string("obj_23_cast_fp16")]; + string query_9_pad_type_0 = const()[name = string("query_9_pad_type_0"), val = string("valid")]; + tensor query_9_strides_0 = const()[name = string("query_9_strides_0"), val = tensor([1, 1])]; + tensor query_9_pad_0 = const()[name = string("query_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_9_dilations_0 = const()[name = string("query_9_dilations_0"), val = tensor([1, 1])]; + int32 query_9_groups_0 = const()[name = string("query_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19173568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19697920))))[name = string("pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_9_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_9_dilations_0, groups = query_9_groups_0, pad = query_9_pad_0, pad_type = query_9_pad_type_0, strides = query_9_strides_0, weight = pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized, x = obj_23_cast_fp16)[name = string("query_9_cast_fp16")]; + string key_9_pad_type_0 = const()[name = string("key_9_pad_type_0"), val = string("valid")]; + tensor key_9_strides_0 = const()[name = string("key_9_strides_0"), val = tensor([1, 1])]; + tensor key_9_pad_0 = const()[name = string("key_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_9_dilations_0 = const()[name = string("key_9_dilations_0"), val = tensor([1, 1])]; + int32 key_9_groups_0 = const()[name = string("key_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19698496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20222848))))[name = string("pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_9_cast_fp16 = conv(dilations = key_9_dilations_0, groups = key_9_groups_0, pad = key_9_pad_0, pad_type = key_9_pad_type_0, strides = key_9_strides_0, weight = pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized, x = obj_23_cast_fp16)[name = string("key_9_cast_fp16")]; + string current_value_5_pad_type_0 = const()[name = string("current_value_5_pad_type_0"), val = string("valid")]; + tensor current_value_5_strides_0 = const()[name = string("current_value_5_strides_0"), val = tensor([1, 1])]; + tensor current_value_5_pad_0 = const()[name = string("current_value_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_5_dilations_0 = const()[name = string("current_value_5_dilations_0"), val = tensor([1, 1])]; + int32 current_value_5_groups_0 = const()[name = string("current_value_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20223424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20747776))))[name = string("pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_5_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_5_dilations_0, groups = current_value_5_groups_0, pad = current_value_5_pad_0, pad_type = current_value_5_pad_type_0, strides = current_value_5_strides_0, weight = pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized, x = obj_23_cast_fp16)[name = string("current_value_5_cast_fp16")]; + tensor var_833 = const()[name = string("op_833"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_13_cast_fp16 = reshape(shape = var_833, x = query_9_cast_fp16)[name = string("mh_q_13_cast_fp16")]; + tensor var_835 = const()[name = string("op_835"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_9_cast_fp16 = reshape(shape = var_835, x = key_9_cast_fp16)[name = string("mh_k_9_cast_fp16")]; + tensor var_839_cast_fp16 = mul(x = mh_q_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_839_cast_fp16")]; + tensor var_844_begin_0 = const()[name = string("op_844_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_844_end_0 = const()[name = string("op_844_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_844_end_mask_0 = const()[name = string("op_844_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_844_cast_fp16 = slice_by_index(begin = var_844_begin_0, end = var_844_end_0, end_mask = var_844_end_mask_0, x = mh_q_13_cast_fp16)[name = string("op_844_cast_fp16")]; + tensor var_850_begin_0 = const()[name = string("op_850_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_850_end_0 = const()[name = string("op_850_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_850_end_mask_0 = const()[name = string("op_850_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_850_cast_fp16 = slice_by_index(begin = var_850_begin_0, end = var_850_end_0, end_mask = var_850_end_mask_0, x = mh_q_13_cast_fp16)[name = string("op_850_cast_fp16")]; + fp16 const_69_promoted_to_fp16 = const()[name = string("const_69_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_852_cast_fp16 = mul(x = var_850_cast_fp16, y = const_69_promoted_to_fp16)[name = string("op_852_cast_fp16")]; + bool var_854_interleave_0 = const()[name = string("op_854_interleave_0"), val = bool(false)]; + tensor var_854_cast_fp16 = concat(axis = var_327, interleave = var_854_interleave_0, values = (var_852_cast_fp16, var_844_cast_fp16))[name = string("op_854_cast_fp16")]; + tensor var_855_cast_fp16 = mul(x = var_854_cast_fp16, y = sin_1_cast_fp16)[name = string("op_855_cast_fp16")]; + tensor mh_q_15_cast_fp16 = add(x = var_839_cast_fp16, y = var_855_cast_fp16)[name = string("mh_q_15_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = mh_k_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor var_862_begin_0 = const()[name = string("op_862_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_862_end_0 = const()[name = string("op_862_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_862_end_mask_0 = const()[name = string("op_862_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_862_cast_fp16 = slice_by_index(begin = var_862_begin_0, end = var_862_end_0, end_mask = var_862_end_mask_0, x = mh_k_9_cast_fp16)[name = string("op_862_cast_fp16")]; + tensor var_868_begin_0 = const()[name = string("op_868_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_868_end_0 = const()[name = string("op_868_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_868_end_mask_0 = const()[name = string("op_868_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_868_cast_fp16 = slice_by_index(begin = var_868_begin_0, end = var_868_end_0, end_mask = var_868_end_mask_0, x = mh_k_9_cast_fp16)[name = string("op_868_cast_fp16")]; + fp16 const_72_promoted_to_fp16 = const()[name = string("const_72_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_870_cast_fp16 = mul(x = var_868_cast_fp16, y = const_72_promoted_to_fp16)[name = string("op_870_cast_fp16")]; + bool var_872_interleave_0 = const()[name = string("op_872_interleave_0"), val = bool(false)]; + tensor var_872_cast_fp16 = concat(axis = var_327, interleave = var_872_interleave_0, values = (var_870_cast_fp16, var_862_cast_fp16))[name = string("op_872_cast_fp16")]; + tensor var_873_cast_fp16 = mul(x = var_872_cast_fp16, y = sin_1_cast_fp16)[name = string("op_873_cast_fp16")]; + tensor mh_k_11_cast_fp16 = add(x = var_857_cast_fp16, y = var_873_cast_fp16)[name = string("mh_k_11_cast_fp16")]; + tensor var_877 = const()[name = string("op_877"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_5_cast_fp16 = reshape(shape = var_877, x = mh_k_11_cast_fp16)[name = string("current_key_5_cast_fp16")]; + tensor var_884_cast_fp16 = mul(x = var_361_cast_fp16_2, y = var_495_cast_fp16)[name = string("op_884_cast_fp16")]; + tensor var_885_cast_fp16 = mul(x = current_key_5_cast_fp16, y = var_493_cast_fp16)[name = string("op_885_cast_fp16")]; + tensor key_11_cast_fp16 = add(x = var_884_cast_fp16, y = var_885_cast_fp16)[name = string("key_11_cast_fp16")]; + tensor var_888_cast_fp16 = mul(x = var_370_cast_fp16_2, y = var_495_cast_fp16)[name = string("op_888_cast_fp16")]; + tensor var_889_cast_fp16 = mul(x = current_value_5_cast_fp16, y = var_493_cast_fp16)[name = string("op_889_cast_fp16")]; + tensor value_5_cast_fp16 = add(x = var_888_cast_fp16, y = var_889_cast_fp16)[name = string("value_5_cast_fp16")]; + fp16 var_895_to_fp16 = const()[name = string("op_895_to_fp16"), val = fp16(0x1p-3)]; + tensor var_896_cast_fp16 = mul(x = mh_q_15_cast_fp16, y = var_895_to_fp16)[name = string("op_896_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 16, 64, 80])]; + tensor var_900_cast_fp16 = reshape(shape = var_899, x = key_11_cast_fp16)[name = string("op_900_cast_fp16")]; + bool mh_w_9_transpose_x_0 = const()[name = string("mh_w_9_transpose_x_0"), val = bool(true)]; + bool mh_w_9_transpose_y_0 = const()[name = string("mh_w_9_transpose_y_0"), val = bool(false)]; + tensor mh_w_9_cast_fp16 = matmul(transpose_x = mh_w_9_transpose_x_0, transpose_y = mh_w_9_transpose_y_0, x = var_896_cast_fp16, y = var_900_cast_fp16)[name = string("mh_w_9_cast_fp16")]; + tensor mh_w_11_cast_fp16 = add(x = mh_w_9_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_11_cast_fp16")]; + tensor var_908_cast_fp16 = softmax(axis = var_332, x = mh_w_11_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor var_909 = const()[name = string("op_909"), val = tensor([1, 16, 64, 80])]; + tensor var_910_cast_fp16 = reshape(shape = var_909, x = value_5_cast_fp16)[name = string("op_910_cast_fp16")]; + bool attn_5_transpose_x_0 = const()[name = string("attn_5_transpose_x_0"), val = bool(false)]; + bool attn_5_transpose_y_0 = const()[name = string("attn_5_transpose_y_0"), val = bool(true)]; + tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_910_cast_fp16, y = var_908_cast_fp16)[name = string("attn_5_cast_fp16")]; + tensor var_913 = const()[name = string("op_913"), val = tensor([1, -1, 1, 1])]; + tensor input_57_cast_fp16 = reshape(shape = var_913, x = attn_5_cast_fp16)[name = string("input_57_cast_fp16")]; + string obj_29_pad_type_0 = const()[name = string("obj_29_pad_type_0"), val = string("valid")]; + tensor obj_29_strides_0 = const()[name = string("obj_29_strides_0"), val = tensor([1, 1])]; + tensor obj_29_pad_0 = const()[name = string("obj_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_29_dilations_0 = const()[name = string("obj_29_dilations_0"), val = tensor([1, 1])]; + int32 obj_29_groups_0 = const()[name = string("obj_29_groups_0"), val = int32(1)]; + tensor op_929_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20748352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21272704))))[name = string("op_929_weight_0_to_fp16_palettized")]; + tensor var_929_bias_0_to_fp16 = const()[name = string("op_929_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21273280)))]; + tensor var_929_cast_fp16 = conv(bias = var_929_bias_0_to_fp16, dilations = obj_29_dilations_0, groups = obj_29_groups_0, pad = obj_29_pad_0, pad_type = obj_29_pad_type_0, strides = obj_29_strides_0, weight = op_929_weight_0_to_fp16_palettized, x = input_57_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor inputs_11_cast_fp16 = add(x = inputs_9_cast_fp16, y = var_929_cast_fp16)[name = string("inputs_11_cast_fp16")]; + tensor inputs_sq_11_cast_fp16 = mul(x = inputs_11_cast_fp16, y = inputs_11_cast_fp16)[name = string("inputs_sq_11_cast_fp16")]; + tensor variance_11_axes_0 = const()[name = string("variance_11_axes_0"), val = tensor([1])]; + bool variance_11_keep_dims_0 = const()[name = string("variance_11_keep_dims_0"), val = bool(true)]; + tensor variance_11_cast_fp16 = reduce_mean(axes = variance_11_axes_0, keep_dims = variance_11_keep_dims_0, x = inputs_sq_11_cast_fp16)[name = string("variance_11_cast_fp16")]; + fp16 var_935_to_fp16 = const()[name = string("op_935_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_936_cast_fp16 = add(x = variance_11_cast_fp16, y = var_935_to_fp16)[name = string("op_936_cast_fp16")]; + fp32 var_937_epsilon_0 = const()[name = string("op_937_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_937_cast_fp16 = rsqrt(epsilon = var_937_epsilon_0, x = var_936_cast_fp16)[name = string("op_937_cast_fp16")]; + tensor hidden_states_15_cast_fp16 = mul(x = inputs_11_cast_fp16, y = var_937_cast_fp16)[name = string("hidden_states_15_cast_fp16")]; + tensor w_11_to_fp16 = const()[name = string("w_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21274368)))]; + tensor input_59_cast_fp16 = mul(x = w_11_to_fp16, y = hidden_states_15_cast_fp16)[name = string("input_59_cast_fp16")]; + string input_61_pad_type_0 = const()[name = string("input_61_pad_type_0"), val = string("valid")]; + tensor input_61_strides_0 = const()[name = string("input_61_strides_0"), val = tensor([1, 1])]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_61_dilations_0 = const()[name = string("input_61_dilations_0"), val = tensor([1, 1])]; + int32 input_61_groups_0 = const()[name = string("input_61_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21275456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21799808))))[name = string("pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_61_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor gate_5_cast_fp16 = silu(x = input_61_cast_fp16)[name = string("gate_5_cast_fp16")]; + string up_5_pad_type_0 = const()[name = string("up_5_pad_type_0"), val = string("valid")]; + tensor up_5_strides_0 = const()[name = string("up_5_strides_0"), val = tensor([1, 1])]; + tensor up_5_pad_0 = const()[name = string("up_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_5_dilations_0 = const()[name = string("up_5_dilations_0"), val = tensor([1, 1])]; + int32 up_5_groups_0 = const()[name = string("up_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21800384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22324736))))[name = string("pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_5_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_5_dilations_0, groups = up_5_groups_0, pad = up_5_pad_0, pad_type = up_5_pad_type_0, strides = up_5_strides_0, weight = pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = string("up_5_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = gate_5_cast_fp16, y = up_5_cast_fp16)[name = string("input_63_cast_fp16")]; + string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")]; + tensor hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)]; + tensor op_971_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22325312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22849664))))[name = string("op_971_weight_0_to_fp16_palettized")]; + tensor var_971_bias_0_to_fp16 = const()[name = string("op_971_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22850240)))]; + tensor var_971_cast_fp16 = conv(bias = var_971_bias_0_to_fp16, dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = op_971_weight_0_to_fp16_palettized, x = input_63_cast_fp16)[name = string("op_971_cast_fp16")]; + tensor inputs_13_cast_fp16 = add(x = inputs_11_cast_fp16, y = var_971_cast_fp16)[name = string("inputs_13_cast_fp16")]; + tensor inputs_sq_13_cast_fp16 = mul(x = inputs_13_cast_fp16, y = inputs_13_cast_fp16)[name = string("inputs_sq_13_cast_fp16")]; + tensor variance_13_axes_0 = const()[name = string("variance_13_axes_0"), val = tensor([1])]; + bool variance_13_keep_dims_0 = const()[name = string("variance_13_keep_dims_0"), val = bool(true)]; + tensor variance_13_cast_fp16 = reduce_mean(axes = variance_13_axes_0, keep_dims = variance_13_keep_dims_0, x = inputs_sq_13_cast_fp16)[name = string("variance_13_cast_fp16")]; + fp16 var_987_to_fp16 = const()[name = string("op_987_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_988_cast_fp16 = add(x = variance_13_cast_fp16, y = var_987_to_fp16)[name = string("op_988_cast_fp16")]; + fp32 var_989_epsilon_0 = const()[name = string("op_989_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_989_cast_fp16 = rsqrt(epsilon = var_989_epsilon_0, x = var_988_cast_fp16)[name = string("op_989_cast_fp16")]; + tensor hidden_states_19_cast_fp16 = mul(x = inputs_13_cast_fp16, y = var_989_cast_fp16)[name = string("hidden_states_19_cast_fp16")]; + tensor w_13_to_fp16 = const()[name = string("w_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22851328)))]; + tensor obj_31_cast_fp16 = mul(x = w_13_to_fp16, y = hidden_states_19_cast_fp16)[name = string("obj_31_cast_fp16")]; + string query_13_pad_type_0 = const()[name = string("query_13_pad_type_0"), val = string("valid")]; + tensor query_13_strides_0 = const()[name = string("query_13_strides_0"), val = tensor([1, 1])]; + tensor query_13_pad_0 = const()[name = string("query_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_13_dilations_0 = const()[name = string("query_13_dilations_0"), val = tensor([1, 1])]; + int32 query_13_groups_0 = const()[name = string("query_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22852416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23376768))))[name = string("pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_13_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_13_dilations_0, groups = query_13_groups_0, pad = query_13_pad_0, pad_type = query_13_pad_type_0, strides = query_13_strides_0, weight = pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized, x = obj_31_cast_fp16)[name = string("query_13_cast_fp16")]; + string key_13_pad_type_0 = const()[name = string("key_13_pad_type_0"), val = string("valid")]; + tensor key_13_strides_0 = const()[name = string("key_13_strides_0"), val = tensor([1, 1])]; + tensor key_13_pad_0 = const()[name = string("key_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_13_dilations_0 = const()[name = string("key_13_dilations_0"), val = tensor([1, 1])]; + int32 key_13_groups_0 = const()[name = string("key_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23377344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23901696))))[name = string("pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_13_cast_fp16 = conv(dilations = key_13_dilations_0, groups = key_13_groups_0, pad = key_13_pad_0, pad_type = key_13_pad_type_0, strides = key_13_strides_0, weight = pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized, x = obj_31_cast_fp16)[name = string("key_13_cast_fp16")]; + string current_value_7_pad_type_0 = const()[name = string("current_value_7_pad_type_0"), val = string("valid")]; + tensor current_value_7_strides_0 = const()[name = string("current_value_7_strides_0"), val = tensor([1, 1])]; + tensor current_value_7_pad_0 = const()[name = string("current_value_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_7_dilations_0 = const()[name = string("current_value_7_dilations_0"), val = tensor([1, 1])]; + int32 current_value_7_groups_0 = const()[name = string("current_value_7_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23902272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24426624))))[name = string("pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_7_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_7_dilations_0, groups = current_value_7_groups_0, pad = current_value_7_pad_0, pad_type = current_value_7_pad_type_0, strides = current_value_7_strides_0, weight = pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized, x = obj_31_cast_fp16)[name = string("current_value_7_cast_fp16")]; + tensor var_1027 = const()[name = string("op_1027"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_19_cast_fp16 = reshape(shape = var_1027, x = query_13_cast_fp16)[name = string("mh_q_19_cast_fp16")]; + tensor var_1029 = const()[name = string("op_1029"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_13_cast_fp16 = reshape(shape = var_1029, x = key_13_cast_fp16)[name = string("mh_k_13_cast_fp16")]; + tensor var_1033_cast_fp16 = mul(x = mh_q_19_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1033_cast_fp16")]; + tensor var_1038_begin_0 = const()[name = string("op_1038_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1038_end_0 = const()[name = string("op_1038_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1038_end_mask_0 = const()[name = string("op_1038_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1038_cast_fp16 = slice_by_index(begin = var_1038_begin_0, end = var_1038_end_0, end_mask = var_1038_end_mask_0, x = mh_q_19_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor var_1044_begin_0 = const()[name = string("op_1044_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1044_end_0 = const()[name = string("op_1044_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1044_end_mask_0 = const()[name = string("op_1044_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1044_cast_fp16 = slice_by_index(begin = var_1044_begin_0, end = var_1044_end_0, end_mask = var_1044_end_mask_0, x = mh_q_19_cast_fp16)[name = string("op_1044_cast_fp16")]; + fp16 const_88_promoted_to_fp16 = const()[name = string("const_88_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1046_cast_fp16 = mul(x = var_1044_cast_fp16, y = const_88_promoted_to_fp16)[name = string("op_1046_cast_fp16")]; + bool var_1048_interleave_0 = const()[name = string("op_1048_interleave_0"), val = bool(false)]; + tensor var_1048_cast_fp16 = concat(axis = var_327, interleave = var_1048_interleave_0, values = (var_1046_cast_fp16, var_1038_cast_fp16))[name = string("op_1048_cast_fp16")]; + tensor var_1049_cast_fp16 = mul(x = var_1048_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1049_cast_fp16")]; + tensor mh_q_21_cast_fp16 = add(x = var_1033_cast_fp16, y = var_1049_cast_fp16)[name = string("mh_q_21_cast_fp16")]; + tensor var_1051_cast_fp16 = mul(x = mh_k_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1051_cast_fp16")]; + tensor var_1056_begin_0 = const()[name = string("op_1056_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1056_end_0 = const()[name = string("op_1056_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1056_end_mask_0 = const()[name = string("op_1056_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1056_cast_fp16 = slice_by_index(begin = var_1056_begin_0, end = var_1056_end_0, end_mask = var_1056_end_mask_0, x = mh_k_13_cast_fp16)[name = string("op_1056_cast_fp16")]; + tensor var_1062_begin_0 = const()[name = string("op_1062_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1062_end_0 = const()[name = string("op_1062_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1062_end_mask_0 = const()[name = string("op_1062_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1062_cast_fp16 = slice_by_index(begin = var_1062_begin_0, end = var_1062_end_0, end_mask = var_1062_end_mask_0, x = mh_k_13_cast_fp16)[name = string("op_1062_cast_fp16")]; + fp16 const_91_promoted_to_fp16 = const()[name = string("const_91_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1064_cast_fp16 = mul(x = var_1062_cast_fp16, y = const_91_promoted_to_fp16)[name = string("op_1064_cast_fp16")]; + bool var_1066_interleave_0 = const()[name = string("op_1066_interleave_0"), val = bool(false)]; + tensor var_1066_cast_fp16 = concat(axis = var_327, interleave = var_1066_interleave_0, values = (var_1064_cast_fp16, var_1056_cast_fp16))[name = string("op_1066_cast_fp16")]; + tensor var_1067_cast_fp16 = mul(x = var_1066_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor mh_k_15_cast_fp16 = add(x = var_1051_cast_fp16, y = var_1067_cast_fp16)[name = string("mh_k_15_cast_fp16")]; + tensor var_1071 = const()[name = string("op_1071"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_7_cast_fp16 = reshape(shape = var_1071, x = mh_k_15_cast_fp16)[name = string("current_key_7_cast_fp16")]; + tensor var_1078_cast_fp16 = mul(x = var_361_cast_fp16_3, y = var_495_cast_fp16)[name = string("op_1078_cast_fp16")]; + tensor var_1079_cast_fp16 = mul(x = current_key_7_cast_fp16, y = var_493_cast_fp16)[name = string("op_1079_cast_fp16")]; + tensor key_15_cast_fp16 = add(x = var_1078_cast_fp16, y = var_1079_cast_fp16)[name = string("key_15_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_370_cast_fp16_3, y = var_495_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor var_1083_cast_fp16 = mul(x = current_value_7_cast_fp16, y = var_493_cast_fp16)[name = string("op_1083_cast_fp16")]; + tensor value_7_cast_fp16 = add(x = var_1082_cast_fp16, y = var_1083_cast_fp16)[name = string("value_7_cast_fp16")]; + fp16 var_1089_to_fp16 = const()[name = string("op_1089_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1090_cast_fp16 = mul(x = mh_q_21_cast_fp16, y = var_1089_to_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1093 = const()[name = string("op_1093"), val = tensor([1, 16, 64, 80])]; + tensor var_1094_cast_fp16 = reshape(shape = var_1093, x = key_15_cast_fp16)[name = string("op_1094_cast_fp16")]; + bool mh_w_13_transpose_x_0 = const()[name = string("mh_w_13_transpose_x_0"), val = bool(true)]; + bool mh_w_13_transpose_y_0 = const()[name = string("mh_w_13_transpose_y_0"), val = bool(false)]; + tensor mh_w_13_cast_fp16 = matmul(transpose_x = mh_w_13_transpose_x_0, transpose_y = mh_w_13_transpose_y_0, x = var_1090_cast_fp16, y = var_1094_cast_fp16)[name = string("mh_w_13_cast_fp16")]; + tensor mh_w_15_cast_fp16 = add(x = mh_w_13_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_15_cast_fp16")]; + tensor var_1102_cast_fp16 = softmax(axis = var_332, x = mh_w_15_cast_fp16)[name = string("op_1102_cast_fp16")]; + tensor var_1103 = const()[name = string("op_1103"), val = tensor([1, 16, 64, 80])]; + tensor var_1104_cast_fp16 = reshape(shape = var_1103, x = value_7_cast_fp16)[name = string("op_1104_cast_fp16")]; + bool attn_7_transpose_x_0 = const()[name = string("attn_7_transpose_x_0"), val = bool(false)]; + bool attn_7_transpose_y_0 = const()[name = string("attn_7_transpose_y_0"), val = bool(true)]; + tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_1104_cast_fp16, y = var_1102_cast_fp16)[name = string("attn_7_cast_fp16")]; + tensor var_1107 = const()[name = string("op_1107"), val = tensor([1, -1, 1, 1])]; + tensor input_65_cast_fp16 = reshape(shape = var_1107, x = attn_7_cast_fp16)[name = string("input_65_cast_fp16")]; + string obj_37_pad_type_0 = const()[name = string("obj_37_pad_type_0"), val = string("valid")]; + tensor obj_37_strides_0 = const()[name = string("obj_37_strides_0"), val = tensor([1, 1])]; + tensor obj_37_pad_0 = const()[name = string("obj_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_37_dilations_0 = const()[name = string("obj_37_dilations_0"), val = tensor([1, 1])]; + int32 obj_37_groups_0 = const()[name = string("obj_37_groups_0"), val = int32(1)]; + tensor op_1123_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24427200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24951552))))[name = string("op_1123_weight_0_to_fp16_palettized")]; + tensor var_1123_bias_0_to_fp16 = const()[name = string("op_1123_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24952128)))]; + tensor var_1123_cast_fp16 = conv(bias = var_1123_bias_0_to_fp16, dilations = obj_37_dilations_0, groups = obj_37_groups_0, pad = obj_37_pad_0, pad_type = obj_37_pad_type_0, strides = obj_37_strides_0, weight = op_1123_weight_0_to_fp16_palettized, x = input_65_cast_fp16)[name = string("op_1123_cast_fp16")]; + tensor inputs_15_cast_fp16 = add(x = inputs_13_cast_fp16, y = var_1123_cast_fp16)[name = string("inputs_15_cast_fp16")]; + tensor inputs_sq_15_cast_fp16 = mul(x = inputs_15_cast_fp16, y = inputs_15_cast_fp16)[name = string("inputs_sq_15_cast_fp16")]; + tensor variance_15_axes_0 = const()[name = string("variance_15_axes_0"), val = tensor([1])]; + bool variance_15_keep_dims_0 = const()[name = string("variance_15_keep_dims_0"), val = bool(true)]; + tensor variance_15_cast_fp16 = reduce_mean(axes = variance_15_axes_0, keep_dims = variance_15_keep_dims_0, x = inputs_sq_15_cast_fp16)[name = string("variance_15_cast_fp16")]; + fp16 var_1129_to_fp16 = const()[name = string("op_1129_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1130_cast_fp16 = add(x = variance_15_cast_fp16, y = var_1129_to_fp16)[name = string("op_1130_cast_fp16")]; + fp32 var_1131_epsilon_0 = const()[name = string("op_1131_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1131_cast_fp16 = rsqrt(epsilon = var_1131_epsilon_0, x = var_1130_cast_fp16)[name = string("op_1131_cast_fp16")]; + tensor hidden_states_21_cast_fp16 = mul(x = inputs_15_cast_fp16, y = var_1131_cast_fp16)[name = string("hidden_states_21_cast_fp16")]; + tensor w_15_to_fp16 = const()[name = string("w_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24953216)))]; + tensor input_67_cast_fp16 = mul(x = w_15_to_fp16, y = hidden_states_21_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_pad_type_0 = const()[name = string("input_69_pad_type_0"), val = string("valid")]; + tensor input_69_strides_0 = const()[name = string("input_69_strides_0"), val = tensor([1, 1])]; + tensor input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_69_dilations_0 = const()[name = string("input_69_dilations_0"), val = tensor([1, 1])]; + int32 input_69_groups_0 = const()[name = string("input_69_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24954304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25478656))))[name = string("pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_69_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor gate_7_cast_fp16 = silu(x = input_69_cast_fp16)[name = string("gate_7_cast_fp16")]; + string up_7_pad_type_0 = const()[name = string("up_7_pad_type_0"), val = string("valid")]; + tensor up_7_strides_0 = const()[name = string("up_7_strides_0"), val = tensor([1, 1])]; + tensor up_7_pad_0 = const()[name = string("up_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_7_dilations_0 = const()[name = string("up_7_dilations_0"), val = tensor([1, 1])]; + int32 up_7_groups_0 = const()[name = string("up_7_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25479232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26003584))))[name = string("pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_7_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_7_dilations_0, groups = up_7_groups_0, pad = up_7_pad_0, pad_type = up_7_pad_type_0, strides = up_7_strides_0, weight = pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = string("up_7_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = gate_7_cast_fp16, y = up_7_cast_fp16)[name = string("input_71_cast_fp16")]; + string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")]; + tensor hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)]; + tensor op_1165_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26004160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26528512))))[name = string("op_1165_weight_0_to_fp16_palettized")]; + tensor var_1165_bias_0_to_fp16 = const()[name = string("op_1165_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26529088)))]; + tensor var_1165_cast_fp16 = conv(bias = var_1165_bias_0_to_fp16, dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = op_1165_weight_0_to_fp16_palettized, x = input_71_cast_fp16)[name = string("op_1165_cast_fp16")]; + tensor inputs_17_cast_fp16 = add(x = inputs_15_cast_fp16, y = var_1165_cast_fp16)[name = string("inputs_17_cast_fp16")]; + tensor inputs_sq_17_cast_fp16 = mul(x = inputs_17_cast_fp16, y = inputs_17_cast_fp16)[name = string("inputs_sq_17_cast_fp16")]; + tensor variance_17_axes_0 = const()[name = string("variance_17_axes_0"), val = tensor([1])]; + bool variance_17_keep_dims_0 = const()[name = string("variance_17_keep_dims_0"), val = bool(true)]; + tensor variance_17_cast_fp16 = reduce_mean(axes = variance_17_axes_0, keep_dims = variance_17_keep_dims_0, x = inputs_sq_17_cast_fp16)[name = string("variance_17_cast_fp16")]; + fp16 var_1181_to_fp16 = const()[name = string("op_1181_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1182_cast_fp16 = add(x = variance_17_cast_fp16, y = var_1181_to_fp16)[name = string("op_1182_cast_fp16")]; + fp32 var_1183_epsilon_0 = const()[name = string("op_1183_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1183_cast_fp16 = rsqrt(epsilon = var_1183_epsilon_0, x = var_1182_cast_fp16)[name = string("op_1183_cast_fp16")]; + tensor hidden_states_25_cast_fp16 = mul(x = inputs_17_cast_fp16, y = var_1183_cast_fp16)[name = string("hidden_states_25_cast_fp16")]; + tensor w_17_to_fp16 = const()[name = string("w_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26530176)))]; + tensor obj_39_cast_fp16 = mul(x = w_17_to_fp16, y = hidden_states_25_cast_fp16)[name = string("obj_39_cast_fp16")]; + string query_17_pad_type_0 = const()[name = string("query_17_pad_type_0"), val = string("valid")]; + tensor query_17_strides_0 = const()[name = string("query_17_strides_0"), val = tensor([1, 1])]; + tensor query_17_pad_0 = const()[name = string("query_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_17_dilations_0 = const()[name = string("query_17_dilations_0"), val = tensor([1, 1])]; + int32 query_17_groups_0 = const()[name = string("query_17_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26531264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27055616))))[name = string("pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_17_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_17_dilations_0, groups = query_17_groups_0, pad = query_17_pad_0, pad_type = query_17_pad_type_0, strides = query_17_strides_0, weight = pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized, x = obj_39_cast_fp16)[name = string("query_17_cast_fp16")]; + string key_17_pad_type_0 = const()[name = string("key_17_pad_type_0"), val = string("valid")]; + tensor key_17_strides_0 = const()[name = string("key_17_strides_0"), val = tensor([1, 1])]; + tensor key_17_pad_0 = const()[name = string("key_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_17_dilations_0 = const()[name = string("key_17_dilations_0"), val = tensor([1, 1])]; + int32 key_17_groups_0 = const()[name = string("key_17_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27056192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27580544))))[name = string("pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_17_cast_fp16 = conv(dilations = key_17_dilations_0, groups = key_17_groups_0, pad = key_17_pad_0, pad_type = key_17_pad_type_0, strides = key_17_strides_0, weight = pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized, x = obj_39_cast_fp16)[name = string("key_17_cast_fp16")]; + string current_value_9_pad_type_0 = const()[name = string("current_value_9_pad_type_0"), val = string("valid")]; + tensor current_value_9_strides_0 = const()[name = string("current_value_9_strides_0"), val = tensor([1, 1])]; + tensor current_value_9_pad_0 = const()[name = string("current_value_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_9_dilations_0 = const()[name = string("current_value_9_dilations_0"), val = tensor([1, 1])]; + int32 current_value_9_groups_0 = const()[name = string("current_value_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27581120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28105472))))[name = string("pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_9_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_9_dilations_0, groups = current_value_9_groups_0, pad = current_value_9_pad_0, pad_type = current_value_9_pad_type_0, strides = current_value_9_strides_0, weight = pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized, x = obj_39_cast_fp16)[name = string("current_value_9_cast_fp16")]; + tensor var_1221 = const()[name = string("op_1221"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_25_cast_fp16 = reshape(shape = var_1221, x = query_17_cast_fp16)[name = string("mh_q_25_cast_fp16")]; + tensor var_1223 = const()[name = string("op_1223"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_17_cast_fp16 = reshape(shape = var_1223, x = key_17_cast_fp16)[name = string("mh_k_17_cast_fp16")]; + tensor var_1227_cast_fp16 = mul(x = mh_q_25_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1227_cast_fp16")]; + tensor var_1232_begin_0 = const()[name = string("op_1232_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1232_end_0 = const()[name = string("op_1232_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1232_end_mask_0 = const()[name = string("op_1232_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1232_cast_fp16 = slice_by_index(begin = var_1232_begin_0, end = var_1232_end_0, end_mask = var_1232_end_mask_0, x = mh_q_25_cast_fp16)[name = string("op_1232_cast_fp16")]; + tensor var_1238_begin_0 = const()[name = string("op_1238_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1238_end_0 = const()[name = string("op_1238_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1238_end_mask_0 = const()[name = string("op_1238_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1238_cast_fp16 = slice_by_index(begin = var_1238_begin_0, end = var_1238_end_0, end_mask = var_1238_end_mask_0, x = mh_q_25_cast_fp16)[name = string("op_1238_cast_fp16")]; + fp16 const_107_promoted_to_fp16 = const()[name = string("const_107_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1240_cast_fp16 = mul(x = var_1238_cast_fp16, y = const_107_promoted_to_fp16)[name = string("op_1240_cast_fp16")]; + bool var_1242_interleave_0 = const()[name = string("op_1242_interleave_0"), val = bool(false)]; + tensor var_1242_cast_fp16 = concat(axis = var_327, interleave = var_1242_interleave_0, values = (var_1240_cast_fp16, var_1232_cast_fp16))[name = string("op_1242_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = var_1242_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor mh_q_27_cast_fp16 = add(x = var_1227_cast_fp16, y = var_1243_cast_fp16)[name = string("mh_q_27_cast_fp16")]; + tensor var_1245_cast_fp16 = mul(x = mh_k_17_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1245_cast_fp16")]; + tensor var_1250_begin_0 = const()[name = string("op_1250_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1250_end_0 = const()[name = string("op_1250_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1250_end_mask_0 = const()[name = string("op_1250_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1250_cast_fp16 = slice_by_index(begin = var_1250_begin_0, end = var_1250_end_0, end_mask = var_1250_end_mask_0, x = mh_k_17_cast_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1256_begin_0 = const()[name = string("op_1256_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1256_end_0 = const()[name = string("op_1256_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1256_end_mask_0 = const()[name = string("op_1256_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1256_cast_fp16 = slice_by_index(begin = var_1256_begin_0, end = var_1256_end_0, end_mask = var_1256_end_mask_0, x = mh_k_17_cast_fp16)[name = string("op_1256_cast_fp16")]; + fp16 const_110_promoted_to_fp16 = const()[name = string("const_110_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1258_cast_fp16 = mul(x = var_1256_cast_fp16, y = const_110_promoted_to_fp16)[name = string("op_1258_cast_fp16")]; + bool var_1260_interleave_0 = const()[name = string("op_1260_interleave_0"), val = bool(false)]; + tensor var_1260_cast_fp16 = concat(axis = var_327, interleave = var_1260_interleave_0, values = (var_1258_cast_fp16, var_1250_cast_fp16))[name = string("op_1260_cast_fp16")]; + tensor var_1261_cast_fp16 = mul(x = var_1260_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1261_cast_fp16")]; + tensor mh_k_19_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1261_cast_fp16)[name = string("mh_k_19_cast_fp16")]; + tensor var_1265 = const()[name = string("op_1265"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_9_cast_fp16 = reshape(shape = var_1265, x = mh_k_19_cast_fp16)[name = string("current_key_9_cast_fp16")]; + tensor var_1272_cast_fp16 = mul(x = var_361_cast_fp16_4, y = var_495_cast_fp16)[name = string("op_1272_cast_fp16")]; + tensor var_1273_cast_fp16 = mul(x = current_key_9_cast_fp16, y = var_493_cast_fp16)[name = string("op_1273_cast_fp16")]; + tensor key_19_cast_fp16 = add(x = var_1272_cast_fp16, y = var_1273_cast_fp16)[name = string("key_19_cast_fp16")]; + tensor var_1276_cast_fp16 = mul(x = var_370_cast_fp16_4, y = var_495_cast_fp16)[name = string("op_1276_cast_fp16")]; + tensor var_1277_cast_fp16 = mul(x = current_value_9_cast_fp16, y = var_493_cast_fp16)[name = string("op_1277_cast_fp16")]; + tensor value_9_cast_fp16 = add(x = var_1276_cast_fp16, y = var_1277_cast_fp16)[name = string("value_9_cast_fp16")]; + fp16 var_1283_to_fp16 = const()[name = string("op_1283_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1284_cast_fp16 = mul(x = mh_q_27_cast_fp16, y = var_1283_to_fp16)[name = string("op_1284_cast_fp16")]; + tensor var_1287 = const()[name = string("op_1287"), val = tensor([1, 16, 64, 80])]; + tensor var_1288_cast_fp16 = reshape(shape = var_1287, x = key_19_cast_fp16)[name = string("op_1288_cast_fp16")]; + bool mh_w_17_transpose_x_0 = const()[name = string("mh_w_17_transpose_x_0"), val = bool(true)]; + bool mh_w_17_transpose_y_0 = const()[name = string("mh_w_17_transpose_y_0"), val = bool(false)]; + tensor mh_w_17_cast_fp16 = matmul(transpose_x = mh_w_17_transpose_x_0, transpose_y = mh_w_17_transpose_y_0, x = var_1284_cast_fp16, y = var_1288_cast_fp16)[name = string("mh_w_17_cast_fp16")]; + tensor mh_w_19_cast_fp16 = add(x = mh_w_17_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_19_cast_fp16")]; + tensor var_1296_cast_fp16 = softmax(axis = var_332, x = mh_w_19_cast_fp16)[name = string("op_1296_cast_fp16")]; + tensor var_1297 = const()[name = string("op_1297"), val = tensor([1, 16, 64, 80])]; + tensor var_1298_cast_fp16 = reshape(shape = var_1297, x = value_9_cast_fp16)[name = string("op_1298_cast_fp16")]; + bool attn_9_transpose_x_0 = const()[name = string("attn_9_transpose_x_0"), val = bool(false)]; + bool attn_9_transpose_y_0 = const()[name = string("attn_9_transpose_y_0"), val = bool(true)]; + tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_1298_cast_fp16, y = var_1296_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor var_1301 = const()[name = string("op_1301"), val = tensor([1, -1, 1, 1])]; + tensor input_73_cast_fp16 = reshape(shape = var_1301, x = attn_9_cast_fp16)[name = string("input_73_cast_fp16")]; + string obj_45_pad_type_0 = const()[name = string("obj_45_pad_type_0"), val = string("valid")]; + tensor obj_45_strides_0 = const()[name = string("obj_45_strides_0"), val = tensor([1, 1])]; + tensor obj_45_pad_0 = const()[name = string("obj_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_45_dilations_0 = const()[name = string("obj_45_dilations_0"), val = tensor([1, 1])]; + int32 obj_45_groups_0 = const()[name = string("obj_45_groups_0"), val = int32(1)]; + tensor op_1317_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28106048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28630400))))[name = string("op_1317_weight_0_to_fp16_palettized")]; + tensor var_1317_bias_0_to_fp16 = const()[name = string("op_1317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28630976)))]; + tensor var_1317_cast_fp16 = conv(bias = var_1317_bias_0_to_fp16, dilations = obj_45_dilations_0, groups = obj_45_groups_0, pad = obj_45_pad_0, pad_type = obj_45_pad_type_0, strides = obj_45_strides_0, weight = op_1317_weight_0_to_fp16_palettized, x = input_73_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor inputs_19_cast_fp16 = add(x = inputs_17_cast_fp16, y = var_1317_cast_fp16)[name = string("inputs_19_cast_fp16")]; + tensor inputs_sq_19_cast_fp16 = mul(x = inputs_19_cast_fp16, y = inputs_19_cast_fp16)[name = string("inputs_sq_19_cast_fp16")]; + tensor variance_19_axes_0 = const()[name = string("variance_19_axes_0"), val = tensor([1])]; + bool variance_19_keep_dims_0 = const()[name = string("variance_19_keep_dims_0"), val = bool(true)]; + tensor variance_19_cast_fp16 = reduce_mean(axes = variance_19_axes_0, keep_dims = variance_19_keep_dims_0, x = inputs_sq_19_cast_fp16)[name = string("variance_19_cast_fp16")]; + fp16 var_1323_to_fp16 = const()[name = string("op_1323_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1324_cast_fp16 = add(x = variance_19_cast_fp16, y = var_1323_to_fp16)[name = string("op_1324_cast_fp16")]; + fp32 var_1325_epsilon_0 = const()[name = string("op_1325_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1325_cast_fp16 = rsqrt(epsilon = var_1325_epsilon_0, x = var_1324_cast_fp16)[name = string("op_1325_cast_fp16")]; + tensor hidden_states_27_cast_fp16 = mul(x = inputs_19_cast_fp16, y = var_1325_cast_fp16)[name = string("hidden_states_27_cast_fp16")]; + tensor w_19_to_fp16 = const()[name = string("w_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28632064)))]; + tensor input_75_cast_fp16 = mul(x = w_19_to_fp16, y = hidden_states_27_cast_fp16)[name = string("input_75_cast_fp16")]; + string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("valid")]; + tensor input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor([1, 1])]; + tensor input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor([1, 1])]; + int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28633152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29157504))))[name = string("pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_77_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor gate_9_cast_fp16 = silu(x = input_77_cast_fp16)[name = string("gate_9_cast_fp16")]; + string up_9_pad_type_0 = const()[name = string("up_9_pad_type_0"), val = string("valid")]; + tensor up_9_strides_0 = const()[name = string("up_9_strides_0"), val = tensor([1, 1])]; + tensor up_9_pad_0 = const()[name = string("up_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_9_dilations_0 = const()[name = string("up_9_dilations_0"), val = tensor([1, 1])]; + int32 up_9_groups_0 = const()[name = string("up_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29158080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29682432))))[name = string("pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_9_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_9_dilations_0, groups = up_9_groups_0, pad = up_9_pad_0, pad_type = up_9_pad_type_0, strides = up_9_strides_0, weight = pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = string("up_9_cast_fp16")]; + tensor input_79_cast_fp16 = mul(x = gate_9_cast_fp16, y = up_9_cast_fp16)[name = string("input_79_cast_fp16")]; + string hidden_states_29_pad_type_0 = const()[name = string("hidden_states_29_pad_type_0"), val = string("valid")]; + tensor hidden_states_29_strides_0 = const()[name = string("hidden_states_29_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_29_pad_0 = const()[name = string("hidden_states_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_29_dilations_0 = const()[name = string("hidden_states_29_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_29_groups_0 = const()[name = string("hidden_states_29_groups_0"), val = int32(1)]; + tensor op_1359_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29683008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30207360))))[name = string("op_1359_weight_0_to_fp16_palettized")]; + tensor var_1359_bias_0_to_fp16 = const()[name = string("op_1359_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30207936)))]; + tensor var_1359_cast_fp16 = conv(bias = var_1359_bias_0_to_fp16, dilations = hidden_states_29_dilations_0, groups = hidden_states_29_groups_0, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = hidden_states_29_strides_0, weight = op_1359_weight_0_to_fp16_palettized, x = input_79_cast_fp16)[name = string("op_1359_cast_fp16")]; + tensor inputs_21_cast_fp16 = add(x = inputs_19_cast_fp16, y = var_1359_cast_fp16)[name = string("inputs_21_cast_fp16")]; + tensor inputs_sq_21_cast_fp16 = mul(x = inputs_21_cast_fp16, y = inputs_21_cast_fp16)[name = string("inputs_sq_21_cast_fp16")]; + tensor variance_21_axes_0 = const()[name = string("variance_21_axes_0"), val = tensor([1])]; + bool variance_21_keep_dims_0 = const()[name = string("variance_21_keep_dims_0"), val = bool(true)]; + tensor variance_21_cast_fp16 = reduce_mean(axes = variance_21_axes_0, keep_dims = variance_21_keep_dims_0, x = inputs_sq_21_cast_fp16)[name = string("variance_21_cast_fp16")]; + fp16 var_1375_to_fp16 = const()[name = string("op_1375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1376_cast_fp16 = add(x = variance_21_cast_fp16, y = var_1375_to_fp16)[name = string("op_1376_cast_fp16")]; + fp32 var_1377_epsilon_0 = const()[name = string("op_1377_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1377_cast_fp16 = rsqrt(epsilon = var_1377_epsilon_0, x = var_1376_cast_fp16)[name = string("op_1377_cast_fp16")]; + tensor hidden_states_31_cast_fp16 = mul(x = inputs_21_cast_fp16, y = var_1377_cast_fp16)[name = string("hidden_states_31_cast_fp16")]; + tensor w_21_to_fp16 = const()[name = string("w_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30209024)))]; + tensor obj_47_cast_fp16 = mul(x = w_21_to_fp16, y = hidden_states_31_cast_fp16)[name = string("obj_47_cast_fp16")]; + string query_21_pad_type_0 = const()[name = string("query_21_pad_type_0"), val = string("valid")]; + tensor query_21_strides_0 = const()[name = string("query_21_strides_0"), val = tensor([1, 1])]; + tensor query_21_pad_0 = const()[name = string("query_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_21_dilations_0 = const()[name = string("query_21_dilations_0"), val = tensor([1, 1])]; + int32 query_21_groups_0 = const()[name = string("query_21_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30210112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30734464))))[name = string("pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_21_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_21_dilations_0, groups = query_21_groups_0, pad = query_21_pad_0, pad_type = query_21_pad_type_0, strides = query_21_strides_0, weight = pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized, x = obj_47_cast_fp16)[name = string("query_21_cast_fp16")]; + string key_21_pad_type_0 = const()[name = string("key_21_pad_type_0"), val = string("valid")]; + tensor key_21_strides_0 = const()[name = string("key_21_strides_0"), val = tensor([1, 1])]; + tensor key_21_pad_0 = const()[name = string("key_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_21_dilations_0 = const()[name = string("key_21_dilations_0"), val = tensor([1, 1])]; + int32 key_21_groups_0 = const()[name = string("key_21_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30735040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31259392))))[name = string("pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_21_cast_fp16 = conv(dilations = key_21_dilations_0, groups = key_21_groups_0, pad = key_21_pad_0, pad_type = key_21_pad_type_0, strides = key_21_strides_0, weight = pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized, x = obj_47_cast_fp16)[name = string("key_21_cast_fp16")]; + string current_value_11_pad_type_0 = const()[name = string("current_value_11_pad_type_0"), val = string("valid")]; + tensor current_value_11_strides_0 = const()[name = string("current_value_11_strides_0"), val = tensor([1, 1])]; + tensor current_value_11_pad_0 = const()[name = string("current_value_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_11_dilations_0 = const()[name = string("current_value_11_dilations_0"), val = tensor([1, 1])]; + int32 current_value_11_groups_0 = const()[name = string("current_value_11_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31259968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31784320))))[name = string("pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_11_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_11_dilations_0, groups = current_value_11_groups_0, pad = current_value_11_pad_0, pad_type = current_value_11_pad_type_0, strides = current_value_11_strides_0, weight = pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized, x = obj_47_cast_fp16)[name = string("current_value_11_cast_fp16")]; + tensor var_1415 = const()[name = string("op_1415"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_31_cast_fp16 = reshape(shape = var_1415, x = query_21_cast_fp16)[name = string("mh_q_31_cast_fp16")]; + tensor var_1417 = const()[name = string("op_1417"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_21_cast_fp16 = reshape(shape = var_1417, x = key_21_cast_fp16)[name = string("mh_k_21_cast_fp16")]; + tensor var_1421_cast_fp16 = mul(x = mh_q_31_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1421_cast_fp16")]; + tensor var_1426_begin_0 = const()[name = string("op_1426_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1426_end_0 = const()[name = string("op_1426_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1426_end_mask_0 = const()[name = string("op_1426_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1426_cast_fp16 = slice_by_index(begin = var_1426_begin_0, end = var_1426_end_0, end_mask = var_1426_end_mask_0, x = mh_q_31_cast_fp16)[name = string("op_1426_cast_fp16")]; + tensor var_1432_begin_0 = const()[name = string("op_1432_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1432_end_0 = const()[name = string("op_1432_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1432_end_mask_0 = const()[name = string("op_1432_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1432_cast_fp16 = slice_by_index(begin = var_1432_begin_0, end = var_1432_end_0, end_mask = var_1432_end_mask_0, x = mh_q_31_cast_fp16)[name = string("op_1432_cast_fp16")]; + fp16 const_126_promoted_to_fp16 = const()[name = string("const_126_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1434_cast_fp16 = mul(x = var_1432_cast_fp16, y = const_126_promoted_to_fp16)[name = string("op_1434_cast_fp16")]; + bool var_1436_interleave_0 = const()[name = string("op_1436_interleave_0"), val = bool(false)]; + tensor var_1436_cast_fp16 = concat(axis = var_327, interleave = var_1436_interleave_0, values = (var_1434_cast_fp16, var_1426_cast_fp16))[name = string("op_1436_cast_fp16")]; + tensor var_1437_cast_fp16 = mul(x = var_1436_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1437_cast_fp16")]; + tensor mh_q_33_cast_fp16 = add(x = var_1421_cast_fp16, y = var_1437_cast_fp16)[name = string("mh_q_33_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = mh_k_21_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1444_begin_0 = const()[name = string("op_1444_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1444_end_0 = const()[name = string("op_1444_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1444_end_mask_0 = const()[name = string("op_1444_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1444_cast_fp16 = slice_by_index(begin = var_1444_begin_0, end = var_1444_end_0, end_mask = var_1444_end_mask_0, x = mh_k_21_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1450_begin_0 = const()[name = string("op_1450_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1450_end_0 = const()[name = string("op_1450_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1450_end_mask_0 = const()[name = string("op_1450_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1450_cast_fp16 = slice_by_index(begin = var_1450_begin_0, end = var_1450_end_0, end_mask = var_1450_end_mask_0, x = mh_k_21_cast_fp16)[name = string("op_1450_cast_fp16")]; + fp16 const_129_promoted_to_fp16 = const()[name = string("const_129_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1452_cast_fp16 = mul(x = var_1450_cast_fp16, y = const_129_promoted_to_fp16)[name = string("op_1452_cast_fp16")]; + bool var_1454_interleave_0 = const()[name = string("op_1454_interleave_0"), val = bool(false)]; + tensor var_1454_cast_fp16 = concat(axis = var_327, interleave = var_1454_interleave_0, values = (var_1452_cast_fp16, var_1444_cast_fp16))[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = var_1454_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor mh_k_23_cast_fp16 = add(x = var_1439_cast_fp16, y = var_1455_cast_fp16)[name = string("mh_k_23_cast_fp16")]; + tensor var_1459 = const()[name = string("op_1459"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_11_cast_fp16 = reshape(shape = var_1459, x = mh_k_23_cast_fp16)[name = string("current_key_11_cast_fp16")]; + tensor var_1466_cast_fp16 = mul(x = var_361_cast_fp16_5, y = var_495_cast_fp16)[name = string("op_1466_cast_fp16")]; + tensor var_1467_cast_fp16 = mul(x = current_key_11_cast_fp16, y = var_493_cast_fp16)[name = string("op_1467_cast_fp16")]; + tensor key_23_cast_fp16 = add(x = var_1466_cast_fp16, y = var_1467_cast_fp16)[name = string("key_23_cast_fp16")]; + tensor var_1470_cast_fp16 = mul(x = var_370_cast_fp16_5, y = var_495_cast_fp16)[name = string("op_1470_cast_fp16")]; + tensor var_1471_cast_fp16 = mul(x = current_value_11_cast_fp16, y = var_493_cast_fp16)[name = string("op_1471_cast_fp16")]; + tensor value_11_cast_fp16 = add(x = var_1470_cast_fp16, y = var_1471_cast_fp16)[name = string("value_11_cast_fp16")]; + fp16 var_1477_to_fp16 = const()[name = string("op_1477_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1478_cast_fp16 = mul(x = mh_q_33_cast_fp16, y = var_1477_to_fp16)[name = string("op_1478_cast_fp16")]; + tensor var_1481 = const()[name = string("op_1481"), val = tensor([1, 16, 64, 80])]; + tensor var_1482_cast_fp16 = reshape(shape = var_1481, x = key_23_cast_fp16)[name = string("op_1482_cast_fp16")]; + bool mh_w_21_transpose_x_0 = const()[name = string("mh_w_21_transpose_x_0"), val = bool(true)]; + bool mh_w_21_transpose_y_0 = const()[name = string("mh_w_21_transpose_y_0"), val = bool(false)]; + tensor mh_w_21_cast_fp16 = matmul(transpose_x = mh_w_21_transpose_x_0, transpose_y = mh_w_21_transpose_y_0, x = var_1478_cast_fp16, y = var_1482_cast_fp16)[name = string("mh_w_21_cast_fp16")]; + tensor mh_w_23_cast_fp16 = add(x = mh_w_21_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_23_cast_fp16")]; + tensor var_1490_cast_fp16 = softmax(axis = var_332, x = mh_w_23_cast_fp16)[name = string("op_1490_cast_fp16")]; + tensor var_1491 = const()[name = string("op_1491"), val = tensor([1, 16, 64, 80])]; + tensor var_1492_cast_fp16 = reshape(shape = var_1491, x = value_11_cast_fp16)[name = string("op_1492_cast_fp16")]; + bool attn_11_transpose_x_0 = const()[name = string("attn_11_transpose_x_0"), val = bool(false)]; + bool attn_11_transpose_y_0 = const()[name = string("attn_11_transpose_y_0"), val = bool(true)]; + tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_1492_cast_fp16, y = var_1490_cast_fp16)[name = string("attn_11_cast_fp16")]; + tensor var_1495 = const()[name = string("op_1495"), val = tensor([1, -1, 1, 1])]; + tensor input_81_cast_fp16 = reshape(shape = var_1495, x = attn_11_cast_fp16)[name = string("input_81_cast_fp16")]; + string obj_53_pad_type_0 = const()[name = string("obj_53_pad_type_0"), val = string("valid")]; + tensor obj_53_strides_0 = const()[name = string("obj_53_strides_0"), val = tensor([1, 1])]; + tensor obj_53_pad_0 = const()[name = string("obj_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_53_dilations_0 = const()[name = string("obj_53_dilations_0"), val = tensor([1, 1])]; + int32 obj_53_groups_0 = const()[name = string("obj_53_groups_0"), val = int32(1)]; + tensor op_1511_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31784896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32309248))))[name = string("op_1511_weight_0_to_fp16_palettized")]; + tensor var_1511_bias_0_to_fp16 = const()[name = string("op_1511_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32309824)))]; + tensor var_1511_cast_fp16 = conv(bias = var_1511_bias_0_to_fp16, dilations = obj_53_dilations_0, groups = obj_53_groups_0, pad = obj_53_pad_0, pad_type = obj_53_pad_type_0, strides = obj_53_strides_0, weight = op_1511_weight_0_to_fp16_palettized, x = input_81_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor inputs_23_cast_fp16 = add(x = inputs_21_cast_fp16, y = var_1511_cast_fp16)[name = string("inputs_23_cast_fp16")]; + tensor inputs_sq_23_cast_fp16 = mul(x = inputs_23_cast_fp16, y = inputs_23_cast_fp16)[name = string("inputs_sq_23_cast_fp16")]; + tensor variance_23_axes_0 = const()[name = string("variance_23_axes_0"), val = tensor([1])]; + bool variance_23_keep_dims_0 = const()[name = string("variance_23_keep_dims_0"), val = bool(true)]; + tensor variance_23_cast_fp16 = reduce_mean(axes = variance_23_axes_0, keep_dims = variance_23_keep_dims_0, x = inputs_sq_23_cast_fp16)[name = string("variance_23_cast_fp16")]; + fp16 var_1517_to_fp16 = const()[name = string("op_1517_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1518_cast_fp16 = add(x = variance_23_cast_fp16, y = var_1517_to_fp16)[name = string("op_1518_cast_fp16")]; + fp32 var_1519_epsilon_0 = const()[name = string("op_1519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1519_cast_fp16 = rsqrt(epsilon = var_1519_epsilon_0, x = var_1518_cast_fp16)[name = string("op_1519_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = mul(x = inputs_23_cast_fp16, y = var_1519_cast_fp16)[name = string("hidden_states_33_cast_fp16")]; + tensor w_23_to_fp16 = const()[name = string("w_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32310912)))]; + tensor input_83_cast_fp16 = mul(x = w_23_to_fp16, y = hidden_states_33_cast_fp16)[name = string("input_83_cast_fp16")]; + string input_85_pad_type_0 = const()[name = string("input_85_pad_type_0"), val = string("valid")]; + tensor input_85_strides_0 = const()[name = string("input_85_strides_0"), val = tensor([1, 1])]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_85_dilations_0 = const()[name = string("input_85_dilations_0"), val = tensor([1, 1])]; + int32 input_85_groups_0 = const()[name = string("input_85_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32312000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32836352))))[name = string("pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_85_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor gate_11_cast_fp16 = silu(x = input_85_cast_fp16)[name = string("gate_11_cast_fp16")]; + string up_11_pad_type_0 = const()[name = string("up_11_pad_type_0"), val = string("valid")]; + tensor up_11_strides_0 = const()[name = string("up_11_strides_0"), val = tensor([1, 1])]; + tensor up_11_pad_0 = const()[name = string("up_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_11_dilations_0 = const()[name = string("up_11_dilations_0"), val = tensor([1, 1])]; + int32 up_11_groups_0 = const()[name = string("up_11_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32836928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33361280))))[name = string("pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_11_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_11_dilations_0, groups = up_11_groups_0, pad = up_11_pad_0, pad_type = up_11_pad_type_0, strides = up_11_strides_0, weight = pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized, x = input_83_cast_fp16)[name = string("up_11_cast_fp16")]; + tensor input_87_cast_fp16 = mul(x = gate_11_cast_fp16, y = up_11_cast_fp16)[name = string("input_87_cast_fp16")]; + string hidden_states_35_pad_type_0 = const()[name = string("hidden_states_35_pad_type_0"), val = string("valid")]; + tensor hidden_states_35_strides_0 = const()[name = string("hidden_states_35_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_0 = const()[name = string("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_35_dilations_0 = const()[name = string("hidden_states_35_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_35_groups_0 = const()[name = string("hidden_states_35_groups_0"), val = int32(1)]; + tensor op_1553_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33361856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33886208))))[name = string("op_1553_weight_0_to_fp16_palettized")]; + tensor var_1553_bias_0_to_fp16 = const()[name = string("op_1553_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33886784)))]; + tensor var_1553_cast_fp16 = conv(bias = var_1553_bias_0_to_fp16, dilations = hidden_states_35_dilations_0, groups = hidden_states_35_groups_0, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = hidden_states_35_strides_0, weight = op_1553_weight_0_to_fp16_palettized, x = input_87_cast_fp16)[name = string("op_1553_cast_fp16")]; + tensor inputs_25_cast_fp16 = add(x = inputs_23_cast_fp16, y = var_1553_cast_fp16)[name = string("inputs_25_cast_fp16")]; + tensor inputs_sq_25_cast_fp16 = mul(x = inputs_25_cast_fp16, y = inputs_25_cast_fp16)[name = string("inputs_sq_25_cast_fp16")]; + tensor variance_25_axes_0 = const()[name = string("variance_25_axes_0"), val = tensor([1])]; + bool variance_25_keep_dims_0 = const()[name = string("variance_25_keep_dims_0"), val = bool(true)]; + tensor variance_25_cast_fp16 = reduce_mean(axes = variance_25_axes_0, keep_dims = variance_25_keep_dims_0, x = inputs_sq_25_cast_fp16)[name = string("variance_25_cast_fp16")]; + fp16 var_1569_to_fp16 = const()[name = string("op_1569_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1570_cast_fp16 = add(x = variance_25_cast_fp16, y = var_1569_to_fp16)[name = string("op_1570_cast_fp16")]; + fp32 var_1571_epsilon_0 = const()[name = string("op_1571_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1571_cast_fp16 = rsqrt(epsilon = var_1571_epsilon_0, x = var_1570_cast_fp16)[name = string("op_1571_cast_fp16")]; + tensor hidden_states_37_cast_fp16 = mul(x = inputs_25_cast_fp16, y = var_1571_cast_fp16)[name = string("hidden_states_37_cast_fp16")]; + tensor w_25_to_fp16 = const()[name = string("w_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33887872)))]; + tensor obj_55_cast_fp16 = mul(x = w_25_to_fp16, y = hidden_states_37_cast_fp16)[name = string("obj_55_cast_fp16")]; + string query_25_pad_type_0 = const()[name = string("query_25_pad_type_0"), val = string("valid")]; + tensor query_25_strides_0 = const()[name = string("query_25_strides_0"), val = tensor([1, 1])]; + tensor query_25_pad_0 = const()[name = string("query_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_25_dilations_0 = const()[name = string("query_25_dilations_0"), val = tensor([1, 1])]; + int32 query_25_groups_0 = const()[name = string("query_25_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33888960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34413312))))[name = string("pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_25_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_25_dilations_0, groups = query_25_groups_0, pad = query_25_pad_0, pad_type = query_25_pad_type_0, strides = query_25_strides_0, weight = pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized, x = obj_55_cast_fp16)[name = string("query_25_cast_fp16")]; + string key_25_pad_type_0 = const()[name = string("key_25_pad_type_0"), val = string("valid")]; + tensor key_25_strides_0 = const()[name = string("key_25_strides_0"), val = tensor([1, 1])]; + tensor key_25_pad_0 = const()[name = string("key_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_25_dilations_0 = const()[name = string("key_25_dilations_0"), val = tensor([1, 1])]; + int32 key_25_groups_0 = const()[name = string("key_25_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34413888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34938240))))[name = string("pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_25_cast_fp16 = conv(dilations = key_25_dilations_0, groups = key_25_groups_0, pad = key_25_pad_0, pad_type = key_25_pad_type_0, strides = key_25_strides_0, weight = pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized, x = obj_55_cast_fp16)[name = string("key_25_cast_fp16")]; + string current_value_13_pad_type_0 = const()[name = string("current_value_13_pad_type_0"), val = string("valid")]; + tensor current_value_13_strides_0 = const()[name = string("current_value_13_strides_0"), val = tensor([1, 1])]; + tensor current_value_13_pad_0 = const()[name = string("current_value_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_13_dilations_0 = const()[name = string("current_value_13_dilations_0"), val = tensor([1, 1])]; + int32 current_value_13_groups_0 = const()[name = string("current_value_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34938816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35463168))))[name = string("pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_13_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_13_dilations_0, groups = current_value_13_groups_0, pad = current_value_13_pad_0, pad_type = current_value_13_pad_type_0, strides = current_value_13_strides_0, weight = pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized, x = obj_55_cast_fp16)[name = string("current_value_13_cast_fp16")]; + tensor var_1609 = const()[name = string("op_1609"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_37_cast_fp16 = reshape(shape = var_1609, x = query_25_cast_fp16)[name = string("mh_q_37_cast_fp16")]; + tensor var_1611 = const()[name = string("op_1611"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_25_cast_fp16 = reshape(shape = var_1611, x = key_25_cast_fp16)[name = string("mh_k_25_cast_fp16")]; + tensor var_1615_cast_fp16 = mul(x = mh_q_37_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1615_cast_fp16")]; + tensor var_1620_begin_0 = const()[name = string("op_1620_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1620_end_0 = const()[name = string("op_1620_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1620_end_mask_0 = const()[name = string("op_1620_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1620_cast_fp16 = slice_by_index(begin = var_1620_begin_0, end = var_1620_end_0, end_mask = var_1620_end_mask_0, x = mh_q_37_cast_fp16)[name = string("op_1620_cast_fp16")]; + tensor var_1626_begin_0 = const()[name = string("op_1626_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1626_end_0 = const()[name = string("op_1626_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1626_end_mask_0 = const()[name = string("op_1626_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1626_cast_fp16 = slice_by_index(begin = var_1626_begin_0, end = var_1626_end_0, end_mask = var_1626_end_mask_0, x = mh_q_37_cast_fp16)[name = string("op_1626_cast_fp16")]; + fp16 const_145_promoted_to_fp16 = const()[name = string("const_145_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1628_cast_fp16 = mul(x = var_1626_cast_fp16, y = const_145_promoted_to_fp16)[name = string("op_1628_cast_fp16")]; + bool var_1630_interleave_0 = const()[name = string("op_1630_interleave_0"), val = bool(false)]; + tensor var_1630_cast_fp16 = concat(axis = var_327, interleave = var_1630_interleave_0, values = (var_1628_cast_fp16, var_1620_cast_fp16))[name = string("op_1630_cast_fp16")]; + tensor var_1631_cast_fp16 = mul(x = var_1630_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1631_cast_fp16")]; + tensor mh_q_39_cast_fp16 = add(x = var_1615_cast_fp16, y = var_1631_cast_fp16)[name = string("mh_q_39_cast_fp16")]; + tensor var_1633_cast_fp16 = mul(x = mh_k_25_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1633_cast_fp16")]; + tensor var_1638_begin_0 = const()[name = string("op_1638_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1638_end_0 = const()[name = string("op_1638_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1638_end_mask_0 = const()[name = string("op_1638_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1638_cast_fp16 = slice_by_index(begin = var_1638_begin_0, end = var_1638_end_0, end_mask = var_1638_end_mask_0, x = mh_k_25_cast_fp16)[name = string("op_1638_cast_fp16")]; + tensor var_1644_begin_0 = const()[name = string("op_1644_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1644_end_0 = const()[name = string("op_1644_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1644_end_mask_0 = const()[name = string("op_1644_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1644_cast_fp16 = slice_by_index(begin = var_1644_begin_0, end = var_1644_end_0, end_mask = var_1644_end_mask_0, x = mh_k_25_cast_fp16)[name = string("op_1644_cast_fp16")]; + fp16 const_148_promoted_to_fp16 = const()[name = string("const_148_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1646_cast_fp16 = mul(x = var_1644_cast_fp16, y = const_148_promoted_to_fp16)[name = string("op_1646_cast_fp16")]; + bool var_1648_interleave_0 = const()[name = string("op_1648_interleave_0"), val = bool(false)]; + tensor var_1648_cast_fp16 = concat(axis = var_327, interleave = var_1648_interleave_0, values = (var_1646_cast_fp16, var_1638_cast_fp16))[name = string("op_1648_cast_fp16")]; + tensor var_1649_cast_fp16 = mul(x = var_1648_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1649_cast_fp16")]; + tensor mh_k_27_cast_fp16 = add(x = var_1633_cast_fp16, y = var_1649_cast_fp16)[name = string("mh_k_27_cast_fp16")]; + tensor var_1653 = const()[name = string("op_1653"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_13_cast_fp16 = reshape(shape = var_1653, x = mh_k_27_cast_fp16)[name = string("current_key_13_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = var_361_cast_fp16_6, y = var_495_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1661_cast_fp16 = mul(x = current_key_13_cast_fp16, y = var_493_cast_fp16)[name = string("op_1661_cast_fp16")]; + tensor key_27_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1661_cast_fp16)[name = string("key_27_cast_fp16")]; + tensor var_1664_cast_fp16 = mul(x = var_370_cast_fp16_6, y = var_495_cast_fp16)[name = string("op_1664_cast_fp16")]; + tensor var_1665_cast_fp16 = mul(x = current_value_13_cast_fp16, y = var_493_cast_fp16)[name = string("op_1665_cast_fp16")]; + tensor value_13_cast_fp16 = add(x = var_1664_cast_fp16, y = var_1665_cast_fp16)[name = string("value_13_cast_fp16")]; + fp16 var_1671_to_fp16 = const()[name = string("op_1671_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1672_cast_fp16 = mul(x = mh_q_39_cast_fp16, y = var_1671_to_fp16)[name = string("op_1672_cast_fp16")]; + tensor var_1675 = const()[name = string("op_1675"), val = tensor([1, 16, 64, 80])]; + tensor var_1676_cast_fp16 = reshape(shape = var_1675, x = key_27_cast_fp16)[name = string("op_1676_cast_fp16")]; + bool mh_w_25_transpose_x_0 = const()[name = string("mh_w_25_transpose_x_0"), val = bool(true)]; + bool mh_w_25_transpose_y_0 = const()[name = string("mh_w_25_transpose_y_0"), val = bool(false)]; + tensor mh_w_25_cast_fp16 = matmul(transpose_x = mh_w_25_transpose_x_0, transpose_y = mh_w_25_transpose_y_0, x = var_1672_cast_fp16, y = var_1676_cast_fp16)[name = string("mh_w_25_cast_fp16")]; + tensor mh_w_27_cast_fp16 = add(x = mh_w_25_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_27_cast_fp16")]; + tensor var_1684_cast_fp16 = softmax(axis = var_332, x = mh_w_27_cast_fp16)[name = string("op_1684_cast_fp16")]; + tensor var_1685 = const()[name = string("op_1685"), val = tensor([1, 16, 64, 80])]; + tensor var_1686_cast_fp16 = reshape(shape = var_1685, x = value_13_cast_fp16)[name = string("op_1686_cast_fp16")]; + bool attn_13_transpose_x_0 = const()[name = string("attn_13_transpose_x_0"), val = bool(false)]; + bool attn_13_transpose_y_0 = const()[name = string("attn_13_transpose_y_0"), val = bool(true)]; + tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1686_cast_fp16, y = var_1684_cast_fp16)[name = string("attn_13_cast_fp16")]; + tensor var_1689 = const()[name = string("op_1689"), val = tensor([1, -1, 1, 1])]; + tensor input_89_cast_fp16 = reshape(shape = var_1689, x = attn_13_cast_fp16)[name = string("input_89_cast_fp16")]; + string obj_61_pad_type_0 = const()[name = string("obj_61_pad_type_0"), val = string("valid")]; + tensor obj_61_strides_0 = const()[name = string("obj_61_strides_0"), val = tensor([1, 1])]; + tensor obj_61_pad_0 = const()[name = string("obj_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_61_dilations_0 = const()[name = string("obj_61_dilations_0"), val = tensor([1, 1])]; + int32 obj_61_groups_0 = const()[name = string("obj_61_groups_0"), val = int32(1)]; + tensor op_1705_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35463744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35988096))))[name = string("op_1705_weight_0_to_fp16_palettized")]; + tensor var_1705_bias_0_to_fp16 = const()[name = string("op_1705_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35988672)))]; + tensor var_1705_cast_fp16 = conv(bias = var_1705_bias_0_to_fp16, dilations = obj_61_dilations_0, groups = obj_61_groups_0, pad = obj_61_pad_0, pad_type = obj_61_pad_type_0, strides = obj_61_strides_0, weight = op_1705_weight_0_to_fp16_palettized, x = input_89_cast_fp16)[name = string("op_1705_cast_fp16")]; + tensor inputs_27_cast_fp16 = add(x = inputs_25_cast_fp16, y = var_1705_cast_fp16)[name = string("inputs_27_cast_fp16")]; + tensor inputs_sq_27_cast_fp16 = mul(x = inputs_27_cast_fp16, y = inputs_27_cast_fp16)[name = string("inputs_sq_27_cast_fp16")]; + tensor variance_27_axes_0 = const()[name = string("variance_27_axes_0"), val = tensor([1])]; + bool variance_27_keep_dims_0 = const()[name = string("variance_27_keep_dims_0"), val = bool(true)]; + tensor variance_27_cast_fp16 = reduce_mean(axes = variance_27_axes_0, keep_dims = variance_27_keep_dims_0, x = inputs_sq_27_cast_fp16)[name = string("variance_27_cast_fp16")]; + fp16 var_1711_to_fp16 = const()[name = string("op_1711_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1712_cast_fp16 = add(x = variance_27_cast_fp16, y = var_1711_to_fp16)[name = string("op_1712_cast_fp16")]; + fp32 var_1713_epsilon_0 = const()[name = string("op_1713_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1713_cast_fp16 = rsqrt(epsilon = var_1713_epsilon_0, x = var_1712_cast_fp16)[name = string("op_1713_cast_fp16")]; + tensor hidden_states_39_cast_fp16 = mul(x = inputs_27_cast_fp16, y = var_1713_cast_fp16)[name = string("hidden_states_39_cast_fp16")]; + tensor w_27_to_fp16 = const()[name = string("w_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35989760)))]; + tensor input_91_cast_fp16 = mul(x = w_27_to_fp16, y = hidden_states_39_cast_fp16)[name = string("input_91_cast_fp16")]; + string input_93_pad_type_0 = const()[name = string("input_93_pad_type_0"), val = string("valid")]; + tensor input_93_strides_0 = const()[name = string("input_93_strides_0"), val = tensor([1, 1])]; + tensor input_93_pad_0 = const()[name = string("input_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_93_dilations_0 = const()[name = string("input_93_dilations_0"), val = tensor([1, 1])]; + int32 input_93_groups_0 = const()[name = string("input_93_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35990848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36515200))))[name = string("pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_93_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized, x = input_91_cast_fp16)[name = string("input_93_cast_fp16")]; + tensor gate_13_cast_fp16 = silu(x = input_93_cast_fp16)[name = string("gate_13_cast_fp16")]; + string up_13_pad_type_0 = const()[name = string("up_13_pad_type_0"), val = string("valid")]; + tensor up_13_strides_0 = const()[name = string("up_13_strides_0"), val = tensor([1, 1])]; + tensor up_13_pad_0 = const()[name = string("up_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_13_dilations_0 = const()[name = string("up_13_dilations_0"), val = tensor([1, 1])]; + int32 up_13_groups_0 = const()[name = string("up_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36515776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37040128))))[name = string("pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_13_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_13_dilations_0, groups = up_13_groups_0, pad = up_13_pad_0, pad_type = up_13_pad_type_0, strides = up_13_strides_0, weight = pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized, x = input_91_cast_fp16)[name = string("up_13_cast_fp16")]; + tensor input_95_cast_fp16 = mul(x = gate_13_cast_fp16, y = up_13_cast_fp16)[name = string("input_95_cast_fp16")]; + string hidden_states_41_pad_type_0 = const()[name = string("hidden_states_41_pad_type_0"), val = string("valid")]; + tensor hidden_states_41_strides_0 = const()[name = string("hidden_states_41_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_41_pad_0 = const()[name = string("hidden_states_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_41_dilations_0 = const()[name = string("hidden_states_41_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_41_groups_0 = const()[name = string("hidden_states_41_groups_0"), val = int32(1)]; + tensor op_1747_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37040704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37565056))))[name = string("op_1747_weight_0_to_fp16_palettized")]; + tensor var_1747_bias_0_to_fp16 = const()[name = string("op_1747_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37565632)))]; + tensor var_1747_cast_fp16 = conv(bias = var_1747_bias_0_to_fp16, dilations = hidden_states_41_dilations_0, groups = hidden_states_41_groups_0, pad = hidden_states_41_pad_0, pad_type = hidden_states_41_pad_type_0, strides = hidden_states_41_strides_0, weight = op_1747_weight_0_to_fp16_palettized, x = input_95_cast_fp16)[name = string("op_1747_cast_fp16")]; + tensor inputs_29_cast_fp16 = add(x = inputs_27_cast_fp16, y = var_1747_cast_fp16)[name = string("inputs_29_cast_fp16")]; + tensor inputs_sq_29_cast_fp16 = mul(x = inputs_29_cast_fp16, y = inputs_29_cast_fp16)[name = string("inputs_sq_29_cast_fp16")]; + tensor variance_29_axes_0 = const()[name = string("variance_29_axes_0"), val = tensor([1])]; + bool variance_29_keep_dims_0 = const()[name = string("variance_29_keep_dims_0"), val = bool(true)]; + tensor variance_29_cast_fp16 = reduce_mean(axes = variance_29_axes_0, keep_dims = variance_29_keep_dims_0, x = inputs_sq_29_cast_fp16)[name = string("variance_29_cast_fp16")]; + fp16 var_1763_to_fp16 = const()[name = string("op_1763_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1764_cast_fp16 = add(x = variance_29_cast_fp16, y = var_1763_to_fp16)[name = string("op_1764_cast_fp16")]; + fp32 var_1765_epsilon_0 = const()[name = string("op_1765_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1765_cast_fp16 = rsqrt(epsilon = var_1765_epsilon_0, x = var_1764_cast_fp16)[name = string("op_1765_cast_fp16")]; + tensor hidden_states_43_cast_fp16 = mul(x = inputs_29_cast_fp16, y = var_1765_cast_fp16)[name = string("hidden_states_43_cast_fp16")]; + tensor w_29_to_fp16 = const()[name = string("w_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37566720)))]; + tensor obj_63_cast_fp16 = mul(x = w_29_to_fp16, y = hidden_states_43_cast_fp16)[name = string("obj_63_cast_fp16")]; + string query_29_pad_type_0 = const()[name = string("query_29_pad_type_0"), val = string("valid")]; + tensor query_29_strides_0 = const()[name = string("query_29_strides_0"), val = tensor([1, 1])]; + tensor query_29_pad_0 = const()[name = string("query_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_29_dilations_0 = const()[name = string("query_29_dilations_0"), val = tensor([1, 1])]; + int32 query_29_groups_0 = const()[name = string("query_29_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37567808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38092160))))[name = string("pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_29_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_29_dilations_0, groups = query_29_groups_0, pad = query_29_pad_0, pad_type = query_29_pad_type_0, strides = query_29_strides_0, weight = pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized, x = obj_63_cast_fp16)[name = string("query_29_cast_fp16")]; + string key_29_pad_type_0 = const()[name = string("key_29_pad_type_0"), val = string("valid")]; + tensor key_29_strides_0 = const()[name = string("key_29_strides_0"), val = tensor([1, 1])]; + tensor key_29_pad_0 = const()[name = string("key_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_29_dilations_0 = const()[name = string("key_29_dilations_0"), val = tensor([1, 1])]; + int32 key_29_groups_0 = const()[name = string("key_29_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38092736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38617088))))[name = string("pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_29_cast_fp16 = conv(dilations = key_29_dilations_0, groups = key_29_groups_0, pad = key_29_pad_0, pad_type = key_29_pad_type_0, strides = key_29_strides_0, weight = pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized, x = obj_63_cast_fp16)[name = string("key_29_cast_fp16")]; + string current_value_pad_type_0 = const()[name = string("current_value_pad_type_0"), val = string("valid")]; + tensor current_value_strides_0 = const()[name = string("current_value_strides_0"), val = tensor([1, 1])]; + tensor current_value_pad_0 = const()[name = string("current_value_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_dilations_0 = const()[name = string("current_value_dilations_0"), val = tensor([1, 1])]; + int32 current_value_groups_0 = const()[name = string("current_value_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38617664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39142016))))[name = string("pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_dilations_0, groups = current_value_groups_0, pad = current_value_pad_0, pad_type = current_value_pad_type_0, strides = current_value_strides_0, weight = pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized, x = obj_63_cast_fp16)[name = string("current_value_cast_fp16")]; + tensor var_1803 = const()[name = string("op_1803"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_43_cast_fp16 = reshape(shape = var_1803, x = query_29_cast_fp16)[name = string("mh_q_43_cast_fp16")]; + tensor var_1805 = const()[name = string("op_1805"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_29_cast_fp16 = reshape(shape = var_1805, x = key_29_cast_fp16)[name = string("mh_k_29_cast_fp16")]; + tensor var_1809_cast_fp16 = mul(x = mh_q_43_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1809_cast_fp16")]; + tensor var_1814_begin_0 = const()[name = string("op_1814_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1814_end_0 = const()[name = string("op_1814_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1814_end_mask_0 = const()[name = string("op_1814_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1814_cast_fp16 = slice_by_index(begin = var_1814_begin_0, end = var_1814_end_0, end_mask = var_1814_end_mask_0, x = mh_q_43_cast_fp16)[name = string("op_1814_cast_fp16")]; + tensor var_1820_begin_0 = const()[name = string("op_1820_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1820_end_0 = const()[name = string("op_1820_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1820_end_mask_0 = const()[name = string("op_1820_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1820_cast_fp16 = slice_by_index(begin = var_1820_begin_0, end = var_1820_end_0, end_mask = var_1820_end_mask_0, x = mh_q_43_cast_fp16)[name = string("op_1820_cast_fp16")]; + fp16 const_164_promoted_to_fp16 = const()[name = string("const_164_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1822_cast_fp16 = mul(x = var_1820_cast_fp16, y = const_164_promoted_to_fp16)[name = string("op_1822_cast_fp16")]; + bool var_1824_interleave_0 = const()[name = string("op_1824_interleave_0"), val = bool(false)]; + tensor var_1824_cast_fp16 = concat(axis = var_327, interleave = var_1824_interleave_0, values = (var_1822_cast_fp16, var_1814_cast_fp16))[name = string("op_1824_cast_fp16")]; + tensor var_1825_cast_fp16 = mul(x = var_1824_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1825_cast_fp16")]; + tensor mh_q_45_cast_fp16 = add(x = var_1809_cast_fp16, y = var_1825_cast_fp16)[name = string("mh_q_45_cast_fp16")]; + tensor var_1827_cast_fp16 = mul(x = mh_k_29_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1827_cast_fp16")]; + tensor var_1832_begin_0 = const()[name = string("op_1832_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1832_end_0 = const()[name = string("op_1832_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1832_end_mask_0 = const()[name = string("op_1832_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1832_cast_fp16 = slice_by_index(begin = var_1832_begin_0, end = var_1832_end_0, end_mask = var_1832_end_mask_0, x = mh_k_29_cast_fp16)[name = string("op_1832_cast_fp16")]; + tensor var_1838_begin_0 = const()[name = string("op_1838_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1838_end_0 = const()[name = string("op_1838_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1838_end_mask_0 = const()[name = string("op_1838_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1838_cast_fp16 = slice_by_index(begin = var_1838_begin_0, end = var_1838_end_0, end_mask = var_1838_end_mask_0, x = mh_k_29_cast_fp16)[name = string("op_1838_cast_fp16")]; + fp16 const_167_promoted_to_fp16 = const()[name = string("const_167_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1840_cast_fp16 = mul(x = var_1838_cast_fp16, y = const_167_promoted_to_fp16)[name = string("op_1840_cast_fp16")]; + bool var_1842_interleave_0 = const()[name = string("op_1842_interleave_0"), val = bool(false)]; + tensor var_1842_cast_fp16 = concat(axis = var_327, interleave = var_1842_interleave_0, values = (var_1840_cast_fp16, var_1832_cast_fp16))[name = string("op_1842_cast_fp16")]; + tensor var_1843_cast_fp16 = mul(x = var_1842_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1843_cast_fp16")]; + tensor mh_k_cast_fp16 = add(x = var_1827_cast_fp16, y = var_1843_cast_fp16)[name = string("mh_k_cast_fp16")]; + tensor var_1847 = const()[name = string("op_1847"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_cast_fp16 = reshape(shape = var_1847, x = mh_k_cast_fp16)[name = string("current_key_cast_fp16")]; + tensor var_1854_cast_fp16 = mul(x = var_361_cast_fp16_7, y = var_495_cast_fp16)[name = string("op_1854_cast_fp16")]; + tensor var_1855_cast_fp16 = mul(x = current_key_cast_fp16, y = var_493_cast_fp16)[name = string("op_1855_cast_fp16")]; + tensor key_cast_fp16 = add(x = var_1854_cast_fp16, y = var_1855_cast_fp16)[name = string("key_cast_fp16")]; + tensor var_1858_cast_fp16 = mul(x = var_370_cast_fp16_7, y = var_495_cast_fp16)[name = string("op_1858_cast_fp16")]; + tensor var_1859_cast_fp16 = mul(x = current_value_cast_fp16, y = var_493_cast_fp16)[name = string("op_1859_cast_fp16")]; + tensor value_cast_fp16 = add(x = var_1858_cast_fp16, y = var_1859_cast_fp16)[name = string("value_cast_fp16")]; + fp16 var_1865_to_fp16 = const()[name = string("op_1865_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1866_cast_fp16 = mul(x = mh_q_45_cast_fp16, y = var_1865_to_fp16)[name = string("op_1866_cast_fp16")]; + tensor var_1869 = const()[name = string("op_1869"), val = tensor([1, 16, 64, 80])]; + tensor var_1870_cast_fp16 = reshape(shape = var_1869, x = key_cast_fp16)[name = string("op_1870_cast_fp16")]; + bool mh_w_29_transpose_x_0 = const()[name = string("mh_w_29_transpose_x_0"), val = bool(true)]; + bool mh_w_29_transpose_y_0 = const()[name = string("mh_w_29_transpose_y_0"), val = bool(false)]; + tensor mh_w_29_cast_fp16 = matmul(transpose_x = mh_w_29_transpose_x_0, transpose_y = mh_w_29_transpose_y_0, x = var_1866_cast_fp16, y = var_1870_cast_fp16)[name = string("mh_w_29_cast_fp16")]; + tensor mh_w_cast_fp16 = add(x = mh_w_29_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_cast_fp16")]; + tensor var_1878_cast_fp16 = softmax(axis = var_332, x = mh_w_cast_fp16)[name = string("op_1878_cast_fp16")]; + tensor var_1879 = const()[name = string("op_1879"), val = tensor([1, 16, 64, 80])]; + tensor var_1880_cast_fp16 = reshape(shape = var_1879, x = value_cast_fp16)[name = string("op_1880_cast_fp16")]; + bool attn_transpose_x_0 = const()[name = string("attn_transpose_x_0"), val = bool(false)]; + bool attn_transpose_y_0 = const()[name = string("attn_transpose_y_0"), val = bool(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_1880_cast_fp16, y = var_1878_cast_fp16)[name = string("attn_cast_fp16")]; + tensor var_1883 = const()[name = string("op_1883"), val = tensor([1, -1, 1, 1])]; + tensor input_97_cast_fp16 = reshape(shape = var_1883, x = attn_cast_fp16)[name = string("input_97_cast_fp16")]; + string obj_pad_type_0 = const()[name = string("obj_pad_type_0"), val = string("valid")]; + tensor obj_strides_0 = const()[name = string("obj_strides_0"), val = tensor([1, 1])]; + tensor obj_pad_0 = const()[name = string("obj_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_dilations_0 = const()[name = string("obj_dilations_0"), val = tensor([1, 1])]; + int32 obj_groups_0 = const()[name = string("obj_groups_0"), val = int32(1)]; + tensor op_1899_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39142592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39666944))))[name = string("op_1899_weight_0_to_fp16_palettized")]; + tensor var_1899_bias_0_to_fp16 = const()[name = string("op_1899_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39667520)))]; + tensor var_1899_cast_fp16 = conv(bias = var_1899_bias_0_to_fp16, dilations = obj_dilations_0, groups = obj_groups_0, pad = obj_pad_0, pad_type = obj_pad_type_0, strides = obj_strides_0, weight = op_1899_weight_0_to_fp16_palettized, x = input_97_cast_fp16)[name = string("op_1899_cast_fp16")]; + tensor inputs_31_cast_fp16 = add(x = inputs_29_cast_fp16, y = var_1899_cast_fp16)[name = string("inputs_31_cast_fp16")]; + tensor inputs_sq_31_cast_fp16 = mul(x = inputs_31_cast_fp16, y = inputs_31_cast_fp16)[name = string("inputs_sq_31_cast_fp16")]; + tensor variance_31_axes_0 = const()[name = string("variance_31_axes_0"), val = tensor([1])]; + bool variance_31_keep_dims_0 = const()[name = string("variance_31_keep_dims_0"), val = bool(true)]; + tensor variance_31_cast_fp16 = reduce_mean(axes = variance_31_axes_0, keep_dims = variance_31_keep_dims_0, x = inputs_sq_31_cast_fp16)[name = string("variance_31_cast_fp16")]; + fp16 var_1905_to_fp16 = const()[name = string("op_1905_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1906_cast_fp16 = add(x = variance_31_cast_fp16, y = var_1905_to_fp16)[name = string("op_1906_cast_fp16")]; + fp32 var_1907_epsilon_0 = const()[name = string("op_1907_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1907_cast_fp16 = rsqrt(epsilon = var_1907_epsilon_0, x = var_1906_cast_fp16)[name = string("op_1907_cast_fp16")]; + tensor hidden_states_45_cast_fp16 = mul(x = inputs_31_cast_fp16, y = var_1907_cast_fp16)[name = string("hidden_states_45_cast_fp16")]; + tensor w_31_to_fp16 = const()[name = string("w_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39668608)))]; + tensor input_99_cast_fp16 = mul(x = w_31_to_fp16, y = hidden_states_45_cast_fp16)[name = string("input_99_cast_fp16")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("valid")]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1, 1])]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1, 1])]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39669696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40194048))))[name = string("pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_101_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("input_101_cast_fp16")]; + tensor gate_cast_fp16 = silu(x = input_101_cast_fp16)[name = string("gate_cast_fp16")]; + string up_pad_type_0 = const()[name = string("up_pad_type_0"), val = string("valid")]; + tensor up_strides_0 = const()[name = string("up_strides_0"), val = tensor([1, 1])]; + tensor up_pad_0 = const()[name = string("up_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_dilations_0 = const()[name = string("up_dilations_0"), val = tensor([1, 1])]; + int32 up_groups_0 = const()[name = string("up_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40194624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40718976))))[name = string("pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_dilations_0, groups = up_groups_0, pad = up_pad_0, pad_type = up_pad_type_0, strides = up_strides_0, weight = pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("up_cast_fp16")]; + tensor input_103_cast_fp16 = mul(x = gate_cast_fp16, y = up_cast_fp16)[name = string("input_103_cast_fp16")]; + string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")]; + tensor hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)]; + tensor op_1941_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40719552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41243904))))[name = string("op_1941_weight_0_to_fp16_palettized")]; + tensor var_1941_bias_0_to_fp16 = const()[name = string("op_1941_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41244480)))]; + tensor var_1941_cast_fp16 = conv(bias = var_1941_bias_0_to_fp16, dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = op_1941_weight_0_to_fp16_palettized, x = input_103_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = inputs_31_cast_fp16, y = var_1941_cast_fp16)[name = string("inputs_cast_fp16")]; + tensor inputs_sq_cast_fp16 = mul(x = inputs_cast_fp16, y = inputs_cast_fp16)[name = string("inputs_sq_cast_fp16")]; + tensor variance_axes_0 = const()[name = string("variance_axes_0"), val = tensor([1])]; + bool variance_keep_dims_0 = const()[name = string("variance_keep_dims_0"), val = bool(true)]; + tensor variance_cast_fp16 = reduce_mean(axes = variance_axes_0, keep_dims = variance_keep_dims_0, x = inputs_sq_cast_fp16)[name = string("variance_cast_fp16")]; + fp16 var_1951_to_fp16 = const()[name = string("op_1951_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1952_cast_fp16 = add(x = variance_cast_fp16, y = var_1951_to_fp16)[name = string("op_1952_cast_fp16")]; + fp32 var_1953_epsilon_0 = const()[name = string("op_1953_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1953_cast_fp16 = rsqrt(epsilon = var_1953_epsilon_0, x = var_1952_cast_fp16)[name = string("op_1953_cast_fp16")]; + tensor hidden_states_49_cast_fp16 = mul(x = inputs_cast_fp16, y = var_1953_cast_fp16)[name = string("hidden_states_49_cast_fp16")]; + tensor w_to_fp16 = const()[name = string("w_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41245568)))]; + tensor input_105_cast_fp16 = mul(x = w_to_fp16, y = hidden_states_49_cast_fp16)[name = string("input_105_cast_fp16")]; + string new_hiddens_pad_type_0 = const()[name = string("new_hiddens_pad_type_0"), val = string("valid")]; + tensor new_hiddens_strides_0 = const()[name = string("new_hiddens_strides_0"), val = tensor([1, 1])]; + tensor new_hiddens_pad_0 = const()[name = string("new_hiddens_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor new_hiddens_dilations_0 = const()[name = string("new_hiddens_dilations_0"), val = tensor([1, 1])]; + int32 new_hiddens_groups_0 = const()[name = string("new_hiddens_groups_0"), val = int32(1)]; + tensor pre_transformer_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41246656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41771008))))[name = string("pre_transformer_output_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_output_proj_bias_to_fp16 = const()[name = string("pre_transformer_output_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41771584)))]; + tensor hidden_context_update = conv(bias = pre_transformer_output_proj_bias_to_fp16, dilations = new_hiddens_dilations_0, groups = new_hiddens_groups_0, pad = new_hiddens_pad_0, pad_type = new_hiddens_pad_type_0, strides = new_hiddens_strides_0, weight = pre_transformer_output_proj_weight_to_fp16_palettized, x = input_105_cast_fp16)[name = string("new_hiddens_cast_fp16")]; + bool var_1966_interleave_0 = const()[name = string("op_1966_interleave_0"), val = bool(false)]; + tensor key_cache_updates = concat(axis = var_338, interleave = var_1966_interleave_0, values = (current_key_1_cast_fp16, current_key_3_cast_fp16, current_key_5_cast_fp16, current_key_7_cast_fp16, current_key_9_cast_fp16, current_key_11_cast_fp16, current_key_13_cast_fp16, current_key_cast_fp16))[name = string("op_1966_cast_fp16")]; + bool var_1968_interleave_0 = const()[name = string("op_1968_interleave_0"), val = bool(false)]; + tensor value_cache_updates = concat(axis = var_338, interleave = var_1968_interleave_0, values = (current_value_1_cast_fp16, current_value_3_cast_fp16, current_value_5_cast_fp16, current_value_7_cast_fp16, current_value_9_cast_fp16, current_value_11_cast_fp16, current_value_13_cast_fp16, current_value_cast_fp16))[name = string("op_1968_cast_fp16")]; + int32 var_1974 = const()[name = string("op_1974"), val = int32(-1)]; + bool hidden_states_51_interleave_0 = const()[name = string("hidden_states_51_interleave_0"), val = bool(false)]; + tensor hidden_states_51_cast_fp16 = concat(axis = var_1974, interleave = hidden_states_51_interleave_0, values = (hidden_context, hidden_context_update))[name = string("hidden_states_51_cast_fp16")]; + int32 var_1991 = const()[name = string("op_1991"), val = int32(-1)]; + string sub_pixels_1_pad_type_0 = const()[name = string("sub_pixels_1_pad_type_0"), val = string("valid")]; + tensor sub_pixels_1_strides_0 = const()[name = string("sub_pixels_1_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_1_pad_0 = const()[name = string("sub_pixels_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_1_dilations_0 = const()[name = string("sub_pixels_1_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_1_groups_0 = const()[name = string("sub_pixels_1_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41773696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43870912))))[name = string("audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43871488)))]; + tensor sub_pixels_1_cast_fp16 = conv(bias = audio_upsampler_upsample_0_0_conv_bias_to_fp16, dilations = sub_pixels_1_dilations_0, groups = sub_pixels_1_groups_0, pad = sub_pixels_1_pad_0, pad_type = sub_pixels_1_pad_type_0, strides = sub_pixels_1_strides_0, weight = audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized, x = hidden_states_51_cast_fp16)[name = string("sub_pixels_1_cast_fp16")]; + tensor var_2042 = const()[name = string("op_2042"), val = tensor([1, 2, 1024, 9])]; + tensor sub_pixels_3_cast_fp16 = reshape(shape = var_2042, x = sub_pixels_1_cast_fp16)[name = string("sub_pixels_3_cast_fp16")]; + tensor var_2044 = const()[name = string("op_2044"), val = tensor([0, 2, 3, 1])]; + tensor var_2049 = const()[name = string("op_2049"), val = tensor([1, 1024, 1, 18])]; + tensor sub_pixels_5_cast_fp16 = transpose(perm = var_2044, x = sub_pixels_3_cast_fp16)[name = string("transpose_9")]; + tensor hidden_states_53_cast_fp16 = reshape(shape = var_2049, x = sub_pixels_5_cast_fp16)[name = string("hidden_states_53_cast_fp16")]; + tensor var_2054 = const()[name = string("op_2054"), val = tensor([1, 1, 8, 1])]; + tensor var_2055_cast_fp16 = reshape(shape = var_2054, x = hidden_context_mask)[name = string("op_2055_cast_fp16")]; + tensor spread_1_reps_0 = const()[name = string("spread_1_reps_0"), val = tensor([1, 1, 1, 2])]; + tensor spread_1_cast_fp16 = tile(reps = spread_1_reps_0, x = var_2055_cast_fp16)[name = string("spread_1_cast_fp16")]; + tensor var_2061 = const()[name = string("op_2061"), val = tensor([1, 1, 1, 16])]; + tensor context_mask_1_cast_fp16 = reshape(shape = var_2061, x = spread_1_cast_fp16)[name = string("context_mask_1_cast_fp16")]; + bool full_mask_1_interleave_0 = const()[name = string("full_mask_1_interleave_0"), val = bool(false)]; + tensor fill_0_to_fp16 = const()[name = string("fill_0_to_fp16"), val = tensor([[[[0x1p+0, 0x1p+0]]]])]; + tensor full_mask_1_cast_fp16 = concat(axis = var_1991, interleave = full_mask_1_interleave_0, values = (context_mask_1_cast_fp16, fill_0_to_fp16))[name = string("full_mask_1_cast_fp16")]; + tensor hidden_states_55_cast_fp16 = mul(x = hidden_states_53_cast_fp16, y = full_mask_1_cast_fp16)[name = string("hidden_states_55_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 6, 0])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("constant")]; + fp16 const_177_to_fp16 = const()[name = string("const_177_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_177_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = hidden_states_55_cast_fp16)[name = string("input_107_cast_fp16")]; + string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")]; + int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1024)]; + tensor hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor([1, 1])]; + tensor audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43875648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43882880))))[name = string("audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43883456)))]; + tensor hidden_states_57_cast_fp16 = conv(bias = audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16, dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("hidden_states_57_cast_fp16")]; + tensor var_2089_axes_0 = const()[name = string("op_2089_axes_0"), val = tensor([2])]; + tensor var_2089_cast_fp16 = squeeze(axes = var_2089_axes_0, x = hidden_states_57_cast_fp16)[name = string("op_2089_cast_fp16")]; + tensor var_2090 = const()[name = string("op_2090"), val = tensor([0, 2, 1])]; + tensor hidden_states_59_axes_0 = const()[name = string("hidden_states_59_axes_0"), val = tensor([-1])]; + tensor audio_upsampler_upsample_0_1_norm_weight_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43885568)))]; + tensor audio_upsampler_upsample_0_1_norm_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43887680)))]; + fp16 var_1995_to_fp16 = const()[name = string("op_1995_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_109_cast_fp16 = transpose(perm = var_2090, x = var_2089_cast_fp16)[name = string("transpose_8")]; + tensor hidden_states_59_cast_fp16 = layer_norm(axes = hidden_states_59_axes_0, beta = audio_upsampler_upsample_0_1_norm_bias_to_fp16, epsilon = var_1995_to_fp16, gamma = audio_upsampler_upsample_0_1_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("hidden_states_59_cast_fp16")]; + tensor var_2096 = const()[name = string("op_2096"), val = tensor([0, 2, 1])]; + tensor input_111_axes_0 = const()[name = string("input_111_axes_0"), val = tensor([2])]; + tensor var_2097_cast_fp16 = transpose(perm = var_2096, x = hidden_states_59_cast_fp16)[name = string("transpose_7")]; + tensor input_111_cast_fp16 = expand_dims(axes = input_111_axes_0, x = var_2097_cast_fp16)[name = string("input_111_cast_fp16")]; + string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("valid")]; + tensor input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor([1, 1])]; + tensor input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor([1, 1])]; + int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43889792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48084160))))[name = string("audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48084736)))]; + tensor input_113_cast_fp16 = conv(bias = audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = string("input_113_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = input_113_cast_fp16)[name = string("input_115_cast_fp16")]; + string hidden_states_61_pad_type_0 = const()[name = string("hidden_states_61_pad_type_0"), val = string("valid")]; + tensor hidden_states_61_strides_0 = const()[name = string("hidden_states_61_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_61_pad_0 = const()[name = string("hidden_states_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_61_dilations_0 = const()[name = string("hidden_states_61_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_61_groups_0 = const()[name = string("hidden_states_61_groups_0"), val = int32(1)]; + tensor hidden_states_63_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48092992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52287360))))[name = string("hidden_states_63_weight_0_to_fp16_palettized")]; + tensor hidden_states_63_bias_0_to_fp16 = const()[name = string("hidden_states_63_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52287936)))]; + tensor hidden_states_63_cast_fp16 = conv(bias = hidden_states_63_bias_0_to_fp16, dilations = hidden_states_61_dilations_0, groups = hidden_states_61_groups_0, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = hidden_states_61_strides_0, weight = hidden_states_63_weight_0_to_fp16_palettized, x = input_115_cast_fp16)[name = string("hidden_states_63_cast_fp16")]; + tensor hidden_states_65_cast_fp16 = add(x = hidden_states_53_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")]; + tensor input_117_begin_0 = const()[name = string("input_117_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor input_117_end_0 = const()[name = string("input_117_end_0"), val = tensor([1, 1024, 1, 18])]; + tensor input_117_end_mask_0 = const()[name = string("input_117_end_mask_0"), val = tensor([true, true, true, true])]; + tensor input_117_cast_fp16 = slice_by_index(begin = input_117_begin_0, end = input_117_end_0, end_mask = input_117_end_mask_0, x = hidden_states_65_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor context_mask_3_begin_0 = const()[name = string("context_mask_3_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor context_mask_3_end_0 = const()[name = string("context_mask_3_end_0"), val = tensor([1, 1, 1, 16])]; + tensor context_mask_3_end_mask_0 = const()[name = string("context_mask_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_3_cast_fp16 = slice_by_index(begin = context_mask_3_begin_0, end = context_mask_3_end_0, end_mask = context_mask_3_end_mask_0, x = context_mask_1_cast_fp16)[name = string("context_mask_3_cast_fp16")]; + string sub_pixels_7_pad_type_0 = const()[name = string("sub_pixels_7_pad_type_0"), val = string("valid")]; + tensor sub_pixels_7_strides_0 = const()[name = string("sub_pixels_7_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_7_pad_0 = const()[name = string("sub_pixels_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_7_dilations_0 = const()[name = string("sub_pixels_7_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_7_groups_0 = const()[name = string("sub_pixels_7_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52290048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54387264))))[name = string("audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54387840)))]; + tensor sub_pixels_7_cast_fp16 = conv(bias = audio_upsampler_upsample_1_0_conv_bias_to_fp16, dilations = sub_pixels_7_dilations_0, groups = sub_pixels_7_groups_0, pad = sub_pixels_7_pad_0, pad_type = sub_pixels_7_pad_type_0, strides = sub_pixels_7_strides_0, weight = audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized, x = input_117_cast_fp16)[name = string("sub_pixels_7_cast_fp16")]; + tensor var_2137 = const()[name = string("op_2137"), val = tensor([1, 2, 1024, 15])]; + tensor sub_pixels_9_cast_fp16 = reshape(shape = var_2137, x = sub_pixels_7_cast_fp16)[name = string("sub_pixels_9_cast_fp16")]; + tensor var_2139 = const()[name = string("op_2139"), val = tensor([0, 2, 3, 1])]; + tensor var_2144 = const()[name = string("op_2144"), val = tensor([1, 1024, 1, 30])]; + tensor sub_pixels_11_cast_fp16 = transpose(perm = var_2139, x = sub_pixels_9_cast_fp16)[name = string("transpose_6")]; + tensor hidden_states_67_cast_fp16 = reshape(shape = var_2144, x = sub_pixels_11_cast_fp16)[name = string("hidden_states_67_cast_fp16")]; + tensor var_2149 = const()[name = string("op_2149"), val = tensor([1, 1, 13, 1])]; + tensor var_2150_cast_fp16 = reshape(shape = var_2149, x = context_mask_3_cast_fp16)[name = string("op_2150_cast_fp16")]; + tensor spread_3_reps_0 = const()[name = string("spread_3_reps_0"), val = tensor([1, 1, 1, 2])]; + tensor spread_3_cast_fp16 = tile(reps = spread_3_reps_0, x = var_2150_cast_fp16)[name = string("spread_3_cast_fp16")]; + tensor var_2156 = const()[name = string("op_2156"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_5_cast_fp16 = reshape(shape = var_2156, x = spread_3_cast_fp16)[name = string("context_mask_5_cast_fp16")]; + tensor residual_1_begin_0 = const()[name = string("residual_1_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_1_end_0 = const()[name = string("residual_1_end_0"), val = tensor([1, 1024, 1, 30])]; + tensor residual_1_end_mask_0 = const()[name = string("residual_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_1_cast_fp16 = slice_by_index(begin = residual_1_begin_0, end = residual_1_end_0, end_mask = residual_1_end_mask_0, x = hidden_states_67_cast_fp16)[name = string("residual_1_cast_fp16")]; + bool full_mask_3_interleave_0 = const()[name = string("full_mask_3_interleave_0"), val = bool(false)]; + tensor fill_1_to_fp16 = const()[name = string("fill_1_to_fp16"), val = tensor([[[[0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0]]]])]; + tensor full_mask_3_cast_fp16 = concat(axis = var_1991, interleave = full_mask_3_interleave_0, values = (context_mask_5_cast_fp16, fill_1_to_fp16))[name = string("full_mask_3_cast_fp16")]; + tensor input_119_cast_fp16 = mul(x = hidden_states_67_cast_fp16, y = full_mask_3_cast_fp16)[name = string("input_119_cast_fp16")]; + string hidden_states_69_pad_type_0 = const()[name = string("hidden_states_69_pad_type_0"), val = string("valid")]; + int32 hidden_states_69_groups_0 = const()[name = string("hidden_states_69_groups_0"), val = int32(1024)]; + tensor hidden_states_69_strides_0 = const()[name = string("hidden_states_69_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_69_pad_0 = const()[name = string("hidden_states_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_69_dilations_0 = const()[name = string("hidden_states_69_dilations_0"), val = tensor([1, 1])]; + tensor audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54392000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54399232))))[name = string("audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54399808)))]; + tensor hidden_states_69_cast_fp16 = conv(bias = audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16, dilations = hidden_states_69_dilations_0, groups = hidden_states_69_groups_0, pad = hidden_states_69_pad_0, pad_type = hidden_states_69_pad_type_0, strides = hidden_states_69_strides_0, weight = audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("hidden_states_69_cast_fp16")]; + tensor var_2183_axes_0 = const()[name = string("op_2183_axes_0"), val = tensor([2])]; + tensor var_2183_cast_fp16 = squeeze(axes = var_2183_axes_0, x = hidden_states_69_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor var_2184 = const()[name = string("op_2184"), val = tensor([0, 2, 1])]; + tensor hidden_states_71_axes_0 = const()[name = string("hidden_states_71_axes_0"), val = tensor([-1])]; + tensor audio_upsampler_upsample_1_1_norm_weight_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54401920)))]; + tensor audio_upsampler_upsample_1_1_norm_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54404032)))]; + tensor input_121_cast_fp16 = transpose(perm = var_2184, x = var_2183_cast_fp16)[name = string("transpose_5")]; + tensor hidden_states_71_cast_fp16 = layer_norm(axes = hidden_states_71_axes_0, beta = audio_upsampler_upsample_1_1_norm_bias_to_fp16, epsilon = var_1995_to_fp16, gamma = audio_upsampler_upsample_1_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("hidden_states_71_cast_fp16")]; + tensor var_2190 = const()[name = string("op_2190"), val = tensor([0, 2, 1])]; + tensor input_123_axes_0 = const()[name = string("input_123_axes_0"), val = tensor([2])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2190, x = hidden_states_71_cast_fp16)[name = string("transpose_4")]; + tensor input_123_cast_fp16 = expand_dims(axes = input_123_axes_0, x = var_2191_cast_fp16)[name = string("input_123_cast_fp16")]; + string input_125_pad_type_0 = const()[name = string("input_125_pad_type_0"), val = string("valid")]; + tensor input_125_strides_0 = const()[name = string("input_125_strides_0"), val = tensor([1, 1])]; + tensor input_125_pad_0 = const()[name = string("input_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_125_dilations_0 = const()[name = string("input_125_dilations_0"), val = tensor([1, 1])]; + int32 input_125_groups_0 = const()[name = string("input_125_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54406144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58600512))))[name = string("audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58601088)))]; + tensor input_125_cast_fp16 = conv(bias = audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("input_125_cast_fp16")]; + string input_127_mode_0 = const()[name = string("input_127_mode_0"), val = string("EXACT")]; + tensor input_127_cast_fp16 = gelu(mode = input_127_mode_0, x = input_125_cast_fp16)[name = string("input_127_cast_fp16")]; + string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")]; + tensor hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)]; + tensor hidden_states_75_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58609344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62803712))))[name = string("hidden_states_75_weight_0_to_fp16_palettized")]; + tensor hidden_states_75_bias_0_to_fp16 = const()[name = string("hidden_states_75_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62804288)))]; + tensor hidden_states_75_cast_fp16 = conv(bias = hidden_states_75_bias_0_to_fp16, dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = hidden_states_75_weight_0_to_fp16_palettized, x = input_127_cast_fp16)[name = string("hidden_states_75_cast_fp16")]; + tensor hidden_states_77_cast_fp16 = add(x = residual_1_cast_fp16, y = hidden_states_75_cast_fp16)[name = string("hidden_states_77_cast_fp16")]; + tensor context_mask_7_begin_0 = const()[name = string("context_mask_7_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_7_end_0 = const()[name = string("context_mask_7_end_0"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_7_end_mask_0 = const()[name = string("context_mask_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_7_cast_fp16 = slice_by_index(begin = context_mask_7_begin_0, end = context_mask_7_end_0, end_mask = context_mask_7_end_mask_0, x = context_mask_5_cast_fp16)[name = string("context_mask_7_cast_fp16")]; + bool full_mask_5_interleave_0 = const()[name = string("full_mask_5_interleave_0"), val = bool(false)]; + tensor fill_2_to_fp16 = const()[name = string("fill_2_to_fp16"), val = tensor([[[[0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0]]]])]; + tensor full_mask_5_cast_fp16 = concat(axis = var_1991, interleave = full_mask_5_interleave_0, values = (context_mask_7_cast_fp16, fill_2_to_fp16))[name = string("full_mask_5_cast_fp16")]; + tensor input_129_cast_fp16 = mul(x = hidden_states_77_cast_fp16, y = full_mask_5_cast_fp16)[name = string("input_129_cast_fp16")]; + string hidden_states_79_pad_type_0 = const()[name = string("hidden_states_79_pad_type_0"), val = string("valid")]; + tensor hidden_states_79_strides_0 = const()[name = string("hidden_states_79_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_79_pad_0 = const()[name = string("hidden_states_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_79_dilations_0 = const()[name = string("hidden_states_79_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_79_groups_0 = const()[name = string("hidden_states_79_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62806400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73816512))))[name = string("audio_upsampler_decoder_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73817088)))]; + tensor hidden_states_79_cast_fp16 = conv(bias = audio_upsampler_decoder_0_conv_bias_to_fp16, dilations = hidden_states_79_dilations_0, groups = hidden_states_79_groups_0, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = hidden_states_79_strides_0, weight = audio_upsampler_decoder_0_conv_weight_to_fp16_palettized, x = input_129_cast_fp16)[name = string("hidden_states_79_cast_fp16")]; + tensor context_mask_9_begin_0 = const()[name = string("context_mask_9_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_9_end_0 = const()[name = string("context_mask_9_end_0"), val = tensor([1, 1, 1, 20])]; + tensor context_mask_9_end_mask_0 = const()[name = string("context_mask_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_9_cast_fp16 = slice_by_index(begin = context_mask_9_begin_0, end = context_mask_9_end_0, end_mask = context_mask_9_end_mask_0, x = context_mask_7_cast_fp16)[name = string("context_mask_9_cast_fp16")]; + tensor alpha_over_pi_1_to_fp16 = const()[name = string("alpha_over_pi_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73820224)))]; + tensor theta_over_pi_1_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = alpha_over_pi_1_to_fp16)[name = string("theta_over_pi_1_cast_fp16")]; + tensor var_2259_cast_fp16 = round(x = theta_over_pi_1_cast_fp16)[name = string("op_2259_cast_fp16")]; + tensor reduced_1_cast_fp16 = sub(x = theta_over_pi_1_cast_fp16, y = var_2259_cast_fp16)[name = string("reduced_1_cast_fp16")]; + tensor reduced_sq_1_cast_fp16 = mul(x = reduced_1_cast_fp16, y = reduced_1_cast_fp16)[name = string("reduced_sq_1_cast_fp16")]; + tensor acc_1_mean_0_to_fp16 = const()[name = string("acc_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73823360)))]; + tensor acc_1_variance_0_to_fp16 = const()[name = string("acc_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73826496)))]; + tensor acc_1_gamma_0_to_fp16 = const()[name = string("acc_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73829632)))]; + tensor acc_1_beta_0_to_fp16 = const()[name = string("acc_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73832768)))]; + fp16 acc_1_epsilon_0_to_fp16 = const()[name = string("acc_1_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_1_cast_fp16 = batch_norm(beta = acc_1_beta_0_to_fp16, epsilon = acc_1_epsilon_0_to_fp16, gamma = acc_1_gamma_0_to_fp16, mean = acc_1_mean_0_to_fp16, variance = acc_1_variance_0_to_fp16, x = reduced_sq_1_cast_fp16)[name = string("acc_1_cast_fp16")]; + tensor var_2272_cast_fp16 = mul(x = acc_1_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2272_cast_fp16")]; + tensor c_1_to_fp16 = const()[name = string("c_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73835904)))]; + tensor acc_3_cast_fp16 = add(x = var_2272_cast_fp16, y = c_1_to_fp16)[name = string("acc_3_cast_fp16")]; + tensor var_2274_cast_fp16 = mul(x = acc_3_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2274_cast_fp16")]; + tensor c_3_to_fp16 = const()[name = string("c_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73839040)))]; + tensor acc_5_cast_fp16 = add(x = var_2274_cast_fp16, y = c_3_to_fp16)[name = string("acc_5_cast_fp16")]; + tensor var_2276_cast_fp16 = mul(x = acc_5_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2276_cast_fp16")]; + tensor hidden_states_81_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = var_2276_cast_fp16)[name = string("hidden_states_81_cast_fp16")]; + bool full_mask_7_interleave_0 = const()[name = string("full_mask_7_interleave_0"), val = bool(false)]; + tensor fill_3_to_fp16 = const()[name = string("fill_3_to_fp16"), val = tensor([[[[0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0]]]])]; + tensor full_mask_7_cast_fp16 = concat(axis = var_1991, interleave = full_mask_7_interleave_0, values = (context_mask_9_cast_fp16, fill_3_to_fp16))[name = string("full_mask_7_cast_fp16")]; + tensor input_131_cast_fp16 = mul(x = hidden_states_81_cast_fp16, y = full_mask_7_cast_fp16)[name = string("input_131_cast_fp16")]; + string sub_pixels_13_pad_type_0 = const()[name = string("sub_pixels_13_pad_type_0"), val = string("valid")]; + tensor sub_pixels_13_strides_0 = const()[name = string("sub_pixels_13_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_13_pad_0 = const()[name = string("sub_pixels_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_13_dilations_0 = const()[name = string("sub_pixels_13_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_13_groups_0 = const()[name = string("sub_pixels_13_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73842176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92716608))))[name = string("audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92717184)))]; + tensor sub_pixels_13_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_1_conv_bias_to_fp16, dilations = sub_pixels_13_dilations_0, groups = sub_pixels_13_groups_0, pad = sub_pixels_13_pad_0, pad_type = sub_pixels_13_pad_type_0, strides = sub_pixels_13_strides_0, weight = audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("sub_pixels_13_cast_fp16")]; + tensor var_2300 = const()[name = string("op_2300"), val = tensor([1, 8, 768, 17])]; + tensor sub_pixels_15_cast_fp16 = reshape(shape = var_2300, x = sub_pixels_13_cast_fp16)[name = string("sub_pixels_15_cast_fp16")]; + tensor var_2302 = const()[name = string("op_2302"), val = tensor([0, 2, 3, 1])]; + tensor var_2307 = const()[name = string("op_2307"), val = tensor([1, 768, 1, 136])]; + tensor sub_pixels_17_cast_fp16 = transpose(perm = var_2302, x = sub_pixels_15_cast_fp16)[name = string("transpose_3")]; + tensor hidden_states_83_cast_fp16 = reshape(shape = var_2307, x = sub_pixels_17_cast_fp16)[name = string("hidden_states_83_cast_fp16")]; + tensor newest_5_begin_0 = const()[name = string("newest_5_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_5_end_0 = const()[name = string("newest_5_end_0"), val = tensor([1, 1, 1, 14])]; + tensor newest_5_end_mask_0 = const()[name = string("newest_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_5_cast_fp16 = slice_by_index(begin = newest_5_begin_0, end = newest_5_end_0, end_mask = newest_5_end_mask_0, x = context_mask_9_cast_fp16)[name = string("newest_5_cast_fp16")]; + tensor var_2312 = const()[name = string("op_2312"), val = tensor([1, 1, 13, 1])]; + tensor var_2313_cast_fp16 = reshape(shape = var_2312, x = newest_5_cast_fp16)[name = string("op_2313_cast_fp16")]; + tensor spread_5_reps_0 = const()[name = string("spread_5_reps_0"), val = tensor([1, 1, 1, 8])]; + tensor spread_5_cast_fp16 = tile(reps = spread_5_reps_0, x = var_2313_cast_fp16)[name = string("spread_5_cast_fp16")]; + tensor var_2319 = const()[name = string("op_2319"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_11_cast_fp16 = reshape(shape = var_2319, x = spread_5_cast_fp16)[name = string("context_mask_11_cast_fp16")]; + tensor residual_3_begin_0 = const()[name = string("residual_3_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_3_end_0 = const()[name = string("residual_3_end_0"), val = tensor([1, 768, 1, 136])]; + tensor residual_3_end_mask_0 = const()[name = string("residual_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_3_cast_fp16 = slice_by_index(begin = residual_3_begin_0, end = residual_3_end_0, end_mask = residual_3_end_mask_0, x = hidden_states_83_cast_fp16)[name = string("residual_3_cast_fp16")]; + tensor alpha_over_pi_3_to_fp16 = const()[name = string("alpha_over_pi_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92729536)))]; + tensor theta_over_pi_3_cast_fp16 = mul(x = hidden_states_83_cast_fp16, y = alpha_over_pi_3_to_fp16)[name = string("theta_over_pi_3_cast_fp16")]; + tensor var_2342_cast_fp16 = round(x = theta_over_pi_3_cast_fp16)[name = string("op_2342_cast_fp16")]; + tensor reduced_3_cast_fp16 = sub(x = theta_over_pi_3_cast_fp16, y = var_2342_cast_fp16)[name = string("reduced_3_cast_fp16")]; + tensor reduced_sq_3_cast_fp16 = mul(x = reduced_3_cast_fp16, y = reduced_3_cast_fp16)[name = string("reduced_sq_3_cast_fp16")]; + tensor acc_7_mean_0_to_fp16 = const()[name = string("acc_7_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92731136)))]; + tensor acc_7_variance_0_to_fp16 = const()[name = string("acc_7_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92732736)))]; + tensor acc_7_gamma_0_to_fp16 = const()[name = string("acc_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92734336)))]; + tensor acc_7_beta_0_to_fp16 = const()[name = string("acc_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92735936)))]; + fp16 acc_7_epsilon_0_to_fp16 = const()[name = string("acc_7_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_7_cast_fp16 = batch_norm(beta = acc_7_beta_0_to_fp16, epsilon = acc_7_epsilon_0_to_fp16, gamma = acc_7_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_3_cast_fp16)[name = string("acc_7_cast_fp16")]; + tensor var_2355_cast_fp16 = mul(x = acc_7_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2355_cast_fp16")]; + tensor c_5_to_fp16 = const()[name = string("c_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92737536)))]; + tensor acc_9_cast_fp16 = add(x = var_2355_cast_fp16, y = c_5_to_fp16)[name = string("acc_9_cast_fp16")]; + tensor var_2357_cast_fp16 = mul(x = acc_9_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2357_cast_fp16")]; + tensor c_7_to_fp16 = const()[name = string("c_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92739136)))]; + tensor acc_11_cast_fp16 = add(x = var_2357_cast_fp16, y = c_7_to_fp16)[name = string("acc_11_cast_fp16")]; + tensor var_2359_cast_fp16 = mul(x = acc_11_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2359_cast_fp16")]; + tensor hidden_states_85_cast_fp16 = add(x = hidden_states_83_cast_fp16, y = var_2359_cast_fp16)[name = string("hidden_states_85_cast_fp16")]; + bool full_mask_9_interleave_0 = const()[name = string("full_mask_9_interleave_0"), val = bool(false)]; + tensor fill_4_to_fp16 = const()[name = string("fill_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740736)))]; + tensor full_mask_9_cast_fp16 = concat(axis = var_1991, interleave = full_mask_9_interleave_0, values = (context_mask_11_cast_fp16, fill_4_to_fp16))[name = string("full_mask_9_cast_fp16")]; + tensor input_133_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = full_mask_9_cast_fp16)[name = string("input_133_cast_fp16")]; + string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")]; + tensor hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96869696))))[name = string("audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96870272)))]; + tensor hidden_states_87_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized, x = input_133_cast_fp16)[name = string("hidden_states_87_cast_fp16")]; + tensor alpha_over_pi_5_to_fp16 = const()[name = string("alpha_over_pi_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96871872)))]; + tensor theta_over_pi_5_cast_fp16 = mul(x = hidden_states_87_cast_fp16, y = alpha_over_pi_5_to_fp16)[name = string("theta_over_pi_5_cast_fp16")]; + tensor var_2396_cast_fp16 = round(x = theta_over_pi_5_cast_fp16)[name = string("op_2396_cast_fp16")]; + tensor reduced_5_cast_fp16 = sub(x = theta_over_pi_5_cast_fp16, y = var_2396_cast_fp16)[name = string("reduced_5_cast_fp16")]; + tensor reduced_sq_5_cast_fp16 = mul(x = reduced_5_cast_fp16, y = reduced_5_cast_fp16)[name = string("reduced_sq_5_cast_fp16")]; + tensor acc_13_gamma_0_to_fp16 = const()[name = string("acc_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96873472)))]; + tensor acc_13_beta_0_to_fp16 = const()[name = string("acc_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96875072)))]; + fp16 acc_13_epsilon_0_to_fp16 = const()[name = string("acc_13_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_13_cast_fp16 = batch_norm(beta = acc_13_beta_0_to_fp16, epsilon = acc_13_epsilon_0_to_fp16, gamma = acc_13_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_5_cast_fp16)[name = string("acc_13_cast_fp16")]; + tensor var_2409_cast_fp16 = mul(x = acc_13_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2409_cast_fp16")]; + tensor c_9_to_fp16 = const()[name = string("c_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96876672)))]; + tensor acc_15_cast_fp16 = add(x = var_2409_cast_fp16, y = c_9_to_fp16)[name = string("acc_15_cast_fp16")]; + tensor var_2411_cast_fp16 = mul(x = acc_15_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2411_cast_fp16")]; + tensor c_11_to_fp16 = const()[name = string("c_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96878272)))]; + tensor acc_17_cast_fp16 = add(x = var_2411_cast_fp16, y = c_11_to_fp16)[name = string("acc_17_cast_fp16")]; + tensor var_2413_cast_fp16 = mul(x = acc_17_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2413_cast_fp16")]; + tensor hidden_states_89_cast_fp16 = add(x = hidden_states_87_cast_fp16, y = var_2413_cast_fp16)[name = string("hidden_states_89_cast_fp16")]; + string hidden_states_91_pad_type_0 = const()[name = string("hidden_states_91_pad_type_0"), val = string("valid")]; + tensor hidden_states_91_strides_0 = const()[name = string("hidden_states_91_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_91_pad_0 = const()[name = string("hidden_states_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_91_dilations_0 = const()[name = string("hidden_states_91_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_91_groups_0 = const()[name = string("hidden_states_91_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96879872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97469760))))[name = string("audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97470336)))]; + tensor hidden_states_91_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_91_dilations_0, groups = hidden_states_91_groups_0, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = hidden_states_91_strides_0, weight = audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_89_cast_fp16)[name = string("hidden_states_91_cast_fp16")]; + tensor hidden_states_93_cast_fp16 = add(x = hidden_states_91_cast_fp16, y = residual_3_cast_fp16)[name = string("hidden_states_93_cast_fp16")]; + tensor context_mask_13_begin_0 = const()[name = string("context_mask_13_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_13_end_0 = const()[name = string("context_mask_13_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_13_end_mask_0 = const()[name = string("context_mask_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_13_cast_fp16 = slice_by_index(begin = context_mask_13_begin_0, end = context_mask_13_end_0, end_mask = context_mask_13_end_mask_0, x = context_mask_11_cast_fp16)[name = string("context_mask_13_cast_fp16")]; + tensor residual_5_begin_0 = const()[name = string("residual_5_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_5_end_0 = const()[name = string("residual_5_end_0"), val = tensor([1, 768, 1, 130])]; + tensor residual_5_end_mask_0 = const()[name = string("residual_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_5_cast_fp16 = slice_by_index(begin = residual_5_begin_0, end = residual_5_end_0, end_mask = residual_5_end_mask_0, x = hidden_states_93_cast_fp16)[name = string("residual_5_cast_fp16")]; + tensor alpha_over_pi_7_to_fp16 = const()[name = string("alpha_over_pi_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97471936)))]; + tensor theta_over_pi_7_cast_fp16 = mul(x = hidden_states_93_cast_fp16, y = alpha_over_pi_7_to_fp16)[name = string("theta_over_pi_7_cast_fp16")]; + tensor var_2448_cast_fp16 = round(x = theta_over_pi_7_cast_fp16)[name = string("op_2448_cast_fp16")]; + tensor reduced_7_cast_fp16 = sub(x = theta_over_pi_7_cast_fp16, y = var_2448_cast_fp16)[name = string("reduced_7_cast_fp16")]; + tensor reduced_sq_7_cast_fp16 = mul(x = reduced_7_cast_fp16, y = reduced_7_cast_fp16)[name = string("reduced_sq_7_cast_fp16")]; + tensor acc_19_gamma_0_to_fp16 = const()[name = string("acc_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97473536)))]; + tensor acc_19_beta_0_to_fp16 = const()[name = string("acc_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97475136)))]; + fp16 acc_19_epsilon_0_to_fp16 = const()[name = string("acc_19_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_19_cast_fp16 = batch_norm(beta = acc_19_beta_0_to_fp16, epsilon = acc_19_epsilon_0_to_fp16, gamma = acc_19_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_7_cast_fp16)[name = string("acc_19_cast_fp16")]; + tensor var_2461_cast_fp16 = mul(x = acc_19_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2461_cast_fp16")]; + tensor c_13_to_fp16 = const()[name = string("c_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97476736)))]; + tensor acc_21_cast_fp16 = add(x = var_2461_cast_fp16, y = c_13_to_fp16)[name = string("acc_21_cast_fp16")]; + tensor var_2463_cast_fp16 = mul(x = acc_21_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2463_cast_fp16")]; + tensor c_15_to_fp16 = const()[name = string("c_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97478336)))]; + tensor acc_23_cast_fp16 = add(x = var_2463_cast_fp16, y = c_15_to_fp16)[name = string("acc_23_cast_fp16")]; + tensor var_2465_cast_fp16 = mul(x = acc_23_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2465_cast_fp16")]; + tensor hidden_states_95_cast_fp16 = add(x = hidden_states_93_cast_fp16, y = var_2465_cast_fp16)[name = string("hidden_states_95_cast_fp16")]; + bool full_mask_11_interleave_0 = const()[name = string("full_mask_11_interleave_0"), val = bool(false)]; + tensor fill_5_to_fp16 = const()[name = string("fill_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740736)))]; + tensor full_mask_11_cast_fp16 = concat(axis = var_1991, interleave = full_mask_11_interleave_0, values = (context_mask_13_cast_fp16, fill_5_to_fp16))[name = string("full_mask_11_cast_fp16")]; + tensor input_137_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = full_mask_11_cast_fp16)[name = string("input_137_cast_fp16")]; + string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")]; + tensor hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97479936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101608768))))[name = string("audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101609344)))]; + tensor hidden_states_97_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized, x = input_137_cast_fp16)[name = string("hidden_states_97_cast_fp16")]; + tensor alpha_over_pi_9_to_fp16 = const()[name = string("alpha_over_pi_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101610944)))]; + tensor theta_over_pi_9_cast_fp16 = mul(x = hidden_states_97_cast_fp16, y = alpha_over_pi_9_to_fp16)[name = string("theta_over_pi_9_cast_fp16")]; + tensor var_2502_cast_fp16 = round(x = theta_over_pi_9_cast_fp16)[name = string("op_2502_cast_fp16")]; + tensor reduced_9_cast_fp16 = sub(x = theta_over_pi_9_cast_fp16, y = var_2502_cast_fp16)[name = string("reduced_9_cast_fp16")]; + tensor reduced_sq_9_cast_fp16 = mul(x = reduced_9_cast_fp16, y = reduced_9_cast_fp16)[name = string("reduced_sq_9_cast_fp16")]; + tensor acc_25_gamma_0_to_fp16 = const()[name = string("acc_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101612544)))]; + tensor acc_25_beta_0_to_fp16 = const()[name = string("acc_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101614144)))]; + fp16 acc_25_epsilon_0_to_fp16 = const()[name = string("acc_25_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_25_cast_fp16 = batch_norm(beta = acc_25_beta_0_to_fp16, epsilon = acc_25_epsilon_0_to_fp16, gamma = acc_25_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_9_cast_fp16)[name = string("acc_25_cast_fp16")]; + tensor var_2515_cast_fp16 = mul(x = acc_25_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2515_cast_fp16")]; + tensor c_17_to_fp16 = const()[name = string("c_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101615744)))]; + tensor acc_27_cast_fp16 = add(x = var_2515_cast_fp16, y = c_17_to_fp16)[name = string("acc_27_cast_fp16")]; + tensor var_2517_cast_fp16 = mul(x = acc_27_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2517_cast_fp16")]; + tensor c_19_to_fp16 = const()[name = string("c_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101617344)))]; + tensor acc_29_cast_fp16 = add(x = var_2517_cast_fp16, y = c_19_to_fp16)[name = string("acc_29_cast_fp16")]; + tensor var_2519_cast_fp16 = mul(x = acc_29_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2519_cast_fp16")]; + tensor hidden_states_99_cast_fp16 = add(x = hidden_states_97_cast_fp16, y = var_2519_cast_fp16)[name = string("hidden_states_99_cast_fp16")]; + string hidden_states_101_pad_type_0 = const()[name = string("hidden_states_101_pad_type_0"), val = string("valid")]; + tensor hidden_states_101_strides_0 = const()[name = string("hidden_states_101_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_101_pad_0 = const()[name = string("hidden_states_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_101_dilations_0 = const()[name = string("hidden_states_101_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_101_groups_0 = const()[name = string("hidden_states_101_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101618944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102208832))))[name = string("audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102209408)))]; + tensor hidden_states_101_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_101_dilations_0, groups = hidden_states_101_groups_0, pad = hidden_states_101_pad_0, pad_type = hidden_states_101_pad_type_0, strides = hidden_states_101_strides_0, weight = audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_99_cast_fp16)[name = string("hidden_states_101_cast_fp16")]; + tensor hidden_states_103_cast_fp16 = add(x = hidden_states_101_cast_fp16, y = residual_5_cast_fp16)[name = string("hidden_states_103_cast_fp16")]; + tensor context_mask_15_begin_0 = const()[name = string("context_mask_15_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_15_end_0 = const()[name = string("context_mask_15_end_0"), val = tensor([1, 1, 1, 98])]; + tensor context_mask_15_end_mask_0 = const()[name = string("context_mask_15_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_15_cast_fp16 = slice_by_index(begin = context_mask_15_begin_0, end = context_mask_15_end_0, end_mask = context_mask_15_end_mask_0, x = context_mask_13_cast_fp16)[name = string("context_mask_15_cast_fp16")]; + tensor residual_7_begin_0 = const()[name = string("residual_7_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_7_end_0 = const()[name = string("residual_7_end_0"), val = tensor([1, 768, 1, 112])]; + tensor residual_7_end_mask_0 = const()[name = string("residual_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_7_cast_fp16 = slice_by_index(begin = residual_7_begin_0, end = residual_7_end_0, end_mask = residual_7_end_mask_0, x = hidden_states_103_cast_fp16)[name = string("residual_7_cast_fp16")]; + tensor alpha_over_pi_11_to_fp16 = const()[name = string("alpha_over_pi_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102211008)))]; + tensor theta_over_pi_11_cast_fp16 = mul(x = hidden_states_103_cast_fp16, y = alpha_over_pi_11_to_fp16)[name = string("theta_over_pi_11_cast_fp16")]; + tensor var_2554_cast_fp16 = round(x = theta_over_pi_11_cast_fp16)[name = string("op_2554_cast_fp16")]; + tensor reduced_11_cast_fp16 = sub(x = theta_over_pi_11_cast_fp16, y = var_2554_cast_fp16)[name = string("reduced_11_cast_fp16")]; + tensor reduced_sq_11_cast_fp16 = mul(x = reduced_11_cast_fp16, y = reduced_11_cast_fp16)[name = string("reduced_sq_11_cast_fp16")]; + tensor acc_31_gamma_0_to_fp16 = const()[name = string("acc_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102212608)))]; + tensor acc_31_beta_0_to_fp16 = const()[name = string("acc_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102214208)))]; + fp16 acc_31_epsilon_0_to_fp16 = const()[name = string("acc_31_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_31_cast_fp16 = batch_norm(beta = acc_31_beta_0_to_fp16, epsilon = acc_31_epsilon_0_to_fp16, gamma = acc_31_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_11_cast_fp16)[name = string("acc_31_cast_fp16")]; + tensor var_2567_cast_fp16 = mul(x = acc_31_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2567_cast_fp16")]; + tensor c_21_to_fp16 = const()[name = string("c_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102215808)))]; + tensor acc_33_cast_fp16 = add(x = var_2567_cast_fp16, y = c_21_to_fp16)[name = string("acc_33_cast_fp16")]; + tensor var_2569_cast_fp16 = mul(x = acc_33_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2569_cast_fp16")]; + tensor c_23_to_fp16 = const()[name = string("c_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102217408)))]; + tensor acc_35_cast_fp16 = add(x = var_2569_cast_fp16, y = c_23_to_fp16)[name = string("acc_35_cast_fp16")]; + tensor var_2571_cast_fp16 = mul(x = acc_35_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2571_cast_fp16")]; + tensor hidden_states_105_cast_fp16 = add(x = hidden_states_103_cast_fp16, y = var_2571_cast_fp16)[name = string("hidden_states_105_cast_fp16")]; + bool full_mask_13_interleave_0 = const()[name = string("full_mask_13_interleave_0"), val = bool(false)]; + tensor fill_6_to_fp16 = const()[name = string("fill_6_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740736)))]; + tensor full_mask_13_cast_fp16 = concat(axis = var_1991, interleave = full_mask_13_interleave_0, values = (context_mask_15_cast_fp16, fill_6_to_fp16))[name = string("full_mask_13_cast_fp16")]; + tensor input_141_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = full_mask_13_cast_fp16)[name = string("input_141_cast_fp16")]; + string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")]; + tensor hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102219008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106347840))))[name = string("audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106348416)))]; + tensor hidden_states_107_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized, x = input_141_cast_fp16)[name = string("hidden_states_107_cast_fp16")]; + tensor alpha_over_pi_13_to_fp16 = const()[name = string("alpha_over_pi_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106350016)))]; + tensor theta_over_pi_13_cast_fp16 = mul(x = hidden_states_107_cast_fp16, y = alpha_over_pi_13_to_fp16)[name = string("theta_over_pi_13_cast_fp16")]; + tensor var_2608_cast_fp16 = round(x = theta_over_pi_13_cast_fp16)[name = string("op_2608_cast_fp16")]; + tensor reduced_13_cast_fp16 = sub(x = theta_over_pi_13_cast_fp16, y = var_2608_cast_fp16)[name = string("reduced_13_cast_fp16")]; + tensor reduced_sq_13_cast_fp16 = mul(x = reduced_13_cast_fp16, y = reduced_13_cast_fp16)[name = string("reduced_sq_13_cast_fp16")]; + tensor acc_37_gamma_0_to_fp16 = const()[name = string("acc_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106351616)))]; + tensor acc_37_beta_0_to_fp16 = const()[name = string("acc_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106353216)))]; + fp16 acc_37_epsilon_0_to_fp16 = const()[name = string("acc_37_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_37_cast_fp16 = batch_norm(beta = acc_37_beta_0_to_fp16, epsilon = acc_37_epsilon_0_to_fp16, gamma = acc_37_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_13_cast_fp16)[name = string("acc_37_cast_fp16")]; + tensor var_2621_cast_fp16 = mul(x = acc_37_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2621_cast_fp16")]; + tensor c_25_to_fp16 = const()[name = string("c_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106354816)))]; + tensor acc_39_cast_fp16 = add(x = var_2621_cast_fp16, y = c_25_to_fp16)[name = string("acc_39_cast_fp16")]; + tensor var_2623_cast_fp16 = mul(x = acc_39_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2623_cast_fp16")]; + tensor c_27_to_fp16 = const()[name = string("c_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106356416)))]; + tensor acc_41_cast_fp16 = add(x = var_2623_cast_fp16, y = c_27_to_fp16)[name = string("acc_41_cast_fp16")]; + tensor var_2625_cast_fp16 = mul(x = acc_41_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2625_cast_fp16")]; + tensor hidden_states_109_cast_fp16 = add(x = hidden_states_107_cast_fp16, y = var_2625_cast_fp16)[name = string("hidden_states_109_cast_fp16")]; + string hidden_states_111_pad_type_0 = const()[name = string("hidden_states_111_pad_type_0"), val = string("valid")]; + tensor hidden_states_111_strides_0 = const()[name = string("hidden_states_111_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_111_pad_0 = const()[name = string("hidden_states_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_111_dilations_0 = const()[name = string("hidden_states_111_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_111_groups_0 = const()[name = string("hidden_states_111_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106358016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106947904))))[name = string("audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106948480)))]; + tensor hidden_states_111_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_111_dilations_0, groups = hidden_states_111_groups_0, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = hidden_states_111_strides_0, weight = audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_109_cast_fp16)[name = string("hidden_states_111_cast_fp16")]; + tensor hidden_states_113_cast_fp16 = add(x = hidden_states_111_cast_fp16, y = residual_7_cast_fp16)[name = string("hidden_states_113_cast_fp16")]; + tensor context_mask_19_begin_0 = const()[name = string("context_mask_19_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_19_end_0 = const()[name = string("context_mask_19_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_19_end_mask_0 = const()[name = string("context_mask_19_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_19_cast_fp16 = slice_by_index(begin = context_mask_19_begin_0, end = context_mask_19_end_0, end_mask = context_mask_19_end_mask_0, x = context_mask_11_cast_fp16)[name = string("context_mask_19_cast_fp16")]; + tensor hidden_states_115_begin_0 = const()[name = string("hidden_states_115_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor hidden_states_115_end_0 = const()[name = string("hidden_states_115_end_0"), val = tensor([1, 768, 1, 58])]; + tensor hidden_states_115_end_mask_0 = const()[name = string("hidden_states_115_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_115_cast_fp16 = slice_by_index(begin = hidden_states_115_begin_0, end = hidden_states_115_end_0, end_mask = hidden_states_115_end_mask_0, x = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")]; + tensor context_mask_21_begin_0 = const()[name = string("context_mask_21_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor context_mask_21_end_0 = const()[name = string("context_mask_21_end_0"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_21_end_mask_0 = const()[name = string("context_mask_21_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_21_cast_fp16 = slice_by_index(begin = context_mask_21_begin_0, end = context_mask_21_end_0, end_mask = context_mask_21_end_mask_0, x = context_mask_19_cast_fp16)[name = string("context_mask_21_cast_fp16")]; + tensor alpha_over_pi_15_to_fp16 = const()[name = string("alpha_over_pi_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106950080)))]; + tensor theta_over_pi_15_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = alpha_over_pi_15_to_fp16)[name = string("theta_over_pi_15_cast_fp16")]; + tensor var_2685_cast_fp16 = round(x = theta_over_pi_15_cast_fp16)[name = string("op_2685_cast_fp16")]; + tensor reduced_15_cast_fp16 = sub(x = theta_over_pi_15_cast_fp16, y = var_2685_cast_fp16)[name = string("reduced_15_cast_fp16")]; + tensor reduced_sq_15_cast_fp16 = mul(x = reduced_15_cast_fp16, y = reduced_15_cast_fp16)[name = string("reduced_sq_15_cast_fp16")]; + tensor acc_43_gamma_0_to_fp16 = const()[name = string("acc_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106951680)))]; + tensor acc_43_beta_0_to_fp16 = const()[name = string("acc_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106953280)))]; + fp16 acc_43_epsilon_0_to_fp16 = const()[name = string("acc_43_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_43_cast_fp16 = batch_norm(beta = acc_43_beta_0_to_fp16, epsilon = acc_43_epsilon_0_to_fp16, gamma = acc_43_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_15_cast_fp16)[name = string("acc_43_cast_fp16")]; + tensor var_2698_cast_fp16 = mul(x = acc_43_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2698_cast_fp16")]; + tensor c_29_to_fp16 = const()[name = string("c_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106954880)))]; + tensor acc_45_cast_fp16 = add(x = var_2698_cast_fp16, y = c_29_to_fp16)[name = string("acc_45_cast_fp16")]; + tensor var_2700_cast_fp16 = mul(x = acc_45_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2700_cast_fp16")]; + tensor c_31_to_fp16 = const()[name = string("c_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106956480)))]; + tensor acc_47_cast_fp16 = add(x = var_2700_cast_fp16, y = c_31_to_fp16)[name = string("acc_47_cast_fp16")]; + tensor var_2702_cast_fp16 = mul(x = acc_47_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2702_cast_fp16")]; + tensor hidden_states_117_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = var_2702_cast_fp16)[name = string("hidden_states_117_cast_fp16")]; + bool full_mask_15_interleave_0 = const()[name = string("full_mask_15_interleave_0"), val = bool(false)]; + tensor fill_7_to_fp16 = const()[name = string("fill_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740736)))]; + tensor full_mask_15_cast_fp16 = concat(axis = var_1991, interleave = full_mask_15_interleave_0, values = (context_mask_21_cast_fp16, fill_7_to_fp16))[name = string("full_mask_15_cast_fp16")]; + tensor input_145_cast_fp16 = mul(x = hidden_states_117_cast_fp16, y = full_mask_15_cast_fp16)[name = string("input_145_cast_fp16")]; + string sub_pixels_19_pad_type_0 = const()[name = string("sub_pixels_19_pad_type_0"), val = string("valid")]; + tensor sub_pixels_19_strides_0 = const()[name = string("sub_pixels_19_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_19_pad_0 = const()[name = string("sub_pixels_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_19_dilations_0 = const()[name = string("sub_pixels_19_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_19_groups_0 = const()[name = string("sub_pixels_19_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106958080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109907264))))[name = string("audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109907840)))]; + tensor sub_pixels_19_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_1_conv_bias_to_fp16, dilations = sub_pixels_19_dilations_0, groups = sub_pixels_19_groups_0, pad = sub_pixels_19_pad_0, pad_type = sub_pixels_19_pad_type_0, strides = sub_pixels_19_strides_0, weight = audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized, x = input_145_cast_fp16)[name = string("sub_pixels_19_cast_fp16")]; + tensor var_2726 = const()[name = string("op_2726"), val = tensor([1, 5, 384, 54])]; + tensor sub_pixels_21_cast_fp16 = reshape(shape = var_2726, x = sub_pixels_19_cast_fp16)[name = string("sub_pixels_21_cast_fp16")]; + tensor var_2728 = const()[name = string("op_2728"), val = tensor([0, 2, 3, 1])]; + tensor var_2733 = const()[name = string("op_2733"), val = tensor([1, 384, 1, 270])]; + tensor sub_pixels_23_cast_fp16 = transpose(perm = var_2728, x = sub_pixels_21_cast_fp16)[name = string("transpose_2")]; + tensor hidden_states_119_cast_fp16 = reshape(shape = var_2733, x = sub_pixels_23_cast_fp16)[name = string("hidden_states_119_cast_fp16")]; + tensor newest_9_begin_0 = const()[name = string("newest_9_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_9_end_0 = const()[name = string("newest_9_end_0"), val = tensor([1, 1, 1, 23])]; + tensor newest_9_end_mask_0 = const()[name = string("newest_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_9_cast_fp16 = slice_by_index(begin = newest_9_begin_0, end = newest_9_end_0, end_mask = newest_9_end_mask_0, x = context_mask_21_cast_fp16)[name = string("newest_9_cast_fp16")]; + tensor var_2738 = const()[name = string("op_2738"), val = tensor([1, 1, 22, 1])]; + tensor var_2739_cast_fp16 = reshape(shape = var_2738, x = newest_9_cast_fp16)[name = string("op_2739_cast_fp16")]; + tensor spread_9_reps_0 = const()[name = string("spread_9_reps_0"), val = tensor([1, 1, 1, 5])]; + tensor spread_9_cast_fp16 = tile(reps = spread_9_reps_0, x = var_2739_cast_fp16)[name = string("spread_9_cast_fp16")]; + tensor var_2745 = const()[name = string("op_2745"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_23_cast_fp16 = reshape(shape = var_2745, x = spread_9_cast_fp16)[name = string("context_mask_23_cast_fp16")]; + tensor residual_9_begin_0 = const()[name = string("residual_9_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_9_end_0 = const()[name = string("residual_9_end_0"), val = tensor([1, 384, 1, 270])]; + tensor residual_9_end_mask_0 = const()[name = string("residual_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_9_cast_fp16 = slice_by_index(begin = residual_9_begin_0, end = residual_9_end_0, end_mask = residual_9_end_mask_0, x = hidden_states_119_cast_fp16)[name = string("residual_9_cast_fp16")]; + tensor alpha_over_pi_17_to_fp16 = const()[name = string("alpha_over_pi_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109911744)))]; + tensor theta_over_pi_17_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = alpha_over_pi_17_to_fp16)[name = string("theta_over_pi_17_cast_fp16")]; + tensor var_2768_cast_fp16 = round(x = theta_over_pi_17_cast_fp16)[name = string("op_2768_cast_fp16")]; + tensor reduced_17_cast_fp16 = sub(x = theta_over_pi_17_cast_fp16, y = var_2768_cast_fp16)[name = string("reduced_17_cast_fp16")]; + tensor reduced_sq_17_cast_fp16 = mul(x = reduced_17_cast_fp16, y = reduced_17_cast_fp16)[name = string("reduced_sq_17_cast_fp16")]; + tensor acc_49_mean_0_to_fp16 = const()[name = string("acc_49_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109912576)))]; + tensor acc_49_variance_0_to_fp16 = const()[name = string("acc_49_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109913408)))]; + tensor acc_49_gamma_0_to_fp16 = const()[name = string("acc_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109914240)))]; + tensor acc_49_beta_0_to_fp16 = const()[name = string("acc_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109915072)))]; + fp16 acc_49_epsilon_0_to_fp16 = const()[name = string("acc_49_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_49_cast_fp16 = batch_norm(beta = acc_49_beta_0_to_fp16, epsilon = acc_49_epsilon_0_to_fp16, gamma = acc_49_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_17_cast_fp16)[name = string("acc_49_cast_fp16")]; + tensor var_2781_cast_fp16 = mul(x = acc_49_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2781_cast_fp16")]; + tensor c_33_to_fp16 = const()[name = string("c_33_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109915904)))]; + tensor acc_51_cast_fp16 = add(x = var_2781_cast_fp16, y = c_33_to_fp16)[name = string("acc_51_cast_fp16")]; + tensor var_2783_cast_fp16 = mul(x = acc_51_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2783_cast_fp16")]; + tensor c_35_to_fp16 = const()[name = string("c_35_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109916736)))]; + tensor acc_53_cast_fp16 = add(x = var_2783_cast_fp16, y = c_35_to_fp16)[name = string("acc_53_cast_fp16")]; + tensor var_2785_cast_fp16 = mul(x = acc_53_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2785_cast_fp16")]; + tensor hidden_states_121_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = var_2785_cast_fp16)[name = string("hidden_states_121_cast_fp16")]; + bool full_mask_17_interleave_0 = const()[name = string("full_mask_17_interleave_0"), val = bool(false)]; + tensor fill_8_to_fp16 = const()[name = string("fill_8_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109917568)))]; + tensor full_mask_17_cast_fp16 = concat(axis = var_1991, interleave = full_mask_17_interleave_0, values = (context_mask_23_cast_fp16, fill_8_to_fp16))[name = string("full_mask_17_cast_fp16")]; + tensor input_147_cast_fp16 = mul(x = hidden_states_121_cast_fp16, y = full_mask_17_cast_fp16)[name = string("input_147_cast_fp16")]; + string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")]; + tensor hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109917952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110950208))))[name = string("audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110950784)))]; + tensor hidden_states_123_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("hidden_states_123_cast_fp16")]; + tensor alpha_over_pi_19_to_fp16 = const()[name = string("alpha_over_pi_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110951616)))]; + tensor theta_over_pi_19_cast_fp16 = mul(x = hidden_states_123_cast_fp16, y = alpha_over_pi_19_to_fp16)[name = string("theta_over_pi_19_cast_fp16")]; + tensor var_2822_cast_fp16 = round(x = theta_over_pi_19_cast_fp16)[name = string("op_2822_cast_fp16")]; + tensor reduced_19_cast_fp16 = sub(x = theta_over_pi_19_cast_fp16, y = var_2822_cast_fp16)[name = string("reduced_19_cast_fp16")]; + tensor reduced_sq_19_cast_fp16 = mul(x = reduced_19_cast_fp16, y = reduced_19_cast_fp16)[name = string("reduced_sq_19_cast_fp16")]; + tensor acc_55_gamma_0_to_fp16 = const()[name = string("acc_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110952448)))]; + tensor acc_55_beta_0_to_fp16 = const()[name = string("acc_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110953280)))]; + fp16 acc_55_epsilon_0_to_fp16 = const()[name = string("acc_55_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_55_cast_fp16 = batch_norm(beta = acc_55_beta_0_to_fp16, epsilon = acc_55_epsilon_0_to_fp16, gamma = acc_55_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_19_cast_fp16)[name = string("acc_55_cast_fp16")]; + tensor var_2835_cast_fp16 = mul(x = acc_55_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2835_cast_fp16")]; + tensor c_37_to_fp16 = const()[name = string("c_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110954112)))]; + tensor acc_57_cast_fp16 = add(x = var_2835_cast_fp16, y = c_37_to_fp16)[name = string("acc_57_cast_fp16")]; + tensor var_2837_cast_fp16 = mul(x = acc_57_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2837_cast_fp16")]; + tensor c_39_to_fp16 = const()[name = string("c_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110954944)))]; + tensor acc_59_cast_fp16 = add(x = var_2837_cast_fp16, y = c_39_to_fp16)[name = string("acc_59_cast_fp16")]; + tensor var_2839_cast_fp16 = mul(x = acc_59_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2839_cast_fp16")]; + tensor hidden_states_125_cast_fp16 = add(x = hidden_states_123_cast_fp16, y = var_2839_cast_fp16)[name = string("hidden_states_125_cast_fp16")]; + string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")]; + tensor hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110955776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111103296))))[name = string("audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111103872)))]; + tensor hidden_states_127_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_125_cast_fp16)[name = string("hidden_states_127_cast_fp16")]; + tensor hidden_states_129_cast_fp16 = add(x = hidden_states_127_cast_fp16, y = residual_9_cast_fp16)[name = string("hidden_states_129_cast_fp16")]; + tensor context_mask_25_begin_0 = const()[name = string("context_mask_25_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_25_end_0 = const()[name = string("context_mask_25_end_0"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_25_end_mask_0 = const()[name = string("context_mask_25_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_25_cast_fp16 = slice_by_index(begin = context_mask_25_begin_0, end = context_mask_25_end_0, end_mask = context_mask_25_end_mask_0, x = context_mask_23_cast_fp16)[name = string("context_mask_25_cast_fp16")]; + tensor residual_11_begin_0 = const()[name = string("residual_11_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_11_end_0 = const()[name = string("residual_11_end_0"), val = tensor([1, 384, 1, 264])]; + tensor residual_11_end_mask_0 = const()[name = string("residual_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_11_cast_fp16 = slice_by_index(begin = residual_11_begin_0, end = residual_11_end_0, end_mask = residual_11_end_mask_0, x = hidden_states_129_cast_fp16)[name = string("residual_11_cast_fp16")]; + tensor alpha_over_pi_21_to_fp16 = const()[name = string("alpha_over_pi_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111104704)))]; + tensor theta_over_pi_21_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = alpha_over_pi_21_to_fp16)[name = string("theta_over_pi_21_cast_fp16")]; + tensor var_2874_cast_fp16 = round(x = theta_over_pi_21_cast_fp16)[name = string("op_2874_cast_fp16")]; + tensor reduced_21_cast_fp16 = sub(x = theta_over_pi_21_cast_fp16, y = var_2874_cast_fp16)[name = string("reduced_21_cast_fp16")]; + tensor reduced_sq_21_cast_fp16 = mul(x = reduced_21_cast_fp16, y = reduced_21_cast_fp16)[name = string("reduced_sq_21_cast_fp16")]; + tensor acc_61_gamma_0_to_fp16 = const()[name = string("acc_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111105536)))]; + tensor acc_61_beta_0_to_fp16 = const()[name = string("acc_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111106368)))]; + fp16 acc_61_epsilon_0_to_fp16 = const()[name = string("acc_61_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_61_cast_fp16 = batch_norm(beta = acc_61_beta_0_to_fp16, epsilon = acc_61_epsilon_0_to_fp16, gamma = acc_61_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_21_cast_fp16)[name = string("acc_61_cast_fp16")]; + tensor var_2887_cast_fp16 = mul(x = acc_61_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2887_cast_fp16")]; + tensor c_41_to_fp16 = const()[name = string("c_41_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111107200)))]; + tensor acc_63_cast_fp16 = add(x = var_2887_cast_fp16, y = c_41_to_fp16)[name = string("acc_63_cast_fp16")]; + tensor var_2889_cast_fp16 = mul(x = acc_63_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2889_cast_fp16")]; + tensor c_43_to_fp16 = const()[name = string("c_43_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111108032)))]; + tensor acc_65_cast_fp16 = add(x = var_2889_cast_fp16, y = c_43_to_fp16)[name = string("acc_65_cast_fp16")]; + tensor var_2891_cast_fp16 = mul(x = acc_65_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2891_cast_fp16")]; + tensor hidden_states_131_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = var_2891_cast_fp16)[name = string("hidden_states_131_cast_fp16")]; + bool full_mask_19_interleave_0 = const()[name = string("full_mask_19_interleave_0"), val = bool(false)]; + tensor full_mask_19_cast_fp16 = concat(axis = var_1991, interleave = full_mask_19_interleave_0, values = (context_mask_25_cast_fp16, fill_8_to_fp16))[name = string("full_mask_19_cast_fp16")]; + tensor input_151_cast_fp16 = mul(x = hidden_states_131_cast_fp16, y = full_mask_19_cast_fp16)[name = string("input_151_cast_fp16")]; + string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")]; + tensor hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111108864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112141120))))[name = string("audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112141696)))]; + tensor hidden_states_133_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized, x = input_151_cast_fp16)[name = string("hidden_states_133_cast_fp16")]; + tensor alpha_over_pi_23_to_fp16 = const()[name = string("alpha_over_pi_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112142528)))]; + tensor theta_over_pi_23_cast_fp16 = mul(x = hidden_states_133_cast_fp16, y = alpha_over_pi_23_to_fp16)[name = string("theta_over_pi_23_cast_fp16")]; + tensor var_2928_cast_fp16 = round(x = theta_over_pi_23_cast_fp16)[name = string("op_2928_cast_fp16")]; + tensor reduced_23_cast_fp16 = sub(x = theta_over_pi_23_cast_fp16, y = var_2928_cast_fp16)[name = string("reduced_23_cast_fp16")]; + tensor reduced_sq_23_cast_fp16 = mul(x = reduced_23_cast_fp16, y = reduced_23_cast_fp16)[name = string("reduced_sq_23_cast_fp16")]; + tensor acc_67_gamma_0_to_fp16 = const()[name = string("acc_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112143360)))]; + tensor acc_67_beta_0_to_fp16 = const()[name = string("acc_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112144192)))]; + fp16 acc_67_epsilon_0_to_fp16 = const()[name = string("acc_67_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_67_cast_fp16 = batch_norm(beta = acc_67_beta_0_to_fp16, epsilon = acc_67_epsilon_0_to_fp16, gamma = acc_67_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_23_cast_fp16)[name = string("acc_67_cast_fp16")]; + tensor var_2941_cast_fp16 = mul(x = acc_67_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2941_cast_fp16")]; + tensor c_45_to_fp16 = const()[name = string("c_45_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112145024)))]; + tensor acc_69_cast_fp16 = add(x = var_2941_cast_fp16, y = c_45_to_fp16)[name = string("acc_69_cast_fp16")]; + tensor var_2943_cast_fp16 = mul(x = acc_69_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2943_cast_fp16")]; + tensor c_47_to_fp16 = const()[name = string("c_47_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112145856)))]; + tensor acc_71_cast_fp16 = add(x = var_2943_cast_fp16, y = c_47_to_fp16)[name = string("acc_71_cast_fp16")]; + tensor var_2945_cast_fp16 = mul(x = acc_71_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2945_cast_fp16")]; + tensor hidden_states_135_cast_fp16 = add(x = hidden_states_133_cast_fp16, y = var_2945_cast_fp16)[name = string("hidden_states_135_cast_fp16")]; + string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")]; + tensor hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112146688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112294208))))[name = string("audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112294784)))]; + tensor hidden_states_137_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_135_cast_fp16)[name = string("hidden_states_137_cast_fp16")]; + tensor hidden_states_139_cast_fp16 = add(x = hidden_states_137_cast_fp16, y = residual_11_cast_fp16)[name = string("hidden_states_139_cast_fp16")]; + tensor context_mask_27_begin_0 = const()[name = string("context_mask_27_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_27_end_0 = const()[name = string("context_mask_27_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_27_end_mask_0 = const()[name = string("context_mask_27_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_27_cast_fp16 = slice_by_index(begin = context_mask_27_begin_0, end = context_mask_27_end_0, end_mask = context_mask_27_end_mask_0, x = context_mask_25_cast_fp16)[name = string("context_mask_27_cast_fp16")]; + tensor residual_13_begin_0 = const()[name = string("residual_13_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_13_end_0 = const()[name = string("residual_13_end_0"), val = tensor([1, 384, 1, 246])]; + tensor residual_13_end_mask_0 = const()[name = string("residual_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_13_cast_fp16 = slice_by_index(begin = residual_13_begin_0, end = residual_13_end_0, end_mask = residual_13_end_mask_0, x = hidden_states_139_cast_fp16)[name = string("residual_13_cast_fp16")]; + tensor alpha_over_pi_25_to_fp16 = const()[name = string("alpha_over_pi_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112295616)))]; + tensor theta_over_pi_25_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = alpha_over_pi_25_to_fp16)[name = string("theta_over_pi_25_cast_fp16")]; + tensor var_2980_cast_fp16 = round(x = theta_over_pi_25_cast_fp16)[name = string("op_2980_cast_fp16")]; + tensor reduced_25_cast_fp16 = sub(x = theta_over_pi_25_cast_fp16, y = var_2980_cast_fp16)[name = string("reduced_25_cast_fp16")]; + tensor reduced_sq_25_cast_fp16 = mul(x = reduced_25_cast_fp16, y = reduced_25_cast_fp16)[name = string("reduced_sq_25_cast_fp16")]; + tensor acc_73_gamma_0_to_fp16 = const()[name = string("acc_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112296448)))]; + tensor acc_73_beta_0_to_fp16 = const()[name = string("acc_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112297280)))]; + fp16 acc_73_epsilon_0_to_fp16 = const()[name = string("acc_73_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_73_cast_fp16 = batch_norm(beta = acc_73_beta_0_to_fp16, epsilon = acc_73_epsilon_0_to_fp16, gamma = acc_73_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_25_cast_fp16)[name = string("acc_73_cast_fp16")]; + tensor var_2993_cast_fp16 = mul(x = acc_73_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_2993_cast_fp16")]; + tensor c_49_to_fp16 = const()[name = string("c_49_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112298112)))]; + tensor acc_75_cast_fp16 = add(x = var_2993_cast_fp16, y = c_49_to_fp16)[name = string("acc_75_cast_fp16")]; + tensor var_2995_cast_fp16 = mul(x = acc_75_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_2995_cast_fp16")]; + tensor c_51_to_fp16 = const()[name = string("c_51_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112298944)))]; + tensor acc_77_cast_fp16 = add(x = var_2995_cast_fp16, y = c_51_to_fp16)[name = string("acc_77_cast_fp16")]; + tensor var_2997_cast_fp16 = mul(x = acc_77_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_2997_cast_fp16")]; + tensor hidden_states_141_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = var_2997_cast_fp16)[name = string("hidden_states_141_cast_fp16")]; + bool full_mask_21_interleave_0 = const()[name = string("full_mask_21_interleave_0"), val = bool(false)]; + tensor full_mask_21_cast_fp16 = concat(axis = var_1991, interleave = full_mask_21_interleave_0, values = (context_mask_27_cast_fp16, fill_8_to_fp16))[name = string("full_mask_21_cast_fp16")]; + tensor input_155_cast_fp16 = mul(x = hidden_states_141_cast_fp16, y = full_mask_21_cast_fp16)[name = string("input_155_cast_fp16")]; + string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")]; + tensor hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112299776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113332032))))[name = string("audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113332608)))]; + tensor hidden_states_143_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized, x = input_155_cast_fp16)[name = string("hidden_states_143_cast_fp16")]; + tensor alpha_over_pi_27_to_fp16 = const()[name = string("alpha_over_pi_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113333440)))]; + tensor theta_over_pi_27_cast_fp16 = mul(x = hidden_states_143_cast_fp16, y = alpha_over_pi_27_to_fp16)[name = string("theta_over_pi_27_cast_fp16")]; + tensor var_3034_cast_fp16 = round(x = theta_over_pi_27_cast_fp16)[name = string("op_3034_cast_fp16")]; + tensor reduced_27_cast_fp16 = sub(x = theta_over_pi_27_cast_fp16, y = var_3034_cast_fp16)[name = string("reduced_27_cast_fp16")]; + tensor reduced_sq_27_cast_fp16 = mul(x = reduced_27_cast_fp16, y = reduced_27_cast_fp16)[name = string("reduced_sq_27_cast_fp16")]; + tensor acc_79_gamma_0_to_fp16 = const()[name = string("acc_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113334272)))]; + tensor acc_79_beta_0_to_fp16 = const()[name = string("acc_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113335104)))]; + fp16 acc_79_epsilon_0_to_fp16 = const()[name = string("acc_79_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_79_cast_fp16 = batch_norm(beta = acc_79_beta_0_to_fp16, epsilon = acc_79_epsilon_0_to_fp16, gamma = acc_79_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_27_cast_fp16)[name = string("acc_79_cast_fp16")]; + tensor var_3047_cast_fp16 = mul(x = acc_79_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3047_cast_fp16")]; + tensor c_53_to_fp16 = const()[name = string("c_53_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113335936)))]; + tensor acc_81_cast_fp16 = add(x = var_3047_cast_fp16, y = c_53_to_fp16)[name = string("acc_81_cast_fp16")]; + tensor var_3049_cast_fp16 = mul(x = acc_81_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3049_cast_fp16")]; + tensor c_55_to_fp16 = const()[name = string("c_55_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113336768)))]; + tensor acc_83_cast_fp16 = add(x = var_3049_cast_fp16, y = c_55_to_fp16)[name = string("acc_83_cast_fp16")]; + tensor var_3051_cast_fp16 = mul(x = acc_83_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3051_cast_fp16")]; + tensor hidden_states_145_cast_fp16 = add(x = hidden_states_143_cast_fp16, y = var_3051_cast_fp16)[name = string("hidden_states_145_cast_fp16")]; + string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")]; + tensor hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113337600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113485120))))[name = string("audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113485696)))]; + tensor hidden_states_147_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_145_cast_fp16)[name = string("hidden_states_147_cast_fp16")]; + tensor hidden_states_149_cast_fp16 = add(x = hidden_states_147_cast_fp16, y = residual_13_cast_fp16)[name = string("hidden_states_149_cast_fp16")]; + tensor context_mask_31_begin_0 = const()[name = string("context_mask_31_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_31_end_0 = const()[name = string("context_mask_31_end_0"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_31_end_mask_0 = const()[name = string("context_mask_31_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_31_cast_fp16 = slice_by_index(begin = context_mask_31_begin_0, end = context_mask_31_end_0, end_mask = context_mask_31_end_mask_0, x = context_mask_23_cast_fp16)[name = string("context_mask_31_cast_fp16")]; + tensor hidden_states_151_begin_0 = const()[name = string("hidden_states_151_begin_0"), val = tensor([0, 0, 0, 4])]; + tensor hidden_states_151_end_0 = const()[name = string("hidden_states_151_end_0"), val = tensor([1, 384, 1, 192])]; + tensor hidden_states_151_end_mask_0 = const()[name = string("hidden_states_151_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_151_cast_fp16 = slice_by_index(begin = hidden_states_151_begin_0, end = hidden_states_151_end_0, end_mask = hidden_states_151_end_mask_0, x = hidden_states_149_cast_fp16)[name = string("hidden_states_151_cast_fp16")]; + tensor context_mask_33_begin_0 = const()[name = string("context_mask_33_begin_0"), val = tensor([0, 0, 0, 4])]; + tensor context_mask_33_end_0 = const()[name = string("context_mask_33_end_0"), val = tensor([1, 1, 1, 32])]; + tensor context_mask_33_end_mask_0 = const()[name = string("context_mask_33_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_33_cast_fp16 = slice_by_index(begin = context_mask_33_begin_0, end = context_mask_33_end_0, end_mask = context_mask_33_end_mask_0, x = context_mask_31_cast_fp16)[name = string("context_mask_33_cast_fp16")]; + tensor alpha_over_pi_29_to_fp16 = const()[name = string("alpha_over_pi_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113486528)))]; + tensor theta_over_pi_29_cast_fp16 = mul(x = hidden_states_151_cast_fp16, y = alpha_over_pi_29_to_fp16)[name = string("theta_over_pi_29_cast_fp16")]; + tensor var_3111_cast_fp16 = round(x = theta_over_pi_29_cast_fp16)[name = string("op_3111_cast_fp16")]; + tensor reduced_29_cast_fp16 = sub(x = theta_over_pi_29_cast_fp16, y = var_3111_cast_fp16)[name = string("reduced_29_cast_fp16")]; + tensor reduced_sq_29_cast_fp16 = mul(x = reduced_29_cast_fp16, y = reduced_29_cast_fp16)[name = string("reduced_sq_29_cast_fp16")]; + tensor acc_85_gamma_0_to_fp16 = const()[name = string("acc_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113487360)))]; + tensor acc_85_beta_0_to_fp16 = const()[name = string("acc_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113488192)))]; + fp16 acc_85_epsilon_0_to_fp16 = const()[name = string("acc_85_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_85_cast_fp16 = batch_norm(beta = acc_85_beta_0_to_fp16, epsilon = acc_85_epsilon_0_to_fp16, gamma = acc_85_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_29_cast_fp16)[name = string("acc_85_cast_fp16")]; + tensor var_3124_cast_fp16 = mul(x = acc_85_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3124_cast_fp16")]; + tensor c_57_to_fp16 = const()[name = string("c_57_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113489024)))]; + tensor acc_87_cast_fp16 = add(x = var_3124_cast_fp16, y = c_57_to_fp16)[name = string("acc_87_cast_fp16")]; + tensor var_3126_cast_fp16 = mul(x = acc_87_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3126_cast_fp16")]; + tensor c_59_to_fp16 = const()[name = string("c_59_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113489856)))]; + tensor acc_89_cast_fp16 = add(x = var_3126_cast_fp16, y = c_59_to_fp16)[name = string("acc_89_cast_fp16")]; + tensor var_3128_cast_fp16 = mul(x = acc_89_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3128_cast_fp16")]; + tensor hidden_states_153_cast_fp16 = add(x = hidden_states_151_cast_fp16, y = var_3128_cast_fp16)[name = string("hidden_states_153_cast_fp16")]; + bool full_mask_23_interleave_0 = const()[name = string("full_mask_23_interleave_0"), val = bool(false)]; + tensor full_mask_23_cast_fp16 = concat(axis = var_1991, interleave = full_mask_23_interleave_0, values = (context_mask_33_cast_fp16, fill_8_to_fp16))[name = string("full_mask_23_cast_fp16")]; + tensor input_159_cast_fp16 = mul(x = hidden_states_153_cast_fp16, y = full_mask_23_cast_fp16)[name = string("input_159_cast_fp16")]; + string sub_pixels_25_pad_type_0 = const()[name = string("sub_pixels_25_pad_type_0"), val = string("valid")]; + tensor sub_pixels_25_strides_0 = const()[name = string("sub_pixels_25_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_25_pad_0 = const()[name = string("sub_pixels_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_25_dilations_0 = const()[name = string("sub_pixels_25_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_25_groups_0 = const()[name = string("sub_pixels_25_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113490688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114080576))))[name = string("audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114081152)))]; + tensor sub_pixels_25_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_1_conv_bias_to_fp16, dilations = sub_pixels_25_dilations_0, groups = sub_pixels_25_groups_0, pad = sub_pixels_25_pad_0, pad_type = sub_pixels_25_pad_type_0, strides = sub_pixels_25_strides_0, weight = audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized, x = input_159_cast_fp16)[name = string("sub_pixels_25_cast_fp16")]; + tensor var_3152 = const()[name = string("op_3152"), val = tensor([1, 4, 192, 187])]; + tensor sub_pixels_27_cast_fp16 = reshape(shape = var_3152, x = sub_pixels_25_cast_fp16)[name = string("sub_pixels_27_cast_fp16")]; + tensor var_3154 = const()[name = string("op_3154"), val = tensor([0, 2, 3, 1])]; + tensor var_3159 = const()[name = string("op_3159"), val = tensor([1, 192, 1, 748])]; + tensor sub_pixels_29_cast_fp16 = transpose(perm = var_3154, x = sub_pixels_27_cast_fp16)[name = string("transpose_1")]; + tensor hidden_states_155_cast_fp16 = reshape(shape = var_3159, x = sub_pixels_29_cast_fp16)[name = string("hidden_states_155_cast_fp16")]; + tensor newest_13_begin_0 = const()[name = string("newest_13_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_13_end_0 = const()[name = string("newest_13_end_0"), val = tensor([1, 1, 1, 28])]; + tensor newest_13_end_mask_0 = const()[name = string("newest_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_13_cast_fp16 = slice_by_index(begin = newest_13_begin_0, end = newest_13_end_0, end_mask = newest_13_end_mask_0, x = context_mask_33_cast_fp16)[name = string("newest_13_cast_fp16")]; + tensor var_3164 = const()[name = string("op_3164"), val = tensor([1, 1, 27, 1])]; + tensor var_3165_cast_fp16 = reshape(shape = var_3164, x = newest_13_cast_fp16)[name = string("op_3165_cast_fp16")]; + tensor spread_13_reps_0 = const()[name = string("spread_13_reps_0"), val = tensor([1, 1, 1, 4])]; + tensor spread_13_cast_fp16 = tile(reps = spread_13_reps_0, x = var_3165_cast_fp16)[name = string("spread_13_cast_fp16")]; + tensor var_3171 = const()[name = string("op_3171"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_35_cast_fp16 = reshape(shape = var_3171, x = spread_13_cast_fp16)[name = string("context_mask_35_cast_fp16")]; + tensor residual_15_begin_0 = const()[name = string("residual_15_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_15_end_0 = const()[name = string("residual_15_end_0"), val = tensor([1, 192, 1, 748])]; + tensor residual_15_end_mask_0 = const()[name = string("residual_15_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_15_cast_fp16 = slice_by_index(begin = residual_15_begin_0, end = residual_15_end_0, end_mask = residual_15_end_mask_0, x = hidden_states_155_cast_fp16)[name = string("residual_15_cast_fp16")]; + tensor alpha_over_pi_31_to_fp16 = const()[name = string("alpha_over_pi_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114082752)))]; + tensor theta_over_pi_31_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = alpha_over_pi_31_to_fp16)[name = string("theta_over_pi_31_cast_fp16")]; + tensor var_3194_cast_fp16 = round(x = theta_over_pi_31_cast_fp16)[name = string("op_3194_cast_fp16")]; + tensor reduced_31_cast_fp16 = sub(x = theta_over_pi_31_cast_fp16, y = var_3194_cast_fp16)[name = string("reduced_31_cast_fp16")]; + tensor reduced_sq_31_cast_fp16 = mul(x = reduced_31_cast_fp16, y = reduced_31_cast_fp16)[name = string("reduced_sq_31_cast_fp16")]; + tensor acc_91_mean_0_to_fp16 = const()[name = string("acc_91_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114083200)))]; + tensor acc_91_variance_0_to_fp16 = const()[name = string("acc_91_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114083648)))]; + tensor acc_91_gamma_0_to_fp16 = const()[name = string("acc_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084096)))]; + tensor acc_91_beta_0_to_fp16 = const()[name = string("acc_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084544)))]; + fp16 acc_91_epsilon_0_to_fp16 = const()[name = string("acc_91_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_91_cast_fp16 = batch_norm(beta = acc_91_beta_0_to_fp16, epsilon = acc_91_epsilon_0_to_fp16, gamma = acc_91_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_31_cast_fp16)[name = string("acc_91_cast_fp16")]; + tensor var_3207_cast_fp16 = mul(x = acc_91_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3207_cast_fp16")]; + tensor c_61_to_fp16 = const()[name = string("c_61_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084992)))]; + tensor acc_93_cast_fp16 = add(x = var_3207_cast_fp16, y = c_61_to_fp16)[name = string("acc_93_cast_fp16")]; + tensor var_3209_cast_fp16 = mul(x = acc_93_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3209_cast_fp16")]; + tensor c_63_to_fp16 = const()[name = string("c_63_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114085440)))]; + tensor acc_95_cast_fp16 = add(x = var_3209_cast_fp16, y = c_63_to_fp16)[name = string("acc_95_cast_fp16")]; + tensor var_3211_cast_fp16 = mul(x = acc_95_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3211_cast_fp16")]; + tensor hidden_states_157_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = var_3211_cast_fp16)[name = string("hidden_states_157_cast_fp16")]; + bool full_mask_25_interleave_0 = const()[name = string("full_mask_25_interleave_0"), val = bool(false)]; + tensor fill_12_to_fp16 = const()[name = string("fill_12_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114085888)))]; + tensor full_mask_25_cast_fp16 = concat(axis = var_1991, interleave = full_mask_25_interleave_0, values = (context_mask_35_cast_fp16, fill_12_to_fp16))[name = string("full_mask_25_cast_fp16")]; + tensor input_161_cast_fp16 = mul(x = hidden_states_157_cast_fp16, y = full_mask_25_cast_fp16)[name = string("input_161_cast_fp16")]; + string hidden_states_159_pad_type_0 = const()[name = string("hidden_states_159_pad_type_0"), val = string("valid")]; + tensor hidden_states_159_strides_0 = const()[name = string("hidden_states_159_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_0 = const()[name = string("hidden_states_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_159_dilations_0 = const()[name = string("hidden_states_159_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_159_groups_0 = const()[name = string("hidden_states_159_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114087232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114345344))))[name = string("audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114345920)))]; + tensor hidden_states_159_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_159_dilations_0, groups = hidden_states_159_groups_0, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = hidden_states_159_strides_0, weight = audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("hidden_states_159_cast_fp16")]; + tensor alpha_over_pi_33_to_fp16 = const()[name = string("alpha_over_pi_33_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114346368)))]; + tensor theta_over_pi_33_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = alpha_over_pi_33_to_fp16)[name = string("theta_over_pi_33_cast_fp16")]; + tensor var_3248_cast_fp16 = round(x = theta_over_pi_33_cast_fp16)[name = string("op_3248_cast_fp16")]; + tensor reduced_33_cast_fp16 = sub(x = theta_over_pi_33_cast_fp16, y = var_3248_cast_fp16)[name = string("reduced_33_cast_fp16")]; + tensor reduced_sq_33_cast_fp16 = mul(x = reduced_33_cast_fp16, y = reduced_33_cast_fp16)[name = string("reduced_sq_33_cast_fp16")]; + tensor acc_97_gamma_0_to_fp16 = const()[name = string("acc_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114346816)))]; + tensor acc_97_beta_0_to_fp16 = const()[name = string("acc_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114347264)))]; + fp16 acc_97_epsilon_0_to_fp16 = const()[name = string("acc_97_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_97_cast_fp16 = batch_norm(beta = acc_97_beta_0_to_fp16, epsilon = acc_97_epsilon_0_to_fp16, gamma = acc_97_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_33_cast_fp16)[name = string("acc_97_cast_fp16")]; + tensor var_3261_cast_fp16 = mul(x = acc_97_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3261_cast_fp16")]; + tensor c_65_to_fp16 = const()[name = string("c_65_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114347712)))]; + tensor acc_99_cast_fp16 = add(x = var_3261_cast_fp16, y = c_65_to_fp16)[name = string("acc_99_cast_fp16")]; + tensor var_3263_cast_fp16 = mul(x = acc_99_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3263_cast_fp16")]; + tensor c_67_to_fp16 = const()[name = string("c_67_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114348160)))]; + tensor acc_101_cast_fp16 = add(x = var_3263_cast_fp16, y = c_67_to_fp16)[name = string("acc_101_cast_fp16")]; + tensor var_3265_cast_fp16 = mul(x = acc_101_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3265_cast_fp16")]; + tensor hidden_states_161_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = var_3265_cast_fp16)[name = string("hidden_states_161_cast_fp16")]; + string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")]; + tensor hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114348608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114385536))))[name = string("audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114386112)))]; + tensor hidden_states_163_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_161_cast_fp16)[name = string("hidden_states_163_cast_fp16")]; + tensor hidden_states_165_cast_fp16 = add(x = hidden_states_163_cast_fp16, y = residual_15_cast_fp16)[name = string("hidden_states_165_cast_fp16")]; + tensor context_mask_37_begin_0 = const()[name = string("context_mask_37_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_37_end_0 = const()[name = string("context_mask_37_end_0"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_37_end_mask_0 = const()[name = string("context_mask_37_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_37_cast_fp16 = slice_by_index(begin = context_mask_37_begin_0, end = context_mask_37_end_0, end_mask = context_mask_37_end_mask_0, x = context_mask_35_cast_fp16)[name = string("context_mask_37_cast_fp16")]; + tensor residual_17_begin_0 = const()[name = string("residual_17_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_17_end_0 = const()[name = string("residual_17_end_0"), val = tensor([1, 192, 1, 742])]; + tensor residual_17_end_mask_0 = const()[name = string("residual_17_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_17_cast_fp16 = slice_by_index(begin = residual_17_begin_0, end = residual_17_end_0, end_mask = residual_17_end_mask_0, x = hidden_states_165_cast_fp16)[name = string("residual_17_cast_fp16")]; + tensor alpha_over_pi_35_to_fp16 = const()[name = string("alpha_over_pi_35_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114386560)))]; + tensor theta_over_pi_35_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = alpha_over_pi_35_to_fp16)[name = string("theta_over_pi_35_cast_fp16")]; + tensor var_3300_cast_fp16 = round(x = theta_over_pi_35_cast_fp16)[name = string("op_3300_cast_fp16")]; + tensor reduced_35_cast_fp16 = sub(x = theta_over_pi_35_cast_fp16, y = var_3300_cast_fp16)[name = string("reduced_35_cast_fp16")]; + tensor reduced_sq_35_cast_fp16 = mul(x = reduced_35_cast_fp16, y = reduced_35_cast_fp16)[name = string("reduced_sq_35_cast_fp16")]; + tensor acc_103_gamma_0_to_fp16 = const()[name = string("acc_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387008)))]; + tensor acc_103_beta_0_to_fp16 = const()[name = string("acc_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387456)))]; + fp16 acc_103_epsilon_0_to_fp16 = const()[name = string("acc_103_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_103_cast_fp16 = batch_norm(beta = acc_103_beta_0_to_fp16, epsilon = acc_103_epsilon_0_to_fp16, gamma = acc_103_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_35_cast_fp16)[name = string("acc_103_cast_fp16")]; + tensor var_3313_cast_fp16 = mul(x = acc_103_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3313_cast_fp16")]; + tensor c_69_to_fp16 = const()[name = string("c_69_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387904)))]; + tensor acc_105_cast_fp16 = add(x = var_3313_cast_fp16, y = c_69_to_fp16)[name = string("acc_105_cast_fp16")]; + tensor var_3315_cast_fp16 = mul(x = acc_105_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3315_cast_fp16")]; + tensor c_71_to_fp16 = const()[name = string("c_71_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114388352)))]; + tensor acc_107_cast_fp16 = add(x = var_3315_cast_fp16, y = c_71_to_fp16)[name = string("acc_107_cast_fp16")]; + tensor var_3317_cast_fp16 = mul(x = acc_107_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3317_cast_fp16")]; + tensor hidden_states_167_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = var_3317_cast_fp16)[name = string("hidden_states_167_cast_fp16")]; + bool full_mask_27_interleave_0 = const()[name = string("full_mask_27_interleave_0"), val = bool(false)]; + tensor full_mask_27_cast_fp16 = concat(axis = var_1991, interleave = full_mask_27_interleave_0, values = (context_mask_37_cast_fp16, fill_12_to_fp16))[name = string("full_mask_27_cast_fp16")]; + tensor input_165_cast_fp16 = mul(x = hidden_states_167_cast_fp16, y = full_mask_27_cast_fp16)[name = string("input_165_cast_fp16")]; + string hidden_states_169_pad_type_0 = const()[name = string("hidden_states_169_pad_type_0"), val = string("valid")]; + tensor hidden_states_169_dilations_0 = const()[name = string("hidden_states_169_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_169_strides_0 = const()[name = string("hidden_states_169_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_169_pad_0 = const()[name = string("hidden_states_169_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_169_groups_0 = const()[name = string("hidden_states_169_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114388800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114646912))))[name = string("audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114647488)))]; + tensor hidden_states_169_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_169_dilations_0, groups = hidden_states_169_groups_0, pad = hidden_states_169_pad_0, pad_type = hidden_states_169_pad_type_0, strides = hidden_states_169_strides_0, weight = audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("hidden_states_169_cast_fp16")]; + tensor alpha_over_pi_37_to_fp16 = const()[name = string("alpha_over_pi_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114647936)))]; + tensor theta_over_pi_37_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = alpha_over_pi_37_to_fp16)[name = string("theta_over_pi_37_cast_fp16")]; + tensor var_3354_cast_fp16 = round(x = theta_over_pi_37_cast_fp16)[name = string("op_3354_cast_fp16")]; + tensor reduced_37_cast_fp16 = sub(x = theta_over_pi_37_cast_fp16, y = var_3354_cast_fp16)[name = string("reduced_37_cast_fp16")]; + tensor reduced_sq_37_cast_fp16 = mul(x = reduced_37_cast_fp16, y = reduced_37_cast_fp16)[name = string("reduced_sq_37_cast_fp16")]; + tensor acc_109_gamma_0_to_fp16 = const()[name = string("acc_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114648384)))]; + tensor acc_109_beta_0_to_fp16 = const()[name = string("acc_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114648832)))]; + fp16 acc_109_epsilon_0_to_fp16 = const()[name = string("acc_109_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_109_cast_fp16 = batch_norm(beta = acc_109_beta_0_to_fp16, epsilon = acc_109_epsilon_0_to_fp16, gamma = acc_109_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_37_cast_fp16)[name = string("acc_109_cast_fp16")]; + tensor var_3367_cast_fp16 = mul(x = acc_109_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3367_cast_fp16")]; + tensor c_73_to_fp16 = const()[name = string("c_73_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114649280)))]; + tensor acc_111_cast_fp16 = add(x = var_3367_cast_fp16, y = c_73_to_fp16)[name = string("acc_111_cast_fp16")]; + tensor var_3369_cast_fp16 = mul(x = acc_111_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3369_cast_fp16")]; + tensor c_75_to_fp16 = const()[name = string("c_75_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114649728)))]; + tensor acc_113_cast_fp16 = add(x = var_3369_cast_fp16, y = c_75_to_fp16)[name = string("acc_113_cast_fp16")]; + tensor var_3371_cast_fp16 = mul(x = acc_113_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3371_cast_fp16")]; + tensor hidden_states_171_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = var_3371_cast_fp16)[name = string("hidden_states_171_cast_fp16")]; + string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")]; + tensor hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114650176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114687104))))[name = string("audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114687680)))]; + tensor hidden_states_173_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_171_cast_fp16)[name = string("hidden_states_173_cast_fp16")]; + tensor hidden_states_175_cast_fp16 = add(x = hidden_states_173_cast_fp16, y = residual_17_cast_fp16)[name = string("hidden_states_175_cast_fp16")]; + tensor context_mask_39_begin_0 = const()[name = string("context_mask_39_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_39_end_0 = const()[name = string("context_mask_39_end_0"), val = tensor([1, 1, 1, 102])]; + tensor context_mask_39_end_mask_0 = const()[name = string("context_mask_39_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_39_cast_fp16 = slice_by_index(begin = context_mask_39_begin_0, end = context_mask_39_end_0, end_mask = context_mask_39_end_mask_0, x = context_mask_37_cast_fp16)[name = string("context_mask_39_cast_fp16")]; + tensor residual_19_begin_0 = const()[name = string("residual_19_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_19_end_0 = const()[name = string("residual_19_end_0"), val = tensor([1, 192, 1, 724])]; + tensor residual_19_end_mask_0 = const()[name = string("residual_19_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_19_cast_fp16 = slice_by_index(begin = residual_19_begin_0, end = residual_19_end_0, end_mask = residual_19_end_mask_0, x = hidden_states_175_cast_fp16)[name = string("residual_19_cast_fp16")]; + tensor alpha_over_pi_39_to_fp16 = const()[name = string("alpha_over_pi_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114688128)))]; + tensor theta_over_pi_39_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = alpha_over_pi_39_to_fp16)[name = string("theta_over_pi_39_cast_fp16")]; + tensor var_3406_cast_fp16 = round(x = theta_over_pi_39_cast_fp16)[name = string("op_3406_cast_fp16")]; + tensor reduced_39_cast_fp16 = sub(x = theta_over_pi_39_cast_fp16, y = var_3406_cast_fp16)[name = string("reduced_39_cast_fp16")]; + tensor reduced_sq_39_cast_fp16 = mul(x = reduced_39_cast_fp16, y = reduced_39_cast_fp16)[name = string("reduced_sq_39_cast_fp16")]; + tensor acc_115_gamma_0_to_fp16 = const()[name = string("acc_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114688576)))]; + tensor acc_115_beta_0_to_fp16 = const()[name = string("acc_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689024)))]; + fp16 acc_115_epsilon_0_to_fp16 = const()[name = string("acc_115_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_115_cast_fp16 = batch_norm(beta = acc_115_beta_0_to_fp16, epsilon = acc_115_epsilon_0_to_fp16, gamma = acc_115_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_39_cast_fp16)[name = string("acc_115_cast_fp16")]; + tensor var_3419_cast_fp16 = mul(x = acc_115_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3419_cast_fp16")]; + tensor c_77_to_fp16 = const()[name = string("c_77_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689472)))]; + tensor acc_117_cast_fp16 = add(x = var_3419_cast_fp16, y = c_77_to_fp16)[name = string("acc_117_cast_fp16")]; + tensor var_3421_cast_fp16 = mul(x = acc_117_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3421_cast_fp16")]; + tensor c_79_to_fp16 = const()[name = string("c_79_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689920)))]; + tensor acc_119_cast_fp16 = add(x = var_3421_cast_fp16, y = c_79_to_fp16)[name = string("acc_119_cast_fp16")]; + tensor var_3423_cast_fp16 = mul(x = acc_119_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3423_cast_fp16")]; + tensor hidden_states_177_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = var_3423_cast_fp16)[name = string("hidden_states_177_cast_fp16")]; + bool full_mask_29_interleave_0 = const()[name = string("full_mask_29_interleave_0"), val = bool(false)]; + tensor full_mask_29_cast_fp16 = concat(axis = var_1991, interleave = full_mask_29_interleave_0, values = (context_mask_39_cast_fp16, fill_12_to_fp16))[name = string("full_mask_29_cast_fp16")]; + tensor input_169_cast_fp16 = mul(x = hidden_states_177_cast_fp16, y = full_mask_29_cast_fp16)[name = string("input_169_cast_fp16")]; + string hidden_states_179_pad_type_0 = const()[name = string("hidden_states_179_pad_type_0"), val = string("valid")]; + tensor hidden_states_179_dilations_0 = const()[name = string("hidden_states_179_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_179_strides_0 = const()[name = string("hidden_states_179_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_179_pad_0 = const()[name = string("hidden_states_179_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_179_groups_0 = const()[name = string("hidden_states_179_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114690368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114948480))))[name = string("audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949056)))]; + tensor hidden_states_179_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_179_dilations_0, groups = hidden_states_179_groups_0, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = hidden_states_179_strides_0, weight = audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized, x = input_169_cast_fp16)[name = string("hidden_states_179_cast_fp16")]; + tensor alpha_over_pi_41_to_fp16 = const()[name = string("alpha_over_pi_41_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949504)))]; + tensor theta_over_pi_41_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = alpha_over_pi_41_to_fp16)[name = string("theta_over_pi_41_cast_fp16")]; + tensor var_3460_cast_fp16 = round(x = theta_over_pi_41_cast_fp16)[name = string("op_3460_cast_fp16")]; + tensor reduced_41_cast_fp16 = sub(x = theta_over_pi_41_cast_fp16, y = var_3460_cast_fp16)[name = string("reduced_41_cast_fp16")]; + tensor reduced_sq_41_cast_fp16 = mul(x = reduced_41_cast_fp16, y = reduced_41_cast_fp16)[name = string("reduced_sq_41_cast_fp16")]; + tensor acc_121_gamma_0_to_fp16 = const()[name = string("acc_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949952)))]; + tensor acc_121_beta_0_to_fp16 = const()[name = string("acc_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114950400)))]; + fp16 acc_121_epsilon_0_to_fp16 = const()[name = string("acc_121_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_121_cast_fp16 = batch_norm(beta = acc_121_beta_0_to_fp16, epsilon = acc_121_epsilon_0_to_fp16, gamma = acc_121_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_41_cast_fp16)[name = string("acc_121_cast_fp16")]; + tensor var_3473_cast_fp16 = mul(x = acc_121_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3473_cast_fp16")]; + tensor c_81_to_fp16 = const()[name = string("c_81_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114950848)))]; + tensor acc_123_cast_fp16 = add(x = var_3473_cast_fp16, y = c_81_to_fp16)[name = string("acc_123_cast_fp16")]; + tensor var_3475_cast_fp16 = mul(x = acc_123_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3475_cast_fp16")]; + tensor c_83_to_fp16 = const()[name = string("c_83_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114951296)))]; + tensor acc_125_cast_fp16 = add(x = var_3475_cast_fp16, y = c_83_to_fp16)[name = string("acc_125_cast_fp16")]; + tensor var_3477_cast_fp16 = mul(x = acc_125_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3477_cast_fp16")]; + tensor hidden_states_181_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = var_3477_cast_fp16)[name = string("hidden_states_181_cast_fp16")]; + string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")]; + tensor hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114951744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114988672))))[name = string("audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114989248)))]; + tensor hidden_states_183_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_181_cast_fp16)[name = string("hidden_states_183_cast_fp16")]; + tensor hidden_states_185_cast_fp16 = add(x = hidden_states_183_cast_fp16, y = residual_19_cast_fp16)[name = string("hidden_states_185_cast_fp16")]; + tensor context_mask_43_begin_0 = const()[name = string("context_mask_43_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_43_end_0 = const()[name = string("context_mask_43_end_0"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_43_end_mask_0 = const()[name = string("context_mask_43_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_43_cast_fp16 = slice_by_index(begin = context_mask_43_begin_0, end = context_mask_43_end_0, end_mask = context_mask_43_end_mask_0, x = context_mask_35_cast_fp16)[name = string("context_mask_43_cast_fp16")]; + tensor hidden_states_187_begin_0 = const()[name = string("hidden_states_187_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor hidden_states_187_end_0 = const()[name = string("hidden_states_187_end_0"), val = tensor([1, 192, 1, 670])]; + tensor hidden_states_187_end_mask_0 = const()[name = string("hidden_states_187_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_187_cast_fp16 = slice_by_index(begin = hidden_states_187_begin_0, end = hidden_states_187_end_0, end_mask = hidden_states_187_end_mask_0, x = hidden_states_185_cast_fp16)[name = string("hidden_states_187_cast_fp16")]; + tensor context_mask_45_begin_0 = const()[name = string("context_mask_45_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor context_mask_45_end_0 = const()[name = string("context_mask_45_end_0"), val = tensor([1, 1, 1, 30])]; + tensor context_mask_45_end_mask_0 = const()[name = string("context_mask_45_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_45_cast_fp16 = slice_by_index(begin = context_mask_45_begin_0, end = context_mask_45_end_0, end_mask = context_mask_45_end_mask_0, x = context_mask_43_cast_fp16)[name = string("context_mask_45_cast_fp16")]; + tensor alpha_over_pi_43_to_fp16 = const()[name = string("alpha_over_pi_43_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114989696)))]; + tensor theta_over_pi_43_cast_fp16 = mul(x = hidden_states_187_cast_fp16, y = alpha_over_pi_43_to_fp16)[name = string("theta_over_pi_43_cast_fp16")]; + tensor var_3537_cast_fp16 = round(x = theta_over_pi_43_cast_fp16)[name = string("op_3537_cast_fp16")]; + tensor reduced_43_cast_fp16 = sub(x = theta_over_pi_43_cast_fp16, y = var_3537_cast_fp16)[name = string("reduced_43_cast_fp16")]; + tensor reduced_sq_43_cast_fp16 = mul(x = reduced_43_cast_fp16, y = reduced_43_cast_fp16)[name = string("reduced_sq_43_cast_fp16")]; + tensor acc_127_gamma_0_to_fp16 = const()[name = string("acc_127_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114990144)))]; + tensor acc_127_beta_0_to_fp16 = const()[name = string("acc_127_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114990592)))]; + fp16 acc_127_epsilon_0_to_fp16 = const()[name = string("acc_127_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_127_cast_fp16 = batch_norm(beta = acc_127_beta_0_to_fp16, epsilon = acc_127_epsilon_0_to_fp16, gamma = acc_127_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_43_cast_fp16)[name = string("acc_127_cast_fp16")]; + tensor var_3550_cast_fp16 = mul(x = acc_127_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3550_cast_fp16")]; + tensor c_85_to_fp16 = const()[name = string("c_85_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991040)))]; + tensor acc_129_cast_fp16 = add(x = var_3550_cast_fp16, y = c_85_to_fp16)[name = string("acc_129_cast_fp16")]; + tensor var_3552_cast_fp16 = mul(x = acc_129_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3552_cast_fp16")]; + tensor c_87_to_fp16 = const()[name = string("c_87_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991488)))]; + tensor acc_131_cast_fp16 = add(x = var_3552_cast_fp16, y = c_87_to_fp16)[name = string("acc_131_cast_fp16")]; + tensor var_3554_cast_fp16 = mul(x = acc_131_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3554_cast_fp16")]; + tensor hidden_states_189_cast_fp16 = add(x = hidden_states_187_cast_fp16, y = var_3554_cast_fp16)[name = string("hidden_states_189_cast_fp16")]; + bool full_mask_31_interleave_0 = const()[name = string("full_mask_31_interleave_0"), val = bool(false)]; + tensor full_mask_31_cast_fp16 = concat(axis = var_1991, interleave = full_mask_31_interleave_0, values = (context_mask_45_cast_fp16, fill_12_to_fp16))[name = string("full_mask_31_cast_fp16")]; + tensor input_173_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = full_mask_31_cast_fp16)[name = string("input_173_cast_fp16")]; + string sub_pixels_31_pad_type_0 = const()[name = string("sub_pixels_31_pad_type_0"), val = string("valid")]; + tensor sub_pixels_31_strides_0 = const()[name = string("sub_pixels_31_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_31_pad_0 = const()[name = string("sub_pixels_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_31_dilations_0 = const()[name = string("sub_pixels_31_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_31_groups_0 = const()[name = string("sub_pixels_31_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115102592))))[name = string("audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115103168)))]; + tensor sub_pixels_31_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_1_conv_bias_to_fp16, dilations = sub_pixels_31_dilations_0, groups = sub_pixels_31_groups_0, pad = sub_pixels_31_pad_0, pad_type = sub_pixels_31_pad_type_0, strides = sub_pixels_31_strides_0, weight = audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("sub_pixels_31_cast_fp16")]; + tensor var_3578 = const()[name = string("op_3578"), val = tensor([1, 3, 96, 668])]; + tensor sub_pixels_33_cast_fp16 = reshape(shape = var_3578, x = sub_pixels_31_cast_fp16)[name = string("sub_pixels_33_cast_fp16")]; + tensor var_3580 = const()[name = string("op_3580"), val = tensor([0, 2, 3, 1])]; + tensor var_3585 = const()[name = string("op_3585"), val = tensor([1, 96, 1, 2004])]; + tensor sub_pixels_cast_fp16 = transpose(perm = var_3580, x = sub_pixels_33_cast_fp16)[name = string("transpose_0")]; + tensor hidden_states_191_cast_fp16 = reshape(shape = var_3585, x = sub_pixels_cast_fp16)[name = string("hidden_states_191_cast_fp16")]; + tensor newest_17_begin_0 = const()[name = string("newest_17_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_17_end_0 = const()[name = string("newest_17_end_0"), val = tensor([1, 1, 1, 29])]; + tensor newest_17_end_mask_0 = const()[name = string("newest_17_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_17_cast_fp16 = slice_by_index(begin = newest_17_begin_0, end = newest_17_end_0, end_mask = newest_17_end_mask_0, x = context_mask_45_cast_fp16)[name = string("newest_17_cast_fp16")]; + tensor var_3590 = const()[name = string("op_3590"), val = tensor([1, 1, 28, 1])]; + tensor var_3591_cast_fp16 = reshape(shape = var_3590, x = newest_17_cast_fp16)[name = string("op_3591_cast_fp16")]; + tensor spread_17_reps_0 = const()[name = string("spread_17_reps_0"), val = tensor([1, 1, 1, 3])]; + tensor spread_17_cast_fp16 = tile(reps = spread_17_reps_0, x = var_3591_cast_fp16)[name = string("spread_17_cast_fp16")]; + tensor var_3597 = const()[name = string("op_3597"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_47_cast_fp16 = reshape(shape = var_3597, x = spread_17_cast_fp16)[name = string("context_mask_47_cast_fp16")]; + tensor residual_21_begin_0 = const()[name = string("residual_21_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_21_end_0 = const()[name = string("residual_21_end_0"), val = tensor([1, 96, 1, 2004])]; + tensor residual_21_end_mask_0 = const()[name = string("residual_21_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_21_cast_fp16 = slice_by_index(begin = residual_21_begin_0, end = residual_21_end_0, end_mask = residual_21_end_mask_0, x = hidden_states_191_cast_fp16)[name = string("residual_21_cast_fp16")]; + tensor alpha_over_pi_45_to_fp16 = const()[name = string("alpha_over_pi_45_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115103808)))]; + tensor theta_over_pi_45_cast_fp16 = mul(x = hidden_states_191_cast_fp16, y = alpha_over_pi_45_to_fp16)[name = string("theta_over_pi_45_cast_fp16")]; + tensor var_3620_cast_fp16 = round(x = theta_over_pi_45_cast_fp16)[name = string("op_3620_cast_fp16")]; + tensor reduced_45_cast_fp16 = sub(x = theta_over_pi_45_cast_fp16, y = var_3620_cast_fp16)[name = string("reduced_45_cast_fp16")]; + tensor reduced_sq_45_cast_fp16 = mul(x = reduced_45_cast_fp16, y = reduced_45_cast_fp16)[name = string("reduced_sq_45_cast_fp16")]; + tensor acc_133_mean_0_to_fp16 = const()[name = string("acc_133_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_133_variance_0_to_fp16 = const()[name = string("acc_133_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_133_gamma_0_to_fp16 = const()[name = string("acc_133_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104576)))]; + tensor acc_133_beta_0_to_fp16 = const()[name = string("acc_133_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104832)))]; + fp16 acc_133_epsilon_0_to_fp16 = const()[name = string("acc_133_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_133_cast_fp16 = batch_norm(beta = acc_133_beta_0_to_fp16, epsilon = acc_133_epsilon_0_to_fp16, gamma = acc_133_gamma_0_to_fp16, mean = acc_133_mean_0_to_fp16, variance = acc_133_variance_0_to_fp16, x = reduced_sq_45_cast_fp16)[name = string("acc_133_cast_fp16")]; + tensor var_3633_cast_fp16 = mul(x = acc_133_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3633_cast_fp16")]; + tensor c_89_to_fp16 = const()[name = string("c_89_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115105088)))]; + tensor acc_135_cast_fp16 = add(x = var_3633_cast_fp16, y = c_89_to_fp16)[name = string("acc_135_cast_fp16")]; + tensor var_3635_cast_fp16 = mul(x = acc_135_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3635_cast_fp16")]; + tensor c_91_to_fp16 = const()[name = string("c_91_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115105344)))]; + tensor acc_137_cast_fp16 = add(x = var_3635_cast_fp16, y = c_91_to_fp16)[name = string("acc_137_cast_fp16")]; + tensor var_3637_cast_fp16 = mul(x = acc_137_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3637_cast_fp16")]; + tensor hidden_states_193_cast_fp16 = add(x = hidden_states_191_cast_fp16, y = var_3637_cast_fp16)[name = string("hidden_states_193_cast_fp16")]; + bool full_mask_33_interleave_0 = const()[name = string("full_mask_33_interleave_0"), val = bool(false)]; + tensor fill_16_to_fp16 = const()[name = string("fill_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115105600)))]; + tensor full_mask_33_cast_fp16 = concat(axis = var_1991, interleave = full_mask_33_interleave_0, values = (context_mask_47_cast_fp16, fill_16_to_fp16))[name = string("full_mask_33_cast_fp16")]; + tensor input_175_cast_fp16 = mul(x = hidden_states_193_cast_fp16, y = full_mask_33_cast_fp16)[name = string("input_175_cast_fp16")]; + string hidden_states_195_pad_type_0 = const()[name = string("hidden_states_195_pad_type_0"), val = string("valid")]; + tensor hidden_states_195_strides_0 = const()[name = string("hidden_states_195_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_0 = const()[name = string("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_195_dilations_0 = const()[name = string("hidden_states_195_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_195_groups_0 = const()[name = string("hidden_states_195_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115109504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174080))))[name = string("audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174656)))]; + tensor hidden_states_195_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_195_dilations_0, groups = hidden_states_195_groups_0, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = hidden_states_195_strides_0, weight = audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized, x = input_175_cast_fp16)[name = string("hidden_states_195_cast_fp16")]; + tensor alpha_over_pi_47_to_fp16 = const()[name = string("alpha_over_pi_47_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174912)))]; + tensor theta_over_pi_47_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = alpha_over_pi_47_to_fp16)[name = string("theta_over_pi_47_cast_fp16")]; + tensor var_3674_cast_fp16 = round(x = theta_over_pi_47_cast_fp16)[name = string("op_3674_cast_fp16")]; + tensor reduced_47_cast_fp16 = sub(x = theta_over_pi_47_cast_fp16, y = var_3674_cast_fp16)[name = string("reduced_47_cast_fp16")]; + tensor reduced_sq_47_cast_fp16 = mul(x = reduced_47_cast_fp16, y = reduced_47_cast_fp16)[name = string("reduced_sq_47_cast_fp16")]; + tensor acc_139_mean_0_to_fp16 = const()[name = string("acc_139_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_139_variance_0_to_fp16 = const()[name = string("acc_139_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_139_gamma_0_to_fp16 = const()[name = string("acc_139_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175168)))]; + tensor acc_139_beta_0_to_fp16 = const()[name = string("acc_139_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175424)))]; + fp16 acc_139_epsilon_0_to_fp16 = const()[name = string("acc_139_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_139_cast_fp16 = batch_norm(beta = acc_139_beta_0_to_fp16, epsilon = acc_139_epsilon_0_to_fp16, gamma = acc_139_gamma_0_to_fp16, mean = acc_139_mean_0_to_fp16, variance = acc_139_variance_0_to_fp16, x = reduced_sq_47_cast_fp16)[name = string("acc_139_cast_fp16")]; + tensor var_3687_cast_fp16 = mul(x = acc_139_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3687_cast_fp16")]; + tensor c_93_to_fp16 = const()[name = string("c_93_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175680)))]; + tensor acc_141_cast_fp16 = add(x = var_3687_cast_fp16, y = c_93_to_fp16)[name = string("acc_141_cast_fp16")]; + tensor var_3689_cast_fp16 = mul(x = acc_141_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3689_cast_fp16")]; + tensor c_95_to_fp16 = const()[name = string("c_95_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175936)))]; + tensor acc_143_cast_fp16 = add(x = var_3689_cast_fp16, y = c_95_to_fp16)[name = string("acc_143_cast_fp16")]; + tensor var_3691_cast_fp16 = mul(x = acc_143_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3691_cast_fp16")]; + tensor hidden_states_197_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = var_3691_cast_fp16)[name = string("hidden_states_197_cast_fp16")]; + string hidden_states_199_pad_type_0 = const()[name = string("hidden_states_199_pad_type_0"), val = string("valid")]; + tensor hidden_states_199_strides_0 = const()[name = string("hidden_states_199_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_0 = const()[name = string("hidden_states_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_199_dilations_0 = const()[name = string("hidden_states_199_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_199_groups_0 = const()[name = string("hidden_states_199_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115176192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115185472))))[name = string("audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186048)))]; + tensor hidden_states_199_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_199_dilations_0, groups = hidden_states_199_groups_0, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = hidden_states_199_strides_0, weight = audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")]; + tensor hidden_states_201_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = residual_21_cast_fp16)[name = string("hidden_states_201_cast_fp16")]; + tensor context_mask_49_begin_0 = const()[name = string("context_mask_49_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_49_end_0 = const()[name = string("context_mask_49_end_0"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_49_end_mask_0 = const()[name = string("context_mask_49_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_49_cast_fp16 = slice_by_index(begin = context_mask_49_begin_0, end = context_mask_49_end_0, end_mask = context_mask_49_end_mask_0, x = context_mask_47_cast_fp16)[name = string("context_mask_49_cast_fp16")]; + tensor residual_23_begin_0 = const()[name = string("residual_23_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_23_end_0 = const()[name = string("residual_23_end_0"), val = tensor([1, 96, 1, 1998])]; + tensor residual_23_end_mask_0 = const()[name = string("residual_23_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_23_cast_fp16 = slice_by_index(begin = residual_23_begin_0, end = residual_23_end_0, end_mask = residual_23_end_mask_0, x = hidden_states_201_cast_fp16)[name = string("residual_23_cast_fp16")]; + tensor alpha_over_pi_49_to_fp16 = const()[name = string("alpha_over_pi_49_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186304)))]; + tensor theta_over_pi_49_cast_fp16 = mul(x = hidden_states_201_cast_fp16, y = alpha_over_pi_49_to_fp16)[name = string("theta_over_pi_49_cast_fp16")]; + tensor var_3726_cast_fp16 = round(x = theta_over_pi_49_cast_fp16)[name = string("op_3726_cast_fp16")]; + tensor reduced_49_cast_fp16 = sub(x = theta_over_pi_49_cast_fp16, y = var_3726_cast_fp16)[name = string("reduced_49_cast_fp16")]; + tensor reduced_sq_49_cast_fp16 = mul(x = reduced_49_cast_fp16, y = reduced_49_cast_fp16)[name = string("reduced_sq_49_cast_fp16")]; + tensor acc_145_mean_0_to_fp16 = const()[name = string("acc_145_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_145_variance_0_to_fp16 = const()[name = string("acc_145_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_145_gamma_0_to_fp16 = const()[name = string("acc_145_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186560)))]; + tensor acc_145_beta_0_to_fp16 = const()[name = string("acc_145_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186816)))]; + fp16 acc_145_epsilon_0_to_fp16 = const()[name = string("acc_145_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_145_cast_fp16 = batch_norm(beta = acc_145_beta_0_to_fp16, epsilon = acc_145_epsilon_0_to_fp16, gamma = acc_145_gamma_0_to_fp16, mean = acc_145_mean_0_to_fp16, variance = acc_145_variance_0_to_fp16, x = reduced_sq_49_cast_fp16)[name = string("acc_145_cast_fp16")]; + tensor var_3739_cast_fp16 = mul(x = acc_145_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3739_cast_fp16")]; + tensor c_97_to_fp16 = const()[name = string("c_97_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187072)))]; + tensor acc_147_cast_fp16 = add(x = var_3739_cast_fp16, y = c_97_to_fp16)[name = string("acc_147_cast_fp16")]; + tensor var_3741_cast_fp16 = mul(x = acc_147_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3741_cast_fp16")]; + tensor c_99_to_fp16 = const()[name = string("c_99_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187328)))]; + tensor acc_149_cast_fp16 = add(x = var_3741_cast_fp16, y = c_99_to_fp16)[name = string("acc_149_cast_fp16")]; + tensor var_3743_cast_fp16 = mul(x = acc_149_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3743_cast_fp16")]; + tensor hidden_states_203_cast_fp16 = add(x = hidden_states_201_cast_fp16, y = var_3743_cast_fp16)[name = string("hidden_states_203_cast_fp16")]; + bool full_mask_35_interleave_0 = const()[name = string("full_mask_35_interleave_0"), val = bool(false)]; + tensor full_mask_35_cast_fp16 = concat(axis = var_1991, interleave = full_mask_35_interleave_0, values = (context_mask_49_cast_fp16, fill_16_to_fp16))[name = string("full_mask_35_cast_fp16")]; + tensor input_179_cast_fp16 = mul(x = hidden_states_203_cast_fp16, y = full_mask_35_cast_fp16)[name = string("input_179_cast_fp16")]; + string hidden_states_205_pad_type_0 = const()[name = string("hidden_states_205_pad_type_0"), val = string("valid")]; + tensor hidden_states_205_dilations_0 = const()[name = string("hidden_states_205_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_205_strides_0 = const()[name = string("hidden_states_205_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_0 = const()[name = string("hidden_states_205_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_205_groups_0 = const()[name = string("hidden_states_205_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252160))))[name = string("audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252736)))]; + tensor hidden_states_205_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_205_dilations_0, groups = hidden_states_205_groups_0, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = hidden_states_205_strides_0, weight = audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized, x = input_179_cast_fp16)[name = string("hidden_states_205_cast_fp16")]; + tensor alpha_over_pi_51_to_fp16 = const()[name = string("alpha_over_pi_51_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252992)))]; + tensor theta_over_pi_51_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = alpha_over_pi_51_to_fp16)[name = string("theta_over_pi_51_cast_fp16")]; + tensor var_3780_cast_fp16 = round(x = theta_over_pi_51_cast_fp16)[name = string("op_3780_cast_fp16")]; + tensor reduced_51_cast_fp16 = sub(x = theta_over_pi_51_cast_fp16, y = var_3780_cast_fp16)[name = string("reduced_51_cast_fp16")]; + tensor reduced_sq_51_cast_fp16 = mul(x = reduced_51_cast_fp16, y = reduced_51_cast_fp16)[name = string("reduced_sq_51_cast_fp16")]; + tensor acc_151_mean_0_to_fp16 = const()[name = string("acc_151_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_151_variance_0_to_fp16 = const()[name = string("acc_151_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_151_gamma_0_to_fp16 = const()[name = string("acc_151_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253248)))]; + tensor acc_151_beta_0_to_fp16 = const()[name = string("acc_151_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253504)))]; + fp16 acc_151_epsilon_0_to_fp16 = const()[name = string("acc_151_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_151_cast_fp16 = batch_norm(beta = acc_151_beta_0_to_fp16, epsilon = acc_151_epsilon_0_to_fp16, gamma = acc_151_gamma_0_to_fp16, mean = acc_151_mean_0_to_fp16, variance = acc_151_variance_0_to_fp16, x = reduced_sq_51_cast_fp16)[name = string("acc_151_cast_fp16")]; + tensor var_3793_cast_fp16 = mul(x = acc_151_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3793_cast_fp16")]; + tensor c_101_to_fp16 = const()[name = string("c_101_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253760)))]; + tensor acc_153_cast_fp16 = add(x = var_3793_cast_fp16, y = c_101_to_fp16)[name = string("acc_153_cast_fp16")]; + tensor var_3795_cast_fp16 = mul(x = acc_153_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3795_cast_fp16")]; + tensor c_103_to_fp16 = const()[name = string("c_103_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115254016)))]; + tensor acc_155_cast_fp16 = add(x = var_3795_cast_fp16, y = c_103_to_fp16)[name = string("acc_155_cast_fp16")]; + tensor var_3797_cast_fp16 = mul(x = acc_155_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3797_cast_fp16")]; + tensor hidden_states_207_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = var_3797_cast_fp16)[name = string("hidden_states_207_cast_fp16")]; + string hidden_states_209_pad_type_0 = const()[name = string("hidden_states_209_pad_type_0"), val = string("valid")]; + tensor hidden_states_209_strides_0 = const()[name = string("hidden_states_209_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_209_pad_0 = const()[name = string("hidden_states_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_209_dilations_0 = const()[name = string("hidden_states_209_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_209_groups_0 = const()[name = string("hidden_states_209_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115254272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115263552))))[name = string("audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264128)))]; + tensor hidden_states_209_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_209_dilations_0, groups = hidden_states_209_groups_0, pad = hidden_states_209_pad_0, pad_type = hidden_states_209_pad_type_0, strides = hidden_states_209_strides_0, weight = audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")]; + tensor hidden_states_211_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = residual_23_cast_fp16)[name = string("hidden_states_211_cast_fp16")]; + tensor context_mask_51_begin_0 = const()[name = string("context_mask_51_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_51_end_0 = const()[name = string("context_mask_51_end_0"), val = tensor([1, 1, 1, 78])]; + tensor context_mask_51_end_mask_0 = const()[name = string("context_mask_51_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_51_cast_fp16 = slice_by_index(begin = context_mask_51_begin_0, end = context_mask_51_end_0, end_mask = context_mask_51_end_mask_0, x = context_mask_49_cast_fp16)[name = string("context_mask_51_cast_fp16")]; + tensor residual_begin_0 = const()[name = string("residual_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_end_0 = const()[name = string("residual_end_0"), val = tensor([1, 96, 1, 1980])]; + tensor residual_end_mask_0 = const()[name = string("residual_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_cast_fp16 = slice_by_index(begin = residual_begin_0, end = residual_end_0, end_mask = residual_end_mask_0, x = hidden_states_211_cast_fp16)[name = string("residual_cast_fp16")]; + tensor alpha_over_pi_53_to_fp16 = const()[name = string("alpha_over_pi_53_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264384)))]; + tensor theta_over_pi_53_cast_fp16 = mul(x = hidden_states_211_cast_fp16, y = alpha_over_pi_53_to_fp16)[name = string("theta_over_pi_53_cast_fp16")]; + tensor var_3832_cast_fp16 = round(x = theta_over_pi_53_cast_fp16)[name = string("op_3832_cast_fp16")]; + tensor reduced_53_cast_fp16 = sub(x = theta_over_pi_53_cast_fp16, y = var_3832_cast_fp16)[name = string("reduced_53_cast_fp16")]; + tensor reduced_sq_53_cast_fp16 = mul(x = reduced_53_cast_fp16, y = reduced_53_cast_fp16)[name = string("reduced_sq_53_cast_fp16")]; + tensor acc_157_mean_0_to_fp16 = const()[name = string("acc_157_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_157_variance_0_to_fp16 = const()[name = string("acc_157_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_157_gamma_0_to_fp16 = const()[name = string("acc_157_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264640)))]; + tensor acc_157_beta_0_to_fp16 = const()[name = string("acc_157_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264896)))]; + fp16 acc_157_epsilon_0_to_fp16 = const()[name = string("acc_157_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_157_cast_fp16 = batch_norm(beta = acc_157_beta_0_to_fp16, epsilon = acc_157_epsilon_0_to_fp16, gamma = acc_157_gamma_0_to_fp16, mean = acc_157_mean_0_to_fp16, variance = acc_157_variance_0_to_fp16, x = reduced_sq_53_cast_fp16)[name = string("acc_157_cast_fp16")]; + tensor var_3845_cast_fp16 = mul(x = acc_157_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3845_cast_fp16")]; + tensor c_105_to_fp16 = const()[name = string("c_105_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265152)))]; + tensor acc_159_cast_fp16 = add(x = var_3845_cast_fp16, y = c_105_to_fp16)[name = string("acc_159_cast_fp16")]; + tensor var_3847_cast_fp16 = mul(x = acc_159_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3847_cast_fp16")]; + tensor c_107_to_fp16 = const()[name = string("c_107_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265408)))]; + tensor acc_161_cast_fp16 = add(x = var_3847_cast_fp16, y = c_107_to_fp16)[name = string("acc_161_cast_fp16")]; + tensor var_3849_cast_fp16 = mul(x = acc_161_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3849_cast_fp16")]; + tensor hidden_states_213_cast_fp16 = add(x = hidden_states_211_cast_fp16, y = var_3849_cast_fp16)[name = string("hidden_states_213_cast_fp16")]; + bool full_mask_37_interleave_0 = const()[name = string("full_mask_37_interleave_0"), val = bool(false)]; + tensor full_mask_37_cast_fp16 = concat(axis = var_1991, interleave = full_mask_37_interleave_0, values = (context_mask_51_cast_fp16, fill_16_to_fp16))[name = string("full_mask_37_cast_fp16")]; + tensor input_183_cast_fp16 = mul(x = hidden_states_213_cast_fp16, y = full_mask_37_cast_fp16)[name = string("input_183_cast_fp16")]; + string hidden_states_215_pad_type_0 = const()[name = string("hidden_states_215_pad_type_0"), val = string("valid")]; + tensor hidden_states_215_dilations_0 = const()[name = string("hidden_states_215_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_215_strides_0 = const()[name = string("hidden_states_215_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_0 = const()[name = string("hidden_states_215_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_215_groups_0 = const()[name = string("hidden_states_215_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115330240))))[name = string("audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115330816)))]; + tensor hidden_states_215_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_215_dilations_0, groups = hidden_states_215_groups_0, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = hidden_states_215_strides_0, weight = audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized, x = input_183_cast_fp16)[name = string("hidden_states_215_cast_fp16")]; + tensor alpha_over_pi_55_to_fp16 = const()[name = string("alpha_over_pi_55_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331072)))]; + tensor theta_over_pi_55_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = alpha_over_pi_55_to_fp16)[name = string("theta_over_pi_55_cast_fp16")]; + tensor var_3886_cast_fp16 = round(x = theta_over_pi_55_cast_fp16)[name = string("op_3886_cast_fp16")]; + tensor reduced_55_cast_fp16 = sub(x = theta_over_pi_55_cast_fp16, y = var_3886_cast_fp16)[name = string("reduced_55_cast_fp16")]; + tensor reduced_sq_55_cast_fp16 = mul(x = reduced_55_cast_fp16, y = reduced_55_cast_fp16)[name = string("reduced_sq_55_cast_fp16")]; + tensor acc_163_mean_0_to_fp16 = const()[name = string("acc_163_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_163_variance_0_to_fp16 = const()[name = string("acc_163_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_163_gamma_0_to_fp16 = const()[name = string("acc_163_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331328)))]; + tensor acc_163_beta_0_to_fp16 = const()[name = string("acc_163_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331584)))]; + fp16 acc_163_epsilon_0_to_fp16 = const()[name = string("acc_163_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_163_cast_fp16 = batch_norm(beta = acc_163_beta_0_to_fp16, epsilon = acc_163_epsilon_0_to_fp16, gamma = acc_163_gamma_0_to_fp16, mean = acc_163_mean_0_to_fp16, variance = acc_163_variance_0_to_fp16, x = reduced_sq_55_cast_fp16)[name = string("acc_163_cast_fp16")]; + tensor var_3899_cast_fp16 = mul(x = acc_163_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3899_cast_fp16")]; + tensor c_109_to_fp16 = const()[name = string("c_109_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331840)))]; + tensor acc_165_cast_fp16 = add(x = var_3899_cast_fp16, y = c_109_to_fp16)[name = string("acc_165_cast_fp16")]; + tensor var_3901_cast_fp16 = mul(x = acc_165_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3901_cast_fp16")]; + tensor c_111_to_fp16 = const()[name = string("c_111_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115332096)))]; + tensor acc_167_cast_fp16 = add(x = var_3901_cast_fp16, y = c_111_to_fp16)[name = string("acc_167_cast_fp16")]; + tensor var_3903_cast_fp16 = mul(x = acc_167_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3903_cast_fp16")]; + tensor hidden_states_217_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = var_3903_cast_fp16)[name = string("hidden_states_217_cast_fp16")]; + string hidden_states_219_pad_type_0 = const()[name = string("hidden_states_219_pad_type_0"), val = string("valid")]; + tensor hidden_states_219_strides_0 = const()[name = string("hidden_states_219_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_0 = const()[name = string("hidden_states_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_219_dilations_0 = const()[name = string("hidden_states_219_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_219_groups_0 = const()[name = string("hidden_states_219_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115332352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115341632))))[name = string("audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342208)))]; + tensor hidden_states_219_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_219_dilations_0, groups = hidden_states_219_groups_0, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = hidden_states_219_strides_0, weight = audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")]; + tensor hidden_states_221_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = residual_cast_fp16)[name = string("hidden_states_221_cast_fp16")]; + tensor context_mask_begin_0 = const()[name = string("context_mask_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_end_0 = const()[name = string("context_mask_end_0"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_end_mask_0 = const()[name = string("context_mask_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_cast_fp16 = slice_by_index(begin = context_mask_begin_0, end = context_mask_end_0, end_mask = context_mask_end_mask_0, x = context_mask_47_cast_fp16)[name = string("context_mask_cast_fp16")]; + tensor alpha_over_pi_to_fp16 = const()[name = string("alpha_over_pi_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342464)))]; + tensor theta_over_pi_cast_fp16 = mul(x = hidden_states_221_cast_fp16, y = alpha_over_pi_to_fp16)[name = string("theta_over_pi_cast_fp16")]; + tensor var_3945_cast_fp16 = round(x = theta_over_pi_cast_fp16)[name = string("op_3945_cast_fp16")]; + tensor reduced_cast_fp16 = sub(x = theta_over_pi_cast_fp16, y = var_3945_cast_fp16)[name = string("reduced_cast_fp16")]; + tensor reduced_sq_cast_fp16 = mul(x = reduced_cast_fp16, y = reduced_cast_fp16)[name = string("reduced_sq_cast_fp16")]; + tensor acc_169_mean_0_to_fp16 = const()[name = string("acc_169_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_169_variance_0_to_fp16 = const()[name = string("acc_169_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_169_gamma_0_to_fp16 = const()[name = string("acc_169_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342720)))]; + tensor acc_169_beta_0_to_fp16 = const()[name = string("acc_169_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342976)))]; + fp16 acc_169_epsilon_0_to_fp16 = const()[name = string("acc_169_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_169_cast_fp16 = batch_norm(beta = acc_169_beta_0_to_fp16, epsilon = acc_169_epsilon_0_to_fp16, gamma = acc_169_gamma_0_to_fp16, mean = acc_169_mean_0_to_fp16, variance = acc_169_variance_0_to_fp16, x = reduced_sq_cast_fp16)[name = string("acc_169_cast_fp16")]; + tensor var_3958_cast_fp16 = mul(x = acc_169_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_3958_cast_fp16")]; + tensor c_113_to_fp16 = const()[name = string("c_113_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343232)))]; + tensor acc_171_cast_fp16 = add(x = var_3958_cast_fp16, y = c_113_to_fp16)[name = string("acc_171_cast_fp16")]; + tensor var_3960_cast_fp16 = mul(x = acc_171_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_3960_cast_fp16")]; + tensor c_to_fp16 = const()[name = string("c_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343488)))]; + tensor acc_cast_fp16 = add(x = var_3960_cast_fp16, y = c_to_fp16)[name = string("acc_cast_fp16")]; + tensor var_3962_cast_fp16 = mul(x = acc_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_3962_cast_fp16")]; + tensor hidden_states_223_cast_fp16 = add(x = hidden_states_221_cast_fp16, y = var_3962_cast_fp16)[name = string("hidden_states_223_cast_fp16")]; + bool full_mask_interleave_0 = const()[name = string("full_mask_interleave_0"), val = bool(false)]; + tensor full_mask_cast_fp16 = concat(axis = var_1991, interleave = full_mask_interleave_0, values = (context_mask_cast_fp16, fill_16_to_fp16))[name = string("full_mask_cast_fp16")]; + tensor input_cast_fp16 = mul(x = hidden_states_223_cast_fp16, y = full_mask_cast_fp16)[name = string("input_cast_fp16")]; + string hidden_states_pad_type_0 = const()[name = string("hidden_states_pad_type_0"), val = string("valid")]; + tensor hidden_states_strides_0 = const()[name = string("hidden_states_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_pad_0 = const()[name = string("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_dilations_0 = const()[name = string("hidden_states_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_groups_0 = const()[name = string("hidden_states_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_6_conv_weight_to_fp16 = const()[name = string("audio_upsampler_decoder_6_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343744)))]; + tensor audio_upsampler_decoder_6_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_6_conv_bias_to_fp16"), val = tensor([-0x1.1p-19])]; + tensor hidden_states_cast_fp16 = conv(bias = audio_upsampler_decoder_6_conv_bias_to_fp16, dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = audio_upsampler_decoder_6_conv_weight_to_fp16, x = input_cast_fp16)[name = string("hidden_states_cast_fp16")]; + fp16 var_1978_to_fp16 = const()[name = string("op_1978_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_1977_to_fp16 = const()[name = string("op_1977_to_fp16"), val = fp16(0x1p+0)]; + tensor audio = clip(alpha = var_1978_to_fp16, beta = var_1977_to_fp16, x = hidden_states_cast_fp16)[name = string("clip_16_cast_fp16")]; + } -> (audio, key_cache_updates, value_cache_updates, hidden_context_update, pre_conv_context_update); + func throughput(tensor audio_codes, tensor cache_length, tensor hidden_context, tensor hidden_context_mask, tensor key_cache, tensor key_padding_mask, tensor kv_cache_update_mask, tensor pre_conv_context, tensor qk_mask, tensor value_cache) { + tensor codes_1_begin_0 = const()[name = string("codes_1_begin_0"), val = tensor([0, 0, 0])]; + tensor codes_1_end_0 = const()[name = string("codes_1_end_0"), val = tensor([1, 1, 4])]; + tensor codes_1_end_mask_0 = const()[name = string("codes_1_end_mask_0"), val = tensor([true, false, true])]; + tensor codes_1 = slice_by_index(begin = codes_1_begin_0, end = codes_1_end_0, end_mask = codes_1_end_mask_0, x = audio_codes)[name = string("codes_1")]; + tensor var_43 = const()[name = string("op_43"), val = tensor([1, 0, 2])]; + tensor input_1_begin_0 = const()[name = string("input_1_begin_0"), val = tensor([0, 0, 0])]; + tensor input_1_end_0 = const()[name = string("input_1_end_0"), val = tensor([1, 1, 4])]; + tensor input_1_end_mask_0 = const()[name = string("input_1_end_mask_0"), val = tensor([false, true, true])]; + tensor input_1_squeeze_mask_0 = const()[name = string("input_1_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor codes_3 = transpose(perm = var_43, x = codes_1)[name = string("transpose_61")]; + tensor input_1 = slice_by_index(begin = input_1_begin_0, end = input_1_end_0, end_mask = input_1_end_mask_0, squeeze_mask = input_1_squeeze_mask_0, x = codes_3)[name = string("input_1")]; + int32 quantized_1_batch_dims_0 = const()[name = string("quantized_1_batch_dims_0"), val = int32(0)]; + bool quantized_1_validate_indices_0 = const()[name = string("quantized_1_validate_indices_0"), val = bool(false)]; + tensor weight_1_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("weight_1_to_fp16_palettized")]; + string input_1_to_int16_dtype_0 = const()[name = string("input_1_to_int16_dtype_0"), val = string("int16")]; + string cast_310_dtype_0 = const()[name = string("cast_310_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor input_1_to_int16 = cast(dtype = input_1_to_int16_dtype_0, x = input_1)[name = string("cast_20")]; + tensor cast_310 = cast(dtype = cast_310_dtype_0, x = input_1_to_int16)[name = string("cast_19")]; + tensor greater_equal_0 = greater_equal(x = cast_310, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_112 = const()[name = string("slice_by_index_112"), val = int32(2048)]; + tensor add_0 = add(x = cast_310, y = slice_by_index_112)[name = string("add_0")]; + tensor select_0 = select(a = cast_310, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_18")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_17")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(2048)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 quantized_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("quantized_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor quantized_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = quantized_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = quantized_1_batch_dims_0, indices = select_0_1, validate_indices = quantized_1_validate_indices_0, x = weight_1_to_fp16_palettized)[name = string("quantized_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor var_56 = const()[name = string("op_56"), val = tensor([0, 2, 1])]; + tensor input_3_axes_0 = const()[name = string("input_3_axes_0"), val = tensor([2])]; + tensor var_57_cast_fp16 = transpose(perm = var_56, x = quantized_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_60")]; + tensor input_3_cast_fp16 = expand_dims(axes = input_3_axes_0, x = var_57_cast_fp16)[name = string("input_3_cast_fp16")]; + string quantized_pad_type_0 = const()[name = string("quantized_pad_type_0"), val = string("valid")]; + tensor quantized_strides_0 = const()[name = string("quantized_strides_0"), val = tensor([1, 1])]; + tensor quantized_pad_0 = const()[name = string("quantized_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor quantized_dilations_0 = const()[name = string("quantized_dilations_0"), val = tensor([1, 1])]; + int32 quantized_groups_0 = const()[name = string("quantized_groups_0"), val = int32(1)]; + tensor quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656128))))[name = string("quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized")]; + tensor quantized_cast_fp16 = conv(dilations = quantized_dilations_0, groups = quantized_groups_0, pad = quantized_pad_0, pad_type = quantized_pad_type_0, strides = quantized_strides_0, weight = quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("quantized_cast_fp16")]; + tensor codes_5_begin_0 = const()[name = string("codes_5_begin_0"), val = tensor([0, 1, 0])]; + tensor codes_5_end_0 = const()[name = string("codes_5_end_0"), val = tensor([1, 16, 4])]; + tensor codes_5_end_mask_0 = const()[name = string("codes_5_end_mask_0"), val = tensor([true, true, true])]; + tensor codes_5 = slice_by_index(begin = codes_5_begin_0, end = codes_5_end_0, end_mask = codes_5_end_mask_0, x = audio_codes)[name = string("codes_5")]; + tensor var_69 = const()[name = string("op_69"), val = tensor([1, 0, 2])]; + tensor input_5_begin_0 = const()[name = string("input_5_begin_0"), val = tensor([0, 0, 0])]; + tensor input_5_end_0 = const()[name = string("input_5_end_0"), val = tensor([1, 1, 4])]; + tensor input_5_end_mask_0 = const()[name = string("input_5_end_mask_0"), val = tensor([false, true, true])]; + tensor input_5_squeeze_mask_0 = const()[name = string("input_5_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor codes = transpose(perm = var_69, x = codes_5)[name = string("transpose_59")]; + tensor input_5 = slice_by_index(begin = input_5_begin_0, end = input_5_end_0, end_mask = input_5_end_mask_0, squeeze_mask = input_5_squeeze_mask_0, x = codes)[name = string("input_5")]; + int32 quantized_3_axis_0 = const()[name = string("quantized_3_axis_0"), val = int32(0)]; + int32 quantized_3_batch_dims_0 = const()[name = string("quantized_3_batch_dims_0"), val = int32(0)]; + bool quantized_3_validate_indices_0 = const()[name = string("quantized_3_validate_indices_0"), val = bool(false)]; + tensor weight_5_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1181056))))[name = string("weight_5_to_fp16_palettized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_16")]; + tensor quantized_3_cast_fp16_cast_uint16 = gather(axis = quantized_3_axis_0, batch_dims = quantized_3_batch_dims_0, indices = input_5_to_uint16, validate_indices = quantized_3_validate_indices_0, x = weight_5_to_fp16_palettized)[name = string("quantized_3_cast_fp16_cast_uint16")]; + tensor var_110 = const()[name = string("op_110"), val = tensor([0, 2, 1])]; + tensor quantized_7_axes_0 = const()[name = string("quantized_7_axes_0"), val = tensor([2])]; + tensor var_111_cast_fp16 = transpose(perm = var_110, x = quantized_3_cast_fp16_cast_uint16)[name = string("transpose_58")]; + tensor quantized_7_cast_fp16 = expand_dims(axes = quantized_7_axes_0, x = var_111_cast_fp16)[name = string("quantized_7_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([1, 0, 0])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 4])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([false, true, true])]; + tensor input_7_squeeze_mask_0 = const()[name = string("input_7_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_7 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, squeeze_mask = input_7_squeeze_mask_0, x = codes)[name = string("input_7")]; + int32 quantized_5_axis_0 = const()[name = string("quantized_5_axis_0"), val = int32(0)]; + int32 quantized_5_batch_dims_0 = const()[name = string("quantized_5_batch_dims_0"), val = int32(0)]; + bool quantized_5_validate_indices_0 = const()[name = string("quantized_5_validate_indices_0"), val = bool(false)]; + tensor weight_7_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1181632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705984))))[name = string("weight_7_to_fp16_palettized")]; + string input_7_to_uint16_dtype_0 = const()[name = string("input_7_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_7_to_uint16 = cast(dtype = input_7_to_uint16_dtype_0, x = input_7)[name = string("cast_15")]; + tensor quantized_5_cast_fp16_cast_uint16 = gather(axis = quantized_5_axis_0, batch_dims = quantized_5_batch_dims_0, indices = input_7_to_uint16, validate_indices = quantized_5_validate_indices_0, x = weight_7_to_fp16_palettized)[name = string("quantized_5_cast_fp16_cast_uint16")]; + tensor var_122 = const()[name = string("op_122"), val = tensor([0, 2, 1])]; + tensor layer_out_1_axes_0 = const()[name = string("layer_out_1_axes_0"), val = tensor([2])]; + tensor var_123_cast_fp16 = transpose(perm = var_122, x = quantized_5_cast_fp16_cast_uint16)[name = string("transpose_57")]; + tensor layer_out_1_cast_fp16 = expand_dims(axes = layer_out_1_axes_0, x = var_123_cast_fp16)[name = string("layer_out_1_cast_fp16")]; + tensor quantized_11_cast_fp16 = add(x = quantized_7_cast_fp16, y = layer_out_1_cast_fp16)[name = string("quantized_11_cast_fp16")]; + tensor input_9_begin_0 = const()[name = string("input_9_begin_0"), val = tensor([2, 0, 0])]; + tensor input_9_end_0 = const()[name = string("input_9_end_0"), val = tensor([3, 1, 4])]; + tensor input_9_end_mask_0 = const()[name = string("input_9_end_mask_0"), val = tensor([false, true, true])]; + tensor input_9_squeeze_mask_0 = const()[name = string("input_9_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_9 = slice_by_index(begin = input_9_begin_0, end = input_9_end_0, end_mask = input_9_end_mask_0, squeeze_mask = input_9_squeeze_mask_0, x = codes)[name = string("input_9")]; + int32 quantized_9_axis_0 = const()[name = string("quantized_9_axis_0"), val = int32(0)]; + int32 quantized_9_batch_dims_0 = const()[name = string("quantized_9_batch_dims_0"), val = int32(0)]; + bool quantized_9_validate_indices_0 = const()[name = string("quantized_9_validate_indices_0"), val = bool(false)]; + tensor weight_9_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1706560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2230912))))[name = string("weight_9_to_fp16_palettized")]; + string input_9_to_uint16_dtype_0 = const()[name = string("input_9_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_9_to_uint16 = cast(dtype = input_9_to_uint16_dtype_0, x = input_9)[name = string("cast_14")]; + tensor quantized_9_cast_fp16_cast_uint16 = gather(axis = quantized_9_axis_0, batch_dims = quantized_9_batch_dims_0, indices = input_9_to_uint16, validate_indices = quantized_9_validate_indices_0, x = weight_9_to_fp16_palettized)[name = string("quantized_9_cast_fp16_cast_uint16")]; + tensor var_135 = const()[name = string("op_135"), val = tensor([0, 2, 1])]; + tensor layer_out_3_axes_0 = const()[name = string("layer_out_3_axes_0"), val = tensor([2])]; + tensor var_136_cast_fp16 = transpose(perm = var_135, x = quantized_9_cast_fp16_cast_uint16)[name = string("transpose_56")]; + tensor layer_out_3_cast_fp16 = expand_dims(axes = layer_out_3_axes_0, x = var_136_cast_fp16)[name = string("layer_out_3_cast_fp16")]; + tensor quantized_15_cast_fp16 = add(x = quantized_11_cast_fp16, y = layer_out_3_cast_fp16)[name = string("quantized_15_cast_fp16")]; + tensor input_11_begin_0 = const()[name = string("input_11_begin_0"), val = tensor([3, 0, 0])]; + tensor input_11_end_0 = const()[name = string("input_11_end_0"), val = tensor([4, 1, 4])]; + tensor input_11_end_mask_0 = const()[name = string("input_11_end_mask_0"), val = tensor([false, true, true])]; + tensor input_11_squeeze_mask_0 = const()[name = string("input_11_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_11 = slice_by_index(begin = input_11_begin_0, end = input_11_end_0, end_mask = input_11_end_mask_0, squeeze_mask = input_11_squeeze_mask_0, x = codes)[name = string("input_11")]; + int32 quantized_13_axis_0 = const()[name = string("quantized_13_axis_0"), val = int32(0)]; + int32 quantized_13_batch_dims_0 = const()[name = string("quantized_13_batch_dims_0"), val = int32(0)]; + bool quantized_13_validate_indices_0 = const()[name = string("quantized_13_validate_indices_0"), val = bool(false)]; + tensor weight_11_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2231488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2755840))))[name = string("weight_11_to_fp16_palettized")]; + string input_11_to_uint16_dtype_0 = const()[name = string("input_11_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_11_to_uint16 = cast(dtype = input_11_to_uint16_dtype_0, x = input_11)[name = string("cast_13")]; + tensor quantized_13_cast_fp16_cast_uint16 = gather(axis = quantized_13_axis_0, batch_dims = quantized_13_batch_dims_0, indices = input_11_to_uint16, validate_indices = quantized_13_validate_indices_0, x = weight_11_to_fp16_palettized)[name = string("quantized_13_cast_fp16_cast_uint16")]; + tensor var_148 = const()[name = string("op_148"), val = tensor([0, 2, 1])]; + tensor layer_out_5_axes_0 = const()[name = string("layer_out_5_axes_0"), val = tensor([2])]; + tensor var_149_cast_fp16 = transpose(perm = var_148, x = quantized_13_cast_fp16_cast_uint16)[name = string("transpose_55")]; + tensor layer_out_5_cast_fp16 = expand_dims(axes = layer_out_5_axes_0, x = var_149_cast_fp16)[name = string("layer_out_5_cast_fp16")]; + tensor quantized_19_cast_fp16 = add(x = quantized_15_cast_fp16, y = layer_out_5_cast_fp16)[name = string("quantized_19_cast_fp16")]; + tensor input_13_begin_0 = const()[name = string("input_13_begin_0"), val = tensor([4, 0, 0])]; + tensor input_13_end_0 = const()[name = string("input_13_end_0"), val = tensor([5, 1, 4])]; + tensor input_13_end_mask_0 = const()[name = string("input_13_end_mask_0"), val = tensor([false, true, true])]; + tensor input_13_squeeze_mask_0 = const()[name = string("input_13_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_13 = slice_by_index(begin = input_13_begin_0, end = input_13_end_0, end_mask = input_13_end_mask_0, squeeze_mask = input_13_squeeze_mask_0, x = codes)[name = string("input_13")]; + int32 quantized_17_axis_0 = const()[name = string("quantized_17_axis_0"), val = int32(0)]; + int32 quantized_17_batch_dims_0 = const()[name = string("quantized_17_batch_dims_0"), val = int32(0)]; + bool quantized_17_validate_indices_0 = const()[name = string("quantized_17_validate_indices_0"), val = bool(false)]; + tensor weight_13_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2756416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3280768))))[name = string("weight_13_to_fp16_palettized")]; + string input_13_to_uint16_dtype_0 = const()[name = string("input_13_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_13_to_uint16 = cast(dtype = input_13_to_uint16_dtype_0, x = input_13)[name = string("cast_12")]; + tensor quantized_17_cast_fp16_cast_uint16 = gather(axis = quantized_17_axis_0, batch_dims = quantized_17_batch_dims_0, indices = input_13_to_uint16, validate_indices = quantized_17_validate_indices_0, x = weight_13_to_fp16_palettized)[name = string("quantized_17_cast_fp16_cast_uint16")]; + tensor var_161 = const()[name = string("op_161"), val = tensor([0, 2, 1])]; + tensor layer_out_7_axes_0 = const()[name = string("layer_out_7_axes_0"), val = tensor([2])]; + tensor var_162_cast_fp16 = transpose(perm = var_161, x = quantized_17_cast_fp16_cast_uint16)[name = string("transpose_54")]; + tensor layer_out_7_cast_fp16 = expand_dims(axes = layer_out_7_axes_0, x = var_162_cast_fp16)[name = string("layer_out_7_cast_fp16")]; + tensor quantized_23_cast_fp16 = add(x = quantized_19_cast_fp16, y = layer_out_7_cast_fp16)[name = string("quantized_23_cast_fp16")]; + tensor input_15_begin_0 = const()[name = string("input_15_begin_0"), val = tensor([5, 0, 0])]; + tensor input_15_end_0 = const()[name = string("input_15_end_0"), val = tensor([6, 1, 4])]; + tensor input_15_end_mask_0 = const()[name = string("input_15_end_mask_0"), val = tensor([false, true, true])]; + tensor input_15_squeeze_mask_0 = const()[name = string("input_15_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_15 = slice_by_index(begin = input_15_begin_0, end = input_15_end_0, end_mask = input_15_end_mask_0, squeeze_mask = input_15_squeeze_mask_0, x = codes)[name = string("input_15")]; + int32 quantized_21_axis_0 = const()[name = string("quantized_21_axis_0"), val = int32(0)]; + int32 quantized_21_batch_dims_0 = const()[name = string("quantized_21_batch_dims_0"), val = int32(0)]; + bool quantized_21_validate_indices_0 = const()[name = string("quantized_21_validate_indices_0"), val = bool(false)]; + tensor weight_15_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3281344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3805696))))[name = string("weight_15_to_fp16_palettized")]; + string input_15_to_uint16_dtype_0 = const()[name = string("input_15_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_15_to_uint16 = cast(dtype = input_15_to_uint16_dtype_0, x = input_15)[name = string("cast_11")]; + tensor quantized_21_cast_fp16_cast_uint16 = gather(axis = quantized_21_axis_0, batch_dims = quantized_21_batch_dims_0, indices = input_15_to_uint16, validate_indices = quantized_21_validate_indices_0, x = weight_15_to_fp16_palettized)[name = string("quantized_21_cast_fp16_cast_uint16")]; + tensor var_174 = const()[name = string("op_174"), val = tensor([0, 2, 1])]; + tensor layer_out_9_axes_0 = const()[name = string("layer_out_9_axes_0"), val = tensor([2])]; + tensor var_175_cast_fp16 = transpose(perm = var_174, x = quantized_21_cast_fp16_cast_uint16)[name = string("transpose_53")]; + tensor layer_out_9_cast_fp16 = expand_dims(axes = layer_out_9_axes_0, x = var_175_cast_fp16)[name = string("layer_out_9_cast_fp16")]; + tensor quantized_27_cast_fp16 = add(x = quantized_23_cast_fp16, y = layer_out_9_cast_fp16)[name = string("quantized_27_cast_fp16")]; + tensor input_17_begin_0 = const()[name = string("input_17_begin_0"), val = tensor([6, 0, 0])]; + tensor input_17_end_0 = const()[name = string("input_17_end_0"), val = tensor([7, 1, 4])]; + tensor input_17_end_mask_0 = const()[name = string("input_17_end_mask_0"), val = tensor([false, true, true])]; + tensor input_17_squeeze_mask_0 = const()[name = string("input_17_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_17 = slice_by_index(begin = input_17_begin_0, end = input_17_end_0, end_mask = input_17_end_mask_0, squeeze_mask = input_17_squeeze_mask_0, x = codes)[name = string("input_17")]; + int32 quantized_25_axis_0 = const()[name = string("quantized_25_axis_0"), val = int32(0)]; + int32 quantized_25_batch_dims_0 = const()[name = string("quantized_25_batch_dims_0"), val = int32(0)]; + bool quantized_25_validate_indices_0 = const()[name = string("quantized_25_validate_indices_0"), val = bool(false)]; + tensor weight_17_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3806272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330624))))[name = string("weight_17_to_fp16_palettized")]; + string input_17_to_uint16_dtype_0 = const()[name = string("input_17_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_17_to_uint16 = cast(dtype = input_17_to_uint16_dtype_0, x = input_17)[name = string("cast_10")]; + tensor quantized_25_cast_fp16_cast_uint16 = gather(axis = quantized_25_axis_0, batch_dims = quantized_25_batch_dims_0, indices = input_17_to_uint16, validate_indices = quantized_25_validate_indices_0, x = weight_17_to_fp16_palettized)[name = string("quantized_25_cast_fp16_cast_uint16")]; + tensor var_187 = const()[name = string("op_187"), val = tensor([0, 2, 1])]; + tensor layer_out_11_axes_0 = const()[name = string("layer_out_11_axes_0"), val = tensor([2])]; + tensor var_188_cast_fp16 = transpose(perm = var_187, x = quantized_25_cast_fp16_cast_uint16)[name = string("transpose_52")]; + tensor layer_out_11_cast_fp16 = expand_dims(axes = layer_out_11_axes_0, x = var_188_cast_fp16)[name = string("layer_out_11_cast_fp16")]; + tensor quantized_31_cast_fp16 = add(x = quantized_27_cast_fp16, y = layer_out_11_cast_fp16)[name = string("quantized_31_cast_fp16")]; + tensor input_19_begin_0 = const()[name = string("input_19_begin_0"), val = tensor([7, 0, 0])]; + tensor input_19_end_0 = const()[name = string("input_19_end_0"), val = tensor([8, 1, 4])]; + tensor input_19_end_mask_0 = const()[name = string("input_19_end_mask_0"), val = tensor([false, true, true])]; + tensor input_19_squeeze_mask_0 = const()[name = string("input_19_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_19 = slice_by_index(begin = input_19_begin_0, end = input_19_end_0, end_mask = input_19_end_mask_0, squeeze_mask = input_19_squeeze_mask_0, x = codes)[name = string("input_19")]; + int32 quantized_29_axis_0 = const()[name = string("quantized_29_axis_0"), val = int32(0)]; + int32 quantized_29_batch_dims_0 = const()[name = string("quantized_29_batch_dims_0"), val = int32(0)]; + bool quantized_29_validate_indices_0 = const()[name = string("quantized_29_validate_indices_0"), val = bool(false)]; + tensor weight_19_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4331200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4855552))))[name = string("weight_19_to_fp16_palettized")]; + string input_19_to_uint16_dtype_0 = const()[name = string("input_19_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_19_to_uint16 = cast(dtype = input_19_to_uint16_dtype_0, x = input_19)[name = string("cast_9")]; + tensor quantized_29_cast_fp16_cast_uint16 = gather(axis = quantized_29_axis_0, batch_dims = quantized_29_batch_dims_0, indices = input_19_to_uint16, validate_indices = quantized_29_validate_indices_0, x = weight_19_to_fp16_palettized)[name = string("quantized_29_cast_fp16_cast_uint16")]; + tensor var_200 = const()[name = string("op_200"), val = tensor([0, 2, 1])]; + tensor layer_out_13_axes_0 = const()[name = string("layer_out_13_axes_0"), val = tensor([2])]; + tensor var_201_cast_fp16 = transpose(perm = var_200, x = quantized_29_cast_fp16_cast_uint16)[name = string("transpose_51")]; + tensor layer_out_13_cast_fp16 = expand_dims(axes = layer_out_13_axes_0, x = var_201_cast_fp16)[name = string("layer_out_13_cast_fp16")]; + tensor quantized_35_cast_fp16 = add(x = quantized_31_cast_fp16, y = layer_out_13_cast_fp16)[name = string("quantized_35_cast_fp16")]; + tensor input_21_begin_0 = const()[name = string("input_21_begin_0"), val = tensor([8, 0, 0])]; + tensor input_21_end_0 = const()[name = string("input_21_end_0"), val = tensor([9, 1, 4])]; + tensor input_21_end_mask_0 = const()[name = string("input_21_end_mask_0"), val = tensor([false, true, true])]; + tensor input_21_squeeze_mask_0 = const()[name = string("input_21_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_21 = slice_by_index(begin = input_21_begin_0, end = input_21_end_0, end_mask = input_21_end_mask_0, squeeze_mask = input_21_squeeze_mask_0, x = codes)[name = string("input_21")]; + int32 quantized_33_axis_0 = const()[name = string("quantized_33_axis_0"), val = int32(0)]; + int32 quantized_33_batch_dims_0 = const()[name = string("quantized_33_batch_dims_0"), val = int32(0)]; + bool quantized_33_validate_indices_0 = const()[name = string("quantized_33_validate_indices_0"), val = bool(false)]; + tensor weight_21_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4856128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5380480))))[name = string("weight_21_to_fp16_palettized")]; + string input_21_to_uint16_dtype_0 = const()[name = string("input_21_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_21_to_uint16 = cast(dtype = input_21_to_uint16_dtype_0, x = input_21)[name = string("cast_8")]; + tensor quantized_33_cast_fp16_cast_uint16 = gather(axis = quantized_33_axis_0, batch_dims = quantized_33_batch_dims_0, indices = input_21_to_uint16, validate_indices = quantized_33_validate_indices_0, x = weight_21_to_fp16_palettized)[name = string("quantized_33_cast_fp16_cast_uint16")]; + tensor var_213 = const()[name = string("op_213"), val = tensor([0, 2, 1])]; + tensor layer_out_15_axes_0 = const()[name = string("layer_out_15_axes_0"), val = tensor([2])]; + tensor var_214_cast_fp16 = transpose(perm = var_213, x = quantized_33_cast_fp16_cast_uint16)[name = string("transpose_50")]; + tensor layer_out_15_cast_fp16 = expand_dims(axes = layer_out_15_axes_0, x = var_214_cast_fp16)[name = string("layer_out_15_cast_fp16")]; + tensor quantized_39_cast_fp16 = add(x = quantized_35_cast_fp16, y = layer_out_15_cast_fp16)[name = string("quantized_39_cast_fp16")]; + tensor input_23_begin_0 = const()[name = string("input_23_begin_0"), val = tensor([9, 0, 0])]; + tensor input_23_end_0 = const()[name = string("input_23_end_0"), val = tensor([10, 1, 4])]; + tensor input_23_end_mask_0 = const()[name = string("input_23_end_mask_0"), val = tensor([false, true, true])]; + tensor input_23_squeeze_mask_0 = const()[name = string("input_23_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_23 = slice_by_index(begin = input_23_begin_0, end = input_23_end_0, end_mask = input_23_end_mask_0, squeeze_mask = input_23_squeeze_mask_0, x = codes)[name = string("input_23")]; + int32 quantized_37_axis_0 = const()[name = string("quantized_37_axis_0"), val = int32(0)]; + int32 quantized_37_batch_dims_0 = const()[name = string("quantized_37_batch_dims_0"), val = int32(0)]; + bool quantized_37_validate_indices_0 = const()[name = string("quantized_37_validate_indices_0"), val = bool(false)]; + tensor weight_23_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5381056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5905408))))[name = string("weight_23_to_fp16_palettized")]; + string input_23_to_uint16_dtype_0 = const()[name = string("input_23_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_23_to_uint16 = cast(dtype = input_23_to_uint16_dtype_0, x = input_23)[name = string("cast_7")]; + tensor quantized_37_cast_fp16_cast_uint16 = gather(axis = quantized_37_axis_0, batch_dims = quantized_37_batch_dims_0, indices = input_23_to_uint16, validate_indices = quantized_37_validate_indices_0, x = weight_23_to_fp16_palettized)[name = string("quantized_37_cast_fp16_cast_uint16")]; + tensor var_226 = const()[name = string("op_226"), val = tensor([0, 2, 1])]; + tensor layer_out_17_axes_0 = const()[name = string("layer_out_17_axes_0"), val = tensor([2])]; + tensor var_227_cast_fp16 = transpose(perm = var_226, x = quantized_37_cast_fp16_cast_uint16)[name = string("transpose_49")]; + tensor layer_out_17_cast_fp16 = expand_dims(axes = layer_out_17_axes_0, x = var_227_cast_fp16)[name = string("layer_out_17_cast_fp16")]; + tensor quantized_43_cast_fp16 = add(x = quantized_39_cast_fp16, y = layer_out_17_cast_fp16)[name = string("quantized_43_cast_fp16")]; + tensor input_25_begin_0 = const()[name = string("input_25_begin_0"), val = tensor([10, 0, 0])]; + tensor input_25_end_0 = const()[name = string("input_25_end_0"), val = tensor([11, 1, 4])]; + tensor input_25_end_mask_0 = const()[name = string("input_25_end_mask_0"), val = tensor([false, true, true])]; + tensor input_25_squeeze_mask_0 = const()[name = string("input_25_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_25 = slice_by_index(begin = input_25_begin_0, end = input_25_end_0, end_mask = input_25_end_mask_0, squeeze_mask = input_25_squeeze_mask_0, x = codes)[name = string("input_25")]; + int32 quantized_41_axis_0 = const()[name = string("quantized_41_axis_0"), val = int32(0)]; + int32 quantized_41_batch_dims_0 = const()[name = string("quantized_41_batch_dims_0"), val = int32(0)]; + bool quantized_41_validate_indices_0 = const()[name = string("quantized_41_validate_indices_0"), val = bool(false)]; + tensor weight_25_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5905984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6430336))))[name = string("weight_25_to_fp16_palettized")]; + string input_25_to_uint16_dtype_0 = const()[name = string("input_25_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_25_to_uint16 = cast(dtype = input_25_to_uint16_dtype_0, x = input_25)[name = string("cast_6")]; + tensor quantized_41_cast_fp16_cast_uint16 = gather(axis = quantized_41_axis_0, batch_dims = quantized_41_batch_dims_0, indices = input_25_to_uint16, validate_indices = quantized_41_validate_indices_0, x = weight_25_to_fp16_palettized)[name = string("quantized_41_cast_fp16_cast_uint16")]; + tensor var_239 = const()[name = string("op_239"), val = tensor([0, 2, 1])]; + tensor layer_out_19_axes_0 = const()[name = string("layer_out_19_axes_0"), val = tensor([2])]; + tensor var_240_cast_fp16 = transpose(perm = var_239, x = quantized_41_cast_fp16_cast_uint16)[name = string("transpose_48")]; + tensor layer_out_19_cast_fp16 = expand_dims(axes = layer_out_19_axes_0, x = var_240_cast_fp16)[name = string("layer_out_19_cast_fp16")]; + tensor quantized_47_cast_fp16 = add(x = quantized_43_cast_fp16, y = layer_out_19_cast_fp16)[name = string("quantized_47_cast_fp16")]; + tensor input_27_begin_0 = const()[name = string("input_27_begin_0"), val = tensor([11, 0, 0])]; + tensor input_27_end_0 = const()[name = string("input_27_end_0"), val = tensor([12, 1, 4])]; + tensor input_27_end_mask_0 = const()[name = string("input_27_end_mask_0"), val = tensor([false, true, true])]; + tensor input_27_squeeze_mask_0 = const()[name = string("input_27_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_27 = slice_by_index(begin = input_27_begin_0, end = input_27_end_0, end_mask = input_27_end_mask_0, squeeze_mask = input_27_squeeze_mask_0, x = codes)[name = string("input_27")]; + int32 quantized_45_axis_0 = const()[name = string("quantized_45_axis_0"), val = int32(0)]; + int32 quantized_45_batch_dims_0 = const()[name = string("quantized_45_batch_dims_0"), val = int32(0)]; + bool quantized_45_validate_indices_0 = const()[name = string("quantized_45_validate_indices_0"), val = bool(false)]; + tensor weight_27_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6430912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6955264))))[name = string("weight_27_to_fp16_palettized")]; + string input_27_to_uint16_dtype_0 = const()[name = string("input_27_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_27_to_uint16 = cast(dtype = input_27_to_uint16_dtype_0, x = input_27)[name = string("cast_5")]; + tensor quantized_45_cast_fp16_cast_uint16 = gather(axis = quantized_45_axis_0, batch_dims = quantized_45_batch_dims_0, indices = input_27_to_uint16, validate_indices = quantized_45_validate_indices_0, x = weight_27_to_fp16_palettized)[name = string("quantized_45_cast_fp16_cast_uint16")]; + tensor var_252 = const()[name = string("op_252"), val = tensor([0, 2, 1])]; + tensor layer_out_21_axes_0 = const()[name = string("layer_out_21_axes_0"), val = tensor([2])]; + tensor var_253_cast_fp16 = transpose(perm = var_252, x = quantized_45_cast_fp16_cast_uint16)[name = string("transpose_47")]; + tensor layer_out_21_cast_fp16 = expand_dims(axes = layer_out_21_axes_0, x = var_253_cast_fp16)[name = string("layer_out_21_cast_fp16")]; + tensor quantized_51_cast_fp16 = add(x = quantized_47_cast_fp16, y = layer_out_21_cast_fp16)[name = string("quantized_51_cast_fp16")]; + tensor input_29_begin_0 = const()[name = string("input_29_begin_0"), val = tensor([12, 0, 0])]; + tensor input_29_end_0 = const()[name = string("input_29_end_0"), val = tensor([13, 1, 4])]; + tensor input_29_end_mask_0 = const()[name = string("input_29_end_mask_0"), val = tensor([false, true, true])]; + tensor input_29_squeeze_mask_0 = const()[name = string("input_29_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_29 = slice_by_index(begin = input_29_begin_0, end = input_29_end_0, end_mask = input_29_end_mask_0, squeeze_mask = input_29_squeeze_mask_0, x = codes)[name = string("input_29")]; + int32 quantized_49_axis_0 = const()[name = string("quantized_49_axis_0"), val = int32(0)]; + int32 quantized_49_batch_dims_0 = const()[name = string("quantized_49_batch_dims_0"), val = int32(0)]; + bool quantized_49_validate_indices_0 = const()[name = string("quantized_49_validate_indices_0"), val = bool(false)]; + tensor weight_29_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6955840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480192))))[name = string("weight_29_to_fp16_palettized")]; + string input_29_to_uint16_dtype_0 = const()[name = string("input_29_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_29_to_uint16 = cast(dtype = input_29_to_uint16_dtype_0, x = input_29)[name = string("cast_4")]; + tensor quantized_49_cast_fp16_cast_uint16 = gather(axis = quantized_49_axis_0, batch_dims = quantized_49_batch_dims_0, indices = input_29_to_uint16, validate_indices = quantized_49_validate_indices_0, x = weight_29_to_fp16_palettized)[name = string("quantized_49_cast_fp16_cast_uint16")]; + tensor var_265 = const()[name = string("op_265"), val = tensor([0, 2, 1])]; + tensor layer_out_23_axes_0 = const()[name = string("layer_out_23_axes_0"), val = tensor([2])]; + tensor var_266_cast_fp16 = transpose(perm = var_265, x = quantized_49_cast_fp16_cast_uint16)[name = string("transpose_46")]; + tensor layer_out_23_cast_fp16 = expand_dims(axes = layer_out_23_axes_0, x = var_266_cast_fp16)[name = string("layer_out_23_cast_fp16")]; + tensor quantized_55_cast_fp16 = add(x = quantized_51_cast_fp16, y = layer_out_23_cast_fp16)[name = string("quantized_55_cast_fp16")]; + tensor input_31_begin_0 = const()[name = string("input_31_begin_0"), val = tensor([13, 0, 0])]; + tensor input_31_end_0 = const()[name = string("input_31_end_0"), val = tensor([14, 1, 4])]; + tensor input_31_end_mask_0 = const()[name = string("input_31_end_mask_0"), val = tensor([false, true, true])]; + tensor input_31_squeeze_mask_0 = const()[name = string("input_31_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_31 = slice_by_index(begin = input_31_begin_0, end = input_31_end_0, end_mask = input_31_end_mask_0, squeeze_mask = input_31_squeeze_mask_0, x = codes)[name = string("input_31")]; + int32 quantized_53_axis_0 = const()[name = string("quantized_53_axis_0"), val = int32(0)]; + int32 quantized_53_batch_dims_0 = const()[name = string("quantized_53_batch_dims_0"), val = int32(0)]; + bool quantized_53_validate_indices_0 = const()[name = string("quantized_53_validate_indices_0"), val = bool(false)]; + tensor weight_31_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8005120))))[name = string("weight_31_to_fp16_palettized")]; + string input_31_to_uint16_dtype_0 = const()[name = string("input_31_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_31_to_uint16 = cast(dtype = input_31_to_uint16_dtype_0, x = input_31)[name = string("cast_3")]; + tensor quantized_53_cast_fp16_cast_uint16 = gather(axis = quantized_53_axis_0, batch_dims = quantized_53_batch_dims_0, indices = input_31_to_uint16, validate_indices = quantized_53_validate_indices_0, x = weight_31_to_fp16_palettized)[name = string("quantized_53_cast_fp16_cast_uint16")]; + tensor var_278 = const()[name = string("op_278"), val = tensor([0, 2, 1])]; + tensor layer_out_25_axes_0 = const()[name = string("layer_out_25_axes_0"), val = tensor([2])]; + tensor var_279_cast_fp16 = transpose(perm = var_278, x = quantized_53_cast_fp16_cast_uint16)[name = string("transpose_45")]; + tensor layer_out_25_cast_fp16 = expand_dims(axes = layer_out_25_axes_0, x = var_279_cast_fp16)[name = string("layer_out_25_cast_fp16")]; + tensor quantized_59_cast_fp16 = add(x = quantized_55_cast_fp16, y = layer_out_25_cast_fp16)[name = string("quantized_59_cast_fp16")]; + tensor input_33_begin_0 = const()[name = string("input_33_begin_0"), val = tensor([14, 0, 0])]; + tensor input_33_end_0 = const()[name = string("input_33_end_0"), val = tensor([15, 1, 4])]; + tensor input_33_end_mask_0 = const()[name = string("input_33_end_mask_0"), val = tensor([false, true, true])]; + tensor input_33_squeeze_mask_0 = const()[name = string("input_33_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_33 = slice_by_index(begin = input_33_begin_0, end = input_33_end_0, end_mask = input_33_end_mask_0, squeeze_mask = input_33_squeeze_mask_0, x = codes)[name = string("input_33")]; + int32 quantized_57_axis_0 = const()[name = string("quantized_57_axis_0"), val = int32(0)]; + int32 quantized_57_batch_dims_0 = const()[name = string("quantized_57_batch_dims_0"), val = int32(0)]; + bool quantized_57_validate_indices_0 = const()[name = string("quantized_57_validate_indices_0"), val = bool(false)]; + tensor weight_33_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8005696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8530048))))[name = string("weight_33_to_fp16_palettized")]; + string input_33_to_uint16_dtype_0 = const()[name = string("input_33_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_33_to_uint16 = cast(dtype = input_33_to_uint16_dtype_0, x = input_33)[name = string("cast_2")]; + tensor quantized_57_cast_fp16_cast_uint16 = gather(axis = quantized_57_axis_0, batch_dims = quantized_57_batch_dims_0, indices = input_33_to_uint16, validate_indices = quantized_57_validate_indices_0, x = weight_33_to_fp16_palettized)[name = string("quantized_57_cast_fp16_cast_uint16")]; + tensor var_291 = const()[name = string("op_291"), val = tensor([0, 2, 1])]; + tensor layer_out_axes_0 = const()[name = string("layer_out_axes_0"), val = tensor([2])]; + tensor var_292_cast_fp16 = transpose(perm = var_291, x = quantized_57_cast_fp16_cast_uint16)[name = string("transpose_44")]; + tensor layer_out_cast_fp16 = expand_dims(axes = layer_out_axes_0, x = var_292_cast_fp16)[name = string("layer_out_cast_fp16")]; + tensor input_35_cast_fp16 = add(x = quantized_59_cast_fp16, y = layer_out_cast_fp16)[name = string("input_35_cast_fp16")]; + string var_300_pad_type_0 = const()[name = string("op_300_pad_type_0"), val = string("valid")]; + tensor var_300_strides_0 = const()[name = string("op_300_strides_0"), val = tensor([1, 1])]; + tensor var_300_pad_0 = const()[name = string("op_300_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_300_dilations_0 = const()[name = string("op_300_dilations_0"), val = tensor([1, 1])]; + int32 var_300_groups_0 = const()[name = string("op_300_groups_0"), val = int32(1)]; + tensor quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8530624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8661760))))[name = string("quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized")]; + tensor var_300_cast_fp16 = conv(dilations = var_300_dilations_0, groups = var_300_groups_0, pad = var_300_pad_0, pad_type = var_300_pad_type_0, strides = var_300_strides_0, weight = quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor pre_conv_context_update = add(x = quantized_cast_fp16, y = var_300_cast_fp16)[name = string("hidden_in_cast_fp16")]; + tensor var_316_axes_0 = const()[name = string("op_316_axes_0"), val = tensor([1])]; + tensor var_316 = expand_dims(axes = var_316_axes_0, x = cache_length)[name = string("op_316")]; + tensor var_317 = const()[name = string("op_317"), val = tensor([[0, 1, 2, 3]])]; + tensor input_37 = add(x = var_316, y = var_317)[name = string("input_37")]; + int32 var_320_axis_0 = const()[name = string("op_320_axis_0"), val = int32(0)]; + int32 var_320_batch_dims_0 = const()[name = string("op_320_batch_dims_0"), val = int32(0)]; + bool var_320_validate_indices_0 = const()[name = string("op_320_validate_indices_0"), val = bool(false)]; + tensor rope_rope_cos_to_fp16 = const()[name = string("rope_rope_cos_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8662336)))]; + string input_37_to_uint16_dtype_0 = const()[name = string("input_37_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_37_to_uint16 = cast(dtype = input_37_to_uint16_dtype_0, x = input_37)[name = string("cast_1")]; + tensor var_320_cast_fp16_cast_uint16 = gather(axis = var_320_axis_0, batch_dims = var_320_batch_dims_0, indices = input_37_to_uint16, validate_indices = var_320_validate_indices_0, x = rope_rope_cos_to_fp16)[name = string("op_320_cast_fp16_cast_uint16")]; + tensor obj_7_perm_0 = const()[name = string("obj_7_perm_0"), val = tensor([0, 2, 1])]; + int32 var_322_axis_0 = const()[name = string("op_322_axis_0"), val = int32(0)]; + int32 var_322_batch_dims_0 = const()[name = string("op_322_batch_dims_0"), val = int32(0)]; + bool var_322_validate_indices_0 = const()[name = string("op_322_validate_indices_0"), val = bool(false)]; + tensor rope_rope_sin_to_fp16 = const()[name = string("rope_rope_sin_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9186688)))]; + tensor var_322_cast_fp16_cast_uint16 = gather(axis = var_322_axis_0, batch_dims = var_322_batch_dims_0, indices = input_37_to_uint16, validate_indices = var_322_validate_indices_0, x = rope_rope_sin_to_fp16)[name = string("op_322_cast_fp16_cast_uint16")]; + tensor obj_9_perm_0 = const()[name = string("obj_9_perm_0"), val = tensor([0, 2, 1])]; + int32 var_333 = const()[name = string("op_333"), val = int32(-2)]; + int32 var_338 = const()[name = string("op_338"), val = int32(3)]; + int32 var_343 = const()[name = string("op_343"), val = int32(-1)]; + int32 var_344 = const()[name = string("op_344"), val = int32(1)]; + tensor tile_0 = const()[name = string("tile_0"), val = tensor([1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024])]; + int32 var_367_axis_0 = const()[name = string("op_367_axis_0"), val = int32(1)]; + tensor var_367_cast_fp16_0, tensor var_367_cast_fp16_1, tensor var_367_cast_fp16_2, tensor var_367_cast_fp16_3, tensor var_367_cast_fp16_4, tensor var_367_cast_fp16_5, tensor var_367_cast_fp16_6, tensor var_367_cast_fp16_7 = split(axis = var_367_axis_0, split_sizes = tile_0, x = key_cache)[name = string("op_367_cast_fp16")]; + tensor tile_1 = const()[name = string("tile_1"), val = tensor([1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024])]; + int32 var_376_axis_0 = const()[name = string("op_376_axis_0"), val = int32(1)]; + tensor var_376_cast_fp16_0, tensor var_376_cast_fp16_1, tensor var_376_cast_fp16_2, tensor var_376_cast_fp16_3, tensor var_376_cast_fp16_4, tensor var_376_cast_fp16_5, tensor var_376_cast_fp16_6, tensor var_376_cast_fp16_7 = split(axis = var_376_axis_0, split_sizes = tile_1, x = value_cache)[name = string("op_376_cast_fp16")]; + bool input_39_interleave_0 = const()[name = string("input_39_interleave_0"), val = bool(false)]; + tensor input_39_cast_fp16 = concat(axis = var_343, interleave = input_39_interleave_0, values = (pre_conv_context, pre_conv_context_update))[name = string("input_39_cast_fp16")]; + string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("valid")]; + tensor input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor([1, 1])]; + tensor input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor([1, 1])]; + int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(1)]; + tensor pre_transformer_pre_conv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9711040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11283968))))[name = string("pre_transformer_pre_conv_conv_weight_to_fp16_palettized")]; + tensor pre_transformer_pre_conv_conv_bias_to_fp16 = const()[name = string("pre_transformer_pre_conv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11284544)))]; + tensor input_41_cast_fp16 = conv(bias = pre_transformer_pre_conv_conv_bias_to_fp16, dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = pre_transformer_pre_conv_conv_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + string inputs_1_pad_type_0 = const()[name = string("inputs_1_pad_type_0"), val = string("valid")]; + tensor inputs_1_strides_0 = const()[name = string("inputs_1_strides_0"), val = tensor([1, 1])]; + tensor inputs_1_pad_0 = const()[name = string("inputs_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor inputs_1_dilations_0 = const()[name = string("inputs_1_dilations_0"), val = tensor([1, 1])]; + int32 inputs_1_groups_0 = const()[name = string("inputs_1_groups_0"), val = int32(1)]; + tensor pre_transformer_input_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11286656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11811008))))[name = string("pre_transformer_input_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_input_proj_bias_to_fp16 = const()[name = string("pre_transformer_input_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11811584)))]; + tensor inputs_1_cast_fp16 = conv(bias = pre_transformer_input_proj_bias_to_fp16, dilations = inputs_1_dilations_0, groups = inputs_1_groups_0, pad = inputs_1_pad_0, pad_type = inputs_1_pad_type_0, strides = inputs_1_strides_0, weight = pre_transformer_input_proj_weight_to_fp16_palettized, x = input_41_cast_fp16)[name = string("inputs_1_cast_fp16")]; + tensor inputs_sq_1_cast_fp16 = mul(x = inputs_1_cast_fp16, y = inputs_1_cast_fp16)[name = string("inputs_sq_1_cast_fp16")]; + tensor variance_1_axes_0 = const()[name = string("variance_1_axes_0"), val = tensor([1])]; + bool variance_1_keep_dims_0 = const()[name = string("variance_1_keep_dims_0"), val = bool(true)]; + tensor variance_1_cast_fp16 = reduce_mean(axes = variance_1_axes_0, keep_dims = variance_1_keep_dims_0, x = inputs_sq_1_cast_fp16)[name = string("variance_1_cast_fp16")]; + fp16 var_411_to_fp16 = const()[name = string("op_411_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_412_cast_fp16 = add(x = variance_1_cast_fp16, y = var_411_to_fp16)[name = string("op_412_cast_fp16")]; + fp32 var_413_epsilon_0 = const()[name = string("op_413_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_413_cast_fp16 = rsqrt(epsilon = var_413_epsilon_0, x = var_412_cast_fp16)[name = string("op_413_cast_fp16")]; + tensor hidden_states_1_cast_fp16 = mul(x = inputs_1_cast_fp16, y = var_413_cast_fp16)[name = string("hidden_states_1_cast_fp16")]; + tensor w_1_to_fp16 = const()[name = string("w_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11812672)))]; + tensor obj_1_cast_fp16 = mul(x = w_1_to_fp16, y = hidden_states_1_cast_fp16)[name = string("obj_1_cast_fp16")]; + string query_1_pad_type_0 = const()[name = string("query_1_pad_type_0"), val = string("valid")]; + tensor query_1_strides_0 = const()[name = string("query_1_strides_0"), val = tensor([1, 1])]; + tensor query_1_pad_0 = const()[name = string("query_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_1_dilations_0 = const()[name = string("query_1_dilations_0"), val = tensor([1, 1])]; + int32 query_1_groups_0 = const()[name = string("query_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11813760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12338112))))[name = string("pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = string("pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12338688)))]; + tensor query_1_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_1_dilations_0, groups = query_1_groups_0, pad = query_1_pad_0, pad_type = query_1_pad_type_0, strides = query_1_strides_0, weight = pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("query_1_cast_fp16")]; + string key_1_pad_type_0 = const()[name = string("key_1_pad_type_0"), val = string("valid")]; + tensor key_1_strides_0 = const()[name = string("key_1_strides_0"), val = tensor([1, 1])]; + tensor key_1_pad_0 = const()[name = string("key_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_1_dilations_0 = const()[name = string("key_1_dilations_0"), val = tensor([1, 1])]; + int32 key_1_groups_0 = const()[name = string("key_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12340800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12865152))))[name = string("pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_1_cast_fp16 = conv(dilations = key_1_dilations_0, groups = key_1_groups_0, pad = key_1_pad_0, pad_type = key_1_pad_type_0, strides = key_1_strides_0, weight = pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("key_1_cast_fp16")]; + string obj_15_pad_type_0 = const()[name = string("obj_15_pad_type_0"), val = string("valid")]; + tensor obj_15_strides_0 = const()[name = string("obj_15_strides_0"), val = tensor([1, 1])]; + tensor obj_15_pad_0 = const()[name = string("obj_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_15_dilations_0 = const()[name = string("obj_15_dilations_0"), val = tensor([1, 1])]; + int32 obj_15_groups_0 = const()[name = string("obj_15_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12865728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13390080))))[name = string("pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_15_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_15_dilations_0, groups = obj_15_groups_0, pad = obj_15_pad_0, pad_type = obj_15_pad_type_0, strides = obj_15_strides_0, weight = pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("obj_15_cast_fp16")]; + tensor var_451 = const()[name = string("op_451"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_1_cast_fp16 = reshape(shape = var_451, x = query_1_cast_fp16)[name = string("mh_q_1_cast_fp16")]; + tensor var_453 = const()[name = string("op_453"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_1_cast_fp16 = reshape(shape = var_453, x = key_1_cast_fp16)[name = string("mh_k_1_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor obj_7_cast_fp16 = transpose(perm = obj_7_perm_0, x = var_320_cast_fp16_cast_uint16)[name = string("transpose_43")]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = obj_7_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor obj_9_cast_fp16 = transpose(perm = obj_9_perm_0, x = var_322_cast_fp16_cast_uint16)[name = string("transpose_42")]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = obj_9_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor var_457_cast_fp16 = mul(x = mh_q_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_457_cast_fp16")]; + tensor var_462_begin_0 = const()[name = string("op_462_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_462_end_0 = const()[name = string("op_462_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_462_end_mask_0 = const()[name = string("op_462_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_462_cast_fp16 = slice_by_index(begin = var_462_begin_0, end = var_462_end_0, end_mask = var_462_end_mask_0, x = mh_q_1_cast_fp16)[name = string("op_462_cast_fp16")]; + tensor var_468_begin_0 = const()[name = string("op_468_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_468_end_0 = const()[name = string("op_468_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_468_end_mask_0 = const()[name = string("op_468_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_468_cast_fp16 = slice_by_index(begin = var_468_begin_0, end = var_468_end_0, end_mask = var_468_end_mask_0, x = mh_q_1_cast_fp16)[name = string("op_468_cast_fp16")]; + fp16 const_33_promoted_to_fp16 = const()[name = string("const_33_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_470_cast_fp16 = mul(x = var_468_cast_fp16, y = const_33_promoted_to_fp16)[name = string("op_470_cast_fp16")]; + bool var_472_interleave_0 = const()[name = string("op_472_interleave_0"), val = bool(false)]; + tensor var_472_cast_fp16 = concat(axis = var_333, interleave = var_472_interleave_0, values = (var_470_cast_fp16, var_462_cast_fp16))[name = string("op_472_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_472_cast_fp16, y = sin_1_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor mh_q_3_cast_fp16 = add(x = var_457_cast_fp16, y = var_473_cast_fp16)[name = string("mh_q_3_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = mh_k_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor var_480_begin_0 = const()[name = string("op_480_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_480_end_0 = const()[name = string("op_480_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_480_end_mask_0 = const()[name = string("op_480_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_480_cast_fp16 = slice_by_index(begin = var_480_begin_0, end = var_480_end_0, end_mask = var_480_end_mask_0, x = mh_k_1_cast_fp16)[name = string("op_480_cast_fp16")]; + tensor var_486_begin_0 = const()[name = string("op_486_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_486_end_0 = const()[name = string("op_486_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_486_end_mask_0 = const()[name = string("op_486_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_486_cast_fp16 = slice_by_index(begin = var_486_begin_0, end = var_486_end_0, end_mask = var_486_end_mask_0, x = mh_k_1_cast_fp16)[name = string("op_486_cast_fp16")]; + fp16 const_36_promoted_to_fp16 = const()[name = string("const_36_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_488_cast_fp16 = mul(x = var_486_cast_fp16, y = const_36_promoted_to_fp16)[name = string("op_488_cast_fp16")]; + bool var_490_interleave_0 = const()[name = string("op_490_interleave_0"), val = bool(false)]; + tensor var_490_cast_fp16 = concat(axis = var_333, interleave = var_490_interleave_0, values = (var_488_cast_fp16, var_480_cast_fp16))[name = string("op_490_cast_fp16")]; + tensor var_491_cast_fp16 = mul(x = var_490_cast_fp16, y = sin_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor mh_k_3_cast_fp16 = add(x = var_475_cast_fp16, y = var_491_cast_fp16)[name = string("mh_k_3_cast_fp16")]; + tensor var_495 = const()[name = string("op_495"), val = tensor([1, 1024, 1, 4])]; + tensor obj_13_cast_fp16 = reshape(shape = var_495, x = mh_k_3_cast_fp16)[name = string("obj_13_cast_fp16")]; + tensor var_498_axes_0 = const()[name = string("op_498_axes_0"), val = tensor([1])]; + bool var_498_keep_dims_0 = const()[name = string("op_498_keep_dims_0"), val = bool(false)]; + tensor var_498_cast_fp16 = reduce_sum(axes = var_498_axes_0, keep_dims = var_498_keep_dims_0, x = kv_cache_update_mask)[name = string("op_498_cast_fp16")]; + fp16 var_332_to_fp16 = const()[name = string("op_332_to_fp16"), val = fp16(0x1p+0)]; + tensor var_499_cast_fp16 = sub(x = var_332_to_fp16, y = var_498_cast_fp16)[name = string("op_499_cast_fp16")]; + tensor var_501_axes_0 = const()[name = string("op_501_axes_0"), val = tensor([1])]; + tensor var_501_cast_fp16 = expand_dims(axes = var_501_axes_0, x = var_499_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_axes_0 = const()[name = string("op_502_axes_0"), val = tensor([2])]; + tensor var_502_cast_fp16 = expand_dims(axes = var_502_axes_0, x = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_5 = const()[name = string("concat_5"), val = tensor([1, 4, 1024])]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = obj_13_cast_fp16)[name = string("transpose_41")]; + tensor reshape_1_cast_fp16 = reshape(shape = concat_5, x = transpose_1_cast_fp16)[name = string("reshape_1_cast_fp16")]; + bool matmul_0_transpose_x_1 = const()[name = string("matmul_0_transpose_x_1"), val = bool(true)]; + bool matmul_0_transpose_y_1 = const()[name = string("matmul_0_transpose_y_1"), val = bool(false)]; + tensor matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_1, transpose_y = matmul_0_transpose_y_1, x = kv_cache_update_mask, y = reshape_1_cast_fp16)[name = string("matmul_0_cast_fp16")]; + tensor concat_9 = const()[name = string("concat_9"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_2_cast_fp16 = reshape(shape = concat_9, x = matmul_0_cast_fp16)[name = string("reshape_2_cast_fp16")]; + tensor key_scatter_1_perm_0 = const()[name = string("key_scatter_1_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_3_perm_0 = const()[name = string("transpose_3_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_15 = const()[name = string("concat_15"), val = tensor([1, 4, 1024])]; + tensor transpose_3_cast_fp16 = transpose(perm = transpose_3_perm_0, x = obj_15_cast_fp16)[name = string("transpose_40")]; + tensor reshape_4_cast_fp16 = reshape(shape = concat_15, x = transpose_3_cast_fp16)[name = string("reshape_4_cast_fp16")]; + bool matmul_1_transpose_x_1 = const()[name = string("matmul_1_transpose_x_1"), val = bool(true)]; + bool matmul_1_transpose_y_1 = const()[name = string("matmul_1_transpose_y_1"), val = bool(false)]; + tensor matmul_1_cast_fp16 = matmul(transpose_x = matmul_1_transpose_x_1, transpose_y = matmul_1_transpose_y_1, x = kv_cache_update_mask, y = reshape_4_cast_fp16)[name = string("matmul_1_cast_fp16")]; + tensor concat_19 = const()[name = string("concat_19"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_5_cast_fp16 = reshape(shape = concat_19, x = matmul_1_cast_fp16)[name = string("reshape_5_cast_fp16")]; + tensor value_scatter_1_perm_0 = const()[name = string("value_scatter_1_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_508_cast_fp16 = mul(x = var_367_cast_fp16_0, y = var_502_cast_fp16)[name = string("op_508_cast_fp16")]; + tensor key_scatter_1_cast_fp16 = transpose(perm = key_scatter_1_perm_0, x = reshape_2_cast_fp16)[name = string("transpose_39")]; + tensor key_3_cast_fp16 = add(x = var_508_cast_fp16, y = key_scatter_1_cast_fp16)[name = string("key_3_cast_fp16")]; + tensor var_510_cast_fp16 = mul(x = var_376_cast_fp16_0, y = var_502_cast_fp16)[name = string("op_510_cast_fp16")]; + tensor value_scatter_1_cast_fp16 = transpose(perm = value_scatter_1_perm_0, x = reshape_5_cast_fp16)[name = string("transpose_38")]; + tensor value_1_cast_fp16 = add(x = var_510_cast_fp16, y = value_scatter_1_cast_fp16)[name = string("value_1_cast_fp16")]; + fp16 var_516_to_fp16 = const()[name = string("op_516_to_fp16"), val = fp16(0x1p-3)]; + tensor var_517_cast_fp16 = mul(x = mh_q_3_cast_fp16, y = var_516_to_fp16)[name = string("op_517_cast_fp16")]; + tensor var_520 = const()[name = string("op_520"), val = tensor([1, 16, 64, 80])]; + tensor var_521_cast_fp16 = reshape(shape = var_520, x = key_3_cast_fp16)[name = string("op_521_cast_fp16")]; + bool mh_w_1_transpose_x_0 = const()[name = string("mh_w_1_transpose_x_0"), val = bool(true)]; + bool mh_w_1_transpose_y_0 = const()[name = string("mh_w_1_transpose_y_0"), val = bool(false)]; + tensor mh_w_1_cast_fp16 = matmul(transpose_x = mh_w_1_transpose_x_0, transpose_y = mh_w_1_transpose_y_0, x = var_517_cast_fp16, y = var_521_cast_fp16)[name = string("mh_w_1_cast_fp16")]; + tensor var_525_axes_0 = const()[name = string("op_525_axes_0"), val = tensor([1])]; + tensor var_525_cast_fp16 = expand_dims(axes = var_525_axes_0, x = key_padding_mask)[name = string("op_525_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([2])]; + tensor var_526_cast_fp16 = expand_dims(axes = var_526_axes_0, x = var_525_cast_fp16)[name = string("op_526_cast_fp16")]; + tensor mh_w_3_cast_fp16 = add(x = mh_w_1_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_3_cast_fp16")]; + tensor qk_mask_3_axes_0 = const()[name = string("qk_mask_3_axes_0"), val = tensor([1])]; + tensor qk_mask_3_cast_fp16 = expand_dims(axes = qk_mask_3_axes_0, x = qk_mask)[name = string("qk_mask_3_cast_fp16")]; + tensor mh_w_5_cast_fp16 = add(x = mh_w_3_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_5_cast_fp16")]; + tensor var_531_cast_fp16 = softmax(axis = var_338, x = mh_w_5_cast_fp16)[name = string("op_531_cast_fp16")]; + tensor var_532 = const()[name = string("op_532"), val = tensor([1, 16, 64, 80])]; + tensor var_533_cast_fp16 = reshape(shape = var_532, x = value_1_cast_fp16)[name = string("op_533_cast_fp16")]; + bool attn_1_transpose_x_0 = const()[name = string("attn_1_transpose_x_0"), val = bool(false)]; + bool attn_1_transpose_y_0 = const()[name = string("attn_1_transpose_y_0"), val = bool(true)]; + tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_533_cast_fp16, y = var_531_cast_fp16)[name = string("attn_1_cast_fp16")]; + tensor var_536 = const()[name = string("op_536"), val = tensor([1, -1, 1, 4])]; + tensor input_43_cast_fp16 = reshape(shape = var_536, x = attn_1_cast_fp16)[name = string("input_43_cast_fp16")]; + string obj_11_pad_type_0 = const()[name = string("obj_11_pad_type_0"), val = string("valid")]; + tensor obj_11_strides_0 = const()[name = string("obj_11_strides_0"), val = tensor([1, 1])]; + tensor obj_11_pad_0 = const()[name = string("obj_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_11_dilations_0 = const()[name = string("obj_11_dilations_0"), val = tensor([1, 1])]; + int32 obj_11_groups_0 = const()[name = string("obj_11_groups_0"), val = int32(1)]; + tensor op_552_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13390656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13915008))))[name = string("op_552_weight_0_to_fp16_palettized")]; + tensor var_552_bias_0_to_fp16 = const()[name = string("op_552_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13915584)))]; + tensor var_552_cast_fp16 = conv(bias = var_552_bias_0_to_fp16, dilations = obj_11_dilations_0, groups = obj_11_groups_0, pad = obj_11_pad_0, pad_type = obj_11_pad_type_0, strides = obj_11_strides_0, weight = op_552_weight_0_to_fp16_palettized, x = input_43_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor inputs_3_cast_fp16 = add(x = inputs_1_cast_fp16, y = var_552_cast_fp16)[name = string("inputs_3_cast_fp16")]; + tensor inputs_sq_3_cast_fp16 = mul(x = inputs_3_cast_fp16, y = inputs_3_cast_fp16)[name = string("inputs_sq_3_cast_fp16")]; + tensor variance_3_axes_0 = const()[name = string("variance_3_axes_0"), val = tensor([1])]; + bool variance_3_keep_dims_0 = const()[name = string("variance_3_keep_dims_0"), val = bool(true)]; + tensor variance_3_cast_fp16 = reduce_mean(axes = variance_3_axes_0, keep_dims = variance_3_keep_dims_0, x = inputs_sq_3_cast_fp16)[name = string("variance_3_cast_fp16")]; + fp16 var_558_to_fp16 = const()[name = string("op_558_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_559_cast_fp16 = add(x = variance_3_cast_fp16, y = var_558_to_fp16)[name = string("op_559_cast_fp16")]; + fp32 var_560_epsilon_0 = const()[name = string("op_560_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_560_cast_fp16 = rsqrt(epsilon = var_560_epsilon_0, x = var_559_cast_fp16)[name = string("op_560_cast_fp16")]; + tensor hidden_states_3_cast_fp16 = mul(x = inputs_3_cast_fp16, y = var_560_cast_fp16)[name = string("hidden_states_3_cast_fp16")]; + tensor w_3_to_fp16 = const()[name = string("w_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13916672)))]; + tensor input_45_cast_fp16 = mul(x = w_3_to_fp16, y = hidden_states_3_cast_fp16)[name = string("input_45_cast_fp16")]; + string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")]; + tensor input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor([1, 1])]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor([1, 1])]; + int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13917760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14442112))))[name = string("pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_47_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor gate_1_cast_fp16 = silu(x = input_47_cast_fp16)[name = string("gate_1_cast_fp16")]; + string up_1_pad_type_0 = const()[name = string("up_1_pad_type_0"), val = string("valid")]; + tensor up_1_strides_0 = const()[name = string("up_1_strides_0"), val = tensor([1, 1])]; + tensor up_1_pad_0 = const()[name = string("up_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_1_dilations_0 = const()[name = string("up_1_dilations_0"), val = tensor([1, 1])]; + int32 up_1_groups_0 = const()[name = string("up_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14442688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14967040))))[name = string("pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_1_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_1_dilations_0, groups = up_1_groups_0, pad = up_1_pad_0, pad_type = up_1_pad_type_0, strides = up_1_strides_0, weight = pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized, x = input_45_cast_fp16)[name = string("up_1_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = gate_1_cast_fp16, y = up_1_cast_fp16)[name = string("input_49_cast_fp16")]; + string hidden_states_5_pad_type_0 = const()[name = string("hidden_states_5_pad_type_0"), val = string("valid")]; + tensor hidden_states_5_strides_0 = const()[name = string("hidden_states_5_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_0 = const()[name = string("hidden_states_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_5_dilations_0 = const()[name = string("hidden_states_5_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_5_groups_0 = const()[name = string("hidden_states_5_groups_0"), val = int32(1)]; + tensor op_594_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14967616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15491968))))[name = string("op_594_weight_0_to_fp16_palettized")]; + tensor var_594_bias_0_to_fp16 = const()[name = string("op_594_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15492544)))]; + tensor var_594_cast_fp16 = conv(bias = var_594_bias_0_to_fp16, dilations = hidden_states_5_dilations_0, groups = hidden_states_5_groups_0, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = hidden_states_5_strides_0, weight = op_594_weight_0_to_fp16_palettized, x = input_49_cast_fp16)[name = string("op_594_cast_fp16")]; + tensor inputs_5_cast_fp16 = add(x = inputs_3_cast_fp16, y = var_594_cast_fp16)[name = string("inputs_5_cast_fp16")]; + tensor inputs_sq_5_cast_fp16 = mul(x = inputs_5_cast_fp16, y = inputs_5_cast_fp16)[name = string("inputs_sq_5_cast_fp16")]; + tensor variance_5_axes_0 = const()[name = string("variance_5_axes_0"), val = tensor([1])]; + bool variance_5_keep_dims_0 = const()[name = string("variance_5_keep_dims_0"), val = bool(true)]; + tensor variance_5_cast_fp16 = reduce_mean(axes = variance_5_axes_0, keep_dims = variance_5_keep_dims_0, x = inputs_sq_5_cast_fp16)[name = string("variance_5_cast_fp16")]; + fp16 var_610_to_fp16 = const()[name = string("op_610_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_611_cast_fp16 = add(x = variance_5_cast_fp16, y = var_610_to_fp16)[name = string("op_611_cast_fp16")]; + fp32 var_612_epsilon_0 = const()[name = string("op_612_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_612_cast_fp16 = rsqrt(epsilon = var_612_epsilon_0, x = var_611_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor hidden_states_7_cast_fp16 = mul(x = inputs_5_cast_fp16, y = var_612_cast_fp16)[name = string("hidden_states_7_cast_fp16")]; + tensor w_5_to_fp16 = const()[name = string("w_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15493632)))]; + tensor obj_17_cast_fp16 = mul(x = w_5_to_fp16, y = hidden_states_7_cast_fp16)[name = string("obj_17_cast_fp16")]; + string query_5_pad_type_0 = const()[name = string("query_5_pad_type_0"), val = string("valid")]; + tensor query_5_strides_0 = const()[name = string("query_5_strides_0"), val = tensor([1, 1])]; + tensor query_5_pad_0 = const()[name = string("query_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_5_dilations_0 = const()[name = string("query_5_dilations_0"), val = tensor([1, 1])]; + int32 query_5_groups_0 = const()[name = string("query_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15494720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16019072))))[name = string("pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_5_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_5_dilations_0, groups = query_5_groups_0, pad = query_5_pad_0, pad_type = query_5_pad_type_0, strides = query_5_strides_0, weight = pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized, x = obj_17_cast_fp16)[name = string("query_5_cast_fp16")]; + string key_5_pad_type_0 = const()[name = string("key_5_pad_type_0"), val = string("valid")]; + tensor key_5_strides_0 = const()[name = string("key_5_strides_0"), val = tensor([1, 1])]; + tensor key_5_pad_0 = const()[name = string("key_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_5_dilations_0 = const()[name = string("key_5_dilations_0"), val = tensor([1, 1])]; + int32 key_5_groups_0 = const()[name = string("key_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16019648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16544000))))[name = string("pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_5_cast_fp16 = conv(dilations = key_5_dilations_0, groups = key_5_groups_0, pad = key_5_pad_0, pad_type = key_5_pad_type_0, strides = key_5_strides_0, weight = pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized, x = obj_17_cast_fp16)[name = string("key_5_cast_fp16")]; + string obj_27_pad_type_0 = const()[name = string("obj_27_pad_type_0"), val = string("valid")]; + tensor obj_27_strides_0 = const()[name = string("obj_27_strides_0"), val = tensor([1, 1])]; + tensor obj_27_pad_0 = const()[name = string("obj_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_27_dilations_0 = const()[name = string("obj_27_dilations_0"), val = tensor([1, 1])]; + int32 obj_27_groups_0 = const()[name = string("obj_27_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16544576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17068928))))[name = string("pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_27_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_27_dilations_0, groups = obj_27_groups_0, pad = obj_27_pad_0, pad_type = obj_27_pad_type_0, strides = obj_27_strides_0, weight = pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized, x = obj_17_cast_fp16)[name = string("obj_27_cast_fp16")]; + tensor var_650 = const()[name = string("op_650"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_7_cast_fp16 = reshape(shape = var_650, x = query_5_cast_fp16)[name = string("mh_q_7_cast_fp16")]; + tensor var_652 = const()[name = string("op_652"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_5_cast_fp16 = reshape(shape = var_652, x = key_5_cast_fp16)[name = string("mh_k_5_cast_fp16")]; + tensor var_656_cast_fp16 = mul(x = mh_q_7_cast_fp16, y = cos_1_cast_fp16)[name = string("op_656_cast_fp16")]; + tensor var_661_begin_0 = const()[name = string("op_661_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_661_end_0 = const()[name = string("op_661_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_661_end_mask_0 = const()[name = string("op_661_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_661_cast_fp16 = slice_by_index(begin = var_661_begin_0, end = var_661_end_0, end_mask = var_661_end_mask_0, x = mh_q_7_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_667_begin_0 = const()[name = string("op_667_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_667_end_0 = const()[name = string("op_667_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_667_end_mask_0 = const()[name = string("op_667_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_667_cast_fp16 = slice_by_index(begin = var_667_begin_0, end = var_667_end_0, end_mask = var_667_end_mask_0, x = mh_q_7_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 const_52_promoted_to_fp16 = const()[name = string("const_52_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_669_cast_fp16 = mul(x = var_667_cast_fp16, y = const_52_promoted_to_fp16)[name = string("op_669_cast_fp16")]; + bool var_671_interleave_0 = const()[name = string("op_671_interleave_0"), val = bool(false)]; + tensor var_671_cast_fp16 = concat(axis = var_333, interleave = var_671_interleave_0, values = (var_669_cast_fp16, var_661_cast_fp16))[name = string("op_671_cast_fp16")]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = sin_1_cast_fp16)[name = string("op_672_cast_fp16")]; + tensor mh_q_9_cast_fp16 = add(x = var_656_cast_fp16, y = var_672_cast_fp16)[name = string("mh_q_9_cast_fp16")]; + tensor var_674_cast_fp16 = mul(x = mh_k_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_674_cast_fp16")]; + tensor var_679_begin_0 = const()[name = string("op_679_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_679_end_0 = const()[name = string("op_679_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_679_end_mask_0 = const()[name = string("op_679_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_679_cast_fp16 = slice_by_index(begin = var_679_begin_0, end = var_679_end_0, end_mask = var_679_end_mask_0, x = mh_k_5_cast_fp16)[name = string("op_679_cast_fp16")]; + tensor var_685_begin_0 = const()[name = string("op_685_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_685_end_0 = const()[name = string("op_685_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_685_end_mask_0 = const()[name = string("op_685_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_685_cast_fp16 = slice_by_index(begin = var_685_begin_0, end = var_685_end_0, end_mask = var_685_end_mask_0, x = mh_k_5_cast_fp16)[name = string("op_685_cast_fp16")]; + fp16 const_55_promoted_to_fp16 = const()[name = string("const_55_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_687_cast_fp16 = mul(x = var_685_cast_fp16, y = const_55_promoted_to_fp16)[name = string("op_687_cast_fp16")]; + bool var_689_interleave_0 = const()[name = string("op_689_interleave_0"), val = bool(false)]; + tensor var_689_cast_fp16 = concat(axis = var_333, interleave = var_689_interleave_0, values = (var_687_cast_fp16, var_679_cast_fp16))[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_689_cast_fp16, y = sin_1_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor mh_k_7_cast_fp16 = add(x = var_674_cast_fp16, y = var_690_cast_fp16)[name = string("mh_k_7_cast_fp16")]; + tensor var_694 = const()[name = string("op_694"), val = tensor([1, 1024, 1, 4])]; + tensor obj_25_cast_fp16 = reshape(shape = var_694, x = mh_k_7_cast_fp16)[name = string("obj_25_cast_fp16")]; + tensor transpose_5_perm_0 = const()[name = string("transpose_5_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_25 = const()[name = string("concat_25"), val = tensor([1, 4, 1024])]; + tensor transpose_5_cast_fp16 = transpose(perm = transpose_5_perm_0, x = obj_25_cast_fp16)[name = string("transpose_37")]; + tensor reshape_7_cast_fp16 = reshape(shape = concat_25, x = transpose_5_cast_fp16)[name = string("reshape_7_cast_fp16")]; + bool matmul_2_transpose_x_1 = const()[name = string("matmul_2_transpose_x_1"), val = bool(true)]; + bool matmul_2_transpose_y_1 = const()[name = string("matmul_2_transpose_y_1"), val = bool(false)]; + tensor matmul_2_cast_fp16 = matmul(transpose_x = matmul_2_transpose_x_1, transpose_y = matmul_2_transpose_y_1, x = kv_cache_update_mask, y = reshape_7_cast_fp16)[name = string("matmul_2_cast_fp16")]; + tensor concat_29 = const()[name = string("concat_29"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_8_cast_fp16 = reshape(shape = concat_29, x = matmul_2_cast_fp16)[name = string("reshape_8_cast_fp16")]; + tensor key_scatter_3_perm_0 = const()[name = string("key_scatter_3_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_7_perm_0 = const()[name = string("transpose_7_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_35 = const()[name = string("concat_35"), val = tensor([1, 4, 1024])]; + tensor transpose_7_cast_fp16 = transpose(perm = transpose_7_perm_0, x = obj_27_cast_fp16)[name = string("transpose_36")]; + tensor reshape_10_cast_fp16 = reshape(shape = concat_35, x = transpose_7_cast_fp16)[name = string("reshape_10_cast_fp16")]; + bool matmul_3_transpose_x_1 = const()[name = string("matmul_3_transpose_x_1"), val = bool(true)]; + bool matmul_3_transpose_y_1 = const()[name = string("matmul_3_transpose_y_1"), val = bool(false)]; + tensor matmul_3_cast_fp16 = matmul(transpose_x = matmul_3_transpose_x_1, transpose_y = matmul_3_transpose_y_1, x = kv_cache_update_mask, y = reshape_10_cast_fp16)[name = string("matmul_3_cast_fp16")]; + tensor concat_39 = const()[name = string("concat_39"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_11_cast_fp16 = reshape(shape = concat_39, x = matmul_3_cast_fp16)[name = string("reshape_11_cast_fp16")]; + tensor value_scatter_3_perm_0 = const()[name = string("value_scatter_3_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_707_cast_fp16 = mul(x = var_367_cast_fp16_1, y = var_502_cast_fp16)[name = string("op_707_cast_fp16")]; + tensor key_scatter_3_cast_fp16 = transpose(perm = key_scatter_3_perm_0, x = reshape_8_cast_fp16)[name = string("transpose_35")]; + tensor key_7_cast_fp16 = add(x = var_707_cast_fp16, y = key_scatter_3_cast_fp16)[name = string("key_7_cast_fp16")]; + tensor var_709_cast_fp16 = mul(x = var_376_cast_fp16_1, y = var_502_cast_fp16)[name = string("op_709_cast_fp16")]; + tensor value_scatter_3_cast_fp16 = transpose(perm = value_scatter_3_perm_0, x = reshape_11_cast_fp16)[name = string("transpose_34")]; + tensor value_3_cast_fp16 = add(x = var_709_cast_fp16, y = value_scatter_3_cast_fp16)[name = string("value_3_cast_fp16")]; + fp16 var_715_to_fp16 = const()[name = string("op_715_to_fp16"), val = fp16(0x1p-3)]; + tensor var_716_cast_fp16 = mul(x = mh_q_9_cast_fp16, y = var_715_to_fp16)[name = string("op_716_cast_fp16")]; + tensor var_719 = const()[name = string("op_719"), val = tensor([1, 16, 64, 80])]; + tensor var_720_cast_fp16 = reshape(shape = var_719, x = key_7_cast_fp16)[name = string("op_720_cast_fp16")]; + bool mh_w_7_transpose_x_0 = const()[name = string("mh_w_7_transpose_x_0"), val = bool(true)]; + bool mh_w_7_transpose_y_0 = const()[name = string("mh_w_7_transpose_y_0"), val = bool(false)]; + tensor mh_w_7_cast_fp16 = matmul(transpose_x = mh_w_7_transpose_x_0, transpose_y = mh_w_7_transpose_y_0, x = var_716_cast_fp16, y = var_720_cast_fp16)[name = string("mh_w_7_cast_fp16")]; + tensor mh_w_9_cast_fp16 = add(x = mh_w_7_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_9_cast_fp16")]; + tensor mh_w_11_cast_fp16 = add(x = mh_w_9_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_11_cast_fp16")]; + tensor var_730_cast_fp16 = softmax(axis = var_338, x = mh_w_11_cast_fp16)[name = string("op_730_cast_fp16")]; + tensor var_731 = const()[name = string("op_731"), val = tensor([1, 16, 64, 80])]; + tensor var_732_cast_fp16 = reshape(shape = var_731, x = value_3_cast_fp16)[name = string("op_732_cast_fp16")]; + bool attn_3_transpose_x_0 = const()[name = string("attn_3_transpose_x_0"), val = bool(false)]; + bool attn_3_transpose_y_0 = const()[name = string("attn_3_transpose_y_0"), val = bool(true)]; + tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_732_cast_fp16, y = var_730_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_735 = const()[name = string("op_735"), val = tensor([1, -1, 1, 4])]; + tensor input_51_cast_fp16 = reshape(shape = var_735, x = attn_3_cast_fp16)[name = string("input_51_cast_fp16")]; + string obj_23_pad_type_0 = const()[name = string("obj_23_pad_type_0"), val = string("valid")]; + tensor obj_23_strides_0 = const()[name = string("obj_23_strides_0"), val = tensor([1, 1])]; + tensor obj_23_pad_0 = const()[name = string("obj_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_23_dilations_0 = const()[name = string("obj_23_dilations_0"), val = tensor([1, 1])]; + int32 obj_23_groups_0 = const()[name = string("obj_23_groups_0"), val = int32(1)]; + tensor op_751_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17069504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17593856))))[name = string("op_751_weight_0_to_fp16_palettized")]; + tensor var_751_bias_0_to_fp16 = const()[name = string("op_751_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17594432)))]; + tensor var_751_cast_fp16 = conv(bias = var_751_bias_0_to_fp16, dilations = obj_23_dilations_0, groups = obj_23_groups_0, pad = obj_23_pad_0, pad_type = obj_23_pad_type_0, strides = obj_23_strides_0, weight = op_751_weight_0_to_fp16_palettized, x = input_51_cast_fp16)[name = string("op_751_cast_fp16")]; + tensor inputs_7_cast_fp16 = add(x = inputs_5_cast_fp16, y = var_751_cast_fp16)[name = string("inputs_7_cast_fp16")]; + tensor inputs_sq_7_cast_fp16 = mul(x = inputs_7_cast_fp16, y = inputs_7_cast_fp16)[name = string("inputs_sq_7_cast_fp16")]; + tensor variance_7_axes_0 = const()[name = string("variance_7_axes_0"), val = tensor([1])]; + bool variance_7_keep_dims_0 = const()[name = string("variance_7_keep_dims_0"), val = bool(true)]; + tensor variance_7_cast_fp16 = reduce_mean(axes = variance_7_axes_0, keep_dims = variance_7_keep_dims_0, x = inputs_sq_7_cast_fp16)[name = string("variance_7_cast_fp16")]; + fp16 var_757_to_fp16 = const()[name = string("op_757_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_758_cast_fp16 = add(x = variance_7_cast_fp16, y = var_757_to_fp16)[name = string("op_758_cast_fp16")]; + fp32 var_759_epsilon_0 = const()[name = string("op_759_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_759_cast_fp16 = rsqrt(epsilon = var_759_epsilon_0, x = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor hidden_states_9_cast_fp16 = mul(x = inputs_7_cast_fp16, y = var_759_cast_fp16)[name = string("hidden_states_9_cast_fp16")]; + tensor w_7_to_fp16 = const()[name = string("w_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17595520)))]; + tensor input_53_cast_fp16 = mul(x = w_7_to_fp16, y = hidden_states_9_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("valid")]; + tensor input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor([1, 1])]; + tensor input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor([1, 1])]; + int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17596608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18120960))))[name = string("pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_55_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor gate_3_cast_fp16 = silu(x = input_55_cast_fp16)[name = string("gate_3_cast_fp16")]; + string up_3_pad_type_0 = const()[name = string("up_3_pad_type_0"), val = string("valid")]; + tensor up_3_strides_0 = const()[name = string("up_3_strides_0"), val = tensor([1, 1])]; + tensor up_3_pad_0 = const()[name = string("up_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_3_dilations_0 = const()[name = string("up_3_dilations_0"), val = tensor([1, 1])]; + int32 up_3_groups_0 = const()[name = string("up_3_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18121536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18645888))))[name = string("pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_3_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_3_dilations_0, groups = up_3_groups_0, pad = up_3_pad_0, pad_type = up_3_pad_type_0, strides = up_3_strides_0, weight = pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized, x = input_53_cast_fp16)[name = string("up_3_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = gate_3_cast_fp16, y = up_3_cast_fp16)[name = string("input_57_cast_fp16")]; + string hidden_states_11_pad_type_0 = const()[name = string("hidden_states_11_pad_type_0"), val = string("valid")]; + tensor hidden_states_11_strides_0 = const()[name = string("hidden_states_11_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_0 = const()[name = string("hidden_states_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_11_dilations_0 = const()[name = string("hidden_states_11_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_11_groups_0 = const()[name = string("hidden_states_11_groups_0"), val = int32(1)]; + tensor op_793_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18646464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19170816))))[name = string("op_793_weight_0_to_fp16_palettized")]; + tensor var_793_bias_0_to_fp16 = const()[name = string("op_793_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19171392)))]; + tensor var_793_cast_fp16 = conv(bias = var_793_bias_0_to_fp16, dilations = hidden_states_11_dilations_0, groups = hidden_states_11_groups_0, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = hidden_states_11_strides_0, weight = op_793_weight_0_to_fp16_palettized, x = input_57_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor inputs_9_cast_fp16 = add(x = inputs_7_cast_fp16, y = var_793_cast_fp16)[name = string("inputs_9_cast_fp16")]; + tensor inputs_sq_9_cast_fp16 = mul(x = inputs_9_cast_fp16, y = inputs_9_cast_fp16)[name = string("inputs_sq_9_cast_fp16")]; + tensor variance_9_axes_0 = const()[name = string("variance_9_axes_0"), val = tensor([1])]; + bool variance_9_keep_dims_0 = const()[name = string("variance_9_keep_dims_0"), val = bool(true)]; + tensor variance_9_cast_fp16 = reduce_mean(axes = variance_9_axes_0, keep_dims = variance_9_keep_dims_0, x = inputs_sq_9_cast_fp16)[name = string("variance_9_cast_fp16")]; + fp16 var_809_to_fp16 = const()[name = string("op_809_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_810_cast_fp16 = add(x = variance_9_cast_fp16, y = var_809_to_fp16)[name = string("op_810_cast_fp16")]; + fp32 var_811_epsilon_0 = const()[name = string("op_811_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_811_cast_fp16 = rsqrt(epsilon = var_811_epsilon_0, x = var_810_cast_fp16)[name = string("op_811_cast_fp16")]; + tensor hidden_states_13_cast_fp16 = mul(x = inputs_9_cast_fp16, y = var_811_cast_fp16)[name = string("hidden_states_13_cast_fp16")]; + tensor w_9_to_fp16 = const()[name = string("w_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19172480)))]; + tensor obj_29_cast_fp16 = mul(x = w_9_to_fp16, y = hidden_states_13_cast_fp16)[name = string("obj_29_cast_fp16")]; + string query_9_pad_type_0 = const()[name = string("query_9_pad_type_0"), val = string("valid")]; + tensor query_9_strides_0 = const()[name = string("query_9_strides_0"), val = tensor([1, 1])]; + tensor query_9_pad_0 = const()[name = string("query_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_9_dilations_0 = const()[name = string("query_9_dilations_0"), val = tensor([1, 1])]; + int32 query_9_groups_0 = const()[name = string("query_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19173568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19697920))))[name = string("pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_9_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_9_dilations_0, groups = query_9_groups_0, pad = query_9_pad_0, pad_type = query_9_pad_type_0, strides = query_9_strides_0, weight = pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized, x = obj_29_cast_fp16)[name = string("query_9_cast_fp16")]; + string key_9_pad_type_0 = const()[name = string("key_9_pad_type_0"), val = string("valid")]; + tensor key_9_strides_0 = const()[name = string("key_9_strides_0"), val = tensor([1, 1])]; + tensor key_9_pad_0 = const()[name = string("key_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_9_dilations_0 = const()[name = string("key_9_dilations_0"), val = tensor([1, 1])]; + int32 key_9_groups_0 = const()[name = string("key_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19698496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20222848))))[name = string("pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_9_cast_fp16 = conv(dilations = key_9_dilations_0, groups = key_9_groups_0, pad = key_9_pad_0, pad_type = key_9_pad_type_0, strides = key_9_strides_0, weight = pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized, x = obj_29_cast_fp16)[name = string("key_9_cast_fp16")]; + string obj_39_pad_type_0 = const()[name = string("obj_39_pad_type_0"), val = string("valid")]; + tensor obj_39_strides_0 = const()[name = string("obj_39_strides_0"), val = tensor([1, 1])]; + tensor obj_39_pad_0 = const()[name = string("obj_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_39_dilations_0 = const()[name = string("obj_39_dilations_0"), val = tensor([1, 1])]; + int32 obj_39_groups_0 = const()[name = string("obj_39_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20223424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20747776))))[name = string("pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_39_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_39_dilations_0, groups = obj_39_groups_0, pad = obj_39_pad_0, pad_type = obj_39_pad_type_0, strides = obj_39_strides_0, weight = pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized, x = obj_29_cast_fp16)[name = string("obj_39_cast_fp16")]; + tensor var_849 = const()[name = string("op_849"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_13_cast_fp16 = reshape(shape = var_849, x = query_9_cast_fp16)[name = string("mh_q_13_cast_fp16")]; + tensor var_851 = const()[name = string("op_851"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_9_cast_fp16 = reshape(shape = var_851, x = key_9_cast_fp16)[name = string("mh_k_9_cast_fp16")]; + tensor var_855_cast_fp16 = mul(x = mh_q_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_855_cast_fp16")]; + tensor var_860_begin_0 = const()[name = string("op_860_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_860_end_0 = const()[name = string("op_860_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_860_end_mask_0 = const()[name = string("op_860_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_860_cast_fp16 = slice_by_index(begin = var_860_begin_0, end = var_860_end_0, end_mask = var_860_end_mask_0, x = mh_q_13_cast_fp16)[name = string("op_860_cast_fp16")]; + tensor var_866_begin_0 = const()[name = string("op_866_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_866_end_0 = const()[name = string("op_866_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_866_end_mask_0 = const()[name = string("op_866_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_866_cast_fp16 = slice_by_index(begin = var_866_begin_0, end = var_866_end_0, end_mask = var_866_end_mask_0, x = mh_q_13_cast_fp16)[name = string("op_866_cast_fp16")]; + fp16 const_71_promoted_to_fp16 = const()[name = string("const_71_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_868_cast_fp16 = mul(x = var_866_cast_fp16, y = const_71_promoted_to_fp16)[name = string("op_868_cast_fp16")]; + bool var_870_interleave_0 = const()[name = string("op_870_interleave_0"), val = bool(false)]; + tensor var_870_cast_fp16 = concat(axis = var_333, interleave = var_870_interleave_0, values = (var_868_cast_fp16, var_860_cast_fp16))[name = string("op_870_cast_fp16")]; + tensor var_871_cast_fp16 = mul(x = var_870_cast_fp16, y = sin_1_cast_fp16)[name = string("op_871_cast_fp16")]; + tensor mh_q_15_cast_fp16 = add(x = var_855_cast_fp16, y = var_871_cast_fp16)[name = string("mh_q_15_cast_fp16")]; + tensor var_873_cast_fp16 = mul(x = mh_k_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_873_cast_fp16")]; + tensor var_878_begin_0 = const()[name = string("op_878_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_878_end_0 = const()[name = string("op_878_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_878_end_mask_0 = const()[name = string("op_878_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_878_cast_fp16 = slice_by_index(begin = var_878_begin_0, end = var_878_end_0, end_mask = var_878_end_mask_0, x = mh_k_9_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor var_884_begin_0 = const()[name = string("op_884_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_884_end_0 = const()[name = string("op_884_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_884_end_mask_0 = const()[name = string("op_884_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_884_cast_fp16 = slice_by_index(begin = var_884_begin_0, end = var_884_end_0, end_mask = var_884_end_mask_0, x = mh_k_9_cast_fp16)[name = string("op_884_cast_fp16")]; + fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_886_cast_fp16 = mul(x = var_884_cast_fp16, y = const_74_promoted_to_fp16)[name = string("op_886_cast_fp16")]; + bool var_888_interleave_0 = const()[name = string("op_888_interleave_0"), val = bool(false)]; + tensor var_888_cast_fp16 = concat(axis = var_333, interleave = var_888_interleave_0, values = (var_886_cast_fp16, var_878_cast_fp16))[name = string("op_888_cast_fp16")]; + tensor var_889_cast_fp16 = mul(x = var_888_cast_fp16, y = sin_1_cast_fp16)[name = string("op_889_cast_fp16")]; + tensor mh_k_11_cast_fp16 = add(x = var_873_cast_fp16, y = var_889_cast_fp16)[name = string("mh_k_11_cast_fp16")]; + tensor var_893 = const()[name = string("op_893"), val = tensor([1, 1024, 1, 4])]; + tensor obj_37_cast_fp16 = reshape(shape = var_893, x = mh_k_11_cast_fp16)[name = string("obj_37_cast_fp16")]; + tensor transpose_9_perm_0 = const()[name = string("transpose_9_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_45 = const()[name = string("concat_45"), val = tensor([1, 4, 1024])]; + tensor transpose_9_cast_fp16 = transpose(perm = transpose_9_perm_0, x = obj_37_cast_fp16)[name = string("transpose_33")]; + tensor reshape_13_cast_fp16 = reshape(shape = concat_45, x = transpose_9_cast_fp16)[name = string("reshape_13_cast_fp16")]; + bool matmul_4_transpose_x_1 = const()[name = string("matmul_4_transpose_x_1"), val = bool(true)]; + bool matmul_4_transpose_y_1 = const()[name = string("matmul_4_transpose_y_1"), val = bool(false)]; + tensor matmul_4_cast_fp16 = matmul(transpose_x = matmul_4_transpose_x_1, transpose_y = matmul_4_transpose_y_1, x = kv_cache_update_mask, y = reshape_13_cast_fp16)[name = string("matmul_4_cast_fp16")]; + tensor concat_49 = const()[name = string("concat_49"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_14_cast_fp16 = reshape(shape = concat_49, x = matmul_4_cast_fp16)[name = string("reshape_14_cast_fp16")]; + tensor key_scatter_5_perm_0 = const()[name = string("key_scatter_5_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_11_perm_0 = const()[name = string("transpose_11_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_55 = const()[name = string("concat_55"), val = tensor([1, 4, 1024])]; + tensor transpose_11_cast_fp16 = transpose(perm = transpose_11_perm_0, x = obj_39_cast_fp16)[name = string("transpose_32")]; + tensor reshape_16_cast_fp16 = reshape(shape = concat_55, x = transpose_11_cast_fp16)[name = string("reshape_16_cast_fp16")]; + bool matmul_5_transpose_x_1 = const()[name = string("matmul_5_transpose_x_1"), val = bool(true)]; + bool matmul_5_transpose_y_1 = const()[name = string("matmul_5_transpose_y_1"), val = bool(false)]; + tensor matmul_5_cast_fp16 = matmul(transpose_x = matmul_5_transpose_x_1, transpose_y = matmul_5_transpose_y_1, x = kv_cache_update_mask, y = reshape_16_cast_fp16)[name = string("matmul_5_cast_fp16")]; + tensor concat_59 = const()[name = string("concat_59"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_17_cast_fp16 = reshape(shape = concat_59, x = matmul_5_cast_fp16)[name = string("reshape_17_cast_fp16")]; + tensor value_scatter_5_perm_0 = const()[name = string("value_scatter_5_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_906_cast_fp16 = mul(x = var_367_cast_fp16_2, y = var_502_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor key_scatter_5_cast_fp16 = transpose(perm = key_scatter_5_perm_0, x = reshape_14_cast_fp16)[name = string("transpose_31")]; + tensor key_11_cast_fp16 = add(x = var_906_cast_fp16, y = key_scatter_5_cast_fp16)[name = string("key_11_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_376_cast_fp16_2, y = var_502_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor value_scatter_5_cast_fp16 = transpose(perm = value_scatter_5_perm_0, x = reshape_17_cast_fp16)[name = string("transpose_30")]; + tensor value_5_cast_fp16 = add(x = var_908_cast_fp16, y = value_scatter_5_cast_fp16)[name = string("value_5_cast_fp16")]; + fp16 var_914_to_fp16 = const()[name = string("op_914_to_fp16"), val = fp16(0x1p-3)]; + tensor var_915_cast_fp16 = mul(x = mh_q_15_cast_fp16, y = var_914_to_fp16)[name = string("op_915_cast_fp16")]; + tensor var_918 = const()[name = string("op_918"), val = tensor([1, 16, 64, 80])]; + tensor var_919_cast_fp16 = reshape(shape = var_918, x = key_11_cast_fp16)[name = string("op_919_cast_fp16")]; + bool mh_w_13_transpose_x_0 = const()[name = string("mh_w_13_transpose_x_0"), val = bool(true)]; + bool mh_w_13_transpose_y_0 = const()[name = string("mh_w_13_transpose_y_0"), val = bool(false)]; + tensor mh_w_13_cast_fp16 = matmul(transpose_x = mh_w_13_transpose_x_0, transpose_y = mh_w_13_transpose_y_0, x = var_915_cast_fp16, y = var_919_cast_fp16)[name = string("mh_w_13_cast_fp16")]; + tensor mh_w_15_cast_fp16 = add(x = mh_w_13_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_15_cast_fp16")]; + tensor mh_w_17_cast_fp16 = add(x = mh_w_15_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_17_cast_fp16")]; + tensor var_929_cast_fp16 = softmax(axis = var_338, x = mh_w_17_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930 = const()[name = string("op_930"), val = tensor([1, 16, 64, 80])]; + tensor var_931_cast_fp16 = reshape(shape = var_930, x = value_5_cast_fp16)[name = string("op_931_cast_fp16")]; + bool attn_5_transpose_x_0 = const()[name = string("attn_5_transpose_x_0"), val = bool(false)]; + bool attn_5_transpose_y_0 = const()[name = string("attn_5_transpose_y_0"), val = bool(true)]; + tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_931_cast_fp16, y = var_929_cast_fp16)[name = string("attn_5_cast_fp16")]; + tensor var_934 = const()[name = string("op_934"), val = tensor([1, -1, 1, 4])]; + tensor input_59_cast_fp16 = reshape(shape = var_934, x = attn_5_cast_fp16)[name = string("input_59_cast_fp16")]; + string obj_35_pad_type_0 = const()[name = string("obj_35_pad_type_0"), val = string("valid")]; + tensor obj_35_strides_0 = const()[name = string("obj_35_strides_0"), val = tensor([1, 1])]; + tensor obj_35_pad_0 = const()[name = string("obj_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_35_dilations_0 = const()[name = string("obj_35_dilations_0"), val = tensor([1, 1])]; + int32 obj_35_groups_0 = const()[name = string("obj_35_groups_0"), val = int32(1)]; + tensor op_950_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20748352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21272704))))[name = string("op_950_weight_0_to_fp16_palettized")]; + tensor var_950_bias_0_to_fp16 = const()[name = string("op_950_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21273280)))]; + tensor var_950_cast_fp16 = conv(bias = var_950_bias_0_to_fp16, dilations = obj_35_dilations_0, groups = obj_35_groups_0, pad = obj_35_pad_0, pad_type = obj_35_pad_type_0, strides = obj_35_strides_0, weight = op_950_weight_0_to_fp16_palettized, x = input_59_cast_fp16)[name = string("op_950_cast_fp16")]; + tensor inputs_11_cast_fp16 = add(x = inputs_9_cast_fp16, y = var_950_cast_fp16)[name = string("inputs_11_cast_fp16")]; + tensor inputs_sq_11_cast_fp16 = mul(x = inputs_11_cast_fp16, y = inputs_11_cast_fp16)[name = string("inputs_sq_11_cast_fp16")]; + tensor variance_11_axes_0 = const()[name = string("variance_11_axes_0"), val = tensor([1])]; + bool variance_11_keep_dims_0 = const()[name = string("variance_11_keep_dims_0"), val = bool(true)]; + tensor variance_11_cast_fp16 = reduce_mean(axes = variance_11_axes_0, keep_dims = variance_11_keep_dims_0, x = inputs_sq_11_cast_fp16)[name = string("variance_11_cast_fp16")]; + fp16 var_956_to_fp16 = const()[name = string("op_956_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_957_cast_fp16 = add(x = variance_11_cast_fp16, y = var_956_to_fp16)[name = string("op_957_cast_fp16")]; + fp32 var_958_epsilon_0 = const()[name = string("op_958_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_958_cast_fp16 = rsqrt(epsilon = var_958_epsilon_0, x = var_957_cast_fp16)[name = string("op_958_cast_fp16")]; + tensor hidden_states_15_cast_fp16 = mul(x = inputs_11_cast_fp16, y = var_958_cast_fp16)[name = string("hidden_states_15_cast_fp16")]; + tensor w_11_to_fp16 = const()[name = string("w_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21274368)))]; + tensor input_61_cast_fp16 = mul(x = w_11_to_fp16, y = hidden_states_15_cast_fp16)[name = string("input_61_cast_fp16")]; + string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("valid")]; + tensor input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor([1, 1])]; + tensor input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor([1, 1])]; + int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21275456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21799808))))[name = string("pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_63_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor gate_5_cast_fp16 = silu(x = input_63_cast_fp16)[name = string("gate_5_cast_fp16")]; + string up_5_pad_type_0 = const()[name = string("up_5_pad_type_0"), val = string("valid")]; + tensor up_5_strides_0 = const()[name = string("up_5_strides_0"), val = tensor([1, 1])]; + tensor up_5_pad_0 = const()[name = string("up_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_5_dilations_0 = const()[name = string("up_5_dilations_0"), val = tensor([1, 1])]; + int32 up_5_groups_0 = const()[name = string("up_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21800384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22324736))))[name = string("pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_5_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_5_dilations_0, groups = up_5_groups_0, pad = up_5_pad_0, pad_type = up_5_pad_type_0, strides = up_5_strides_0, weight = pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("up_5_cast_fp16")]; + tensor input_65_cast_fp16 = mul(x = gate_5_cast_fp16, y = up_5_cast_fp16)[name = string("input_65_cast_fp16")]; + string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")]; + tensor hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)]; + tensor op_992_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22325312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22849664))))[name = string("op_992_weight_0_to_fp16_palettized")]; + tensor var_992_bias_0_to_fp16 = const()[name = string("op_992_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22850240)))]; + tensor var_992_cast_fp16 = conv(bias = var_992_bias_0_to_fp16, dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = op_992_weight_0_to_fp16_palettized, x = input_65_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor inputs_13_cast_fp16 = add(x = inputs_11_cast_fp16, y = var_992_cast_fp16)[name = string("inputs_13_cast_fp16")]; + tensor inputs_sq_13_cast_fp16 = mul(x = inputs_13_cast_fp16, y = inputs_13_cast_fp16)[name = string("inputs_sq_13_cast_fp16")]; + tensor variance_13_axes_0 = const()[name = string("variance_13_axes_0"), val = tensor([1])]; + bool variance_13_keep_dims_0 = const()[name = string("variance_13_keep_dims_0"), val = bool(true)]; + tensor variance_13_cast_fp16 = reduce_mean(axes = variance_13_axes_0, keep_dims = variance_13_keep_dims_0, x = inputs_sq_13_cast_fp16)[name = string("variance_13_cast_fp16")]; + fp16 var_1008_to_fp16 = const()[name = string("op_1008_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1009_cast_fp16 = add(x = variance_13_cast_fp16, y = var_1008_to_fp16)[name = string("op_1009_cast_fp16")]; + fp32 var_1010_epsilon_0 = const()[name = string("op_1010_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1010_cast_fp16 = rsqrt(epsilon = var_1010_epsilon_0, x = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor hidden_states_19_cast_fp16 = mul(x = inputs_13_cast_fp16, y = var_1010_cast_fp16)[name = string("hidden_states_19_cast_fp16")]; + tensor w_13_to_fp16 = const()[name = string("w_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22851328)))]; + tensor obj_41_cast_fp16 = mul(x = w_13_to_fp16, y = hidden_states_19_cast_fp16)[name = string("obj_41_cast_fp16")]; + string query_13_pad_type_0 = const()[name = string("query_13_pad_type_0"), val = string("valid")]; + tensor query_13_strides_0 = const()[name = string("query_13_strides_0"), val = tensor([1, 1])]; + tensor query_13_pad_0 = const()[name = string("query_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_13_dilations_0 = const()[name = string("query_13_dilations_0"), val = tensor([1, 1])]; + int32 query_13_groups_0 = const()[name = string("query_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22852416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23376768))))[name = string("pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_13_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_13_dilations_0, groups = query_13_groups_0, pad = query_13_pad_0, pad_type = query_13_pad_type_0, strides = query_13_strides_0, weight = pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized, x = obj_41_cast_fp16)[name = string("query_13_cast_fp16")]; + string key_13_pad_type_0 = const()[name = string("key_13_pad_type_0"), val = string("valid")]; + tensor key_13_strides_0 = const()[name = string("key_13_strides_0"), val = tensor([1, 1])]; + tensor key_13_pad_0 = const()[name = string("key_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_13_dilations_0 = const()[name = string("key_13_dilations_0"), val = tensor([1, 1])]; + int32 key_13_groups_0 = const()[name = string("key_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23377344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23901696))))[name = string("pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_13_cast_fp16 = conv(dilations = key_13_dilations_0, groups = key_13_groups_0, pad = key_13_pad_0, pad_type = key_13_pad_type_0, strides = key_13_strides_0, weight = pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized, x = obj_41_cast_fp16)[name = string("key_13_cast_fp16")]; + string obj_51_pad_type_0 = const()[name = string("obj_51_pad_type_0"), val = string("valid")]; + tensor obj_51_strides_0 = const()[name = string("obj_51_strides_0"), val = tensor([1, 1])]; + tensor obj_51_pad_0 = const()[name = string("obj_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_51_dilations_0 = const()[name = string("obj_51_dilations_0"), val = tensor([1, 1])]; + int32 obj_51_groups_0 = const()[name = string("obj_51_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23902272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24426624))))[name = string("pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_51_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_51_dilations_0, groups = obj_51_groups_0, pad = obj_51_pad_0, pad_type = obj_51_pad_type_0, strides = obj_51_strides_0, weight = pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized, x = obj_41_cast_fp16)[name = string("obj_51_cast_fp16")]; + tensor var_1048 = const()[name = string("op_1048"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_19_cast_fp16 = reshape(shape = var_1048, x = query_13_cast_fp16)[name = string("mh_q_19_cast_fp16")]; + tensor var_1050 = const()[name = string("op_1050"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_13_cast_fp16 = reshape(shape = var_1050, x = key_13_cast_fp16)[name = string("mh_k_13_cast_fp16")]; + tensor var_1054_cast_fp16 = mul(x = mh_q_19_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1059_begin_0 = const()[name = string("op_1059_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1059_end_0 = const()[name = string("op_1059_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1059_end_mask_0 = const()[name = string("op_1059_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1059_cast_fp16 = slice_by_index(begin = var_1059_begin_0, end = var_1059_end_0, end_mask = var_1059_end_mask_0, x = mh_q_19_cast_fp16)[name = string("op_1059_cast_fp16")]; + tensor var_1065_begin_0 = const()[name = string("op_1065_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1065_end_0 = const()[name = string("op_1065_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1065_end_mask_0 = const()[name = string("op_1065_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1065_cast_fp16 = slice_by_index(begin = var_1065_begin_0, end = var_1065_end_0, end_mask = var_1065_end_mask_0, x = mh_q_19_cast_fp16)[name = string("op_1065_cast_fp16")]; + fp16 const_90_promoted_to_fp16 = const()[name = string("const_90_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1067_cast_fp16 = mul(x = var_1065_cast_fp16, y = const_90_promoted_to_fp16)[name = string("op_1067_cast_fp16")]; + bool var_1069_interleave_0 = const()[name = string("op_1069_interleave_0"), val = bool(false)]; + tensor var_1069_cast_fp16 = concat(axis = var_333, interleave = var_1069_interleave_0, values = (var_1067_cast_fp16, var_1059_cast_fp16))[name = string("op_1069_cast_fp16")]; + tensor var_1070_cast_fp16 = mul(x = var_1069_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1070_cast_fp16")]; + tensor mh_q_21_cast_fp16 = add(x = var_1054_cast_fp16, y = var_1070_cast_fp16)[name = string("mh_q_21_cast_fp16")]; + tensor var_1072_cast_fp16 = mul(x = mh_k_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1072_cast_fp16")]; + tensor var_1077_begin_0 = const()[name = string("op_1077_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1077_end_0 = const()[name = string("op_1077_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1077_end_mask_0 = const()[name = string("op_1077_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1077_cast_fp16 = slice_by_index(begin = var_1077_begin_0, end = var_1077_end_0, end_mask = var_1077_end_mask_0, x = mh_k_13_cast_fp16)[name = string("op_1077_cast_fp16")]; + tensor var_1083_begin_0 = const()[name = string("op_1083_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1083_end_0 = const()[name = string("op_1083_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1083_end_mask_0 = const()[name = string("op_1083_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1083_cast_fp16 = slice_by_index(begin = var_1083_begin_0, end = var_1083_end_0, end_mask = var_1083_end_mask_0, x = mh_k_13_cast_fp16)[name = string("op_1083_cast_fp16")]; + fp16 const_93_promoted_to_fp16 = const()[name = string("const_93_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1085_cast_fp16 = mul(x = var_1083_cast_fp16, y = const_93_promoted_to_fp16)[name = string("op_1085_cast_fp16")]; + bool var_1087_interleave_0 = const()[name = string("op_1087_interleave_0"), val = bool(false)]; + tensor var_1087_cast_fp16 = concat(axis = var_333, interleave = var_1087_interleave_0, values = (var_1085_cast_fp16, var_1077_cast_fp16))[name = string("op_1087_cast_fp16")]; + tensor var_1088_cast_fp16 = mul(x = var_1087_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1088_cast_fp16")]; + tensor mh_k_15_cast_fp16 = add(x = var_1072_cast_fp16, y = var_1088_cast_fp16)[name = string("mh_k_15_cast_fp16")]; + tensor var_1092 = const()[name = string("op_1092"), val = tensor([1, 1024, 1, 4])]; + tensor obj_49_cast_fp16 = reshape(shape = var_1092, x = mh_k_15_cast_fp16)[name = string("obj_49_cast_fp16")]; + tensor transpose_13_perm_0 = const()[name = string("transpose_13_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_65 = const()[name = string("concat_65"), val = tensor([1, 4, 1024])]; + tensor transpose_13_cast_fp16 = transpose(perm = transpose_13_perm_0, x = obj_49_cast_fp16)[name = string("transpose_29")]; + tensor reshape_19_cast_fp16 = reshape(shape = concat_65, x = transpose_13_cast_fp16)[name = string("reshape_19_cast_fp16")]; + bool matmul_6_transpose_x_1 = const()[name = string("matmul_6_transpose_x_1"), val = bool(true)]; + bool matmul_6_transpose_y_1 = const()[name = string("matmul_6_transpose_y_1"), val = bool(false)]; + tensor matmul_6_cast_fp16 = matmul(transpose_x = matmul_6_transpose_x_1, transpose_y = matmul_6_transpose_y_1, x = kv_cache_update_mask, y = reshape_19_cast_fp16)[name = string("matmul_6_cast_fp16")]; + tensor concat_69 = const()[name = string("concat_69"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_20_cast_fp16 = reshape(shape = concat_69, x = matmul_6_cast_fp16)[name = string("reshape_20_cast_fp16")]; + tensor key_scatter_7_perm_0 = const()[name = string("key_scatter_7_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_15_perm_0 = const()[name = string("transpose_15_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_75 = const()[name = string("concat_75"), val = tensor([1, 4, 1024])]; + tensor transpose_15_cast_fp16 = transpose(perm = transpose_15_perm_0, x = obj_51_cast_fp16)[name = string("transpose_28")]; + tensor reshape_22_cast_fp16 = reshape(shape = concat_75, x = transpose_15_cast_fp16)[name = string("reshape_22_cast_fp16")]; + bool matmul_7_transpose_x_1 = const()[name = string("matmul_7_transpose_x_1"), val = bool(true)]; + bool matmul_7_transpose_y_1 = const()[name = string("matmul_7_transpose_y_1"), val = bool(false)]; + tensor matmul_7_cast_fp16 = matmul(transpose_x = matmul_7_transpose_x_1, transpose_y = matmul_7_transpose_y_1, x = kv_cache_update_mask, y = reshape_22_cast_fp16)[name = string("matmul_7_cast_fp16")]; + tensor concat_79 = const()[name = string("concat_79"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_23_cast_fp16 = reshape(shape = concat_79, x = matmul_7_cast_fp16)[name = string("reshape_23_cast_fp16")]; + tensor value_scatter_7_perm_0 = const()[name = string("value_scatter_7_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_1105_cast_fp16 = mul(x = var_367_cast_fp16_3, y = var_502_cast_fp16)[name = string("op_1105_cast_fp16")]; + tensor key_scatter_7_cast_fp16 = transpose(perm = key_scatter_7_perm_0, x = reshape_20_cast_fp16)[name = string("transpose_27")]; + tensor key_15_cast_fp16 = add(x = var_1105_cast_fp16, y = key_scatter_7_cast_fp16)[name = string("key_15_cast_fp16")]; + tensor var_1107_cast_fp16 = mul(x = var_376_cast_fp16_3, y = var_502_cast_fp16)[name = string("op_1107_cast_fp16")]; + tensor value_scatter_7_cast_fp16 = transpose(perm = value_scatter_7_perm_0, x = reshape_23_cast_fp16)[name = string("transpose_26")]; + tensor value_7_cast_fp16 = add(x = var_1107_cast_fp16, y = value_scatter_7_cast_fp16)[name = string("value_7_cast_fp16")]; + fp16 var_1113_to_fp16 = const()[name = string("op_1113_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1114_cast_fp16 = mul(x = mh_q_21_cast_fp16, y = var_1113_to_fp16)[name = string("op_1114_cast_fp16")]; + tensor var_1117 = const()[name = string("op_1117"), val = tensor([1, 16, 64, 80])]; + tensor var_1118_cast_fp16 = reshape(shape = var_1117, x = key_15_cast_fp16)[name = string("op_1118_cast_fp16")]; + bool mh_w_19_transpose_x_0 = const()[name = string("mh_w_19_transpose_x_0"), val = bool(true)]; + bool mh_w_19_transpose_y_0 = const()[name = string("mh_w_19_transpose_y_0"), val = bool(false)]; + tensor mh_w_19_cast_fp16 = matmul(transpose_x = mh_w_19_transpose_x_0, transpose_y = mh_w_19_transpose_y_0, x = var_1114_cast_fp16, y = var_1118_cast_fp16)[name = string("mh_w_19_cast_fp16")]; + tensor mh_w_21_cast_fp16 = add(x = mh_w_19_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_21_cast_fp16")]; + tensor mh_w_23_cast_fp16 = add(x = mh_w_21_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_23_cast_fp16")]; + tensor var_1128_cast_fp16 = softmax(axis = var_338, x = mh_w_23_cast_fp16)[name = string("op_1128_cast_fp16")]; + tensor var_1129 = const()[name = string("op_1129"), val = tensor([1, 16, 64, 80])]; + tensor var_1130_cast_fp16 = reshape(shape = var_1129, x = value_7_cast_fp16)[name = string("op_1130_cast_fp16")]; + bool attn_7_transpose_x_0 = const()[name = string("attn_7_transpose_x_0"), val = bool(false)]; + bool attn_7_transpose_y_0 = const()[name = string("attn_7_transpose_y_0"), val = bool(true)]; + tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_1130_cast_fp16, y = var_1128_cast_fp16)[name = string("attn_7_cast_fp16")]; + tensor var_1133 = const()[name = string("op_1133"), val = tensor([1, -1, 1, 4])]; + tensor input_67_cast_fp16 = reshape(shape = var_1133, x = attn_7_cast_fp16)[name = string("input_67_cast_fp16")]; + string obj_47_pad_type_0 = const()[name = string("obj_47_pad_type_0"), val = string("valid")]; + tensor obj_47_strides_0 = const()[name = string("obj_47_strides_0"), val = tensor([1, 1])]; + tensor obj_47_pad_0 = const()[name = string("obj_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_47_dilations_0 = const()[name = string("obj_47_dilations_0"), val = tensor([1, 1])]; + int32 obj_47_groups_0 = const()[name = string("obj_47_groups_0"), val = int32(1)]; + tensor op_1149_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24427200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24951552))))[name = string("op_1149_weight_0_to_fp16_palettized")]; + tensor var_1149_bias_0_to_fp16 = const()[name = string("op_1149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24952128)))]; + tensor var_1149_cast_fp16 = conv(bias = var_1149_bias_0_to_fp16, dilations = obj_47_dilations_0, groups = obj_47_groups_0, pad = obj_47_pad_0, pad_type = obj_47_pad_type_0, strides = obj_47_strides_0, weight = op_1149_weight_0_to_fp16_palettized, x = input_67_cast_fp16)[name = string("op_1149_cast_fp16")]; + tensor inputs_15_cast_fp16 = add(x = inputs_13_cast_fp16, y = var_1149_cast_fp16)[name = string("inputs_15_cast_fp16")]; + tensor inputs_sq_15_cast_fp16 = mul(x = inputs_15_cast_fp16, y = inputs_15_cast_fp16)[name = string("inputs_sq_15_cast_fp16")]; + tensor variance_15_axes_0 = const()[name = string("variance_15_axes_0"), val = tensor([1])]; + bool variance_15_keep_dims_0 = const()[name = string("variance_15_keep_dims_0"), val = bool(true)]; + tensor variance_15_cast_fp16 = reduce_mean(axes = variance_15_axes_0, keep_dims = variance_15_keep_dims_0, x = inputs_sq_15_cast_fp16)[name = string("variance_15_cast_fp16")]; + fp16 var_1155_to_fp16 = const()[name = string("op_1155_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1156_cast_fp16 = add(x = variance_15_cast_fp16, y = var_1155_to_fp16)[name = string("op_1156_cast_fp16")]; + fp32 var_1157_epsilon_0 = const()[name = string("op_1157_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1157_cast_fp16 = rsqrt(epsilon = var_1157_epsilon_0, x = var_1156_cast_fp16)[name = string("op_1157_cast_fp16")]; + tensor hidden_states_21_cast_fp16 = mul(x = inputs_15_cast_fp16, y = var_1157_cast_fp16)[name = string("hidden_states_21_cast_fp16")]; + tensor w_15_to_fp16 = const()[name = string("w_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24953216)))]; + tensor input_69_cast_fp16 = mul(x = w_15_to_fp16, y = hidden_states_21_cast_fp16)[name = string("input_69_cast_fp16")]; + string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("valid")]; + tensor input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor([1, 1])]; + tensor input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor([1, 1])]; + int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24954304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25478656))))[name = string("pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_71_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor gate_7_cast_fp16 = silu(x = input_71_cast_fp16)[name = string("gate_7_cast_fp16")]; + string up_7_pad_type_0 = const()[name = string("up_7_pad_type_0"), val = string("valid")]; + tensor up_7_strides_0 = const()[name = string("up_7_strides_0"), val = tensor([1, 1])]; + tensor up_7_pad_0 = const()[name = string("up_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_7_dilations_0 = const()[name = string("up_7_dilations_0"), val = tensor([1, 1])]; + int32 up_7_groups_0 = const()[name = string("up_7_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25479232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26003584))))[name = string("pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_7_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_7_dilations_0, groups = up_7_groups_0, pad = up_7_pad_0, pad_type = up_7_pad_type_0, strides = up_7_strides_0, weight = pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("up_7_cast_fp16")]; + tensor input_73_cast_fp16 = mul(x = gate_7_cast_fp16, y = up_7_cast_fp16)[name = string("input_73_cast_fp16")]; + string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")]; + tensor hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)]; + tensor op_1191_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26004160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26528512))))[name = string("op_1191_weight_0_to_fp16_palettized")]; + tensor var_1191_bias_0_to_fp16 = const()[name = string("op_1191_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26529088)))]; + tensor var_1191_cast_fp16 = conv(bias = var_1191_bias_0_to_fp16, dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = op_1191_weight_0_to_fp16_palettized, x = input_73_cast_fp16)[name = string("op_1191_cast_fp16")]; + tensor inputs_17_cast_fp16 = add(x = inputs_15_cast_fp16, y = var_1191_cast_fp16)[name = string("inputs_17_cast_fp16")]; + tensor inputs_sq_17_cast_fp16 = mul(x = inputs_17_cast_fp16, y = inputs_17_cast_fp16)[name = string("inputs_sq_17_cast_fp16")]; + tensor variance_17_axes_0 = const()[name = string("variance_17_axes_0"), val = tensor([1])]; + bool variance_17_keep_dims_0 = const()[name = string("variance_17_keep_dims_0"), val = bool(true)]; + tensor variance_17_cast_fp16 = reduce_mean(axes = variance_17_axes_0, keep_dims = variance_17_keep_dims_0, x = inputs_sq_17_cast_fp16)[name = string("variance_17_cast_fp16")]; + fp16 var_1207_to_fp16 = const()[name = string("op_1207_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1208_cast_fp16 = add(x = variance_17_cast_fp16, y = var_1207_to_fp16)[name = string("op_1208_cast_fp16")]; + fp32 var_1209_epsilon_0 = const()[name = string("op_1209_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1209_cast_fp16 = rsqrt(epsilon = var_1209_epsilon_0, x = var_1208_cast_fp16)[name = string("op_1209_cast_fp16")]; + tensor hidden_states_25_cast_fp16 = mul(x = inputs_17_cast_fp16, y = var_1209_cast_fp16)[name = string("hidden_states_25_cast_fp16")]; + tensor w_17_to_fp16 = const()[name = string("w_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26530176)))]; + tensor obj_53_cast_fp16 = mul(x = w_17_to_fp16, y = hidden_states_25_cast_fp16)[name = string("obj_53_cast_fp16")]; + string query_17_pad_type_0 = const()[name = string("query_17_pad_type_0"), val = string("valid")]; + tensor query_17_strides_0 = const()[name = string("query_17_strides_0"), val = tensor([1, 1])]; + tensor query_17_pad_0 = const()[name = string("query_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_17_dilations_0 = const()[name = string("query_17_dilations_0"), val = tensor([1, 1])]; + int32 query_17_groups_0 = const()[name = string("query_17_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26531264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27055616))))[name = string("pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_17_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_17_dilations_0, groups = query_17_groups_0, pad = query_17_pad_0, pad_type = query_17_pad_type_0, strides = query_17_strides_0, weight = pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized, x = obj_53_cast_fp16)[name = string("query_17_cast_fp16")]; + string key_17_pad_type_0 = const()[name = string("key_17_pad_type_0"), val = string("valid")]; + tensor key_17_strides_0 = const()[name = string("key_17_strides_0"), val = tensor([1, 1])]; + tensor key_17_pad_0 = const()[name = string("key_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_17_dilations_0 = const()[name = string("key_17_dilations_0"), val = tensor([1, 1])]; + int32 key_17_groups_0 = const()[name = string("key_17_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27056192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27580544))))[name = string("pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_17_cast_fp16 = conv(dilations = key_17_dilations_0, groups = key_17_groups_0, pad = key_17_pad_0, pad_type = key_17_pad_type_0, strides = key_17_strides_0, weight = pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized, x = obj_53_cast_fp16)[name = string("key_17_cast_fp16")]; + string obj_63_pad_type_0 = const()[name = string("obj_63_pad_type_0"), val = string("valid")]; + tensor obj_63_strides_0 = const()[name = string("obj_63_strides_0"), val = tensor([1, 1])]; + tensor obj_63_pad_0 = const()[name = string("obj_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_63_dilations_0 = const()[name = string("obj_63_dilations_0"), val = tensor([1, 1])]; + int32 obj_63_groups_0 = const()[name = string("obj_63_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27581120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28105472))))[name = string("pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_63_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_63_dilations_0, groups = obj_63_groups_0, pad = obj_63_pad_0, pad_type = obj_63_pad_type_0, strides = obj_63_strides_0, weight = pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized, x = obj_53_cast_fp16)[name = string("obj_63_cast_fp16")]; + tensor var_1247 = const()[name = string("op_1247"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_25_cast_fp16 = reshape(shape = var_1247, x = query_17_cast_fp16)[name = string("mh_q_25_cast_fp16")]; + tensor var_1249 = const()[name = string("op_1249"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_17_cast_fp16 = reshape(shape = var_1249, x = key_17_cast_fp16)[name = string("mh_k_17_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = mh_q_25_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1258_begin_0 = const()[name = string("op_1258_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1258_end_0 = const()[name = string("op_1258_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1258_end_mask_0 = const()[name = string("op_1258_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1258_cast_fp16 = slice_by_index(begin = var_1258_begin_0, end = var_1258_end_0, end_mask = var_1258_end_mask_0, x = mh_q_25_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1264_begin_0 = const()[name = string("op_1264_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1264_end_0 = const()[name = string("op_1264_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1264_end_mask_0 = const()[name = string("op_1264_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1264_cast_fp16 = slice_by_index(begin = var_1264_begin_0, end = var_1264_end_0, end_mask = var_1264_end_mask_0, x = mh_q_25_cast_fp16)[name = string("op_1264_cast_fp16")]; + fp16 const_109_promoted_to_fp16 = const()[name = string("const_109_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1266_cast_fp16 = mul(x = var_1264_cast_fp16, y = const_109_promoted_to_fp16)[name = string("op_1266_cast_fp16")]; + bool var_1268_interleave_0 = const()[name = string("op_1268_interleave_0"), val = bool(false)]; + tensor var_1268_cast_fp16 = concat(axis = var_333, interleave = var_1268_interleave_0, values = (var_1266_cast_fp16, var_1258_cast_fp16))[name = string("op_1268_cast_fp16")]; + tensor var_1269_cast_fp16 = mul(x = var_1268_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1269_cast_fp16")]; + tensor mh_q_27_cast_fp16 = add(x = var_1253_cast_fp16, y = var_1269_cast_fp16)[name = string("mh_q_27_cast_fp16")]; + tensor var_1271_cast_fp16 = mul(x = mh_k_17_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1271_cast_fp16")]; + tensor var_1276_begin_0 = const()[name = string("op_1276_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1276_end_0 = const()[name = string("op_1276_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1276_end_mask_0 = const()[name = string("op_1276_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1276_cast_fp16 = slice_by_index(begin = var_1276_begin_0, end = var_1276_end_0, end_mask = var_1276_end_mask_0, x = mh_k_17_cast_fp16)[name = string("op_1276_cast_fp16")]; + tensor var_1282_begin_0 = const()[name = string("op_1282_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1282_end_0 = const()[name = string("op_1282_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1282_end_mask_0 = const()[name = string("op_1282_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1282_cast_fp16 = slice_by_index(begin = var_1282_begin_0, end = var_1282_end_0, end_mask = var_1282_end_mask_0, x = mh_k_17_cast_fp16)[name = string("op_1282_cast_fp16")]; + fp16 const_112_promoted_to_fp16 = const()[name = string("const_112_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1284_cast_fp16 = mul(x = var_1282_cast_fp16, y = const_112_promoted_to_fp16)[name = string("op_1284_cast_fp16")]; + bool var_1286_interleave_0 = const()[name = string("op_1286_interleave_0"), val = bool(false)]; + tensor var_1286_cast_fp16 = concat(axis = var_333, interleave = var_1286_interleave_0, values = (var_1284_cast_fp16, var_1276_cast_fp16))[name = string("op_1286_cast_fp16")]; + tensor var_1287_cast_fp16 = mul(x = var_1286_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1287_cast_fp16")]; + tensor mh_k_19_cast_fp16 = add(x = var_1271_cast_fp16, y = var_1287_cast_fp16)[name = string("mh_k_19_cast_fp16")]; + tensor var_1291 = const()[name = string("op_1291"), val = tensor([1, 1024, 1, 4])]; + tensor obj_61_cast_fp16 = reshape(shape = var_1291, x = mh_k_19_cast_fp16)[name = string("obj_61_cast_fp16")]; + tensor transpose_17_perm_0 = const()[name = string("transpose_17_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_85 = const()[name = string("concat_85"), val = tensor([1, 4, 1024])]; + tensor transpose_17_cast_fp16 = transpose(perm = transpose_17_perm_0, x = obj_61_cast_fp16)[name = string("transpose_25")]; + tensor reshape_25_cast_fp16 = reshape(shape = concat_85, x = transpose_17_cast_fp16)[name = string("reshape_25_cast_fp16")]; + bool matmul_8_transpose_x_1 = const()[name = string("matmul_8_transpose_x_1"), val = bool(true)]; + bool matmul_8_transpose_y_1 = const()[name = string("matmul_8_transpose_y_1"), val = bool(false)]; + tensor matmul_8_cast_fp16 = matmul(transpose_x = matmul_8_transpose_x_1, transpose_y = matmul_8_transpose_y_1, x = kv_cache_update_mask, y = reshape_25_cast_fp16)[name = string("matmul_8_cast_fp16")]; + tensor concat_89 = const()[name = string("concat_89"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_26_cast_fp16 = reshape(shape = concat_89, x = matmul_8_cast_fp16)[name = string("reshape_26_cast_fp16")]; + tensor key_scatter_9_perm_0 = const()[name = string("key_scatter_9_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_19_perm_0 = const()[name = string("transpose_19_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_95 = const()[name = string("concat_95"), val = tensor([1, 4, 1024])]; + tensor transpose_19_cast_fp16 = transpose(perm = transpose_19_perm_0, x = obj_63_cast_fp16)[name = string("transpose_24")]; + tensor reshape_28_cast_fp16 = reshape(shape = concat_95, x = transpose_19_cast_fp16)[name = string("reshape_28_cast_fp16")]; + bool matmul_9_transpose_x_1 = const()[name = string("matmul_9_transpose_x_1"), val = bool(true)]; + bool matmul_9_transpose_y_1 = const()[name = string("matmul_9_transpose_y_1"), val = bool(false)]; + tensor matmul_9_cast_fp16 = matmul(transpose_x = matmul_9_transpose_x_1, transpose_y = matmul_9_transpose_y_1, x = kv_cache_update_mask, y = reshape_28_cast_fp16)[name = string("matmul_9_cast_fp16")]; + tensor concat_99 = const()[name = string("concat_99"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_29_cast_fp16 = reshape(shape = concat_99, x = matmul_9_cast_fp16)[name = string("reshape_29_cast_fp16")]; + tensor value_scatter_9_perm_0 = const()[name = string("value_scatter_9_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_1304_cast_fp16 = mul(x = var_367_cast_fp16_4, y = var_502_cast_fp16)[name = string("op_1304_cast_fp16")]; + tensor key_scatter_9_cast_fp16 = transpose(perm = key_scatter_9_perm_0, x = reshape_26_cast_fp16)[name = string("transpose_23")]; + tensor key_19_cast_fp16 = add(x = var_1304_cast_fp16, y = key_scatter_9_cast_fp16)[name = string("key_19_cast_fp16")]; + tensor var_1306_cast_fp16 = mul(x = var_376_cast_fp16_4, y = var_502_cast_fp16)[name = string("op_1306_cast_fp16")]; + tensor value_scatter_9_cast_fp16 = transpose(perm = value_scatter_9_perm_0, x = reshape_29_cast_fp16)[name = string("transpose_22")]; + tensor value_9_cast_fp16 = add(x = var_1306_cast_fp16, y = value_scatter_9_cast_fp16)[name = string("value_9_cast_fp16")]; + fp16 var_1312_to_fp16 = const()[name = string("op_1312_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1313_cast_fp16 = mul(x = mh_q_27_cast_fp16, y = var_1312_to_fp16)[name = string("op_1313_cast_fp16")]; + tensor var_1316 = const()[name = string("op_1316"), val = tensor([1, 16, 64, 80])]; + tensor var_1317_cast_fp16 = reshape(shape = var_1316, x = key_19_cast_fp16)[name = string("op_1317_cast_fp16")]; + bool mh_w_25_transpose_x_0 = const()[name = string("mh_w_25_transpose_x_0"), val = bool(true)]; + bool mh_w_25_transpose_y_0 = const()[name = string("mh_w_25_transpose_y_0"), val = bool(false)]; + tensor mh_w_25_cast_fp16 = matmul(transpose_x = mh_w_25_transpose_x_0, transpose_y = mh_w_25_transpose_y_0, x = var_1313_cast_fp16, y = var_1317_cast_fp16)[name = string("mh_w_25_cast_fp16")]; + tensor mh_w_27_cast_fp16 = add(x = mh_w_25_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_27_cast_fp16")]; + tensor mh_w_29_cast_fp16 = add(x = mh_w_27_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_29_cast_fp16")]; + tensor var_1327_cast_fp16 = softmax(axis = var_338, x = mh_w_29_cast_fp16)[name = string("op_1327_cast_fp16")]; + tensor var_1328 = const()[name = string("op_1328"), val = tensor([1, 16, 64, 80])]; + tensor var_1329_cast_fp16 = reshape(shape = var_1328, x = value_9_cast_fp16)[name = string("op_1329_cast_fp16")]; + bool attn_9_transpose_x_0 = const()[name = string("attn_9_transpose_x_0"), val = bool(false)]; + bool attn_9_transpose_y_0 = const()[name = string("attn_9_transpose_y_0"), val = bool(true)]; + tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_1329_cast_fp16, y = var_1327_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor var_1332 = const()[name = string("op_1332"), val = tensor([1, -1, 1, 4])]; + tensor input_75_cast_fp16 = reshape(shape = var_1332, x = attn_9_cast_fp16)[name = string("input_75_cast_fp16")]; + string obj_59_pad_type_0 = const()[name = string("obj_59_pad_type_0"), val = string("valid")]; + tensor obj_59_strides_0 = const()[name = string("obj_59_strides_0"), val = tensor([1, 1])]; + tensor obj_59_pad_0 = const()[name = string("obj_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_59_dilations_0 = const()[name = string("obj_59_dilations_0"), val = tensor([1, 1])]; + int32 obj_59_groups_0 = const()[name = string("obj_59_groups_0"), val = int32(1)]; + tensor op_1348_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28106048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28630400))))[name = string("op_1348_weight_0_to_fp16_palettized")]; + tensor var_1348_bias_0_to_fp16 = const()[name = string("op_1348_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28630976)))]; + tensor var_1348_cast_fp16 = conv(bias = var_1348_bias_0_to_fp16, dilations = obj_59_dilations_0, groups = obj_59_groups_0, pad = obj_59_pad_0, pad_type = obj_59_pad_type_0, strides = obj_59_strides_0, weight = op_1348_weight_0_to_fp16_palettized, x = input_75_cast_fp16)[name = string("op_1348_cast_fp16")]; + tensor inputs_19_cast_fp16 = add(x = inputs_17_cast_fp16, y = var_1348_cast_fp16)[name = string("inputs_19_cast_fp16")]; + tensor inputs_sq_19_cast_fp16 = mul(x = inputs_19_cast_fp16, y = inputs_19_cast_fp16)[name = string("inputs_sq_19_cast_fp16")]; + tensor variance_19_axes_0 = const()[name = string("variance_19_axes_0"), val = tensor([1])]; + bool variance_19_keep_dims_0 = const()[name = string("variance_19_keep_dims_0"), val = bool(true)]; + tensor variance_19_cast_fp16 = reduce_mean(axes = variance_19_axes_0, keep_dims = variance_19_keep_dims_0, x = inputs_sq_19_cast_fp16)[name = string("variance_19_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1355_cast_fp16 = add(x = variance_19_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor hidden_states_27_cast_fp16 = mul(x = inputs_19_cast_fp16, y = var_1356_cast_fp16)[name = string("hidden_states_27_cast_fp16")]; + tensor w_19_to_fp16 = const()[name = string("w_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28632064)))]; + tensor input_77_cast_fp16 = mul(x = w_19_to_fp16, y = hidden_states_27_cast_fp16)[name = string("input_77_cast_fp16")]; + string input_79_pad_type_0 = const()[name = string("input_79_pad_type_0"), val = string("valid")]; + tensor input_79_strides_0 = const()[name = string("input_79_strides_0"), val = tensor([1, 1])]; + tensor input_79_pad_0 = const()[name = string("input_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_79_dilations_0 = const()[name = string("input_79_dilations_0"), val = tensor([1, 1])]; + int32 input_79_groups_0 = const()[name = string("input_79_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28633152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29157504))))[name = string("pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_79_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_79_dilations_0, groups = input_79_groups_0, pad = input_79_pad_0, pad_type = input_79_pad_type_0, strides = input_79_strides_0, weight = pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor gate_9_cast_fp16 = silu(x = input_79_cast_fp16)[name = string("gate_9_cast_fp16")]; + string up_9_pad_type_0 = const()[name = string("up_9_pad_type_0"), val = string("valid")]; + tensor up_9_strides_0 = const()[name = string("up_9_strides_0"), val = tensor([1, 1])]; + tensor up_9_pad_0 = const()[name = string("up_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_9_dilations_0 = const()[name = string("up_9_dilations_0"), val = tensor([1, 1])]; + int32 up_9_groups_0 = const()[name = string("up_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29158080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29682432))))[name = string("pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_9_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_9_dilations_0, groups = up_9_groups_0, pad = up_9_pad_0, pad_type = up_9_pad_type_0, strides = up_9_strides_0, weight = pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("up_9_cast_fp16")]; + tensor input_81_cast_fp16 = mul(x = gate_9_cast_fp16, y = up_9_cast_fp16)[name = string("input_81_cast_fp16")]; + string hidden_states_29_pad_type_0 = const()[name = string("hidden_states_29_pad_type_0"), val = string("valid")]; + tensor hidden_states_29_strides_0 = const()[name = string("hidden_states_29_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_29_pad_0 = const()[name = string("hidden_states_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_29_dilations_0 = const()[name = string("hidden_states_29_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_29_groups_0 = const()[name = string("hidden_states_29_groups_0"), val = int32(1)]; + tensor op_1390_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29683008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30207360))))[name = string("op_1390_weight_0_to_fp16_palettized")]; + tensor var_1390_bias_0_to_fp16 = const()[name = string("op_1390_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30207936)))]; + tensor var_1390_cast_fp16 = conv(bias = var_1390_bias_0_to_fp16, dilations = hidden_states_29_dilations_0, groups = hidden_states_29_groups_0, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = hidden_states_29_strides_0, weight = op_1390_weight_0_to_fp16_palettized, x = input_81_cast_fp16)[name = string("op_1390_cast_fp16")]; + tensor inputs_21_cast_fp16 = add(x = inputs_19_cast_fp16, y = var_1390_cast_fp16)[name = string("inputs_21_cast_fp16")]; + tensor inputs_sq_21_cast_fp16 = mul(x = inputs_21_cast_fp16, y = inputs_21_cast_fp16)[name = string("inputs_sq_21_cast_fp16")]; + tensor variance_21_axes_0 = const()[name = string("variance_21_axes_0"), val = tensor([1])]; + bool variance_21_keep_dims_0 = const()[name = string("variance_21_keep_dims_0"), val = bool(true)]; + tensor variance_21_cast_fp16 = reduce_mean(axes = variance_21_axes_0, keep_dims = variance_21_keep_dims_0, x = inputs_sq_21_cast_fp16)[name = string("variance_21_cast_fp16")]; + fp16 var_1406_to_fp16 = const()[name = string("op_1406_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1407_cast_fp16 = add(x = variance_21_cast_fp16, y = var_1406_to_fp16)[name = string("op_1407_cast_fp16")]; + fp32 var_1408_epsilon_0 = const()[name = string("op_1408_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1408_cast_fp16 = rsqrt(epsilon = var_1408_epsilon_0, x = var_1407_cast_fp16)[name = string("op_1408_cast_fp16")]; + tensor hidden_states_31_cast_fp16 = mul(x = inputs_21_cast_fp16, y = var_1408_cast_fp16)[name = string("hidden_states_31_cast_fp16")]; + tensor w_21_to_fp16 = const()[name = string("w_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30209024)))]; + tensor obj_65_cast_fp16 = mul(x = w_21_to_fp16, y = hidden_states_31_cast_fp16)[name = string("obj_65_cast_fp16")]; + string query_21_pad_type_0 = const()[name = string("query_21_pad_type_0"), val = string("valid")]; + tensor query_21_strides_0 = const()[name = string("query_21_strides_0"), val = tensor([1, 1])]; + tensor query_21_pad_0 = const()[name = string("query_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_21_dilations_0 = const()[name = string("query_21_dilations_0"), val = tensor([1, 1])]; + int32 query_21_groups_0 = const()[name = string("query_21_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30210112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30734464))))[name = string("pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_21_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_21_dilations_0, groups = query_21_groups_0, pad = query_21_pad_0, pad_type = query_21_pad_type_0, strides = query_21_strides_0, weight = pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized, x = obj_65_cast_fp16)[name = string("query_21_cast_fp16")]; + string key_21_pad_type_0 = const()[name = string("key_21_pad_type_0"), val = string("valid")]; + tensor key_21_strides_0 = const()[name = string("key_21_strides_0"), val = tensor([1, 1])]; + tensor key_21_pad_0 = const()[name = string("key_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_21_dilations_0 = const()[name = string("key_21_dilations_0"), val = tensor([1, 1])]; + int32 key_21_groups_0 = const()[name = string("key_21_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30735040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31259392))))[name = string("pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_21_cast_fp16 = conv(dilations = key_21_dilations_0, groups = key_21_groups_0, pad = key_21_pad_0, pad_type = key_21_pad_type_0, strides = key_21_strides_0, weight = pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized, x = obj_65_cast_fp16)[name = string("key_21_cast_fp16")]; + string obj_75_pad_type_0 = const()[name = string("obj_75_pad_type_0"), val = string("valid")]; + tensor obj_75_strides_0 = const()[name = string("obj_75_strides_0"), val = tensor([1, 1])]; + tensor obj_75_pad_0 = const()[name = string("obj_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_75_dilations_0 = const()[name = string("obj_75_dilations_0"), val = tensor([1, 1])]; + int32 obj_75_groups_0 = const()[name = string("obj_75_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31259968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31784320))))[name = string("pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_75_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_75_dilations_0, groups = obj_75_groups_0, pad = obj_75_pad_0, pad_type = obj_75_pad_type_0, strides = obj_75_strides_0, weight = pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized, x = obj_65_cast_fp16)[name = string("obj_75_cast_fp16")]; + tensor var_1446 = const()[name = string("op_1446"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_31_cast_fp16 = reshape(shape = var_1446, x = query_21_cast_fp16)[name = string("mh_q_31_cast_fp16")]; + tensor var_1448 = const()[name = string("op_1448"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_21_cast_fp16 = reshape(shape = var_1448, x = key_21_cast_fp16)[name = string("mh_k_21_cast_fp16")]; + tensor var_1452_cast_fp16 = mul(x = mh_q_31_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1452_cast_fp16")]; + tensor var_1457_begin_0 = const()[name = string("op_1457_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1457_end_0 = const()[name = string("op_1457_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1457_end_mask_0 = const()[name = string("op_1457_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1457_cast_fp16 = slice_by_index(begin = var_1457_begin_0, end = var_1457_end_0, end_mask = var_1457_end_mask_0, x = mh_q_31_cast_fp16)[name = string("op_1457_cast_fp16")]; + tensor var_1463_begin_0 = const()[name = string("op_1463_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1463_end_0 = const()[name = string("op_1463_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1463_end_mask_0 = const()[name = string("op_1463_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1463_cast_fp16 = slice_by_index(begin = var_1463_begin_0, end = var_1463_end_0, end_mask = var_1463_end_mask_0, x = mh_q_31_cast_fp16)[name = string("op_1463_cast_fp16")]; + fp16 const_128_promoted_to_fp16 = const()[name = string("const_128_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1465_cast_fp16 = mul(x = var_1463_cast_fp16, y = const_128_promoted_to_fp16)[name = string("op_1465_cast_fp16")]; + bool var_1467_interleave_0 = const()[name = string("op_1467_interleave_0"), val = bool(false)]; + tensor var_1467_cast_fp16 = concat(axis = var_333, interleave = var_1467_interleave_0, values = (var_1465_cast_fp16, var_1457_cast_fp16))[name = string("op_1467_cast_fp16")]; + tensor var_1468_cast_fp16 = mul(x = var_1467_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1468_cast_fp16")]; + tensor mh_q_33_cast_fp16 = add(x = var_1452_cast_fp16, y = var_1468_cast_fp16)[name = string("mh_q_33_cast_fp16")]; + tensor var_1470_cast_fp16 = mul(x = mh_k_21_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1470_cast_fp16")]; + tensor var_1475_begin_0 = const()[name = string("op_1475_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1475_end_0 = const()[name = string("op_1475_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1475_end_mask_0 = const()[name = string("op_1475_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1475_cast_fp16 = slice_by_index(begin = var_1475_begin_0, end = var_1475_end_0, end_mask = var_1475_end_mask_0, x = mh_k_21_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor var_1481_begin_0 = const()[name = string("op_1481_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1481_end_0 = const()[name = string("op_1481_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1481_end_mask_0 = const()[name = string("op_1481_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1481_cast_fp16 = slice_by_index(begin = var_1481_begin_0, end = var_1481_end_0, end_mask = var_1481_end_mask_0, x = mh_k_21_cast_fp16)[name = string("op_1481_cast_fp16")]; + fp16 const_131_promoted_to_fp16 = const()[name = string("const_131_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1483_cast_fp16 = mul(x = var_1481_cast_fp16, y = const_131_promoted_to_fp16)[name = string("op_1483_cast_fp16")]; + bool var_1485_interleave_0 = const()[name = string("op_1485_interleave_0"), val = bool(false)]; + tensor var_1485_cast_fp16 = concat(axis = var_333, interleave = var_1485_interleave_0, values = (var_1483_cast_fp16, var_1475_cast_fp16))[name = string("op_1485_cast_fp16")]; + tensor var_1486_cast_fp16 = mul(x = var_1485_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1486_cast_fp16")]; + tensor mh_k_23_cast_fp16 = add(x = var_1470_cast_fp16, y = var_1486_cast_fp16)[name = string("mh_k_23_cast_fp16")]; + tensor var_1490 = const()[name = string("op_1490"), val = tensor([1, 1024, 1, 4])]; + tensor obj_73_cast_fp16 = reshape(shape = var_1490, x = mh_k_23_cast_fp16)[name = string("obj_73_cast_fp16")]; + tensor transpose_21_perm_0 = const()[name = string("transpose_21_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_105 = const()[name = string("concat_105"), val = tensor([1, 4, 1024])]; + tensor transpose_21_cast_fp16 = transpose(perm = transpose_21_perm_0, x = obj_73_cast_fp16)[name = string("transpose_21")]; + tensor reshape_31_cast_fp16 = reshape(shape = concat_105, x = transpose_21_cast_fp16)[name = string("reshape_31_cast_fp16")]; + bool matmul_10_transpose_x_1 = const()[name = string("matmul_10_transpose_x_1"), val = bool(true)]; + bool matmul_10_transpose_y_1 = const()[name = string("matmul_10_transpose_y_1"), val = bool(false)]; + tensor matmul_10_cast_fp16 = matmul(transpose_x = matmul_10_transpose_x_1, transpose_y = matmul_10_transpose_y_1, x = kv_cache_update_mask, y = reshape_31_cast_fp16)[name = string("matmul_10_cast_fp16")]; + tensor concat_109 = const()[name = string("concat_109"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_32_cast_fp16 = reshape(shape = concat_109, x = matmul_10_cast_fp16)[name = string("reshape_32_cast_fp16")]; + tensor key_scatter_11_perm_0 = const()[name = string("key_scatter_11_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_23_perm_0 = const()[name = string("transpose_23_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_115 = const()[name = string("concat_115"), val = tensor([1, 4, 1024])]; + tensor transpose_23_cast_fp16 = transpose(perm = transpose_23_perm_0, x = obj_75_cast_fp16)[name = string("transpose_20")]; + tensor reshape_34_cast_fp16 = reshape(shape = concat_115, x = transpose_23_cast_fp16)[name = string("reshape_34_cast_fp16")]; + bool matmul_11_transpose_x_1 = const()[name = string("matmul_11_transpose_x_1"), val = bool(true)]; + bool matmul_11_transpose_y_1 = const()[name = string("matmul_11_transpose_y_1"), val = bool(false)]; + tensor matmul_11_cast_fp16 = matmul(transpose_x = matmul_11_transpose_x_1, transpose_y = matmul_11_transpose_y_1, x = kv_cache_update_mask, y = reshape_34_cast_fp16)[name = string("matmul_11_cast_fp16")]; + tensor concat_119 = const()[name = string("concat_119"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_35_cast_fp16 = reshape(shape = concat_119, x = matmul_11_cast_fp16)[name = string("reshape_35_cast_fp16")]; + tensor value_scatter_11_perm_0 = const()[name = string("value_scatter_11_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_1503_cast_fp16 = mul(x = var_367_cast_fp16_5, y = var_502_cast_fp16)[name = string("op_1503_cast_fp16")]; + tensor key_scatter_11_cast_fp16 = transpose(perm = key_scatter_11_perm_0, x = reshape_32_cast_fp16)[name = string("transpose_19")]; + tensor key_23_cast_fp16 = add(x = var_1503_cast_fp16, y = key_scatter_11_cast_fp16)[name = string("key_23_cast_fp16")]; + tensor var_1505_cast_fp16 = mul(x = var_376_cast_fp16_5, y = var_502_cast_fp16)[name = string("op_1505_cast_fp16")]; + tensor value_scatter_11_cast_fp16 = transpose(perm = value_scatter_11_perm_0, x = reshape_35_cast_fp16)[name = string("transpose_18")]; + tensor value_11_cast_fp16 = add(x = var_1505_cast_fp16, y = value_scatter_11_cast_fp16)[name = string("value_11_cast_fp16")]; + fp16 var_1511_to_fp16 = const()[name = string("op_1511_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1512_cast_fp16 = mul(x = mh_q_33_cast_fp16, y = var_1511_to_fp16)[name = string("op_1512_cast_fp16")]; + tensor var_1515 = const()[name = string("op_1515"), val = tensor([1, 16, 64, 80])]; + tensor var_1516_cast_fp16 = reshape(shape = var_1515, x = key_23_cast_fp16)[name = string("op_1516_cast_fp16")]; + bool mh_w_31_transpose_x_0 = const()[name = string("mh_w_31_transpose_x_0"), val = bool(true)]; + bool mh_w_31_transpose_y_0 = const()[name = string("mh_w_31_transpose_y_0"), val = bool(false)]; + tensor mh_w_31_cast_fp16 = matmul(transpose_x = mh_w_31_transpose_x_0, transpose_y = mh_w_31_transpose_y_0, x = var_1512_cast_fp16, y = var_1516_cast_fp16)[name = string("mh_w_31_cast_fp16")]; + tensor mh_w_33_cast_fp16 = add(x = mh_w_31_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_33_cast_fp16")]; + tensor mh_w_35_cast_fp16 = add(x = mh_w_33_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_35_cast_fp16")]; + tensor var_1526_cast_fp16 = softmax(axis = var_338, x = mh_w_35_cast_fp16)[name = string("op_1526_cast_fp16")]; + tensor var_1527 = const()[name = string("op_1527"), val = tensor([1, 16, 64, 80])]; + tensor var_1528_cast_fp16 = reshape(shape = var_1527, x = value_11_cast_fp16)[name = string("op_1528_cast_fp16")]; + bool attn_11_transpose_x_0 = const()[name = string("attn_11_transpose_x_0"), val = bool(false)]; + bool attn_11_transpose_y_0 = const()[name = string("attn_11_transpose_y_0"), val = bool(true)]; + tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_1528_cast_fp16, y = var_1526_cast_fp16)[name = string("attn_11_cast_fp16")]; + tensor var_1531 = const()[name = string("op_1531"), val = tensor([1, -1, 1, 4])]; + tensor input_83_cast_fp16 = reshape(shape = var_1531, x = attn_11_cast_fp16)[name = string("input_83_cast_fp16")]; + string obj_71_pad_type_0 = const()[name = string("obj_71_pad_type_0"), val = string("valid")]; + tensor obj_71_strides_0 = const()[name = string("obj_71_strides_0"), val = tensor([1, 1])]; + tensor obj_71_pad_0 = const()[name = string("obj_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_71_dilations_0 = const()[name = string("obj_71_dilations_0"), val = tensor([1, 1])]; + int32 obj_71_groups_0 = const()[name = string("obj_71_groups_0"), val = int32(1)]; + tensor op_1547_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31784896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32309248))))[name = string("op_1547_weight_0_to_fp16_palettized")]; + tensor var_1547_bias_0_to_fp16 = const()[name = string("op_1547_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32309824)))]; + tensor var_1547_cast_fp16 = conv(bias = var_1547_bias_0_to_fp16, dilations = obj_71_dilations_0, groups = obj_71_groups_0, pad = obj_71_pad_0, pad_type = obj_71_pad_type_0, strides = obj_71_strides_0, weight = op_1547_weight_0_to_fp16_palettized, x = input_83_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor inputs_23_cast_fp16 = add(x = inputs_21_cast_fp16, y = var_1547_cast_fp16)[name = string("inputs_23_cast_fp16")]; + tensor inputs_sq_23_cast_fp16 = mul(x = inputs_23_cast_fp16, y = inputs_23_cast_fp16)[name = string("inputs_sq_23_cast_fp16")]; + tensor variance_23_axes_0 = const()[name = string("variance_23_axes_0"), val = tensor([1])]; + bool variance_23_keep_dims_0 = const()[name = string("variance_23_keep_dims_0"), val = bool(true)]; + tensor variance_23_cast_fp16 = reduce_mean(axes = variance_23_axes_0, keep_dims = variance_23_keep_dims_0, x = inputs_sq_23_cast_fp16)[name = string("variance_23_cast_fp16")]; + fp16 var_1553_to_fp16 = const()[name = string("op_1553_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1554_cast_fp16 = add(x = variance_23_cast_fp16, y = var_1553_to_fp16)[name = string("op_1554_cast_fp16")]; + fp32 var_1555_epsilon_0 = const()[name = string("op_1555_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1555_cast_fp16 = rsqrt(epsilon = var_1555_epsilon_0, x = var_1554_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = mul(x = inputs_23_cast_fp16, y = var_1555_cast_fp16)[name = string("hidden_states_33_cast_fp16")]; + tensor w_23_to_fp16 = const()[name = string("w_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32310912)))]; + tensor input_85_cast_fp16 = mul(x = w_23_to_fp16, y = hidden_states_33_cast_fp16)[name = string("input_85_cast_fp16")]; + string input_87_pad_type_0 = const()[name = string("input_87_pad_type_0"), val = string("valid")]; + tensor input_87_strides_0 = const()[name = string("input_87_strides_0"), val = tensor([1, 1])]; + tensor input_87_pad_0 = const()[name = string("input_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_87_dilations_0 = const()[name = string("input_87_dilations_0"), val = tensor([1, 1])]; + int32 input_87_groups_0 = const()[name = string("input_87_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32312000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32836352))))[name = string("pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_87_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("input_87_cast_fp16")]; + tensor gate_11_cast_fp16 = silu(x = input_87_cast_fp16)[name = string("gate_11_cast_fp16")]; + string up_11_pad_type_0 = const()[name = string("up_11_pad_type_0"), val = string("valid")]; + tensor up_11_strides_0 = const()[name = string("up_11_strides_0"), val = tensor([1, 1])]; + tensor up_11_pad_0 = const()[name = string("up_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_11_dilations_0 = const()[name = string("up_11_dilations_0"), val = tensor([1, 1])]; + int32 up_11_groups_0 = const()[name = string("up_11_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32836928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33361280))))[name = string("pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_11_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_11_dilations_0, groups = up_11_groups_0, pad = up_11_pad_0, pad_type = up_11_pad_type_0, strides = up_11_strides_0, weight = pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("up_11_cast_fp16")]; + tensor input_89_cast_fp16 = mul(x = gate_11_cast_fp16, y = up_11_cast_fp16)[name = string("input_89_cast_fp16")]; + string hidden_states_35_pad_type_0 = const()[name = string("hidden_states_35_pad_type_0"), val = string("valid")]; + tensor hidden_states_35_strides_0 = const()[name = string("hidden_states_35_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_0 = const()[name = string("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_35_dilations_0 = const()[name = string("hidden_states_35_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_35_groups_0 = const()[name = string("hidden_states_35_groups_0"), val = int32(1)]; + tensor op_1589_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33361856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33886208))))[name = string("op_1589_weight_0_to_fp16_palettized")]; + tensor var_1589_bias_0_to_fp16 = const()[name = string("op_1589_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33886784)))]; + tensor var_1589_cast_fp16 = conv(bias = var_1589_bias_0_to_fp16, dilations = hidden_states_35_dilations_0, groups = hidden_states_35_groups_0, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = hidden_states_35_strides_0, weight = op_1589_weight_0_to_fp16_palettized, x = input_89_cast_fp16)[name = string("op_1589_cast_fp16")]; + tensor inputs_25_cast_fp16 = add(x = inputs_23_cast_fp16, y = var_1589_cast_fp16)[name = string("inputs_25_cast_fp16")]; + tensor inputs_sq_25_cast_fp16 = mul(x = inputs_25_cast_fp16, y = inputs_25_cast_fp16)[name = string("inputs_sq_25_cast_fp16")]; + tensor variance_25_axes_0 = const()[name = string("variance_25_axes_0"), val = tensor([1])]; + bool variance_25_keep_dims_0 = const()[name = string("variance_25_keep_dims_0"), val = bool(true)]; + tensor variance_25_cast_fp16 = reduce_mean(axes = variance_25_axes_0, keep_dims = variance_25_keep_dims_0, x = inputs_sq_25_cast_fp16)[name = string("variance_25_cast_fp16")]; + fp16 var_1605_to_fp16 = const()[name = string("op_1605_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1606_cast_fp16 = add(x = variance_25_cast_fp16, y = var_1605_to_fp16)[name = string("op_1606_cast_fp16")]; + fp32 var_1607_epsilon_0 = const()[name = string("op_1607_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1607_cast_fp16 = rsqrt(epsilon = var_1607_epsilon_0, x = var_1606_cast_fp16)[name = string("op_1607_cast_fp16")]; + tensor hidden_states_37_cast_fp16 = mul(x = inputs_25_cast_fp16, y = var_1607_cast_fp16)[name = string("hidden_states_37_cast_fp16")]; + tensor w_25_to_fp16 = const()[name = string("w_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33887872)))]; + tensor obj_77_cast_fp16 = mul(x = w_25_to_fp16, y = hidden_states_37_cast_fp16)[name = string("obj_77_cast_fp16")]; + string query_25_pad_type_0 = const()[name = string("query_25_pad_type_0"), val = string("valid")]; + tensor query_25_strides_0 = const()[name = string("query_25_strides_0"), val = tensor([1, 1])]; + tensor query_25_pad_0 = const()[name = string("query_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_25_dilations_0 = const()[name = string("query_25_dilations_0"), val = tensor([1, 1])]; + int32 query_25_groups_0 = const()[name = string("query_25_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33888960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34413312))))[name = string("pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_25_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_25_dilations_0, groups = query_25_groups_0, pad = query_25_pad_0, pad_type = query_25_pad_type_0, strides = query_25_strides_0, weight = pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized, x = obj_77_cast_fp16)[name = string("query_25_cast_fp16")]; + string key_25_pad_type_0 = const()[name = string("key_25_pad_type_0"), val = string("valid")]; + tensor key_25_strides_0 = const()[name = string("key_25_strides_0"), val = tensor([1, 1])]; + tensor key_25_pad_0 = const()[name = string("key_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_25_dilations_0 = const()[name = string("key_25_dilations_0"), val = tensor([1, 1])]; + int32 key_25_groups_0 = const()[name = string("key_25_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34413888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34938240))))[name = string("pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_25_cast_fp16 = conv(dilations = key_25_dilations_0, groups = key_25_groups_0, pad = key_25_pad_0, pad_type = key_25_pad_type_0, strides = key_25_strides_0, weight = pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized, x = obj_77_cast_fp16)[name = string("key_25_cast_fp16")]; + string obj_87_pad_type_0 = const()[name = string("obj_87_pad_type_0"), val = string("valid")]; + tensor obj_87_strides_0 = const()[name = string("obj_87_strides_0"), val = tensor([1, 1])]; + tensor obj_87_pad_0 = const()[name = string("obj_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_87_dilations_0 = const()[name = string("obj_87_dilations_0"), val = tensor([1, 1])]; + int32 obj_87_groups_0 = const()[name = string("obj_87_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34938816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35463168))))[name = string("pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_87_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_87_dilations_0, groups = obj_87_groups_0, pad = obj_87_pad_0, pad_type = obj_87_pad_type_0, strides = obj_87_strides_0, weight = pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized, x = obj_77_cast_fp16)[name = string("obj_87_cast_fp16")]; + tensor var_1645 = const()[name = string("op_1645"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_37_cast_fp16 = reshape(shape = var_1645, x = query_25_cast_fp16)[name = string("mh_q_37_cast_fp16")]; + tensor var_1647 = const()[name = string("op_1647"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_25_cast_fp16 = reshape(shape = var_1647, x = key_25_cast_fp16)[name = string("mh_k_25_cast_fp16")]; + tensor var_1651_cast_fp16 = mul(x = mh_q_37_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1651_cast_fp16")]; + tensor var_1656_begin_0 = const()[name = string("op_1656_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1656_end_0 = const()[name = string("op_1656_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1656_end_mask_0 = const()[name = string("op_1656_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1656_cast_fp16 = slice_by_index(begin = var_1656_begin_0, end = var_1656_end_0, end_mask = var_1656_end_mask_0, x = mh_q_37_cast_fp16)[name = string("op_1656_cast_fp16")]; + tensor var_1662_begin_0 = const()[name = string("op_1662_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1662_end_0 = const()[name = string("op_1662_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1662_end_mask_0 = const()[name = string("op_1662_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1662_cast_fp16 = slice_by_index(begin = var_1662_begin_0, end = var_1662_end_0, end_mask = var_1662_end_mask_0, x = mh_q_37_cast_fp16)[name = string("op_1662_cast_fp16")]; + fp16 const_147_promoted_to_fp16 = const()[name = string("const_147_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1664_cast_fp16 = mul(x = var_1662_cast_fp16, y = const_147_promoted_to_fp16)[name = string("op_1664_cast_fp16")]; + bool var_1666_interleave_0 = const()[name = string("op_1666_interleave_0"), val = bool(false)]; + tensor var_1666_cast_fp16 = concat(axis = var_333, interleave = var_1666_interleave_0, values = (var_1664_cast_fp16, var_1656_cast_fp16))[name = string("op_1666_cast_fp16")]; + tensor var_1667_cast_fp16 = mul(x = var_1666_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1667_cast_fp16")]; + tensor mh_q_39_cast_fp16 = add(x = var_1651_cast_fp16, y = var_1667_cast_fp16)[name = string("mh_q_39_cast_fp16")]; + tensor var_1669_cast_fp16 = mul(x = mh_k_25_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1669_cast_fp16")]; + tensor var_1674_begin_0 = const()[name = string("op_1674_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1674_end_0 = const()[name = string("op_1674_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1674_end_mask_0 = const()[name = string("op_1674_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1674_cast_fp16 = slice_by_index(begin = var_1674_begin_0, end = var_1674_end_0, end_mask = var_1674_end_mask_0, x = mh_k_25_cast_fp16)[name = string("op_1674_cast_fp16")]; + tensor var_1680_begin_0 = const()[name = string("op_1680_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1680_end_0 = const()[name = string("op_1680_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1680_end_mask_0 = const()[name = string("op_1680_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1680_cast_fp16 = slice_by_index(begin = var_1680_begin_0, end = var_1680_end_0, end_mask = var_1680_end_mask_0, x = mh_k_25_cast_fp16)[name = string("op_1680_cast_fp16")]; + fp16 const_150_promoted_to_fp16 = const()[name = string("const_150_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1682_cast_fp16 = mul(x = var_1680_cast_fp16, y = const_150_promoted_to_fp16)[name = string("op_1682_cast_fp16")]; + bool var_1684_interleave_0 = const()[name = string("op_1684_interleave_0"), val = bool(false)]; + tensor var_1684_cast_fp16 = concat(axis = var_333, interleave = var_1684_interleave_0, values = (var_1682_cast_fp16, var_1674_cast_fp16))[name = string("op_1684_cast_fp16")]; + tensor var_1685_cast_fp16 = mul(x = var_1684_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1685_cast_fp16")]; + tensor mh_k_27_cast_fp16 = add(x = var_1669_cast_fp16, y = var_1685_cast_fp16)[name = string("mh_k_27_cast_fp16")]; + tensor var_1689 = const()[name = string("op_1689"), val = tensor([1, 1024, 1, 4])]; + tensor obj_85_cast_fp16 = reshape(shape = var_1689, x = mh_k_27_cast_fp16)[name = string("obj_85_cast_fp16")]; + tensor transpose_25_perm_0 = const()[name = string("transpose_25_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_125 = const()[name = string("concat_125"), val = tensor([1, 4, 1024])]; + tensor transpose_25_cast_fp16 = transpose(perm = transpose_25_perm_0, x = obj_85_cast_fp16)[name = string("transpose_17")]; + tensor reshape_37_cast_fp16 = reshape(shape = concat_125, x = transpose_25_cast_fp16)[name = string("reshape_37_cast_fp16")]; + bool matmul_12_transpose_x_1 = const()[name = string("matmul_12_transpose_x_1"), val = bool(true)]; + bool matmul_12_transpose_y_1 = const()[name = string("matmul_12_transpose_y_1"), val = bool(false)]; + tensor matmul_12_cast_fp16 = matmul(transpose_x = matmul_12_transpose_x_1, transpose_y = matmul_12_transpose_y_1, x = kv_cache_update_mask, y = reshape_37_cast_fp16)[name = string("matmul_12_cast_fp16")]; + tensor concat_129 = const()[name = string("concat_129"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_38_cast_fp16 = reshape(shape = concat_129, x = matmul_12_cast_fp16)[name = string("reshape_38_cast_fp16")]; + tensor key_scatter_13_perm_0 = const()[name = string("key_scatter_13_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_27_perm_0 = const()[name = string("transpose_27_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_135 = const()[name = string("concat_135"), val = tensor([1, 4, 1024])]; + tensor transpose_27_cast_fp16 = transpose(perm = transpose_27_perm_0, x = obj_87_cast_fp16)[name = string("transpose_16")]; + tensor reshape_40_cast_fp16 = reshape(shape = concat_135, x = transpose_27_cast_fp16)[name = string("reshape_40_cast_fp16")]; + bool matmul_13_transpose_x_1 = const()[name = string("matmul_13_transpose_x_1"), val = bool(true)]; + bool matmul_13_transpose_y_1 = const()[name = string("matmul_13_transpose_y_1"), val = bool(false)]; + tensor matmul_13_cast_fp16 = matmul(transpose_x = matmul_13_transpose_x_1, transpose_y = matmul_13_transpose_y_1, x = kv_cache_update_mask, y = reshape_40_cast_fp16)[name = string("matmul_13_cast_fp16")]; + tensor concat_139 = const()[name = string("concat_139"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_41_cast_fp16 = reshape(shape = concat_139, x = matmul_13_cast_fp16)[name = string("reshape_41_cast_fp16")]; + tensor value_scatter_13_perm_0 = const()[name = string("value_scatter_13_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_1702_cast_fp16 = mul(x = var_367_cast_fp16_6, y = var_502_cast_fp16)[name = string("op_1702_cast_fp16")]; + tensor key_scatter_13_cast_fp16 = transpose(perm = key_scatter_13_perm_0, x = reshape_38_cast_fp16)[name = string("transpose_15")]; + tensor key_27_cast_fp16 = add(x = var_1702_cast_fp16, y = key_scatter_13_cast_fp16)[name = string("key_27_cast_fp16")]; + tensor var_1704_cast_fp16 = mul(x = var_376_cast_fp16_6, y = var_502_cast_fp16)[name = string("op_1704_cast_fp16")]; + tensor value_scatter_13_cast_fp16 = transpose(perm = value_scatter_13_perm_0, x = reshape_41_cast_fp16)[name = string("transpose_14")]; + tensor value_13_cast_fp16 = add(x = var_1704_cast_fp16, y = value_scatter_13_cast_fp16)[name = string("value_13_cast_fp16")]; + fp16 var_1710_to_fp16 = const()[name = string("op_1710_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1711_cast_fp16 = mul(x = mh_q_39_cast_fp16, y = var_1710_to_fp16)[name = string("op_1711_cast_fp16")]; + tensor var_1714 = const()[name = string("op_1714"), val = tensor([1, 16, 64, 80])]; + tensor var_1715_cast_fp16 = reshape(shape = var_1714, x = key_27_cast_fp16)[name = string("op_1715_cast_fp16")]; + bool mh_w_37_transpose_x_0 = const()[name = string("mh_w_37_transpose_x_0"), val = bool(true)]; + bool mh_w_37_transpose_y_0 = const()[name = string("mh_w_37_transpose_y_0"), val = bool(false)]; + tensor mh_w_37_cast_fp16 = matmul(transpose_x = mh_w_37_transpose_x_0, transpose_y = mh_w_37_transpose_y_0, x = var_1711_cast_fp16, y = var_1715_cast_fp16)[name = string("mh_w_37_cast_fp16")]; + tensor mh_w_39_cast_fp16 = add(x = mh_w_37_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_39_cast_fp16")]; + tensor mh_w_41_cast_fp16 = add(x = mh_w_39_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_41_cast_fp16")]; + tensor var_1725_cast_fp16 = softmax(axis = var_338, x = mh_w_41_cast_fp16)[name = string("op_1725_cast_fp16")]; + tensor var_1726 = const()[name = string("op_1726"), val = tensor([1, 16, 64, 80])]; + tensor var_1727_cast_fp16 = reshape(shape = var_1726, x = value_13_cast_fp16)[name = string("op_1727_cast_fp16")]; + bool attn_13_transpose_x_0 = const()[name = string("attn_13_transpose_x_0"), val = bool(false)]; + bool attn_13_transpose_y_0 = const()[name = string("attn_13_transpose_y_0"), val = bool(true)]; + tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1727_cast_fp16, y = var_1725_cast_fp16)[name = string("attn_13_cast_fp16")]; + tensor var_1730 = const()[name = string("op_1730"), val = tensor([1, -1, 1, 4])]; + tensor input_91_cast_fp16 = reshape(shape = var_1730, x = attn_13_cast_fp16)[name = string("input_91_cast_fp16")]; + string obj_83_pad_type_0 = const()[name = string("obj_83_pad_type_0"), val = string("valid")]; + tensor obj_83_strides_0 = const()[name = string("obj_83_strides_0"), val = tensor([1, 1])]; + tensor obj_83_pad_0 = const()[name = string("obj_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_83_dilations_0 = const()[name = string("obj_83_dilations_0"), val = tensor([1, 1])]; + int32 obj_83_groups_0 = const()[name = string("obj_83_groups_0"), val = int32(1)]; + tensor op_1746_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35463744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35988096))))[name = string("op_1746_weight_0_to_fp16_palettized")]; + tensor var_1746_bias_0_to_fp16 = const()[name = string("op_1746_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35988672)))]; + tensor var_1746_cast_fp16 = conv(bias = var_1746_bias_0_to_fp16, dilations = obj_83_dilations_0, groups = obj_83_groups_0, pad = obj_83_pad_0, pad_type = obj_83_pad_type_0, strides = obj_83_strides_0, weight = op_1746_weight_0_to_fp16_palettized, x = input_91_cast_fp16)[name = string("op_1746_cast_fp16")]; + tensor inputs_27_cast_fp16 = add(x = inputs_25_cast_fp16, y = var_1746_cast_fp16)[name = string("inputs_27_cast_fp16")]; + tensor inputs_sq_27_cast_fp16 = mul(x = inputs_27_cast_fp16, y = inputs_27_cast_fp16)[name = string("inputs_sq_27_cast_fp16")]; + tensor variance_27_axes_0 = const()[name = string("variance_27_axes_0"), val = tensor([1])]; + bool variance_27_keep_dims_0 = const()[name = string("variance_27_keep_dims_0"), val = bool(true)]; + tensor variance_27_cast_fp16 = reduce_mean(axes = variance_27_axes_0, keep_dims = variance_27_keep_dims_0, x = inputs_sq_27_cast_fp16)[name = string("variance_27_cast_fp16")]; + fp16 var_1752_to_fp16 = const()[name = string("op_1752_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1753_cast_fp16 = add(x = variance_27_cast_fp16, y = var_1752_to_fp16)[name = string("op_1753_cast_fp16")]; + fp32 var_1754_epsilon_0 = const()[name = string("op_1754_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1754_cast_fp16 = rsqrt(epsilon = var_1754_epsilon_0, x = var_1753_cast_fp16)[name = string("op_1754_cast_fp16")]; + tensor hidden_states_39_cast_fp16 = mul(x = inputs_27_cast_fp16, y = var_1754_cast_fp16)[name = string("hidden_states_39_cast_fp16")]; + tensor w_27_to_fp16 = const()[name = string("w_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35989760)))]; + tensor input_93_cast_fp16 = mul(x = w_27_to_fp16, y = hidden_states_39_cast_fp16)[name = string("input_93_cast_fp16")]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("valid")]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([1, 1])]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1, 1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35990848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36515200))))[name = string("pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_95_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor gate_13_cast_fp16 = silu(x = input_95_cast_fp16)[name = string("gate_13_cast_fp16")]; + string up_13_pad_type_0 = const()[name = string("up_13_pad_type_0"), val = string("valid")]; + tensor up_13_strides_0 = const()[name = string("up_13_strides_0"), val = tensor([1, 1])]; + tensor up_13_pad_0 = const()[name = string("up_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_13_dilations_0 = const()[name = string("up_13_dilations_0"), val = tensor([1, 1])]; + int32 up_13_groups_0 = const()[name = string("up_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36515776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37040128))))[name = string("pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_13_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_13_dilations_0, groups = up_13_groups_0, pad = up_13_pad_0, pad_type = up_13_pad_type_0, strides = up_13_strides_0, weight = pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("up_13_cast_fp16")]; + tensor input_97_cast_fp16 = mul(x = gate_13_cast_fp16, y = up_13_cast_fp16)[name = string("input_97_cast_fp16")]; + string hidden_states_41_pad_type_0 = const()[name = string("hidden_states_41_pad_type_0"), val = string("valid")]; + tensor hidden_states_41_strides_0 = const()[name = string("hidden_states_41_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_41_pad_0 = const()[name = string("hidden_states_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_41_dilations_0 = const()[name = string("hidden_states_41_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_41_groups_0 = const()[name = string("hidden_states_41_groups_0"), val = int32(1)]; + tensor op_1788_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37040704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37565056))))[name = string("op_1788_weight_0_to_fp16_palettized")]; + tensor var_1788_bias_0_to_fp16 = const()[name = string("op_1788_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37565632)))]; + tensor var_1788_cast_fp16 = conv(bias = var_1788_bias_0_to_fp16, dilations = hidden_states_41_dilations_0, groups = hidden_states_41_groups_0, pad = hidden_states_41_pad_0, pad_type = hidden_states_41_pad_type_0, strides = hidden_states_41_strides_0, weight = op_1788_weight_0_to_fp16_palettized, x = input_97_cast_fp16)[name = string("op_1788_cast_fp16")]; + tensor inputs_29_cast_fp16 = add(x = inputs_27_cast_fp16, y = var_1788_cast_fp16)[name = string("inputs_29_cast_fp16")]; + tensor inputs_sq_29_cast_fp16 = mul(x = inputs_29_cast_fp16, y = inputs_29_cast_fp16)[name = string("inputs_sq_29_cast_fp16")]; + tensor variance_29_axes_0 = const()[name = string("variance_29_axes_0"), val = tensor([1])]; + bool variance_29_keep_dims_0 = const()[name = string("variance_29_keep_dims_0"), val = bool(true)]; + tensor variance_29_cast_fp16 = reduce_mean(axes = variance_29_axes_0, keep_dims = variance_29_keep_dims_0, x = inputs_sq_29_cast_fp16)[name = string("variance_29_cast_fp16")]; + fp16 var_1804_to_fp16 = const()[name = string("op_1804_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1805_cast_fp16 = add(x = variance_29_cast_fp16, y = var_1804_to_fp16)[name = string("op_1805_cast_fp16")]; + fp32 var_1806_epsilon_0 = const()[name = string("op_1806_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1806_cast_fp16 = rsqrt(epsilon = var_1806_epsilon_0, x = var_1805_cast_fp16)[name = string("op_1806_cast_fp16")]; + tensor hidden_states_43_cast_fp16 = mul(x = inputs_29_cast_fp16, y = var_1806_cast_fp16)[name = string("hidden_states_43_cast_fp16")]; + tensor w_29_to_fp16 = const()[name = string("w_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37566720)))]; + tensor obj_89_cast_fp16 = mul(x = w_29_to_fp16, y = hidden_states_43_cast_fp16)[name = string("obj_89_cast_fp16")]; + string query_29_pad_type_0 = const()[name = string("query_29_pad_type_0"), val = string("valid")]; + tensor query_29_strides_0 = const()[name = string("query_29_strides_0"), val = tensor([1, 1])]; + tensor query_29_pad_0 = const()[name = string("query_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_29_dilations_0 = const()[name = string("query_29_dilations_0"), val = tensor([1, 1])]; + int32 query_29_groups_0 = const()[name = string("query_29_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37567808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38092160))))[name = string("pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_29_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_29_dilations_0, groups = query_29_groups_0, pad = query_29_pad_0, pad_type = query_29_pad_type_0, strides = query_29_strides_0, weight = pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized, x = obj_89_cast_fp16)[name = string("query_29_cast_fp16")]; + string key_29_pad_type_0 = const()[name = string("key_29_pad_type_0"), val = string("valid")]; + tensor key_29_strides_0 = const()[name = string("key_29_strides_0"), val = tensor([1, 1])]; + tensor key_29_pad_0 = const()[name = string("key_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_29_dilations_0 = const()[name = string("key_29_dilations_0"), val = tensor([1, 1])]; + int32 key_29_groups_0 = const()[name = string("key_29_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38092736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38617088))))[name = string("pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_29_cast_fp16 = conv(dilations = key_29_dilations_0, groups = key_29_groups_0, pad = key_29_pad_0, pad_type = key_29_pad_type_0, strides = key_29_strides_0, weight = pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized, x = obj_89_cast_fp16)[name = string("key_29_cast_fp16")]; + string obj_pad_type_0 = const()[name = string("obj_pad_type_0"), val = string("valid")]; + tensor obj_strides_0 = const()[name = string("obj_strides_0"), val = tensor([1, 1])]; + tensor obj_pad_0 = const()[name = string("obj_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_dilations_0 = const()[name = string("obj_dilations_0"), val = tensor([1, 1])]; + int32 obj_groups_0 = const()[name = string("obj_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38617664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39142016))))[name = string("pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_dilations_0, groups = obj_groups_0, pad = obj_pad_0, pad_type = obj_pad_type_0, strides = obj_strides_0, weight = pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized, x = obj_89_cast_fp16)[name = string("obj_cast_fp16")]; + tensor var_1844 = const()[name = string("op_1844"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_43_cast_fp16 = reshape(shape = var_1844, x = query_29_cast_fp16)[name = string("mh_q_43_cast_fp16")]; + tensor var_1846 = const()[name = string("op_1846"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_29_cast_fp16 = reshape(shape = var_1846, x = key_29_cast_fp16)[name = string("mh_k_29_cast_fp16")]; + tensor var_1850_cast_fp16 = mul(x = mh_q_43_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor var_1855_begin_0 = const()[name = string("op_1855_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1855_end_0 = const()[name = string("op_1855_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1855_end_mask_0 = const()[name = string("op_1855_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1855_cast_fp16 = slice_by_index(begin = var_1855_begin_0, end = var_1855_end_0, end_mask = var_1855_end_mask_0, x = mh_q_43_cast_fp16)[name = string("op_1855_cast_fp16")]; + tensor var_1861_begin_0 = const()[name = string("op_1861_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1861_end_0 = const()[name = string("op_1861_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1861_end_mask_0 = const()[name = string("op_1861_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1861_cast_fp16 = slice_by_index(begin = var_1861_begin_0, end = var_1861_end_0, end_mask = var_1861_end_mask_0, x = mh_q_43_cast_fp16)[name = string("op_1861_cast_fp16")]; + fp16 const_166_promoted_to_fp16 = const()[name = string("const_166_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1863_cast_fp16 = mul(x = var_1861_cast_fp16, y = const_166_promoted_to_fp16)[name = string("op_1863_cast_fp16")]; + bool var_1865_interleave_0 = const()[name = string("op_1865_interleave_0"), val = bool(false)]; + tensor var_1865_cast_fp16 = concat(axis = var_333, interleave = var_1865_interleave_0, values = (var_1863_cast_fp16, var_1855_cast_fp16))[name = string("op_1865_cast_fp16")]; + tensor var_1866_cast_fp16 = mul(x = var_1865_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1866_cast_fp16")]; + tensor mh_q_45_cast_fp16 = add(x = var_1850_cast_fp16, y = var_1866_cast_fp16)[name = string("mh_q_45_cast_fp16")]; + tensor var_1868_cast_fp16 = mul(x = mh_k_29_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1868_cast_fp16")]; + tensor var_1873_begin_0 = const()[name = string("op_1873_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1873_end_0 = const()[name = string("op_1873_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1873_end_mask_0 = const()[name = string("op_1873_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1873_cast_fp16 = slice_by_index(begin = var_1873_begin_0, end = var_1873_end_0, end_mask = var_1873_end_mask_0, x = mh_k_29_cast_fp16)[name = string("op_1873_cast_fp16")]; + tensor var_1879_begin_0 = const()[name = string("op_1879_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1879_end_0 = const()[name = string("op_1879_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1879_end_mask_0 = const()[name = string("op_1879_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1879_cast_fp16 = slice_by_index(begin = var_1879_begin_0, end = var_1879_end_0, end_mask = var_1879_end_mask_0, x = mh_k_29_cast_fp16)[name = string("op_1879_cast_fp16")]; + fp16 const_169_promoted_to_fp16 = const()[name = string("const_169_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1881_cast_fp16 = mul(x = var_1879_cast_fp16, y = const_169_promoted_to_fp16)[name = string("op_1881_cast_fp16")]; + bool var_1883_interleave_0 = const()[name = string("op_1883_interleave_0"), val = bool(false)]; + tensor var_1883_cast_fp16 = concat(axis = var_333, interleave = var_1883_interleave_0, values = (var_1881_cast_fp16, var_1873_cast_fp16))[name = string("op_1883_cast_fp16")]; + tensor var_1884_cast_fp16 = mul(x = var_1883_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1884_cast_fp16")]; + tensor mh_k_cast_fp16 = add(x = var_1868_cast_fp16, y = var_1884_cast_fp16)[name = string("mh_k_cast_fp16")]; + tensor var_1888 = const()[name = string("op_1888"), val = tensor([1, 1024, 1, 4])]; + tensor obj_97_cast_fp16 = reshape(shape = var_1888, x = mh_k_cast_fp16)[name = string("obj_97_cast_fp16")]; + tensor transpose_29_perm_0 = const()[name = string("transpose_29_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_145 = const()[name = string("concat_145"), val = tensor([1, 4, 1024])]; + tensor transpose_29_cast_fp16 = transpose(perm = transpose_29_perm_0, x = obj_97_cast_fp16)[name = string("transpose_13")]; + tensor reshape_43_cast_fp16 = reshape(shape = concat_145, x = transpose_29_cast_fp16)[name = string("reshape_43_cast_fp16")]; + bool matmul_14_transpose_x_1 = const()[name = string("matmul_14_transpose_x_1"), val = bool(true)]; + bool matmul_14_transpose_y_1 = const()[name = string("matmul_14_transpose_y_1"), val = bool(false)]; + tensor matmul_14_cast_fp16 = matmul(transpose_x = matmul_14_transpose_x_1, transpose_y = matmul_14_transpose_y_1, x = kv_cache_update_mask, y = reshape_43_cast_fp16)[name = string("matmul_14_cast_fp16")]; + tensor concat_149 = const()[name = string("concat_149"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_44_cast_fp16 = reshape(shape = concat_149, x = matmul_14_cast_fp16)[name = string("reshape_44_cast_fp16")]; + tensor key_scatter_perm_0 = const()[name = string("key_scatter_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_31_perm_0 = const()[name = string("transpose_31_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_155 = const()[name = string("concat_155"), val = tensor([1, 4, 1024])]; + tensor transpose_31_cast_fp16 = transpose(perm = transpose_31_perm_0, x = obj_cast_fp16)[name = string("transpose_12")]; + tensor reshape_46_cast_fp16 = reshape(shape = concat_155, x = transpose_31_cast_fp16)[name = string("reshape_46_cast_fp16")]; + bool matmul_15_transpose_x_1 = const()[name = string("matmul_15_transpose_x_1"), val = bool(true)]; + bool matmul_15_transpose_y_1 = const()[name = string("matmul_15_transpose_y_1"), val = bool(false)]; + tensor matmul_15_cast_fp16 = matmul(transpose_x = matmul_15_transpose_x_1, transpose_y = matmul_15_transpose_y_1, x = kv_cache_update_mask, y = reshape_46_cast_fp16)[name = string("matmul_15_cast_fp16")]; + tensor concat_159 = const()[name = string("concat_159"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_47_cast_fp16 = reshape(shape = concat_159, x = matmul_15_cast_fp16)[name = string("reshape_47_cast_fp16")]; + tensor value_scatter_perm_0 = const()[name = string("value_scatter_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_1901_cast_fp16 = mul(x = var_367_cast_fp16_7, y = var_502_cast_fp16)[name = string("op_1901_cast_fp16")]; + tensor key_scatter_cast_fp16 = transpose(perm = key_scatter_perm_0, x = reshape_44_cast_fp16)[name = string("transpose_11")]; + tensor key_cast_fp16 = add(x = var_1901_cast_fp16, y = key_scatter_cast_fp16)[name = string("key_cast_fp16")]; + tensor var_1903_cast_fp16 = mul(x = var_376_cast_fp16_7, y = var_502_cast_fp16)[name = string("op_1903_cast_fp16")]; + tensor value_scatter_cast_fp16 = transpose(perm = value_scatter_perm_0, x = reshape_47_cast_fp16)[name = string("transpose_10")]; + tensor value_cast_fp16 = add(x = var_1903_cast_fp16, y = value_scatter_cast_fp16)[name = string("value_cast_fp16")]; + fp16 var_1909_to_fp16 = const()[name = string("op_1909_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1910_cast_fp16 = mul(x = mh_q_45_cast_fp16, y = var_1909_to_fp16)[name = string("op_1910_cast_fp16")]; + tensor var_1913 = const()[name = string("op_1913"), val = tensor([1, 16, 64, 80])]; + tensor var_1914_cast_fp16 = reshape(shape = var_1913, x = key_cast_fp16)[name = string("op_1914_cast_fp16")]; + bool mh_w_43_transpose_x_0 = const()[name = string("mh_w_43_transpose_x_0"), val = bool(true)]; + bool mh_w_43_transpose_y_0 = const()[name = string("mh_w_43_transpose_y_0"), val = bool(false)]; + tensor mh_w_43_cast_fp16 = matmul(transpose_x = mh_w_43_transpose_x_0, transpose_y = mh_w_43_transpose_y_0, x = var_1910_cast_fp16, y = var_1914_cast_fp16)[name = string("mh_w_43_cast_fp16")]; + tensor mh_w_45_cast_fp16 = add(x = mh_w_43_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_45_cast_fp16")]; + tensor mh_w_cast_fp16 = add(x = mh_w_45_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_cast_fp16")]; + tensor var_1924_cast_fp16 = softmax(axis = var_338, x = mh_w_cast_fp16)[name = string("op_1924_cast_fp16")]; + tensor var_1925 = const()[name = string("op_1925"), val = tensor([1, 16, 64, 80])]; + tensor var_1926_cast_fp16 = reshape(shape = var_1925, x = value_cast_fp16)[name = string("op_1926_cast_fp16")]; + bool attn_transpose_x_0 = const()[name = string("attn_transpose_x_0"), val = bool(false)]; + bool attn_transpose_y_0 = const()[name = string("attn_transpose_y_0"), val = bool(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_1926_cast_fp16, y = var_1924_cast_fp16)[name = string("attn_cast_fp16")]; + tensor var_1929 = const()[name = string("op_1929"), val = tensor([1, -1, 1, 4])]; + tensor input_99_cast_fp16 = reshape(shape = var_1929, x = attn_cast_fp16)[name = string("input_99_cast_fp16")]; + string obj_95_pad_type_0 = const()[name = string("obj_95_pad_type_0"), val = string("valid")]; + tensor obj_95_strides_0 = const()[name = string("obj_95_strides_0"), val = tensor([1, 1])]; + tensor obj_95_pad_0 = const()[name = string("obj_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_95_dilations_0 = const()[name = string("obj_95_dilations_0"), val = tensor([1, 1])]; + int32 obj_95_groups_0 = const()[name = string("obj_95_groups_0"), val = int32(1)]; + tensor op_1945_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39142592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39666944))))[name = string("op_1945_weight_0_to_fp16_palettized")]; + tensor var_1945_bias_0_to_fp16 = const()[name = string("op_1945_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39667520)))]; + tensor var_1945_cast_fp16 = conv(bias = var_1945_bias_0_to_fp16, dilations = obj_95_dilations_0, groups = obj_95_groups_0, pad = obj_95_pad_0, pad_type = obj_95_pad_type_0, strides = obj_95_strides_0, weight = op_1945_weight_0_to_fp16_palettized, x = input_99_cast_fp16)[name = string("op_1945_cast_fp16")]; + tensor inputs_31_cast_fp16 = add(x = inputs_29_cast_fp16, y = var_1945_cast_fp16)[name = string("inputs_31_cast_fp16")]; + tensor inputs_sq_31_cast_fp16 = mul(x = inputs_31_cast_fp16, y = inputs_31_cast_fp16)[name = string("inputs_sq_31_cast_fp16")]; + tensor variance_31_axes_0 = const()[name = string("variance_31_axes_0"), val = tensor([1])]; + bool variance_31_keep_dims_0 = const()[name = string("variance_31_keep_dims_0"), val = bool(true)]; + tensor variance_31_cast_fp16 = reduce_mean(axes = variance_31_axes_0, keep_dims = variance_31_keep_dims_0, x = inputs_sq_31_cast_fp16)[name = string("variance_31_cast_fp16")]; + fp16 var_1951_to_fp16 = const()[name = string("op_1951_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1952_cast_fp16 = add(x = variance_31_cast_fp16, y = var_1951_to_fp16)[name = string("op_1952_cast_fp16")]; + fp32 var_1953_epsilon_0 = const()[name = string("op_1953_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1953_cast_fp16 = rsqrt(epsilon = var_1953_epsilon_0, x = var_1952_cast_fp16)[name = string("op_1953_cast_fp16")]; + tensor hidden_states_45_cast_fp16 = mul(x = inputs_31_cast_fp16, y = var_1953_cast_fp16)[name = string("hidden_states_45_cast_fp16")]; + tensor w_31_to_fp16 = const()[name = string("w_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39668608)))]; + tensor input_101_cast_fp16 = mul(x = w_31_to_fp16, y = hidden_states_45_cast_fp16)[name = string("input_101_cast_fp16")]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([1, 1])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1, 1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39669696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40194048))))[name = string("pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_103_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor gate_cast_fp16 = silu(x = input_103_cast_fp16)[name = string("gate_cast_fp16")]; + string up_pad_type_0 = const()[name = string("up_pad_type_0"), val = string("valid")]; + tensor up_strides_0 = const()[name = string("up_strides_0"), val = tensor([1, 1])]; + tensor up_pad_0 = const()[name = string("up_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_dilations_0 = const()[name = string("up_dilations_0"), val = tensor([1, 1])]; + int32 up_groups_0 = const()[name = string("up_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40194624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40718976))))[name = string("pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_dilations_0, groups = up_groups_0, pad = up_pad_0, pad_type = up_pad_type_0, strides = up_strides_0, weight = pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = string("up_cast_fp16")]; + tensor input_105_cast_fp16 = mul(x = gate_cast_fp16, y = up_cast_fp16)[name = string("input_105_cast_fp16")]; + string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")]; + tensor hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)]; + tensor op_1987_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40719552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41243904))))[name = string("op_1987_weight_0_to_fp16_palettized")]; + tensor var_1987_bias_0_to_fp16 = const()[name = string("op_1987_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41244480)))]; + tensor var_1987_cast_fp16 = conv(bias = var_1987_bias_0_to_fp16, dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = op_1987_weight_0_to_fp16_palettized, x = input_105_cast_fp16)[name = string("op_1987_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = inputs_31_cast_fp16, y = var_1987_cast_fp16)[name = string("inputs_cast_fp16")]; + tensor inputs_sq_cast_fp16 = mul(x = inputs_cast_fp16, y = inputs_cast_fp16)[name = string("inputs_sq_cast_fp16")]; + tensor variance_axes_0 = const()[name = string("variance_axes_0"), val = tensor([1])]; + bool variance_keep_dims_0 = const()[name = string("variance_keep_dims_0"), val = bool(true)]; + tensor variance_cast_fp16 = reduce_mean(axes = variance_axes_0, keep_dims = variance_keep_dims_0, x = inputs_sq_cast_fp16)[name = string("variance_cast_fp16")]; + fp16 var_1997_to_fp16 = const()[name = string("op_1997_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1998_cast_fp16 = add(x = variance_cast_fp16, y = var_1997_to_fp16)[name = string("op_1998_cast_fp16")]; + fp32 var_1999_epsilon_0 = const()[name = string("op_1999_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1999_cast_fp16 = rsqrt(epsilon = var_1999_epsilon_0, x = var_1998_cast_fp16)[name = string("op_1999_cast_fp16")]; + tensor hidden_states_49_cast_fp16 = mul(x = inputs_cast_fp16, y = var_1999_cast_fp16)[name = string("hidden_states_49_cast_fp16")]; + tensor w_to_fp16 = const()[name = string("w_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41245568)))]; + tensor input_107_cast_fp16 = mul(x = w_to_fp16, y = hidden_states_49_cast_fp16)[name = string("input_107_cast_fp16")]; + string new_hiddens_pad_type_0 = const()[name = string("new_hiddens_pad_type_0"), val = string("valid")]; + tensor new_hiddens_strides_0 = const()[name = string("new_hiddens_strides_0"), val = tensor([1, 1])]; + tensor new_hiddens_pad_0 = const()[name = string("new_hiddens_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor new_hiddens_dilations_0 = const()[name = string("new_hiddens_dilations_0"), val = tensor([1, 1])]; + int32 new_hiddens_groups_0 = const()[name = string("new_hiddens_groups_0"), val = int32(1)]; + tensor pre_transformer_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41246656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41771008))))[name = string("pre_transformer_output_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_output_proj_bias_to_fp16 = const()[name = string("pre_transformer_output_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41771584)))]; + tensor hidden_context_update = conv(bias = pre_transformer_output_proj_bias_to_fp16, dilations = new_hiddens_dilations_0, groups = new_hiddens_groups_0, pad = new_hiddens_pad_0, pad_type = new_hiddens_pad_type_0, strides = new_hiddens_strides_0, weight = pre_transformer_output_proj_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("new_hiddens_cast_fp16")]; + bool var_2012_interleave_0 = const()[name = string("op_2012_interleave_0"), val = bool(false)]; + tensor key_cache_updates = concat(axis = var_344, interleave = var_2012_interleave_0, values = (obj_13_cast_fp16, obj_25_cast_fp16, obj_37_cast_fp16, obj_49_cast_fp16, obj_61_cast_fp16, obj_73_cast_fp16, obj_85_cast_fp16, obj_97_cast_fp16))[name = string("op_2012_cast_fp16")]; + bool var_2014_interleave_0 = const()[name = string("op_2014_interleave_0"), val = bool(false)]; + tensor value_cache_updates = concat(axis = var_344, interleave = var_2014_interleave_0, values = (obj_15_cast_fp16, obj_27_cast_fp16, obj_39_cast_fp16, obj_51_cast_fp16, obj_63_cast_fp16, obj_75_cast_fp16, obj_87_cast_fp16, obj_cast_fp16))[name = string("op_2014_cast_fp16")]; + int32 var_2020 = const()[name = string("op_2020"), val = int32(-1)]; + bool hidden_states_51_interleave_0 = const()[name = string("hidden_states_51_interleave_0"), val = bool(false)]; + tensor hidden_states_51_cast_fp16 = concat(axis = var_2020, interleave = hidden_states_51_interleave_0, values = (hidden_context, hidden_context_update))[name = string("hidden_states_51_cast_fp16")]; + int32 var_2037 = const()[name = string("op_2037"), val = int32(-1)]; + string sub_pixels_1_pad_type_0 = const()[name = string("sub_pixels_1_pad_type_0"), val = string("valid")]; + tensor sub_pixels_1_strides_0 = const()[name = string("sub_pixels_1_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_1_pad_0 = const()[name = string("sub_pixels_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_1_dilations_0 = const()[name = string("sub_pixels_1_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_1_groups_0 = const()[name = string("sub_pixels_1_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41773696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43870912))))[name = string("audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43871488)))]; + tensor sub_pixels_1_cast_fp16 = conv(bias = audio_upsampler_upsample_0_0_conv_bias_to_fp16, dilations = sub_pixels_1_dilations_0, groups = sub_pixels_1_groups_0, pad = sub_pixels_1_pad_0, pad_type = sub_pixels_1_pad_type_0, strides = sub_pixels_1_strides_0, weight = audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized, x = hidden_states_51_cast_fp16)[name = string("sub_pixels_1_cast_fp16")]; + tensor var_2088 = const()[name = string("op_2088"), val = tensor([1, 2, 1024, 12])]; + tensor sub_pixels_3_cast_fp16 = reshape(shape = var_2088, x = sub_pixels_1_cast_fp16)[name = string("sub_pixels_3_cast_fp16")]; + tensor var_2090 = const()[name = string("op_2090"), val = tensor([0, 2, 3, 1])]; + tensor var_2095 = const()[name = string("op_2095"), val = tensor([1, 1024, 1, 24])]; + tensor sub_pixels_5_cast_fp16 = transpose(perm = var_2090, x = sub_pixels_3_cast_fp16)[name = string("transpose_9")]; + tensor hidden_states_53_cast_fp16 = reshape(shape = var_2095, x = sub_pixels_5_cast_fp16)[name = string("hidden_states_53_cast_fp16")]; + tensor var_2100 = const()[name = string("op_2100"), val = tensor([1, 1, 8, 1])]; + tensor var_2101_cast_fp16 = reshape(shape = var_2100, x = hidden_context_mask)[name = string("op_2101_cast_fp16")]; + tensor spread_1_reps_0 = const()[name = string("spread_1_reps_0"), val = tensor([1, 1, 1, 2])]; + tensor spread_1_cast_fp16 = tile(reps = spread_1_reps_0, x = var_2101_cast_fp16)[name = string("spread_1_cast_fp16")]; + tensor var_2107 = const()[name = string("op_2107"), val = tensor([1, 1, 1, 16])]; + tensor context_mask_1_cast_fp16 = reshape(shape = var_2107, x = spread_1_cast_fp16)[name = string("context_mask_1_cast_fp16")]; + bool full_mask_1_interleave_0 = const()[name = string("full_mask_1_interleave_0"), val = bool(false)]; + tensor fill_0_to_fp16 = const()[name = string("fill_0_to_fp16"), val = tensor([[[[0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0]]]])]; + tensor full_mask_1_cast_fp16 = concat(axis = var_2037, interleave = full_mask_1_interleave_0, values = (context_mask_1_cast_fp16, fill_0_to_fp16))[name = string("full_mask_1_cast_fp16")]; + tensor hidden_states_55_cast_fp16 = mul(x = hidden_states_53_cast_fp16, y = full_mask_1_cast_fp16)[name = string("hidden_states_55_cast_fp16")]; + tensor input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 6, 0])]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("constant")]; + fp16 const_179_to_fp16 = const()[name = string("const_179_to_fp16"), val = fp16(0x0p+0)]; + tensor input_109_cast_fp16 = pad(constant_val = const_179_to_fp16, mode = input_109_mode_0, pad = input_109_pad_0, x = hidden_states_55_cast_fp16)[name = string("input_109_cast_fp16")]; + string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")]; + int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1024)]; + tensor hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor([1, 1])]; + tensor audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43875648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43882880))))[name = string("audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43883456)))]; + tensor hidden_states_57_cast_fp16 = conv(bias = audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16, dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized, x = input_109_cast_fp16)[name = string("hidden_states_57_cast_fp16")]; + tensor var_2135_axes_0 = const()[name = string("op_2135_axes_0"), val = tensor([2])]; + tensor var_2135_cast_fp16 = squeeze(axes = var_2135_axes_0, x = hidden_states_57_cast_fp16)[name = string("op_2135_cast_fp16")]; + tensor var_2136 = const()[name = string("op_2136"), val = tensor([0, 2, 1])]; + tensor hidden_states_59_axes_0 = const()[name = string("hidden_states_59_axes_0"), val = tensor([-1])]; + tensor audio_upsampler_upsample_0_1_norm_weight_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43885568)))]; + tensor audio_upsampler_upsample_0_1_norm_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43887680)))]; + fp16 var_2041_to_fp16 = const()[name = string("op_2041_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = var_2136, x = var_2135_cast_fp16)[name = string("transpose_8")]; + tensor hidden_states_59_cast_fp16 = layer_norm(axes = hidden_states_59_axes_0, beta = audio_upsampler_upsample_0_1_norm_bias_to_fp16, epsilon = var_2041_to_fp16, gamma = audio_upsampler_upsample_0_1_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("hidden_states_59_cast_fp16")]; + tensor var_2142 = const()[name = string("op_2142"), val = tensor([0, 2, 1])]; + tensor input_113_axes_0 = const()[name = string("input_113_axes_0"), val = tensor([2])]; + tensor var_2143_cast_fp16 = transpose(perm = var_2142, x = hidden_states_59_cast_fp16)[name = string("transpose_7")]; + tensor input_113_cast_fp16 = expand_dims(axes = input_113_axes_0, x = var_2143_cast_fp16)[name = string("input_113_cast_fp16")]; + string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("valid")]; + tensor input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor([1, 1])]; + tensor input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor([1, 1])]; + int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43889792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48084160))))[name = string("audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48084736)))]; + tensor input_115_cast_fp16 = conv(bias = audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = string("input_115_cast_fp16")]; + string input_117_mode_0 = const()[name = string("input_117_mode_0"), val = string("EXACT")]; + tensor input_117_cast_fp16 = gelu(mode = input_117_mode_0, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")]; + string hidden_states_61_pad_type_0 = const()[name = string("hidden_states_61_pad_type_0"), val = string("valid")]; + tensor hidden_states_61_strides_0 = const()[name = string("hidden_states_61_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_61_pad_0 = const()[name = string("hidden_states_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_61_dilations_0 = const()[name = string("hidden_states_61_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_61_groups_0 = const()[name = string("hidden_states_61_groups_0"), val = int32(1)]; + tensor hidden_states_63_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48092992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52287360))))[name = string("hidden_states_63_weight_0_to_fp16_palettized")]; + tensor hidden_states_63_bias_0_to_fp16 = const()[name = string("hidden_states_63_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52287936)))]; + tensor hidden_states_63_cast_fp16 = conv(bias = hidden_states_63_bias_0_to_fp16, dilations = hidden_states_61_dilations_0, groups = hidden_states_61_groups_0, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = hidden_states_61_strides_0, weight = hidden_states_63_weight_0_to_fp16_palettized, x = input_117_cast_fp16)[name = string("hidden_states_63_cast_fp16")]; + tensor hidden_states_65_cast_fp16 = add(x = hidden_states_53_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")]; + tensor input_119_begin_0 = const()[name = string("input_119_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor input_119_end_0 = const()[name = string("input_119_end_0"), val = tensor([1, 1024, 1, 24])]; + tensor input_119_end_mask_0 = const()[name = string("input_119_end_mask_0"), val = tensor([true, true, true, true])]; + tensor input_119_cast_fp16 = slice_by_index(begin = input_119_begin_0, end = input_119_end_0, end_mask = input_119_end_mask_0, x = hidden_states_65_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor context_mask_3_begin_0 = const()[name = string("context_mask_3_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor context_mask_3_end_0 = const()[name = string("context_mask_3_end_0"), val = tensor([1, 1, 1, 16])]; + tensor context_mask_3_end_mask_0 = const()[name = string("context_mask_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_3_cast_fp16 = slice_by_index(begin = context_mask_3_begin_0, end = context_mask_3_end_0, end_mask = context_mask_3_end_mask_0, x = context_mask_1_cast_fp16)[name = string("context_mask_3_cast_fp16")]; + string sub_pixels_7_pad_type_0 = const()[name = string("sub_pixels_7_pad_type_0"), val = string("valid")]; + tensor sub_pixels_7_strides_0 = const()[name = string("sub_pixels_7_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_7_pad_0 = const()[name = string("sub_pixels_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_7_dilations_0 = const()[name = string("sub_pixels_7_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_7_groups_0 = const()[name = string("sub_pixels_7_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52290048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54387264))))[name = string("audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54387840)))]; + tensor sub_pixels_7_cast_fp16 = conv(bias = audio_upsampler_upsample_1_0_conv_bias_to_fp16, dilations = sub_pixels_7_dilations_0, groups = sub_pixels_7_groups_0, pad = sub_pixels_7_pad_0, pad_type = sub_pixels_7_pad_type_0, strides = sub_pixels_7_strides_0, weight = audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("sub_pixels_7_cast_fp16")]; + tensor var_2183 = const()[name = string("op_2183"), val = tensor([1, 2, 1024, 21])]; + tensor sub_pixels_9_cast_fp16 = reshape(shape = var_2183, x = sub_pixels_7_cast_fp16)[name = string("sub_pixels_9_cast_fp16")]; + tensor var_2185 = const()[name = string("op_2185"), val = tensor([0, 2, 3, 1])]; + tensor var_2190 = const()[name = string("op_2190"), val = tensor([1, 1024, 1, 42])]; + tensor sub_pixels_11_cast_fp16 = transpose(perm = var_2185, x = sub_pixels_9_cast_fp16)[name = string("transpose_6")]; + tensor hidden_states_67_cast_fp16 = reshape(shape = var_2190, x = sub_pixels_11_cast_fp16)[name = string("hidden_states_67_cast_fp16")]; + tensor var_2195 = const()[name = string("op_2195"), val = tensor([1, 1, 13, 1])]; + tensor var_2196_cast_fp16 = reshape(shape = var_2195, x = context_mask_3_cast_fp16)[name = string("op_2196_cast_fp16")]; + tensor spread_3_reps_0 = const()[name = string("spread_3_reps_0"), val = tensor([1, 1, 1, 2])]; + tensor spread_3_cast_fp16 = tile(reps = spread_3_reps_0, x = var_2196_cast_fp16)[name = string("spread_3_cast_fp16")]; + tensor var_2202 = const()[name = string("op_2202"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_5_cast_fp16 = reshape(shape = var_2202, x = spread_3_cast_fp16)[name = string("context_mask_5_cast_fp16")]; + tensor residual_1_begin_0 = const()[name = string("residual_1_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_1_end_0 = const()[name = string("residual_1_end_0"), val = tensor([1, 1024, 1, 42])]; + tensor residual_1_end_mask_0 = const()[name = string("residual_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_1_cast_fp16 = slice_by_index(begin = residual_1_begin_0, end = residual_1_end_0, end_mask = residual_1_end_mask_0, x = hidden_states_67_cast_fp16)[name = string("residual_1_cast_fp16")]; + bool full_mask_3_interleave_0 = const()[name = string("full_mask_3_interleave_0"), val = bool(false)]; + tensor fill_1_to_fp16 = const()[name = string("fill_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115345152)))]; + tensor full_mask_3_cast_fp16 = concat(axis = var_2037, interleave = full_mask_3_interleave_0, values = (context_mask_5_cast_fp16, fill_1_to_fp16))[name = string("full_mask_3_cast_fp16")]; + tensor input_121_cast_fp16 = mul(x = hidden_states_67_cast_fp16, y = full_mask_3_cast_fp16)[name = string("input_121_cast_fp16")]; + string hidden_states_69_pad_type_0 = const()[name = string("hidden_states_69_pad_type_0"), val = string("valid")]; + int32 hidden_states_69_groups_0 = const()[name = string("hidden_states_69_groups_0"), val = int32(1024)]; + tensor hidden_states_69_strides_0 = const()[name = string("hidden_states_69_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_69_pad_0 = const()[name = string("hidden_states_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_69_dilations_0 = const()[name = string("hidden_states_69_dilations_0"), val = tensor([1, 1])]; + tensor audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54392000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115345280))))[name = string("audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54399808)))]; + tensor hidden_states_69_cast_fp16 = conv(bias = audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16, dilations = hidden_states_69_dilations_0, groups = hidden_states_69_groups_0, pad = hidden_states_69_pad_0, pad_type = hidden_states_69_pad_type_0, strides = hidden_states_69_strides_0, weight = audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized, x = input_121_cast_fp16)[name = string("hidden_states_69_cast_fp16")]; + tensor var_2229_axes_0 = const()[name = string("op_2229_axes_0"), val = tensor([2])]; + tensor var_2229_cast_fp16 = squeeze(axes = var_2229_axes_0, x = hidden_states_69_cast_fp16)[name = string("op_2229_cast_fp16")]; + tensor var_2230 = const()[name = string("op_2230"), val = tensor([0, 2, 1])]; + tensor hidden_states_71_axes_0 = const()[name = string("hidden_states_71_axes_0"), val = tensor([-1])]; + tensor audio_upsampler_upsample_1_1_norm_weight_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54401920)))]; + tensor audio_upsampler_upsample_1_1_norm_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54404032)))]; + tensor input_123_cast_fp16 = transpose(perm = var_2230, x = var_2229_cast_fp16)[name = string("transpose_5")]; + tensor hidden_states_71_cast_fp16 = layer_norm(axes = hidden_states_71_axes_0, beta = audio_upsampler_upsample_1_1_norm_bias_to_fp16, epsilon = var_2041_to_fp16, gamma = audio_upsampler_upsample_1_1_norm_weight_to_fp16, x = input_123_cast_fp16)[name = string("hidden_states_71_cast_fp16")]; + tensor var_2236 = const()[name = string("op_2236"), val = tensor([0, 2, 1])]; + tensor input_125_axes_0 = const()[name = string("input_125_axes_0"), val = tensor([2])]; + tensor var_2237_cast_fp16 = transpose(perm = var_2236, x = hidden_states_71_cast_fp16)[name = string("transpose_4")]; + tensor input_125_cast_fp16 = expand_dims(axes = input_125_axes_0, x = var_2237_cast_fp16)[name = string("input_125_cast_fp16")]; + string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("valid")]; + tensor input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor([1, 1])]; + tensor input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor([1, 1])]; + int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54406144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58600512))))[name = string("audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58601088)))]; + tensor input_127_cast_fp16 = conv(bias = audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized, x = input_125_cast_fp16)[name = string("input_127_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = input_127_cast_fp16)[name = string("input_129_cast_fp16")]; + string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")]; + tensor hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)]; + tensor hidden_states_75_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58609344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62803712))))[name = string("hidden_states_75_weight_0_to_fp16_palettized")]; + tensor hidden_states_75_bias_0_to_fp16 = const()[name = string("hidden_states_75_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62804288)))]; + tensor hidden_states_75_cast_fp16 = conv(bias = hidden_states_75_bias_0_to_fp16, dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = hidden_states_75_weight_0_to_fp16_palettized, x = input_129_cast_fp16)[name = string("hidden_states_75_cast_fp16")]; + tensor hidden_states_77_cast_fp16 = add(x = residual_1_cast_fp16, y = hidden_states_75_cast_fp16)[name = string("hidden_states_77_cast_fp16")]; + tensor context_mask_7_begin_0 = const()[name = string("context_mask_7_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_7_end_0 = const()[name = string("context_mask_7_end_0"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_7_end_mask_0 = const()[name = string("context_mask_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_7_cast_fp16 = slice_by_index(begin = context_mask_7_begin_0, end = context_mask_7_end_0, end_mask = context_mask_7_end_mask_0, x = context_mask_5_cast_fp16)[name = string("context_mask_7_cast_fp16")]; + bool full_mask_5_interleave_0 = const()[name = string("full_mask_5_interleave_0"), val = bool(false)]; + tensor fill_2_to_fp16 = const()[name = string("fill_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115345152)))]; + tensor full_mask_5_cast_fp16 = concat(axis = var_2037, interleave = full_mask_5_interleave_0, values = (context_mask_7_cast_fp16, fill_2_to_fp16))[name = string("full_mask_5_cast_fp16")]; + tensor input_131_cast_fp16 = mul(x = hidden_states_77_cast_fp16, y = full_mask_5_cast_fp16)[name = string("input_131_cast_fp16")]; + string hidden_states_79_pad_type_0 = const()[name = string("hidden_states_79_pad_type_0"), val = string("valid")]; + tensor hidden_states_79_strides_0 = const()[name = string("hidden_states_79_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_79_pad_0 = const()[name = string("hidden_states_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_79_dilations_0 = const()[name = string("hidden_states_79_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_79_groups_0 = const()[name = string("hidden_states_79_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62806400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73816512))))[name = string("audio_upsampler_decoder_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73817088)))]; + tensor hidden_states_79_cast_fp16 = conv(bias = audio_upsampler_decoder_0_conv_bias_to_fp16, dilations = hidden_states_79_dilations_0, groups = hidden_states_79_groups_0, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = hidden_states_79_strides_0, weight = audio_upsampler_decoder_0_conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("hidden_states_79_cast_fp16")]; + tensor context_mask_9_begin_0 = const()[name = string("context_mask_9_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_9_end_0 = const()[name = string("context_mask_9_end_0"), val = tensor([1, 1, 1, 20])]; + tensor context_mask_9_end_mask_0 = const()[name = string("context_mask_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_9_cast_fp16 = slice_by_index(begin = context_mask_9_begin_0, end = context_mask_9_end_0, end_mask = context_mask_9_end_mask_0, x = context_mask_7_cast_fp16)[name = string("context_mask_9_cast_fp16")]; + tensor alpha_over_pi_1_to_fp16 = const()[name = string("alpha_over_pi_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73820224)))]; + tensor theta_over_pi_1_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = alpha_over_pi_1_to_fp16)[name = string("theta_over_pi_1_cast_fp16")]; + tensor var_2305_cast_fp16 = round(x = theta_over_pi_1_cast_fp16)[name = string("op_2305_cast_fp16")]; + tensor reduced_1_cast_fp16 = sub(x = theta_over_pi_1_cast_fp16, y = var_2305_cast_fp16)[name = string("reduced_1_cast_fp16")]; + tensor reduced_sq_1_cast_fp16 = mul(x = reduced_1_cast_fp16, y = reduced_1_cast_fp16)[name = string("reduced_sq_1_cast_fp16")]; + tensor acc_1_mean_0_to_fp16 = const()[name = string("acc_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73823360)))]; + tensor acc_1_variance_0_to_fp16 = const()[name = string("acc_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73826496)))]; + tensor acc_1_gamma_0_to_fp16 = const()[name = string("acc_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73829632)))]; + tensor acc_1_beta_0_to_fp16 = const()[name = string("acc_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73832768)))]; + fp16 acc_1_epsilon_0_to_fp16 = const()[name = string("acc_1_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_1_cast_fp16 = batch_norm(beta = acc_1_beta_0_to_fp16, epsilon = acc_1_epsilon_0_to_fp16, gamma = acc_1_gamma_0_to_fp16, mean = acc_1_mean_0_to_fp16, variance = acc_1_variance_0_to_fp16, x = reduced_sq_1_cast_fp16)[name = string("acc_1_cast_fp16")]; + tensor var_2318_cast_fp16 = mul(x = acc_1_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2318_cast_fp16")]; + tensor c_1_to_fp16 = const()[name = string("c_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73835904)))]; + tensor acc_3_cast_fp16 = add(x = var_2318_cast_fp16, y = c_1_to_fp16)[name = string("acc_3_cast_fp16")]; + tensor var_2320_cast_fp16 = mul(x = acc_3_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2320_cast_fp16")]; + tensor c_3_to_fp16 = const()[name = string("c_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73839040)))]; + tensor acc_5_cast_fp16 = add(x = var_2320_cast_fp16, y = c_3_to_fp16)[name = string("acc_5_cast_fp16")]; + tensor var_2322_cast_fp16 = mul(x = acc_5_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2322_cast_fp16")]; + tensor hidden_states_81_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = var_2322_cast_fp16)[name = string("hidden_states_81_cast_fp16")]; + bool full_mask_7_interleave_0 = const()[name = string("full_mask_7_interleave_0"), val = bool(false)]; + tensor fill_3_to_fp16 = const()[name = string("fill_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115345152)))]; + tensor full_mask_7_cast_fp16 = concat(axis = var_2037, interleave = full_mask_7_interleave_0, values = (context_mask_9_cast_fp16, fill_3_to_fp16))[name = string("full_mask_7_cast_fp16")]; + tensor input_133_cast_fp16 = mul(x = hidden_states_81_cast_fp16, y = full_mask_7_cast_fp16)[name = string("input_133_cast_fp16")]; + string sub_pixels_13_pad_type_0 = const()[name = string("sub_pixels_13_pad_type_0"), val = string("valid")]; + tensor sub_pixels_13_strides_0 = const()[name = string("sub_pixels_13_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_13_pad_0 = const()[name = string("sub_pixels_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_13_dilations_0 = const()[name = string("sub_pixels_13_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_13_groups_0 = const()[name = string("sub_pixels_13_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73842176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92716608))))[name = string("audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92717184)))]; + tensor sub_pixels_13_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_1_conv_bias_to_fp16, dilations = sub_pixels_13_dilations_0, groups = sub_pixels_13_groups_0, pad = sub_pixels_13_pad_0, pad_type = sub_pixels_13_pad_type_0, strides = sub_pixels_13_strides_0, weight = audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized, x = input_133_cast_fp16)[name = string("sub_pixels_13_cast_fp16")]; + tensor var_2346 = const()[name = string("op_2346"), val = tensor([1, 8, 768, 29])]; + tensor sub_pixels_15_cast_fp16 = reshape(shape = var_2346, x = sub_pixels_13_cast_fp16)[name = string("sub_pixels_15_cast_fp16")]; + tensor var_2348 = const()[name = string("op_2348"), val = tensor([0, 2, 3, 1])]; + tensor var_2353 = const()[name = string("op_2353"), val = tensor([1, 768, 1, 232])]; + tensor sub_pixels_17_cast_fp16 = transpose(perm = var_2348, x = sub_pixels_15_cast_fp16)[name = string("transpose_3")]; + tensor hidden_states_83_cast_fp16 = reshape(shape = var_2353, x = sub_pixels_17_cast_fp16)[name = string("hidden_states_83_cast_fp16")]; + tensor newest_5_begin_0 = const()[name = string("newest_5_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_5_end_0 = const()[name = string("newest_5_end_0"), val = tensor([1, 1, 1, 14])]; + tensor newest_5_end_mask_0 = const()[name = string("newest_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_5_cast_fp16 = slice_by_index(begin = newest_5_begin_0, end = newest_5_end_0, end_mask = newest_5_end_mask_0, x = context_mask_9_cast_fp16)[name = string("newest_5_cast_fp16")]; + tensor var_2358 = const()[name = string("op_2358"), val = tensor([1, 1, 13, 1])]; + tensor var_2359_cast_fp16 = reshape(shape = var_2358, x = newest_5_cast_fp16)[name = string("op_2359_cast_fp16")]; + tensor spread_5_reps_0 = const()[name = string("spread_5_reps_0"), val = tensor([1, 1, 1, 8])]; + tensor spread_5_cast_fp16 = tile(reps = spread_5_reps_0, x = var_2359_cast_fp16)[name = string("spread_5_cast_fp16")]; + tensor var_2365 = const()[name = string("op_2365"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_11_cast_fp16 = reshape(shape = var_2365, x = spread_5_cast_fp16)[name = string("context_mask_11_cast_fp16")]; + tensor residual_3_begin_0 = const()[name = string("residual_3_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_3_end_0 = const()[name = string("residual_3_end_0"), val = tensor([1, 768, 1, 232])]; + tensor residual_3_end_mask_0 = const()[name = string("residual_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_3_cast_fp16 = slice_by_index(begin = residual_3_begin_0, end = residual_3_end_0, end_mask = residual_3_end_mask_0, x = hidden_states_83_cast_fp16)[name = string("residual_3_cast_fp16")]; + tensor alpha_over_pi_3_to_fp16 = const()[name = string("alpha_over_pi_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92729536)))]; + tensor theta_over_pi_3_cast_fp16 = mul(x = hidden_states_83_cast_fp16, y = alpha_over_pi_3_to_fp16)[name = string("theta_over_pi_3_cast_fp16")]; + tensor var_2388_cast_fp16 = round(x = theta_over_pi_3_cast_fp16)[name = string("op_2388_cast_fp16")]; + tensor reduced_3_cast_fp16 = sub(x = theta_over_pi_3_cast_fp16, y = var_2388_cast_fp16)[name = string("reduced_3_cast_fp16")]; + tensor reduced_sq_3_cast_fp16 = mul(x = reduced_3_cast_fp16, y = reduced_3_cast_fp16)[name = string("reduced_sq_3_cast_fp16")]; + tensor acc_7_mean_0_to_fp16 = const()[name = string("acc_7_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92731136)))]; + tensor acc_7_variance_0_to_fp16 = const()[name = string("acc_7_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92732736)))]; + tensor acc_7_gamma_0_to_fp16 = const()[name = string("acc_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92734336)))]; + tensor acc_7_beta_0_to_fp16 = const()[name = string("acc_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92735936)))]; + fp16 acc_7_epsilon_0_to_fp16 = const()[name = string("acc_7_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_7_cast_fp16 = batch_norm(beta = acc_7_beta_0_to_fp16, epsilon = acc_7_epsilon_0_to_fp16, gamma = acc_7_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_3_cast_fp16)[name = string("acc_7_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = acc_7_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor c_5_to_fp16 = const()[name = string("c_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92737536)))]; + tensor acc_9_cast_fp16 = add(x = var_2401_cast_fp16, y = c_5_to_fp16)[name = string("acc_9_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = acc_9_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor c_7_to_fp16 = const()[name = string("c_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92739136)))]; + tensor acc_11_cast_fp16 = add(x = var_2403_cast_fp16, y = c_7_to_fp16)[name = string("acc_11_cast_fp16")]; + tensor var_2405_cast_fp16 = mul(x = acc_11_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2405_cast_fp16")]; + tensor hidden_states_85_cast_fp16 = add(x = hidden_states_83_cast_fp16, y = var_2405_cast_fp16)[name = string("hidden_states_85_cast_fp16")]; + bool full_mask_9_interleave_0 = const()[name = string("full_mask_9_interleave_0"), val = bool(false)]; + tensor fill_4_to_fp16 = const()[name = string("fill_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115345856)))]; + tensor full_mask_9_cast_fp16 = concat(axis = var_2037, interleave = full_mask_9_interleave_0, values = (context_mask_11_cast_fp16, fill_4_to_fp16))[name = string("full_mask_9_cast_fp16")]; + tensor input_135_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = full_mask_9_cast_fp16)[name = string("input_135_cast_fp16")]; + string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")]; + tensor hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96869696))))[name = string("audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96870272)))]; + tensor hidden_states_87_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("hidden_states_87_cast_fp16")]; + tensor alpha_over_pi_5_to_fp16 = const()[name = string("alpha_over_pi_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96871872)))]; + tensor theta_over_pi_5_cast_fp16 = mul(x = hidden_states_87_cast_fp16, y = alpha_over_pi_5_to_fp16)[name = string("theta_over_pi_5_cast_fp16")]; + tensor var_2442_cast_fp16 = round(x = theta_over_pi_5_cast_fp16)[name = string("op_2442_cast_fp16")]; + tensor reduced_5_cast_fp16 = sub(x = theta_over_pi_5_cast_fp16, y = var_2442_cast_fp16)[name = string("reduced_5_cast_fp16")]; + tensor reduced_sq_5_cast_fp16 = mul(x = reduced_5_cast_fp16, y = reduced_5_cast_fp16)[name = string("reduced_sq_5_cast_fp16")]; + tensor acc_13_gamma_0_to_fp16 = const()[name = string("acc_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96873472)))]; + tensor acc_13_beta_0_to_fp16 = const()[name = string("acc_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96875072)))]; + fp16 acc_13_epsilon_0_to_fp16 = const()[name = string("acc_13_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_13_cast_fp16 = batch_norm(beta = acc_13_beta_0_to_fp16, epsilon = acc_13_epsilon_0_to_fp16, gamma = acc_13_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_5_cast_fp16)[name = string("acc_13_cast_fp16")]; + tensor var_2455_cast_fp16 = mul(x = acc_13_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2455_cast_fp16")]; + tensor c_9_to_fp16 = const()[name = string("c_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96876672)))]; + tensor acc_15_cast_fp16 = add(x = var_2455_cast_fp16, y = c_9_to_fp16)[name = string("acc_15_cast_fp16")]; + tensor var_2457_cast_fp16 = mul(x = acc_15_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2457_cast_fp16")]; + tensor c_11_to_fp16 = const()[name = string("c_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96878272)))]; + tensor acc_17_cast_fp16 = add(x = var_2457_cast_fp16, y = c_11_to_fp16)[name = string("acc_17_cast_fp16")]; + tensor var_2459_cast_fp16 = mul(x = acc_17_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2459_cast_fp16")]; + tensor hidden_states_89_cast_fp16 = add(x = hidden_states_87_cast_fp16, y = var_2459_cast_fp16)[name = string("hidden_states_89_cast_fp16")]; + string hidden_states_91_pad_type_0 = const()[name = string("hidden_states_91_pad_type_0"), val = string("valid")]; + tensor hidden_states_91_strides_0 = const()[name = string("hidden_states_91_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_91_pad_0 = const()[name = string("hidden_states_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_91_dilations_0 = const()[name = string("hidden_states_91_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_91_groups_0 = const()[name = string("hidden_states_91_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96879872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97469760))))[name = string("audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97470336)))]; + tensor hidden_states_91_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_91_dilations_0, groups = hidden_states_91_groups_0, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = hidden_states_91_strides_0, weight = audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_89_cast_fp16)[name = string("hidden_states_91_cast_fp16")]; + tensor hidden_states_93_cast_fp16 = add(x = hidden_states_91_cast_fp16, y = residual_3_cast_fp16)[name = string("hidden_states_93_cast_fp16")]; + tensor context_mask_13_begin_0 = const()[name = string("context_mask_13_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_13_end_0 = const()[name = string("context_mask_13_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_13_end_mask_0 = const()[name = string("context_mask_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_13_cast_fp16 = slice_by_index(begin = context_mask_13_begin_0, end = context_mask_13_end_0, end_mask = context_mask_13_end_mask_0, x = context_mask_11_cast_fp16)[name = string("context_mask_13_cast_fp16")]; + tensor residual_5_begin_0 = const()[name = string("residual_5_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_5_end_0 = const()[name = string("residual_5_end_0"), val = tensor([1, 768, 1, 226])]; + tensor residual_5_end_mask_0 = const()[name = string("residual_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_5_cast_fp16 = slice_by_index(begin = residual_5_begin_0, end = residual_5_end_0, end_mask = residual_5_end_mask_0, x = hidden_states_93_cast_fp16)[name = string("residual_5_cast_fp16")]; + tensor alpha_over_pi_7_to_fp16 = const()[name = string("alpha_over_pi_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97471936)))]; + tensor theta_over_pi_7_cast_fp16 = mul(x = hidden_states_93_cast_fp16, y = alpha_over_pi_7_to_fp16)[name = string("theta_over_pi_7_cast_fp16")]; + tensor var_2494_cast_fp16 = round(x = theta_over_pi_7_cast_fp16)[name = string("op_2494_cast_fp16")]; + tensor reduced_7_cast_fp16 = sub(x = theta_over_pi_7_cast_fp16, y = var_2494_cast_fp16)[name = string("reduced_7_cast_fp16")]; + tensor reduced_sq_7_cast_fp16 = mul(x = reduced_7_cast_fp16, y = reduced_7_cast_fp16)[name = string("reduced_sq_7_cast_fp16")]; + tensor acc_19_gamma_0_to_fp16 = const()[name = string("acc_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97473536)))]; + tensor acc_19_beta_0_to_fp16 = const()[name = string("acc_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97475136)))]; + fp16 acc_19_epsilon_0_to_fp16 = const()[name = string("acc_19_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_19_cast_fp16 = batch_norm(beta = acc_19_beta_0_to_fp16, epsilon = acc_19_epsilon_0_to_fp16, gamma = acc_19_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_7_cast_fp16)[name = string("acc_19_cast_fp16")]; + tensor var_2507_cast_fp16 = mul(x = acc_19_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2507_cast_fp16")]; + tensor c_13_to_fp16 = const()[name = string("c_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97476736)))]; + tensor acc_21_cast_fp16 = add(x = var_2507_cast_fp16, y = c_13_to_fp16)[name = string("acc_21_cast_fp16")]; + tensor var_2509_cast_fp16 = mul(x = acc_21_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2509_cast_fp16")]; + tensor c_15_to_fp16 = const()[name = string("c_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97478336)))]; + tensor acc_23_cast_fp16 = add(x = var_2509_cast_fp16, y = c_15_to_fp16)[name = string("acc_23_cast_fp16")]; + tensor var_2511_cast_fp16 = mul(x = acc_23_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2511_cast_fp16")]; + tensor hidden_states_95_cast_fp16 = add(x = hidden_states_93_cast_fp16, y = var_2511_cast_fp16)[name = string("hidden_states_95_cast_fp16")]; + bool full_mask_11_interleave_0 = const()[name = string("full_mask_11_interleave_0"), val = bool(false)]; + tensor full_mask_11_cast_fp16 = concat(axis = var_2037, interleave = full_mask_11_interleave_0, values = (context_mask_13_cast_fp16, fill_4_to_fp16))[name = string("full_mask_11_cast_fp16")]; + tensor input_139_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = full_mask_11_cast_fp16)[name = string("input_139_cast_fp16")]; + string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")]; + tensor hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97479936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101608768))))[name = string("audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101609344)))]; + tensor hidden_states_97_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized, x = input_139_cast_fp16)[name = string("hidden_states_97_cast_fp16")]; + tensor alpha_over_pi_9_to_fp16 = const()[name = string("alpha_over_pi_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101610944)))]; + tensor theta_over_pi_9_cast_fp16 = mul(x = hidden_states_97_cast_fp16, y = alpha_over_pi_9_to_fp16)[name = string("theta_over_pi_9_cast_fp16")]; + tensor var_2548_cast_fp16 = round(x = theta_over_pi_9_cast_fp16)[name = string("op_2548_cast_fp16")]; + tensor reduced_9_cast_fp16 = sub(x = theta_over_pi_9_cast_fp16, y = var_2548_cast_fp16)[name = string("reduced_9_cast_fp16")]; + tensor reduced_sq_9_cast_fp16 = mul(x = reduced_9_cast_fp16, y = reduced_9_cast_fp16)[name = string("reduced_sq_9_cast_fp16")]; + tensor acc_25_gamma_0_to_fp16 = const()[name = string("acc_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101612544)))]; + tensor acc_25_beta_0_to_fp16 = const()[name = string("acc_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101614144)))]; + fp16 acc_25_epsilon_0_to_fp16 = const()[name = string("acc_25_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_25_cast_fp16 = batch_norm(beta = acc_25_beta_0_to_fp16, epsilon = acc_25_epsilon_0_to_fp16, gamma = acc_25_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_9_cast_fp16)[name = string("acc_25_cast_fp16")]; + tensor var_2561_cast_fp16 = mul(x = acc_25_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2561_cast_fp16")]; + tensor c_17_to_fp16 = const()[name = string("c_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101615744)))]; + tensor acc_27_cast_fp16 = add(x = var_2561_cast_fp16, y = c_17_to_fp16)[name = string("acc_27_cast_fp16")]; + tensor var_2563_cast_fp16 = mul(x = acc_27_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2563_cast_fp16")]; + tensor c_19_to_fp16 = const()[name = string("c_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101617344)))]; + tensor acc_29_cast_fp16 = add(x = var_2563_cast_fp16, y = c_19_to_fp16)[name = string("acc_29_cast_fp16")]; + tensor var_2565_cast_fp16 = mul(x = acc_29_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2565_cast_fp16")]; + tensor hidden_states_99_cast_fp16 = add(x = hidden_states_97_cast_fp16, y = var_2565_cast_fp16)[name = string("hidden_states_99_cast_fp16")]; + string hidden_states_101_pad_type_0 = const()[name = string("hidden_states_101_pad_type_0"), val = string("valid")]; + tensor hidden_states_101_strides_0 = const()[name = string("hidden_states_101_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_101_pad_0 = const()[name = string("hidden_states_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_101_dilations_0 = const()[name = string("hidden_states_101_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_101_groups_0 = const()[name = string("hidden_states_101_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101618944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102208832))))[name = string("audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102209408)))]; + tensor hidden_states_101_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_101_dilations_0, groups = hidden_states_101_groups_0, pad = hidden_states_101_pad_0, pad_type = hidden_states_101_pad_type_0, strides = hidden_states_101_strides_0, weight = audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_99_cast_fp16)[name = string("hidden_states_101_cast_fp16")]; + tensor hidden_states_103_cast_fp16 = add(x = hidden_states_101_cast_fp16, y = residual_5_cast_fp16)[name = string("hidden_states_103_cast_fp16")]; + tensor context_mask_15_begin_0 = const()[name = string("context_mask_15_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_15_end_0 = const()[name = string("context_mask_15_end_0"), val = tensor([1, 1, 1, 98])]; + tensor context_mask_15_end_mask_0 = const()[name = string("context_mask_15_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_15_cast_fp16 = slice_by_index(begin = context_mask_15_begin_0, end = context_mask_15_end_0, end_mask = context_mask_15_end_mask_0, x = context_mask_13_cast_fp16)[name = string("context_mask_15_cast_fp16")]; + tensor residual_7_begin_0 = const()[name = string("residual_7_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_7_end_0 = const()[name = string("residual_7_end_0"), val = tensor([1, 768, 1, 208])]; + tensor residual_7_end_mask_0 = const()[name = string("residual_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_7_cast_fp16 = slice_by_index(begin = residual_7_begin_0, end = residual_7_end_0, end_mask = residual_7_end_mask_0, x = hidden_states_103_cast_fp16)[name = string("residual_7_cast_fp16")]; + tensor alpha_over_pi_11_to_fp16 = const()[name = string("alpha_over_pi_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102211008)))]; + tensor theta_over_pi_11_cast_fp16 = mul(x = hidden_states_103_cast_fp16, y = alpha_over_pi_11_to_fp16)[name = string("theta_over_pi_11_cast_fp16")]; + tensor var_2600_cast_fp16 = round(x = theta_over_pi_11_cast_fp16)[name = string("op_2600_cast_fp16")]; + tensor reduced_11_cast_fp16 = sub(x = theta_over_pi_11_cast_fp16, y = var_2600_cast_fp16)[name = string("reduced_11_cast_fp16")]; + tensor reduced_sq_11_cast_fp16 = mul(x = reduced_11_cast_fp16, y = reduced_11_cast_fp16)[name = string("reduced_sq_11_cast_fp16")]; + tensor acc_31_gamma_0_to_fp16 = const()[name = string("acc_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102212608)))]; + tensor acc_31_beta_0_to_fp16 = const()[name = string("acc_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102214208)))]; + fp16 acc_31_epsilon_0_to_fp16 = const()[name = string("acc_31_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_31_cast_fp16 = batch_norm(beta = acc_31_beta_0_to_fp16, epsilon = acc_31_epsilon_0_to_fp16, gamma = acc_31_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_11_cast_fp16)[name = string("acc_31_cast_fp16")]; + tensor var_2613_cast_fp16 = mul(x = acc_31_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor c_21_to_fp16 = const()[name = string("c_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102215808)))]; + tensor acc_33_cast_fp16 = add(x = var_2613_cast_fp16, y = c_21_to_fp16)[name = string("acc_33_cast_fp16")]; + tensor var_2615_cast_fp16 = mul(x = acc_33_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2615_cast_fp16")]; + tensor c_23_to_fp16 = const()[name = string("c_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102217408)))]; + tensor acc_35_cast_fp16 = add(x = var_2615_cast_fp16, y = c_23_to_fp16)[name = string("acc_35_cast_fp16")]; + tensor var_2617_cast_fp16 = mul(x = acc_35_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2617_cast_fp16")]; + tensor hidden_states_105_cast_fp16 = add(x = hidden_states_103_cast_fp16, y = var_2617_cast_fp16)[name = string("hidden_states_105_cast_fp16")]; + bool full_mask_13_interleave_0 = const()[name = string("full_mask_13_interleave_0"), val = bool(false)]; + tensor full_mask_13_cast_fp16 = concat(axis = var_2037, interleave = full_mask_13_interleave_0, values = (context_mask_15_cast_fp16, fill_4_to_fp16))[name = string("full_mask_13_cast_fp16")]; + tensor input_143_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = full_mask_13_cast_fp16)[name = string("input_143_cast_fp16")]; + string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")]; + tensor hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102219008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106347840))))[name = string("audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106348416)))]; + tensor hidden_states_107_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("hidden_states_107_cast_fp16")]; + tensor alpha_over_pi_13_to_fp16 = const()[name = string("alpha_over_pi_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106350016)))]; + tensor theta_over_pi_13_cast_fp16 = mul(x = hidden_states_107_cast_fp16, y = alpha_over_pi_13_to_fp16)[name = string("theta_over_pi_13_cast_fp16")]; + tensor var_2654_cast_fp16 = round(x = theta_over_pi_13_cast_fp16)[name = string("op_2654_cast_fp16")]; + tensor reduced_13_cast_fp16 = sub(x = theta_over_pi_13_cast_fp16, y = var_2654_cast_fp16)[name = string("reduced_13_cast_fp16")]; + tensor reduced_sq_13_cast_fp16 = mul(x = reduced_13_cast_fp16, y = reduced_13_cast_fp16)[name = string("reduced_sq_13_cast_fp16")]; + tensor acc_37_gamma_0_to_fp16 = const()[name = string("acc_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106351616)))]; + tensor acc_37_beta_0_to_fp16 = const()[name = string("acc_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106353216)))]; + fp16 acc_37_epsilon_0_to_fp16 = const()[name = string("acc_37_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_37_cast_fp16 = batch_norm(beta = acc_37_beta_0_to_fp16, epsilon = acc_37_epsilon_0_to_fp16, gamma = acc_37_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_13_cast_fp16)[name = string("acc_37_cast_fp16")]; + tensor var_2667_cast_fp16 = mul(x = acc_37_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2667_cast_fp16")]; + tensor c_25_to_fp16 = const()[name = string("c_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106354816)))]; + tensor acc_39_cast_fp16 = add(x = var_2667_cast_fp16, y = c_25_to_fp16)[name = string("acc_39_cast_fp16")]; + tensor var_2669_cast_fp16 = mul(x = acc_39_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2669_cast_fp16")]; + tensor c_27_to_fp16 = const()[name = string("c_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106356416)))]; + tensor acc_41_cast_fp16 = add(x = var_2669_cast_fp16, y = c_27_to_fp16)[name = string("acc_41_cast_fp16")]; + tensor var_2671_cast_fp16 = mul(x = acc_41_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2671_cast_fp16")]; + tensor hidden_states_109_cast_fp16 = add(x = hidden_states_107_cast_fp16, y = var_2671_cast_fp16)[name = string("hidden_states_109_cast_fp16")]; + string hidden_states_111_pad_type_0 = const()[name = string("hidden_states_111_pad_type_0"), val = string("valid")]; + tensor hidden_states_111_strides_0 = const()[name = string("hidden_states_111_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_111_pad_0 = const()[name = string("hidden_states_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_111_dilations_0 = const()[name = string("hidden_states_111_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_111_groups_0 = const()[name = string("hidden_states_111_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106358016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106947904))))[name = string("audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106948480)))]; + tensor hidden_states_111_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_111_dilations_0, groups = hidden_states_111_groups_0, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = hidden_states_111_strides_0, weight = audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_109_cast_fp16)[name = string("hidden_states_111_cast_fp16")]; + tensor hidden_states_113_cast_fp16 = add(x = hidden_states_111_cast_fp16, y = residual_7_cast_fp16)[name = string("hidden_states_113_cast_fp16")]; + tensor context_mask_19_begin_0 = const()[name = string("context_mask_19_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_19_end_0 = const()[name = string("context_mask_19_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_19_end_mask_0 = const()[name = string("context_mask_19_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_19_cast_fp16 = slice_by_index(begin = context_mask_19_begin_0, end = context_mask_19_end_0, end_mask = context_mask_19_end_mask_0, x = context_mask_11_cast_fp16)[name = string("context_mask_19_cast_fp16")]; + tensor hidden_states_115_begin_0 = const()[name = string("hidden_states_115_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor hidden_states_115_end_0 = const()[name = string("hidden_states_115_end_0"), val = tensor([1, 768, 1, 154])]; + tensor hidden_states_115_end_mask_0 = const()[name = string("hidden_states_115_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_115_cast_fp16 = slice_by_index(begin = hidden_states_115_begin_0, end = hidden_states_115_end_0, end_mask = hidden_states_115_end_mask_0, x = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")]; + tensor context_mask_21_begin_0 = const()[name = string("context_mask_21_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor context_mask_21_end_0 = const()[name = string("context_mask_21_end_0"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_21_end_mask_0 = const()[name = string("context_mask_21_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_21_cast_fp16 = slice_by_index(begin = context_mask_21_begin_0, end = context_mask_21_end_0, end_mask = context_mask_21_end_mask_0, x = context_mask_19_cast_fp16)[name = string("context_mask_21_cast_fp16")]; + tensor alpha_over_pi_15_to_fp16 = const()[name = string("alpha_over_pi_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106950080)))]; + tensor theta_over_pi_15_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = alpha_over_pi_15_to_fp16)[name = string("theta_over_pi_15_cast_fp16")]; + tensor var_2731_cast_fp16 = round(x = theta_over_pi_15_cast_fp16)[name = string("op_2731_cast_fp16")]; + tensor reduced_15_cast_fp16 = sub(x = theta_over_pi_15_cast_fp16, y = var_2731_cast_fp16)[name = string("reduced_15_cast_fp16")]; + tensor reduced_sq_15_cast_fp16 = mul(x = reduced_15_cast_fp16, y = reduced_15_cast_fp16)[name = string("reduced_sq_15_cast_fp16")]; + tensor acc_43_gamma_0_to_fp16 = const()[name = string("acc_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106951680)))]; + tensor acc_43_beta_0_to_fp16 = const()[name = string("acc_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106953280)))]; + fp16 acc_43_epsilon_0_to_fp16 = const()[name = string("acc_43_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_43_cast_fp16 = batch_norm(beta = acc_43_beta_0_to_fp16, epsilon = acc_43_epsilon_0_to_fp16, gamma = acc_43_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_15_cast_fp16)[name = string("acc_43_cast_fp16")]; + tensor var_2744_cast_fp16 = mul(x = acc_43_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2744_cast_fp16")]; + tensor c_29_to_fp16 = const()[name = string("c_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106954880)))]; + tensor acc_45_cast_fp16 = add(x = var_2744_cast_fp16, y = c_29_to_fp16)[name = string("acc_45_cast_fp16")]; + tensor var_2746_cast_fp16 = mul(x = acc_45_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2746_cast_fp16")]; + tensor c_31_to_fp16 = const()[name = string("c_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106956480)))]; + tensor acc_47_cast_fp16 = add(x = var_2746_cast_fp16, y = c_31_to_fp16)[name = string("acc_47_cast_fp16")]; + tensor var_2748_cast_fp16 = mul(x = acc_47_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2748_cast_fp16")]; + tensor hidden_states_117_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = var_2748_cast_fp16)[name = string("hidden_states_117_cast_fp16")]; + bool full_mask_15_interleave_0 = const()[name = string("full_mask_15_interleave_0"), val = bool(false)]; + tensor full_mask_15_cast_fp16 = concat(axis = var_2037, interleave = full_mask_15_interleave_0, values = (context_mask_21_cast_fp16, fill_4_to_fp16))[name = string("full_mask_15_cast_fp16")]; + tensor input_147_cast_fp16 = mul(x = hidden_states_117_cast_fp16, y = full_mask_15_cast_fp16)[name = string("input_147_cast_fp16")]; + string sub_pixels_19_pad_type_0 = const()[name = string("sub_pixels_19_pad_type_0"), val = string("valid")]; + tensor sub_pixels_19_strides_0 = const()[name = string("sub_pixels_19_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_19_pad_0 = const()[name = string("sub_pixels_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_19_dilations_0 = const()[name = string("sub_pixels_19_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_19_groups_0 = const()[name = string("sub_pixels_19_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106958080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109907264))))[name = string("audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109907840)))]; + tensor sub_pixels_19_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_1_conv_bias_to_fp16, dilations = sub_pixels_19_dilations_0, groups = sub_pixels_19_groups_0, pad = sub_pixels_19_pad_0, pad_type = sub_pixels_19_pad_type_0, strides = sub_pixels_19_strides_0, weight = audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("sub_pixels_19_cast_fp16")]; + tensor var_2772 = const()[name = string("op_2772"), val = tensor([1, 5, 384, 150])]; + tensor sub_pixels_21_cast_fp16 = reshape(shape = var_2772, x = sub_pixels_19_cast_fp16)[name = string("sub_pixels_21_cast_fp16")]; + tensor var_2774 = const()[name = string("op_2774"), val = tensor([0, 2, 3, 1])]; + tensor var_2779 = const()[name = string("op_2779"), val = tensor([1, 384, 1, 750])]; + tensor sub_pixels_23_cast_fp16 = transpose(perm = var_2774, x = sub_pixels_21_cast_fp16)[name = string("transpose_2")]; + tensor hidden_states_119_cast_fp16 = reshape(shape = var_2779, x = sub_pixels_23_cast_fp16)[name = string("hidden_states_119_cast_fp16")]; + tensor newest_9_begin_0 = const()[name = string("newest_9_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_9_end_0 = const()[name = string("newest_9_end_0"), val = tensor([1, 1, 1, 23])]; + tensor newest_9_end_mask_0 = const()[name = string("newest_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_9_cast_fp16 = slice_by_index(begin = newest_9_begin_0, end = newest_9_end_0, end_mask = newest_9_end_mask_0, x = context_mask_21_cast_fp16)[name = string("newest_9_cast_fp16")]; + tensor var_2784 = const()[name = string("op_2784"), val = tensor([1, 1, 22, 1])]; + tensor var_2785_cast_fp16 = reshape(shape = var_2784, x = newest_9_cast_fp16)[name = string("op_2785_cast_fp16")]; + tensor spread_9_reps_0 = const()[name = string("spread_9_reps_0"), val = tensor([1, 1, 1, 5])]; + tensor spread_9_cast_fp16 = tile(reps = spread_9_reps_0, x = var_2785_cast_fp16)[name = string("spread_9_cast_fp16")]; + tensor var_2791 = const()[name = string("op_2791"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_23_cast_fp16 = reshape(shape = var_2791, x = spread_9_cast_fp16)[name = string("context_mask_23_cast_fp16")]; + tensor residual_9_begin_0 = const()[name = string("residual_9_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_9_end_0 = const()[name = string("residual_9_end_0"), val = tensor([1, 384, 1, 750])]; + tensor residual_9_end_mask_0 = const()[name = string("residual_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_9_cast_fp16 = slice_by_index(begin = residual_9_begin_0, end = residual_9_end_0, end_mask = residual_9_end_mask_0, x = hidden_states_119_cast_fp16)[name = string("residual_9_cast_fp16")]; + tensor alpha_over_pi_17_to_fp16 = const()[name = string("alpha_over_pi_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109911744)))]; + tensor theta_over_pi_17_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = alpha_over_pi_17_to_fp16)[name = string("theta_over_pi_17_cast_fp16")]; + tensor var_2814_cast_fp16 = round(x = theta_over_pi_17_cast_fp16)[name = string("op_2814_cast_fp16")]; + tensor reduced_17_cast_fp16 = sub(x = theta_over_pi_17_cast_fp16, y = var_2814_cast_fp16)[name = string("reduced_17_cast_fp16")]; + tensor reduced_sq_17_cast_fp16 = mul(x = reduced_17_cast_fp16, y = reduced_17_cast_fp16)[name = string("reduced_sq_17_cast_fp16")]; + tensor acc_49_mean_0_to_fp16 = const()[name = string("acc_49_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109912576)))]; + tensor acc_49_variance_0_to_fp16 = const()[name = string("acc_49_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109913408)))]; + tensor acc_49_gamma_0_to_fp16 = const()[name = string("acc_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109914240)))]; + tensor acc_49_beta_0_to_fp16 = const()[name = string("acc_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109915072)))]; + fp16 acc_49_epsilon_0_to_fp16 = const()[name = string("acc_49_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_49_cast_fp16 = batch_norm(beta = acc_49_beta_0_to_fp16, epsilon = acc_49_epsilon_0_to_fp16, gamma = acc_49_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_17_cast_fp16)[name = string("acc_49_cast_fp16")]; + tensor var_2827_cast_fp16 = mul(x = acc_49_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2827_cast_fp16")]; + tensor c_33_to_fp16 = const()[name = string("c_33_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109915904)))]; + tensor acc_51_cast_fp16 = add(x = var_2827_cast_fp16, y = c_33_to_fp16)[name = string("acc_51_cast_fp16")]; + tensor var_2829_cast_fp16 = mul(x = acc_51_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2829_cast_fp16")]; + tensor c_35_to_fp16 = const()[name = string("c_35_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109916736)))]; + tensor acc_53_cast_fp16 = add(x = var_2829_cast_fp16, y = c_35_to_fp16)[name = string("acc_53_cast_fp16")]; + tensor var_2831_cast_fp16 = mul(x = acc_53_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2831_cast_fp16")]; + tensor hidden_states_121_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = var_2831_cast_fp16)[name = string("hidden_states_121_cast_fp16")]; + bool full_mask_17_interleave_0 = const()[name = string("full_mask_17_interleave_0"), val = bool(false)]; + tensor fill_8_to_fp16 = const()[name = string("fill_8_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114085888)))]; + tensor full_mask_17_cast_fp16 = concat(axis = var_2037, interleave = full_mask_17_interleave_0, values = (context_mask_23_cast_fp16, fill_8_to_fp16))[name = string("full_mask_17_cast_fp16")]; + tensor input_149_cast_fp16 = mul(x = hidden_states_121_cast_fp16, y = full_mask_17_cast_fp16)[name = string("input_149_cast_fp16")]; + string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")]; + tensor hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109917952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110950208))))[name = string("audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110950784)))]; + tensor hidden_states_123_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized, x = input_149_cast_fp16)[name = string("hidden_states_123_cast_fp16")]; + tensor alpha_over_pi_19_to_fp16 = const()[name = string("alpha_over_pi_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110951616)))]; + tensor theta_over_pi_19_cast_fp16 = mul(x = hidden_states_123_cast_fp16, y = alpha_over_pi_19_to_fp16)[name = string("theta_over_pi_19_cast_fp16")]; + tensor var_2868_cast_fp16 = round(x = theta_over_pi_19_cast_fp16)[name = string("op_2868_cast_fp16")]; + tensor reduced_19_cast_fp16 = sub(x = theta_over_pi_19_cast_fp16, y = var_2868_cast_fp16)[name = string("reduced_19_cast_fp16")]; + tensor reduced_sq_19_cast_fp16 = mul(x = reduced_19_cast_fp16, y = reduced_19_cast_fp16)[name = string("reduced_sq_19_cast_fp16")]; + tensor acc_55_gamma_0_to_fp16 = const()[name = string("acc_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110952448)))]; + tensor acc_55_beta_0_to_fp16 = const()[name = string("acc_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110953280)))]; + fp16 acc_55_epsilon_0_to_fp16 = const()[name = string("acc_55_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_55_cast_fp16 = batch_norm(beta = acc_55_beta_0_to_fp16, epsilon = acc_55_epsilon_0_to_fp16, gamma = acc_55_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_19_cast_fp16)[name = string("acc_55_cast_fp16")]; + tensor var_2881_cast_fp16 = mul(x = acc_55_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2881_cast_fp16")]; + tensor c_37_to_fp16 = const()[name = string("c_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110954112)))]; + tensor acc_57_cast_fp16 = add(x = var_2881_cast_fp16, y = c_37_to_fp16)[name = string("acc_57_cast_fp16")]; + tensor var_2883_cast_fp16 = mul(x = acc_57_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2883_cast_fp16")]; + tensor c_39_to_fp16 = const()[name = string("c_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110954944)))]; + tensor acc_59_cast_fp16 = add(x = var_2883_cast_fp16, y = c_39_to_fp16)[name = string("acc_59_cast_fp16")]; + tensor var_2885_cast_fp16 = mul(x = acc_59_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2885_cast_fp16")]; + tensor hidden_states_125_cast_fp16 = add(x = hidden_states_123_cast_fp16, y = var_2885_cast_fp16)[name = string("hidden_states_125_cast_fp16")]; + string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")]; + tensor hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110955776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111103296))))[name = string("audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111103872)))]; + tensor hidden_states_127_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_125_cast_fp16)[name = string("hidden_states_127_cast_fp16")]; + tensor hidden_states_129_cast_fp16 = add(x = hidden_states_127_cast_fp16, y = residual_9_cast_fp16)[name = string("hidden_states_129_cast_fp16")]; + tensor context_mask_25_begin_0 = const()[name = string("context_mask_25_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_25_end_0 = const()[name = string("context_mask_25_end_0"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_25_end_mask_0 = const()[name = string("context_mask_25_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_25_cast_fp16 = slice_by_index(begin = context_mask_25_begin_0, end = context_mask_25_end_0, end_mask = context_mask_25_end_mask_0, x = context_mask_23_cast_fp16)[name = string("context_mask_25_cast_fp16")]; + tensor residual_11_begin_0 = const()[name = string("residual_11_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_11_end_0 = const()[name = string("residual_11_end_0"), val = tensor([1, 384, 1, 744])]; + tensor residual_11_end_mask_0 = const()[name = string("residual_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_11_cast_fp16 = slice_by_index(begin = residual_11_begin_0, end = residual_11_end_0, end_mask = residual_11_end_mask_0, x = hidden_states_129_cast_fp16)[name = string("residual_11_cast_fp16")]; + tensor alpha_over_pi_21_to_fp16 = const()[name = string("alpha_over_pi_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111104704)))]; + tensor theta_over_pi_21_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = alpha_over_pi_21_to_fp16)[name = string("theta_over_pi_21_cast_fp16")]; + tensor var_2920_cast_fp16 = round(x = theta_over_pi_21_cast_fp16)[name = string("op_2920_cast_fp16")]; + tensor reduced_21_cast_fp16 = sub(x = theta_over_pi_21_cast_fp16, y = var_2920_cast_fp16)[name = string("reduced_21_cast_fp16")]; + tensor reduced_sq_21_cast_fp16 = mul(x = reduced_21_cast_fp16, y = reduced_21_cast_fp16)[name = string("reduced_sq_21_cast_fp16")]; + tensor acc_61_gamma_0_to_fp16 = const()[name = string("acc_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111105536)))]; + tensor acc_61_beta_0_to_fp16 = const()[name = string("acc_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111106368)))]; + fp16 acc_61_epsilon_0_to_fp16 = const()[name = string("acc_61_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_61_cast_fp16 = batch_norm(beta = acc_61_beta_0_to_fp16, epsilon = acc_61_epsilon_0_to_fp16, gamma = acc_61_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_21_cast_fp16)[name = string("acc_61_cast_fp16")]; + tensor var_2933_cast_fp16 = mul(x = acc_61_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2933_cast_fp16")]; + tensor c_41_to_fp16 = const()[name = string("c_41_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111107200)))]; + tensor acc_63_cast_fp16 = add(x = var_2933_cast_fp16, y = c_41_to_fp16)[name = string("acc_63_cast_fp16")]; + tensor var_2935_cast_fp16 = mul(x = acc_63_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2935_cast_fp16")]; + tensor c_43_to_fp16 = const()[name = string("c_43_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111108032)))]; + tensor acc_65_cast_fp16 = add(x = var_2935_cast_fp16, y = c_43_to_fp16)[name = string("acc_65_cast_fp16")]; + tensor var_2937_cast_fp16 = mul(x = acc_65_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2937_cast_fp16")]; + tensor hidden_states_131_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = var_2937_cast_fp16)[name = string("hidden_states_131_cast_fp16")]; + bool full_mask_19_interleave_0 = const()[name = string("full_mask_19_interleave_0"), val = bool(false)]; + tensor full_mask_19_cast_fp16 = concat(axis = var_2037, interleave = full_mask_19_interleave_0, values = (context_mask_25_cast_fp16, fill_8_to_fp16))[name = string("full_mask_19_cast_fp16")]; + tensor input_153_cast_fp16 = mul(x = hidden_states_131_cast_fp16, y = full_mask_19_cast_fp16)[name = string("input_153_cast_fp16")]; + string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")]; + tensor hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111108864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112141120))))[name = string("audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112141696)))]; + tensor hidden_states_133_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized, x = input_153_cast_fp16)[name = string("hidden_states_133_cast_fp16")]; + tensor alpha_over_pi_23_to_fp16 = const()[name = string("alpha_over_pi_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112142528)))]; + tensor theta_over_pi_23_cast_fp16 = mul(x = hidden_states_133_cast_fp16, y = alpha_over_pi_23_to_fp16)[name = string("theta_over_pi_23_cast_fp16")]; + tensor var_2974_cast_fp16 = round(x = theta_over_pi_23_cast_fp16)[name = string("op_2974_cast_fp16")]; + tensor reduced_23_cast_fp16 = sub(x = theta_over_pi_23_cast_fp16, y = var_2974_cast_fp16)[name = string("reduced_23_cast_fp16")]; + tensor reduced_sq_23_cast_fp16 = mul(x = reduced_23_cast_fp16, y = reduced_23_cast_fp16)[name = string("reduced_sq_23_cast_fp16")]; + tensor acc_67_gamma_0_to_fp16 = const()[name = string("acc_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112143360)))]; + tensor acc_67_beta_0_to_fp16 = const()[name = string("acc_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112144192)))]; + fp16 acc_67_epsilon_0_to_fp16 = const()[name = string("acc_67_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_67_cast_fp16 = batch_norm(beta = acc_67_beta_0_to_fp16, epsilon = acc_67_epsilon_0_to_fp16, gamma = acc_67_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_23_cast_fp16)[name = string("acc_67_cast_fp16")]; + tensor var_2987_cast_fp16 = mul(x = acc_67_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2987_cast_fp16")]; + tensor c_45_to_fp16 = const()[name = string("c_45_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112145024)))]; + tensor acc_69_cast_fp16 = add(x = var_2987_cast_fp16, y = c_45_to_fp16)[name = string("acc_69_cast_fp16")]; + tensor var_2989_cast_fp16 = mul(x = acc_69_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2989_cast_fp16")]; + tensor c_47_to_fp16 = const()[name = string("c_47_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112145856)))]; + tensor acc_71_cast_fp16 = add(x = var_2989_cast_fp16, y = c_47_to_fp16)[name = string("acc_71_cast_fp16")]; + tensor var_2991_cast_fp16 = mul(x = acc_71_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2991_cast_fp16")]; + tensor hidden_states_135_cast_fp16 = add(x = hidden_states_133_cast_fp16, y = var_2991_cast_fp16)[name = string("hidden_states_135_cast_fp16")]; + string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")]; + tensor hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112146688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112294208))))[name = string("audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112294784)))]; + tensor hidden_states_137_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_135_cast_fp16)[name = string("hidden_states_137_cast_fp16")]; + tensor hidden_states_139_cast_fp16 = add(x = hidden_states_137_cast_fp16, y = residual_11_cast_fp16)[name = string("hidden_states_139_cast_fp16")]; + tensor context_mask_27_begin_0 = const()[name = string("context_mask_27_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_27_end_0 = const()[name = string("context_mask_27_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_27_end_mask_0 = const()[name = string("context_mask_27_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_27_cast_fp16 = slice_by_index(begin = context_mask_27_begin_0, end = context_mask_27_end_0, end_mask = context_mask_27_end_mask_0, x = context_mask_25_cast_fp16)[name = string("context_mask_27_cast_fp16")]; + tensor residual_13_begin_0 = const()[name = string("residual_13_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_13_end_0 = const()[name = string("residual_13_end_0"), val = tensor([1, 384, 1, 726])]; + tensor residual_13_end_mask_0 = const()[name = string("residual_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_13_cast_fp16 = slice_by_index(begin = residual_13_begin_0, end = residual_13_end_0, end_mask = residual_13_end_mask_0, x = hidden_states_139_cast_fp16)[name = string("residual_13_cast_fp16")]; + tensor alpha_over_pi_25_to_fp16 = const()[name = string("alpha_over_pi_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112295616)))]; + tensor theta_over_pi_25_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = alpha_over_pi_25_to_fp16)[name = string("theta_over_pi_25_cast_fp16")]; + tensor var_3026_cast_fp16 = round(x = theta_over_pi_25_cast_fp16)[name = string("op_3026_cast_fp16")]; + tensor reduced_25_cast_fp16 = sub(x = theta_over_pi_25_cast_fp16, y = var_3026_cast_fp16)[name = string("reduced_25_cast_fp16")]; + tensor reduced_sq_25_cast_fp16 = mul(x = reduced_25_cast_fp16, y = reduced_25_cast_fp16)[name = string("reduced_sq_25_cast_fp16")]; + tensor acc_73_gamma_0_to_fp16 = const()[name = string("acc_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112296448)))]; + tensor acc_73_beta_0_to_fp16 = const()[name = string("acc_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112297280)))]; + fp16 acc_73_epsilon_0_to_fp16 = const()[name = string("acc_73_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_73_cast_fp16 = batch_norm(beta = acc_73_beta_0_to_fp16, epsilon = acc_73_epsilon_0_to_fp16, gamma = acc_73_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_25_cast_fp16)[name = string("acc_73_cast_fp16")]; + tensor var_3039_cast_fp16 = mul(x = acc_73_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_3039_cast_fp16")]; + tensor c_49_to_fp16 = const()[name = string("c_49_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112298112)))]; + tensor acc_75_cast_fp16 = add(x = var_3039_cast_fp16, y = c_49_to_fp16)[name = string("acc_75_cast_fp16")]; + tensor var_3041_cast_fp16 = mul(x = acc_75_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_3041_cast_fp16")]; + tensor c_51_to_fp16 = const()[name = string("c_51_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112298944)))]; + tensor acc_77_cast_fp16 = add(x = var_3041_cast_fp16, y = c_51_to_fp16)[name = string("acc_77_cast_fp16")]; + tensor var_3043_cast_fp16 = mul(x = acc_77_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_3043_cast_fp16")]; + tensor hidden_states_141_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = var_3043_cast_fp16)[name = string("hidden_states_141_cast_fp16")]; + bool full_mask_21_interleave_0 = const()[name = string("full_mask_21_interleave_0"), val = bool(false)]; + tensor full_mask_21_cast_fp16 = concat(axis = var_2037, interleave = full_mask_21_interleave_0, values = (context_mask_27_cast_fp16, fill_8_to_fp16))[name = string("full_mask_21_cast_fp16")]; + tensor input_157_cast_fp16 = mul(x = hidden_states_141_cast_fp16, y = full_mask_21_cast_fp16)[name = string("input_157_cast_fp16")]; + string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")]; + tensor hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112299776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113332032))))[name = string("audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113332608)))]; + tensor hidden_states_143_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("hidden_states_143_cast_fp16")]; + tensor alpha_over_pi_27_to_fp16 = const()[name = string("alpha_over_pi_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113333440)))]; + tensor theta_over_pi_27_cast_fp16 = mul(x = hidden_states_143_cast_fp16, y = alpha_over_pi_27_to_fp16)[name = string("theta_over_pi_27_cast_fp16")]; + tensor var_3080_cast_fp16 = round(x = theta_over_pi_27_cast_fp16)[name = string("op_3080_cast_fp16")]; + tensor reduced_27_cast_fp16 = sub(x = theta_over_pi_27_cast_fp16, y = var_3080_cast_fp16)[name = string("reduced_27_cast_fp16")]; + tensor reduced_sq_27_cast_fp16 = mul(x = reduced_27_cast_fp16, y = reduced_27_cast_fp16)[name = string("reduced_sq_27_cast_fp16")]; + tensor acc_79_gamma_0_to_fp16 = const()[name = string("acc_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113334272)))]; + tensor acc_79_beta_0_to_fp16 = const()[name = string("acc_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113335104)))]; + fp16 acc_79_epsilon_0_to_fp16 = const()[name = string("acc_79_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_79_cast_fp16 = batch_norm(beta = acc_79_beta_0_to_fp16, epsilon = acc_79_epsilon_0_to_fp16, gamma = acc_79_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_27_cast_fp16)[name = string("acc_79_cast_fp16")]; + tensor var_3093_cast_fp16 = mul(x = acc_79_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3093_cast_fp16")]; + tensor c_53_to_fp16 = const()[name = string("c_53_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113335936)))]; + tensor acc_81_cast_fp16 = add(x = var_3093_cast_fp16, y = c_53_to_fp16)[name = string("acc_81_cast_fp16")]; + tensor var_3095_cast_fp16 = mul(x = acc_81_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3095_cast_fp16")]; + tensor c_55_to_fp16 = const()[name = string("c_55_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113336768)))]; + tensor acc_83_cast_fp16 = add(x = var_3095_cast_fp16, y = c_55_to_fp16)[name = string("acc_83_cast_fp16")]; + tensor var_3097_cast_fp16 = mul(x = acc_83_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3097_cast_fp16")]; + tensor hidden_states_145_cast_fp16 = add(x = hidden_states_143_cast_fp16, y = var_3097_cast_fp16)[name = string("hidden_states_145_cast_fp16")]; + string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")]; + tensor hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113337600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113485120))))[name = string("audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113485696)))]; + tensor hidden_states_147_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_145_cast_fp16)[name = string("hidden_states_147_cast_fp16")]; + tensor hidden_states_149_cast_fp16 = add(x = hidden_states_147_cast_fp16, y = residual_13_cast_fp16)[name = string("hidden_states_149_cast_fp16")]; + tensor context_mask_31_begin_0 = const()[name = string("context_mask_31_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_31_end_0 = const()[name = string("context_mask_31_end_0"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_31_end_mask_0 = const()[name = string("context_mask_31_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_31_cast_fp16 = slice_by_index(begin = context_mask_31_begin_0, end = context_mask_31_end_0, end_mask = context_mask_31_end_mask_0, x = context_mask_23_cast_fp16)[name = string("context_mask_31_cast_fp16")]; + tensor hidden_states_151_begin_0 = const()[name = string("hidden_states_151_begin_0"), val = tensor([0, 0, 0, 4])]; + tensor hidden_states_151_end_0 = const()[name = string("hidden_states_151_end_0"), val = tensor([1, 384, 1, 672])]; + tensor hidden_states_151_end_mask_0 = const()[name = string("hidden_states_151_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_151_cast_fp16 = slice_by_index(begin = hidden_states_151_begin_0, end = hidden_states_151_end_0, end_mask = hidden_states_151_end_mask_0, x = hidden_states_149_cast_fp16)[name = string("hidden_states_151_cast_fp16")]; + tensor context_mask_33_begin_0 = const()[name = string("context_mask_33_begin_0"), val = tensor([0, 0, 0, 4])]; + tensor context_mask_33_end_0 = const()[name = string("context_mask_33_end_0"), val = tensor([1, 1, 1, 32])]; + tensor context_mask_33_end_mask_0 = const()[name = string("context_mask_33_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_33_cast_fp16 = slice_by_index(begin = context_mask_33_begin_0, end = context_mask_33_end_0, end_mask = context_mask_33_end_mask_0, x = context_mask_31_cast_fp16)[name = string("context_mask_33_cast_fp16")]; + tensor alpha_over_pi_29_to_fp16 = const()[name = string("alpha_over_pi_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113486528)))]; + tensor theta_over_pi_29_cast_fp16 = mul(x = hidden_states_151_cast_fp16, y = alpha_over_pi_29_to_fp16)[name = string("theta_over_pi_29_cast_fp16")]; + tensor var_3157_cast_fp16 = round(x = theta_over_pi_29_cast_fp16)[name = string("op_3157_cast_fp16")]; + tensor reduced_29_cast_fp16 = sub(x = theta_over_pi_29_cast_fp16, y = var_3157_cast_fp16)[name = string("reduced_29_cast_fp16")]; + tensor reduced_sq_29_cast_fp16 = mul(x = reduced_29_cast_fp16, y = reduced_29_cast_fp16)[name = string("reduced_sq_29_cast_fp16")]; + tensor acc_85_gamma_0_to_fp16 = const()[name = string("acc_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113487360)))]; + tensor acc_85_beta_0_to_fp16 = const()[name = string("acc_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113488192)))]; + fp16 acc_85_epsilon_0_to_fp16 = const()[name = string("acc_85_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_85_cast_fp16 = batch_norm(beta = acc_85_beta_0_to_fp16, epsilon = acc_85_epsilon_0_to_fp16, gamma = acc_85_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_29_cast_fp16)[name = string("acc_85_cast_fp16")]; + tensor var_3170_cast_fp16 = mul(x = acc_85_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3170_cast_fp16")]; + tensor c_57_to_fp16 = const()[name = string("c_57_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113489024)))]; + tensor acc_87_cast_fp16 = add(x = var_3170_cast_fp16, y = c_57_to_fp16)[name = string("acc_87_cast_fp16")]; + tensor var_3172_cast_fp16 = mul(x = acc_87_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3172_cast_fp16")]; + tensor c_59_to_fp16 = const()[name = string("c_59_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113489856)))]; + tensor acc_89_cast_fp16 = add(x = var_3172_cast_fp16, y = c_59_to_fp16)[name = string("acc_89_cast_fp16")]; + tensor var_3174_cast_fp16 = mul(x = acc_89_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3174_cast_fp16")]; + tensor hidden_states_153_cast_fp16 = add(x = hidden_states_151_cast_fp16, y = var_3174_cast_fp16)[name = string("hidden_states_153_cast_fp16")]; + bool full_mask_23_interleave_0 = const()[name = string("full_mask_23_interleave_0"), val = bool(false)]; + tensor full_mask_23_cast_fp16 = concat(axis = var_2037, interleave = full_mask_23_interleave_0, values = (context_mask_33_cast_fp16, fill_8_to_fp16))[name = string("full_mask_23_cast_fp16")]; + tensor input_161_cast_fp16 = mul(x = hidden_states_153_cast_fp16, y = full_mask_23_cast_fp16)[name = string("input_161_cast_fp16")]; + string sub_pixels_25_pad_type_0 = const()[name = string("sub_pixels_25_pad_type_0"), val = string("valid")]; + tensor sub_pixels_25_strides_0 = const()[name = string("sub_pixels_25_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_25_pad_0 = const()[name = string("sub_pixels_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_25_dilations_0 = const()[name = string("sub_pixels_25_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_25_groups_0 = const()[name = string("sub_pixels_25_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113490688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114080576))))[name = string("audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114081152)))]; + tensor sub_pixels_25_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_1_conv_bias_to_fp16, dilations = sub_pixels_25_dilations_0, groups = sub_pixels_25_groups_0, pad = sub_pixels_25_pad_0, pad_type = sub_pixels_25_pad_type_0, strides = sub_pixels_25_strides_0, weight = audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("sub_pixels_25_cast_fp16")]; + tensor var_3198 = const()[name = string("op_3198"), val = tensor([1, 4, 192, 667])]; + tensor sub_pixels_27_cast_fp16 = reshape(shape = var_3198, x = sub_pixels_25_cast_fp16)[name = string("sub_pixels_27_cast_fp16")]; + tensor var_3200 = const()[name = string("op_3200"), val = tensor([0, 2, 3, 1])]; + tensor var_3205 = const()[name = string("op_3205"), val = tensor([1, 192, 1, 2668])]; + tensor sub_pixels_29_cast_fp16 = transpose(perm = var_3200, x = sub_pixels_27_cast_fp16)[name = string("transpose_1")]; + tensor hidden_states_155_cast_fp16 = reshape(shape = var_3205, x = sub_pixels_29_cast_fp16)[name = string("hidden_states_155_cast_fp16")]; + tensor newest_13_begin_0 = const()[name = string("newest_13_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_13_end_0 = const()[name = string("newest_13_end_0"), val = tensor([1, 1, 1, 28])]; + tensor newest_13_end_mask_0 = const()[name = string("newest_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_13_cast_fp16 = slice_by_index(begin = newest_13_begin_0, end = newest_13_end_0, end_mask = newest_13_end_mask_0, x = context_mask_33_cast_fp16)[name = string("newest_13_cast_fp16")]; + tensor var_3210 = const()[name = string("op_3210"), val = tensor([1, 1, 27, 1])]; + tensor var_3211_cast_fp16 = reshape(shape = var_3210, x = newest_13_cast_fp16)[name = string("op_3211_cast_fp16")]; + tensor spread_13_reps_0 = const()[name = string("spread_13_reps_0"), val = tensor([1, 1, 1, 4])]; + tensor spread_13_cast_fp16 = tile(reps = spread_13_reps_0, x = var_3211_cast_fp16)[name = string("spread_13_cast_fp16")]; + tensor var_3217 = const()[name = string("op_3217"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_35_cast_fp16 = reshape(shape = var_3217, x = spread_13_cast_fp16)[name = string("context_mask_35_cast_fp16")]; + tensor residual_15_begin_0 = const()[name = string("residual_15_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_15_end_0 = const()[name = string("residual_15_end_0"), val = tensor([1, 192, 1, 2668])]; + tensor residual_15_end_mask_0 = const()[name = string("residual_15_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_15_cast_fp16 = slice_by_index(begin = residual_15_begin_0, end = residual_15_end_0, end_mask = residual_15_end_mask_0, x = hidden_states_155_cast_fp16)[name = string("residual_15_cast_fp16")]; + tensor alpha_over_pi_31_to_fp16 = const()[name = string("alpha_over_pi_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114082752)))]; + tensor theta_over_pi_31_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = alpha_over_pi_31_to_fp16)[name = string("theta_over_pi_31_cast_fp16")]; + tensor var_3240_cast_fp16 = round(x = theta_over_pi_31_cast_fp16)[name = string("op_3240_cast_fp16")]; + tensor reduced_31_cast_fp16 = sub(x = theta_over_pi_31_cast_fp16, y = var_3240_cast_fp16)[name = string("reduced_31_cast_fp16")]; + tensor reduced_sq_31_cast_fp16 = mul(x = reduced_31_cast_fp16, y = reduced_31_cast_fp16)[name = string("reduced_sq_31_cast_fp16")]; + tensor acc_91_mean_0_to_fp16 = const()[name = string("acc_91_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114083200)))]; + tensor acc_91_variance_0_to_fp16 = const()[name = string("acc_91_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114083648)))]; + tensor acc_91_gamma_0_to_fp16 = const()[name = string("acc_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084096)))]; + tensor acc_91_beta_0_to_fp16 = const()[name = string("acc_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084544)))]; + fp16 acc_91_epsilon_0_to_fp16 = const()[name = string("acc_91_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_91_cast_fp16 = batch_norm(beta = acc_91_beta_0_to_fp16, epsilon = acc_91_epsilon_0_to_fp16, gamma = acc_91_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_31_cast_fp16)[name = string("acc_91_cast_fp16")]; + tensor var_3253_cast_fp16 = mul(x = acc_91_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3253_cast_fp16")]; + tensor c_61_to_fp16 = const()[name = string("c_61_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084992)))]; + tensor acc_93_cast_fp16 = add(x = var_3253_cast_fp16, y = c_61_to_fp16)[name = string("acc_93_cast_fp16")]; + tensor var_3255_cast_fp16 = mul(x = acc_93_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3255_cast_fp16")]; + tensor c_63_to_fp16 = const()[name = string("c_63_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114085440)))]; + tensor acc_95_cast_fp16 = add(x = var_3255_cast_fp16, y = c_63_to_fp16)[name = string("acc_95_cast_fp16")]; + tensor var_3257_cast_fp16 = mul(x = acc_95_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3257_cast_fp16")]; + tensor hidden_states_157_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = var_3257_cast_fp16)[name = string("hidden_states_157_cast_fp16")]; + bool full_mask_25_interleave_0 = const()[name = string("full_mask_25_interleave_0"), val = bool(false)]; + tensor fill_12_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115346176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115348800))))[name = string("fill_12_to_fp16_palettized")]; + tensor full_mask_25_cast_fp16 = concat(axis = var_2037, interleave = full_mask_25_interleave_0, values = (context_mask_35_cast_fp16, fill_12_to_fp16_palettized))[name = string("full_mask_25_cast_fp16")]; + tensor input_163_cast_fp16 = mul(x = hidden_states_157_cast_fp16, y = full_mask_25_cast_fp16)[name = string("input_163_cast_fp16")]; + string hidden_states_159_pad_type_0 = const()[name = string("hidden_states_159_pad_type_0"), val = string("valid")]; + tensor hidden_states_159_strides_0 = const()[name = string("hidden_states_159_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_0 = const()[name = string("hidden_states_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_159_dilations_0 = const()[name = string("hidden_states_159_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_159_groups_0 = const()[name = string("hidden_states_159_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114087232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114345344))))[name = string("audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114345920)))]; + tensor hidden_states_159_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_159_dilations_0, groups = hidden_states_159_groups_0, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = hidden_states_159_strides_0, weight = audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized, x = input_163_cast_fp16)[name = string("hidden_states_159_cast_fp16")]; + tensor alpha_over_pi_33_to_fp16 = const()[name = string("alpha_over_pi_33_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114346368)))]; + tensor theta_over_pi_33_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = alpha_over_pi_33_to_fp16)[name = string("theta_over_pi_33_cast_fp16")]; + tensor var_3294_cast_fp16 = round(x = theta_over_pi_33_cast_fp16)[name = string("op_3294_cast_fp16")]; + tensor reduced_33_cast_fp16 = sub(x = theta_over_pi_33_cast_fp16, y = var_3294_cast_fp16)[name = string("reduced_33_cast_fp16")]; + tensor reduced_sq_33_cast_fp16 = mul(x = reduced_33_cast_fp16, y = reduced_33_cast_fp16)[name = string("reduced_sq_33_cast_fp16")]; + tensor acc_97_gamma_0_to_fp16 = const()[name = string("acc_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114346816)))]; + tensor acc_97_beta_0_to_fp16 = const()[name = string("acc_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114347264)))]; + fp16 acc_97_epsilon_0_to_fp16 = const()[name = string("acc_97_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_97_cast_fp16 = batch_norm(beta = acc_97_beta_0_to_fp16, epsilon = acc_97_epsilon_0_to_fp16, gamma = acc_97_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_33_cast_fp16)[name = string("acc_97_cast_fp16")]; + tensor var_3307_cast_fp16 = mul(x = acc_97_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3307_cast_fp16")]; + tensor c_65_to_fp16 = const()[name = string("c_65_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114347712)))]; + tensor acc_99_cast_fp16 = add(x = var_3307_cast_fp16, y = c_65_to_fp16)[name = string("acc_99_cast_fp16")]; + tensor var_3309_cast_fp16 = mul(x = acc_99_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3309_cast_fp16")]; + tensor c_67_to_fp16 = const()[name = string("c_67_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114348160)))]; + tensor acc_101_cast_fp16 = add(x = var_3309_cast_fp16, y = c_67_to_fp16)[name = string("acc_101_cast_fp16")]; + tensor var_3311_cast_fp16 = mul(x = acc_101_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3311_cast_fp16")]; + tensor hidden_states_161_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = var_3311_cast_fp16)[name = string("hidden_states_161_cast_fp16")]; + string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")]; + tensor hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114348608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114385536))))[name = string("audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114386112)))]; + tensor hidden_states_163_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_161_cast_fp16)[name = string("hidden_states_163_cast_fp16")]; + tensor hidden_states_165_cast_fp16 = add(x = hidden_states_163_cast_fp16, y = residual_15_cast_fp16)[name = string("hidden_states_165_cast_fp16")]; + tensor context_mask_37_begin_0 = const()[name = string("context_mask_37_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_37_end_0 = const()[name = string("context_mask_37_end_0"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_37_end_mask_0 = const()[name = string("context_mask_37_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_37_cast_fp16 = slice_by_index(begin = context_mask_37_begin_0, end = context_mask_37_end_0, end_mask = context_mask_37_end_mask_0, x = context_mask_35_cast_fp16)[name = string("context_mask_37_cast_fp16")]; + tensor residual_17_begin_0 = const()[name = string("residual_17_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_17_end_0 = const()[name = string("residual_17_end_0"), val = tensor([1, 192, 1, 2662])]; + tensor residual_17_end_mask_0 = const()[name = string("residual_17_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_17_cast_fp16 = slice_by_index(begin = residual_17_begin_0, end = residual_17_end_0, end_mask = residual_17_end_mask_0, x = hidden_states_165_cast_fp16)[name = string("residual_17_cast_fp16")]; + tensor alpha_over_pi_35_to_fp16 = const()[name = string("alpha_over_pi_35_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114386560)))]; + tensor theta_over_pi_35_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = alpha_over_pi_35_to_fp16)[name = string("theta_over_pi_35_cast_fp16")]; + tensor var_3346_cast_fp16 = round(x = theta_over_pi_35_cast_fp16)[name = string("op_3346_cast_fp16")]; + tensor reduced_35_cast_fp16 = sub(x = theta_over_pi_35_cast_fp16, y = var_3346_cast_fp16)[name = string("reduced_35_cast_fp16")]; + tensor reduced_sq_35_cast_fp16 = mul(x = reduced_35_cast_fp16, y = reduced_35_cast_fp16)[name = string("reduced_sq_35_cast_fp16")]; + tensor acc_103_gamma_0_to_fp16 = const()[name = string("acc_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387008)))]; + tensor acc_103_beta_0_to_fp16 = const()[name = string("acc_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387456)))]; + fp16 acc_103_epsilon_0_to_fp16 = const()[name = string("acc_103_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_103_cast_fp16 = batch_norm(beta = acc_103_beta_0_to_fp16, epsilon = acc_103_epsilon_0_to_fp16, gamma = acc_103_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_35_cast_fp16)[name = string("acc_103_cast_fp16")]; + tensor var_3359_cast_fp16 = mul(x = acc_103_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3359_cast_fp16")]; + tensor c_69_to_fp16 = const()[name = string("c_69_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387904)))]; + tensor acc_105_cast_fp16 = add(x = var_3359_cast_fp16, y = c_69_to_fp16)[name = string("acc_105_cast_fp16")]; + tensor var_3361_cast_fp16 = mul(x = acc_105_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3361_cast_fp16")]; + tensor c_71_to_fp16 = const()[name = string("c_71_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114388352)))]; + tensor acc_107_cast_fp16 = add(x = var_3361_cast_fp16, y = c_71_to_fp16)[name = string("acc_107_cast_fp16")]; + tensor var_3363_cast_fp16 = mul(x = acc_107_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3363_cast_fp16")]; + tensor hidden_states_167_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = var_3363_cast_fp16)[name = string("hidden_states_167_cast_fp16")]; + bool full_mask_27_interleave_0 = const()[name = string("full_mask_27_interleave_0"), val = bool(false)]; + tensor full_mask_27_cast_fp16 = concat(axis = var_2037, interleave = full_mask_27_interleave_0, values = (context_mask_37_cast_fp16, fill_12_to_fp16_palettized))[name = string("full_mask_27_cast_fp16")]; + tensor input_167_cast_fp16 = mul(x = hidden_states_167_cast_fp16, y = full_mask_27_cast_fp16)[name = string("input_167_cast_fp16")]; + string hidden_states_169_pad_type_0 = const()[name = string("hidden_states_169_pad_type_0"), val = string("valid")]; + tensor hidden_states_169_dilations_0 = const()[name = string("hidden_states_169_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_169_strides_0 = const()[name = string("hidden_states_169_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_169_pad_0 = const()[name = string("hidden_states_169_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_169_groups_0 = const()[name = string("hidden_states_169_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114388800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114646912))))[name = string("audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114647488)))]; + tensor hidden_states_169_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_169_dilations_0, groups = hidden_states_169_groups_0, pad = hidden_states_169_pad_0, pad_type = hidden_states_169_pad_type_0, strides = hidden_states_169_strides_0, weight = audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized, x = input_167_cast_fp16)[name = string("hidden_states_169_cast_fp16")]; + tensor alpha_over_pi_37_to_fp16 = const()[name = string("alpha_over_pi_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114647936)))]; + tensor theta_over_pi_37_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = alpha_over_pi_37_to_fp16)[name = string("theta_over_pi_37_cast_fp16")]; + tensor var_3400_cast_fp16 = round(x = theta_over_pi_37_cast_fp16)[name = string("op_3400_cast_fp16")]; + tensor reduced_37_cast_fp16 = sub(x = theta_over_pi_37_cast_fp16, y = var_3400_cast_fp16)[name = string("reduced_37_cast_fp16")]; + tensor reduced_sq_37_cast_fp16 = mul(x = reduced_37_cast_fp16, y = reduced_37_cast_fp16)[name = string("reduced_sq_37_cast_fp16")]; + tensor acc_109_gamma_0_to_fp16 = const()[name = string("acc_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114648384)))]; + tensor acc_109_beta_0_to_fp16 = const()[name = string("acc_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114648832)))]; + fp16 acc_109_epsilon_0_to_fp16 = const()[name = string("acc_109_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_109_cast_fp16 = batch_norm(beta = acc_109_beta_0_to_fp16, epsilon = acc_109_epsilon_0_to_fp16, gamma = acc_109_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_37_cast_fp16)[name = string("acc_109_cast_fp16")]; + tensor var_3413_cast_fp16 = mul(x = acc_109_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3413_cast_fp16")]; + tensor c_73_to_fp16 = const()[name = string("c_73_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114649280)))]; + tensor acc_111_cast_fp16 = add(x = var_3413_cast_fp16, y = c_73_to_fp16)[name = string("acc_111_cast_fp16")]; + tensor var_3415_cast_fp16 = mul(x = acc_111_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3415_cast_fp16")]; + tensor c_75_to_fp16 = const()[name = string("c_75_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114649728)))]; + tensor acc_113_cast_fp16 = add(x = var_3415_cast_fp16, y = c_75_to_fp16)[name = string("acc_113_cast_fp16")]; + tensor var_3417_cast_fp16 = mul(x = acc_113_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3417_cast_fp16")]; + tensor hidden_states_171_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = var_3417_cast_fp16)[name = string("hidden_states_171_cast_fp16")]; + string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")]; + tensor hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114650176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114687104))))[name = string("audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114687680)))]; + tensor hidden_states_173_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_171_cast_fp16)[name = string("hidden_states_173_cast_fp16")]; + tensor hidden_states_175_cast_fp16 = add(x = hidden_states_173_cast_fp16, y = residual_17_cast_fp16)[name = string("hidden_states_175_cast_fp16")]; + tensor context_mask_39_begin_0 = const()[name = string("context_mask_39_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_39_end_0 = const()[name = string("context_mask_39_end_0"), val = tensor([1, 1, 1, 102])]; + tensor context_mask_39_end_mask_0 = const()[name = string("context_mask_39_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_39_cast_fp16 = slice_by_index(begin = context_mask_39_begin_0, end = context_mask_39_end_0, end_mask = context_mask_39_end_mask_0, x = context_mask_37_cast_fp16)[name = string("context_mask_39_cast_fp16")]; + tensor residual_19_begin_0 = const()[name = string("residual_19_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_19_end_0 = const()[name = string("residual_19_end_0"), val = tensor([1, 192, 1, 2644])]; + tensor residual_19_end_mask_0 = const()[name = string("residual_19_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_19_cast_fp16 = slice_by_index(begin = residual_19_begin_0, end = residual_19_end_0, end_mask = residual_19_end_mask_0, x = hidden_states_175_cast_fp16)[name = string("residual_19_cast_fp16")]; + tensor alpha_over_pi_39_to_fp16 = const()[name = string("alpha_over_pi_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114688128)))]; + tensor theta_over_pi_39_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = alpha_over_pi_39_to_fp16)[name = string("theta_over_pi_39_cast_fp16")]; + tensor var_3452_cast_fp16 = round(x = theta_over_pi_39_cast_fp16)[name = string("op_3452_cast_fp16")]; + tensor reduced_39_cast_fp16 = sub(x = theta_over_pi_39_cast_fp16, y = var_3452_cast_fp16)[name = string("reduced_39_cast_fp16")]; + tensor reduced_sq_39_cast_fp16 = mul(x = reduced_39_cast_fp16, y = reduced_39_cast_fp16)[name = string("reduced_sq_39_cast_fp16")]; + tensor acc_115_gamma_0_to_fp16 = const()[name = string("acc_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114688576)))]; + tensor acc_115_beta_0_to_fp16 = const()[name = string("acc_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689024)))]; + fp16 acc_115_epsilon_0_to_fp16 = const()[name = string("acc_115_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_115_cast_fp16 = batch_norm(beta = acc_115_beta_0_to_fp16, epsilon = acc_115_epsilon_0_to_fp16, gamma = acc_115_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_39_cast_fp16)[name = string("acc_115_cast_fp16")]; + tensor var_3465_cast_fp16 = mul(x = acc_115_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3465_cast_fp16")]; + tensor c_77_to_fp16 = const()[name = string("c_77_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689472)))]; + tensor acc_117_cast_fp16 = add(x = var_3465_cast_fp16, y = c_77_to_fp16)[name = string("acc_117_cast_fp16")]; + tensor var_3467_cast_fp16 = mul(x = acc_117_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3467_cast_fp16")]; + tensor c_79_to_fp16 = const()[name = string("c_79_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689920)))]; + tensor acc_119_cast_fp16 = add(x = var_3467_cast_fp16, y = c_79_to_fp16)[name = string("acc_119_cast_fp16")]; + tensor var_3469_cast_fp16 = mul(x = acc_119_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3469_cast_fp16")]; + tensor hidden_states_177_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = var_3469_cast_fp16)[name = string("hidden_states_177_cast_fp16")]; + bool full_mask_29_interleave_0 = const()[name = string("full_mask_29_interleave_0"), val = bool(false)]; + tensor full_mask_29_cast_fp16 = concat(axis = var_2037, interleave = full_mask_29_interleave_0, values = (context_mask_39_cast_fp16, fill_12_to_fp16_palettized))[name = string("full_mask_29_cast_fp16")]; + tensor input_171_cast_fp16 = mul(x = hidden_states_177_cast_fp16, y = full_mask_29_cast_fp16)[name = string("input_171_cast_fp16")]; + string hidden_states_179_pad_type_0 = const()[name = string("hidden_states_179_pad_type_0"), val = string("valid")]; + tensor hidden_states_179_dilations_0 = const()[name = string("hidden_states_179_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_179_strides_0 = const()[name = string("hidden_states_179_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_179_pad_0 = const()[name = string("hidden_states_179_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_179_groups_0 = const()[name = string("hidden_states_179_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114690368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114948480))))[name = string("audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949056)))]; + tensor hidden_states_179_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_179_dilations_0, groups = hidden_states_179_groups_0, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = hidden_states_179_strides_0, weight = audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized, x = input_171_cast_fp16)[name = string("hidden_states_179_cast_fp16")]; + tensor alpha_over_pi_41_to_fp16 = const()[name = string("alpha_over_pi_41_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949504)))]; + tensor theta_over_pi_41_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = alpha_over_pi_41_to_fp16)[name = string("theta_over_pi_41_cast_fp16")]; + tensor var_3506_cast_fp16 = round(x = theta_over_pi_41_cast_fp16)[name = string("op_3506_cast_fp16")]; + tensor reduced_41_cast_fp16 = sub(x = theta_over_pi_41_cast_fp16, y = var_3506_cast_fp16)[name = string("reduced_41_cast_fp16")]; + tensor reduced_sq_41_cast_fp16 = mul(x = reduced_41_cast_fp16, y = reduced_41_cast_fp16)[name = string("reduced_sq_41_cast_fp16")]; + tensor acc_121_gamma_0_to_fp16 = const()[name = string("acc_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949952)))]; + tensor acc_121_beta_0_to_fp16 = const()[name = string("acc_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114950400)))]; + fp16 acc_121_epsilon_0_to_fp16 = const()[name = string("acc_121_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_121_cast_fp16 = batch_norm(beta = acc_121_beta_0_to_fp16, epsilon = acc_121_epsilon_0_to_fp16, gamma = acc_121_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_41_cast_fp16)[name = string("acc_121_cast_fp16")]; + tensor var_3519_cast_fp16 = mul(x = acc_121_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3519_cast_fp16")]; + tensor c_81_to_fp16 = const()[name = string("c_81_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114950848)))]; + tensor acc_123_cast_fp16 = add(x = var_3519_cast_fp16, y = c_81_to_fp16)[name = string("acc_123_cast_fp16")]; + tensor var_3521_cast_fp16 = mul(x = acc_123_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3521_cast_fp16")]; + tensor c_83_to_fp16 = const()[name = string("c_83_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114951296)))]; + tensor acc_125_cast_fp16 = add(x = var_3521_cast_fp16, y = c_83_to_fp16)[name = string("acc_125_cast_fp16")]; + tensor var_3523_cast_fp16 = mul(x = acc_125_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3523_cast_fp16")]; + tensor hidden_states_181_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = var_3523_cast_fp16)[name = string("hidden_states_181_cast_fp16")]; + string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")]; + tensor hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114951744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114988672))))[name = string("audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114989248)))]; + tensor hidden_states_183_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_181_cast_fp16)[name = string("hidden_states_183_cast_fp16")]; + tensor hidden_states_185_cast_fp16 = add(x = hidden_states_183_cast_fp16, y = residual_19_cast_fp16)[name = string("hidden_states_185_cast_fp16")]; + tensor context_mask_43_begin_0 = const()[name = string("context_mask_43_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_43_end_0 = const()[name = string("context_mask_43_end_0"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_43_end_mask_0 = const()[name = string("context_mask_43_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_43_cast_fp16 = slice_by_index(begin = context_mask_43_begin_0, end = context_mask_43_end_0, end_mask = context_mask_43_end_mask_0, x = context_mask_35_cast_fp16)[name = string("context_mask_43_cast_fp16")]; + tensor hidden_states_187_begin_0 = const()[name = string("hidden_states_187_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor hidden_states_187_end_0 = const()[name = string("hidden_states_187_end_0"), val = tensor([1, 192, 1, 2590])]; + tensor hidden_states_187_end_mask_0 = const()[name = string("hidden_states_187_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_187_cast_fp16 = slice_by_index(begin = hidden_states_187_begin_0, end = hidden_states_187_end_0, end_mask = hidden_states_187_end_mask_0, x = hidden_states_185_cast_fp16)[name = string("hidden_states_187_cast_fp16")]; + tensor context_mask_45_begin_0 = const()[name = string("context_mask_45_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor context_mask_45_end_0 = const()[name = string("context_mask_45_end_0"), val = tensor([1, 1, 1, 30])]; + tensor context_mask_45_end_mask_0 = const()[name = string("context_mask_45_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_45_cast_fp16 = slice_by_index(begin = context_mask_45_begin_0, end = context_mask_45_end_0, end_mask = context_mask_45_end_mask_0, x = context_mask_43_cast_fp16)[name = string("context_mask_45_cast_fp16")]; + tensor alpha_over_pi_43_to_fp16 = const()[name = string("alpha_over_pi_43_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114989696)))]; + tensor theta_over_pi_43_cast_fp16 = mul(x = hidden_states_187_cast_fp16, y = alpha_over_pi_43_to_fp16)[name = string("theta_over_pi_43_cast_fp16")]; + tensor var_3583_cast_fp16 = round(x = theta_over_pi_43_cast_fp16)[name = string("op_3583_cast_fp16")]; + tensor reduced_43_cast_fp16 = sub(x = theta_over_pi_43_cast_fp16, y = var_3583_cast_fp16)[name = string("reduced_43_cast_fp16")]; + tensor reduced_sq_43_cast_fp16 = mul(x = reduced_43_cast_fp16, y = reduced_43_cast_fp16)[name = string("reduced_sq_43_cast_fp16")]; + tensor acc_127_gamma_0_to_fp16 = const()[name = string("acc_127_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114990144)))]; + tensor acc_127_beta_0_to_fp16 = const()[name = string("acc_127_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114990592)))]; + fp16 acc_127_epsilon_0_to_fp16 = const()[name = string("acc_127_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_127_cast_fp16 = batch_norm(beta = acc_127_beta_0_to_fp16, epsilon = acc_127_epsilon_0_to_fp16, gamma = acc_127_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_43_cast_fp16)[name = string("acc_127_cast_fp16")]; + tensor var_3596_cast_fp16 = mul(x = acc_127_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3596_cast_fp16")]; + tensor c_85_to_fp16 = const()[name = string("c_85_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991040)))]; + tensor acc_129_cast_fp16 = add(x = var_3596_cast_fp16, y = c_85_to_fp16)[name = string("acc_129_cast_fp16")]; + tensor var_3598_cast_fp16 = mul(x = acc_129_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3598_cast_fp16")]; + tensor c_87_to_fp16 = const()[name = string("c_87_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991488)))]; + tensor acc_131_cast_fp16 = add(x = var_3598_cast_fp16, y = c_87_to_fp16)[name = string("acc_131_cast_fp16")]; + tensor var_3600_cast_fp16 = mul(x = acc_131_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3600_cast_fp16")]; + tensor hidden_states_189_cast_fp16 = add(x = hidden_states_187_cast_fp16, y = var_3600_cast_fp16)[name = string("hidden_states_189_cast_fp16")]; + bool full_mask_31_interleave_0 = const()[name = string("full_mask_31_interleave_0"), val = bool(false)]; + tensor full_mask_31_cast_fp16 = concat(axis = var_2037, interleave = full_mask_31_interleave_0, values = (context_mask_45_cast_fp16, fill_12_to_fp16_palettized))[name = string("full_mask_31_cast_fp16")]; + tensor input_175_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = full_mask_31_cast_fp16)[name = string("input_175_cast_fp16")]; + string sub_pixels_31_pad_type_0 = const()[name = string("sub_pixels_31_pad_type_0"), val = string("valid")]; + tensor sub_pixels_31_strides_0 = const()[name = string("sub_pixels_31_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_31_pad_0 = const()[name = string("sub_pixels_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_31_dilations_0 = const()[name = string("sub_pixels_31_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_31_groups_0 = const()[name = string("sub_pixels_31_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115102592))))[name = string("audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115103168)))]; + tensor sub_pixels_31_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_1_conv_bias_to_fp16, dilations = sub_pixels_31_dilations_0, groups = sub_pixels_31_groups_0, pad = sub_pixels_31_pad_0, pad_type = sub_pixels_31_pad_type_0, strides = sub_pixels_31_strides_0, weight = audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized, x = input_175_cast_fp16)[name = string("sub_pixels_31_cast_fp16")]; + tensor var_3624 = const()[name = string("op_3624"), val = tensor([1, 3, 96, 2588])]; + tensor sub_pixels_33_cast_fp16 = reshape(shape = var_3624, x = sub_pixels_31_cast_fp16)[name = string("sub_pixels_33_cast_fp16")]; + tensor var_3626 = const()[name = string("op_3626"), val = tensor([0, 2, 3, 1])]; + tensor var_3631 = const()[name = string("op_3631"), val = tensor([1, 96, 1, 7764])]; + tensor sub_pixels_cast_fp16 = transpose(perm = var_3626, x = sub_pixels_33_cast_fp16)[name = string("transpose_0")]; + tensor hidden_states_191_cast_fp16 = reshape(shape = var_3631, x = sub_pixels_cast_fp16)[name = string("hidden_states_191_cast_fp16")]; + tensor newest_17_begin_0 = const()[name = string("newest_17_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_17_end_0 = const()[name = string("newest_17_end_0"), val = tensor([1, 1, 1, 29])]; + tensor newest_17_end_mask_0 = const()[name = string("newest_17_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_17_cast_fp16 = slice_by_index(begin = newest_17_begin_0, end = newest_17_end_0, end_mask = newest_17_end_mask_0, x = context_mask_45_cast_fp16)[name = string("newest_17_cast_fp16")]; + tensor var_3636 = const()[name = string("op_3636"), val = tensor([1, 1, 28, 1])]; + tensor var_3637_cast_fp16 = reshape(shape = var_3636, x = newest_17_cast_fp16)[name = string("op_3637_cast_fp16")]; + tensor spread_17_reps_0 = const()[name = string("spread_17_reps_0"), val = tensor([1, 1, 1, 3])]; + tensor spread_17_cast_fp16 = tile(reps = spread_17_reps_0, x = var_3637_cast_fp16)[name = string("spread_17_cast_fp16")]; + tensor var_3643 = const()[name = string("op_3643"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_47_cast_fp16 = reshape(shape = var_3643, x = spread_17_cast_fp16)[name = string("context_mask_47_cast_fp16")]; + tensor residual_21_begin_0 = const()[name = string("residual_21_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_21_end_0 = const()[name = string("residual_21_end_0"), val = tensor([1, 96, 1, 7764])]; + tensor residual_21_end_mask_0 = const()[name = string("residual_21_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_21_cast_fp16 = slice_by_index(begin = residual_21_begin_0, end = residual_21_end_0, end_mask = residual_21_end_mask_0, x = hidden_states_191_cast_fp16)[name = string("residual_21_cast_fp16")]; + tensor alpha_over_pi_45_to_fp16 = const()[name = string("alpha_over_pi_45_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115103808)))]; + tensor theta_over_pi_45_cast_fp16 = mul(x = hidden_states_191_cast_fp16, y = alpha_over_pi_45_to_fp16)[name = string("theta_over_pi_45_cast_fp16")]; + tensor var_3666_cast_fp16 = round(x = theta_over_pi_45_cast_fp16)[name = string("op_3666_cast_fp16")]; + tensor reduced_45_cast_fp16 = sub(x = theta_over_pi_45_cast_fp16, y = var_3666_cast_fp16)[name = string("reduced_45_cast_fp16")]; + tensor reduced_sq_45_cast_fp16 = mul(x = reduced_45_cast_fp16, y = reduced_45_cast_fp16)[name = string("reduced_sq_45_cast_fp16")]; + tensor acc_133_mean_0_to_fp16 = const()[name = string("acc_133_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_133_variance_0_to_fp16 = const()[name = string("acc_133_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_133_gamma_0_to_fp16 = const()[name = string("acc_133_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104576)))]; + tensor acc_133_beta_0_to_fp16 = const()[name = string("acc_133_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104832)))]; + fp16 acc_133_epsilon_0_to_fp16 = const()[name = string("acc_133_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_133_cast_fp16 = batch_norm(beta = acc_133_beta_0_to_fp16, epsilon = acc_133_epsilon_0_to_fp16, gamma = acc_133_gamma_0_to_fp16, mean = acc_133_mean_0_to_fp16, variance = acc_133_variance_0_to_fp16, x = reduced_sq_45_cast_fp16)[name = string("acc_133_cast_fp16")]; + tensor var_3679_cast_fp16 = mul(x = acc_133_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3679_cast_fp16")]; + tensor c_89_to_fp16 = const()[name = string("c_89_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115105088)))]; + tensor acc_135_cast_fp16 = add(x = var_3679_cast_fp16, y = c_89_to_fp16)[name = string("acc_135_cast_fp16")]; + tensor var_3681_cast_fp16 = mul(x = acc_135_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3681_cast_fp16")]; + tensor c_91_to_fp16 = const()[name = string("c_91_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115105344)))]; + tensor acc_137_cast_fp16 = add(x = var_3681_cast_fp16, y = c_91_to_fp16)[name = string("acc_137_cast_fp16")]; + tensor var_3683_cast_fp16 = mul(x = acc_137_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3683_cast_fp16")]; + tensor hidden_states_193_cast_fp16 = add(x = hidden_states_191_cast_fp16, y = var_3683_cast_fp16)[name = string("hidden_states_193_cast_fp16")]; + bool full_mask_33_interleave_0 = const()[name = string("full_mask_33_interleave_0"), val = bool(false)]; + tensor fill_16_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115349376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115348800))))[name = string("fill_16_to_fp16_palettized")]; + tensor full_mask_33_cast_fp16 = concat(axis = var_2037, interleave = full_mask_33_interleave_0, values = (context_mask_47_cast_fp16, fill_16_to_fp16_palettized))[name = string("full_mask_33_cast_fp16")]; + tensor input_177_cast_fp16 = mul(x = hidden_states_193_cast_fp16, y = full_mask_33_cast_fp16)[name = string("input_177_cast_fp16")]; + string hidden_states_195_pad_type_0 = const()[name = string("hidden_states_195_pad_type_0"), val = string("valid")]; + tensor hidden_states_195_strides_0 = const()[name = string("hidden_states_195_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_0 = const()[name = string("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_195_dilations_0 = const()[name = string("hidden_states_195_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_195_groups_0 = const()[name = string("hidden_states_195_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115109504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174080))))[name = string("audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174656)))]; + tensor hidden_states_195_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_195_dilations_0, groups = hidden_states_195_groups_0, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = hidden_states_195_strides_0, weight = audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized, x = input_177_cast_fp16)[name = string("hidden_states_195_cast_fp16")]; + tensor alpha_over_pi_47_to_fp16 = const()[name = string("alpha_over_pi_47_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174912)))]; + tensor theta_over_pi_47_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = alpha_over_pi_47_to_fp16)[name = string("theta_over_pi_47_cast_fp16")]; + tensor var_3720_cast_fp16 = round(x = theta_over_pi_47_cast_fp16)[name = string("op_3720_cast_fp16")]; + tensor reduced_47_cast_fp16 = sub(x = theta_over_pi_47_cast_fp16, y = var_3720_cast_fp16)[name = string("reduced_47_cast_fp16")]; + tensor reduced_sq_47_cast_fp16 = mul(x = reduced_47_cast_fp16, y = reduced_47_cast_fp16)[name = string("reduced_sq_47_cast_fp16")]; + tensor acc_139_mean_0_to_fp16 = const()[name = string("acc_139_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_139_variance_0_to_fp16 = const()[name = string("acc_139_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_139_gamma_0_to_fp16 = const()[name = string("acc_139_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175168)))]; + tensor acc_139_beta_0_to_fp16 = const()[name = string("acc_139_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175424)))]; + fp16 acc_139_epsilon_0_to_fp16 = const()[name = string("acc_139_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_139_cast_fp16 = batch_norm(beta = acc_139_beta_0_to_fp16, epsilon = acc_139_epsilon_0_to_fp16, gamma = acc_139_gamma_0_to_fp16, mean = acc_139_mean_0_to_fp16, variance = acc_139_variance_0_to_fp16, x = reduced_sq_47_cast_fp16)[name = string("acc_139_cast_fp16")]; + tensor var_3733_cast_fp16 = mul(x = acc_139_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3733_cast_fp16")]; + tensor c_93_to_fp16 = const()[name = string("c_93_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175680)))]; + tensor acc_141_cast_fp16 = add(x = var_3733_cast_fp16, y = c_93_to_fp16)[name = string("acc_141_cast_fp16")]; + tensor var_3735_cast_fp16 = mul(x = acc_141_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3735_cast_fp16")]; + tensor c_95_to_fp16 = const()[name = string("c_95_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175936)))]; + tensor acc_143_cast_fp16 = add(x = var_3735_cast_fp16, y = c_95_to_fp16)[name = string("acc_143_cast_fp16")]; + tensor var_3737_cast_fp16 = mul(x = acc_143_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3737_cast_fp16")]; + tensor hidden_states_197_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = var_3737_cast_fp16)[name = string("hidden_states_197_cast_fp16")]; + string hidden_states_199_pad_type_0 = const()[name = string("hidden_states_199_pad_type_0"), val = string("valid")]; + tensor hidden_states_199_strides_0 = const()[name = string("hidden_states_199_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_0 = const()[name = string("hidden_states_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_199_dilations_0 = const()[name = string("hidden_states_199_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_199_groups_0 = const()[name = string("hidden_states_199_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115176192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115185472))))[name = string("audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186048)))]; + tensor hidden_states_199_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_199_dilations_0, groups = hidden_states_199_groups_0, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = hidden_states_199_strides_0, weight = audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")]; + tensor hidden_states_201_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = residual_21_cast_fp16)[name = string("hidden_states_201_cast_fp16")]; + tensor context_mask_49_begin_0 = const()[name = string("context_mask_49_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_49_end_0 = const()[name = string("context_mask_49_end_0"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_49_end_mask_0 = const()[name = string("context_mask_49_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_49_cast_fp16 = slice_by_index(begin = context_mask_49_begin_0, end = context_mask_49_end_0, end_mask = context_mask_49_end_mask_0, x = context_mask_47_cast_fp16)[name = string("context_mask_49_cast_fp16")]; + tensor residual_23_begin_0 = const()[name = string("residual_23_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_23_end_0 = const()[name = string("residual_23_end_0"), val = tensor([1, 96, 1, 7758])]; + tensor residual_23_end_mask_0 = const()[name = string("residual_23_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_23_cast_fp16 = slice_by_index(begin = residual_23_begin_0, end = residual_23_end_0, end_mask = residual_23_end_mask_0, x = hidden_states_201_cast_fp16)[name = string("residual_23_cast_fp16")]; + tensor alpha_over_pi_49_to_fp16 = const()[name = string("alpha_over_pi_49_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186304)))]; + tensor theta_over_pi_49_cast_fp16 = mul(x = hidden_states_201_cast_fp16, y = alpha_over_pi_49_to_fp16)[name = string("theta_over_pi_49_cast_fp16")]; + tensor var_3772_cast_fp16 = round(x = theta_over_pi_49_cast_fp16)[name = string("op_3772_cast_fp16")]; + tensor reduced_49_cast_fp16 = sub(x = theta_over_pi_49_cast_fp16, y = var_3772_cast_fp16)[name = string("reduced_49_cast_fp16")]; + tensor reduced_sq_49_cast_fp16 = mul(x = reduced_49_cast_fp16, y = reduced_49_cast_fp16)[name = string("reduced_sq_49_cast_fp16")]; + tensor acc_145_mean_0_to_fp16 = const()[name = string("acc_145_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_145_variance_0_to_fp16 = const()[name = string("acc_145_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_145_gamma_0_to_fp16 = const()[name = string("acc_145_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186560)))]; + tensor acc_145_beta_0_to_fp16 = const()[name = string("acc_145_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186816)))]; + fp16 acc_145_epsilon_0_to_fp16 = const()[name = string("acc_145_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_145_cast_fp16 = batch_norm(beta = acc_145_beta_0_to_fp16, epsilon = acc_145_epsilon_0_to_fp16, gamma = acc_145_gamma_0_to_fp16, mean = acc_145_mean_0_to_fp16, variance = acc_145_variance_0_to_fp16, x = reduced_sq_49_cast_fp16)[name = string("acc_145_cast_fp16")]; + tensor var_3785_cast_fp16 = mul(x = acc_145_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3785_cast_fp16")]; + tensor c_97_to_fp16 = const()[name = string("c_97_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187072)))]; + tensor acc_147_cast_fp16 = add(x = var_3785_cast_fp16, y = c_97_to_fp16)[name = string("acc_147_cast_fp16")]; + tensor var_3787_cast_fp16 = mul(x = acc_147_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3787_cast_fp16")]; + tensor c_99_to_fp16 = const()[name = string("c_99_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187328)))]; + tensor acc_149_cast_fp16 = add(x = var_3787_cast_fp16, y = c_99_to_fp16)[name = string("acc_149_cast_fp16")]; + tensor var_3789_cast_fp16 = mul(x = acc_149_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3789_cast_fp16")]; + tensor hidden_states_203_cast_fp16 = add(x = hidden_states_201_cast_fp16, y = var_3789_cast_fp16)[name = string("hidden_states_203_cast_fp16")]; + bool full_mask_35_interleave_0 = const()[name = string("full_mask_35_interleave_0"), val = bool(false)]; + tensor full_mask_35_cast_fp16 = concat(axis = var_2037, interleave = full_mask_35_interleave_0, values = (context_mask_49_cast_fp16, fill_16_to_fp16_palettized))[name = string("full_mask_35_cast_fp16")]; + tensor input_181_cast_fp16 = mul(x = hidden_states_203_cast_fp16, y = full_mask_35_cast_fp16)[name = string("input_181_cast_fp16")]; + string hidden_states_205_pad_type_0 = const()[name = string("hidden_states_205_pad_type_0"), val = string("valid")]; + tensor hidden_states_205_dilations_0 = const()[name = string("hidden_states_205_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_205_strides_0 = const()[name = string("hidden_states_205_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_0 = const()[name = string("hidden_states_205_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_205_groups_0 = const()[name = string("hidden_states_205_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252160))))[name = string("audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252736)))]; + tensor hidden_states_205_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_205_dilations_0, groups = hidden_states_205_groups_0, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = hidden_states_205_strides_0, weight = audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("hidden_states_205_cast_fp16")]; + tensor alpha_over_pi_51_to_fp16 = const()[name = string("alpha_over_pi_51_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252992)))]; + tensor theta_over_pi_51_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = alpha_over_pi_51_to_fp16)[name = string("theta_over_pi_51_cast_fp16")]; + tensor var_3826_cast_fp16 = round(x = theta_over_pi_51_cast_fp16)[name = string("op_3826_cast_fp16")]; + tensor reduced_51_cast_fp16 = sub(x = theta_over_pi_51_cast_fp16, y = var_3826_cast_fp16)[name = string("reduced_51_cast_fp16")]; + tensor reduced_sq_51_cast_fp16 = mul(x = reduced_51_cast_fp16, y = reduced_51_cast_fp16)[name = string("reduced_sq_51_cast_fp16")]; + tensor acc_151_mean_0_to_fp16 = const()[name = string("acc_151_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_151_variance_0_to_fp16 = const()[name = string("acc_151_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_151_gamma_0_to_fp16 = const()[name = string("acc_151_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253248)))]; + tensor acc_151_beta_0_to_fp16 = const()[name = string("acc_151_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253504)))]; + fp16 acc_151_epsilon_0_to_fp16 = const()[name = string("acc_151_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_151_cast_fp16 = batch_norm(beta = acc_151_beta_0_to_fp16, epsilon = acc_151_epsilon_0_to_fp16, gamma = acc_151_gamma_0_to_fp16, mean = acc_151_mean_0_to_fp16, variance = acc_151_variance_0_to_fp16, x = reduced_sq_51_cast_fp16)[name = string("acc_151_cast_fp16")]; + tensor var_3839_cast_fp16 = mul(x = acc_151_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3839_cast_fp16")]; + tensor c_101_to_fp16 = const()[name = string("c_101_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253760)))]; + tensor acc_153_cast_fp16 = add(x = var_3839_cast_fp16, y = c_101_to_fp16)[name = string("acc_153_cast_fp16")]; + tensor var_3841_cast_fp16 = mul(x = acc_153_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3841_cast_fp16")]; + tensor c_103_to_fp16 = const()[name = string("c_103_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115254016)))]; + tensor acc_155_cast_fp16 = add(x = var_3841_cast_fp16, y = c_103_to_fp16)[name = string("acc_155_cast_fp16")]; + tensor var_3843_cast_fp16 = mul(x = acc_155_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3843_cast_fp16")]; + tensor hidden_states_207_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = var_3843_cast_fp16)[name = string("hidden_states_207_cast_fp16")]; + string hidden_states_209_pad_type_0 = const()[name = string("hidden_states_209_pad_type_0"), val = string("valid")]; + tensor hidden_states_209_strides_0 = const()[name = string("hidden_states_209_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_209_pad_0 = const()[name = string("hidden_states_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_209_dilations_0 = const()[name = string("hidden_states_209_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_209_groups_0 = const()[name = string("hidden_states_209_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115254272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115357120))))[name = string("audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264128)))]; + tensor hidden_states_209_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_209_dilations_0, groups = hidden_states_209_groups_0, pad = hidden_states_209_pad_0, pad_type = hidden_states_209_pad_type_0, strides = hidden_states_209_strides_0, weight = audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")]; + tensor hidden_states_211_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = residual_23_cast_fp16)[name = string("hidden_states_211_cast_fp16")]; + tensor context_mask_51_begin_0 = const()[name = string("context_mask_51_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_51_end_0 = const()[name = string("context_mask_51_end_0"), val = tensor([1, 1, 1, 78])]; + tensor context_mask_51_end_mask_0 = const()[name = string("context_mask_51_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_51_cast_fp16 = slice_by_index(begin = context_mask_51_begin_0, end = context_mask_51_end_0, end_mask = context_mask_51_end_mask_0, x = context_mask_49_cast_fp16)[name = string("context_mask_51_cast_fp16")]; + tensor residual_begin_0 = const()[name = string("residual_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_end_0 = const()[name = string("residual_end_0"), val = tensor([1, 96, 1, 7740])]; + tensor residual_end_mask_0 = const()[name = string("residual_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_cast_fp16 = slice_by_index(begin = residual_begin_0, end = residual_end_0, end_mask = residual_end_mask_0, x = hidden_states_211_cast_fp16)[name = string("residual_cast_fp16")]; + tensor alpha_over_pi_53_to_fp16 = const()[name = string("alpha_over_pi_53_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264384)))]; + tensor theta_over_pi_53_cast_fp16 = mul(x = hidden_states_211_cast_fp16, y = alpha_over_pi_53_to_fp16)[name = string("theta_over_pi_53_cast_fp16")]; + tensor var_3878_cast_fp16 = round(x = theta_over_pi_53_cast_fp16)[name = string("op_3878_cast_fp16")]; + tensor reduced_53_cast_fp16 = sub(x = theta_over_pi_53_cast_fp16, y = var_3878_cast_fp16)[name = string("reduced_53_cast_fp16")]; + tensor reduced_sq_53_cast_fp16 = mul(x = reduced_53_cast_fp16, y = reduced_53_cast_fp16)[name = string("reduced_sq_53_cast_fp16")]; + tensor acc_157_mean_0_to_fp16 = const()[name = string("acc_157_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_157_variance_0_to_fp16 = const()[name = string("acc_157_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_157_gamma_0_to_fp16 = const()[name = string("acc_157_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264640)))]; + tensor acc_157_beta_0_to_fp16 = const()[name = string("acc_157_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264896)))]; + fp16 acc_157_epsilon_0_to_fp16 = const()[name = string("acc_157_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_157_cast_fp16 = batch_norm(beta = acc_157_beta_0_to_fp16, epsilon = acc_157_epsilon_0_to_fp16, gamma = acc_157_gamma_0_to_fp16, mean = acc_157_mean_0_to_fp16, variance = acc_157_variance_0_to_fp16, x = reduced_sq_53_cast_fp16)[name = string("acc_157_cast_fp16")]; + tensor var_3891_cast_fp16 = mul(x = acc_157_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3891_cast_fp16")]; + tensor c_105_to_fp16 = const()[name = string("c_105_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265152)))]; + tensor acc_159_cast_fp16 = add(x = var_3891_cast_fp16, y = c_105_to_fp16)[name = string("acc_159_cast_fp16")]; + tensor var_3893_cast_fp16 = mul(x = acc_159_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3893_cast_fp16")]; + tensor c_107_to_fp16 = const()[name = string("c_107_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265408)))]; + tensor acc_161_cast_fp16 = add(x = var_3893_cast_fp16, y = c_107_to_fp16)[name = string("acc_161_cast_fp16")]; + tensor var_3895_cast_fp16 = mul(x = acc_161_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3895_cast_fp16")]; + tensor hidden_states_213_cast_fp16 = add(x = hidden_states_211_cast_fp16, y = var_3895_cast_fp16)[name = string("hidden_states_213_cast_fp16")]; + bool full_mask_37_interleave_0 = const()[name = string("full_mask_37_interleave_0"), val = bool(false)]; + tensor full_mask_37_cast_fp16 = concat(axis = var_2037, interleave = full_mask_37_interleave_0, values = (context_mask_51_cast_fp16, fill_16_to_fp16_palettized))[name = string("full_mask_37_cast_fp16")]; + tensor input_185_cast_fp16 = mul(x = hidden_states_213_cast_fp16, y = full_mask_37_cast_fp16)[name = string("input_185_cast_fp16")]; + string hidden_states_215_pad_type_0 = const()[name = string("hidden_states_215_pad_type_0"), val = string("valid")]; + tensor hidden_states_215_dilations_0 = const()[name = string("hidden_states_215_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_215_strides_0 = const()[name = string("hidden_states_215_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_0 = const()[name = string("hidden_states_215_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_215_groups_0 = const()[name = string("hidden_states_215_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115330240))))[name = string("audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115330816)))]; + tensor hidden_states_215_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_215_dilations_0, groups = hidden_states_215_groups_0, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = hidden_states_215_strides_0, weight = audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("hidden_states_215_cast_fp16")]; + tensor alpha_over_pi_55_to_fp16 = const()[name = string("alpha_over_pi_55_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331072)))]; + tensor theta_over_pi_55_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = alpha_over_pi_55_to_fp16)[name = string("theta_over_pi_55_cast_fp16")]; + tensor var_3932_cast_fp16 = round(x = theta_over_pi_55_cast_fp16)[name = string("op_3932_cast_fp16")]; + tensor reduced_55_cast_fp16 = sub(x = theta_over_pi_55_cast_fp16, y = var_3932_cast_fp16)[name = string("reduced_55_cast_fp16")]; + tensor reduced_sq_55_cast_fp16 = mul(x = reduced_55_cast_fp16, y = reduced_55_cast_fp16)[name = string("reduced_sq_55_cast_fp16")]; + tensor acc_163_mean_0_to_fp16 = const()[name = string("acc_163_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_163_variance_0_to_fp16 = const()[name = string("acc_163_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_163_gamma_0_to_fp16 = const()[name = string("acc_163_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331328)))]; + tensor acc_163_beta_0_to_fp16 = const()[name = string("acc_163_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331584)))]; + fp16 acc_163_epsilon_0_to_fp16 = const()[name = string("acc_163_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_163_cast_fp16 = batch_norm(beta = acc_163_beta_0_to_fp16, epsilon = acc_163_epsilon_0_to_fp16, gamma = acc_163_gamma_0_to_fp16, mean = acc_163_mean_0_to_fp16, variance = acc_163_variance_0_to_fp16, x = reduced_sq_55_cast_fp16)[name = string("acc_163_cast_fp16")]; + tensor var_3945_cast_fp16 = mul(x = acc_163_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3945_cast_fp16")]; + tensor c_109_to_fp16 = const()[name = string("c_109_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331840)))]; + tensor acc_165_cast_fp16 = add(x = var_3945_cast_fp16, y = c_109_to_fp16)[name = string("acc_165_cast_fp16")]; + tensor var_3947_cast_fp16 = mul(x = acc_165_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3947_cast_fp16")]; + tensor c_111_to_fp16 = const()[name = string("c_111_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115332096)))]; + tensor acc_167_cast_fp16 = add(x = var_3947_cast_fp16, y = c_111_to_fp16)[name = string("acc_167_cast_fp16")]; + tensor var_3949_cast_fp16 = mul(x = acc_167_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3949_cast_fp16")]; + tensor hidden_states_217_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = var_3949_cast_fp16)[name = string("hidden_states_217_cast_fp16")]; + string hidden_states_219_pad_type_0 = const()[name = string("hidden_states_219_pad_type_0"), val = string("valid")]; + tensor hidden_states_219_strides_0 = const()[name = string("hidden_states_219_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_0 = const()[name = string("hidden_states_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_219_dilations_0 = const()[name = string("hidden_states_219_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_219_groups_0 = const()[name = string("hidden_states_219_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115332352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115357696))))[name = string("audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342208)))]; + tensor hidden_states_219_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_219_dilations_0, groups = hidden_states_219_groups_0, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = hidden_states_219_strides_0, weight = audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")]; + tensor hidden_states_221_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = residual_cast_fp16)[name = string("hidden_states_221_cast_fp16")]; + tensor context_mask_begin_0 = const()[name = string("context_mask_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_end_0 = const()[name = string("context_mask_end_0"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_end_mask_0 = const()[name = string("context_mask_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_cast_fp16 = slice_by_index(begin = context_mask_begin_0, end = context_mask_end_0, end_mask = context_mask_end_mask_0, x = context_mask_47_cast_fp16)[name = string("context_mask_cast_fp16")]; + tensor alpha_over_pi_to_fp16 = const()[name = string("alpha_over_pi_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342464)))]; + tensor theta_over_pi_cast_fp16 = mul(x = hidden_states_221_cast_fp16, y = alpha_over_pi_to_fp16)[name = string("theta_over_pi_cast_fp16")]; + tensor var_3991_cast_fp16 = round(x = theta_over_pi_cast_fp16)[name = string("op_3991_cast_fp16")]; + tensor reduced_cast_fp16 = sub(x = theta_over_pi_cast_fp16, y = var_3991_cast_fp16)[name = string("reduced_cast_fp16")]; + tensor reduced_sq_cast_fp16 = mul(x = reduced_cast_fp16, y = reduced_cast_fp16)[name = string("reduced_sq_cast_fp16")]; + tensor acc_169_mean_0_to_fp16 = const()[name = string("acc_169_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_169_variance_0_to_fp16 = const()[name = string("acc_169_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_169_gamma_0_to_fp16 = const()[name = string("acc_169_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342720)))]; + tensor acc_169_beta_0_to_fp16 = const()[name = string("acc_169_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342976)))]; + fp16 acc_169_epsilon_0_to_fp16 = const()[name = string("acc_169_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_169_cast_fp16 = batch_norm(beta = acc_169_beta_0_to_fp16, epsilon = acc_169_epsilon_0_to_fp16, gamma = acc_169_gamma_0_to_fp16, mean = acc_169_mean_0_to_fp16, variance = acc_169_variance_0_to_fp16, x = reduced_sq_cast_fp16)[name = string("acc_169_cast_fp16")]; + tensor var_4004_cast_fp16 = mul(x = acc_169_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_4004_cast_fp16")]; + tensor c_113_to_fp16 = const()[name = string("c_113_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343232)))]; + tensor acc_171_cast_fp16 = add(x = var_4004_cast_fp16, y = c_113_to_fp16)[name = string("acc_171_cast_fp16")]; + tensor var_4006_cast_fp16 = mul(x = acc_171_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_4006_cast_fp16")]; + tensor c_to_fp16 = const()[name = string("c_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343488)))]; + tensor acc_cast_fp16 = add(x = var_4006_cast_fp16, y = c_to_fp16)[name = string("acc_cast_fp16")]; + tensor var_4008_cast_fp16 = mul(x = acc_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_4008_cast_fp16")]; + tensor hidden_states_223_cast_fp16 = add(x = hidden_states_221_cast_fp16, y = var_4008_cast_fp16)[name = string("hidden_states_223_cast_fp16")]; + bool full_mask_interleave_0 = const()[name = string("full_mask_interleave_0"), val = bool(false)]; + tensor full_mask_cast_fp16 = concat(axis = var_2037, interleave = full_mask_interleave_0, values = (context_mask_cast_fp16, fill_16_to_fp16_palettized))[name = string("full_mask_cast_fp16")]; + tensor input_cast_fp16 = mul(x = hidden_states_223_cast_fp16, y = full_mask_cast_fp16)[name = string("input_cast_fp16")]; + string hidden_states_pad_type_0 = const()[name = string("hidden_states_pad_type_0"), val = string("valid")]; + tensor hidden_states_strides_0 = const()[name = string("hidden_states_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_pad_0 = const()[name = string("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_dilations_0 = const()[name = string("hidden_states_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_groups_0 = const()[name = string("hidden_states_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_6_conv_weight_to_fp16 = const()[name = string("audio_upsampler_decoder_6_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343744)))]; + tensor audio_upsampler_decoder_6_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_6_conv_bias_to_fp16"), val = tensor([-0x1.1p-19])]; + tensor hidden_states_cast_fp16 = conv(bias = audio_upsampler_decoder_6_conv_bias_to_fp16, dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = audio_upsampler_decoder_6_conv_weight_to_fp16, x = input_cast_fp16)[name = string("hidden_states_cast_fp16")]; + fp16 var_2024_to_fp16 = const()[name = string("op_2024_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_2023_to_fp16 = const()[name = string("op_2023_to_fp16"), val = fp16(0x1p+0)]; + tensor audio = clip(alpha = var_2024_to_fp16, beta = var_2023_to_fp16, x = hidden_states_cast_fp16)[name = string("clip_16_cast_fp16")]; + } -> (audio, key_cache_updates, value_cache_updates, hidden_context_update, pre_conv_context_update); +} \ No newline at end of file diff --git a/qwen3_tts/speech_decoder/12hz-0.6b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/weights/weight.bin b/qwen3_tts/speech_decoder/12hz-0.6b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..79d225c5dd3e3011574b6d1f79e30077495ede3b --- /dev/null +++ b/qwen3_tts/speech_decoder/12hz-0.6b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:827114b7e98262297d55ed977405a56e3a26f431c22968e31604a3ce7d7dec50 +size 115358272 diff --git a/qwen3_tts/speech_decoder/12hz-1.7b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/analytics/coremldata.bin 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a/qwen3_tts/speech_decoder/12hz-1.7b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/model.mil b/qwen3_tts/speech_decoder/12hz-1.7b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..4b4d5825c720e3ade79ddd7427c79a8b24ae9ac3 --- /dev/null +++ b/qwen3_tts/speech_decoder/12hz-1.7b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/model.mil @@ -0,0 +1,5458 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})] +{ + func latency(tensor audio_codes, tensor cache_length, tensor hidden_context, tensor hidden_context_mask, tensor key_cache, tensor key_padding_mask, tensor kv_cache_update_mask, tensor pre_conv_context, tensor value_cache) { + tensor codes_1_begin_0 = const()[name = string("codes_1_begin_0"), val = tensor([0, 0, 0])]; + tensor codes_1_end_0 = const()[name = string("codes_1_end_0"), val = tensor([1, 1, 1])]; + tensor codes_1_end_mask_0 = const()[name = string("codes_1_end_mask_0"), val = tensor([true, false, true])]; + tensor codes_1 = slice_by_index(begin = codes_1_begin_0, end = codes_1_end_0, end_mask = codes_1_end_mask_0, x = audio_codes)[name = string("codes_1")]; + tensor var_44 = const()[name = string("op_44"), val = tensor([1, 0, 2])]; + tensor input_1_begin_0 = const()[name = string("input_1_begin_0"), val = tensor([0, 0, 0])]; + tensor input_1_end_0 = const()[name = string("input_1_end_0"), val = tensor([1, 1, 1])]; + tensor input_1_end_mask_0 = const()[name = string("input_1_end_mask_0"), val = tensor([false, true, true])]; + tensor input_1_squeeze_mask_0 = const()[name = string("input_1_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor codes_3 = transpose(perm = var_44, x = codes_1)[name = string("transpose_27")]; + tensor input_1 = slice_by_index(begin = input_1_begin_0, end = input_1_end_0, end_mask = input_1_end_mask_0, squeeze_mask = input_1_squeeze_mask_0, x = codes_3)[name = string("input_1")]; + int32 quantized_1_axis_0 = const()[name = string("quantized_1_axis_0"), val = int32(0)]; + int32 quantized_1_batch_dims_0 = const()[name = string("quantized_1_batch_dims_0"), val = int32(0)]; + bool quantized_1_validate_indices_0 = const()[name = string("quantized_1_validate_indices_0"), val = bool(false)]; + tensor weight_1_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("weight_1_to_fp16_palettized")]; + string input_1_to_uint16_dtype_0 = const()[name = string("input_1_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_1_to_uint16 = cast(dtype = input_1_to_uint16_dtype_0, x = input_1)[name = string("cast_16")]; + tensor quantized_1_cast_fp16_cast_uint16 = gather(axis = quantized_1_axis_0, batch_dims = quantized_1_batch_dims_0, indices = input_1_to_uint16, validate_indices = quantized_1_validate_indices_0, x = weight_1_to_fp16_palettized)[name = string("quantized_1_cast_fp16_cast_uint16")]; + tensor var_57 = const()[name = string("op_57"), val = tensor([0, 2, 1])]; + tensor input_3_axes_0 = const()[name = string("input_3_axes_0"), val = tensor([2])]; + tensor var_58_cast_fp16 = transpose(perm = var_57, x = quantized_1_cast_fp16_cast_uint16)[name = string("transpose_26")]; + tensor input_3_cast_fp16 = expand_dims(axes = input_3_axes_0, x = var_58_cast_fp16)[name = string("input_3_cast_fp16")]; + string quantized_pad_type_0 = const()[name = string("quantized_pad_type_0"), val = string("valid")]; + tensor quantized_strides_0 = const()[name = string("quantized_strides_0"), val = tensor([1, 1])]; + tensor quantized_pad_0 = const()[name = string("quantized_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor quantized_dilations_0 = const()[name = string("quantized_dilations_0"), val = tensor([1, 1])]; + int32 quantized_groups_0 = const()[name = string("quantized_groups_0"), val = int32(1)]; + tensor quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656128))))[name = string("quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized")]; + tensor quantized_cast_fp16 = conv(dilations = quantized_dilations_0, groups = quantized_groups_0, pad = quantized_pad_0, pad_type = quantized_pad_type_0, strides = quantized_strides_0, weight = quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("quantized_cast_fp16")]; + tensor codes_5_begin_0 = const()[name = string("codes_5_begin_0"), val = tensor([0, 1, 0])]; + tensor codes_5_end_0 = const()[name = string("codes_5_end_0"), val = tensor([1, 16, 1])]; + tensor codes_5_end_mask_0 = const()[name = string("codes_5_end_mask_0"), val = tensor([true, true, true])]; + tensor codes_5 = slice_by_index(begin = codes_5_begin_0, end = codes_5_end_0, end_mask = codes_5_end_mask_0, x = audio_codes)[name = string("codes_5")]; + tensor var_70 = const()[name = string("op_70"), val = tensor([1, 0, 2])]; + tensor input_5_begin_0 = const()[name = string("input_5_begin_0"), val = tensor([0, 0, 0])]; + tensor input_5_end_0 = const()[name = string("input_5_end_0"), val = tensor([1, 1, 1])]; + tensor input_5_end_mask_0 = const()[name = string("input_5_end_mask_0"), val = tensor([false, true, true])]; + tensor input_5_squeeze_mask_0 = const()[name = string("input_5_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor codes = transpose(perm = var_70, x = codes_5)[name = string("transpose_25")]; + tensor input_5 = slice_by_index(begin = input_5_begin_0, end = input_5_end_0, end_mask = input_5_end_mask_0, squeeze_mask = input_5_squeeze_mask_0, x = codes)[name = string("input_5")]; + int32 quantized_3_axis_0 = const()[name = string("quantized_3_axis_0"), val = int32(0)]; + int32 quantized_3_batch_dims_0 = const()[name = string("quantized_3_batch_dims_0"), val = int32(0)]; + bool quantized_3_validate_indices_0 = const()[name = string("quantized_3_validate_indices_0"), val = bool(false)]; + tensor weight_5_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1181056))))[name = string("weight_5_to_fp16_palettized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_15")]; + tensor quantized_3_cast_fp16_cast_uint16 = gather(axis = quantized_3_axis_0, batch_dims = quantized_3_batch_dims_0, indices = input_5_to_uint16, validate_indices = quantized_3_validate_indices_0, x = weight_5_to_fp16_palettized)[name = string("quantized_3_cast_fp16_cast_uint16")]; + tensor var_111 = const()[name = string("op_111"), val = tensor([0, 2, 1])]; + tensor quantized_7_axes_0 = const()[name = string("quantized_7_axes_0"), val = tensor([2])]; + tensor var_112_cast_fp16 = transpose(perm = var_111, x = quantized_3_cast_fp16_cast_uint16)[name = string("transpose_24")]; + tensor quantized_7_cast_fp16 = expand_dims(axes = quantized_7_axes_0, x = var_112_cast_fp16)[name = string("quantized_7_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([1, 0, 0])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 1])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([false, true, true])]; + tensor input_7_squeeze_mask_0 = const()[name = string("input_7_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_7 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, squeeze_mask = input_7_squeeze_mask_0, x = codes)[name = string("input_7")]; + int32 quantized_5_axis_0 = const()[name = string("quantized_5_axis_0"), val = int32(0)]; + int32 quantized_5_batch_dims_0 = const()[name = string("quantized_5_batch_dims_0"), val = int32(0)]; + bool quantized_5_validate_indices_0 = const()[name = string("quantized_5_validate_indices_0"), val = bool(false)]; + tensor weight_7_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1181632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705984))))[name = string("weight_7_to_fp16_palettized")]; + string input_7_to_uint16_dtype_0 = const()[name = string("input_7_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_7_to_uint16 = cast(dtype = input_7_to_uint16_dtype_0, x = input_7)[name = string("cast_14")]; + tensor quantized_5_cast_fp16_cast_uint16 = gather(axis = quantized_5_axis_0, batch_dims = quantized_5_batch_dims_0, indices = input_7_to_uint16, validate_indices = quantized_5_validate_indices_0, x = weight_7_to_fp16_palettized)[name = string("quantized_5_cast_fp16_cast_uint16")]; + tensor var_123 = const()[name = string("op_123"), val = tensor([0, 2, 1])]; + tensor layer_out_1_axes_0 = const()[name = string("layer_out_1_axes_0"), val = tensor([2])]; + tensor var_124_cast_fp16 = transpose(perm = var_123, x = quantized_5_cast_fp16_cast_uint16)[name = string("transpose_23")]; + tensor layer_out_1_cast_fp16 = expand_dims(axes = layer_out_1_axes_0, x = var_124_cast_fp16)[name = string("layer_out_1_cast_fp16")]; + tensor quantized_11_cast_fp16 = add(x = quantized_7_cast_fp16, y = layer_out_1_cast_fp16)[name = string("quantized_11_cast_fp16")]; + tensor input_9_begin_0 = const()[name = string("input_9_begin_0"), val = tensor([2, 0, 0])]; + tensor input_9_end_0 = const()[name = string("input_9_end_0"), val = tensor([3, 1, 1])]; + tensor input_9_end_mask_0 = const()[name = string("input_9_end_mask_0"), val = tensor([false, true, true])]; + tensor input_9_squeeze_mask_0 = const()[name = string("input_9_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_9 = slice_by_index(begin = input_9_begin_0, end = input_9_end_0, end_mask = input_9_end_mask_0, squeeze_mask = input_9_squeeze_mask_0, x = codes)[name = string("input_9")]; + int32 quantized_9_axis_0 = const()[name = string("quantized_9_axis_0"), val = int32(0)]; + int32 quantized_9_batch_dims_0 = const()[name = string("quantized_9_batch_dims_0"), val = int32(0)]; + bool quantized_9_validate_indices_0 = const()[name = string("quantized_9_validate_indices_0"), val = bool(false)]; + tensor weight_9_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1706560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2230912))))[name = string("weight_9_to_fp16_palettized")]; + string input_9_to_uint16_dtype_0 = const()[name = string("input_9_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_9_to_uint16 = cast(dtype = input_9_to_uint16_dtype_0, x = input_9)[name = string("cast_13")]; + tensor quantized_9_cast_fp16_cast_uint16 = gather(axis = quantized_9_axis_0, batch_dims = quantized_9_batch_dims_0, indices = input_9_to_uint16, validate_indices = quantized_9_validate_indices_0, x = weight_9_to_fp16_palettized)[name = string("quantized_9_cast_fp16_cast_uint16")]; + tensor var_136 = const()[name = string("op_136"), val = tensor([0, 2, 1])]; + tensor layer_out_3_axes_0 = const()[name = string("layer_out_3_axes_0"), val = tensor([2])]; + tensor var_137_cast_fp16 = transpose(perm = var_136, x = quantized_9_cast_fp16_cast_uint16)[name = string("transpose_22")]; + tensor layer_out_3_cast_fp16 = expand_dims(axes = layer_out_3_axes_0, x = var_137_cast_fp16)[name = string("layer_out_3_cast_fp16")]; + tensor quantized_15_cast_fp16 = add(x = quantized_11_cast_fp16, y = layer_out_3_cast_fp16)[name = string("quantized_15_cast_fp16")]; + tensor input_11_begin_0 = const()[name = string("input_11_begin_0"), val = tensor([3, 0, 0])]; + tensor input_11_end_0 = const()[name = string("input_11_end_0"), val = tensor([4, 1, 1])]; + tensor input_11_end_mask_0 = const()[name = string("input_11_end_mask_0"), val = tensor([false, true, true])]; + tensor input_11_squeeze_mask_0 = const()[name = string("input_11_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_11 = slice_by_index(begin = input_11_begin_0, end = input_11_end_0, end_mask = input_11_end_mask_0, squeeze_mask = input_11_squeeze_mask_0, x = codes)[name = string("input_11")]; + int32 quantized_13_axis_0 = const()[name = string("quantized_13_axis_0"), val = int32(0)]; + int32 quantized_13_batch_dims_0 = const()[name = string("quantized_13_batch_dims_0"), val = int32(0)]; + bool quantized_13_validate_indices_0 = const()[name = string("quantized_13_validate_indices_0"), val = bool(false)]; + tensor weight_11_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2231488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2755840))))[name = string("weight_11_to_fp16_palettized")]; + string input_11_to_uint16_dtype_0 = const()[name = string("input_11_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_11_to_uint16 = cast(dtype = input_11_to_uint16_dtype_0, x = input_11)[name = string("cast_12")]; + tensor quantized_13_cast_fp16_cast_uint16 = gather(axis = quantized_13_axis_0, batch_dims = quantized_13_batch_dims_0, indices = input_11_to_uint16, validate_indices = quantized_13_validate_indices_0, x = weight_11_to_fp16_palettized)[name = string("quantized_13_cast_fp16_cast_uint16")]; + tensor var_149 = const()[name = string("op_149"), val = tensor([0, 2, 1])]; + tensor layer_out_5_axes_0 = const()[name = string("layer_out_5_axes_0"), val = tensor([2])]; + tensor var_150_cast_fp16 = transpose(perm = var_149, x = quantized_13_cast_fp16_cast_uint16)[name = string("transpose_21")]; + tensor layer_out_5_cast_fp16 = expand_dims(axes = layer_out_5_axes_0, x = var_150_cast_fp16)[name = string("layer_out_5_cast_fp16")]; + tensor quantized_19_cast_fp16 = add(x = quantized_15_cast_fp16, y = layer_out_5_cast_fp16)[name = string("quantized_19_cast_fp16")]; + tensor input_13_begin_0 = const()[name = string("input_13_begin_0"), val = tensor([4, 0, 0])]; + tensor input_13_end_0 = const()[name = string("input_13_end_0"), val = tensor([5, 1, 1])]; + tensor input_13_end_mask_0 = const()[name = string("input_13_end_mask_0"), val = tensor([false, true, true])]; + tensor input_13_squeeze_mask_0 = const()[name = string("input_13_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_13 = slice_by_index(begin = input_13_begin_0, end = input_13_end_0, end_mask = input_13_end_mask_0, squeeze_mask = input_13_squeeze_mask_0, x = codes)[name = string("input_13")]; + int32 quantized_17_axis_0 = const()[name = string("quantized_17_axis_0"), val = int32(0)]; + int32 quantized_17_batch_dims_0 = const()[name = string("quantized_17_batch_dims_0"), val = int32(0)]; + bool quantized_17_validate_indices_0 = const()[name = string("quantized_17_validate_indices_0"), val = bool(false)]; + tensor weight_13_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2756416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3280768))))[name = string("weight_13_to_fp16_palettized")]; + string input_13_to_uint16_dtype_0 = const()[name = string("input_13_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_13_to_uint16 = cast(dtype = input_13_to_uint16_dtype_0, x = input_13)[name = string("cast_11")]; + tensor quantized_17_cast_fp16_cast_uint16 = gather(axis = quantized_17_axis_0, batch_dims = quantized_17_batch_dims_0, indices = input_13_to_uint16, validate_indices = quantized_17_validate_indices_0, x = weight_13_to_fp16_palettized)[name = string("quantized_17_cast_fp16_cast_uint16")]; + tensor var_162 = const()[name = string("op_162"), val = tensor([0, 2, 1])]; + tensor layer_out_7_axes_0 = const()[name = string("layer_out_7_axes_0"), val = tensor([2])]; + tensor var_163_cast_fp16 = transpose(perm = var_162, x = quantized_17_cast_fp16_cast_uint16)[name = string("transpose_20")]; + tensor layer_out_7_cast_fp16 = expand_dims(axes = layer_out_7_axes_0, x = var_163_cast_fp16)[name = string("layer_out_7_cast_fp16")]; + tensor quantized_23_cast_fp16 = add(x = quantized_19_cast_fp16, y = layer_out_7_cast_fp16)[name = string("quantized_23_cast_fp16")]; + tensor input_15_begin_0 = const()[name = string("input_15_begin_0"), val = tensor([5, 0, 0])]; + tensor input_15_end_0 = const()[name = string("input_15_end_0"), val = tensor([6, 1, 1])]; + tensor input_15_end_mask_0 = const()[name = string("input_15_end_mask_0"), val = tensor([false, true, true])]; + tensor input_15_squeeze_mask_0 = const()[name = string("input_15_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_15 = slice_by_index(begin = input_15_begin_0, end = input_15_end_0, end_mask = input_15_end_mask_0, squeeze_mask = input_15_squeeze_mask_0, x = codes)[name = string("input_15")]; + int32 quantized_21_axis_0 = const()[name = string("quantized_21_axis_0"), val = int32(0)]; + int32 quantized_21_batch_dims_0 = const()[name = string("quantized_21_batch_dims_0"), val = int32(0)]; + bool quantized_21_validate_indices_0 = const()[name = string("quantized_21_validate_indices_0"), val = bool(false)]; + tensor weight_15_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3281344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3805696))))[name = string("weight_15_to_fp16_palettized")]; + string input_15_to_uint16_dtype_0 = const()[name = string("input_15_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_15_to_uint16 = cast(dtype = input_15_to_uint16_dtype_0, x = input_15)[name = string("cast_10")]; + tensor quantized_21_cast_fp16_cast_uint16 = gather(axis = quantized_21_axis_0, batch_dims = quantized_21_batch_dims_0, indices = input_15_to_uint16, validate_indices = quantized_21_validate_indices_0, x = weight_15_to_fp16_palettized)[name = string("quantized_21_cast_fp16_cast_uint16")]; + tensor var_175 = const()[name = string("op_175"), val = tensor([0, 2, 1])]; + tensor layer_out_9_axes_0 = const()[name = string("layer_out_9_axes_0"), val = tensor([2])]; + tensor var_176_cast_fp16 = transpose(perm = var_175, x = quantized_21_cast_fp16_cast_uint16)[name = string("transpose_19")]; + tensor layer_out_9_cast_fp16 = expand_dims(axes = layer_out_9_axes_0, x = var_176_cast_fp16)[name = string("layer_out_9_cast_fp16")]; + tensor quantized_27_cast_fp16 = add(x = quantized_23_cast_fp16, y = layer_out_9_cast_fp16)[name = string("quantized_27_cast_fp16")]; + tensor input_17_begin_0 = const()[name = string("input_17_begin_0"), val = tensor([6, 0, 0])]; + tensor input_17_end_0 = const()[name = string("input_17_end_0"), val = tensor([7, 1, 1])]; + tensor input_17_end_mask_0 = const()[name = string("input_17_end_mask_0"), val = tensor([false, true, true])]; + tensor input_17_squeeze_mask_0 = const()[name = string("input_17_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_17 = slice_by_index(begin = input_17_begin_0, end = input_17_end_0, end_mask = input_17_end_mask_0, squeeze_mask = input_17_squeeze_mask_0, x = codes)[name = string("input_17")]; + int32 quantized_25_axis_0 = const()[name = string("quantized_25_axis_0"), val = int32(0)]; + int32 quantized_25_batch_dims_0 = const()[name = string("quantized_25_batch_dims_0"), val = int32(0)]; + bool quantized_25_validate_indices_0 = const()[name = string("quantized_25_validate_indices_0"), val = bool(false)]; + tensor weight_17_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3806272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330624))))[name = string("weight_17_to_fp16_palettized")]; + string input_17_to_uint16_dtype_0 = const()[name = string("input_17_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_17_to_uint16 = cast(dtype = input_17_to_uint16_dtype_0, x = input_17)[name = string("cast_9")]; + tensor quantized_25_cast_fp16_cast_uint16 = gather(axis = quantized_25_axis_0, batch_dims = quantized_25_batch_dims_0, indices = input_17_to_uint16, validate_indices = quantized_25_validate_indices_0, x = weight_17_to_fp16_palettized)[name = string("quantized_25_cast_fp16_cast_uint16")]; + tensor var_188 = const()[name = string("op_188"), val = tensor([0, 2, 1])]; + tensor layer_out_11_axes_0 = const()[name = string("layer_out_11_axes_0"), val = tensor([2])]; + tensor var_189_cast_fp16 = transpose(perm = var_188, x = quantized_25_cast_fp16_cast_uint16)[name = string("transpose_18")]; + tensor layer_out_11_cast_fp16 = expand_dims(axes = layer_out_11_axes_0, x = var_189_cast_fp16)[name = string("layer_out_11_cast_fp16")]; + tensor quantized_31_cast_fp16 = add(x = quantized_27_cast_fp16, y = layer_out_11_cast_fp16)[name = string("quantized_31_cast_fp16")]; + tensor input_19_begin_0 = const()[name = string("input_19_begin_0"), val = tensor([7, 0, 0])]; + tensor input_19_end_0 = const()[name = string("input_19_end_0"), val = tensor([8, 1, 1])]; + tensor input_19_end_mask_0 = const()[name = string("input_19_end_mask_0"), val = tensor([false, true, true])]; + tensor input_19_squeeze_mask_0 = const()[name = string("input_19_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_19 = slice_by_index(begin = input_19_begin_0, end = input_19_end_0, end_mask = input_19_end_mask_0, squeeze_mask = input_19_squeeze_mask_0, x = codes)[name = string("input_19")]; + int32 quantized_29_axis_0 = const()[name = string("quantized_29_axis_0"), val = int32(0)]; + int32 quantized_29_batch_dims_0 = const()[name = string("quantized_29_batch_dims_0"), val = int32(0)]; + bool quantized_29_validate_indices_0 = const()[name = string("quantized_29_validate_indices_0"), val = bool(false)]; + tensor weight_19_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4331200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4855552))))[name = string("weight_19_to_fp16_palettized")]; + string input_19_to_uint16_dtype_0 = const()[name = string("input_19_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_19_to_uint16 = cast(dtype = input_19_to_uint16_dtype_0, x = input_19)[name = string("cast_8")]; + tensor quantized_29_cast_fp16_cast_uint16 = gather(axis = quantized_29_axis_0, batch_dims = quantized_29_batch_dims_0, indices = input_19_to_uint16, validate_indices = quantized_29_validate_indices_0, x = weight_19_to_fp16_palettized)[name = string("quantized_29_cast_fp16_cast_uint16")]; + tensor var_201 = const()[name = string("op_201"), val = tensor([0, 2, 1])]; + tensor layer_out_13_axes_0 = const()[name = string("layer_out_13_axes_0"), val = tensor([2])]; + tensor var_202_cast_fp16 = transpose(perm = var_201, x = quantized_29_cast_fp16_cast_uint16)[name = string("transpose_17")]; + tensor layer_out_13_cast_fp16 = expand_dims(axes = layer_out_13_axes_0, x = var_202_cast_fp16)[name = string("layer_out_13_cast_fp16")]; + tensor quantized_35_cast_fp16 = add(x = quantized_31_cast_fp16, y = layer_out_13_cast_fp16)[name = string("quantized_35_cast_fp16")]; + tensor input_21_begin_0 = const()[name = string("input_21_begin_0"), val = tensor([8, 0, 0])]; + tensor input_21_end_0 = const()[name = string("input_21_end_0"), val = tensor([9, 1, 1])]; + tensor input_21_end_mask_0 = const()[name = string("input_21_end_mask_0"), val = tensor([false, true, true])]; + tensor input_21_squeeze_mask_0 = const()[name = string("input_21_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_21 = slice_by_index(begin = input_21_begin_0, end = input_21_end_0, end_mask = input_21_end_mask_0, squeeze_mask = input_21_squeeze_mask_0, x = codes)[name = string("input_21")]; + int32 quantized_33_axis_0 = const()[name = string("quantized_33_axis_0"), val = int32(0)]; + int32 quantized_33_batch_dims_0 = const()[name = string("quantized_33_batch_dims_0"), val = int32(0)]; + bool quantized_33_validate_indices_0 = const()[name = string("quantized_33_validate_indices_0"), val = bool(false)]; + tensor weight_21_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4856128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5380480))))[name = string("weight_21_to_fp16_palettized")]; + string input_21_to_uint16_dtype_0 = const()[name = string("input_21_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_21_to_uint16 = cast(dtype = input_21_to_uint16_dtype_0, x = input_21)[name = string("cast_7")]; + tensor quantized_33_cast_fp16_cast_uint16 = gather(axis = quantized_33_axis_0, batch_dims = quantized_33_batch_dims_0, indices = input_21_to_uint16, validate_indices = quantized_33_validate_indices_0, x = weight_21_to_fp16_palettized)[name = string("quantized_33_cast_fp16_cast_uint16")]; + tensor var_214 = const()[name = string("op_214"), val = tensor([0, 2, 1])]; + tensor layer_out_15_axes_0 = const()[name = string("layer_out_15_axes_0"), val = tensor([2])]; + tensor var_215_cast_fp16 = transpose(perm = var_214, x = quantized_33_cast_fp16_cast_uint16)[name = string("transpose_16")]; + tensor layer_out_15_cast_fp16 = expand_dims(axes = layer_out_15_axes_0, x = var_215_cast_fp16)[name = string("layer_out_15_cast_fp16")]; + tensor quantized_39_cast_fp16 = add(x = quantized_35_cast_fp16, y = layer_out_15_cast_fp16)[name = string("quantized_39_cast_fp16")]; + tensor input_23_begin_0 = const()[name = string("input_23_begin_0"), val = tensor([9, 0, 0])]; + tensor input_23_end_0 = const()[name = string("input_23_end_0"), val = tensor([10, 1, 1])]; + tensor input_23_end_mask_0 = const()[name = string("input_23_end_mask_0"), val = tensor([false, true, true])]; + tensor input_23_squeeze_mask_0 = const()[name = string("input_23_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_23 = slice_by_index(begin = input_23_begin_0, end = input_23_end_0, end_mask = input_23_end_mask_0, squeeze_mask = input_23_squeeze_mask_0, x = codes)[name = string("input_23")]; + int32 quantized_37_axis_0 = const()[name = string("quantized_37_axis_0"), val = int32(0)]; + int32 quantized_37_batch_dims_0 = const()[name = string("quantized_37_batch_dims_0"), val = int32(0)]; + bool quantized_37_validate_indices_0 = const()[name = string("quantized_37_validate_indices_0"), val = bool(false)]; + tensor weight_23_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5381056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5905408))))[name = string("weight_23_to_fp16_palettized")]; + string input_23_to_uint16_dtype_0 = const()[name = string("input_23_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_23_to_uint16 = cast(dtype = input_23_to_uint16_dtype_0, x = input_23)[name = string("cast_6")]; + tensor quantized_37_cast_fp16_cast_uint16 = gather(axis = quantized_37_axis_0, batch_dims = quantized_37_batch_dims_0, indices = input_23_to_uint16, validate_indices = quantized_37_validate_indices_0, x = weight_23_to_fp16_palettized)[name = string("quantized_37_cast_fp16_cast_uint16")]; + tensor var_227 = const()[name = string("op_227"), val = tensor([0, 2, 1])]; + tensor layer_out_17_axes_0 = const()[name = string("layer_out_17_axes_0"), val = tensor([2])]; + tensor var_228_cast_fp16 = transpose(perm = var_227, x = quantized_37_cast_fp16_cast_uint16)[name = string("transpose_15")]; + tensor layer_out_17_cast_fp16 = expand_dims(axes = layer_out_17_axes_0, x = var_228_cast_fp16)[name = string("layer_out_17_cast_fp16")]; + tensor quantized_43_cast_fp16 = add(x = quantized_39_cast_fp16, y = layer_out_17_cast_fp16)[name = string("quantized_43_cast_fp16")]; + tensor input_25_begin_0 = const()[name = string("input_25_begin_0"), val = tensor([10, 0, 0])]; + tensor input_25_end_0 = const()[name = string("input_25_end_0"), val = tensor([11, 1, 1])]; + tensor input_25_end_mask_0 = const()[name = string("input_25_end_mask_0"), val = tensor([false, true, true])]; + tensor input_25_squeeze_mask_0 = const()[name = string("input_25_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_25 = slice_by_index(begin = input_25_begin_0, end = input_25_end_0, end_mask = input_25_end_mask_0, squeeze_mask = input_25_squeeze_mask_0, x = codes)[name = string("input_25")]; + int32 quantized_41_axis_0 = const()[name = string("quantized_41_axis_0"), val = int32(0)]; + int32 quantized_41_batch_dims_0 = const()[name = string("quantized_41_batch_dims_0"), val = int32(0)]; + bool quantized_41_validate_indices_0 = const()[name = string("quantized_41_validate_indices_0"), val = bool(false)]; + tensor weight_25_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5905984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6430336))))[name = string("weight_25_to_fp16_palettized")]; + string input_25_to_uint16_dtype_0 = const()[name = string("input_25_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_25_to_uint16 = cast(dtype = input_25_to_uint16_dtype_0, x = input_25)[name = string("cast_5")]; + tensor quantized_41_cast_fp16_cast_uint16 = gather(axis = quantized_41_axis_0, batch_dims = quantized_41_batch_dims_0, indices = input_25_to_uint16, validate_indices = quantized_41_validate_indices_0, x = weight_25_to_fp16_palettized)[name = string("quantized_41_cast_fp16_cast_uint16")]; + tensor var_240 = const()[name = string("op_240"), val = tensor([0, 2, 1])]; + tensor layer_out_19_axes_0 = const()[name = string("layer_out_19_axes_0"), val = tensor([2])]; + tensor var_241_cast_fp16 = transpose(perm = var_240, x = quantized_41_cast_fp16_cast_uint16)[name = string("transpose_14")]; + tensor layer_out_19_cast_fp16 = expand_dims(axes = layer_out_19_axes_0, x = var_241_cast_fp16)[name = string("layer_out_19_cast_fp16")]; + tensor quantized_47_cast_fp16 = add(x = quantized_43_cast_fp16, y = layer_out_19_cast_fp16)[name = string("quantized_47_cast_fp16")]; + tensor input_27_begin_0 = const()[name = string("input_27_begin_0"), val = tensor([11, 0, 0])]; + tensor input_27_end_0 = const()[name = string("input_27_end_0"), val = tensor([12, 1, 1])]; + tensor input_27_end_mask_0 = const()[name = string("input_27_end_mask_0"), val = tensor([false, true, true])]; + tensor input_27_squeeze_mask_0 = const()[name = string("input_27_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_27 = slice_by_index(begin = input_27_begin_0, end = input_27_end_0, end_mask = input_27_end_mask_0, squeeze_mask = input_27_squeeze_mask_0, x = codes)[name = string("input_27")]; + int32 quantized_45_axis_0 = const()[name = string("quantized_45_axis_0"), val = int32(0)]; + int32 quantized_45_batch_dims_0 = const()[name = string("quantized_45_batch_dims_0"), val = int32(0)]; + bool quantized_45_validate_indices_0 = const()[name = string("quantized_45_validate_indices_0"), val = bool(false)]; + tensor weight_27_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6430912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6955264))))[name = string("weight_27_to_fp16_palettized")]; + string input_27_to_uint16_dtype_0 = const()[name = string("input_27_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_27_to_uint16 = cast(dtype = input_27_to_uint16_dtype_0, x = input_27)[name = string("cast_4")]; + tensor quantized_45_cast_fp16_cast_uint16 = gather(axis = quantized_45_axis_0, batch_dims = quantized_45_batch_dims_0, indices = input_27_to_uint16, validate_indices = quantized_45_validate_indices_0, x = weight_27_to_fp16_palettized)[name = string("quantized_45_cast_fp16_cast_uint16")]; + tensor var_253 = const()[name = string("op_253"), val = tensor([0, 2, 1])]; + tensor layer_out_21_axes_0 = const()[name = string("layer_out_21_axes_0"), val = tensor([2])]; + tensor var_254_cast_fp16 = transpose(perm = var_253, x = quantized_45_cast_fp16_cast_uint16)[name = string("transpose_13")]; + tensor layer_out_21_cast_fp16 = expand_dims(axes = layer_out_21_axes_0, x = var_254_cast_fp16)[name = string("layer_out_21_cast_fp16")]; + tensor quantized_51_cast_fp16 = add(x = quantized_47_cast_fp16, y = layer_out_21_cast_fp16)[name = string("quantized_51_cast_fp16")]; + tensor input_29_begin_0 = const()[name = string("input_29_begin_0"), val = tensor([12, 0, 0])]; + tensor input_29_end_0 = const()[name = string("input_29_end_0"), val = tensor([13, 1, 1])]; + tensor input_29_end_mask_0 = const()[name = string("input_29_end_mask_0"), val = tensor([false, true, true])]; + tensor input_29_squeeze_mask_0 = const()[name = string("input_29_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_29 = slice_by_index(begin = input_29_begin_0, end = input_29_end_0, end_mask = input_29_end_mask_0, squeeze_mask = input_29_squeeze_mask_0, x = codes)[name = string("input_29")]; + int32 quantized_49_axis_0 = const()[name = string("quantized_49_axis_0"), val = int32(0)]; + int32 quantized_49_batch_dims_0 = const()[name = string("quantized_49_batch_dims_0"), val = int32(0)]; + bool quantized_49_validate_indices_0 = const()[name = string("quantized_49_validate_indices_0"), val = bool(false)]; + tensor weight_29_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6955840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480192))))[name = string("weight_29_to_fp16_palettized")]; + string input_29_to_uint16_dtype_0 = const()[name = string("input_29_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_29_to_uint16 = cast(dtype = input_29_to_uint16_dtype_0, x = input_29)[name = string("cast_3")]; + tensor quantized_49_cast_fp16_cast_uint16 = gather(axis = quantized_49_axis_0, batch_dims = quantized_49_batch_dims_0, indices = input_29_to_uint16, validate_indices = quantized_49_validate_indices_0, x = weight_29_to_fp16_palettized)[name = string("quantized_49_cast_fp16_cast_uint16")]; + tensor var_266 = const()[name = string("op_266"), val = tensor([0, 2, 1])]; + tensor layer_out_23_axes_0 = const()[name = string("layer_out_23_axes_0"), val = tensor([2])]; + tensor var_267_cast_fp16 = transpose(perm = var_266, x = quantized_49_cast_fp16_cast_uint16)[name = string("transpose_12")]; + tensor layer_out_23_cast_fp16 = expand_dims(axes = layer_out_23_axes_0, x = var_267_cast_fp16)[name = string("layer_out_23_cast_fp16")]; + tensor quantized_55_cast_fp16 = add(x = quantized_51_cast_fp16, y = layer_out_23_cast_fp16)[name = string("quantized_55_cast_fp16")]; + tensor input_31_begin_0 = const()[name = string("input_31_begin_0"), val = tensor([13, 0, 0])]; + tensor input_31_end_0 = const()[name = string("input_31_end_0"), val = tensor([14, 1, 1])]; + tensor input_31_end_mask_0 = const()[name = string("input_31_end_mask_0"), val = tensor([false, true, true])]; + tensor input_31_squeeze_mask_0 = const()[name = string("input_31_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_31 = slice_by_index(begin = input_31_begin_0, end = input_31_end_0, end_mask = input_31_end_mask_0, squeeze_mask = input_31_squeeze_mask_0, x = codes)[name = string("input_31")]; + int32 quantized_53_axis_0 = const()[name = string("quantized_53_axis_0"), val = int32(0)]; + int32 quantized_53_batch_dims_0 = const()[name = string("quantized_53_batch_dims_0"), val = int32(0)]; + bool quantized_53_validate_indices_0 = const()[name = string("quantized_53_validate_indices_0"), val = bool(false)]; + tensor weight_31_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8005120))))[name = string("weight_31_to_fp16_palettized")]; + string input_31_to_uint16_dtype_0 = const()[name = string("input_31_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_31_to_uint16 = cast(dtype = input_31_to_uint16_dtype_0, x = input_31)[name = string("cast_2")]; + tensor quantized_53_cast_fp16_cast_uint16 = gather(axis = quantized_53_axis_0, batch_dims = quantized_53_batch_dims_0, indices = input_31_to_uint16, validate_indices = quantized_53_validate_indices_0, x = weight_31_to_fp16_palettized)[name = string("quantized_53_cast_fp16_cast_uint16")]; + tensor var_279 = const()[name = string("op_279"), val = tensor([0, 2, 1])]; + tensor layer_out_25_axes_0 = const()[name = string("layer_out_25_axes_0"), val = tensor([2])]; + tensor var_280_cast_fp16 = transpose(perm = var_279, x = quantized_53_cast_fp16_cast_uint16)[name = string("transpose_11")]; + tensor layer_out_25_cast_fp16 = expand_dims(axes = layer_out_25_axes_0, x = var_280_cast_fp16)[name = string("layer_out_25_cast_fp16")]; + tensor quantized_59_cast_fp16 = add(x = quantized_55_cast_fp16, y = layer_out_25_cast_fp16)[name = string("quantized_59_cast_fp16")]; + tensor input_33_begin_0 = const()[name = string("input_33_begin_0"), val = tensor([14, 0, 0])]; + tensor input_33_end_0 = const()[name = string("input_33_end_0"), val = tensor([15, 1, 1])]; + tensor input_33_end_mask_0 = const()[name = string("input_33_end_mask_0"), val = tensor([false, true, true])]; + tensor input_33_squeeze_mask_0 = const()[name = string("input_33_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_33 = slice_by_index(begin = input_33_begin_0, end = input_33_end_0, end_mask = input_33_end_mask_0, squeeze_mask = input_33_squeeze_mask_0, x = codes)[name = string("input_33")]; + int32 quantized_57_axis_0 = const()[name = string("quantized_57_axis_0"), val = int32(0)]; + int32 quantized_57_batch_dims_0 = const()[name = string("quantized_57_batch_dims_0"), val = int32(0)]; + bool quantized_57_validate_indices_0 = const()[name = string("quantized_57_validate_indices_0"), val = bool(false)]; + tensor weight_33_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8005696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8530048))))[name = string("weight_33_to_fp16_palettized")]; + string input_33_to_uint16_dtype_0 = const()[name = string("input_33_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_33_to_uint16 = cast(dtype = input_33_to_uint16_dtype_0, x = input_33)[name = string("cast_1")]; + tensor quantized_57_cast_fp16_cast_uint16 = gather(axis = quantized_57_axis_0, batch_dims = quantized_57_batch_dims_0, indices = input_33_to_uint16, validate_indices = quantized_57_validate_indices_0, x = weight_33_to_fp16_palettized)[name = string("quantized_57_cast_fp16_cast_uint16")]; + tensor var_292 = const()[name = string("op_292"), val = tensor([0, 2, 1])]; + tensor layer_out_axes_0 = const()[name = string("layer_out_axes_0"), val = tensor([2])]; + tensor var_293_cast_fp16 = transpose(perm = var_292, x = quantized_57_cast_fp16_cast_uint16)[name = string("transpose_10")]; + tensor layer_out_cast_fp16 = expand_dims(axes = layer_out_axes_0, x = var_293_cast_fp16)[name = string("layer_out_cast_fp16")]; + tensor input_35_cast_fp16 = add(x = quantized_59_cast_fp16, y = layer_out_cast_fp16)[name = string("input_35_cast_fp16")]; + string var_301_pad_type_0 = const()[name = string("op_301_pad_type_0"), val = string("valid")]; + tensor var_301_strides_0 = const()[name = string("op_301_strides_0"), val = tensor([1, 1])]; + tensor var_301_pad_0 = const()[name = string("op_301_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_301_dilations_0 = const()[name = string("op_301_dilations_0"), val = tensor([1, 1])]; + int32 var_301_groups_0 = const()[name = string("op_301_groups_0"), val = int32(1)]; + tensor quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8530624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8661760))))[name = string("quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized")]; + tensor var_301_cast_fp16 = conv(dilations = var_301_dilations_0, groups = var_301_groups_0, pad = var_301_pad_0, pad_type = var_301_pad_type_0, strides = var_301_strides_0, weight = quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("op_301_cast_fp16")]; + tensor pre_conv_context_update = add(x = quantized_cast_fp16, y = var_301_cast_fp16)[name = string("hidden_in_cast_fp16")]; + int32 var_311_axis_0 = const()[name = string("op_311_axis_0"), val = int32(0)]; + int32 var_311_batch_dims_0 = const()[name = string("op_311_batch_dims_0"), val = int32(0)]; + bool var_311_validate_indices_0 = const()[name = string("op_311_validate_indices_0"), val = bool(false)]; + tensor rope_rope_cos_to_fp16 = const()[name = string("rope_rope_cos_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8662336)))]; + string cache_length_to_uint16_dtype_0 = const()[name = string("cache_length_to_uint16_dtype_0"), val = string("uint16")]; + tensor cache_length_to_uint16 = cast(dtype = cache_length_to_uint16_dtype_0, x = cache_length)[name = string("cast_0")]; + tensor var_311_cast_fp16_cast_uint16 = gather(axis = var_311_axis_0, batch_dims = var_311_batch_dims_0, indices = cache_length_to_uint16, validate_indices = var_311_validate_indices_0, x = rope_rope_cos_to_fp16)[name = string("op_311_cast_fp16_cast_uint16")]; + tensor obj_9_axes_0 = const()[name = string("obj_9_axes_0"), val = tensor([2])]; + tensor obj_9_cast_fp16 = expand_dims(axes = obj_9_axes_0, x = var_311_cast_fp16_cast_uint16)[name = string("obj_9_cast_fp16")]; + int32 var_315_axis_0 = const()[name = string("op_315_axis_0"), val = int32(0)]; + int32 var_315_batch_dims_0 = const()[name = string("op_315_batch_dims_0"), val = int32(0)]; + bool var_315_validate_indices_0 = const()[name = string("op_315_validate_indices_0"), val = bool(false)]; + tensor rope_rope_sin_to_fp16 = const()[name = string("rope_rope_sin_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9186688)))]; + tensor var_315_cast_fp16_cast_uint16 = gather(axis = var_315_axis_0, batch_dims = var_315_batch_dims_0, indices = cache_length_to_uint16, validate_indices = var_315_validate_indices_0, x = rope_rope_sin_to_fp16)[name = string("op_315_cast_fp16_cast_uint16")]; + tensor obj_11_axes_0 = const()[name = string("obj_11_axes_0"), val = tensor([2])]; + tensor obj_11_cast_fp16 = expand_dims(axes = obj_11_axes_0, x = var_315_cast_fp16_cast_uint16)[name = string("obj_11_cast_fp16")]; + int32 var_327 = const()[name = string("op_327"), val = int32(-2)]; + int32 var_332 = const()[name = string("op_332"), val = int32(3)]; + int32 var_337 = const()[name = string("op_337"), val = int32(-1)]; + int32 var_338 = const()[name = string("op_338"), val = int32(1)]; + tensor tile_0 = const()[name = string("tile_0"), val = tensor([1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024])]; + int32 var_361_axis_0 = const()[name = string("op_361_axis_0"), val = int32(1)]; + tensor var_361_cast_fp16_0, tensor var_361_cast_fp16_1, tensor var_361_cast_fp16_2, tensor var_361_cast_fp16_3, tensor var_361_cast_fp16_4, tensor var_361_cast_fp16_5, tensor var_361_cast_fp16_6, tensor var_361_cast_fp16_7 = split(axis = var_361_axis_0, split_sizes = tile_0, x = key_cache)[name = string("op_361_cast_fp16")]; + tensor tile_1 = const()[name = string("tile_1"), val = tensor([1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024])]; + int32 var_370_axis_0 = const()[name = string("op_370_axis_0"), val = int32(1)]; + tensor var_370_cast_fp16_0, tensor var_370_cast_fp16_1, tensor var_370_cast_fp16_2, tensor var_370_cast_fp16_3, tensor var_370_cast_fp16_4, tensor var_370_cast_fp16_5, tensor var_370_cast_fp16_6, tensor var_370_cast_fp16_7 = split(axis = var_370_axis_0, split_sizes = tile_1, x = value_cache)[name = string("op_370_cast_fp16")]; + bool input_37_interleave_0 = const()[name = string("input_37_interleave_0"), val = bool(false)]; + tensor input_37_cast_fp16 = concat(axis = var_337, interleave = input_37_interleave_0, values = (pre_conv_context, pre_conv_context_update))[name = string("input_37_cast_fp16")]; + string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")]; + tensor input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor([1, 1])]; + int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)]; + tensor pre_transformer_pre_conv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9711040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11283968))))[name = string("pre_transformer_pre_conv_conv_weight_to_fp16_palettized")]; + tensor pre_transformer_pre_conv_conv_bias_to_fp16 = const()[name = string("pre_transformer_pre_conv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11284544)))]; + tensor input_39_cast_fp16 = conv(bias = pre_transformer_pre_conv_conv_bias_to_fp16, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = pre_transformer_pre_conv_conv_weight_to_fp16_palettized, x = input_37_cast_fp16)[name = string("input_39_cast_fp16")]; + string inputs_1_pad_type_0 = const()[name = string("inputs_1_pad_type_0"), val = string("valid")]; + tensor inputs_1_strides_0 = const()[name = string("inputs_1_strides_0"), val = tensor([1, 1])]; + tensor inputs_1_pad_0 = const()[name = string("inputs_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor inputs_1_dilations_0 = const()[name = string("inputs_1_dilations_0"), val = tensor([1, 1])]; + int32 inputs_1_groups_0 = const()[name = string("inputs_1_groups_0"), val = int32(1)]; + tensor pre_transformer_input_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11286656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11811008))))[name = string("pre_transformer_input_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_input_proj_bias_to_fp16 = const()[name = string("pre_transformer_input_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11811584)))]; + tensor inputs_1_cast_fp16 = conv(bias = pre_transformer_input_proj_bias_to_fp16, dilations = inputs_1_dilations_0, groups = inputs_1_groups_0, pad = inputs_1_pad_0, pad_type = inputs_1_pad_type_0, strides = inputs_1_strides_0, weight = pre_transformer_input_proj_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("inputs_1_cast_fp16")]; + tensor inputs_sq_1_cast_fp16 = mul(x = inputs_1_cast_fp16, y = inputs_1_cast_fp16)[name = string("inputs_sq_1_cast_fp16")]; + tensor variance_1_axes_0 = const()[name = string("variance_1_axes_0"), val = tensor([1])]; + bool variance_1_keep_dims_0 = const()[name = string("variance_1_keep_dims_0"), val = bool(true)]; + tensor variance_1_cast_fp16 = reduce_mean(axes = variance_1_axes_0, keep_dims = variance_1_keep_dims_0, x = inputs_sq_1_cast_fp16)[name = string("variance_1_cast_fp16")]; + fp16 var_405_to_fp16 = const()[name = string("op_405_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_406_cast_fp16 = add(x = variance_1_cast_fp16, y = var_405_to_fp16)[name = string("op_406_cast_fp16")]; + fp32 var_407_epsilon_0 = const()[name = string("op_407_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_407_cast_fp16 = rsqrt(epsilon = var_407_epsilon_0, x = var_406_cast_fp16)[name = string("op_407_cast_fp16")]; + tensor hidden_states_1_cast_fp16 = mul(x = inputs_1_cast_fp16, y = var_407_cast_fp16)[name = string("hidden_states_1_cast_fp16")]; + tensor w_1_to_fp16 = const()[name = string("w_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11812672)))]; + tensor obj_1_cast_fp16 = mul(x = w_1_to_fp16, y = hidden_states_1_cast_fp16)[name = string("obj_1_cast_fp16")]; + string query_1_pad_type_0 = const()[name = string("query_1_pad_type_0"), val = string("valid")]; + tensor query_1_strides_0 = const()[name = string("query_1_strides_0"), val = tensor([1, 1])]; + tensor query_1_pad_0 = const()[name = string("query_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_1_dilations_0 = const()[name = string("query_1_dilations_0"), val = tensor([1, 1])]; + int32 query_1_groups_0 = const()[name = string("query_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11813760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12338112))))[name = string("pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = string("pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12338688)))]; + tensor query_1_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_1_dilations_0, groups = query_1_groups_0, pad = query_1_pad_0, pad_type = query_1_pad_type_0, strides = query_1_strides_0, weight = pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("query_1_cast_fp16")]; + string key_1_pad_type_0 = const()[name = string("key_1_pad_type_0"), val = string("valid")]; + tensor key_1_strides_0 = const()[name = string("key_1_strides_0"), val = tensor([1, 1])]; + tensor key_1_pad_0 = const()[name = string("key_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_1_dilations_0 = const()[name = string("key_1_dilations_0"), val = tensor([1, 1])]; + int32 key_1_groups_0 = const()[name = string("key_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12340800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12865152))))[name = string("pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_1_cast_fp16 = conv(dilations = key_1_dilations_0, groups = key_1_groups_0, pad = key_1_pad_0, pad_type = key_1_pad_type_0, strides = key_1_strides_0, weight = pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("key_1_cast_fp16")]; + string current_value_1_pad_type_0 = const()[name = string("current_value_1_pad_type_0"), val = string("valid")]; + tensor current_value_1_strides_0 = const()[name = string("current_value_1_strides_0"), val = tensor([1, 1])]; + tensor current_value_1_pad_0 = const()[name = string("current_value_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_1_dilations_0 = const()[name = string("current_value_1_dilations_0"), val = tensor([1, 1])]; + int32 current_value_1_groups_0 = const()[name = string("current_value_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12865728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13390080))))[name = string("pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_1_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_1_dilations_0, groups = current_value_1_groups_0, pad = current_value_1_pad_0, pad_type = current_value_1_pad_type_0, strides = current_value_1_strides_0, weight = pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("current_value_1_cast_fp16")]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_1_cast_fp16 = reshape(shape = var_445, x = query_1_cast_fp16)[name = string("mh_q_1_cast_fp16")]; + tensor var_447 = const()[name = string("op_447"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_1_cast_fp16 = reshape(shape = var_447, x = key_1_cast_fp16)[name = string("mh_k_1_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = obj_9_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = obj_11_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = mh_q_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor var_456_begin_0 = const()[name = string("op_456_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_456_end_0 = const()[name = string("op_456_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_456_end_mask_0 = const()[name = string("op_456_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_456_cast_fp16 = slice_by_index(begin = var_456_begin_0, end = var_456_end_0, end_mask = var_456_end_mask_0, x = mh_q_1_cast_fp16)[name = string("op_456_cast_fp16")]; + tensor var_462_begin_0 = const()[name = string("op_462_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_462_end_0 = const()[name = string("op_462_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_462_end_mask_0 = const()[name = string("op_462_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_462_cast_fp16 = slice_by_index(begin = var_462_begin_0, end = var_462_end_0, end_mask = var_462_end_mask_0, x = mh_q_1_cast_fp16)[name = string("op_462_cast_fp16")]; + fp16 const_31_promoted_to_fp16 = const()[name = string("const_31_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_464_cast_fp16 = mul(x = var_462_cast_fp16, y = const_31_promoted_to_fp16)[name = string("op_464_cast_fp16")]; + bool var_466_interleave_0 = const()[name = string("op_466_interleave_0"), val = bool(false)]; + tensor var_466_cast_fp16 = concat(axis = var_327, interleave = var_466_interleave_0, values = (var_464_cast_fp16, var_456_cast_fp16))[name = string("op_466_cast_fp16")]; + tensor var_467_cast_fp16 = mul(x = var_466_cast_fp16, y = sin_1_cast_fp16)[name = string("op_467_cast_fp16")]; + tensor mh_q_3_cast_fp16 = add(x = var_451_cast_fp16, y = var_467_cast_fp16)[name = string("mh_q_3_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = mh_k_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_474_begin_0 = const()[name = string("op_474_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_474_end_0 = const()[name = string("op_474_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_474_end_mask_0 = const()[name = string("op_474_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_474_cast_fp16 = slice_by_index(begin = var_474_begin_0, end = var_474_end_0, end_mask = var_474_end_mask_0, x = mh_k_1_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_480_begin_0 = const()[name = string("op_480_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_480_end_0 = const()[name = string("op_480_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_480_end_mask_0 = const()[name = string("op_480_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_480_cast_fp16 = slice_by_index(begin = var_480_begin_0, end = var_480_end_0, end_mask = var_480_end_mask_0, x = mh_k_1_cast_fp16)[name = string("op_480_cast_fp16")]; + fp16 const_34_promoted_to_fp16 = const()[name = string("const_34_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_482_cast_fp16 = mul(x = var_480_cast_fp16, y = const_34_promoted_to_fp16)[name = string("op_482_cast_fp16")]; + bool var_484_interleave_0 = const()[name = string("op_484_interleave_0"), val = bool(false)]; + tensor var_484_cast_fp16 = concat(axis = var_327, interleave = var_484_interleave_0, values = (var_482_cast_fp16, var_474_cast_fp16))[name = string("op_484_cast_fp16")]; + tensor var_485_cast_fp16 = mul(x = var_484_cast_fp16, y = sin_1_cast_fp16)[name = string("op_485_cast_fp16")]; + tensor mh_k_3_cast_fp16 = add(x = var_469_cast_fp16, y = var_485_cast_fp16)[name = string("mh_k_3_cast_fp16")]; + tensor var_489 = const()[name = string("op_489"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_1_cast_fp16 = reshape(shape = var_489, x = mh_k_3_cast_fp16)[name = string("current_key_1_cast_fp16")]; + tensor var_493_axes_0 = const()[name = string("op_493_axes_0"), val = tensor([2])]; + tensor var_493_cast_fp16 = expand_dims(axes = var_493_axes_0, x = kv_cache_update_mask)[name = string("op_493_cast_fp16")]; + fp16 var_326_to_fp16 = const()[name = string("op_326_to_fp16"), val = fp16(0x1p+0)]; + tensor var_495_cast_fp16 = sub(x = var_326_to_fp16, y = var_493_cast_fp16)[name = string("op_495_cast_fp16")]; + tensor var_496_cast_fp16 = mul(x = var_361_cast_fp16_0, y = var_495_cast_fp16)[name = string("op_496_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = current_key_1_cast_fp16, y = var_493_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor key_3_cast_fp16 = add(x = var_496_cast_fp16, y = var_497_cast_fp16)[name = string("key_3_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = var_370_cast_fp16_0, y = var_495_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = current_value_1_cast_fp16, y = var_493_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor value_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("value_1_cast_fp16")]; + fp16 var_507_to_fp16 = const()[name = string("op_507_to_fp16"), val = fp16(0x1p-3)]; + tensor var_508_cast_fp16 = mul(x = mh_q_3_cast_fp16, y = var_507_to_fp16)[name = string("op_508_cast_fp16")]; + tensor var_511 = const()[name = string("op_511"), val = tensor([1, 16, 64, 80])]; + tensor var_512_cast_fp16 = reshape(shape = var_511, x = key_3_cast_fp16)[name = string("op_512_cast_fp16")]; + bool mh_w_1_transpose_x_0 = const()[name = string("mh_w_1_transpose_x_0"), val = bool(true)]; + bool mh_w_1_transpose_y_0 = const()[name = string("mh_w_1_transpose_y_0"), val = bool(false)]; + tensor mh_w_1_cast_fp16 = matmul(transpose_x = mh_w_1_transpose_x_0, transpose_y = mh_w_1_transpose_y_0, x = var_508_cast_fp16, y = var_512_cast_fp16)[name = string("mh_w_1_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([1])]; + tensor var_516_cast_fp16 = expand_dims(axes = var_516_axes_0, x = key_padding_mask)[name = string("op_516_cast_fp16")]; + tensor var_517_axes_0 = const()[name = string("op_517_axes_0"), val = tensor([2])]; + tensor var_517_cast_fp16 = expand_dims(axes = var_517_axes_0, x = var_516_cast_fp16)[name = string("op_517_cast_fp16")]; + tensor mh_w_3_cast_fp16 = add(x = mh_w_1_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_3_cast_fp16")]; + tensor var_520_cast_fp16 = softmax(axis = var_332, x = mh_w_3_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor var_521 = const()[name = string("op_521"), val = tensor([1, 16, 64, 80])]; + tensor var_522_cast_fp16 = reshape(shape = var_521, x = value_1_cast_fp16)[name = string("op_522_cast_fp16")]; + bool attn_1_transpose_x_0 = const()[name = string("attn_1_transpose_x_0"), val = bool(false)]; + bool attn_1_transpose_y_0 = const()[name = string("attn_1_transpose_y_0"), val = bool(true)]; + tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_522_cast_fp16, y = var_520_cast_fp16)[name = string("attn_1_cast_fp16")]; + tensor var_525 = const()[name = string("op_525"), val = tensor([1, -1, 1, 1])]; + tensor input_41_cast_fp16 = reshape(shape = var_525, x = attn_1_cast_fp16)[name = string("input_41_cast_fp16")]; + string obj_13_pad_type_0 = const()[name = string("obj_13_pad_type_0"), val = string("valid")]; + tensor obj_13_strides_0 = const()[name = string("obj_13_strides_0"), val = tensor([1, 1])]; + tensor obj_13_pad_0 = const()[name = string("obj_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_13_dilations_0 = const()[name = string("obj_13_dilations_0"), val = tensor([1, 1])]; + int32 obj_13_groups_0 = const()[name = string("obj_13_groups_0"), val = int32(1)]; + tensor op_541_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13390656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13915008))))[name = string("op_541_weight_0_to_fp16_palettized")]; + tensor var_541_bias_0_to_fp16 = const()[name = string("op_541_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13915584)))]; + tensor var_541_cast_fp16 = conv(bias = var_541_bias_0_to_fp16, dilations = obj_13_dilations_0, groups = obj_13_groups_0, pad = obj_13_pad_0, pad_type = obj_13_pad_type_0, strides = obj_13_strides_0, weight = op_541_weight_0_to_fp16_palettized, x = input_41_cast_fp16)[name = string("op_541_cast_fp16")]; + tensor inputs_3_cast_fp16 = add(x = inputs_1_cast_fp16, y = var_541_cast_fp16)[name = string("inputs_3_cast_fp16")]; + tensor inputs_sq_3_cast_fp16 = mul(x = inputs_3_cast_fp16, y = inputs_3_cast_fp16)[name = string("inputs_sq_3_cast_fp16")]; + tensor variance_3_axes_0 = const()[name = string("variance_3_axes_0"), val = tensor([1])]; + bool variance_3_keep_dims_0 = const()[name = string("variance_3_keep_dims_0"), val = bool(true)]; + tensor variance_3_cast_fp16 = reduce_mean(axes = variance_3_axes_0, keep_dims = variance_3_keep_dims_0, x = inputs_sq_3_cast_fp16)[name = string("variance_3_cast_fp16")]; + fp16 var_547_to_fp16 = const()[name = string("op_547_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_548_cast_fp16 = add(x = variance_3_cast_fp16, y = var_547_to_fp16)[name = string("op_548_cast_fp16")]; + fp32 var_549_epsilon_0 = const()[name = string("op_549_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_549_cast_fp16 = rsqrt(epsilon = var_549_epsilon_0, x = var_548_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor hidden_states_3_cast_fp16 = mul(x = inputs_3_cast_fp16, y = var_549_cast_fp16)[name = string("hidden_states_3_cast_fp16")]; + tensor w_3_to_fp16 = const()[name = string("w_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13916672)))]; + tensor input_43_cast_fp16 = mul(x = w_3_to_fp16, y = hidden_states_3_cast_fp16)[name = string("input_43_cast_fp16")]; + string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")]; + tensor input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor([1, 1])]; + tensor input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor([1, 1])]; + int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13917760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14442112))))[name = string("pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_45_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor gate_1_cast_fp16 = silu(x = input_45_cast_fp16)[name = string("gate_1_cast_fp16")]; + string up_1_pad_type_0 = const()[name = string("up_1_pad_type_0"), val = string("valid")]; + tensor up_1_strides_0 = const()[name = string("up_1_strides_0"), val = tensor([1, 1])]; + tensor up_1_pad_0 = const()[name = string("up_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_1_dilations_0 = const()[name = string("up_1_dilations_0"), val = tensor([1, 1])]; + int32 up_1_groups_0 = const()[name = string("up_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14442688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14967040))))[name = string("pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_1_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_1_dilations_0, groups = up_1_groups_0, pad = up_1_pad_0, pad_type = up_1_pad_type_0, strides = up_1_strides_0, weight = pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = string("up_1_cast_fp16")]; + tensor input_47_cast_fp16 = mul(x = gate_1_cast_fp16, y = up_1_cast_fp16)[name = string("input_47_cast_fp16")]; + string hidden_states_5_pad_type_0 = const()[name = string("hidden_states_5_pad_type_0"), val = string("valid")]; + tensor hidden_states_5_strides_0 = const()[name = string("hidden_states_5_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_0 = const()[name = string("hidden_states_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_5_dilations_0 = const()[name = string("hidden_states_5_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_5_groups_0 = const()[name = string("hidden_states_5_groups_0"), val = int32(1)]; + tensor op_583_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14967616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15491968))))[name = string("op_583_weight_0_to_fp16_palettized")]; + tensor var_583_bias_0_to_fp16 = const()[name = string("op_583_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15492544)))]; + tensor var_583_cast_fp16 = conv(bias = var_583_bias_0_to_fp16, dilations = hidden_states_5_dilations_0, groups = hidden_states_5_groups_0, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = hidden_states_5_strides_0, weight = op_583_weight_0_to_fp16_palettized, x = input_47_cast_fp16)[name = string("op_583_cast_fp16")]; + tensor inputs_5_cast_fp16 = add(x = inputs_3_cast_fp16, y = var_583_cast_fp16)[name = string("inputs_5_cast_fp16")]; + tensor inputs_sq_5_cast_fp16 = mul(x = inputs_5_cast_fp16, y = inputs_5_cast_fp16)[name = string("inputs_sq_5_cast_fp16")]; + tensor variance_5_axes_0 = const()[name = string("variance_5_axes_0"), val = tensor([1])]; + bool variance_5_keep_dims_0 = const()[name = string("variance_5_keep_dims_0"), val = bool(true)]; + tensor variance_5_cast_fp16 = reduce_mean(axes = variance_5_axes_0, keep_dims = variance_5_keep_dims_0, x = inputs_sq_5_cast_fp16)[name = string("variance_5_cast_fp16")]; + fp16 var_599_to_fp16 = const()[name = string("op_599_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_600_cast_fp16 = add(x = variance_5_cast_fp16, y = var_599_to_fp16)[name = string("op_600_cast_fp16")]; + fp32 var_601_epsilon_0 = const()[name = string("op_601_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_601_cast_fp16 = rsqrt(epsilon = var_601_epsilon_0, x = var_600_cast_fp16)[name = string("op_601_cast_fp16")]; + tensor hidden_states_7_cast_fp16 = mul(x = inputs_5_cast_fp16, y = var_601_cast_fp16)[name = string("hidden_states_7_cast_fp16")]; + tensor w_5_to_fp16 = const()[name = string("w_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15493632)))]; + tensor obj_15_cast_fp16 = mul(x = w_5_to_fp16, y = hidden_states_7_cast_fp16)[name = string("obj_15_cast_fp16")]; + string query_5_pad_type_0 = const()[name = string("query_5_pad_type_0"), val = string("valid")]; + tensor query_5_strides_0 = const()[name = string("query_5_strides_0"), val = tensor([1, 1])]; + tensor query_5_pad_0 = const()[name = string("query_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_5_dilations_0 = const()[name = string("query_5_dilations_0"), val = tensor([1, 1])]; + int32 query_5_groups_0 = const()[name = string("query_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15494720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16019072))))[name = string("pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_5_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_5_dilations_0, groups = query_5_groups_0, pad = query_5_pad_0, pad_type = query_5_pad_type_0, strides = query_5_strides_0, weight = pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized, x = obj_15_cast_fp16)[name = string("query_5_cast_fp16")]; + string key_5_pad_type_0 = const()[name = string("key_5_pad_type_0"), val = string("valid")]; + tensor key_5_strides_0 = const()[name = string("key_5_strides_0"), val = tensor([1, 1])]; + tensor key_5_pad_0 = const()[name = string("key_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_5_dilations_0 = const()[name = string("key_5_dilations_0"), val = tensor([1, 1])]; + int32 key_5_groups_0 = const()[name = string("key_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16019648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16544000))))[name = string("pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_5_cast_fp16 = conv(dilations = key_5_dilations_0, groups = key_5_groups_0, pad = key_5_pad_0, pad_type = key_5_pad_type_0, strides = key_5_strides_0, weight = pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized, x = obj_15_cast_fp16)[name = string("key_5_cast_fp16")]; + string current_value_3_pad_type_0 = const()[name = string("current_value_3_pad_type_0"), val = string("valid")]; + tensor current_value_3_strides_0 = const()[name = string("current_value_3_strides_0"), val = tensor([1, 1])]; + tensor current_value_3_pad_0 = const()[name = string("current_value_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_3_dilations_0 = const()[name = string("current_value_3_dilations_0"), val = tensor([1, 1])]; + int32 current_value_3_groups_0 = const()[name = string("current_value_3_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16544576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17068928))))[name = string("pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_3_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_3_dilations_0, groups = current_value_3_groups_0, pad = current_value_3_pad_0, pad_type = current_value_3_pad_type_0, strides = current_value_3_strides_0, weight = pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized, x = obj_15_cast_fp16)[name = string("current_value_3_cast_fp16")]; + tensor var_639 = const()[name = string("op_639"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_7_cast_fp16 = reshape(shape = var_639, x = query_5_cast_fp16)[name = string("mh_q_7_cast_fp16")]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_5_cast_fp16 = reshape(shape = var_641, x = key_5_cast_fp16)[name = string("mh_k_5_cast_fp16")]; + tensor var_645_cast_fp16 = mul(x = mh_q_7_cast_fp16, y = cos_1_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_650_begin_0 = const()[name = string("op_650_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_650_end_0 = const()[name = string("op_650_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_650_end_mask_0 = const()[name = string("op_650_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_650_cast_fp16 = slice_by_index(begin = var_650_begin_0, end = var_650_end_0, end_mask = var_650_end_mask_0, x = mh_q_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor var_656_begin_0 = const()[name = string("op_656_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_656_end_0 = const()[name = string("op_656_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_656_end_mask_0 = const()[name = string("op_656_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_656_cast_fp16 = slice_by_index(begin = var_656_begin_0, end = var_656_end_0, end_mask = var_656_end_mask_0, x = mh_q_7_cast_fp16)[name = string("op_656_cast_fp16")]; + fp16 const_50_promoted_to_fp16 = const()[name = string("const_50_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_658_cast_fp16 = mul(x = var_656_cast_fp16, y = const_50_promoted_to_fp16)[name = string("op_658_cast_fp16")]; + bool var_660_interleave_0 = const()[name = string("op_660_interleave_0"), val = bool(false)]; + tensor var_660_cast_fp16 = concat(axis = var_327, interleave = var_660_interleave_0, values = (var_658_cast_fp16, var_650_cast_fp16))[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = var_660_cast_fp16, y = sin_1_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor mh_q_9_cast_fp16 = add(x = var_645_cast_fp16, y = var_661_cast_fp16)[name = string("mh_q_9_cast_fp16")]; + tensor var_663_cast_fp16 = mul(x = mh_k_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor var_668_begin_0 = const()[name = string("op_668_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_668_end_0 = const()[name = string("op_668_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_668_end_mask_0 = const()[name = string("op_668_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_668_cast_fp16 = slice_by_index(begin = var_668_begin_0, end = var_668_end_0, end_mask = var_668_end_mask_0, x = mh_k_5_cast_fp16)[name = string("op_668_cast_fp16")]; + tensor var_674_begin_0 = const()[name = string("op_674_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_674_end_0 = const()[name = string("op_674_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_674_end_mask_0 = const()[name = string("op_674_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_674_cast_fp16 = slice_by_index(begin = var_674_begin_0, end = var_674_end_0, end_mask = var_674_end_mask_0, x = mh_k_5_cast_fp16)[name = string("op_674_cast_fp16")]; + fp16 const_53_promoted_to_fp16 = const()[name = string("const_53_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_676_cast_fp16 = mul(x = var_674_cast_fp16, y = const_53_promoted_to_fp16)[name = string("op_676_cast_fp16")]; + bool var_678_interleave_0 = const()[name = string("op_678_interleave_0"), val = bool(false)]; + tensor var_678_cast_fp16 = concat(axis = var_327, interleave = var_678_interleave_0, values = (var_676_cast_fp16, var_668_cast_fp16))[name = string("op_678_cast_fp16")]; + tensor var_679_cast_fp16 = mul(x = var_678_cast_fp16, y = sin_1_cast_fp16)[name = string("op_679_cast_fp16")]; + tensor mh_k_7_cast_fp16 = add(x = var_663_cast_fp16, y = var_679_cast_fp16)[name = string("mh_k_7_cast_fp16")]; + tensor var_683 = const()[name = string("op_683"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_3_cast_fp16 = reshape(shape = var_683, x = mh_k_7_cast_fp16)[name = string("current_key_3_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_361_cast_fp16_1, y = var_495_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_cast_fp16 = mul(x = current_key_3_cast_fp16, y = var_493_cast_fp16)[name = string("op_691_cast_fp16")]; + tensor key_7_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("key_7_cast_fp16")]; + tensor var_694_cast_fp16 = mul(x = var_370_cast_fp16_1, y = var_495_cast_fp16)[name = string("op_694_cast_fp16")]; + tensor var_695_cast_fp16 = mul(x = current_value_3_cast_fp16, y = var_493_cast_fp16)[name = string("op_695_cast_fp16")]; + tensor value_3_cast_fp16 = add(x = var_694_cast_fp16, y = var_695_cast_fp16)[name = string("value_3_cast_fp16")]; + fp16 var_701_to_fp16 = const()[name = string("op_701_to_fp16"), val = fp16(0x1p-3)]; + tensor var_702_cast_fp16 = mul(x = mh_q_9_cast_fp16, y = var_701_to_fp16)[name = string("op_702_cast_fp16")]; + tensor var_705 = const()[name = string("op_705"), val = tensor([1, 16, 64, 80])]; + tensor var_706_cast_fp16 = reshape(shape = var_705, x = key_7_cast_fp16)[name = string("op_706_cast_fp16")]; + bool mh_w_5_transpose_x_0 = const()[name = string("mh_w_5_transpose_x_0"), val = bool(true)]; + bool mh_w_5_transpose_y_0 = const()[name = string("mh_w_5_transpose_y_0"), val = bool(false)]; + tensor mh_w_5_cast_fp16 = matmul(transpose_x = mh_w_5_transpose_x_0, transpose_y = mh_w_5_transpose_y_0, x = var_702_cast_fp16, y = var_706_cast_fp16)[name = string("mh_w_5_cast_fp16")]; + tensor mh_w_7_cast_fp16 = add(x = mh_w_5_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_7_cast_fp16")]; + tensor var_714_cast_fp16 = softmax(axis = var_332, x = mh_w_7_cast_fp16)[name = string("op_714_cast_fp16")]; + tensor var_715 = const()[name = string("op_715"), val = tensor([1, 16, 64, 80])]; + tensor var_716_cast_fp16 = reshape(shape = var_715, x = value_3_cast_fp16)[name = string("op_716_cast_fp16")]; + bool attn_3_transpose_x_0 = const()[name = string("attn_3_transpose_x_0"), val = bool(false)]; + bool attn_3_transpose_y_0 = const()[name = string("attn_3_transpose_y_0"), val = bool(true)]; + tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_716_cast_fp16, y = var_714_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_719 = const()[name = string("op_719"), val = tensor([1, -1, 1, 1])]; + tensor input_49_cast_fp16 = reshape(shape = var_719, x = attn_3_cast_fp16)[name = string("input_49_cast_fp16")]; + string obj_21_pad_type_0 = const()[name = string("obj_21_pad_type_0"), val = string("valid")]; + tensor obj_21_strides_0 = const()[name = string("obj_21_strides_0"), val = tensor([1, 1])]; + tensor obj_21_pad_0 = const()[name = string("obj_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_21_dilations_0 = const()[name = string("obj_21_dilations_0"), val = tensor([1, 1])]; + int32 obj_21_groups_0 = const()[name = string("obj_21_groups_0"), val = int32(1)]; + tensor op_735_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17069504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17593856))))[name = string("op_735_weight_0_to_fp16_palettized")]; + tensor var_735_bias_0_to_fp16 = const()[name = string("op_735_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17594432)))]; + tensor var_735_cast_fp16 = conv(bias = var_735_bias_0_to_fp16, dilations = obj_21_dilations_0, groups = obj_21_groups_0, pad = obj_21_pad_0, pad_type = obj_21_pad_type_0, strides = obj_21_strides_0, weight = op_735_weight_0_to_fp16_palettized, x = input_49_cast_fp16)[name = string("op_735_cast_fp16")]; + tensor inputs_7_cast_fp16 = add(x = inputs_5_cast_fp16, y = var_735_cast_fp16)[name = string("inputs_7_cast_fp16")]; + tensor inputs_sq_7_cast_fp16 = mul(x = inputs_7_cast_fp16, y = inputs_7_cast_fp16)[name = string("inputs_sq_7_cast_fp16")]; + tensor variance_7_axes_0 = const()[name = string("variance_7_axes_0"), val = tensor([1])]; + bool variance_7_keep_dims_0 = const()[name = string("variance_7_keep_dims_0"), val = bool(true)]; + tensor variance_7_cast_fp16 = reduce_mean(axes = variance_7_axes_0, keep_dims = variance_7_keep_dims_0, x = inputs_sq_7_cast_fp16)[name = string("variance_7_cast_fp16")]; + fp16 var_741_to_fp16 = const()[name = string("op_741_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_742_cast_fp16 = add(x = variance_7_cast_fp16, y = var_741_to_fp16)[name = string("op_742_cast_fp16")]; + fp32 var_743_epsilon_0 = const()[name = string("op_743_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_743_cast_fp16 = rsqrt(epsilon = var_743_epsilon_0, x = var_742_cast_fp16)[name = string("op_743_cast_fp16")]; + tensor hidden_states_9_cast_fp16 = mul(x = inputs_7_cast_fp16, y = var_743_cast_fp16)[name = string("hidden_states_9_cast_fp16")]; + tensor w_7_to_fp16 = const()[name = string("w_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17595520)))]; + tensor input_51_cast_fp16 = mul(x = w_7_to_fp16, y = hidden_states_9_cast_fp16)[name = string("input_51_cast_fp16")]; + string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")]; + tensor input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor([1, 1])]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor([1, 1])]; + int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17596608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18120960))))[name = string("pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_53_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor gate_3_cast_fp16 = silu(x = input_53_cast_fp16)[name = string("gate_3_cast_fp16")]; + string up_3_pad_type_0 = const()[name = string("up_3_pad_type_0"), val = string("valid")]; + tensor up_3_strides_0 = const()[name = string("up_3_strides_0"), val = tensor([1, 1])]; + tensor up_3_pad_0 = const()[name = string("up_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_3_dilations_0 = const()[name = string("up_3_dilations_0"), val = tensor([1, 1])]; + int32 up_3_groups_0 = const()[name = string("up_3_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18121536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18645888))))[name = string("pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_3_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_3_dilations_0, groups = up_3_groups_0, pad = up_3_pad_0, pad_type = up_3_pad_type_0, strides = up_3_strides_0, weight = pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("up_3_cast_fp16")]; + tensor input_55_cast_fp16 = mul(x = gate_3_cast_fp16, y = up_3_cast_fp16)[name = string("input_55_cast_fp16")]; + string hidden_states_11_pad_type_0 = const()[name = string("hidden_states_11_pad_type_0"), val = string("valid")]; + tensor hidden_states_11_strides_0 = const()[name = string("hidden_states_11_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_0 = const()[name = string("hidden_states_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_11_dilations_0 = const()[name = string("hidden_states_11_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_11_groups_0 = const()[name = string("hidden_states_11_groups_0"), val = int32(1)]; + tensor op_777_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18646464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19170816))))[name = string("op_777_weight_0_to_fp16_palettized")]; + tensor var_777_bias_0_to_fp16 = const()[name = string("op_777_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19171392)))]; + tensor var_777_cast_fp16 = conv(bias = var_777_bias_0_to_fp16, dilations = hidden_states_11_dilations_0, groups = hidden_states_11_groups_0, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = hidden_states_11_strides_0, weight = op_777_weight_0_to_fp16_palettized, x = input_55_cast_fp16)[name = string("op_777_cast_fp16")]; + tensor inputs_9_cast_fp16 = add(x = inputs_7_cast_fp16, y = var_777_cast_fp16)[name = string("inputs_9_cast_fp16")]; + tensor inputs_sq_9_cast_fp16 = mul(x = inputs_9_cast_fp16, y = inputs_9_cast_fp16)[name = string("inputs_sq_9_cast_fp16")]; + tensor variance_9_axes_0 = const()[name = string("variance_9_axes_0"), val = tensor([1])]; + bool variance_9_keep_dims_0 = const()[name = string("variance_9_keep_dims_0"), val = bool(true)]; + tensor variance_9_cast_fp16 = reduce_mean(axes = variance_9_axes_0, keep_dims = variance_9_keep_dims_0, x = inputs_sq_9_cast_fp16)[name = string("variance_9_cast_fp16")]; + fp16 var_793_to_fp16 = const()[name = string("op_793_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_794_cast_fp16 = add(x = variance_9_cast_fp16, y = var_793_to_fp16)[name = string("op_794_cast_fp16")]; + fp32 var_795_epsilon_0 = const()[name = string("op_795_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_795_cast_fp16 = rsqrt(epsilon = var_795_epsilon_0, x = var_794_cast_fp16)[name = string("op_795_cast_fp16")]; + tensor hidden_states_13_cast_fp16 = mul(x = inputs_9_cast_fp16, y = var_795_cast_fp16)[name = string("hidden_states_13_cast_fp16")]; + tensor w_9_to_fp16 = const()[name = string("w_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19172480)))]; + tensor obj_23_cast_fp16 = mul(x = w_9_to_fp16, y = hidden_states_13_cast_fp16)[name = string("obj_23_cast_fp16")]; + string query_9_pad_type_0 = const()[name = string("query_9_pad_type_0"), val = string("valid")]; + tensor query_9_strides_0 = const()[name = string("query_9_strides_0"), val = tensor([1, 1])]; + tensor query_9_pad_0 = const()[name = string("query_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_9_dilations_0 = const()[name = string("query_9_dilations_0"), val = tensor([1, 1])]; + int32 query_9_groups_0 = const()[name = string("query_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19173568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19697920))))[name = string("pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_9_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_9_dilations_0, groups = query_9_groups_0, pad = query_9_pad_0, pad_type = query_9_pad_type_0, strides = query_9_strides_0, weight = pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized, x = obj_23_cast_fp16)[name = string("query_9_cast_fp16")]; + string key_9_pad_type_0 = const()[name = string("key_9_pad_type_0"), val = string("valid")]; + tensor key_9_strides_0 = const()[name = string("key_9_strides_0"), val = tensor([1, 1])]; + tensor key_9_pad_0 = const()[name = string("key_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_9_dilations_0 = const()[name = string("key_9_dilations_0"), val = tensor([1, 1])]; + int32 key_9_groups_0 = const()[name = string("key_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19698496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20222848))))[name = string("pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_9_cast_fp16 = conv(dilations = key_9_dilations_0, groups = key_9_groups_0, pad = key_9_pad_0, pad_type = key_9_pad_type_0, strides = key_9_strides_0, weight = pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized, x = obj_23_cast_fp16)[name = string("key_9_cast_fp16")]; + string current_value_5_pad_type_0 = const()[name = string("current_value_5_pad_type_0"), val = string("valid")]; + tensor current_value_5_strides_0 = const()[name = string("current_value_5_strides_0"), val = tensor([1, 1])]; + tensor current_value_5_pad_0 = const()[name = string("current_value_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_5_dilations_0 = const()[name = string("current_value_5_dilations_0"), val = tensor([1, 1])]; + int32 current_value_5_groups_0 = const()[name = string("current_value_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20223424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20747776))))[name = string("pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_5_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_5_dilations_0, groups = current_value_5_groups_0, pad = current_value_5_pad_0, pad_type = current_value_5_pad_type_0, strides = current_value_5_strides_0, weight = pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized, x = obj_23_cast_fp16)[name = string("current_value_5_cast_fp16")]; + tensor var_833 = const()[name = string("op_833"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_13_cast_fp16 = reshape(shape = var_833, x = query_9_cast_fp16)[name = string("mh_q_13_cast_fp16")]; + tensor var_835 = const()[name = string("op_835"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_9_cast_fp16 = reshape(shape = var_835, x = key_9_cast_fp16)[name = string("mh_k_9_cast_fp16")]; + tensor var_839_cast_fp16 = mul(x = mh_q_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_839_cast_fp16")]; + tensor var_844_begin_0 = const()[name = string("op_844_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_844_end_0 = const()[name = string("op_844_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_844_end_mask_0 = const()[name = string("op_844_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_844_cast_fp16 = slice_by_index(begin = var_844_begin_0, end = var_844_end_0, end_mask = var_844_end_mask_0, x = mh_q_13_cast_fp16)[name = string("op_844_cast_fp16")]; + tensor var_850_begin_0 = const()[name = string("op_850_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_850_end_0 = const()[name = string("op_850_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_850_end_mask_0 = const()[name = string("op_850_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_850_cast_fp16 = slice_by_index(begin = var_850_begin_0, end = var_850_end_0, end_mask = var_850_end_mask_0, x = mh_q_13_cast_fp16)[name = string("op_850_cast_fp16")]; + fp16 const_69_promoted_to_fp16 = const()[name = string("const_69_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_852_cast_fp16 = mul(x = var_850_cast_fp16, y = const_69_promoted_to_fp16)[name = string("op_852_cast_fp16")]; + bool var_854_interleave_0 = const()[name = string("op_854_interleave_0"), val = bool(false)]; + tensor var_854_cast_fp16 = concat(axis = var_327, interleave = var_854_interleave_0, values = (var_852_cast_fp16, var_844_cast_fp16))[name = string("op_854_cast_fp16")]; + tensor var_855_cast_fp16 = mul(x = var_854_cast_fp16, y = sin_1_cast_fp16)[name = string("op_855_cast_fp16")]; + tensor mh_q_15_cast_fp16 = add(x = var_839_cast_fp16, y = var_855_cast_fp16)[name = string("mh_q_15_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = mh_k_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor var_862_begin_0 = const()[name = string("op_862_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_862_end_0 = const()[name = string("op_862_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_862_end_mask_0 = const()[name = string("op_862_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_862_cast_fp16 = slice_by_index(begin = var_862_begin_0, end = var_862_end_0, end_mask = var_862_end_mask_0, x = mh_k_9_cast_fp16)[name = string("op_862_cast_fp16")]; + tensor var_868_begin_0 = const()[name = string("op_868_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_868_end_0 = const()[name = string("op_868_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_868_end_mask_0 = const()[name = string("op_868_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_868_cast_fp16 = slice_by_index(begin = var_868_begin_0, end = var_868_end_0, end_mask = var_868_end_mask_0, x = mh_k_9_cast_fp16)[name = string("op_868_cast_fp16")]; + fp16 const_72_promoted_to_fp16 = const()[name = string("const_72_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_870_cast_fp16 = mul(x = var_868_cast_fp16, y = const_72_promoted_to_fp16)[name = string("op_870_cast_fp16")]; + bool var_872_interleave_0 = const()[name = string("op_872_interleave_0"), val = bool(false)]; + tensor var_872_cast_fp16 = concat(axis = var_327, interleave = var_872_interleave_0, values = (var_870_cast_fp16, var_862_cast_fp16))[name = string("op_872_cast_fp16")]; + tensor var_873_cast_fp16 = mul(x = var_872_cast_fp16, y = sin_1_cast_fp16)[name = string("op_873_cast_fp16")]; + tensor mh_k_11_cast_fp16 = add(x = var_857_cast_fp16, y = var_873_cast_fp16)[name = string("mh_k_11_cast_fp16")]; + tensor var_877 = const()[name = string("op_877"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_5_cast_fp16 = reshape(shape = var_877, x = mh_k_11_cast_fp16)[name = string("current_key_5_cast_fp16")]; + tensor var_884_cast_fp16 = mul(x = var_361_cast_fp16_2, y = var_495_cast_fp16)[name = string("op_884_cast_fp16")]; + tensor var_885_cast_fp16 = mul(x = current_key_5_cast_fp16, y = var_493_cast_fp16)[name = string("op_885_cast_fp16")]; + tensor key_11_cast_fp16 = add(x = var_884_cast_fp16, y = var_885_cast_fp16)[name = string("key_11_cast_fp16")]; + tensor var_888_cast_fp16 = mul(x = var_370_cast_fp16_2, y = var_495_cast_fp16)[name = string("op_888_cast_fp16")]; + tensor var_889_cast_fp16 = mul(x = current_value_5_cast_fp16, y = var_493_cast_fp16)[name = string("op_889_cast_fp16")]; + tensor value_5_cast_fp16 = add(x = var_888_cast_fp16, y = var_889_cast_fp16)[name = string("value_5_cast_fp16")]; + fp16 var_895_to_fp16 = const()[name = string("op_895_to_fp16"), val = fp16(0x1p-3)]; + tensor var_896_cast_fp16 = mul(x = mh_q_15_cast_fp16, y = var_895_to_fp16)[name = string("op_896_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 16, 64, 80])]; + tensor var_900_cast_fp16 = reshape(shape = var_899, x = key_11_cast_fp16)[name = string("op_900_cast_fp16")]; + bool mh_w_9_transpose_x_0 = const()[name = string("mh_w_9_transpose_x_0"), val = bool(true)]; + bool mh_w_9_transpose_y_0 = const()[name = string("mh_w_9_transpose_y_0"), val = bool(false)]; + tensor mh_w_9_cast_fp16 = matmul(transpose_x = mh_w_9_transpose_x_0, transpose_y = mh_w_9_transpose_y_0, x = var_896_cast_fp16, y = var_900_cast_fp16)[name = string("mh_w_9_cast_fp16")]; + tensor mh_w_11_cast_fp16 = add(x = mh_w_9_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_11_cast_fp16")]; + tensor var_908_cast_fp16 = softmax(axis = var_332, x = mh_w_11_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor var_909 = const()[name = string("op_909"), val = tensor([1, 16, 64, 80])]; + tensor var_910_cast_fp16 = reshape(shape = var_909, x = value_5_cast_fp16)[name = string("op_910_cast_fp16")]; + bool attn_5_transpose_x_0 = const()[name = string("attn_5_transpose_x_0"), val = bool(false)]; + bool attn_5_transpose_y_0 = const()[name = string("attn_5_transpose_y_0"), val = bool(true)]; + tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_910_cast_fp16, y = var_908_cast_fp16)[name = string("attn_5_cast_fp16")]; + tensor var_913 = const()[name = string("op_913"), val = tensor([1, -1, 1, 1])]; + tensor input_57_cast_fp16 = reshape(shape = var_913, x = attn_5_cast_fp16)[name = string("input_57_cast_fp16")]; + string obj_29_pad_type_0 = const()[name = string("obj_29_pad_type_0"), val = string("valid")]; + tensor obj_29_strides_0 = const()[name = string("obj_29_strides_0"), val = tensor([1, 1])]; + tensor obj_29_pad_0 = const()[name = string("obj_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_29_dilations_0 = const()[name = string("obj_29_dilations_0"), val = tensor([1, 1])]; + int32 obj_29_groups_0 = const()[name = string("obj_29_groups_0"), val = int32(1)]; + tensor op_929_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20748352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21272704))))[name = string("op_929_weight_0_to_fp16_palettized")]; + tensor var_929_bias_0_to_fp16 = const()[name = string("op_929_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21273280)))]; + tensor var_929_cast_fp16 = conv(bias = var_929_bias_0_to_fp16, dilations = obj_29_dilations_0, groups = obj_29_groups_0, pad = obj_29_pad_0, pad_type = obj_29_pad_type_0, strides = obj_29_strides_0, weight = op_929_weight_0_to_fp16_palettized, x = input_57_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor inputs_11_cast_fp16 = add(x = inputs_9_cast_fp16, y = var_929_cast_fp16)[name = string("inputs_11_cast_fp16")]; + tensor inputs_sq_11_cast_fp16 = mul(x = inputs_11_cast_fp16, y = inputs_11_cast_fp16)[name = string("inputs_sq_11_cast_fp16")]; + tensor variance_11_axes_0 = const()[name = string("variance_11_axes_0"), val = tensor([1])]; + bool variance_11_keep_dims_0 = const()[name = string("variance_11_keep_dims_0"), val = bool(true)]; + tensor variance_11_cast_fp16 = reduce_mean(axes = variance_11_axes_0, keep_dims = variance_11_keep_dims_0, x = inputs_sq_11_cast_fp16)[name = string("variance_11_cast_fp16")]; + fp16 var_935_to_fp16 = const()[name = string("op_935_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_936_cast_fp16 = add(x = variance_11_cast_fp16, y = var_935_to_fp16)[name = string("op_936_cast_fp16")]; + fp32 var_937_epsilon_0 = const()[name = string("op_937_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_937_cast_fp16 = rsqrt(epsilon = var_937_epsilon_0, x = var_936_cast_fp16)[name = string("op_937_cast_fp16")]; + tensor hidden_states_15_cast_fp16 = mul(x = inputs_11_cast_fp16, y = var_937_cast_fp16)[name = string("hidden_states_15_cast_fp16")]; + tensor w_11_to_fp16 = const()[name = string("w_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21274368)))]; + tensor input_59_cast_fp16 = mul(x = w_11_to_fp16, y = hidden_states_15_cast_fp16)[name = string("input_59_cast_fp16")]; + string input_61_pad_type_0 = const()[name = string("input_61_pad_type_0"), val = string("valid")]; + tensor input_61_strides_0 = const()[name = string("input_61_strides_0"), val = tensor([1, 1])]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_61_dilations_0 = const()[name = string("input_61_dilations_0"), val = tensor([1, 1])]; + int32 input_61_groups_0 = const()[name = string("input_61_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21275456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21799808))))[name = string("pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_61_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor gate_5_cast_fp16 = silu(x = input_61_cast_fp16)[name = string("gate_5_cast_fp16")]; + string up_5_pad_type_0 = const()[name = string("up_5_pad_type_0"), val = string("valid")]; + tensor up_5_strides_0 = const()[name = string("up_5_strides_0"), val = tensor([1, 1])]; + tensor up_5_pad_0 = const()[name = string("up_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_5_dilations_0 = const()[name = string("up_5_dilations_0"), val = tensor([1, 1])]; + int32 up_5_groups_0 = const()[name = string("up_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21800384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22324736))))[name = string("pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_5_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_5_dilations_0, groups = up_5_groups_0, pad = up_5_pad_0, pad_type = up_5_pad_type_0, strides = up_5_strides_0, weight = pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = string("up_5_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = gate_5_cast_fp16, y = up_5_cast_fp16)[name = string("input_63_cast_fp16")]; + string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")]; + tensor hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)]; + tensor op_971_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22325312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22849664))))[name = string("op_971_weight_0_to_fp16_palettized")]; + tensor var_971_bias_0_to_fp16 = const()[name = string("op_971_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22850240)))]; + tensor var_971_cast_fp16 = conv(bias = var_971_bias_0_to_fp16, dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = op_971_weight_0_to_fp16_palettized, x = input_63_cast_fp16)[name = string("op_971_cast_fp16")]; + tensor inputs_13_cast_fp16 = add(x = inputs_11_cast_fp16, y = var_971_cast_fp16)[name = string("inputs_13_cast_fp16")]; + tensor inputs_sq_13_cast_fp16 = mul(x = inputs_13_cast_fp16, y = inputs_13_cast_fp16)[name = string("inputs_sq_13_cast_fp16")]; + tensor variance_13_axes_0 = const()[name = string("variance_13_axes_0"), val = tensor([1])]; + bool variance_13_keep_dims_0 = const()[name = string("variance_13_keep_dims_0"), val = bool(true)]; + tensor variance_13_cast_fp16 = reduce_mean(axes = variance_13_axes_0, keep_dims = variance_13_keep_dims_0, x = inputs_sq_13_cast_fp16)[name = string("variance_13_cast_fp16")]; + fp16 var_987_to_fp16 = const()[name = string("op_987_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_988_cast_fp16 = add(x = variance_13_cast_fp16, y = var_987_to_fp16)[name = string("op_988_cast_fp16")]; + fp32 var_989_epsilon_0 = const()[name = string("op_989_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_989_cast_fp16 = rsqrt(epsilon = var_989_epsilon_0, x = var_988_cast_fp16)[name = string("op_989_cast_fp16")]; + tensor hidden_states_19_cast_fp16 = mul(x = inputs_13_cast_fp16, y = var_989_cast_fp16)[name = string("hidden_states_19_cast_fp16")]; + tensor w_13_to_fp16 = const()[name = string("w_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22851328)))]; + tensor obj_31_cast_fp16 = mul(x = w_13_to_fp16, y = hidden_states_19_cast_fp16)[name = string("obj_31_cast_fp16")]; + string query_13_pad_type_0 = const()[name = string("query_13_pad_type_0"), val = string("valid")]; + tensor query_13_strides_0 = const()[name = string("query_13_strides_0"), val = tensor([1, 1])]; + tensor query_13_pad_0 = const()[name = string("query_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_13_dilations_0 = const()[name = string("query_13_dilations_0"), val = tensor([1, 1])]; + int32 query_13_groups_0 = const()[name = string("query_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22852416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23376768))))[name = string("pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_13_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_13_dilations_0, groups = query_13_groups_0, pad = query_13_pad_0, pad_type = query_13_pad_type_0, strides = query_13_strides_0, weight = pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized, x = obj_31_cast_fp16)[name = string("query_13_cast_fp16")]; + string key_13_pad_type_0 = const()[name = string("key_13_pad_type_0"), val = string("valid")]; + tensor key_13_strides_0 = const()[name = string("key_13_strides_0"), val = tensor([1, 1])]; + tensor key_13_pad_0 = const()[name = string("key_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_13_dilations_0 = const()[name = string("key_13_dilations_0"), val = tensor([1, 1])]; + int32 key_13_groups_0 = const()[name = string("key_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23377344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23901696))))[name = string("pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_13_cast_fp16 = conv(dilations = key_13_dilations_0, groups = key_13_groups_0, pad = key_13_pad_0, pad_type = key_13_pad_type_0, strides = key_13_strides_0, weight = pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized, x = obj_31_cast_fp16)[name = string("key_13_cast_fp16")]; + string current_value_7_pad_type_0 = const()[name = string("current_value_7_pad_type_0"), val = string("valid")]; + tensor current_value_7_strides_0 = const()[name = string("current_value_7_strides_0"), val = tensor([1, 1])]; + tensor current_value_7_pad_0 = const()[name = string("current_value_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_7_dilations_0 = const()[name = string("current_value_7_dilations_0"), val = tensor([1, 1])]; + int32 current_value_7_groups_0 = const()[name = string("current_value_7_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23902272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24426624))))[name = string("pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_7_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_7_dilations_0, groups = current_value_7_groups_0, pad = current_value_7_pad_0, pad_type = current_value_7_pad_type_0, strides = current_value_7_strides_0, weight = pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized, x = obj_31_cast_fp16)[name = string("current_value_7_cast_fp16")]; + tensor var_1027 = const()[name = string("op_1027"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_19_cast_fp16 = reshape(shape = var_1027, x = query_13_cast_fp16)[name = string("mh_q_19_cast_fp16")]; + tensor var_1029 = const()[name = string("op_1029"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_13_cast_fp16 = reshape(shape = var_1029, x = key_13_cast_fp16)[name = string("mh_k_13_cast_fp16")]; + tensor var_1033_cast_fp16 = mul(x = mh_q_19_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1033_cast_fp16")]; + tensor var_1038_begin_0 = const()[name = string("op_1038_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1038_end_0 = const()[name = string("op_1038_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1038_end_mask_0 = const()[name = string("op_1038_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1038_cast_fp16 = slice_by_index(begin = var_1038_begin_0, end = var_1038_end_0, end_mask = var_1038_end_mask_0, x = mh_q_19_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor var_1044_begin_0 = const()[name = string("op_1044_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1044_end_0 = const()[name = string("op_1044_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1044_end_mask_0 = const()[name = string("op_1044_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1044_cast_fp16 = slice_by_index(begin = var_1044_begin_0, end = var_1044_end_0, end_mask = var_1044_end_mask_0, x = mh_q_19_cast_fp16)[name = string("op_1044_cast_fp16")]; + fp16 const_88_promoted_to_fp16 = const()[name = string("const_88_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1046_cast_fp16 = mul(x = var_1044_cast_fp16, y = const_88_promoted_to_fp16)[name = string("op_1046_cast_fp16")]; + bool var_1048_interleave_0 = const()[name = string("op_1048_interleave_0"), val = bool(false)]; + tensor var_1048_cast_fp16 = concat(axis = var_327, interleave = var_1048_interleave_0, values = (var_1046_cast_fp16, var_1038_cast_fp16))[name = string("op_1048_cast_fp16")]; + tensor var_1049_cast_fp16 = mul(x = var_1048_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1049_cast_fp16")]; + tensor mh_q_21_cast_fp16 = add(x = var_1033_cast_fp16, y = var_1049_cast_fp16)[name = string("mh_q_21_cast_fp16")]; + tensor var_1051_cast_fp16 = mul(x = mh_k_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1051_cast_fp16")]; + tensor var_1056_begin_0 = const()[name = string("op_1056_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1056_end_0 = const()[name = string("op_1056_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1056_end_mask_0 = const()[name = string("op_1056_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1056_cast_fp16 = slice_by_index(begin = var_1056_begin_0, end = var_1056_end_0, end_mask = var_1056_end_mask_0, x = mh_k_13_cast_fp16)[name = string("op_1056_cast_fp16")]; + tensor var_1062_begin_0 = const()[name = string("op_1062_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1062_end_0 = const()[name = string("op_1062_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1062_end_mask_0 = const()[name = string("op_1062_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1062_cast_fp16 = slice_by_index(begin = var_1062_begin_0, end = var_1062_end_0, end_mask = var_1062_end_mask_0, x = mh_k_13_cast_fp16)[name = string("op_1062_cast_fp16")]; + fp16 const_91_promoted_to_fp16 = const()[name = string("const_91_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1064_cast_fp16 = mul(x = var_1062_cast_fp16, y = const_91_promoted_to_fp16)[name = string("op_1064_cast_fp16")]; + bool var_1066_interleave_0 = const()[name = string("op_1066_interleave_0"), val = bool(false)]; + tensor var_1066_cast_fp16 = concat(axis = var_327, interleave = var_1066_interleave_0, values = (var_1064_cast_fp16, var_1056_cast_fp16))[name = string("op_1066_cast_fp16")]; + tensor var_1067_cast_fp16 = mul(x = var_1066_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor mh_k_15_cast_fp16 = add(x = var_1051_cast_fp16, y = var_1067_cast_fp16)[name = string("mh_k_15_cast_fp16")]; + tensor var_1071 = const()[name = string("op_1071"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_7_cast_fp16 = reshape(shape = var_1071, x = mh_k_15_cast_fp16)[name = string("current_key_7_cast_fp16")]; + tensor var_1078_cast_fp16 = mul(x = var_361_cast_fp16_3, y = var_495_cast_fp16)[name = string("op_1078_cast_fp16")]; + tensor var_1079_cast_fp16 = mul(x = current_key_7_cast_fp16, y = var_493_cast_fp16)[name = string("op_1079_cast_fp16")]; + tensor key_15_cast_fp16 = add(x = var_1078_cast_fp16, y = var_1079_cast_fp16)[name = string("key_15_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_370_cast_fp16_3, y = var_495_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor var_1083_cast_fp16 = mul(x = current_value_7_cast_fp16, y = var_493_cast_fp16)[name = string("op_1083_cast_fp16")]; + tensor value_7_cast_fp16 = add(x = var_1082_cast_fp16, y = var_1083_cast_fp16)[name = string("value_7_cast_fp16")]; + fp16 var_1089_to_fp16 = const()[name = string("op_1089_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1090_cast_fp16 = mul(x = mh_q_21_cast_fp16, y = var_1089_to_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1093 = const()[name = string("op_1093"), val = tensor([1, 16, 64, 80])]; + tensor var_1094_cast_fp16 = reshape(shape = var_1093, x = key_15_cast_fp16)[name = string("op_1094_cast_fp16")]; + bool mh_w_13_transpose_x_0 = const()[name = string("mh_w_13_transpose_x_0"), val = bool(true)]; + bool mh_w_13_transpose_y_0 = const()[name = string("mh_w_13_transpose_y_0"), val = bool(false)]; + tensor mh_w_13_cast_fp16 = matmul(transpose_x = mh_w_13_transpose_x_0, transpose_y = mh_w_13_transpose_y_0, x = var_1090_cast_fp16, y = var_1094_cast_fp16)[name = string("mh_w_13_cast_fp16")]; + tensor mh_w_15_cast_fp16 = add(x = mh_w_13_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_15_cast_fp16")]; + tensor var_1102_cast_fp16 = softmax(axis = var_332, x = mh_w_15_cast_fp16)[name = string("op_1102_cast_fp16")]; + tensor var_1103 = const()[name = string("op_1103"), val = tensor([1, 16, 64, 80])]; + tensor var_1104_cast_fp16 = reshape(shape = var_1103, x = value_7_cast_fp16)[name = string("op_1104_cast_fp16")]; + bool attn_7_transpose_x_0 = const()[name = string("attn_7_transpose_x_0"), val = bool(false)]; + bool attn_7_transpose_y_0 = const()[name = string("attn_7_transpose_y_0"), val = bool(true)]; + tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_1104_cast_fp16, y = var_1102_cast_fp16)[name = string("attn_7_cast_fp16")]; + tensor var_1107 = const()[name = string("op_1107"), val = tensor([1, -1, 1, 1])]; + tensor input_65_cast_fp16 = reshape(shape = var_1107, x = attn_7_cast_fp16)[name = string("input_65_cast_fp16")]; + string obj_37_pad_type_0 = const()[name = string("obj_37_pad_type_0"), val = string("valid")]; + tensor obj_37_strides_0 = const()[name = string("obj_37_strides_0"), val = tensor([1, 1])]; + tensor obj_37_pad_0 = const()[name = string("obj_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_37_dilations_0 = const()[name = string("obj_37_dilations_0"), val = tensor([1, 1])]; + int32 obj_37_groups_0 = const()[name = string("obj_37_groups_0"), val = int32(1)]; + tensor op_1123_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24427200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24951552))))[name = string("op_1123_weight_0_to_fp16_palettized")]; + tensor var_1123_bias_0_to_fp16 = const()[name = string("op_1123_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24952128)))]; + tensor var_1123_cast_fp16 = conv(bias = var_1123_bias_0_to_fp16, dilations = obj_37_dilations_0, groups = obj_37_groups_0, pad = obj_37_pad_0, pad_type = obj_37_pad_type_0, strides = obj_37_strides_0, weight = op_1123_weight_0_to_fp16_palettized, x = input_65_cast_fp16)[name = string("op_1123_cast_fp16")]; + tensor inputs_15_cast_fp16 = add(x = inputs_13_cast_fp16, y = var_1123_cast_fp16)[name = string("inputs_15_cast_fp16")]; + tensor inputs_sq_15_cast_fp16 = mul(x = inputs_15_cast_fp16, y = inputs_15_cast_fp16)[name = string("inputs_sq_15_cast_fp16")]; + tensor variance_15_axes_0 = const()[name = string("variance_15_axes_0"), val = tensor([1])]; + bool variance_15_keep_dims_0 = const()[name = string("variance_15_keep_dims_0"), val = bool(true)]; + tensor variance_15_cast_fp16 = reduce_mean(axes = variance_15_axes_0, keep_dims = variance_15_keep_dims_0, x = inputs_sq_15_cast_fp16)[name = string("variance_15_cast_fp16")]; + fp16 var_1129_to_fp16 = const()[name = string("op_1129_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1130_cast_fp16 = add(x = variance_15_cast_fp16, y = var_1129_to_fp16)[name = string("op_1130_cast_fp16")]; + fp32 var_1131_epsilon_0 = const()[name = string("op_1131_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1131_cast_fp16 = rsqrt(epsilon = var_1131_epsilon_0, x = var_1130_cast_fp16)[name = string("op_1131_cast_fp16")]; + tensor hidden_states_21_cast_fp16 = mul(x = inputs_15_cast_fp16, y = var_1131_cast_fp16)[name = string("hidden_states_21_cast_fp16")]; + tensor w_15_to_fp16 = const()[name = string("w_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24953216)))]; + tensor input_67_cast_fp16 = mul(x = w_15_to_fp16, y = hidden_states_21_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_pad_type_0 = const()[name = string("input_69_pad_type_0"), val = string("valid")]; + tensor input_69_strides_0 = const()[name = string("input_69_strides_0"), val = tensor([1, 1])]; + tensor input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_69_dilations_0 = const()[name = string("input_69_dilations_0"), val = tensor([1, 1])]; + int32 input_69_groups_0 = const()[name = string("input_69_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24954304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25478656))))[name = string("pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_69_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor gate_7_cast_fp16 = silu(x = input_69_cast_fp16)[name = string("gate_7_cast_fp16")]; + string up_7_pad_type_0 = const()[name = string("up_7_pad_type_0"), val = string("valid")]; + tensor up_7_strides_0 = const()[name = string("up_7_strides_0"), val = tensor([1, 1])]; + tensor up_7_pad_0 = const()[name = string("up_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_7_dilations_0 = const()[name = string("up_7_dilations_0"), val = tensor([1, 1])]; + int32 up_7_groups_0 = const()[name = string("up_7_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25479232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26003584))))[name = string("pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_7_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_7_dilations_0, groups = up_7_groups_0, pad = up_7_pad_0, pad_type = up_7_pad_type_0, strides = up_7_strides_0, weight = pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = string("up_7_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = gate_7_cast_fp16, y = up_7_cast_fp16)[name = string("input_71_cast_fp16")]; + string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")]; + tensor hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)]; + tensor op_1165_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26004160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26528512))))[name = string("op_1165_weight_0_to_fp16_palettized")]; + tensor var_1165_bias_0_to_fp16 = const()[name = string("op_1165_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26529088)))]; + tensor var_1165_cast_fp16 = conv(bias = var_1165_bias_0_to_fp16, dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = op_1165_weight_0_to_fp16_palettized, x = input_71_cast_fp16)[name = string("op_1165_cast_fp16")]; + tensor inputs_17_cast_fp16 = add(x = inputs_15_cast_fp16, y = var_1165_cast_fp16)[name = string("inputs_17_cast_fp16")]; + tensor inputs_sq_17_cast_fp16 = mul(x = inputs_17_cast_fp16, y = inputs_17_cast_fp16)[name = string("inputs_sq_17_cast_fp16")]; + tensor variance_17_axes_0 = const()[name = string("variance_17_axes_0"), val = tensor([1])]; + bool variance_17_keep_dims_0 = const()[name = string("variance_17_keep_dims_0"), val = bool(true)]; + tensor variance_17_cast_fp16 = reduce_mean(axes = variance_17_axes_0, keep_dims = variance_17_keep_dims_0, x = inputs_sq_17_cast_fp16)[name = string("variance_17_cast_fp16")]; + fp16 var_1181_to_fp16 = const()[name = string("op_1181_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1182_cast_fp16 = add(x = variance_17_cast_fp16, y = var_1181_to_fp16)[name = string("op_1182_cast_fp16")]; + fp32 var_1183_epsilon_0 = const()[name = string("op_1183_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1183_cast_fp16 = rsqrt(epsilon = var_1183_epsilon_0, x = var_1182_cast_fp16)[name = string("op_1183_cast_fp16")]; + tensor hidden_states_25_cast_fp16 = mul(x = inputs_17_cast_fp16, y = var_1183_cast_fp16)[name = string("hidden_states_25_cast_fp16")]; + tensor w_17_to_fp16 = const()[name = string("w_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26530176)))]; + tensor obj_39_cast_fp16 = mul(x = w_17_to_fp16, y = hidden_states_25_cast_fp16)[name = string("obj_39_cast_fp16")]; + string query_17_pad_type_0 = const()[name = string("query_17_pad_type_0"), val = string("valid")]; + tensor query_17_strides_0 = const()[name = string("query_17_strides_0"), val = tensor([1, 1])]; + tensor query_17_pad_0 = const()[name = string("query_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_17_dilations_0 = const()[name = string("query_17_dilations_0"), val = tensor([1, 1])]; + int32 query_17_groups_0 = const()[name = string("query_17_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26531264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27055616))))[name = string("pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_17_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_17_dilations_0, groups = query_17_groups_0, pad = query_17_pad_0, pad_type = query_17_pad_type_0, strides = query_17_strides_0, weight = pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized, x = obj_39_cast_fp16)[name = string("query_17_cast_fp16")]; + string key_17_pad_type_0 = const()[name = string("key_17_pad_type_0"), val = string("valid")]; + tensor key_17_strides_0 = const()[name = string("key_17_strides_0"), val = tensor([1, 1])]; + tensor key_17_pad_0 = const()[name = string("key_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_17_dilations_0 = const()[name = string("key_17_dilations_0"), val = tensor([1, 1])]; + int32 key_17_groups_0 = const()[name = string("key_17_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27056192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27580544))))[name = string("pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_17_cast_fp16 = conv(dilations = key_17_dilations_0, groups = key_17_groups_0, pad = key_17_pad_0, pad_type = key_17_pad_type_0, strides = key_17_strides_0, weight = pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized, x = obj_39_cast_fp16)[name = string("key_17_cast_fp16")]; + string current_value_9_pad_type_0 = const()[name = string("current_value_9_pad_type_0"), val = string("valid")]; + tensor current_value_9_strides_0 = const()[name = string("current_value_9_strides_0"), val = tensor([1, 1])]; + tensor current_value_9_pad_0 = const()[name = string("current_value_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_9_dilations_0 = const()[name = string("current_value_9_dilations_0"), val = tensor([1, 1])]; + int32 current_value_9_groups_0 = const()[name = string("current_value_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27581120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28105472))))[name = string("pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_9_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_9_dilations_0, groups = current_value_9_groups_0, pad = current_value_9_pad_0, pad_type = current_value_9_pad_type_0, strides = current_value_9_strides_0, weight = pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized, x = obj_39_cast_fp16)[name = string("current_value_9_cast_fp16")]; + tensor var_1221 = const()[name = string("op_1221"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_25_cast_fp16 = reshape(shape = var_1221, x = query_17_cast_fp16)[name = string("mh_q_25_cast_fp16")]; + tensor var_1223 = const()[name = string("op_1223"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_17_cast_fp16 = reshape(shape = var_1223, x = key_17_cast_fp16)[name = string("mh_k_17_cast_fp16")]; + tensor var_1227_cast_fp16 = mul(x = mh_q_25_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1227_cast_fp16")]; + tensor var_1232_begin_0 = const()[name = string("op_1232_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1232_end_0 = const()[name = string("op_1232_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1232_end_mask_0 = const()[name = string("op_1232_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1232_cast_fp16 = slice_by_index(begin = var_1232_begin_0, end = var_1232_end_0, end_mask = var_1232_end_mask_0, x = mh_q_25_cast_fp16)[name = string("op_1232_cast_fp16")]; + tensor var_1238_begin_0 = const()[name = string("op_1238_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1238_end_0 = const()[name = string("op_1238_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1238_end_mask_0 = const()[name = string("op_1238_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1238_cast_fp16 = slice_by_index(begin = var_1238_begin_0, end = var_1238_end_0, end_mask = var_1238_end_mask_0, x = mh_q_25_cast_fp16)[name = string("op_1238_cast_fp16")]; + fp16 const_107_promoted_to_fp16 = const()[name = string("const_107_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1240_cast_fp16 = mul(x = var_1238_cast_fp16, y = const_107_promoted_to_fp16)[name = string("op_1240_cast_fp16")]; + bool var_1242_interleave_0 = const()[name = string("op_1242_interleave_0"), val = bool(false)]; + tensor var_1242_cast_fp16 = concat(axis = var_327, interleave = var_1242_interleave_0, values = (var_1240_cast_fp16, var_1232_cast_fp16))[name = string("op_1242_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = var_1242_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor mh_q_27_cast_fp16 = add(x = var_1227_cast_fp16, y = var_1243_cast_fp16)[name = string("mh_q_27_cast_fp16")]; + tensor var_1245_cast_fp16 = mul(x = mh_k_17_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1245_cast_fp16")]; + tensor var_1250_begin_0 = const()[name = string("op_1250_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1250_end_0 = const()[name = string("op_1250_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1250_end_mask_0 = const()[name = string("op_1250_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1250_cast_fp16 = slice_by_index(begin = var_1250_begin_0, end = var_1250_end_0, end_mask = var_1250_end_mask_0, x = mh_k_17_cast_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1256_begin_0 = const()[name = string("op_1256_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1256_end_0 = const()[name = string("op_1256_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1256_end_mask_0 = const()[name = string("op_1256_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1256_cast_fp16 = slice_by_index(begin = var_1256_begin_0, end = var_1256_end_0, end_mask = var_1256_end_mask_0, x = mh_k_17_cast_fp16)[name = string("op_1256_cast_fp16")]; + fp16 const_110_promoted_to_fp16 = const()[name = string("const_110_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1258_cast_fp16 = mul(x = var_1256_cast_fp16, y = const_110_promoted_to_fp16)[name = string("op_1258_cast_fp16")]; + bool var_1260_interleave_0 = const()[name = string("op_1260_interleave_0"), val = bool(false)]; + tensor var_1260_cast_fp16 = concat(axis = var_327, interleave = var_1260_interleave_0, values = (var_1258_cast_fp16, var_1250_cast_fp16))[name = string("op_1260_cast_fp16")]; + tensor var_1261_cast_fp16 = mul(x = var_1260_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1261_cast_fp16")]; + tensor mh_k_19_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1261_cast_fp16)[name = string("mh_k_19_cast_fp16")]; + tensor var_1265 = const()[name = string("op_1265"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_9_cast_fp16 = reshape(shape = var_1265, x = mh_k_19_cast_fp16)[name = string("current_key_9_cast_fp16")]; + tensor var_1272_cast_fp16 = mul(x = var_361_cast_fp16_4, y = var_495_cast_fp16)[name = string("op_1272_cast_fp16")]; + tensor var_1273_cast_fp16 = mul(x = current_key_9_cast_fp16, y = var_493_cast_fp16)[name = string("op_1273_cast_fp16")]; + tensor key_19_cast_fp16 = add(x = var_1272_cast_fp16, y = var_1273_cast_fp16)[name = string("key_19_cast_fp16")]; + tensor var_1276_cast_fp16 = mul(x = var_370_cast_fp16_4, y = var_495_cast_fp16)[name = string("op_1276_cast_fp16")]; + tensor var_1277_cast_fp16 = mul(x = current_value_9_cast_fp16, y = var_493_cast_fp16)[name = string("op_1277_cast_fp16")]; + tensor value_9_cast_fp16 = add(x = var_1276_cast_fp16, y = var_1277_cast_fp16)[name = string("value_9_cast_fp16")]; + fp16 var_1283_to_fp16 = const()[name = string("op_1283_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1284_cast_fp16 = mul(x = mh_q_27_cast_fp16, y = var_1283_to_fp16)[name = string("op_1284_cast_fp16")]; + tensor var_1287 = const()[name = string("op_1287"), val = tensor([1, 16, 64, 80])]; + tensor var_1288_cast_fp16 = reshape(shape = var_1287, x = key_19_cast_fp16)[name = string("op_1288_cast_fp16")]; + bool mh_w_17_transpose_x_0 = const()[name = string("mh_w_17_transpose_x_0"), val = bool(true)]; + bool mh_w_17_transpose_y_0 = const()[name = string("mh_w_17_transpose_y_0"), val = bool(false)]; + tensor mh_w_17_cast_fp16 = matmul(transpose_x = mh_w_17_transpose_x_0, transpose_y = mh_w_17_transpose_y_0, x = var_1284_cast_fp16, y = var_1288_cast_fp16)[name = string("mh_w_17_cast_fp16")]; + tensor mh_w_19_cast_fp16 = add(x = mh_w_17_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_19_cast_fp16")]; + tensor var_1296_cast_fp16 = softmax(axis = var_332, x = mh_w_19_cast_fp16)[name = string("op_1296_cast_fp16")]; + tensor var_1297 = const()[name = string("op_1297"), val = tensor([1, 16, 64, 80])]; + tensor var_1298_cast_fp16 = reshape(shape = var_1297, x = value_9_cast_fp16)[name = string("op_1298_cast_fp16")]; + bool attn_9_transpose_x_0 = const()[name = string("attn_9_transpose_x_0"), val = bool(false)]; + bool attn_9_transpose_y_0 = const()[name = string("attn_9_transpose_y_0"), val = bool(true)]; + tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_1298_cast_fp16, y = var_1296_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor var_1301 = const()[name = string("op_1301"), val = tensor([1, -1, 1, 1])]; + tensor input_73_cast_fp16 = reshape(shape = var_1301, x = attn_9_cast_fp16)[name = string("input_73_cast_fp16")]; + string obj_45_pad_type_0 = const()[name = string("obj_45_pad_type_0"), val = string("valid")]; + tensor obj_45_strides_0 = const()[name = string("obj_45_strides_0"), val = tensor([1, 1])]; + tensor obj_45_pad_0 = const()[name = string("obj_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_45_dilations_0 = const()[name = string("obj_45_dilations_0"), val = tensor([1, 1])]; + int32 obj_45_groups_0 = const()[name = string("obj_45_groups_0"), val = int32(1)]; + tensor op_1317_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28106048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28630400))))[name = string("op_1317_weight_0_to_fp16_palettized")]; + tensor var_1317_bias_0_to_fp16 = const()[name = string("op_1317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28630976)))]; + tensor var_1317_cast_fp16 = conv(bias = var_1317_bias_0_to_fp16, dilations = obj_45_dilations_0, groups = obj_45_groups_0, pad = obj_45_pad_0, pad_type = obj_45_pad_type_0, strides = obj_45_strides_0, weight = op_1317_weight_0_to_fp16_palettized, x = input_73_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor inputs_19_cast_fp16 = add(x = inputs_17_cast_fp16, y = var_1317_cast_fp16)[name = string("inputs_19_cast_fp16")]; + tensor inputs_sq_19_cast_fp16 = mul(x = inputs_19_cast_fp16, y = inputs_19_cast_fp16)[name = string("inputs_sq_19_cast_fp16")]; + tensor variance_19_axes_0 = const()[name = string("variance_19_axes_0"), val = tensor([1])]; + bool variance_19_keep_dims_0 = const()[name = string("variance_19_keep_dims_0"), val = bool(true)]; + tensor variance_19_cast_fp16 = reduce_mean(axes = variance_19_axes_0, keep_dims = variance_19_keep_dims_0, x = inputs_sq_19_cast_fp16)[name = string("variance_19_cast_fp16")]; + fp16 var_1323_to_fp16 = const()[name = string("op_1323_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1324_cast_fp16 = add(x = variance_19_cast_fp16, y = var_1323_to_fp16)[name = string("op_1324_cast_fp16")]; + fp32 var_1325_epsilon_0 = const()[name = string("op_1325_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1325_cast_fp16 = rsqrt(epsilon = var_1325_epsilon_0, x = var_1324_cast_fp16)[name = string("op_1325_cast_fp16")]; + tensor hidden_states_27_cast_fp16 = mul(x = inputs_19_cast_fp16, y = var_1325_cast_fp16)[name = string("hidden_states_27_cast_fp16")]; + tensor w_19_to_fp16 = const()[name = string("w_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28632064)))]; + tensor input_75_cast_fp16 = mul(x = w_19_to_fp16, y = hidden_states_27_cast_fp16)[name = string("input_75_cast_fp16")]; + string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("valid")]; + tensor input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor([1, 1])]; + tensor input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor([1, 1])]; + int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28633152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29157504))))[name = string("pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_77_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor gate_9_cast_fp16 = silu(x = input_77_cast_fp16)[name = string("gate_9_cast_fp16")]; + string up_9_pad_type_0 = const()[name = string("up_9_pad_type_0"), val = string("valid")]; + tensor up_9_strides_0 = const()[name = string("up_9_strides_0"), val = tensor([1, 1])]; + tensor up_9_pad_0 = const()[name = string("up_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_9_dilations_0 = const()[name = string("up_9_dilations_0"), val = tensor([1, 1])]; + int32 up_9_groups_0 = const()[name = string("up_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29158080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29682432))))[name = string("pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_9_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_9_dilations_0, groups = up_9_groups_0, pad = up_9_pad_0, pad_type = up_9_pad_type_0, strides = up_9_strides_0, weight = pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = string("up_9_cast_fp16")]; + tensor input_79_cast_fp16 = mul(x = gate_9_cast_fp16, y = up_9_cast_fp16)[name = string("input_79_cast_fp16")]; + string hidden_states_29_pad_type_0 = const()[name = string("hidden_states_29_pad_type_0"), val = string("valid")]; + tensor hidden_states_29_strides_0 = const()[name = string("hidden_states_29_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_29_pad_0 = const()[name = string("hidden_states_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_29_dilations_0 = const()[name = string("hidden_states_29_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_29_groups_0 = const()[name = string("hidden_states_29_groups_0"), val = int32(1)]; + tensor op_1359_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29683008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30207360))))[name = string("op_1359_weight_0_to_fp16_palettized")]; + tensor var_1359_bias_0_to_fp16 = const()[name = string("op_1359_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30207936)))]; + tensor var_1359_cast_fp16 = conv(bias = var_1359_bias_0_to_fp16, dilations = hidden_states_29_dilations_0, groups = hidden_states_29_groups_0, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = hidden_states_29_strides_0, weight = op_1359_weight_0_to_fp16_palettized, x = input_79_cast_fp16)[name = string("op_1359_cast_fp16")]; + tensor inputs_21_cast_fp16 = add(x = inputs_19_cast_fp16, y = var_1359_cast_fp16)[name = string("inputs_21_cast_fp16")]; + tensor inputs_sq_21_cast_fp16 = mul(x = inputs_21_cast_fp16, y = inputs_21_cast_fp16)[name = string("inputs_sq_21_cast_fp16")]; + tensor variance_21_axes_0 = const()[name = string("variance_21_axes_0"), val = tensor([1])]; + bool variance_21_keep_dims_0 = const()[name = string("variance_21_keep_dims_0"), val = bool(true)]; + tensor variance_21_cast_fp16 = reduce_mean(axes = variance_21_axes_0, keep_dims = variance_21_keep_dims_0, x = inputs_sq_21_cast_fp16)[name = string("variance_21_cast_fp16")]; + fp16 var_1375_to_fp16 = const()[name = string("op_1375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1376_cast_fp16 = add(x = variance_21_cast_fp16, y = var_1375_to_fp16)[name = string("op_1376_cast_fp16")]; + fp32 var_1377_epsilon_0 = const()[name = string("op_1377_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1377_cast_fp16 = rsqrt(epsilon = var_1377_epsilon_0, x = var_1376_cast_fp16)[name = string("op_1377_cast_fp16")]; + tensor hidden_states_31_cast_fp16 = mul(x = inputs_21_cast_fp16, y = var_1377_cast_fp16)[name = string("hidden_states_31_cast_fp16")]; + tensor w_21_to_fp16 = const()[name = string("w_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30209024)))]; + tensor obj_47_cast_fp16 = mul(x = w_21_to_fp16, y = hidden_states_31_cast_fp16)[name = string("obj_47_cast_fp16")]; + string query_21_pad_type_0 = const()[name = string("query_21_pad_type_0"), val = string("valid")]; + tensor query_21_strides_0 = const()[name = string("query_21_strides_0"), val = tensor([1, 1])]; + tensor query_21_pad_0 = const()[name = string("query_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_21_dilations_0 = const()[name = string("query_21_dilations_0"), val = tensor([1, 1])]; + int32 query_21_groups_0 = const()[name = string("query_21_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30210112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30734464))))[name = string("pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_21_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_21_dilations_0, groups = query_21_groups_0, pad = query_21_pad_0, pad_type = query_21_pad_type_0, strides = query_21_strides_0, weight = pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized, x = obj_47_cast_fp16)[name = string("query_21_cast_fp16")]; + string key_21_pad_type_0 = const()[name = string("key_21_pad_type_0"), val = string("valid")]; + tensor key_21_strides_0 = const()[name = string("key_21_strides_0"), val = tensor([1, 1])]; + tensor key_21_pad_0 = const()[name = string("key_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_21_dilations_0 = const()[name = string("key_21_dilations_0"), val = tensor([1, 1])]; + int32 key_21_groups_0 = const()[name = string("key_21_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30735040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31259392))))[name = string("pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_21_cast_fp16 = conv(dilations = key_21_dilations_0, groups = key_21_groups_0, pad = key_21_pad_0, pad_type = key_21_pad_type_0, strides = key_21_strides_0, weight = pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized, x = obj_47_cast_fp16)[name = string("key_21_cast_fp16")]; + string current_value_11_pad_type_0 = const()[name = string("current_value_11_pad_type_0"), val = string("valid")]; + tensor current_value_11_strides_0 = const()[name = string("current_value_11_strides_0"), val = tensor([1, 1])]; + tensor current_value_11_pad_0 = const()[name = string("current_value_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_11_dilations_0 = const()[name = string("current_value_11_dilations_0"), val = tensor([1, 1])]; + int32 current_value_11_groups_0 = const()[name = string("current_value_11_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31259968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31784320))))[name = string("pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_11_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_11_dilations_0, groups = current_value_11_groups_0, pad = current_value_11_pad_0, pad_type = current_value_11_pad_type_0, strides = current_value_11_strides_0, weight = pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized, x = obj_47_cast_fp16)[name = string("current_value_11_cast_fp16")]; + tensor var_1415 = const()[name = string("op_1415"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_31_cast_fp16 = reshape(shape = var_1415, x = query_21_cast_fp16)[name = string("mh_q_31_cast_fp16")]; + tensor var_1417 = const()[name = string("op_1417"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_21_cast_fp16 = reshape(shape = var_1417, x = key_21_cast_fp16)[name = string("mh_k_21_cast_fp16")]; + tensor var_1421_cast_fp16 = mul(x = mh_q_31_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1421_cast_fp16")]; + tensor var_1426_begin_0 = const()[name = string("op_1426_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1426_end_0 = const()[name = string("op_1426_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1426_end_mask_0 = const()[name = string("op_1426_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1426_cast_fp16 = slice_by_index(begin = var_1426_begin_0, end = var_1426_end_0, end_mask = var_1426_end_mask_0, x = mh_q_31_cast_fp16)[name = string("op_1426_cast_fp16")]; + tensor var_1432_begin_0 = const()[name = string("op_1432_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1432_end_0 = const()[name = string("op_1432_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1432_end_mask_0 = const()[name = string("op_1432_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1432_cast_fp16 = slice_by_index(begin = var_1432_begin_0, end = var_1432_end_0, end_mask = var_1432_end_mask_0, x = mh_q_31_cast_fp16)[name = string("op_1432_cast_fp16")]; + fp16 const_126_promoted_to_fp16 = const()[name = string("const_126_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1434_cast_fp16 = mul(x = var_1432_cast_fp16, y = const_126_promoted_to_fp16)[name = string("op_1434_cast_fp16")]; + bool var_1436_interleave_0 = const()[name = string("op_1436_interleave_0"), val = bool(false)]; + tensor var_1436_cast_fp16 = concat(axis = var_327, interleave = var_1436_interleave_0, values = (var_1434_cast_fp16, var_1426_cast_fp16))[name = string("op_1436_cast_fp16")]; + tensor var_1437_cast_fp16 = mul(x = var_1436_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1437_cast_fp16")]; + tensor mh_q_33_cast_fp16 = add(x = var_1421_cast_fp16, y = var_1437_cast_fp16)[name = string("mh_q_33_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = mh_k_21_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1444_begin_0 = const()[name = string("op_1444_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1444_end_0 = const()[name = string("op_1444_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1444_end_mask_0 = const()[name = string("op_1444_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1444_cast_fp16 = slice_by_index(begin = var_1444_begin_0, end = var_1444_end_0, end_mask = var_1444_end_mask_0, x = mh_k_21_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1450_begin_0 = const()[name = string("op_1450_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1450_end_0 = const()[name = string("op_1450_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1450_end_mask_0 = const()[name = string("op_1450_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1450_cast_fp16 = slice_by_index(begin = var_1450_begin_0, end = var_1450_end_0, end_mask = var_1450_end_mask_0, x = mh_k_21_cast_fp16)[name = string("op_1450_cast_fp16")]; + fp16 const_129_promoted_to_fp16 = const()[name = string("const_129_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1452_cast_fp16 = mul(x = var_1450_cast_fp16, y = const_129_promoted_to_fp16)[name = string("op_1452_cast_fp16")]; + bool var_1454_interleave_0 = const()[name = string("op_1454_interleave_0"), val = bool(false)]; + tensor var_1454_cast_fp16 = concat(axis = var_327, interleave = var_1454_interleave_0, values = (var_1452_cast_fp16, var_1444_cast_fp16))[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = var_1454_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor mh_k_23_cast_fp16 = add(x = var_1439_cast_fp16, y = var_1455_cast_fp16)[name = string("mh_k_23_cast_fp16")]; + tensor var_1459 = const()[name = string("op_1459"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_11_cast_fp16 = reshape(shape = var_1459, x = mh_k_23_cast_fp16)[name = string("current_key_11_cast_fp16")]; + tensor var_1466_cast_fp16 = mul(x = var_361_cast_fp16_5, y = var_495_cast_fp16)[name = string("op_1466_cast_fp16")]; + tensor var_1467_cast_fp16 = mul(x = current_key_11_cast_fp16, y = var_493_cast_fp16)[name = string("op_1467_cast_fp16")]; + tensor key_23_cast_fp16 = add(x = var_1466_cast_fp16, y = var_1467_cast_fp16)[name = string("key_23_cast_fp16")]; + tensor var_1470_cast_fp16 = mul(x = var_370_cast_fp16_5, y = var_495_cast_fp16)[name = string("op_1470_cast_fp16")]; + tensor var_1471_cast_fp16 = mul(x = current_value_11_cast_fp16, y = var_493_cast_fp16)[name = string("op_1471_cast_fp16")]; + tensor value_11_cast_fp16 = add(x = var_1470_cast_fp16, y = var_1471_cast_fp16)[name = string("value_11_cast_fp16")]; + fp16 var_1477_to_fp16 = const()[name = string("op_1477_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1478_cast_fp16 = mul(x = mh_q_33_cast_fp16, y = var_1477_to_fp16)[name = string("op_1478_cast_fp16")]; + tensor var_1481 = const()[name = string("op_1481"), val = tensor([1, 16, 64, 80])]; + tensor var_1482_cast_fp16 = reshape(shape = var_1481, x = key_23_cast_fp16)[name = string("op_1482_cast_fp16")]; + bool mh_w_21_transpose_x_0 = const()[name = string("mh_w_21_transpose_x_0"), val = bool(true)]; + bool mh_w_21_transpose_y_0 = const()[name = string("mh_w_21_transpose_y_0"), val = bool(false)]; + tensor mh_w_21_cast_fp16 = matmul(transpose_x = mh_w_21_transpose_x_0, transpose_y = mh_w_21_transpose_y_0, x = var_1478_cast_fp16, y = var_1482_cast_fp16)[name = string("mh_w_21_cast_fp16")]; + tensor mh_w_23_cast_fp16 = add(x = mh_w_21_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_23_cast_fp16")]; + tensor var_1490_cast_fp16 = softmax(axis = var_332, x = mh_w_23_cast_fp16)[name = string("op_1490_cast_fp16")]; + tensor var_1491 = const()[name = string("op_1491"), val = tensor([1, 16, 64, 80])]; + tensor var_1492_cast_fp16 = reshape(shape = var_1491, x = value_11_cast_fp16)[name = string("op_1492_cast_fp16")]; + bool attn_11_transpose_x_0 = const()[name = string("attn_11_transpose_x_0"), val = bool(false)]; + bool attn_11_transpose_y_0 = const()[name = string("attn_11_transpose_y_0"), val = bool(true)]; + tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_1492_cast_fp16, y = var_1490_cast_fp16)[name = string("attn_11_cast_fp16")]; + tensor var_1495 = const()[name = string("op_1495"), val = tensor([1, -1, 1, 1])]; + tensor input_81_cast_fp16 = reshape(shape = var_1495, x = attn_11_cast_fp16)[name = string("input_81_cast_fp16")]; + string obj_53_pad_type_0 = const()[name = string("obj_53_pad_type_0"), val = string("valid")]; + tensor obj_53_strides_0 = const()[name = string("obj_53_strides_0"), val = tensor([1, 1])]; + tensor obj_53_pad_0 = const()[name = string("obj_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_53_dilations_0 = const()[name = string("obj_53_dilations_0"), val = tensor([1, 1])]; + int32 obj_53_groups_0 = const()[name = string("obj_53_groups_0"), val = int32(1)]; + tensor op_1511_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31784896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32309248))))[name = string("op_1511_weight_0_to_fp16_palettized")]; + tensor var_1511_bias_0_to_fp16 = const()[name = string("op_1511_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32309824)))]; + tensor var_1511_cast_fp16 = conv(bias = var_1511_bias_0_to_fp16, dilations = obj_53_dilations_0, groups = obj_53_groups_0, pad = obj_53_pad_0, pad_type = obj_53_pad_type_0, strides = obj_53_strides_0, weight = op_1511_weight_0_to_fp16_palettized, x = input_81_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor inputs_23_cast_fp16 = add(x = inputs_21_cast_fp16, y = var_1511_cast_fp16)[name = string("inputs_23_cast_fp16")]; + tensor inputs_sq_23_cast_fp16 = mul(x = inputs_23_cast_fp16, y = inputs_23_cast_fp16)[name = string("inputs_sq_23_cast_fp16")]; + tensor variance_23_axes_0 = const()[name = string("variance_23_axes_0"), val = tensor([1])]; + bool variance_23_keep_dims_0 = const()[name = string("variance_23_keep_dims_0"), val = bool(true)]; + tensor variance_23_cast_fp16 = reduce_mean(axes = variance_23_axes_0, keep_dims = variance_23_keep_dims_0, x = inputs_sq_23_cast_fp16)[name = string("variance_23_cast_fp16")]; + fp16 var_1517_to_fp16 = const()[name = string("op_1517_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1518_cast_fp16 = add(x = variance_23_cast_fp16, y = var_1517_to_fp16)[name = string("op_1518_cast_fp16")]; + fp32 var_1519_epsilon_0 = const()[name = string("op_1519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1519_cast_fp16 = rsqrt(epsilon = var_1519_epsilon_0, x = var_1518_cast_fp16)[name = string("op_1519_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = mul(x = inputs_23_cast_fp16, y = var_1519_cast_fp16)[name = string("hidden_states_33_cast_fp16")]; + tensor w_23_to_fp16 = const()[name = string("w_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32310912)))]; + tensor input_83_cast_fp16 = mul(x = w_23_to_fp16, y = hidden_states_33_cast_fp16)[name = string("input_83_cast_fp16")]; + string input_85_pad_type_0 = const()[name = string("input_85_pad_type_0"), val = string("valid")]; + tensor input_85_strides_0 = const()[name = string("input_85_strides_0"), val = tensor([1, 1])]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_85_dilations_0 = const()[name = string("input_85_dilations_0"), val = tensor([1, 1])]; + int32 input_85_groups_0 = const()[name = string("input_85_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32312000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32836352))))[name = string("pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_85_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor gate_11_cast_fp16 = silu(x = input_85_cast_fp16)[name = string("gate_11_cast_fp16")]; + string up_11_pad_type_0 = const()[name = string("up_11_pad_type_0"), val = string("valid")]; + tensor up_11_strides_0 = const()[name = string("up_11_strides_0"), val = tensor([1, 1])]; + tensor up_11_pad_0 = const()[name = string("up_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_11_dilations_0 = const()[name = string("up_11_dilations_0"), val = tensor([1, 1])]; + int32 up_11_groups_0 = const()[name = string("up_11_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32836928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33361280))))[name = string("pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_11_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_11_dilations_0, groups = up_11_groups_0, pad = up_11_pad_0, pad_type = up_11_pad_type_0, strides = up_11_strides_0, weight = pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized, x = input_83_cast_fp16)[name = string("up_11_cast_fp16")]; + tensor input_87_cast_fp16 = mul(x = gate_11_cast_fp16, y = up_11_cast_fp16)[name = string("input_87_cast_fp16")]; + string hidden_states_35_pad_type_0 = const()[name = string("hidden_states_35_pad_type_0"), val = string("valid")]; + tensor hidden_states_35_strides_0 = const()[name = string("hidden_states_35_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_0 = const()[name = string("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_35_dilations_0 = const()[name = string("hidden_states_35_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_35_groups_0 = const()[name = string("hidden_states_35_groups_0"), val = int32(1)]; + tensor op_1553_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33361856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33886208))))[name = string("op_1553_weight_0_to_fp16_palettized")]; + tensor var_1553_bias_0_to_fp16 = const()[name = string("op_1553_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33886784)))]; + tensor var_1553_cast_fp16 = conv(bias = var_1553_bias_0_to_fp16, dilations = hidden_states_35_dilations_0, groups = hidden_states_35_groups_0, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = hidden_states_35_strides_0, weight = op_1553_weight_0_to_fp16_palettized, x = input_87_cast_fp16)[name = string("op_1553_cast_fp16")]; + tensor inputs_25_cast_fp16 = add(x = inputs_23_cast_fp16, y = var_1553_cast_fp16)[name = string("inputs_25_cast_fp16")]; + tensor inputs_sq_25_cast_fp16 = mul(x = inputs_25_cast_fp16, y = inputs_25_cast_fp16)[name = string("inputs_sq_25_cast_fp16")]; + tensor variance_25_axes_0 = const()[name = string("variance_25_axes_0"), val = tensor([1])]; + bool variance_25_keep_dims_0 = const()[name = string("variance_25_keep_dims_0"), val = bool(true)]; + tensor variance_25_cast_fp16 = reduce_mean(axes = variance_25_axes_0, keep_dims = variance_25_keep_dims_0, x = inputs_sq_25_cast_fp16)[name = string("variance_25_cast_fp16")]; + fp16 var_1569_to_fp16 = const()[name = string("op_1569_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1570_cast_fp16 = add(x = variance_25_cast_fp16, y = var_1569_to_fp16)[name = string("op_1570_cast_fp16")]; + fp32 var_1571_epsilon_0 = const()[name = string("op_1571_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1571_cast_fp16 = rsqrt(epsilon = var_1571_epsilon_0, x = var_1570_cast_fp16)[name = string("op_1571_cast_fp16")]; + tensor hidden_states_37_cast_fp16 = mul(x = inputs_25_cast_fp16, y = var_1571_cast_fp16)[name = string("hidden_states_37_cast_fp16")]; + tensor w_25_to_fp16 = const()[name = string("w_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33887872)))]; + tensor obj_55_cast_fp16 = mul(x = w_25_to_fp16, y = hidden_states_37_cast_fp16)[name = string("obj_55_cast_fp16")]; + string query_25_pad_type_0 = const()[name = string("query_25_pad_type_0"), val = string("valid")]; + tensor query_25_strides_0 = const()[name = string("query_25_strides_0"), val = tensor([1, 1])]; + tensor query_25_pad_0 = const()[name = string("query_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_25_dilations_0 = const()[name = string("query_25_dilations_0"), val = tensor([1, 1])]; + int32 query_25_groups_0 = const()[name = string("query_25_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33888960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34413312))))[name = string("pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_25_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_25_dilations_0, groups = query_25_groups_0, pad = query_25_pad_0, pad_type = query_25_pad_type_0, strides = query_25_strides_0, weight = pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized, x = obj_55_cast_fp16)[name = string("query_25_cast_fp16")]; + string key_25_pad_type_0 = const()[name = string("key_25_pad_type_0"), val = string("valid")]; + tensor key_25_strides_0 = const()[name = string("key_25_strides_0"), val = tensor([1, 1])]; + tensor key_25_pad_0 = const()[name = string("key_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_25_dilations_0 = const()[name = string("key_25_dilations_0"), val = tensor([1, 1])]; + int32 key_25_groups_0 = const()[name = string("key_25_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34413888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34938240))))[name = string("pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_25_cast_fp16 = conv(dilations = key_25_dilations_0, groups = key_25_groups_0, pad = key_25_pad_0, pad_type = key_25_pad_type_0, strides = key_25_strides_0, weight = pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized, x = obj_55_cast_fp16)[name = string("key_25_cast_fp16")]; + string current_value_13_pad_type_0 = const()[name = string("current_value_13_pad_type_0"), val = string("valid")]; + tensor current_value_13_strides_0 = const()[name = string("current_value_13_strides_0"), val = tensor([1, 1])]; + tensor current_value_13_pad_0 = const()[name = string("current_value_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_13_dilations_0 = const()[name = string("current_value_13_dilations_0"), val = tensor([1, 1])]; + int32 current_value_13_groups_0 = const()[name = string("current_value_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34938816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35463168))))[name = string("pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_13_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_13_dilations_0, groups = current_value_13_groups_0, pad = current_value_13_pad_0, pad_type = current_value_13_pad_type_0, strides = current_value_13_strides_0, weight = pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized, x = obj_55_cast_fp16)[name = string("current_value_13_cast_fp16")]; + tensor var_1609 = const()[name = string("op_1609"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_37_cast_fp16 = reshape(shape = var_1609, x = query_25_cast_fp16)[name = string("mh_q_37_cast_fp16")]; + tensor var_1611 = const()[name = string("op_1611"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_25_cast_fp16 = reshape(shape = var_1611, x = key_25_cast_fp16)[name = string("mh_k_25_cast_fp16")]; + tensor var_1615_cast_fp16 = mul(x = mh_q_37_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1615_cast_fp16")]; + tensor var_1620_begin_0 = const()[name = string("op_1620_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1620_end_0 = const()[name = string("op_1620_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1620_end_mask_0 = const()[name = string("op_1620_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1620_cast_fp16 = slice_by_index(begin = var_1620_begin_0, end = var_1620_end_0, end_mask = var_1620_end_mask_0, x = mh_q_37_cast_fp16)[name = string("op_1620_cast_fp16")]; + tensor var_1626_begin_0 = const()[name = string("op_1626_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1626_end_0 = const()[name = string("op_1626_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1626_end_mask_0 = const()[name = string("op_1626_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1626_cast_fp16 = slice_by_index(begin = var_1626_begin_0, end = var_1626_end_0, end_mask = var_1626_end_mask_0, x = mh_q_37_cast_fp16)[name = string("op_1626_cast_fp16")]; + fp16 const_145_promoted_to_fp16 = const()[name = string("const_145_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1628_cast_fp16 = mul(x = var_1626_cast_fp16, y = const_145_promoted_to_fp16)[name = string("op_1628_cast_fp16")]; + bool var_1630_interleave_0 = const()[name = string("op_1630_interleave_0"), val = bool(false)]; + tensor var_1630_cast_fp16 = concat(axis = var_327, interleave = var_1630_interleave_0, values = (var_1628_cast_fp16, var_1620_cast_fp16))[name = string("op_1630_cast_fp16")]; + tensor var_1631_cast_fp16 = mul(x = var_1630_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1631_cast_fp16")]; + tensor mh_q_39_cast_fp16 = add(x = var_1615_cast_fp16, y = var_1631_cast_fp16)[name = string("mh_q_39_cast_fp16")]; + tensor var_1633_cast_fp16 = mul(x = mh_k_25_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1633_cast_fp16")]; + tensor var_1638_begin_0 = const()[name = string("op_1638_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1638_end_0 = const()[name = string("op_1638_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1638_end_mask_0 = const()[name = string("op_1638_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1638_cast_fp16 = slice_by_index(begin = var_1638_begin_0, end = var_1638_end_0, end_mask = var_1638_end_mask_0, x = mh_k_25_cast_fp16)[name = string("op_1638_cast_fp16")]; + tensor var_1644_begin_0 = const()[name = string("op_1644_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1644_end_0 = const()[name = string("op_1644_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1644_end_mask_0 = const()[name = string("op_1644_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1644_cast_fp16 = slice_by_index(begin = var_1644_begin_0, end = var_1644_end_0, end_mask = var_1644_end_mask_0, x = mh_k_25_cast_fp16)[name = string("op_1644_cast_fp16")]; + fp16 const_148_promoted_to_fp16 = const()[name = string("const_148_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1646_cast_fp16 = mul(x = var_1644_cast_fp16, y = const_148_promoted_to_fp16)[name = string("op_1646_cast_fp16")]; + bool var_1648_interleave_0 = const()[name = string("op_1648_interleave_0"), val = bool(false)]; + tensor var_1648_cast_fp16 = concat(axis = var_327, interleave = var_1648_interleave_0, values = (var_1646_cast_fp16, var_1638_cast_fp16))[name = string("op_1648_cast_fp16")]; + tensor var_1649_cast_fp16 = mul(x = var_1648_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1649_cast_fp16")]; + tensor mh_k_27_cast_fp16 = add(x = var_1633_cast_fp16, y = var_1649_cast_fp16)[name = string("mh_k_27_cast_fp16")]; + tensor var_1653 = const()[name = string("op_1653"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_13_cast_fp16 = reshape(shape = var_1653, x = mh_k_27_cast_fp16)[name = string("current_key_13_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = var_361_cast_fp16_6, y = var_495_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1661_cast_fp16 = mul(x = current_key_13_cast_fp16, y = var_493_cast_fp16)[name = string("op_1661_cast_fp16")]; + tensor key_27_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1661_cast_fp16)[name = string("key_27_cast_fp16")]; + tensor var_1664_cast_fp16 = mul(x = var_370_cast_fp16_6, y = var_495_cast_fp16)[name = string("op_1664_cast_fp16")]; + tensor var_1665_cast_fp16 = mul(x = current_value_13_cast_fp16, y = var_493_cast_fp16)[name = string("op_1665_cast_fp16")]; + tensor value_13_cast_fp16 = add(x = var_1664_cast_fp16, y = var_1665_cast_fp16)[name = string("value_13_cast_fp16")]; + fp16 var_1671_to_fp16 = const()[name = string("op_1671_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1672_cast_fp16 = mul(x = mh_q_39_cast_fp16, y = var_1671_to_fp16)[name = string("op_1672_cast_fp16")]; + tensor var_1675 = const()[name = string("op_1675"), val = tensor([1, 16, 64, 80])]; + tensor var_1676_cast_fp16 = reshape(shape = var_1675, x = key_27_cast_fp16)[name = string("op_1676_cast_fp16")]; + bool mh_w_25_transpose_x_0 = const()[name = string("mh_w_25_transpose_x_0"), val = bool(true)]; + bool mh_w_25_transpose_y_0 = const()[name = string("mh_w_25_transpose_y_0"), val = bool(false)]; + tensor mh_w_25_cast_fp16 = matmul(transpose_x = mh_w_25_transpose_x_0, transpose_y = mh_w_25_transpose_y_0, x = var_1672_cast_fp16, y = var_1676_cast_fp16)[name = string("mh_w_25_cast_fp16")]; + tensor mh_w_27_cast_fp16 = add(x = mh_w_25_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_27_cast_fp16")]; + tensor var_1684_cast_fp16 = softmax(axis = var_332, x = mh_w_27_cast_fp16)[name = string("op_1684_cast_fp16")]; + tensor var_1685 = const()[name = string("op_1685"), val = tensor([1, 16, 64, 80])]; + tensor var_1686_cast_fp16 = reshape(shape = var_1685, x = value_13_cast_fp16)[name = string("op_1686_cast_fp16")]; + bool attn_13_transpose_x_0 = const()[name = string("attn_13_transpose_x_0"), val = bool(false)]; + bool attn_13_transpose_y_0 = const()[name = string("attn_13_transpose_y_0"), val = bool(true)]; + tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1686_cast_fp16, y = var_1684_cast_fp16)[name = string("attn_13_cast_fp16")]; + tensor var_1689 = const()[name = string("op_1689"), val = tensor([1, -1, 1, 1])]; + tensor input_89_cast_fp16 = reshape(shape = var_1689, x = attn_13_cast_fp16)[name = string("input_89_cast_fp16")]; + string obj_61_pad_type_0 = const()[name = string("obj_61_pad_type_0"), val = string("valid")]; + tensor obj_61_strides_0 = const()[name = string("obj_61_strides_0"), val = tensor([1, 1])]; + tensor obj_61_pad_0 = const()[name = string("obj_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_61_dilations_0 = const()[name = string("obj_61_dilations_0"), val = tensor([1, 1])]; + int32 obj_61_groups_0 = const()[name = string("obj_61_groups_0"), val = int32(1)]; + tensor op_1705_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35463744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35988096))))[name = string("op_1705_weight_0_to_fp16_palettized")]; + tensor var_1705_bias_0_to_fp16 = const()[name = string("op_1705_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35988672)))]; + tensor var_1705_cast_fp16 = conv(bias = var_1705_bias_0_to_fp16, dilations = obj_61_dilations_0, groups = obj_61_groups_0, pad = obj_61_pad_0, pad_type = obj_61_pad_type_0, strides = obj_61_strides_0, weight = op_1705_weight_0_to_fp16_palettized, x = input_89_cast_fp16)[name = string("op_1705_cast_fp16")]; + tensor inputs_27_cast_fp16 = add(x = inputs_25_cast_fp16, y = var_1705_cast_fp16)[name = string("inputs_27_cast_fp16")]; + tensor inputs_sq_27_cast_fp16 = mul(x = inputs_27_cast_fp16, y = inputs_27_cast_fp16)[name = string("inputs_sq_27_cast_fp16")]; + tensor variance_27_axes_0 = const()[name = string("variance_27_axes_0"), val = tensor([1])]; + bool variance_27_keep_dims_0 = const()[name = string("variance_27_keep_dims_0"), val = bool(true)]; + tensor variance_27_cast_fp16 = reduce_mean(axes = variance_27_axes_0, keep_dims = variance_27_keep_dims_0, x = inputs_sq_27_cast_fp16)[name = string("variance_27_cast_fp16")]; + fp16 var_1711_to_fp16 = const()[name = string("op_1711_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1712_cast_fp16 = add(x = variance_27_cast_fp16, y = var_1711_to_fp16)[name = string("op_1712_cast_fp16")]; + fp32 var_1713_epsilon_0 = const()[name = string("op_1713_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1713_cast_fp16 = rsqrt(epsilon = var_1713_epsilon_0, x = var_1712_cast_fp16)[name = string("op_1713_cast_fp16")]; + tensor hidden_states_39_cast_fp16 = mul(x = inputs_27_cast_fp16, y = var_1713_cast_fp16)[name = string("hidden_states_39_cast_fp16")]; + tensor w_27_to_fp16 = const()[name = string("w_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35989760)))]; + tensor input_91_cast_fp16 = mul(x = w_27_to_fp16, y = hidden_states_39_cast_fp16)[name = string("input_91_cast_fp16")]; + string input_93_pad_type_0 = const()[name = string("input_93_pad_type_0"), val = string("valid")]; + tensor input_93_strides_0 = const()[name = string("input_93_strides_0"), val = tensor([1, 1])]; + tensor input_93_pad_0 = const()[name = string("input_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_93_dilations_0 = const()[name = string("input_93_dilations_0"), val = tensor([1, 1])]; + int32 input_93_groups_0 = const()[name = string("input_93_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35990848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36515200))))[name = string("pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_93_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized, x = input_91_cast_fp16)[name = string("input_93_cast_fp16")]; + tensor gate_13_cast_fp16 = silu(x = input_93_cast_fp16)[name = string("gate_13_cast_fp16")]; + string up_13_pad_type_0 = const()[name = string("up_13_pad_type_0"), val = string("valid")]; + tensor up_13_strides_0 = const()[name = string("up_13_strides_0"), val = tensor([1, 1])]; + tensor up_13_pad_0 = const()[name = string("up_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_13_dilations_0 = const()[name = string("up_13_dilations_0"), val = tensor([1, 1])]; + int32 up_13_groups_0 = const()[name = string("up_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36515776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37040128))))[name = string("pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_13_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_13_dilations_0, groups = up_13_groups_0, pad = up_13_pad_0, pad_type = up_13_pad_type_0, strides = up_13_strides_0, weight = pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized, x = input_91_cast_fp16)[name = string("up_13_cast_fp16")]; + tensor input_95_cast_fp16 = mul(x = gate_13_cast_fp16, y = up_13_cast_fp16)[name = string("input_95_cast_fp16")]; + string hidden_states_41_pad_type_0 = const()[name = string("hidden_states_41_pad_type_0"), val = string("valid")]; + tensor hidden_states_41_strides_0 = const()[name = string("hidden_states_41_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_41_pad_0 = const()[name = string("hidden_states_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_41_dilations_0 = const()[name = string("hidden_states_41_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_41_groups_0 = const()[name = string("hidden_states_41_groups_0"), val = int32(1)]; + tensor op_1747_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37040704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37565056))))[name = string("op_1747_weight_0_to_fp16_palettized")]; + tensor var_1747_bias_0_to_fp16 = const()[name = string("op_1747_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37565632)))]; + tensor var_1747_cast_fp16 = conv(bias = var_1747_bias_0_to_fp16, dilations = hidden_states_41_dilations_0, groups = hidden_states_41_groups_0, pad = hidden_states_41_pad_0, pad_type = hidden_states_41_pad_type_0, strides = hidden_states_41_strides_0, weight = op_1747_weight_0_to_fp16_palettized, x = input_95_cast_fp16)[name = string("op_1747_cast_fp16")]; + tensor inputs_29_cast_fp16 = add(x = inputs_27_cast_fp16, y = var_1747_cast_fp16)[name = string("inputs_29_cast_fp16")]; + tensor inputs_sq_29_cast_fp16 = mul(x = inputs_29_cast_fp16, y = inputs_29_cast_fp16)[name = string("inputs_sq_29_cast_fp16")]; + tensor variance_29_axes_0 = const()[name = string("variance_29_axes_0"), val = tensor([1])]; + bool variance_29_keep_dims_0 = const()[name = string("variance_29_keep_dims_0"), val = bool(true)]; + tensor variance_29_cast_fp16 = reduce_mean(axes = variance_29_axes_0, keep_dims = variance_29_keep_dims_0, x = inputs_sq_29_cast_fp16)[name = string("variance_29_cast_fp16")]; + fp16 var_1763_to_fp16 = const()[name = string("op_1763_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1764_cast_fp16 = add(x = variance_29_cast_fp16, y = var_1763_to_fp16)[name = string("op_1764_cast_fp16")]; + fp32 var_1765_epsilon_0 = const()[name = string("op_1765_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1765_cast_fp16 = rsqrt(epsilon = var_1765_epsilon_0, x = var_1764_cast_fp16)[name = string("op_1765_cast_fp16")]; + tensor hidden_states_43_cast_fp16 = mul(x = inputs_29_cast_fp16, y = var_1765_cast_fp16)[name = string("hidden_states_43_cast_fp16")]; + tensor w_29_to_fp16 = const()[name = string("w_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37566720)))]; + tensor obj_63_cast_fp16 = mul(x = w_29_to_fp16, y = hidden_states_43_cast_fp16)[name = string("obj_63_cast_fp16")]; + string query_29_pad_type_0 = const()[name = string("query_29_pad_type_0"), val = string("valid")]; + tensor query_29_strides_0 = const()[name = string("query_29_strides_0"), val = tensor([1, 1])]; + tensor query_29_pad_0 = const()[name = string("query_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_29_dilations_0 = const()[name = string("query_29_dilations_0"), val = tensor([1, 1])]; + int32 query_29_groups_0 = const()[name = string("query_29_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37567808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38092160))))[name = string("pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_29_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_29_dilations_0, groups = query_29_groups_0, pad = query_29_pad_0, pad_type = query_29_pad_type_0, strides = query_29_strides_0, weight = pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized, x = obj_63_cast_fp16)[name = string("query_29_cast_fp16")]; + string key_29_pad_type_0 = const()[name = string("key_29_pad_type_0"), val = string("valid")]; + tensor key_29_strides_0 = const()[name = string("key_29_strides_0"), val = tensor([1, 1])]; + tensor key_29_pad_0 = const()[name = string("key_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_29_dilations_0 = const()[name = string("key_29_dilations_0"), val = tensor([1, 1])]; + int32 key_29_groups_0 = const()[name = string("key_29_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38092736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38617088))))[name = string("pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_29_cast_fp16 = conv(dilations = key_29_dilations_0, groups = key_29_groups_0, pad = key_29_pad_0, pad_type = key_29_pad_type_0, strides = key_29_strides_0, weight = pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized, x = obj_63_cast_fp16)[name = string("key_29_cast_fp16")]; + string current_value_pad_type_0 = const()[name = string("current_value_pad_type_0"), val = string("valid")]; + tensor current_value_strides_0 = const()[name = string("current_value_strides_0"), val = tensor([1, 1])]; + tensor current_value_pad_0 = const()[name = string("current_value_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor current_value_dilations_0 = const()[name = string("current_value_dilations_0"), val = tensor([1, 1])]; + int32 current_value_groups_0 = const()[name = string("current_value_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38617664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39142016))))[name = string("pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor current_value_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = current_value_dilations_0, groups = current_value_groups_0, pad = current_value_pad_0, pad_type = current_value_pad_type_0, strides = current_value_strides_0, weight = pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized, x = obj_63_cast_fp16)[name = string("current_value_cast_fp16")]; + tensor var_1803 = const()[name = string("op_1803"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_43_cast_fp16 = reshape(shape = var_1803, x = query_29_cast_fp16)[name = string("mh_q_43_cast_fp16")]; + tensor var_1805 = const()[name = string("op_1805"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_29_cast_fp16 = reshape(shape = var_1805, x = key_29_cast_fp16)[name = string("mh_k_29_cast_fp16")]; + tensor var_1809_cast_fp16 = mul(x = mh_q_43_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1809_cast_fp16")]; + tensor var_1814_begin_0 = const()[name = string("op_1814_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1814_end_0 = const()[name = string("op_1814_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1814_end_mask_0 = const()[name = string("op_1814_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1814_cast_fp16 = slice_by_index(begin = var_1814_begin_0, end = var_1814_end_0, end_mask = var_1814_end_mask_0, x = mh_q_43_cast_fp16)[name = string("op_1814_cast_fp16")]; + tensor var_1820_begin_0 = const()[name = string("op_1820_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1820_end_0 = const()[name = string("op_1820_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1820_end_mask_0 = const()[name = string("op_1820_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1820_cast_fp16 = slice_by_index(begin = var_1820_begin_0, end = var_1820_end_0, end_mask = var_1820_end_mask_0, x = mh_q_43_cast_fp16)[name = string("op_1820_cast_fp16")]; + fp16 const_164_promoted_to_fp16 = const()[name = string("const_164_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1822_cast_fp16 = mul(x = var_1820_cast_fp16, y = const_164_promoted_to_fp16)[name = string("op_1822_cast_fp16")]; + bool var_1824_interleave_0 = const()[name = string("op_1824_interleave_0"), val = bool(false)]; + tensor var_1824_cast_fp16 = concat(axis = var_327, interleave = var_1824_interleave_0, values = (var_1822_cast_fp16, var_1814_cast_fp16))[name = string("op_1824_cast_fp16")]; + tensor var_1825_cast_fp16 = mul(x = var_1824_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1825_cast_fp16")]; + tensor mh_q_45_cast_fp16 = add(x = var_1809_cast_fp16, y = var_1825_cast_fp16)[name = string("mh_q_45_cast_fp16")]; + tensor var_1827_cast_fp16 = mul(x = mh_k_29_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1827_cast_fp16")]; + tensor var_1832_begin_0 = const()[name = string("op_1832_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1832_end_0 = const()[name = string("op_1832_end_0"), val = tensor([1, 16, 32, 1])]; + tensor var_1832_end_mask_0 = const()[name = string("op_1832_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1832_cast_fp16 = slice_by_index(begin = var_1832_begin_0, end = var_1832_end_0, end_mask = var_1832_end_mask_0, x = mh_k_29_cast_fp16)[name = string("op_1832_cast_fp16")]; + tensor var_1838_begin_0 = const()[name = string("op_1838_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1838_end_0 = const()[name = string("op_1838_end_0"), val = tensor([1, 16, 64, 1])]; + tensor var_1838_end_mask_0 = const()[name = string("op_1838_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1838_cast_fp16 = slice_by_index(begin = var_1838_begin_0, end = var_1838_end_0, end_mask = var_1838_end_mask_0, x = mh_k_29_cast_fp16)[name = string("op_1838_cast_fp16")]; + fp16 const_167_promoted_to_fp16 = const()[name = string("const_167_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1840_cast_fp16 = mul(x = var_1838_cast_fp16, y = const_167_promoted_to_fp16)[name = string("op_1840_cast_fp16")]; + bool var_1842_interleave_0 = const()[name = string("op_1842_interleave_0"), val = bool(false)]; + tensor var_1842_cast_fp16 = concat(axis = var_327, interleave = var_1842_interleave_0, values = (var_1840_cast_fp16, var_1832_cast_fp16))[name = string("op_1842_cast_fp16")]; + tensor var_1843_cast_fp16 = mul(x = var_1842_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1843_cast_fp16")]; + tensor mh_k_cast_fp16 = add(x = var_1827_cast_fp16, y = var_1843_cast_fp16)[name = string("mh_k_cast_fp16")]; + tensor var_1847 = const()[name = string("op_1847"), val = tensor([1, 1024, 1, 1])]; + tensor current_key_cast_fp16 = reshape(shape = var_1847, x = mh_k_cast_fp16)[name = string("current_key_cast_fp16")]; + tensor var_1854_cast_fp16 = mul(x = var_361_cast_fp16_7, y = var_495_cast_fp16)[name = string("op_1854_cast_fp16")]; + tensor var_1855_cast_fp16 = mul(x = current_key_cast_fp16, y = var_493_cast_fp16)[name = string("op_1855_cast_fp16")]; + tensor key_cast_fp16 = add(x = var_1854_cast_fp16, y = var_1855_cast_fp16)[name = string("key_cast_fp16")]; + tensor var_1858_cast_fp16 = mul(x = var_370_cast_fp16_7, y = var_495_cast_fp16)[name = string("op_1858_cast_fp16")]; + tensor var_1859_cast_fp16 = mul(x = current_value_cast_fp16, y = var_493_cast_fp16)[name = string("op_1859_cast_fp16")]; + tensor value_cast_fp16 = add(x = var_1858_cast_fp16, y = var_1859_cast_fp16)[name = string("value_cast_fp16")]; + fp16 var_1865_to_fp16 = const()[name = string("op_1865_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1866_cast_fp16 = mul(x = mh_q_45_cast_fp16, y = var_1865_to_fp16)[name = string("op_1866_cast_fp16")]; + tensor var_1869 = const()[name = string("op_1869"), val = tensor([1, 16, 64, 80])]; + tensor var_1870_cast_fp16 = reshape(shape = var_1869, x = key_cast_fp16)[name = string("op_1870_cast_fp16")]; + bool mh_w_29_transpose_x_0 = const()[name = string("mh_w_29_transpose_x_0"), val = bool(true)]; + bool mh_w_29_transpose_y_0 = const()[name = string("mh_w_29_transpose_y_0"), val = bool(false)]; + tensor mh_w_29_cast_fp16 = matmul(transpose_x = mh_w_29_transpose_x_0, transpose_y = mh_w_29_transpose_y_0, x = var_1866_cast_fp16, y = var_1870_cast_fp16)[name = string("mh_w_29_cast_fp16")]; + tensor mh_w_cast_fp16 = add(x = mh_w_29_cast_fp16, y = var_517_cast_fp16)[name = string("mh_w_cast_fp16")]; + tensor var_1878_cast_fp16 = softmax(axis = var_332, x = mh_w_cast_fp16)[name = string("op_1878_cast_fp16")]; + tensor var_1879 = const()[name = string("op_1879"), val = tensor([1, 16, 64, 80])]; + tensor var_1880_cast_fp16 = reshape(shape = var_1879, x = value_cast_fp16)[name = string("op_1880_cast_fp16")]; + bool attn_transpose_x_0 = const()[name = string("attn_transpose_x_0"), val = bool(false)]; + bool attn_transpose_y_0 = const()[name = string("attn_transpose_y_0"), val = bool(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_1880_cast_fp16, y = var_1878_cast_fp16)[name = string("attn_cast_fp16")]; + tensor var_1883 = const()[name = string("op_1883"), val = tensor([1, -1, 1, 1])]; + tensor input_97_cast_fp16 = reshape(shape = var_1883, x = attn_cast_fp16)[name = string("input_97_cast_fp16")]; + string obj_pad_type_0 = const()[name = string("obj_pad_type_0"), val = string("valid")]; + tensor obj_strides_0 = const()[name = string("obj_strides_0"), val = tensor([1, 1])]; + tensor obj_pad_0 = const()[name = string("obj_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_dilations_0 = const()[name = string("obj_dilations_0"), val = tensor([1, 1])]; + int32 obj_groups_0 = const()[name = string("obj_groups_0"), val = int32(1)]; + tensor op_1899_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39142592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39666944))))[name = string("op_1899_weight_0_to_fp16_palettized")]; + tensor var_1899_bias_0_to_fp16 = const()[name = string("op_1899_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39667520)))]; + tensor var_1899_cast_fp16 = conv(bias = var_1899_bias_0_to_fp16, dilations = obj_dilations_0, groups = obj_groups_0, pad = obj_pad_0, pad_type = obj_pad_type_0, strides = obj_strides_0, weight = op_1899_weight_0_to_fp16_palettized, x = input_97_cast_fp16)[name = string("op_1899_cast_fp16")]; + tensor inputs_31_cast_fp16 = add(x = inputs_29_cast_fp16, y = var_1899_cast_fp16)[name = string("inputs_31_cast_fp16")]; + tensor inputs_sq_31_cast_fp16 = mul(x = inputs_31_cast_fp16, y = inputs_31_cast_fp16)[name = string("inputs_sq_31_cast_fp16")]; + tensor variance_31_axes_0 = const()[name = string("variance_31_axes_0"), val = tensor([1])]; + bool variance_31_keep_dims_0 = const()[name = string("variance_31_keep_dims_0"), val = bool(true)]; + tensor variance_31_cast_fp16 = reduce_mean(axes = variance_31_axes_0, keep_dims = variance_31_keep_dims_0, x = inputs_sq_31_cast_fp16)[name = string("variance_31_cast_fp16")]; + fp16 var_1905_to_fp16 = const()[name = string("op_1905_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1906_cast_fp16 = add(x = variance_31_cast_fp16, y = var_1905_to_fp16)[name = string("op_1906_cast_fp16")]; + fp32 var_1907_epsilon_0 = const()[name = string("op_1907_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1907_cast_fp16 = rsqrt(epsilon = var_1907_epsilon_0, x = var_1906_cast_fp16)[name = string("op_1907_cast_fp16")]; + tensor hidden_states_45_cast_fp16 = mul(x = inputs_31_cast_fp16, y = var_1907_cast_fp16)[name = string("hidden_states_45_cast_fp16")]; + tensor w_31_to_fp16 = const()[name = string("w_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39668608)))]; + tensor input_99_cast_fp16 = mul(x = w_31_to_fp16, y = hidden_states_45_cast_fp16)[name = string("input_99_cast_fp16")]; + string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("valid")]; + tensor input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor([1, 1])]; + tensor input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor([1, 1])]; + int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39669696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40194048))))[name = string("pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_101_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("input_101_cast_fp16")]; + tensor gate_cast_fp16 = silu(x = input_101_cast_fp16)[name = string("gate_cast_fp16")]; + string up_pad_type_0 = const()[name = string("up_pad_type_0"), val = string("valid")]; + tensor up_strides_0 = const()[name = string("up_strides_0"), val = tensor([1, 1])]; + tensor up_pad_0 = const()[name = string("up_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_dilations_0 = const()[name = string("up_dilations_0"), val = tensor([1, 1])]; + int32 up_groups_0 = const()[name = string("up_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40194624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40718976))))[name = string("pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_dilations_0, groups = up_groups_0, pad = up_pad_0, pad_type = up_pad_type_0, strides = up_strides_0, weight = pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("up_cast_fp16")]; + tensor input_103_cast_fp16 = mul(x = gate_cast_fp16, y = up_cast_fp16)[name = string("input_103_cast_fp16")]; + string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")]; + tensor hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)]; + tensor op_1941_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40719552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41243904))))[name = string("op_1941_weight_0_to_fp16_palettized")]; + tensor var_1941_bias_0_to_fp16 = const()[name = string("op_1941_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41244480)))]; + tensor var_1941_cast_fp16 = conv(bias = var_1941_bias_0_to_fp16, dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = op_1941_weight_0_to_fp16_palettized, x = input_103_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = inputs_31_cast_fp16, y = var_1941_cast_fp16)[name = string("inputs_cast_fp16")]; + tensor inputs_sq_cast_fp16 = mul(x = inputs_cast_fp16, y = inputs_cast_fp16)[name = string("inputs_sq_cast_fp16")]; + tensor variance_axes_0 = const()[name = string("variance_axes_0"), val = tensor([1])]; + bool variance_keep_dims_0 = const()[name = string("variance_keep_dims_0"), val = bool(true)]; + tensor variance_cast_fp16 = reduce_mean(axes = variance_axes_0, keep_dims = variance_keep_dims_0, x = inputs_sq_cast_fp16)[name = string("variance_cast_fp16")]; + fp16 var_1951_to_fp16 = const()[name = string("op_1951_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1952_cast_fp16 = add(x = variance_cast_fp16, y = var_1951_to_fp16)[name = string("op_1952_cast_fp16")]; + fp32 var_1953_epsilon_0 = const()[name = string("op_1953_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1953_cast_fp16 = rsqrt(epsilon = var_1953_epsilon_0, x = var_1952_cast_fp16)[name = string("op_1953_cast_fp16")]; + tensor hidden_states_49_cast_fp16 = mul(x = inputs_cast_fp16, y = var_1953_cast_fp16)[name = string("hidden_states_49_cast_fp16")]; + tensor w_to_fp16 = const()[name = string("w_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41245568)))]; + tensor input_105_cast_fp16 = mul(x = w_to_fp16, y = hidden_states_49_cast_fp16)[name = string("input_105_cast_fp16")]; + string new_hiddens_pad_type_0 = const()[name = string("new_hiddens_pad_type_0"), val = string("valid")]; + tensor new_hiddens_strides_0 = const()[name = string("new_hiddens_strides_0"), val = tensor([1, 1])]; + tensor new_hiddens_pad_0 = const()[name = string("new_hiddens_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor new_hiddens_dilations_0 = const()[name = string("new_hiddens_dilations_0"), val = tensor([1, 1])]; + int32 new_hiddens_groups_0 = const()[name = string("new_hiddens_groups_0"), val = int32(1)]; + tensor pre_transformer_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41246656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41771008))))[name = string("pre_transformer_output_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_output_proj_bias_to_fp16 = const()[name = string("pre_transformer_output_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41771584)))]; + tensor hidden_context_update = conv(bias = pre_transformer_output_proj_bias_to_fp16, dilations = new_hiddens_dilations_0, groups = new_hiddens_groups_0, pad = new_hiddens_pad_0, pad_type = new_hiddens_pad_type_0, strides = new_hiddens_strides_0, weight = pre_transformer_output_proj_weight_to_fp16_palettized, x = input_105_cast_fp16)[name = string("new_hiddens_cast_fp16")]; + bool var_1966_interleave_0 = const()[name = string("op_1966_interleave_0"), val = bool(false)]; + tensor key_cache_updates = concat(axis = var_338, interleave = var_1966_interleave_0, values = (current_key_1_cast_fp16, current_key_3_cast_fp16, current_key_5_cast_fp16, current_key_7_cast_fp16, current_key_9_cast_fp16, current_key_11_cast_fp16, current_key_13_cast_fp16, current_key_cast_fp16))[name = string("op_1966_cast_fp16")]; + bool var_1968_interleave_0 = const()[name = string("op_1968_interleave_0"), val = bool(false)]; + tensor value_cache_updates = concat(axis = var_338, interleave = var_1968_interleave_0, values = (current_value_1_cast_fp16, current_value_3_cast_fp16, current_value_5_cast_fp16, current_value_7_cast_fp16, current_value_9_cast_fp16, current_value_11_cast_fp16, current_value_13_cast_fp16, current_value_cast_fp16))[name = string("op_1968_cast_fp16")]; + int32 var_1974 = const()[name = string("op_1974"), val = int32(-1)]; + bool hidden_states_51_interleave_0 = const()[name = string("hidden_states_51_interleave_0"), val = bool(false)]; + tensor hidden_states_51_cast_fp16 = concat(axis = var_1974, interleave = hidden_states_51_interleave_0, values = (hidden_context, hidden_context_update))[name = string("hidden_states_51_cast_fp16")]; + int32 var_1991 = const()[name = string("op_1991"), val = int32(-1)]; + string sub_pixels_1_pad_type_0 = const()[name = string("sub_pixels_1_pad_type_0"), val = string("valid")]; + tensor sub_pixels_1_strides_0 = const()[name = string("sub_pixels_1_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_1_pad_0 = const()[name = string("sub_pixels_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_1_dilations_0 = const()[name = string("sub_pixels_1_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_1_groups_0 = const()[name = string("sub_pixels_1_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41773696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43870912))))[name = string("audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43871488)))]; + tensor sub_pixels_1_cast_fp16 = conv(bias = audio_upsampler_upsample_0_0_conv_bias_to_fp16, dilations = sub_pixels_1_dilations_0, groups = sub_pixels_1_groups_0, pad = sub_pixels_1_pad_0, pad_type = sub_pixels_1_pad_type_0, strides = sub_pixels_1_strides_0, weight = audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized, x = hidden_states_51_cast_fp16)[name = string("sub_pixels_1_cast_fp16")]; + tensor var_2042 = const()[name = string("op_2042"), val = tensor([1, 2, 1024, 9])]; + tensor sub_pixels_3_cast_fp16 = reshape(shape = var_2042, x = sub_pixels_1_cast_fp16)[name = string("sub_pixels_3_cast_fp16")]; + tensor var_2044 = const()[name = string("op_2044"), val = tensor([0, 2, 3, 1])]; + tensor var_2049 = const()[name = string("op_2049"), val = tensor([1, 1024, 1, 18])]; + tensor sub_pixels_5_cast_fp16 = transpose(perm = var_2044, x = sub_pixels_3_cast_fp16)[name = string("transpose_9")]; + tensor hidden_states_53_cast_fp16 = reshape(shape = var_2049, x = sub_pixels_5_cast_fp16)[name = string("hidden_states_53_cast_fp16")]; + tensor var_2054 = const()[name = string("op_2054"), val = tensor([1, 1, 8, 1])]; + tensor var_2055_cast_fp16 = reshape(shape = var_2054, x = hidden_context_mask)[name = string("op_2055_cast_fp16")]; + tensor spread_1_reps_0 = const()[name = string("spread_1_reps_0"), val = tensor([1, 1, 1, 2])]; + tensor spread_1_cast_fp16 = tile(reps = spread_1_reps_0, x = var_2055_cast_fp16)[name = string("spread_1_cast_fp16")]; + tensor var_2061 = const()[name = string("op_2061"), val = tensor([1, 1, 1, 16])]; + tensor context_mask_1_cast_fp16 = reshape(shape = var_2061, x = spread_1_cast_fp16)[name = string("context_mask_1_cast_fp16")]; + bool full_mask_1_interleave_0 = const()[name = string("full_mask_1_interleave_0"), val = bool(false)]; + tensor fill_0_to_fp16 = const()[name = string("fill_0_to_fp16"), val = tensor([[[[0x1p+0, 0x1p+0]]]])]; + tensor full_mask_1_cast_fp16 = concat(axis = var_1991, interleave = full_mask_1_interleave_0, values = (context_mask_1_cast_fp16, fill_0_to_fp16))[name = string("full_mask_1_cast_fp16")]; + tensor hidden_states_55_cast_fp16 = mul(x = hidden_states_53_cast_fp16, y = full_mask_1_cast_fp16)[name = string("hidden_states_55_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 6, 0])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("constant")]; + fp16 const_177_to_fp16 = const()[name = string("const_177_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_177_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = hidden_states_55_cast_fp16)[name = string("input_107_cast_fp16")]; + string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")]; + int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1024)]; + tensor hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor([1, 1])]; + tensor audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43875648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43882880))))[name = string("audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43883456)))]; + tensor hidden_states_57_cast_fp16 = conv(bias = audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16, dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("hidden_states_57_cast_fp16")]; + tensor var_2089_axes_0 = const()[name = string("op_2089_axes_0"), val = tensor([2])]; + tensor var_2089_cast_fp16 = squeeze(axes = var_2089_axes_0, x = hidden_states_57_cast_fp16)[name = string("op_2089_cast_fp16")]; + tensor var_2090 = const()[name = string("op_2090"), val = tensor([0, 2, 1])]; + tensor hidden_states_59_axes_0 = const()[name = string("hidden_states_59_axes_0"), val = tensor([-1])]; + tensor audio_upsampler_upsample_0_1_norm_weight_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43885568)))]; + tensor audio_upsampler_upsample_0_1_norm_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43887680)))]; + fp16 var_1995_to_fp16 = const()[name = string("op_1995_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_109_cast_fp16 = transpose(perm = var_2090, x = var_2089_cast_fp16)[name = string("transpose_8")]; + tensor hidden_states_59_cast_fp16 = layer_norm(axes = hidden_states_59_axes_0, beta = audio_upsampler_upsample_0_1_norm_bias_to_fp16, epsilon = var_1995_to_fp16, gamma = audio_upsampler_upsample_0_1_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("hidden_states_59_cast_fp16")]; + tensor var_2096 = const()[name = string("op_2096"), val = tensor([0, 2, 1])]; + tensor input_111_axes_0 = const()[name = string("input_111_axes_0"), val = tensor([2])]; + tensor var_2097_cast_fp16 = transpose(perm = var_2096, x = hidden_states_59_cast_fp16)[name = string("transpose_7")]; + tensor input_111_cast_fp16 = expand_dims(axes = input_111_axes_0, x = var_2097_cast_fp16)[name = string("input_111_cast_fp16")]; + string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("valid")]; + tensor input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor([1, 1])]; + tensor input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor([1, 1])]; + int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43889792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48084160))))[name = string("audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48084736)))]; + tensor input_113_cast_fp16 = conv(bias = audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = string("input_113_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = input_113_cast_fp16)[name = string("input_115_cast_fp16")]; + string hidden_states_61_pad_type_0 = const()[name = string("hidden_states_61_pad_type_0"), val = string("valid")]; + tensor hidden_states_61_strides_0 = const()[name = string("hidden_states_61_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_61_pad_0 = const()[name = string("hidden_states_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_61_dilations_0 = const()[name = string("hidden_states_61_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_61_groups_0 = const()[name = string("hidden_states_61_groups_0"), val = int32(1)]; + tensor hidden_states_63_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48092992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52287360))))[name = string("hidden_states_63_weight_0_to_fp16_palettized")]; + tensor hidden_states_63_bias_0_to_fp16 = const()[name = string("hidden_states_63_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52287936)))]; + tensor hidden_states_63_cast_fp16 = conv(bias = hidden_states_63_bias_0_to_fp16, dilations = hidden_states_61_dilations_0, groups = hidden_states_61_groups_0, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = hidden_states_61_strides_0, weight = hidden_states_63_weight_0_to_fp16_palettized, x = input_115_cast_fp16)[name = string("hidden_states_63_cast_fp16")]; + tensor hidden_states_65_cast_fp16 = add(x = hidden_states_53_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")]; + tensor input_117_begin_0 = const()[name = string("input_117_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor input_117_end_0 = const()[name = string("input_117_end_0"), val = tensor([1, 1024, 1, 18])]; + tensor input_117_end_mask_0 = const()[name = string("input_117_end_mask_0"), val = tensor([true, true, true, true])]; + tensor input_117_cast_fp16 = slice_by_index(begin = input_117_begin_0, end = input_117_end_0, end_mask = input_117_end_mask_0, x = hidden_states_65_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor context_mask_3_begin_0 = const()[name = string("context_mask_3_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor context_mask_3_end_0 = const()[name = string("context_mask_3_end_0"), val = tensor([1, 1, 1, 16])]; + tensor context_mask_3_end_mask_0 = const()[name = string("context_mask_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_3_cast_fp16 = slice_by_index(begin = context_mask_3_begin_0, end = context_mask_3_end_0, end_mask = context_mask_3_end_mask_0, x = context_mask_1_cast_fp16)[name = string("context_mask_3_cast_fp16")]; + string sub_pixels_7_pad_type_0 = const()[name = string("sub_pixels_7_pad_type_0"), val = string("valid")]; + tensor sub_pixels_7_strides_0 = const()[name = string("sub_pixels_7_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_7_pad_0 = const()[name = string("sub_pixels_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_7_dilations_0 = const()[name = string("sub_pixels_7_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_7_groups_0 = const()[name = string("sub_pixels_7_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52290048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54387264))))[name = string("audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54387840)))]; + tensor sub_pixels_7_cast_fp16 = conv(bias = audio_upsampler_upsample_1_0_conv_bias_to_fp16, dilations = sub_pixels_7_dilations_0, groups = sub_pixels_7_groups_0, pad = sub_pixels_7_pad_0, pad_type = sub_pixels_7_pad_type_0, strides = sub_pixels_7_strides_0, weight = audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized, x = input_117_cast_fp16)[name = string("sub_pixels_7_cast_fp16")]; + tensor var_2137 = const()[name = string("op_2137"), val = tensor([1, 2, 1024, 15])]; + tensor sub_pixels_9_cast_fp16 = reshape(shape = var_2137, x = sub_pixels_7_cast_fp16)[name = string("sub_pixels_9_cast_fp16")]; + tensor var_2139 = const()[name = string("op_2139"), val = tensor([0, 2, 3, 1])]; + tensor var_2144 = const()[name = string("op_2144"), val = tensor([1, 1024, 1, 30])]; + tensor sub_pixels_11_cast_fp16 = transpose(perm = var_2139, x = sub_pixels_9_cast_fp16)[name = string("transpose_6")]; + tensor hidden_states_67_cast_fp16 = reshape(shape = var_2144, x = sub_pixels_11_cast_fp16)[name = string("hidden_states_67_cast_fp16")]; + tensor var_2149 = const()[name = string("op_2149"), val = tensor([1, 1, 13, 1])]; + tensor var_2150_cast_fp16 = reshape(shape = var_2149, x = context_mask_3_cast_fp16)[name = string("op_2150_cast_fp16")]; + tensor spread_3_reps_0 = const()[name = string("spread_3_reps_0"), val = tensor([1, 1, 1, 2])]; + tensor spread_3_cast_fp16 = tile(reps = spread_3_reps_0, x = var_2150_cast_fp16)[name = string("spread_3_cast_fp16")]; + tensor var_2156 = const()[name = string("op_2156"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_5_cast_fp16 = reshape(shape = var_2156, x = spread_3_cast_fp16)[name = string("context_mask_5_cast_fp16")]; + tensor residual_1_begin_0 = const()[name = string("residual_1_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_1_end_0 = const()[name = string("residual_1_end_0"), val = tensor([1, 1024, 1, 30])]; + tensor residual_1_end_mask_0 = const()[name = string("residual_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_1_cast_fp16 = slice_by_index(begin = residual_1_begin_0, end = residual_1_end_0, end_mask = residual_1_end_mask_0, x = hidden_states_67_cast_fp16)[name = string("residual_1_cast_fp16")]; + bool full_mask_3_interleave_0 = const()[name = string("full_mask_3_interleave_0"), val = bool(false)]; + tensor fill_1_to_fp16 = const()[name = string("fill_1_to_fp16"), val = tensor([[[[0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0]]]])]; + tensor full_mask_3_cast_fp16 = concat(axis = var_1991, interleave = full_mask_3_interleave_0, values = (context_mask_5_cast_fp16, fill_1_to_fp16))[name = string("full_mask_3_cast_fp16")]; + tensor input_119_cast_fp16 = mul(x = hidden_states_67_cast_fp16, y = full_mask_3_cast_fp16)[name = string("input_119_cast_fp16")]; + string hidden_states_69_pad_type_0 = const()[name = string("hidden_states_69_pad_type_0"), val = string("valid")]; + int32 hidden_states_69_groups_0 = const()[name = string("hidden_states_69_groups_0"), val = int32(1024)]; + tensor hidden_states_69_strides_0 = const()[name = string("hidden_states_69_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_69_pad_0 = const()[name = string("hidden_states_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_69_dilations_0 = const()[name = string("hidden_states_69_dilations_0"), val = tensor([1, 1])]; + tensor audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54392000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54399232))))[name = string("audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54399808)))]; + tensor hidden_states_69_cast_fp16 = conv(bias = audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16, dilations = hidden_states_69_dilations_0, groups = hidden_states_69_groups_0, pad = hidden_states_69_pad_0, pad_type = hidden_states_69_pad_type_0, strides = hidden_states_69_strides_0, weight = audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("hidden_states_69_cast_fp16")]; + tensor var_2183_axes_0 = const()[name = string("op_2183_axes_0"), val = tensor([2])]; + tensor var_2183_cast_fp16 = squeeze(axes = var_2183_axes_0, x = hidden_states_69_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor var_2184 = const()[name = string("op_2184"), val = tensor([0, 2, 1])]; + tensor hidden_states_71_axes_0 = const()[name = string("hidden_states_71_axes_0"), val = tensor([-1])]; + tensor audio_upsampler_upsample_1_1_norm_weight_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54401920)))]; + tensor audio_upsampler_upsample_1_1_norm_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54404032)))]; + tensor input_121_cast_fp16 = transpose(perm = var_2184, x = var_2183_cast_fp16)[name = string("transpose_5")]; + tensor hidden_states_71_cast_fp16 = layer_norm(axes = hidden_states_71_axes_0, beta = audio_upsampler_upsample_1_1_norm_bias_to_fp16, epsilon = var_1995_to_fp16, gamma = audio_upsampler_upsample_1_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("hidden_states_71_cast_fp16")]; + tensor var_2190 = const()[name = string("op_2190"), val = tensor([0, 2, 1])]; + tensor input_123_axes_0 = const()[name = string("input_123_axes_0"), val = tensor([2])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2190, x = hidden_states_71_cast_fp16)[name = string("transpose_4")]; + tensor input_123_cast_fp16 = expand_dims(axes = input_123_axes_0, x = var_2191_cast_fp16)[name = string("input_123_cast_fp16")]; + string input_125_pad_type_0 = const()[name = string("input_125_pad_type_0"), val = string("valid")]; + tensor input_125_strides_0 = const()[name = string("input_125_strides_0"), val = tensor([1, 1])]; + tensor input_125_pad_0 = const()[name = string("input_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_125_dilations_0 = const()[name = string("input_125_dilations_0"), val = tensor([1, 1])]; + int32 input_125_groups_0 = const()[name = string("input_125_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54406144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58600512))))[name = string("audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58601088)))]; + tensor input_125_cast_fp16 = conv(bias = audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("input_125_cast_fp16")]; + string input_127_mode_0 = const()[name = string("input_127_mode_0"), val = string("EXACT")]; + tensor input_127_cast_fp16 = gelu(mode = input_127_mode_0, x = input_125_cast_fp16)[name = string("input_127_cast_fp16")]; + string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")]; + tensor hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)]; + tensor hidden_states_75_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58609344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62803712))))[name = string("hidden_states_75_weight_0_to_fp16_palettized")]; + tensor hidden_states_75_bias_0_to_fp16 = const()[name = string("hidden_states_75_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62804288)))]; + tensor hidden_states_75_cast_fp16 = conv(bias = hidden_states_75_bias_0_to_fp16, dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = hidden_states_75_weight_0_to_fp16_palettized, x = input_127_cast_fp16)[name = string("hidden_states_75_cast_fp16")]; + tensor hidden_states_77_cast_fp16 = add(x = residual_1_cast_fp16, y = hidden_states_75_cast_fp16)[name = string("hidden_states_77_cast_fp16")]; + tensor context_mask_7_begin_0 = const()[name = string("context_mask_7_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_7_end_0 = const()[name = string("context_mask_7_end_0"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_7_end_mask_0 = const()[name = string("context_mask_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_7_cast_fp16 = slice_by_index(begin = context_mask_7_begin_0, end = context_mask_7_end_0, end_mask = context_mask_7_end_mask_0, x = context_mask_5_cast_fp16)[name = string("context_mask_7_cast_fp16")]; + bool full_mask_5_interleave_0 = const()[name = string("full_mask_5_interleave_0"), val = bool(false)]; + tensor fill_2_to_fp16 = const()[name = string("fill_2_to_fp16"), val = tensor([[[[0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0]]]])]; + tensor full_mask_5_cast_fp16 = concat(axis = var_1991, interleave = full_mask_5_interleave_0, values = (context_mask_7_cast_fp16, fill_2_to_fp16))[name = string("full_mask_5_cast_fp16")]; + tensor input_129_cast_fp16 = mul(x = hidden_states_77_cast_fp16, y = full_mask_5_cast_fp16)[name = string("input_129_cast_fp16")]; + string hidden_states_79_pad_type_0 = const()[name = string("hidden_states_79_pad_type_0"), val = string("valid")]; + tensor hidden_states_79_strides_0 = const()[name = string("hidden_states_79_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_79_pad_0 = const()[name = string("hidden_states_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_79_dilations_0 = const()[name = string("hidden_states_79_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_79_groups_0 = const()[name = string("hidden_states_79_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62806400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73816512))))[name = string("audio_upsampler_decoder_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73817088)))]; + tensor hidden_states_79_cast_fp16 = conv(bias = audio_upsampler_decoder_0_conv_bias_to_fp16, dilations = hidden_states_79_dilations_0, groups = hidden_states_79_groups_0, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = hidden_states_79_strides_0, weight = audio_upsampler_decoder_0_conv_weight_to_fp16_palettized, x = input_129_cast_fp16)[name = string("hidden_states_79_cast_fp16")]; + tensor context_mask_9_begin_0 = const()[name = string("context_mask_9_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_9_end_0 = const()[name = string("context_mask_9_end_0"), val = tensor([1, 1, 1, 20])]; + tensor context_mask_9_end_mask_0 = const()[name = string("context_mask_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_9_cast_fp16 = slice_by_index(begin = context_mask_9_begin_0, end = context_mask_9_end_0, end_mask = context_mask_9_end_mask_0, x = context_mask_7_cast_fp16)[name = string("context_mask_9_cast_fp16")]; + tensor alpha_over_pi_1_to_fp16 = const()[name = string("alpha_over_pi_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73820224)))]; + tensor theta_over_pi_1_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = alpha_over_pi_1_to_fp16)[name = string("theta_over_pi_1_cast_fp16")]; + tensor var_2259_cast_fp16 = round(x = theta_over_pi_1_cast_fp16)[name = string("op_2259_cast_fp16")]; + tensor reduced_1_cast_fp16 = sub(x = theta_over_pi_1_cast_fp16, y = var_2259_cast_fp16)[name = string("reduced_1_cast_fp16")]; + tensor reduced_sq_1_cast_fp16 = mul(x = reduced_1_cast_fp16, y = reduced_1_cast_fp16)[name = string("reduced_sq_1_cast_fp16")]; + tensor acc_1_mean_0_to_fp16 = const()[name = string("acc_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73823360)))]; + tensor acc_1_variance_0_to_fp16 = const()[name = string("acc_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73826496)))]; + tensor acc_1_gamma_0_to_fp16 = const()[name = string("acc_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73829632)))]; + tensor acc_1_beta_0_to_fp16 = const()[name = string("acc_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73832768)))]; + fp16 acc_1_epsilon_0_to_fp16 = const()[name = string("acc_1_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_1_cast_fp16 = batch_norm(beta = acc_1_beta_0_to_fp16, epsilon = acc_1_epsilon_0_to_fp16, gamma = acc_1_gamma_0_to_fp16, mean = acc_1_mean_0_to_fp16, variance = acc_1_variance_0_to_fp16, x = reduced_sq_1_cast_fp16)[name = string("acc_1_cast_fp16")]; + tensor var_2272_cast_fp16 = mul(x = acc_1_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2272_cast_fp16")]; + tensor c_1_to_fp16 = const()[name = string("c_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73835904)))]; + tensor acc_3_cast_fp16 = add(x = var_2272_cast_fp16, y = c_1_to_fp16)[name = string("acc_3_cast_fp16")]; + tensor var_2274_cast_fp16 = mul(x = acc_3_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2274_cast_fp16")]; + tensor c_3_to_fp16 = const()[name = string("c_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73839040)))]; + tensor acc_5_cast_fp16 = add(x = var_2274_cast_fp16, y = c_3_to_fp16)[name = string("acc_5_cast_fp16")]; + tensor var_2276_cast_fp16 = mul(x = acc_5_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2276_cast_fp16")]; + tensor hidden_states_81_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = var_2276_cast_fp16)[name = string("hidden_states_81_cast_fp16")]; + bool full_mask_7_interleave_0 = const()[name = string("full_mask_7_interleave_0"), val = bool(false)]; + tensor fill_3_to_fp16 = const()[name = string("fill_3_to_fp16"), val = tensor([[[[0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0]]]])]; + tensor full_mask_7_cast_fp16 = concat(axis = var_1991, interleave = full_mask_7_interleave_0, values = (context_mask_9_cast_fp16, fill_3_to_fp16))[name = string("full_mask_7_cast_fp16")]; + tensor input_131_cast_fp16 = mul(x = hidden_states_81_cast_fp16, y = full_mask_7_cast_fp16)[name = string("input_131_cast_fp16")]; + string sub_pixels_13_pad_type_0 = const()[name = string("sub_pixels_13_pad_type_0"), val = string("valid")]; + tensor sub_pixels_13_strides_0 = const()[name = string("sub_pixels_13_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_13_pad_0 = const()[name = string("sub_pixels_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_13_dilations_0 = const()[name = string("sub_pixels_13_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_13_groups_0 = const()[name = string("sub_pixels_13_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73842176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92716608))))[name = string("audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92717184)))]; + tensor sub_pixels_13_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_1_conv_bias_to_fp16, dilations = sub_pixels_13_dilations_0, groups = sub_pixels_13_groups_0, pad = sub_pixels_13_pad_0, pad_type = sub_pixels_13_pad_type_0, strides = sub_pixels_13_strides_0, weight = audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("sub_pixels_13_cast_fp16")]; + tensor var_2300 = const()[name = string("op_2300"), val = tensor([1, 8, 768, 17])]; + tensor sub_pixels_15_cast_fp16 = reshape(shape = var_2300, x = sub_pixels_13_cast_fp16)[name = string("sub_pixels_15_cast_fp16")]; + tensor var_2302 = const()[name = string("op_2302"), val = tensor([0, 2, 3, 1])]; + tensor var_2307 = const()[name = string("op_2307"), val = tensor([1, 768, 1, 136])]; + tensor sub_pixels_17_cast_fp16 = transpose(perm = var_2302, x = sub_pixels_15_cast_fp16)[name = string("transpose_3")]; + tensor hidden_states_83_cast_fp16 = reshape(shape = var_2307, x = sub_pixels_17_cast_fp16)[name = string("hidden_states_83_cast_fp16")]; + tensor newest_5_begin_0 = const()[name = string("newest_5_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_5_end_0 = const()[name = string("newest_5_end_0"), val = tensor([1, 1, 1, 14])]; + tensor newest_5_end_mask_0 = const()[name = string("newest_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_5_cast_fp16 = slice_by_index(begin = newest_5_begin_0, end = newest_5_end_0, end_mask = newest_5_end_mask_0, x = context_mask_9_cast_fp16)[name = string("newest_5_cast_fp16")]; + tensor var_2312 = const()[name = string("op_2312"), val = tensor([1, 1, 13, 1])]; + tensor var_2313_cast_fp16 = reshape(shape = var_2312, x = newest_5_cast_fp16)[name = string("op_2313_cast_fp16")]; + tensor spread_5_reps_0 = const()[name = string("spread_5_reps_0"), val = tensor([1, 1, 1, 8])]; + tensor spread_5_cast_fp16 = tile(reps = spread_5_reps_0, x = var_2313_cast_fp16)[name = string("spread_5_cast_fp16")]; + tensor var_2319 = const()[name = string("op_2319"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_11_cast_fp16 = reshape(shape = var_2319, x = spread_5_cast_fp16)[name = string("context_mask_11_cast_fp16")]; + tensor residual_3_begin_0 = const()[name = string("residual_3_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_3_end_0 = const()[name = string("residual_3_end_0"), val = tensor([1, 768, 1, 136])]; + tensor residual_3_end_mask_0 = const()[name = string("residual_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_3_cast_fp16 = slice_by_index(begin = residual_3_begin_0, end = residual_3_end_0, end_mask = residual_3_end_mask_0, x = hidden_states_83_cast_fp16)[name = string("residual_3_cast_fp16")]; + tensor alpha_over_pi_3_to_fp16 = const()[name = string("alpha_over_pi_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92729536)))]; + tensor theta_over_pi_3_cast_fp16 = mul(x = hidden_states_83_cast_fp16, y = alpha_over_pi_3_to_fp16)[name = string("theta_over_pi_3_cast_fp16")]; + tensor var_2342_cast_fp16 = round(x = theta_over_pi_3_cast_fp16)[name = string("op_2342_cast_fp16")]; + tensor reduced_3_cast_fp16 = sub(x = theta_over_pi_3_cast_fp16, y = var_2342_cast_fp16)[name = string("reduced_3_cast_fp16")]; + tensor reduced_sq_3_cast_fp16 = mul(x = reduced_3_cast_fp16, y = reduced_3_cast_fp16)[name = string("reduced_sq_3_cast_fp16")]; + tensor acc_7_mean_0_to_fp16 = const()[name = string("acc_7_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92731136)))]; + tensor acc_7_variance_0_to_fp16 = const()[name = string("acc_7_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92732736)))]; + tensor acc_7_gamma_0_to_fp16 = const()[name = string("acc_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92734336)))]; + tensor acc_7_beta_0_to_fp16 = const()[name = string("acc_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92735936)))]; + fp16 acc_7_epsilon_0_to_fp16 = const()[name = string("acc_7_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_7_cast_fp16 = batch_norm(beta = acc_7_beta_0_to_fp16, epsilon = acc_7_epsilon_0_to_fp16, gamma = acc_7_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_3_cast_fp16)[name = string("acc_7_cast_fp16")]; + tensor var_2355_cast_fp16 = mul(x = acc_7_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2355_cast_fp16")]; + tensor c_5_to_fp16 = const()[name = string("c_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92737536)))]; + tensor acc_9_cast_fp16 = add(x = var_2355_cast_fp16, y = c_5_to_fp16)[name = string("acc_9_cast_fp16")]; + tensor var_2357_cast_fp16 = mul(x = acc_9_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2357_cast_fp16")]; + tensor c_7_to_fp16 = const()[name = string("c_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92739136)))]; + tensor acc_11_cast_fp16 = add(x = var_2357_cast_fp16, y = c_7_to_fp16)[name = string("acc_11_cast_fp16")]; + tensor var_2359_cast_fp16 = mul(x = acc_11_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2359_cast_fp16")]; + tensor hidden_states_85_cast_fp16 = add(x = hidden_states_83_cast_fp16, y = var_2359_cast_fp16)[name = string("hidden_states_85_cast_fp16")]; + bool full_mask_9_interleave_0 = const()[name = string("full_mask_9_interleave_0"), val = bool(false)]; + tensor fill_4_to_fp16 = const()[name = string("fill_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740736)))]; + tensor full_mask_9_cast_fp16 = concat(axis = var_1991, interleave = full_mask_9_interleave_0, values = (context_mask_11_cast_fp16, fill_4_to_fp16))[name = string("full_mask_9_cast_fp16")]; + tensor input_133_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = full_mask_9_cast_fp16)[name = string("input_133_cast_fp16")]; + string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")]; + tensor hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96869696))))[name = string("audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96870272)))]; + tensor hidden_states_87_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized, x = input_133_cast_fp16)[name = string("hidden_states_87_cast_fp16")]; + tensor alpha_over_pi_5_to_fp16 = const()[name = string("alpha_over_pi_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96871872)))]; + tensor theta_over_pi_5_cast_fp16 = mul(x = hidden_states_87_cast_fp16, y = alpha_over_pi_5_to_fp16)[name = string("theta_over_pi_5_cast_fp16")]; + tensor var_2396_cast_fp16 = round(x = theta_over_pi_5_cast_fp16)[name = string("op_2396_cast_fp16")]; + tensor reduced_5_cast_fp16 = sub(x = theta_over_pi_5_cast_fp16, y = var_2396_cast_fp16)[name = string("reduced_5_cast_fp16")]; + tensor reduced_sq_5_cast_fp16 = mul(x = reduced_5_cast_fp16, y = reduced_5_cast_fp16)[name = string("reduced_sq_5_cast_fp16")]; + tensor acc_13_gamma_0_to_fp16 = const()[name = string("acc_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96873472)))]; + tensor acc_13_beta_0_to_fp16 = const()[name = string("acc_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96875072)))]; + fp16 acc_13_epsilon_0_to_fp16 = const()[name = string("acc_13_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_13_cast_fp16 = batch_norm(beta = acc_13_beta_0_to_fp16, epsilon = acc_13_epsilon_0_to_fp16, gamma = acc_13_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_5_cast_fp16)[name = string("acc_13_cast_fp16")]; + tensor var_2409_cast_fp16 = mul(x = acc_13_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2409_cast_fp16")]; + tensor c_9_to_fp16 = const()[name = string("c_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96876672)))]; + tensor acc_15_cast_fp16 = add(x = var_2409_cast_fp16, y = c_9_to_fp16)[name = string("acc_15_cast_fp16")]; + tensor var_2411_cast_fp16 = mul(x = acc_15_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2411_cast_fp16")]; + tensor c_11_to_fp16 = const()[name = string("c_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96878272)))]; + tensor acc_17_cast_fp16 = add(x = var_2411_cast_fp16, y = c_11_to_fp16)[name = string("acc_17_cast_fp16")]; + tensor var_2413_cast_fp16 = mul(x = acc_17_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2413_cast_fp16")]; + tensor hidden_states_89_cast_fp16 = add(x = hidden_states_87_cast_fp16, y = var_2413_cast_fp16)[name = string("hidden_states_89_cast_fp16")]; + string hidden_states_91_pad_type_0 = const()[name = string("hidden_states_91_pad_type_0"), val = string("valid")]; + tensor hidden_states_91_strides_0 = const()[name = string("hidden_states_91_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_91_pad_0 = const()[name = string("hidden_states_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_91_dilations_0 = const()[name = string("hidden_states_91_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_91_groups_0 = const()[name = string("hidden_states_91_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96879872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97469760))))[name = string("audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97470336)))]; + tensor hidden_states_91_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_91_dilations_0, groups = hidden_states_91_groups_0, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = hidden_states_91_strides_0, weight = audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_89_cast_fp16)[name = string("hidden_states_91_cast_fp16")]; + tensor hidden_states_93_cast_fp16 = add(x = hidden_states_91_cast_fp16, y = residual_3_cast_fp16)[name = string("hidden_states_93_cast_fp16")]; + tensor context_mask_13_begin_0 = const()[name = string("context_mask_13_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_13_end_0 = const()[name = string("context_mask_13_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_13_end_mask_0 = const()[name = string("context_mask_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_13_cast_fp16 = slice_by_index(begin = context_mask_13_begin_0, end = context_mask_13_end_0, end_mask = context_mask_13_end_mask_0, x = context_mask_11_cast_fp16)[name = string("context_mask_13_cast_fp16")]; + tensor residual_5_begin_0 = const()[name = string("residual_5_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_5_end_0 = const()[name = string("residual_5_end_0"), val = tensor([1, 768, 1, 130])]; + tensor residual_5_end_mask_0 = const()[name = string("residual_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_5_cast_fp16 = slice_by_index(begin = residual_5_begin_0, end = residual_5_end_0, end_mask = residual_5_end_mask_0, x = hidden_states_93_cast_fp16)[name = string("residual_5_cast_fp16")]; + tensor alpha_over_pi_7_to_fp16 = const()[name = string("alpha_over_pi_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97471936)))]; + tensor theta_over_pi_7_cast_fp16 = mul(x = hidden_states_93_cast_fp16, y = alpha_over_pi_7_to_fp16)[name = string("theta_over_pi_7_cast_fp16")]; + tensor var_2448_cast_fp16 = round(x = theta_over_pi_7_cast_fp16)[name = string("op_2448_cast_fp16")]; + tensor reduced_7_cast_fp16 = sub(x = theta_over_pi_7_cast_fp16, y = var_2448_cast_fp16)[name = string("reduced_7_cast_fp16")]; + tensor reduced_sq_7_cast_fp16 = mul(x = reduced_7_cast_fp16, y = reduced_7_cast_fp16)[name = string("reduced_sq_7_cast_fp16")]; + tensor acc_19_gamma_0_to_fp16 = const()[name = string("acc_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97473536)))]; + tensor acc_19_beta_0_to_fp16 = const()[name = string("acc_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97475136)))]; + fp16 acc_19_epsilon_0_to_fp16 = const()[name = string("acc_19_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_19_cast_fp16 = batch_norm(beta = acc_19_beta_0_to_fp16, epsilon = acc_19_epsilon_0_to_fp16, gamma = acc_19_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_7_cast_fp16)[name = string("acc_19_cast_fp16")]; + tensor var_2461_cast_fp16 = mul(x = acc_19_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2461_cast_fp16")]; + tensor c_13_to_fp16 = const()[name = string("c_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97476736)))]; + tensor acc_21_cast_fp16 = add(x = var_2461_cast_fp16, y = c_13_to_fp16)[name = string("acc_21_cast_fp16")]; + tensor var_2463_cast_fp16 = mul(x = acc_21_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2463_cast_fp16")]; + tensor c_15_to_fp16 = const()[name = string("c_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97478336)))]; + tensor acc_23_cast_fp16 = add(x = var_2463_cast_fp16, y = c_15_to_fp16)[name = string("acc_23_cast_fp16")]; + tensor var_2465_cast_fp16 = mul(x = acc_23_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2465_cast_fp16")]; + tensor hidden_states_95_cast_fp16 = add(x = hidden_states_93_cast_fp16, y = var_2465_cast_fp16)[name = string("hidden_states_95_cast_fp16")]; + bool full_mask_11_interleave_0 = const()[name = string("full_mask_11_interleave_0"), val = bool(false)]; + tensor fill_5_to_fp16 = const()[name = string("fill_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740736)))]; + tensor full_mask_11_cast_fp16 = concat(axis = var_1991, interleave = full_mask_11_interleave_0, values = (context_mask_13_cast_fp16, fill_5_to_fp16))[name = string("full_mask_11_cast_fp16")]; + tensor input_137_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = full_mask_11_cast_fp16)[name = string("input_137_cast_fp16")]; + string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")]; + tensor hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97479936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101608768))))[name = string("audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101609344)))]; + tensor hidden_states_97_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized, x = input_137_cast_fp16)[name = string("hidden_states_97_cast_fp16")]; + tensor alpha_over_pi_9_to_fp16 = const()[name = string("alpha_over_pi_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101610944)))]; + tensor theta_over_pi_9_cast_fp16 = mul(x = hidden_states_97_cast_fp16, y = alpha_over_pi_9_to_fp16)[name = string("theta_over_pi_9_cast_fp16")]; + tensor var_2502_cast_fp16 = round(x = theta_over_pi_9_cast_fp16)[name = string("op_2502_cast_fp16")]; + tensor reduced_9_cast_fp16 = sub(x = theta_over_pi_9_cast_fp16, y = var_2502_cast_fp16)[name = string("reduced_9_cast_fp16")]; + tensor reduced_sq_9_cast_fp16 = mul(x = reduced_9_cast_fp16, y = reduced_9_cast_fp16)[name = string("reduced_sq_9_cast_fp16")]; + tensor acc_25_gamma_0_to_fp16 = const()[name = string("acc_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101612544)))]; + tensor acc_25_beta_0_to_fp16 = const()[name = string("acc_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101614144)))]; + fp16 acc_25_epsilon_0_to_fp16 = const()[name = string("acc_25_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_25_cast_fp16 = batch_norm(beta = acc_25_beta_0_to_fp16, epsilon = acc_25_epsilon_0_to_fp16, gamma = acc_25_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_9_cast_fp16)[name = string("acc_25_cast_fp16")]; + tensor var_2515_cast_fp16 = mul(x = acc_25_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2515_cast_fp16")]; + tensor c_17_to_fp16 = const()[name = string("c_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101615744)))]; + tensor acc_27_cast_fp16 = add(x = var_2515_cast_fp16, y = c_17_to_fp16)[name = string("acc_27_cast_fp16")]; + tensor var_2517_cast_fp16 = mul(x = acc_27_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2517_cast_fp16")]; + tensor c_19_to_fp16 = const()[name = string("c_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101617344)))]; + tensor acc_29_cast_fp16 = add(x = var_2517_cast_fp16, y = c_19_to_fp16)[name = string("acc_29_cast_fp16")]; + tensor var_2519_cast_fp16 = mul(x = acc_29_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2519_cast_fp16")]; + tensor hidden_states_99_cast_fp16 = add(x = hidden_states_97_cast_fp16, y = var_2519_cast_fp16)[name = string("hidden_states_99_cast_fp16")]; + string hidden_states_101_pad_type_0 = const()[name = string("hidden_states_101_pad_type_0"), val = string("valid")]; + tensor hidden_states_101_strides_0 = const()[name = string("hidden_states_101_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_101_pad_0 = const()[name = string("hidden_states_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_101_dilations_0 = const()[name = string("hidden_states_101_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_101_groups_0 = const()[name = string("hidden_states_101_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101618944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102208832))))[name = string("audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102209408)))]; + tensor hidden_states_101_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_101_dilations_0, groups = hidden_states_101_groups_0, pad = hidden_states_101_pad_0, pad_type = hidden_states_101_pad_type_0, strides = hidden_states_101_strides_0, weight = audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_99_cast_fp16)[name = string("hidden_states_101_cast_fp16")]; + tensor hidden_states_103_cast_fp16 = add(x = hidden_states_101_cast_fp16, y = residual_5_cast_fp16)[name = string("hidden_states_103_cast_fp16")]; + tensor context_mask_15_begin_0 = const()[name = string("context_mask_15_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_15_end_0 = const()[name = string("context_mask_15_end_0"), val = tensor([1, 1, 1, 98])]; + tensor context_mask_15_end_mask_0 = const()[name = string("context_mask_15_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_15_cast_fp16 = slice_by_index(begin = context_mask_15_begin_0, end = context_mask_15_end_0, end_mask = context_mask_15_end_mask_0, x = context_mask_13_cast_fp16)[name = string("context_mask_15_cast_fp16")]; + tensor residual_7_begin_0 = const()[name = string("residual_7_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_7_end_0 = const()[name = string("residual_7_end_0"), val = tensor([1, 768, 1, 112])]; + tensor residual_7_end_mask_0 = const()[name = string("residual_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_7_cast_fp16 = slice_by_index(begin = residual_7_begin_0, end = residual_7_end_0, end_mask = residual_7_end_mask_0, x = hidden_states_103_cast_fp16)[name = string("residual_7_cast_fp16")]; + tensor alpha_over_pi_11_to_fp16 = const()[name = string("alpha_over_pi_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102211008)))]; + tensor theta_over_pi_11_cast_fp16 = mul(x = hidden_states_103_cast_fp16, y = alpha_over_pi_11_to_fp16)[name = string("theta_over_pi_11_cast_fp16")]; + tensor var_2554_cast_fp16 = round(x = theta_over_pi_11_cast_fp16)[name = string("op_2554_cast_fp16")]; + tensor reduced_11_cast_fp16 = sub(x = theta_over_pi_11_cast_fp16, y = var_2554_cast_fp16)[name = string("reduced_11_cast_fp16")]; + tensor reduced_sq_11_cast_fp16 = mul(x = reduced_11_cast_fp16, y = reduced_11_cast_fp16)[name = string("reduced_sq_11_cast_fp16")]; + tensor acc_31_gamma_0_to_fp16 = const()[name = string("acc_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102212608)))]; + tensor acc_31_beta_0_to_fp16 = const()[name = string("acc_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102214208)))]; + fp16 acc_31_epsilon_0_to_fp16 = const()[name = string("acc_31_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_31_cast_fp16 = batch_norm(beta = acc_31_beta_0_to_fp16, epsilon = acc_31_epsilon_0_to_fp16, gamma = acc_31_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_11_cast_fp16)[name = string("acc_31_cast_fp16")]; + tensor var_2567_cast_fp16 = mul(x = acc_31_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2567_cast_fp16")]; + tensor c_21_to_fp16 = const()[name = string("c_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102215808)))]; + tensor acc_33_cast_fp16 = add(x = var_2567_cast_fp16, y = c_21_to_fp16)[name = string("acc_33_cast_fp16")]; + tensor var_2569_cast_fp16 = mul(x = acc_33_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2569_cast_fp16")]; + tensor c_23_to_fp16 = const()[name = string("c_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102217408)))]; + tensor acc_35_cast_fp16 = add(x = var_2569_cast_fp16, y = c_23_to_fp16)[name = string("acc_35_cast_fp16")]; + tensor var_2571_cast_fp16 = mul(x = acc_35_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2571_cast_fp16")]; + tensor hidden_states_105_cast_fp16 = add(x = hidden_states_103_cast_fp16, y = var_2571_cast_fp16)[name = string("hidden_states_105_cast_fp16")]; + bool full_mask_13_interleave_0 = const()[name = string("full_mask_13_interleave_0"), val = bool(false)]; + tensor fill_6_to_fp16 = const()[name = string("fill_6_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740736)))]; + tensor full_mask_13_cast_fp16 = concat(axis = var_1991, interleave = full_mask_13_interleave_0, values = (context_mask_15_cast_fp16, fill_6_to_fp16))[name = string("full_mask_13_cast_fp16")]; + tensor input_141_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = full_mask_13_cast_fp16)[name = string("input_141_cast_fp16")]; + string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")]; + tensor hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102219008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106347840))))[name = string("audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106348416)))]; + tensor hidden_states_107_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized, x = input_141_cast_fp16)[name = string("hidden_states_107_cast_fp16")]; + tensor alpha_over_pi_13_to_fp16 = const()[name = string("alpha_over_pi_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106350016)))]; + tensor theta_over_pi_13_cast_fp16 = mul(x = hidden_states_107_cast_fp16, y = alpha_over_pi_13_to_fp16)[name = string("theta_over_pi_13_cast_fp16")]; + tensor var_2608_cast_fp16 = round(x = theta_over_pi_13_cast_fp16)[name = string("op_2608_cast_fp16")]; + tensor reduced_13_cast_fp16 = sub(x = theta_over_pi_13_cast_fp16, y = var_2608_cast_fp16)[name = string("reduced_13_cast_fp16")]; + tensor reduced_sq_13_cast_fp16 = mul(x = reduced_13_cast_fp16, y = reduced_13_cast_fp16)[name = string("reduced_sq_13_cast_fp16")]; + tensor acc_37_gamma_0_to_fp16 = const()[name = string("acc_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106351616)))]; + tensor acc_37_beta_0_to_fp16 = const()[name = string("acc_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106353216)))]; + fp16 acc_37_epsilon_0_to_fp16 = const()[name = string("acc_37_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_37_cast_fp16 = batch_norm(beta = acc_37_beta_0_to_fp16, epsilon = acc_37_epsilon_0_to_fp16, gamma = acc_37_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_13_cast_fp16)[name = string("acc_37_cast_fp16")]; + tensor var_2621_cast_fp16 = mul(x = acc_37_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2621_cast_fp16")]; + tensor c_25_to_fp16 = const()[name = string("c_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106354816)))]; + tensor acc_39_cast_fp16 = add(x = var_2621_cast_fp16, y = c_25_to_fp16)[name = string("acc_39_cast_fp16")]; + tensor var_2623_cast_fp16 = mul(x = acc_39_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2623_cast_fp16")]; + tensor c_27_to_fp16 = const()[name = string("c_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106356416)))]; + tensor acc_41_cast_fp16 = add(x = var_2623_cast_fp16, y = c_27_to_fp16)[name = string("acc_41_cast_fp16")]; + tensor var_2625_cast_fp16 = mul(x = acc_41_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2625_cast_fp16")]; + tensor hidden_states_109_cast_fp16 = add(x = hidden_states_107_cast_fp16, y = var_2625_cast_fp16)[name = string("hidden_states_109_cast_fp16")]; + string hidden_states_111_pad_type_0 = const()[name = string("hidden_states_111_pad_type_0"), val = string("valid")]; + tensor hidden_states_111_strides_0 = const()[name = string("hidden_states_111_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_111_pad_0 = const()[name = string("hidden_states_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_111_dilations_0 = const()[name = string("hidden_states_111_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_111_groups_0 = const()[name = string("hidden_states_111_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106358016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106947904))))[name = string("audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106948480)))]; + tensor hidden_states_111_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_111_dilations_0, groups = hidden_states_111_groups_0, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = hidden_states_111_strides_0, weight = audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_109_cast_fp16)[name = string("hidden_states_111_cast_fp16")]; + tensor hidden_states_113_cast_fp16 = add(x = hidden_states_111_cast_fp16, y = residual_7_cast_fp16)[name = string("hidden_states_113_cast_fp16")]; + tensor context_mask_19_begin_0 = const()[name = string("context_mask_19_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_19_end_0 = const()[name = string("context_mask_19_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_19_end_mask_0 = const()[name = string("context_mask_19_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_19_cast_fp16 = slice_by_index(begin = context_mask_19_begin_0, end = context_mask_19_end_0, end_mask = context_mask_19_end_mask_0, x = context_mask_11_cast_fp16)[name = string("context_mask_19_cast_fp16")]; + tensor hidden_states_115_begin_0 = const()[name = string("hidden_states_115_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor hidden_states_115_end_0 = const()[name = string("hidden_states_115_end_0"), val = tensor([1, 768, 1, 58])]; + tensor hidden_states_115_end_mask_0 = const()[name = string("hidden_states_115_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_115_cast_fp16 = slice_by_index(begin = hidden_states_115_begin_0, end = hidden_states_115_end_0, end_mask = hidden_states_115_end_mask_0, x = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")]; + tensor context_mask_21_begin_0 = const()[name = string("context_mask_21_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor context_mask_21_end_0 = const()[name = string("context_mask_21_end_0"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_21_end_mask_0 = const()[name = string("context_mask_21_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_21_cast_fp16 = slice_by_index(begin = context_mask_21_begin_0, end = context_mask_21_end_0, end_mask = context_mask_21_end_mask_0, x = context_mask_19_cast_fp16)[name = string("context_mask_21_cast_fp16")]; + tensor alpha_over_pi_15_to_fp16 = const()[name = string("alpha_over_pi_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106950080)))]; + tensor theta_over_pi_15_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = alpha_over_pi_15_to_fp16)[name = string("theta_over_pi_15_cast_fp16")]; + tensor var_2685_cast_fp16 = round(x = theta_over_pi_15_cast_fp16)[name = string("op_2685_cast_fp16")]; + tensor reduced_15_cast_fp16 = sub(x = theta_over_pi_15_cast_fp16, y = var_2685_cast_fp16)[name = string("reduced_15_cast_fp16")]; + tensor reduced_sq_15_cast_fp16 = mul(x = reduced_15_cast_fp16, y = reduced_15_cast_fp16)[name = string("reduced_sq_15_cast_fp16")]; + tensor acc_43_gamma_0_to_fp16 = const()[name = string("acc_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106951680)))]; + tensor acc_43_beta_0_to_fp16 = const()[name = string("acc_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106953280)))]; + fp16 acc_43_epsilon_0_to_fp16 = const()[name = string("acc_43_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_43_cast_fp16 = batch_norm(beta = acc_43_beta_0_to_fp16, epsilon = acc_43_epsilon_0_to_fp16, gamma = acc_43_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_15_cast_fp16)[name = string("acc_43_cast_fp16")]; + tensor var_2698_cast_fp16 = mul(x = acc_43_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2698_cast_fp16")]; + tensor c_29_to_fp16 = const()[name = string("c_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106954880)))]; + tensor acc_45_cast_fp16 = add(x = var_2698_cast_fp16, y = c_29_to_fp16)[name = string("acc_45_cast_fp16")]; + tensor var_2700_cast_fp16 = mul(x = acc_45_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2700_cast_fp16")]; + tensor c_31_to_fp16 = const()[name = string("c_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106956480)))]; + tensor acc_47_cast_fp16 = add(x = var_2700_cast_fp16, y = c_31_to_fp16)[name = string("acc_47_cast_fp16")]; + tensor var_2702_cast_fp16 = mul(x = acc_47_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2702_cast_fp16")]; + tensor hidden_states_117_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = var_2702_cast_fp16)[name = string("hidden_states_117_cast_fp16")]; + bool full_mask_15_interleave_0 = const()[name = string("full_mask_15_interleave_0"), val = bool(false)]; + tensor fill_7_to_fp16 = const()[name = string("fill_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740736)))]; + tensor full_mask_15_cast_fp16 = concat(axis = var_1991, interleave = full_mask_15_interleave_0, values = (context_mask_21_cast_fp16, fill_7_to_fp16))[name = string("full_mask_15_cast_fp16")]; + tensor input_145_cast_fp16 = mul(x = hidden_states_117_cast_fp16, y = full_mask_15_cast_fp16)[name = string("input_145_cast_fp16")]; + string sub_pixels_19_pad_type_0 = const()[name = string("sub_pixels_19_pad_type_0"), val = string("valid")]; + tensor sub_pixels_19_strides_0 = const()[name = string("sub_pixels_19_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_19_pad_0 = const()[name = string("sub_pixels_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_19_dilations_0 = const()[name = string("sub_pixels_19_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_19_groups_0 = const()[name = string("sub_pixels_19_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106958080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109907264))))[name = string("audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109907840)))]; + tensor sub_pixels_19_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_1_conv_bias_to_fp16, dilations = sub_pixels_19_dilations_0, groups = sub_pixels_19_groups_0, pad = sub_pixels_19_pad_0, pad_type = sub_pixels_19_pad_type_0, strides = sub_pixels_19_strides_0, weight = audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized, x = input_145_cast_fp16)[name = string("sub_pixels_19_cast_fp16")]; + tensor var_2726 = const()[name = string("op_2726"), val = tensor([1, 5, 384, 54])]; + tensor sub_pixels_21_cast_fp16 = reshape(shape = var_2726, x = sub_pixels_19_cast_fp16)[name = string("sub_pixels_21_cast_fp16")]; + tensor var_2728 = const()[name = string("op_2728"), val = tensor([0, 2, 3, 1])]; + tensor var_2733 = const()[name = string("op_2733"), val = tensor([1, 384, 1, 270])]; + tensor sub_pixels_23_cast_fp16 = transpose(perm = var_2728, x = sub_pixels_21_cast_fp16)[name = string("transpose_2")]; + tensor hidden_states_119_cast_fp16 = reshape(shape = var_2733, x = sub_pixels_23_cast_fp16)[name = string("hidden_states_119_cast_fp16")]; + tensor newest_9_begin_0 = const()[name = string("newest_9_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_9_end_0 = const()[name = string("newest_9_end_0"), val = tensor([1, 1, 1, 23])]; + tensor newest_9_end_mask_0 = const()[name = string("newest_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_9_cast_fp16 = slice_by_index(begin = newest_9_begin_0, end = newest_9_end_0, end_mask = newest_9_end_mask_0, x = context_mask_21_cast_fp16)[name = string("newest_9_cast_fp16")]; + tensor var_2738 = const()[name = string("op_2738"), val = tensor([1, 1, 22, 1])]; + tensor var_2739_cast_fp16 = reshape(shape = var_2738, x = newest_9_cast_fp16)[name = string("op_2739_cast_fp16")]; + tensor spread_9_reps_0 = const()[name = string("spread_9_reps_0"), val = tensor([1, 1, 1, 5])]; + tensor spread_9_cast_fp16 = tile(reps = spread_9_reps_0, x = var_2739_cast_fp16)[name = string("spread_9_cast_fp16")]; + tensor var_2745 = const()[name = string("op_2745"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_23_cast_fp16 = reshape(shape = var_2745, x = spread_9_cast_fp16)[name = string("context_mask_23_cast_fp16")]; + tensor residual_9_begin_0 = const()[name = string("residual_9_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_9_end_0 = const()[name = string("residual_9_end_0"), val = tensor([1, 384, 1, 270])]; + tensor residual_9_end_mask_0 = const()[name = string("residual_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_9_cast_fp16 = slice_by_index(begin = residual_9_begin_0, end = residual_9_end_0, end_mask = residual_9_end_mask_0, x = hidden_states_119_cast_fp16)[name = string("residual_9_cast_fp16")]; + tensor alpha_over_pi_17_to_fp16 = const()[name = string("alpha_over_pi_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109911744)))]; + tensor theta_over_pi_17_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = alpha_over_pi_17_to_fp16)[name = string("theta_over_pi_17_cast_fp16")]; + tensor var_2768_cast_fp16 = round(x = theta_over_pi_17_cast_fp16)[name = string("op_2768_cast_fp16")]; + tensor reduced_17_cast_fp16 = sub(x = theta_over_pi_17_cast_fp16, y = var_2768_cast_fp16)[name = string("reduced_17_cast_fp16")]; + tensor reduced_sq_17_cast_fp16 = mul(x = reduced_17_cast_fp16, y = reduced_17_cast_fp16)[name = string("reduced_sq_17_cast_fp16")]; + tensor acc_49_mean_0_to_fp16 = const()[name = string("acc_49_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109912576)))]; + tensor acc_49_variance_0_to_fp16 = const()[name = string("acc_49_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109913408)))]; + tensor acc_49_gamma_0_to_fp16 = const()[name = string("acc_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109914240)))]; + tensor acc_49_beta_0_to_fp16 = const()[name = string("acc_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109915072)))]; + fp16 acc_49_epsilon_0_to_fp16 = const()[name = string("acc_49_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_49_cast_fp16 = batch_norm(beta = acc_49_beta_0_to_fp16, epsilon = acc_49_epsilon_0_to_fp16, gamma = acc_49_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_17_cast_fp16)[name = string("acc_49_cast_fp16")]; + tensor var_2781_cast_fp16 = mul(x = acc_49_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2781_cast_fp16")]; + tensor c_33_to_fp16 = const()[name = string("c_33_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109915904)))]; + tensor acc_51_cast_fp16 = add(x = var_2781_cast_fp16, y = c_33_to_fp16)[name = string("acc_51_cast_fp16")]; + tensor var_2783_cast_fp16 = mul(x = acc_51_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2783_cast_fp16")]; + tensor c_35_to_fp16 = const()[name = string("c_35_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109916736)))]; + tensor acc_53_cast_fp16 = add(x = var_2783_cast_fp16, y = c_35_to_fp16)[name = string("acc_53_cast_fp16")]; + tensor var_2785_cast_fp16 = mul(x = acc_53_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2785_cast_fp16")]; + tensor hidden_states_121_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = var_2785_cast_fp16)[name = string("hidden_states_121_cast_fp16")]; + bool full_mask_17_interleave_0 = const()[name = string("full_mask_17_interleave_0"), val = bool(false)]; + tensor fill_8_to_fp16 = const()[name = string("fill_8_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109917568)))]; + tensor full_mask_17_cast_fp16 = concat(axis = var_1991, interleave = full_mask_17_interleave_0, values = (context_mask_23_cast_fp16, fill_8_to_fp16))[name = string("full_mask_17_cast_fp16")]; + tensor input_147_cast_fp16 = mul(x = hidden_states_121_cast_fp16, y = full_mask_17_cast_fp16)[name = string("input_147_cast_fp16")]; + string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")]; + tensor hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109917952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110950208))))[name = string("audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110950784)))]; + tensor hidden_states_123_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("hidden_states_123_cast_fp16")]; + tensor alpha_over_pi_19_to_fp16 = const()[name = string("alpha_over_pi_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110951616)))]; + tensor theta_over_pi_19_cast_fp16 = mul(x = hidden_states_123_cast_fp16, y = alpha_over_pi_19_to_fp16)[name = string("theta_over_pi_19_cast_fp16")]; + tensor var_2822_cast_fp16 = round(x = theta_over_pi_19_cast_fp16)[name = string("op_2822_cast_fp16")]; + tensor reduced_19_cast_fp16 = sub(x = theta_over_pi_19_cast_fp16, y = var_2822_cast_fp16)[name = string("reduced_19_cast_fp16")]; + tensor reduced_sq_19_cast_fp16 = mul(x = reduced_19_cast_fp16, y = reduced_19_cast_fp16)[name = string("reduced_sq_19_cast_fp16")]; + tensor acc_55_gamma_0_to_fp16 = const()[name = string("acc_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110952448)))]; + tensor acc_55_beta_0_to_fp16 = const()[name = string("acc_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110953280)))]; + fp16 acc_55_epsilon_0_to_fp16 = const()[name = string("acc_55_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_55_cast_fp16 = batch_norm(beta = acc_55_beta_0_to_fp16, epsilon = acc_55_epsilon_0_to_fp16, gamma = acc_55_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_19_cast_fp16)[name = string("acc_55_cast_fp16")]; + tensor var_2835_cast_fp16 = mul(x = acc_55_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2835_cast_fp16")]; + tensor c_37_to_fp16 = const()[name = string("c_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110954112)))]; + tensor acc_57_cast_fp16 = add(x = var_2835_cast_fp16, y = c_37_to_fp16)[name = string("acc_57_cast_fp16")]; + tensor var_2837_cast_fp16 = mul(x = acc_57_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2837_cast_fp16")]; + tensor c_39_to_fp16 = const()[name = string("c_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110954944)))]; + tensor acc_59_cast_fp16 = add(x = var_2837_cast_fp16, y = c_39_to_fp16)[name = string("acc_59_cast_fp16")]; + tensor var_2839_cast_fp16 = mul(x = acc_59_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2839_cast_fp16")]; + tensor hidden_states_125_cast_fp16 = add(x = hidden_states_123_cast_fp16, y = var_2839_cast_fp16)[name = string("hidden_states_125_cast_fp16")]; + string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")]; + tensor hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110955776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111103296))))[name = string("audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111103872)))]; + tensor hidden_states_127_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_125_cast_fp16)[name = string("hidden_states_127_cast_fp16")]; + tensor hidden_states_129_cast_fp16 = add(x = hidden_states_127_cast_fp16, y = residual_9_cast_fp16)[name = string("hidden_states_129_cast_fp16")]; + tensor context_mask_25_begin_0 = const()[name = string("context_mask_25_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_25_end_0 = const()[name = string("context_mask_25_end_0"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_25_end_mask_0 = const()[name = string("context_mask_25_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_25_cast_fp16 = slice_by_index(begin = context_mask_25_begin_0, end = context_mask_25_end_0, end_mask = context_mask_25_end_mask_0, x = context_mask_23_cast_fp16)[name = string("context_mask_25_cast_fp16")]; + tensor residual_11_begin_0 = const()[name = string("residual_11_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_11_end_0 = const()[name = string("residual_11_end_0"), val = tensor([1, 384, 1, 264])]; + tensor residual_11_end_mask_0 = const()[name = string("residual_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_11_cast_fp16 = slice_by_index(begin = residual_11_begin_0, end = residual_11_end_0, end_mask = residual_11_end_mask_0, x = hidden_states_129_cast_fp16)[name = string("residual_11_cast_fp16")]; + tensor alpha_over_pi_21_to_fp16 = const()[name = string("alpha_over_pi_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111104704)))]; + tensor theta_over_pi_21_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = alpha_over_pi_21_to_fp16)[name = string("theta_over_pi_21_cast_fp16")]; + tensor var_2874_cast_fp16 = round(x = theta_over_pi_21_cast_fp16)[name = string("op_2874_cast_fp16")]; + tensor reduced_21_cast_fp16 = sub(x = theta_over_pi_21_cast_fp16, y = var_2874_cast_fp16)[name = string("reduced_21_cast_fp16")]; + tensor reduced_sq_21_cast_fp16 = mul(x = reduced_21_cast_fp16, y = reduced_21_cast_fp16)[name = string("reduced_sq_21_cast_fp16")]; + tensor acc_61_gamma_0_to_fp16 = const()[name = string("acc_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111105536)))]; + tensor acc_61_beta_0_to_fp16 = const()[name = string("acc_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111106368)))]; + fp16 acc_61_epsilon_0_to_fp16 = const()[name = string("acc_61_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_61_cast_fp16 = batch_norm(beta = acc_61_beta_0_to_fp16, epsilon = acc_61_epsilon_0_to_fp16, gamma = acc_61_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_21_cast_fp16)[name = string("acc_61_cast_fp16")]; + tensor var_2887_cast_fp16 = mul(x = acc_61_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2887_cast_fp16")]; + tensor c_41_to_fp16 = const()[name = string("c_41_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111107200)))]; + tensor acc_63_cast_fp16 = add(x = var_2887_cast_fp16, y = c_41_to_fp16)[name = string("acc_63_cast_fp16")]; + tensor var_2889_cast_fp16 = mul(x = acc_63_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2889_cast_fp16")]; + tensor c_43_to_fp16 = const()[name = string("c_43_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111108032)))]; + tensor acc_65_cast_fp16 = add(x = var_2889_cast_fp16, y = c_43_to_fp16)[name = string("acc_65_cast_fp16")]; + tensor var_2891_cast_fp16 = mul(x = acc_65_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2891_cast_fp16")]; + tensor hidden_states_131_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = var_2891_cast_fp16)[name = string("hidden_states_131_cast_fp16")]; + bool full_mask_19_interleave_0 = const()[name = string("full_mask_19_interleave_0"), val = bool(false)]; + tensor full_mask_19_cast_fp16 = concat(axis = var_1991, interleave = full_mask_19_interleave_0, values = (context_mask_25_cast_fp16, fill_8_to_fp16))[name = string("full_mask_19_cast_fp16")]; + tensor input_151_cast_fp16 = mul(x = hidden_states_131_cast_fp16, y = full_mask_19_cast_fp16)[name = string("input_151_cast_fp16")]; + string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")]; + tensor hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111108864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112141120))))[name = string("audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112141696)))]; + tensor hidden_states_133_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized, x = input_151_cast_fp16)[name = string("hidden_states_133_cast_fp16")]; + tensor alpha_over_pi_23_to_fp16 = const()[name = string("alpha_over_pi_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112142528)))]; + tensor theta_over_pi_23_cast_fp16 = mul(x = hidden_states_133_cast_fp16, y = alpha_over_pi_23_to_fp16)[name = string("theta_over_pi_23_cast_fp16")]; + tensor var_2928_cast_fp16 = round(x = theta_over_pi_23_cast_fp16)[name = string("op_2928_cast_fp16")]; + tensor reduced_23_cast_fp16 = sub(x = theta_over_pi_23_cast_fp16, y = var_2928_cast_fp16)[name = string("reduced_23_cast_fp16")]; + tensor reduced_sq_23_cast_fp16 = mul(x = reduced_23_cast_fp16, y = reduced_23_cast_fp16)[name = string("reduced_sq_23_cast_fp16")]; + tensor acc_67_gamma_0_to_fp16 = const()[name = string("acc_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112143360)))]; + tensor acc_67_beta_0_to_fp16 = const()[name = string("acc_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112144192)))]; + fp16 acc_67_epsilon_0_to_fp16 = const()[name = string("acc_67_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_67_cast_fp16 = batch_norm(beta = acc_67_beta_0_to_fp16, epsilon = acc_67_epsilon_0_to_fp16, gamma = acc_67_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_23_cast_fp16)[name = string("acc_67_cast_fp16")]; + tensor var_2941_cast_fp16 = mul(x = acc_67_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2941_cast_fp16")]; + tensor c_45_to_fp16 = const()[name = string("c_45_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112145024)))]; + tensor acc_69_cast_fp16 = add(x = var_2941_cast_fp16, y = c_45_to_fp16)[name = string("acc_69_cast_fp16")]; + tensor var_2943_cast_fp16 = mul(x = acc_69_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2943_cast_fp16")]; + tensor c_47_to_fp16 = const()[name = string("c_47_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112145856)))]; + tensor acc_71_cast_fp16 = add(x = var_2943_cast_fp16, y = c_47_to_fp16)[name = string("acc_71_cast_fp16")]; + tensor var_2945_cast_fp16 = mul(x = acc_71_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2945_cast_fp16")]; + tensor hidden_states_135_cast_fp16 = add(x = hidden_states_133_cast_fp16, y = var_2945_cast_fp16)[name = string("hidden_states_135_cast_fp16")]; + string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")]; + tensor hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112146688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112294208))))[name = string("audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112294784)))]; + tensor hidden_states_137_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_135_cast_fp16)[name = string("hidden_states_137_cast_fp16")]; + tensor hidden_states_139_cast_fp16 = add(x = hidden_states_137_cast_fp16, y = residual_11_cast_fp16)[name = string("hidden_states_139_cast_fp16")]; + tensor context_mask_27_begin_0 = const()[name = string("context_mask_27_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_27_end_0 = const()[name = string("context_mask_27_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_27_end_mask_0 = const()[name = string("context_mask_27_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_27_cast_fp16 = slice_by_index(begin = context_mask_27_begin_0, end = context_mask_27_end_0, end_mask = context_mask_27_end_mask_0, x = context_mask_25_cast_fp16)[name = string("context_mask_27_cast_fp16")]; + tensor residual_13_begin_0 = const()[name = string("residual_13_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_13_end_0 = const()[name = string("residual_13_end_0"), val = tensor([1, 384, 1, 246])]; + tensor residual_13_end_mask_0 = const()[name = string("residual_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_13_cast_fp16 = slice_by_index(begin = residual_13_begin_0, end = residual_13_end_0, end_mask = residual_13_end_mask_0, x = hidden_states_139_cast_fp16)[name = string("residual_13_cast_fp16")]; + tensor alpha_over_pi_25_to_fp16 = const()[name = string("alpha_over_pi_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112295616)))]; + tensor theta_over_pi_25_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = alpha_over_pi_25_to_fp16)[name = string("theta_over_pi_25_cast_fp16")]; + tensor var_2980_cast_fp16 = round(x = theta_over_pi_25_cast_fp16)[name = string("op_2980_cast_fp16")]; + tensor reduced_25_cast_fp16 = sub(x = theta_over_pi_25_cast_fp16, y = var_2980_cast_fp16)[name = string("reduced_25_cast_fp16")]; + tensor reduced_sq_25_cast_fp16 = mul(x = reduced_25_cast_fp16, y = reduced_25_cast_fp16)[name = string("reduced_sq_25_cast_fp16")]; + tensor acc_73_gamma_0_to_fp16 = const()[name = string("acc_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112296448)))]; + tensor acc_73_beta_0_to_fp16 = const()[name = string("acc_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112297280)))]; + fp16 acc_73_epsilon_0_to_fp16 = const()[name = string("acc_73_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_73_cast_fp16 = batch_norm(beta = acc_73_beta_0_to_fp16, epsilon = acc_73_epsilon_0_to_fp16, gamma = acc_73_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_25_cast_fp16)[name = string("acc_73_cast_fp16")]; + tensor var_2993_cast_fp16 = mul(x = acc_73_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_2993_cast_fp16")]; + tensor c_49_to_fp16 = const()[name = string("c_49_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112298112)))]; + tensor acc_75_cast_fp16 = add(x = var_2993_cast_fp16, y = c_49_to_fp16)[name = string("acc_75_cast_fp16")]; + tensor var_2995_cast_fp16 = mul(x = acc_75_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_2995_cast_fp16")]; + tensor c_51_to_fp16 = const()[name = string("c_51_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112298944)))]; + tensor acc_77_cast_fp16 = add(x = var_2995_cast_fp16, y = c_51_to_fp16)[name = string("acc_77_cast_fp16")]; + tensor var_2997_cast_fp16 = mul(x = acc_77_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_2997_cast_fp16")]; + tensor hidden_states_141_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = var_2997_cast_fp16)[name = string("hidden_states_141_cast_fp16")]; + bool full_mask_21_interleave_0 = const()[name = string("full_mask_21_interleave_0"), val = bool(false)]; + tensor full_mask_21_cast_fp16 = concat(axis = var_1991, interleave = full_mask_21_interleave_0, values = (context_mask_27_cast_fp16, fill_8_to_fp16))[name = string("full_mask_21_cast_fp16")]; + tensor input_155_cast_fp16 = mul(x = hidden_states_141_cast_fp16, y = full_mask_21_cast_fp16)[name = string("input_155_cast_fp16")]; + string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")]; + tensor hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112299776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113332032))))[name = string("audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113332608)))]; + tensor hidden_states_143_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized, x = input_155_cast_fp16)[name = string("hidden_states_143_cast_fp16")]; + tensor alpha_over_pi_27_to_fp16 = const()[name = string("alpha_over_pi_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113333440)))]; + tensor theta_over_pi_27_cast_fp16 = mul(x = hidden_states_143_cast_fp16, y = alpha_over_pi_27_to_fp16)[name = string("theta_over_pi_27_cast_fp16")]; + tensor var_3034_cast_fp16 = round(x = theta_over_pi_27_cast_fp16)[name = string("op_3034_cast_fp16")]; + tensor reduced_27_cast_fp16 = sub(x = theta_over_pi_27_cast_fp16, y = var_3034_cast_fp16)[name = string("reduced_27_cast_fp16")]; + tensor reduced_sq_27_cast_fp16 = mul(x = reduced_27_cast_fp16, y = reduced_27_cast_fp16)[name = string("reduced_sq_27_cast_fp16")]; + tensor acc_79_gamma_0_to_fp16 = const()[name = string("acc_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113334272)))]; + tensor acc_79_beta_0_to_fp16 = const()[name = string("acc_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113335104)))]; + fp16 acc_79_epsilon_0_to_fp16 = const()[name = string("acc_79_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_79_cast_fp16 = batch_norm(beta = acc_79_beta_0_to_fp16, epsilon = acc_79_epsilon_0_to_fp16, gamma = acc_79_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_27_cast_fp16)[name = string("acc_79_cast_fp16")]; + tensor var_3047_cast_fp16 = mul(x = acc_79_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3047_cast_fp16")]; + tensor c_53_to_fp16 = const()[name = string("c_53_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113335936)))]; + tensor acc_81_cast_fp16 = add(x = var_3047_cast_fp16, y = c_53_to_fp16)[name = string("acc_81_cast_fp16")]; + tensor var_3049_cast_fp16 = mul(x = acc_81_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3049_cast_fp16")]; + tensor c_55_to_fp16 = const()[name = string("c_55_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113336768)))]; + tensor acc_83_cast_fp16 = add(x = var_3049_cast_fp16, y = c_55_to_fp16)[name = string("acc_83_cast_fp16")]; + tensor var_3051_cast_fp16 = mul(x = acc_83_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3051_cast_fp16")]; + tensor hidden_states_145_cast_fp16 = add(x = hidden_states_143_cast_fp16, y = var_3051_cast_fp16)[name = string("hidden_states_145_cast_fp16")]; + string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")]; + tensor hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113337600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113485120))))[name = string("audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113485696)))]; + tensor hidden_states_147_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_145_cast_fp16)[name = string("hidden_states_147_cast_fp16")]; + tensor hidden_states_149_cast_fp16 = add(x = hidden_states_147_cast_fp16, y = residual_13_cast_fp16)[name = string("hidden_states_149_cast_fp16")]; + tensor context_mask_31_begin_0 = const()[name = string("context_mask_31_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_31_end_0 = const()[name = string("context_mask_31_end_0"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_31_end_mask_0 = const()[name = string("context_mask_31_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_31_cast_fp16 = slice_by_index(begin = context_mask_31_begin_0, end = context_mask_31_end_0, end_mask = context_mask_31_end_mask_0, x = context_mask_23_cast_fp16)[name = string("context_mask_31_cast_fp16")]; + tensor hidden_states_151_begin_0 = const()[name = string("hidden_states_151_begin_0"), val = tensor([0, 0, 0, 4])]; + tensor hidden_states_151_end_0 = const()[name = string("hidden_states_151_end_0"), val = tensor([1, 384, 1, 192])]; + tensor hidden_states_151_end_mask_0 = const()[name = string("hidden_states_151_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_151_cast_fp16 = slice_by_index(begin = hidden_states_151_begin_0, end = hidden_states_151_end_0, end_mask = hidden_states_151_end_mask_0, x = hidden_states_149_cast_fp16)[name = string("hidden_states_151_cast_fp16")]; + tensor context_mask_33_begin_0 = const()[name = string("context_mask_33_begin_0"), val = tensor([0, 0, 0, 4])]; + tensor context_mask_33_end_0 = const()[name = string("context_mask_33_end_0"), val = tensor([1, 1, 1, 32])]; + tensor context_mask_33_end_mask_0 = const()[name = string("context_mask_33_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_33_cast_fp16 = slice_by_index(begin = context_mask_33_begin_0, end = context_mask_33_end_0, end_mask = context_mask_33_end_mask_0, x = context_mask_31_cast_fp16)[name = string("context_mask_33_cast_fp16")]; + tensor alpha_over_pi_29_to_fp16 = const()[name = string("alpha_over_pi_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113486528)))]; + tensor theta_over_pi_29_cast_fp16 = mul(x = hidden_states_151_cast_fp16, y = alpha_over_pi_29_to_fp16)[name = string("theta_over_pi_29_cast_fp16")]; + tensor var_3111_cast_fp16 = round(x = theta_over_pi_29_cast_fp16)[name = string("op_3111_cast_fp16")]; + tensor reduced_29_cast_fp16 = sub(x = theta_over_pi_29_cast_fp16, y = var_3111_cast_fp16)[name = string("reduced_29_cast_fp16")]; + tensor reduced_sq_29_cast_fp16 = mul(x = reduced_29_cast_fp16, y = reduced_29_cast_fp16)[name = string("reduced_sq_29_cast_fp16")]; + tensor acc_85_gamma_0_to_fp16 = const()[name = string("acc_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113487360)))]; + tensor acc_85_beta_0_to_fp16 = const()[name = string("acc_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113488192)))]; + fp16 acc_85_epsilon_0_to_fp16 = const()[name = string("acc_85_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_85_cast_fp16 = batch_norm(beta = acc_85_beta_0_to_fp16, epsilon = acc_85_epsilon_0_to_fp16, gamma = acc_85_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_29_cast_fp16)[name = string("acc_85_cast_fp16")]; + tensor var_3124_cast_fp16 = mul(x = acc_85_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3124_cast_fp16")]; + tensor c_57_to_fp16 = const()[name = string("c_57_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113489024)))]; + tensor acc_87_cast_fp16 = add(x = var_3124_cast_fp16, y = c_57_to_fp16)[name = string("acc_87_cast_fp16")]; + tensor var_3126_cast_fp16 = mul(x = acc_87_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3126_cast_fp16")]; + tensor c_59_to_fp16 = const()[name = string("c_59_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113489856)))]; + tensor acc_89_cast_fp16 = add(x = var_3126_cast_fp16, y = c_59_to_fp16)[name = string("acc_89_cast_fp16")]; + tensor var_3128_cast_fp16 = mul(x = acc_89_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3128_cast_fp16")]; + tensor hidden_states_153_cast_fp16 = add(x = hidden_states_151_cast_fp16, y = var_3128_cast_fp16)[name = string("hidden_states_153_cast_fp16")]; + bool full_mask_23_interleave_0 = const()[name = string("full_mask_23_interleave_0"), val = bool(false)]; + tensor full_mask_23_cast_fp16 = concat(axis = var_1991, interleave = full_mask_23_interleave_0, values = (context_mask_33_cast_fp16, fill_8_to_fp16))[name = string("full_mask_23_cast_fp16")]; + tensor input_159_cast_fp16 = mul(x = hidden_states_153_cast_fp16, y = full_mask_23_cast_fp16)[name = string("input_159_cast_fp16")]; + string sub_pixels_25_pad_type_0 = const()[name = string("sub_pixels_25_pad_type_0"), val = string("valid")]; + tensor sub_pixels_25_strides_0 = const()[name = string("sub_pixels_25_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_25_pad_0 = const()[name = string("sub_pixels_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_25_dilations_0 = const()[name = string("sub_pixels_25_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_25_groups_0 = const()[name = string("sub_pixels_25_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113490688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114080576))))[name = string("audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114081152)))]; + tensor sub_pixels_25_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_1_conv_bias_to_fp16, dilations = sub_pixels_25_dilations_0, groups = sub_pixels_25_groups_0, pad = sub_pixels_25_pad_0, pad_type = sub_pixels_25_pad_type_0, strides = sub_pixels_25_strides_0, weight = audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized, x = input_159_cast_fp16)[name = string("sub_pixels_25_cast_fp16")]; + tensor var_3152 = const()[name = string("op_3152"), val = tensor([1, 4, 192, 187])]; + tensor sub_pixels_27_cast_fp16 = reshape(shape = var_3152, x = sub_pixels_25_cast_fp16)[name = string("sub_pixels_27_cast_fp16")]; + tensor var_3154 = const()[name = string("op_3154"), val = tensor([0, 2, 3, 1])]; + tensor var_3159 = const()[name = string("op_3159"), val = tensor([1, 192, 1, 748])]; + tensor sub_pixels_29_cast_fp16 = transpose(perm = var_3154, x = sub_pixels_27_cast_fp16)[name = string("transpose_1")]; + tensor hidden_states_155_cast_fp16 = reshape(shape = var_3159, x = sub_pixels_29_cast_fp16)[name = string("hidden_states_155_cast_fp16")]; + tensor newest_13_begin_0 = const()[name = string("newest_13_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_13_end_0 = const()[name = string("newest_13_end_0"), val = tensor([1, 1, 1, 28])]; + tensor newest_13_end_mask_0 = const()[name = string("newest_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_13_cast_fp16 = slice_by_index(begin = newest_13_begin_0, end = newest_13_end_0, end_mask = newest_13_end_mask_0, x = context_mask_33_cast_fp16)[name = string("newest_13_cast_fp16")]; + tensor var_3164 = const()[name = string("op_3164"), val = tensor([1, 1, 27, 1])]; + tensor var_3165_cast_fp16 = reshape(shape = var_3164, x = newest_13_cast_fp16)[name = string("op_3165_cast_fp16")]; + tensor spread_13_reps_0 = const()[name = string("spread_13_reps_0"), val = tensor([1, 1, 1, 4])]; + tensor spread_13_cast_fp16 = tile(reps = spread_13_reps_0, x = var_3165_cast_fp16)[name = string("spread_13_cast_fp16")]; + tensor var_3171 = const()[name = string("op_3171"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_35_cast_fp16 = reshape(shape = var_3171, x = spread_13_cast_fp16)[name = string("context_mask_35_cast_fp16")]; + tensor residual_15_begin_0 = const()[name = string("residual_15_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_15_end_0 = const()[name = string("residual_15_end_0"), val = tensor([1, 192, 1, 748])]; + tensor residual_15_end_mask_0 = const()[name = string("residual_15_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_15_cast_fp16 = slice_by_index(begin = residual_15_begin_0, end = residual_15_end_0, end_mask = residual_15_end_mask_0, x = hidden_states_155_cast_fp16)[name = string("residual_15_cast_fp16")]; + tensor alpha_over_pi_31_to_fp16 = const()[name = string("alpha_over_pi_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114082752)))]; + tensor theta_over_pi_31_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = alpha_over_pi_31_to_fp16)[name = string("theta_over_pi_31_cast_fp16")]; + tensor var_3194_cast_fp16 = round(x = theta_over_pi_31_cast_fp16)[name = string("op_3194_cast_fp16")]; + tensor reduced_31_cast_fp16 = sub(x = theta_over_pi_31_cast_fp16, y = var_3194_cast_fp16)[name = string("reduced_31_cast_fp16")]; + tensor reduced_sq_31_cast_fp16 = mul(x = reduced_31_cast_fp16, y = reduced_31_cast_fp16)[name = string("reduced_sq_31_cast_fp16")]; + tensor acc_91_mean_0_to_fp16 = const()[name = string("acc_91_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114083200)))]; + tensor acc_91_variance_0_to_fp16 = const()[name = string("acc_91_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114083648)))]; + tensor acc_91_gamma_0_to_fp16 = const()[name = string("acc_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084096)))]; + tensor acc_91_beta_0_to_fp16 = const()[name = string("acc_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084544)))]; + fp16 acc_91_epsilon_0_to_fp16 = const()[name = string("acc_91_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_91_cast_fp16 = batch_norm(beta = acc_91_beta_0_to_fp16, epsilon = acc_91_epsilon_0_to_fp16, gamma = acc_91_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_31_cast_fp16)[name = string("acc_91_cast_fp16")]; + tensor var_3207_cast_fp16 = mul(x = acc_91_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3207_cast_fp16")]; + tensor c_61_to_fp16 = const()[name = string("c_61_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084992)))]; + tensor acc_93_cast_fp16 = add(x = var_3207_cast_fp16, y = c_61_to_fp16)[name = string("acc_93_cast_fp16")]; + tensor var_3209_cast_fp16 = mul(x = acc_93_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3209_cast_fp16")]; + tensor c_63_to_fp16 = const()[name = string("c_63_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114085440)))]; + tensor acc_95_cast_fp16 = add(x = var_3209_cast_fp16, y = c_63_to_fp16)[name = string("acc_95_cast_fp16")]; + tensor var_3211_cast_fp16 = mul(x = acc_95_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3211_cast_fp16")]; + tensor hidden_states_157_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = var_3211_cast_fp16)[name = string("hidden_states_157_cast_fp16")]; + bool full_mask_25_interleave_0 = const()[name = string("full_mask_25_interleave_0"), val = bool(false)]; + tensor fill_12_to_fp16 = const()[name = string("fill_12_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114085888)))]; + tensor full_mask_25_cast_fp16 = concat(axis = var_1991, interleave = full_mask_25_interleave_0, values = (context_mask_35_cast_fp16, fill_12_to_fp16))[name = string("full_mask_25_cast_fp16")]; + tensor input_161_cast_fp16 = mul(x = hidden_states_157_cast_fp16, y = full_mask_25_cast_fp16)[name = string("input_161_cast_fp16")]; + string hidden_states_159_pad_type_0 = const()[name = string("hidden_states_159_pad_type_0"), val = string("valid")]; + tensor hidden_states_159_strides_0 = const()[name = string("hidden_states_159_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_0 = const()[name = string("hidden_states_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_159_dilations_0 = const()[name = string("hidden_states_159_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_159_groups_0 = const()[name = string("hidden_states_159_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114087232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114345344))))[name = string("audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114345920)))]; + tensor hidden_states_159_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_159_dilations_0, groups = hidden_states_159_groups_0, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = hidden_states_159_strides_0, weight = audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("hidden_states_159_cast_fp16")]; + tensor alpha_over_pi_33_to_fp16 = const()[name = string("alpha_over_pi_33_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114346368)))]; + tensor theta_over_pi_33_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = alpha_over_pi_33_to_fp16)[name = string("theta_over_pi_33_cast_fp16")]; + tensor var_3248_cast_fp16 = round(x = theta_over_pi_33_cast_fp16)[name = string("op_3248_cast_fp16")]; + tensor reduced_33_cast_fp16 = sub(x = theta_over_pi_33_cast_fp16, y = var_3248_cast_fp16)[name = string("reduced_33_cast_fp16")]; + tensor reduced_sq_33_cast_fp16 = mul(x = reduced_33_cast_fp16, y = reduced_33_cast_fp16)[name = string("reduced_sq_33_cast_fp16")]; + tensor acc_97_gamma_0_to_fp16 = const()[name = string("acc_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114346816)))]; + tensor acc_97_beta_0_to_fp16 = const()[name = string("acc_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114347264)))]; + fp16 acc_97_epsilon_0_to_fp16 = const()[name = string("acc_97_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_97_cast_fp16 = batch_norm(beta = acc_97_beta_0_to_fp16, epsilon = acc_97_epsilon_0_to_fp16, gamma = acc_97_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_33_cast_fp16)[name = string("acc_97_cast_fp16")]; + tensor var_3261_cast_fp16 = mul(x = acc_97_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3261_cast_fp16")]; + tensor c_65_to_fp16 = const()[name = string("c_65_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114347712)))]; + tensor acc_99_cast_fp16 = add(x = var_3261_cast_fp16, y = c_65_to_fp16)[name = string("acc_99_cast_fp16")]; + tensor var_3263_cast_fp16 = mul(x = acc_99_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3263_cast_fp16")]; + tensor c_67_to_fp16 = const()[name = string("c_67_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114348160)))]; + tensor acc_101_cast_fp16 = add(x = var_3263_cast_fp16, y = c_67_to_fp16)[name = string("acc_101_cast_fp16")]; + tensor var_3265_cast_fp16 = mul(x = acc_101_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3265_cast_fp16")]; + tensor hidden_states_161_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = var_3265_cast_fp16)[name = string("hidden_states_161_cast_fp16")]; + string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")]; + tensor hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114348608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114385536))))[name = string("audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114386112)))]; + tensor hidden_states_163_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_161_cast_fp16)[name = string("hidden_states_163_cast_fp16")]; + tensor hidden_states_165_cast_fp16 = add(x = hidden_states_163_cast_fp16, y = residual_15_cast_fp16)[name = string("hidden_states_165_cast_fp16")]; + tensor context_mask_37_begin_0 = const()[name = string("context_mask_37_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_37_end_0 = const()[name = string("context_mask_37_end_0"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_37_end_mask_0 = const()[name = string("context_mask_37_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_37_cast_fp16 = slice_by_index(begin = context_mask_37_begin_0, end = context_mask_37_end_0, end_mask = context_mask_37_end_mask_0, x = context_mask_35_cast_fp16)[name = string("context_mask_37_cast_fp16")]; + tensor residual_17_begin_0 = const()[name = string("residual_17_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_17_end_0 = const()[name = string("residual_17_end_0"), val = tensor([1, 192, 1, 742])]; + tensor residual_17_end_mask_0 = const()[name = string("residual_17_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_17_cast_fp16 = slice_by_index(begin = residual_17_begin_0, end = residual_17_end_0, end_mask = residual_17_end_mask_0, x = hidden_states_165_cast_fp16)[name = string("residual_17_cast_fp16")]; + tensor alpha_over_pi_35_to_fp16 = const()[name = string("alpha_over_pi_35_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114386560)))]; + tensor theta_over_pi_35_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = alpha_over_pi_35_to_fp16)[name = string("theta_over_pi_35_cast_fp16")]; + tensor var_3300_cast_fp16 = round(x = theta_over_pi_35_cast_fp16)[name = string("op_3300_cast_fp16")]; + tensor reduced_35_cast_fp16 = sub(x = theta_over_pi_35_cast_fp16, y = var_3300_cast_fp16)[name = string("reduced_35_cast_fp16")]; + tensor reduced_sq_35_cast_fp16 = mul(x = reduced_35_cast_fp16, y = reduced_35_cast_fp16)[name = string("reduced_sq_35_cast_fp16")]; + tensor acc_103_gamma_0_to_fp16 = const()[name = string("acc_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387008)))]; + tensor acc_103_beta_0_to_fp16 = const()[name = string("acc_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387456)))]; + fp16 acc_103_epsilon_0_to_fp16 = const()[name = string("acc_103_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_103_cast_fp16 = batch_norm(beta = acc_103_beta_0_to_fp16, epsilon = acc_103_epsilon_0_to_fp16, gamma = acc_103_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_35_cast_fp16)[name = string("acc_103_cast_fp16")]; + tensor var_3313_cast_fp16 = mul(x = acc_103_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3313_cast_fp16")]; + tensor c_69_to_fp16 = const()[name = string("c_69_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387904)))]; + tensor acc_105_cast_fp16 = add(x = var_3313_cast_fp16, y = c_69_to_fp16)[name = string("acc_105_cast_fp16")]; + tensor var_3315_cast_fp16 = mul(x = acc_105_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3315_cast_fp16")]; + tensor c_71_to_fp16 = const()[name = string("c_71_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114388352)))]; + tensor acc_107_cast_fp16 = add(x = var_3315_cast_fp16, y = c_71_to_fp16)[name = string("acc_107_cast_fp16")]; + tensor var_3317_cast_fp16 = mul(x = acc_107_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3317_cast_fp16")]; + tensor hidden_states_167_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = var_3317_cast_fp16)[name = string("hidden_states_167_cast_fp16")]; + bool full_mask_27_interleave_0 = const()[name = string("full_mask_27_interleave_0"), val = bool(false)]; + tensor full_mask_27_cast_fp16 = concat(axis = var_1991, interleave = full_mask_27_interleave_0, values = (context_mask_37_cast_fp16, fill_12_to_fp16))[name = string("full_mask_27_cast_fp16")]; + tensor input_165_cast_fp16 = mul(x = hidden_states_167_cast_fp16, y = full_mask_27_cast_fp16)[name = string("input_165_cast_fp16")]; + string hidden_states_169_pad_type_0 = const()[name = string("hidden_states_169_pad_type_0"), val = string("valid")]; + tensor hidden_states_169_dilations_0 = const()[name = string("hidden_states_169_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_169_strides_0 = const()[name = string("hidden_states_169_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_169_pad_0 = const()[name = string("hidden_states_169_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_169_groups_0 = const()[name = string("hidden_states_169_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114388800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114646912))))[name = string("audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114647488)))]; + tensor hidden_states_169_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_169_dilations_0, groups = hidden_states_169_groups_0, pad = hidden_states_169_pad_0, pad_type = hidden_states_169_pad_type_0, strides = hidden_states_169_strides_0, weight = audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("hidden_states_169_cast_fp16")]; + tensor alpha_over_pi_37_to_fp16 = const()[name = string("alpha_over_pi_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114647936)))]; + tensor theta_over_pi_37_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = alpha_over_pi_37_to_fp16)[name = string("theta_over_pi_37_cast_fp16")]; + tensor var_3354_cast_fp16 = round(x = theta_over_pi_37_cast_fp16)[name = string("op_3354_cast_fp16")]; + tensor reduced_37_cast_fp16 = sub(x = theta_over_pi_37_cast_fp16, y = var_3354_cast_fp16)[name = string("reduced_37_cast_fp16")]; + tensor reduced_sq_37_cast_fp16 = mul(x = reduced_37_cast_fp16, y = reduced_37_cast_fp16)[name = string("reduced_sq_37_cast_fp16")]; + tensor acc_109_gamma_0_to_fp16 = const()[name = string("acc_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114648384)))]; + tensor acc_109_beta_0_to_fp16 = const()[name = string("acc_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114648832)))]; + fp16 acc_109_epsilon_0_to_fp16 = const()[name = string("acc_109_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_109_cast_fp16 = batch_norm(beta = acc_109_beta_0_to_fp16, epsilon = acc_109_epsilon_0_to_fp16, gamma = acc_109_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_37_cast_fp16)[name = string("acc_109_cast_fp16")]; + tensor var_3367_cast_fp16 = mul(x = acc_109_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3367_cast_fp16")]; + tensor c_73_to_fp16 = const()[name = string("c_73_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114649280)))]; + tensor acc_111_cast_fp16 = add(x = var_3367_cast_fp16, y = c_73_to_fp16)[name = string("acc_111_cast_fp16")]; + tensor var_3369_cast_fp16 = mul(x = acc_111_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3369_cast_fp16")]; + tensor c_75_to_fp16 = const()[name = string("c_75_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114649728)))]; + tensor acc_113_cast_fp16 = add(x = var_3369_cast_fp16, y = c_75_to_fp16)[name = string("acc_113_cast_fp16")]; + tensor var_3371_cast_fp16 = mul(x = acc_113_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3371_cast_fp16")]; + tensor hidden_states_171_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = var_3371_cast_fp16)[name = string("hidden_states_171_cast_fp16")]; + string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")]; + tensor hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114650176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114687104))))[name = string("audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114687680)))]; + tensor hidden_states_173_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_171_cast_fp16)[name = string("hidden_states_173_cast_fp16")]; + tensor hidden_states_175_cast_fp16 = add(x = hidden_states_173_cast_fp16, y = residual_17_cast_fp16)[name = string("hidden_states_175_cast_fp16")]; + tensor context_mask_39_begin_0 = const()[name = string("context_mask_39_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_39_end_0 = const()[name = string("context_mask_39_end_0"), val = tensor([1, 1, 1, 102])]; + tensor context_mask_39_end_mask_0 = const()[name = string("context_mask_39_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_39_cast_fp16 = slice_by_index(begin = context_mask_39_begin_0, end = context_mask_39_end_0, end_mask = context_mask_39_end_mask_0, x = context_mask_37_cast_fp16)[name = string("context_mask_39_cast_fp16")]; + tensor residual_19_begin_0 = const()[name = string("residual_19_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_19_end_0 = const()[name = string("residual_19_end_0"), val = tensor([1, 192, 1, 724])]; + tensor residual_19_end_mask_0 = const()[name = string("residual_19_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_19_cast_fp16 = slice_by_index(begin = residual_19_begin_0, end = residual_19_end_0, end_mask = residual_19_end_mask_0, x = hidden_states_175_cast_fp16)[name = string("residual_19_cast_fp16")]; + tensor alpha_over_pi_39_to_fp16 = const()[name = string("alpha_over_pi_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114688128)))]; + tensor theta_over_pi_39_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = alpha_over_pi_39_to_fp16)[name = string("theta_over_pi_39_cast_fp16")]; + tensor var_3406_cast_fp16 = round(x = theta_over_pi_39_cast_fp16)[name = string("op_3406_cast_fp16")]; + tensor reduced_39_cast_fp16 = sub(x = theta_over_pi_39_cast_fp16, y = var_3406_cast_fp16)[name = string("reduced_39_cast_fp16")]; + tensor reduced_sq_39_cast_fp16 = mul(x = reduced_39_cast_fp16, y = reduced_39_cast_fp16)[name = string("reduced_sq_39_cast_fp16")]; + tensor acc_115_gamma_0_to_fp16 = const()[name = string("acc_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114688576)))]; + tensor acc_115_beta_0_to_fp16 = const()[name = string("acc_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689024)))]; + fp16 acc_115_epsilon_0_to_fp16 = const()[name = string("acc_115_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_115_cast_fp16 = batch_norm(beta = acc_115_beta_0_to_fp16, epsilon = acc_115_epsilon_0_to_fp16, gamma = acc_115_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_39_cast_fp16)[name = string("acc_115_cast_fp16")]; + tensor var_3419_cast_fp16 = mul(x = acc_115_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3419_cast_fp16")]; + tensor c_77_to_fp16 = const()[name = string("c_77_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689472)))]; + tensor acc_117_cast_fp16 = add(x = var_3419_cast_fp16, y = c_77_to_fp16)[name = string("acc_117_cast_fp16")]; + tensor var_3421_cast_fp16 = mul(x = acc_117_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3421_cast_fp16")]; + tensor c_79_to_fp16 = const()[name = string("c_79_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689920)))]; + tensor acc_119_cast_fp16 = add(x = var_3421_cast_fp16, y = c_79_to_fp16)[name = string("acc_119_cast_fp16")]; + tensor var_3423_cast_fp16 = mul(x = acc_119_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3423_cast_fp16")]; + tensor hidden_states_177_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = var_3423_cast_fp16)[name = string("hidden_states_177_cast_fp16")]; + bool full_mask_29_interleave_0 = const()[name = string("full_mask_29_interleave_0"), val = bool(false)]; + tensor full_mask_29_cast_fp16 = concat(axis = var_1991, interleave = full_mask_29_interleave_0, values = (context_mask_39_cast_fp16, fill_12_to_fp16))[name = string("full_mask_29_cast_fp16")]; + tensor input_169_cast_fp16 = mul(x = hidden_states_177_cast_fp16, y = full_mask_29_cast_fp16)[name = string("input_169_cast_fp16")]; + string hidden_states_179_pad_type_0 = const()[name = string("hidden_states_179_pad_type_0"), val = string("valid")]; + tensor hidden_states_179_dilations_0 = const()[name = string("hidden_states_179_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_179_strides_0 = const()[name = string("hidden_states_179_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_179_pad_0 = const()[name = string("hidden_states_179_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_179_groups_0 = const()[name = string("hidden_states_179_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114690368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114948480))))[name = string("audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949056)))]; + tensor hidden_states_179_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_179_dilations_0, groups = hidden_states_179_groups_0, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = hidden_states_179_strides_0, weight = audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized, x = input_169_cast_fp16)[name = string("hidden_states_179_cast_fp16")]; + tensor alpha_over_pi_41_to_fp16 = const()[name = string("alpha_over_pi_41_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949504)))]; + tensor theta_over_pi_41_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = alpha_over_pi_41_to_fp16)[name = string("theta_over_pi_41_cast_fp16")]; + tensor var_3460_cast_fp16 = round(x = theta_over_pi_41_cast_fp16)[name = string("op_3460_cast_fp16")]; + tensor reduced_41_cast_fp16 = sub(x = theta_over_pi_41_cast_fp16, y = var_3460_cast_fp16)[name = string("reduced_41_cast_fp16")]; + tensor reduced_sq_41_cast_fp16 = mul(x = reduced_41_cast_fp16, y = reduced_41_cast_fp16)[name = string("reduced_sq_41_cast_fp16")]; + tensor acc_121_gamma_0_to_fp16 = const()[name = string("acc_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949952)))]; + tensor acc_121_beta_0_to_fp16 = const()[name = string("acc_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114950400)))]; + fp16 acc_121_epsilon_0_to_fp16 = const()[name = string("acc_121_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_121_cast_fp16 = batch_norm(beta = acc_121_beta_0_to_fp16, epsilon = acc_121_epsilon_0_to_fp16, gamma = acc_121_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_41_cast_fp16)[name = string("acc_121_cast_fp16")]; + tensor var_3473_cast_fp16 = mul(x = acc_121_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3473_cast_fp16")]; + tensor c_81_to_fp16 = const()[name = string("c_81_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114950848)))]; + tensor acc_123_cast_fp16 = add(x = var_3473_cast_fp16, y = c_81_to_fp16)[name = string("acc_123_cast_fp16")]; + tensor var_3475_cast_fp16 = mul(x = acc_123_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3475_cast_fp16")]; + tensor c_83_to_fp16 = const()[name = string("c_83_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114951296)))]; + tensor acc_125_cast_fp16 = add(x = var_3475_cast_fp16, y = c_83_to_fp16)[name = string("acc_125_cast_fp16")]; + tensor var_3477_cast_fp16 = mul(x = acc_125_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3477_cast_fp16")]; + tensor hidden_states_181_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = var_3477_cast_fp16)[name = string("hidden_states_181_cast_fp16")]; + string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")]; + tensor hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114951744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114988672))))[name = string("audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114989248)))]; + tensor hidden_states_183_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_181_cast_fp16)[name = string("hidden_states_183_cast_fp16")]; + tensor hidden_states_185_cast_fp16 = add(x = hidden_states_183_cast_fp16, y = residual_19_cast_fp16)[name = string("hidden_states_185_cast_fp16")]; + tensor context_mask_43_begin_0 = const()[name = string("context_mask_43_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_43_end_0 = const()[name = string("context_mask_43_end_0"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_43_end_mask_0 = const()[name = string("context_mask_43_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_43_cast_fp16 = slice_by_index(begin = context_mask_43_begin_0, end = context_mask_43_end_0, end_mask = context_mask_43_end_mask_0, x = context_mask_35_cast_fp16)[name = string("context_mask_43_cast_fp16")]; + tensor hidden_states_187_begin_0 = const()[name = string("hidden_states_187_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor hidden_states_187_end_0 = const()[name = string("hidden_states_187_end_0"), val = tensor([1, 192, 1, 670])]; + tensor hidden_states_187_end_mask_0 = const()[name = string("hidden_states_187_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_187_cast_fp16 = slice_by_index(begin = hidden_states_187_begin_0, end = hidden_states_187_end_0, end_mask = hidden_states_187_end_mask_0, x = hidden_states_185_cast_fp16)[name = string("hidden_states_187_cast_fp16")]; + tensor context_mask_45_begin_0 = const()[name = string("context_mask_45_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor context_mask_45_end_0 = const()[name = string("context_mask_45_end_0"), val = tensor([1, 1, 1, 30])]; + tensor context_mask_45_end_mask_0 = const()[name = string("context_mask_45_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_45_cast_fp16 = slice_by_index(begin = context_mask_45_begin_0, end = context_mask_45_end_0, end_mask = context_mask_45_end_mask_0, x = context_mask_43_cast_fp16)[name = string("context_mask_45_cast_fp16")]; + tensor alpha_over_pi_43_to_fp16 = const()[name = string("alpha_over_pi_43_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114989696)))]; + tensor theta_over_pi_43_cast_fp16 = mul(x = hidden_states_187_cast_fp16, y = alpha_over_pi_43_to_fp16)[name = string("theta_over_pi_43_cast_fp16")]; + tensor var_3537_cast_fp16 = round(x = theta_over_pi_43_cast_fp16)[name = string("op_3537_cast_fp16")]; + tensor reduced_43_cast_fp16 = sub(x = theta_over_pi_43_cast_fp16, y = var_3537_cast_fp16)[name = string("reduced_43_cast_fp16")]; + tensor reduced_sq_43_cast_fp16 = mul(x = reduced_43_cast_fp16, y = reduced_43_cast_fp16)[name = string("reduced_sq_43_cast_fp16")]; + tensor acc_127_gamma_0_to_fp16 = const()[name = string("acc_127_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114990144)))]; + tensor acc_127_beta_0_to_fp16 = const()[name = string("acc_127_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114990592)))]; + fp16 acc_127_epsilon_0_to_fp16 = const()[name = string("acc_127_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_127_cast_fp16 = batch_norm(beta = acc_127_beta_0_to_fp16, epsilon = acc_127_epsilon_0_to_fp16, gamma = acc_127_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_43_cast_fp16)[name = string("acc_127_cast_fp16")]; + tensor var_3550_cast_fp16 = mul(x = acc_127_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3550_cast_fp16")]; + tensor c_85_to_fp16 = const()[name = string("c_85_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991040)))]; + tensor acc_129_cast_fp16 = add(x = var_3550_cast_fp16, y = c_85_to_fp16)[name = string("acc_129_cast_fp16")]; + tensor var_3552_cast_fp16 = mul(x = acc_129_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3552_cast_fp16")]; + tensor c_87_to_fp16 = const()[name = string("c_87_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991488)))]; + tensor acc_131_cast_fp16 = add(x = var_3552_cast_fp16, y = c_87_to_fp16)[name = string("acc_131_cast_fp16")]; + tensor var_3554_cast_fp16 = mul(x = acc_131_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3554_cast_fp16")]; + tensor hidden_states_189_cast_fp16 = add(x = hidden_states_187_cast_fp16, y = var_3554_cast_fp16)[name = string("hidden_states_189_cast_fp16")]; + bool full_mask_31_interleave_0 = const()[name = string("full_mask_31_interleave_0"), val = bool(false)]; + tensor full_mask_31_cast_fp16 = concat(axis = var_1991, interleave = full_mask_31_interleave_0, values = (context_mask_45_cast_fp16, fill_12_to_fp16))[name = string("full_mask_31_cast_fp16")]; + tensor input_173_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = full_mask_31_cast_fp16)[name = string("input_173_cast_fp16")]; + string sub_pixels_31_pad_type_0 = const()[name = string("sub_pixels_31_pad_type_0"), val = string("valid")]; + tensor sub_pixels_31_strides_0 = const()[name = string("sub_pixels_31_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_31_pad_0 = const()[name = string("sub_pixels_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_31_dilations_0 = const()[name = string("sub_pixels_31_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_31_groups_0 = const()[name = string("sub_pixels_31_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115102592))))[name = string("audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115103168)))]; + tensor sub_pixels_31_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_1_conv_bias_to_fp16, dilations = sub_pixels_31_dilations_0, groups = sub_pixels_31_groups_0, pad = sub_pixels_31_pad_0, pad_type = sub_pixels_31_pad_type_0, strides = sub_pixels_31_strides_0, weight = audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("sub_pixels_31_cast_fp16")]; + tensor var_3578 = const()[name = string("op_3578"), val = tensor([1, 3, 96, 668])]; + tensor sub_pixels_33_cast_fp16 = reshape(shape = var_3578, x = sub_pixels_31_cast_fp16)[name = string("sub_pixels_33_cast_fp16")]; + tensor var_3580 = const()[name = string("op_3580"), val = tensor([0, 2, 3, 1])]; + tensor var_3585 = const()[name = string("op_3585"), val = tensor([1, 96, 1, 2004])]; + tensor sub_pixels_cast_fp16 = transpose(perm = var_3580, x = sub_pixels_33_cast_fp16)[name = string("transpose_0")]; + tensor hidden_states_191_cast_fp16 = reshape(shape = var_3585, x = sub_pixels_cast_fp16)[name = string("hidden_states_191_cast_fp16")]; + tensor newest_17_begin_0 = const()[name = string("newest_17_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_17_end_0 = const()[name = string("newest_17_end_0"), val = tensor([1, 1, 1, 29])]; + tensor newest_17_end_mask_0 = const()[name = string("newest_17_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_17_cast_fp16 = slice_by_index(begin = newest_17_begin_0, end = newest_17_end_0, end_mask = newest_17_end_mask_0, x = context_mask_45_cast_fp16)[name = string("newest_17_cast_fp16")]; + tensor var_3590 = const()[name = string("op_3590"), val = tensor([1, 1, 28, 1])]; + tensor var_3591_cast_fp16 = reshape(shape = var_3590, x = newest_17_cast_fp16)[name = string("op_3591_cast_fp16")]; + tensor spread_17_reps_0 = const()[name = string("spread_17_reps_0"), val = tensor([1, 1, 1, 3])]; + tensor spread_17_cast_fp16 = tile(reps = spread_17_reps_0, x = var_3591_cast_fp16)[name = string("spread_17_cast_fp16")]; + tensor var_3597 = const()[name = string("op_3597"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_47_cast_fp16 = reshape(shape = var_3597, x = spread_17_cast_fp16)[name = string("context_mask_47_cast_fp16")]; + tensor residual_21_begin_0 = const()[name = string("residual_21_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_21_end_0 = const()[name = string("residual_21_end_0"), val = tensor([1, 96, 1, 2004])]; + tensor residual_21_end_mask_0 = const()[name = string("residual_21_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_21_cast_fp16 = slice_by_index(begin = residual_21_begin_0, end = residual_21_end_0, end_mask = residual_21_end_mask_0, x = hidden_states_191_cast_fp16)[name = string("residual_21_cast_fp16")]; + tensor alpha_over_pi_45_to_fp16 = const()[name = string("alpha_over_pi_45_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115103808)))]; + tensor theta_over_pi_45_cast_fp16 = mul(x = hidden_states_191_cast_fp16, y = alpha_over_pi_45_to_fp16)[name = string("theta_over_pi_45_cast_fp16")]; + tensor var_3620_cast_fp16 = round(x = theta_over_pi_45_cast_fp16)[name = string("op_3620_cast_fp16")]; + tensor reduced_45_cast_fp16 = sub(x = theta_over_pi_45_cast_fp16, y = var_3620_cast_fp16)[name = string("reduced_45_cast_fp16")]; + tensor reduced_sq_45_cast_fp16 = mul(x = reduced_45_cast_fp16, y = reduced_45_cast_fp16)[name = string("reduced_sq_45_cast_fp16")]; + tensor acc_133_mean_0_to_fp16 = const()[name = string("acc_133_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_133_variance_0_to_fp16 = const()[name = string("acc_133_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_133_gamma_0_to_fp16 = const()[name = string("acc_133_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104576)))]; + tensor acc_133_beta_0_to_fp16 = const()[name = string("acc_133_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104832)))]; + fp16 acc_133_epsilon_0_to_fp16 = const()[name = string("acc_133_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_133_cast_fp16 = batch_norm(beta = acc_133_beta_0_to_fp16, epsilon = acc_133_epsilon_0_to_fp16, gamma = acc_133_gamma_0_to_fp16, mean = acc_133_mean_0_to_fp16, variance = acc_133_variance_0_to_fp16, x = reduced_sq_45_cast_fp16)[name = string("acc_133_cast_fp16")]; + tensor var_3633_cast_fp16 = mul(x = acc_133_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3633_cast_fp16")]; + tensor c_89_to_fp16 = const()[name = string("c_89_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115105088)))]; + tensor acc_135_cast_fp16 = add(x = var_3633_cast_fp16, y = c_89_to_fp16)[name = string("acc_135_cast_fp16")]; + tensor var_3635_cast_fp16 = mul(x = acc_135_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3635_cast_fp16")]; + tensor c_91_to_fp16 = const()[name = string("c_91_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115105344)))]; + tensor acc_137_cast_fp16 = add(x = var_3635_cast_fp16, y = c_91_to_fp16)[name = string("acc_137_cast_fp16")]; + tensor var_3637_cast_fp16 = mul(x = acc_137_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3637_cast_fp16")]; + tensor hidden_states_193_cast_fp16 = add(x = hidden_states_191_cast_fp16, y = var_3637_cast_fp16)[name = string("hidden_states_193_cast_fp16")]; + bool full_mask_33_interleave_0 = const()[name = string("full_mask_33_interleave_0"), val = bool(false)]; + tensor fill_16_to_fp16 = const()[name = string("fill_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115105600)))]; + tensor full_mask_33_cast_fp16 = concat(axis = var_1991, interleave = full_mask_33_interleave_0, values = (context_mask_47_cast_fp16, fill_16_to_fp16))[name = string("full_mask_33_cast_fp16")]; + tensor input_175_cast_fp16 = mul(x = hidden_states_193_cast_fp16, y = full_mask_33_cast_fp16)[name = string("input_175_cast_fp16")]; + string hidden_states_195_pad_type_0 = const()[name = string("hidden_states_195_pad_type_0"), val = string("valid")]; + tensor hidden_states_195_strides_0 = const()[name = string("hidden_states_195_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_0 = const()[name = string("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_195_dilations_0 = const()[name = string("hidden_states_195_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_195_groups_0 = const()[name = string("hidden_states_195_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115109504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174080))))[name = string("audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174656)))]; + tensor hidden_states_195_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_195_dilations_0, groups = hidden_states_195_groups_0, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = hidden_states_195_strides_0, weight = audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized, x = input_175_cast_fp16)[name = string("hidden_states_195_cast_fp16")]; + tensor alpha_over_pi_47_to_fp16 = const()[name = string("alpha_over_pi_47_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174912)))]; + tensor theta_over_pi_47_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = alpha_over_pi_47_to_fp16)[name = string("theta_over_pi_47_cast_fp16")]; + tensor var_3674_cast_fp16 = round(x = theta_over_pi_47_cast_fp16)[name = string("op_3674_cast_fp16")]; + tensor reduced_47_cast_fp16 = sub(x = theta_over_pi_47_cast_fp16, y = var_3674_cast_fp16)[name = string("reduced_47_cast_fp16")]; + tensor reduced_sq_47_cast_fp16 = mul(x = reduced_47_cast_fp16, y = reduced_47_cast_fp16)[name = string("reduced_sq_47_cast_fp16")]; + tensor acc_139_mean_0_to_fp16 = const()[name = string("acc_139_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_139_variance_0_to_fp16 = const()[name = string("acc_139_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_139_gamma_0_to_fp16 = const()[name = string("acc_139_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175168)))]; + tensor acc_139_beta_0_to_fp16 = const()[name = string("acc_139_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175424)))]; + fp16 acc_139_epsilon_0_to_fp16 = const()[name = string("acc_139_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_139_cast_fp16 = batch_norm(beta = acc_139_beta_0_to_fp16, epsilon = acc_139_epsilon_0_to_fp16, gamma = acc_139_gamma_0_to_fp16, mean = acc_139_mean_0_to_fp16, variance = acc_139_variance_0_to_fp16, x = reduced_sq_47_cast_fp16)[name = string("acc_139_cast_fp16")]; + tensor var_3687_cast_fp16 = mul(x = acc_139_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3687_cast_fp16")]; + tensor c_93_to_fp16 = const()[name = string("c_93_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175680)))]; + tensor acc_141_cast_fp16 = add(x = var_3687_cast_fp16, y = c_93_to_fp16)[name = string("acc_141_cast_fp16")]; + tensor var_3689_cast_fp16 = mul(x = acc_141_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3689_cast_fp16")]; + tensor c_95_to_fp16 = const()[name = string("c_95_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175936)))]; + tensor acc_143_cast_fp16 = add(x = var_3689_cast_fp16, y = c_95_to_fp16)[name = string("acc_143_cast_fp16")]; + tensor var_3691_cast_fp16 = mul(x = acc_143_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3691_cast_fp16")]; + tensor hidden_states_197_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = var_3691_cast_fp16)[name = string("hidden_states_197_cast_fp16")]; + string hidden_states_199_pad_type_0 = const()[name = string("hidden_states_199_pad_type_0"), val = string("valid")]; + tensor hidden_states_199_strides_0 = const()[name = string("hidden_states_199_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_0 = const()[name = string("hidden_states_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_199_dilations_0 = const()[name = string("hidden_states_199_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_199_groups_0 = const()[name = string("hidden_states_199_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115176192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115185472))))[name = string("audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186048)))]; + tensor hidden_states_199_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_199_dilations_0, groups = hidden_states_199_groups_0, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = hidden_states_199_strides_0, weight = audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")]; + tensor hidden_states_201_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = residual_21_cast_fp16)[name = string("hidden_states_201_cast_fp16")]; + tensor context_mask_49_begin_0 = const()[name = string("context_mask_49_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_49_end_0 = const()[name = string("context_mask_49_end_0"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_49_end_mask_0 = const()[name = string("context_mask_49_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_49_cast_fp16 = slice_by_index(begin = context_mask_49_begin_0, end = context_mask_49_end_0, end_mask = context_mask_49_end_mask_0, x = context_mask_47_cast_fp16)[name = string("context_mask_49_cast_fp16")]; + tensor residual_23_begin_0 = const()[name = string("residual_23_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_23_end_0 = const()[name = string("residual_23_end_0"), val = tensor([1, 96, 1, 1998])]; + tensor residual_23_end_mask_0 = const()[name = string("residual_23_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_23_cast_fp16 = slice_by_index(begin = residual_23_begin_0, end = residual_23_end_0, end_mask = residual_23_end_mask_0, x = hidden_states_201_cast_fp16)[name = string("residual_23_cast_fp16")]; + tensor alpha_over_pi_49_to_fp16 = const()[name = string("alpha_over_pi_49_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186304)))]; + tensor theta_over_pi_49_cast_fp16 = mul(x = hidden_states_201_cast_fp16, y = alpha_over_pi_49_to_fp16)[name = string("theta_over_pi_49_cast_fp16")]; + tensor var_3726_cast_fp16 = round(x = theta_over_pi_49_cast_fp16)[name = string("op_3726_cast_fp16")]; + tensor reduced_49_cast_fp16 = sub(x = theta_over_pi_49_cast_fp16, y = var_3726_cast_fp16)[name = string("reduced_49_cast_fp16")]; + tensor reduced_sq_49_cast_fp16 = mul(x = reduced_49_cast_fp16, y = reduced_49_cast_fp16)[name = string("reduced_sq_49_cast_fp16")]; + tensor acc_145_mean_0_to_fp16 = const()[name = string("acc_145_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_145_variance_0_to_fp16 = const()[name = string("acc_145_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_145_gamma_0_to_fp16 = const()[name = string("acc_145_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186560)))]; + tensor acc_145_beta_0_to_fp16 = const()[name = string("acc_145_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186816)))]; + fp16 acc_145_epsilon_0_to_fp16 = const()[name = string("acc_145_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_145_cast_fp16 = batch_norm(beta = acc_145_beta_0_to_fp16, epsilon = acc_145_epsilon_0_to_fp16, gamma = acc_145_gamma_0_to_fp16, mean = acc_145_mean_0_to_fp16, variance = acc_145_variance_0_to_fp16, x = reduced_sq_49_cast_fp16)[name = string("acc_145_cast_fp16")]; + tensor var_3739_cast_fp16 = mul(x = acc_145_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3739_cast_fp16")]; + tensor c_97_to_fp16 = const()[name = string("c_97_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187072)))]; + tensor acc_147_cast_fp16 = add(x = var_3739_cast_fp16, y = c_97_to_fp16)[name = string("acc_147_cast_fp16")]; + tensor var_3741_cast_fp16 = mul(x = acc_147_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3741_cast_fp16")]; + tensor c_99_to_fp16 = const()[name = string("c_99_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187328)))]; + tensor acc_149_cast_fp16 = add(x = var_3741_cast_fp16, y = c_99_to_fp16)[name = string("acc_149_cast_fp16")]; + tensor var_3743_cast_fp16 = mul(x = acc_149_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3743_cast_fp16")]; + tensor hidden_states_203_cast_fp16 = add(x = hidden_states_201_cast_fp16, y = var_3743_cast_fp16)[name = string("hidden_states_203_cast_fp16")]; + bool full_mask_35_interleave_0 = const()[name = string("full_mask_35_interleave_0"), val = bool(false)]; + tensor full_mask_35_cast_fp16 = concat(axis = var_1991, interleave = full_mask_35_interleave_0, values = (context_mask_49_cast_fp16, fill_16_to_fp16))[name = string("full_mask_35_cast_fp16")]; + tensor input_179_cast_fp16 = mul(x = hidden_states_203_cast_fp16, y = full_mask_35_cast_fp16)[name = string("input_179_cast_fp16")]; + string hidden_states_205_pad_type_0 = const()[name = string("hidden_states_205_pad_type_0"), val = string("valid")]; + tensor hidden_states_205_dilations_0 = const()[name = string("hidden_states_205_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_205_strides_0 = const()[name = string("hidden_states_205_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_0 = const()[name = string("hidden_states_205_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_205_groups_0 = const()[name = string("hidden_states_205_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252160))))[name = string("audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252736)))]; + tensor hidden_states_205_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_205_dilations_0, groups = hidden_states_205_groups_0, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = hidden_states_205_strides_0, weight = audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized, x = input_179_cast_fp16)[name = string("hidden_states_205_cast_fp16")]; + tensor alpha_over_pi_51_to_fp16 = const()[name = string("alpha_over_pi_51_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252992)))]; + tensor theta_over_pi_51_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = alpha_over_pi_51_to_fp16)[name = string("theta_over_pi_51_cast_fp16")]; + tensor var_3780_cast_fp16 = round(x = theta_over_pi_51_cast_fp16)[name = string("op_3780_cast_fp16")]; + tensor reduced_51_cast_fp16 = sub(x = theta_over_pi_51_cast_fp16, y = var_3780_cast_fp16)[name = string("reduced_51_cast_fp16")]; + tensor reduced_sq_51_cast_fp16 = mul(x = reduced_51_cast_fp16, y = reduced_51_cast_fp16)[name = string("reduced_sq_51_cast_fp16")]; + tensor acc_151_mean_0_to_fp16 = const()[name = string("acc_151_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_151_variance_0_to_fp16 = const()[name = string("acc_151_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_151_gamma_0_to_fp16 = const()[name = string("acc_151_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253248)))]; + tensor acc_151_beta_0_to_fp16 = const()[name = string("acc_151_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253504)))]; + fp16 acc_151_epsilon_0_to_fp16 = const()[name = string("acc_151_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_151_cast_fp16 = batch_norm(beta = acc_151_beta_0_to_fp16, epsilon = acc_151_epsilon_0_to_fp16, gamma = acc_151_gamma_0_to_fp16, mean = acc_151_mean_0_to_fp16, variance = acc_151_variance_0_to_fp16, x = reduced_sq_51_cast_fp16)[name = string("acc_151_cast_fp16")]; + tensor var_3793_cast_fp16 = mul(x = acc_151_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3793_cast_fp16")]; + tensor c_101_to_fp16 = const()[name = string("c_101_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253760)))]; + tensor acc_153_cast_fp16 = add(x = var_3793_cast_fp16, y = c_101_to_fp16)[name = string("acc_153_cast_fp16")]; + tensor var_3795_cast_fp16 = mul(x = acc_153_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3795_cast_fp16")]; + tensor c_103_to_fp16 = const()[name = string("c_103_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115254016)))]; + tensor acc_155_cast_fp16 = add(x = var_3795_cast_fp16, y = c_103_to_fp16)[name = string("acc_155_cast_fp16")]; + tensor var_3797_cast_fp16 = mul(x = acc_155_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3797_cast_fp16")]; + tensor hidden_states_207_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = var_3797_cast_fp16)[name = string("hidden_states_207_cast_fp16")]; + string hidden_states_209_pad_type_0 = const()[name = string("hidden_states_209_pad_type_0"), val = string("valid")]; + tensor hidden_states_209_strides_0 = const()[name = string("hidden_states_209_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_209_pad_0 = const()[name = string("hidden_states_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_209_dilations_0 = const()[name = string("hidden_states_209_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_209_groups_0 = const()[name = string("hidden_states_209_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115254272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115263552))))[name = string("audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264128)))]; + tensor hidden_states_209_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_209_dilations_0, groups = hidden_states_209_groups_0, pad = hidden_states_209_pad_0, pad_type = hidden_states_209_pad_type_0, strides = hidden_states_209_strides_0, weight = audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")]; + tensor hidden_states_211_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = residual_23_cast_fp16)[name = string("hidden_states_211_cast_fp16")]; + tensor context_mask_51_begin_0 = const()[name = string("context_mask_51_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_51_end_0 = const()[name = string("context_mask_51_end_0"), val = tensor([1, 1, 1, 78])]; + tensor context_mask_51_end_mask_0 = const()[name = string("context_mask_51_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_51_cast_fp16 = slice_by_index(begin = context_mask_51_begin_0, end = context_mask_51_end_0, end_mask = context_mask_51_end_mask_0, x = context_mask_49_cast_fp16)[name = string("context_mask_51_cast_fp16")]; + tensor residual_begin_0 = const()[name = string("residual_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_end_0 = const()[name = string("residual_end_0"), val = tensor([1, 96, 1, 1980])]; + tensor residual_end_mask_0 = const()[name = string("residual_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_cast_fp16 = slice_by_index(begin = residual_begin_0, end = residual_end_0, end_mask = residual_end_mask_0, x = hidden_states_211_cast_fp16)[name = string("residual_cast_fp16")]; + tensor alpha_over_pi_53_to_fp16 = const()[name = string("alpha_over_pi_53_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264384)))]; + tensor theta_over_pi_53_cast_fp16 = mul(x = hidden_states_211_cast_fp16, y = alpha_over_pi_53_to_fp16)[name = string("theta_over_pi_53_cast_fp16")]; + tensor var_3832_cast_fp16 = round(x = theta_over_pi_53_cast_fp16)[name = string("op_3832_cast_fp16")]; + tensor reduced_53_cast_fp16 = sub(x = theta_over_pi_53_cast_fp16, y = var_3832_cast_fp16)[name = string("reduced_53_cast_fp16")]; + tensor reduced_sq_53_cast_fp16 = mul(x = reduced_53_cast_fp16, y = reduced_53_cast_fp16)[name = string("reduced_sq_53_cast_fp16")]; + tensor acc_157_mean_0_to_fp16 = const()[name = string("acc_157_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_157_variance_0_to_fp16 = const()[name = string("acc_157_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_157_gamma_0_to_fp16 = const()[name = string("acc_157_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264640)))]; + tensor acc_157_beta_0_to_fp16 = const()[name = string("acc_157_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264896)))]; + fp16 acc_157_epsilon_0_to_fp16 = const()[name = string("acc_157_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_157_cast_fp16 = batch_norm(beta = acc_157_beta_0_to_fp16, epsilon = acc_157_epsilon_0_to_fp16, gamma = acc_157_gamma_0_to_fp16, mean = acc_157_mean_0_to_fp16, variance = acc_157_variance_0_to_fp16, x = reduced_sq_53_cast_fp16)[name = string("acc_157_cast_fp16")]; + tensor var_3845_cast_fp16 = mul(x = acc_157_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3845_cast_fp16")]; + tensor c_105_to_fp16 = const()[name = string("c_105_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265152)))]; + tensor acc_159_cast_fp16 = add(x = var_3845_cast_fp16, y = c_105_to_fp16)[name = string("acc_159_cast_fp16")]; + tensor var_3847_cast_fp16 = mul(x = acc_159_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3847_cast_fp16")]; + tensor c_107_to_fp16 = const()[name = string("c_107_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265408)))]; + tensor acc_161_cast_fp16 = add(x = var_3847_cast_fp16, y = c_107_to_fp16)[name = string("acc_161_cast_fp16")]; + tensor var_3849_cast_fp16 = mul(x = acc_161_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3849_cast_fp16")]; + tensor hidden_states_213_cast_fp16 = add(x = hidden_states_211_cast_fp16, y = var_3849_cast_fp16)[name = string("hidden_states_213_cast_fp16")]; + bool full_mask_37_interleave_0 = const()[name = string("full_mask_37_interleave_0"), val = bool(false)]; + tensor full_mask_37_cast_fp16 = concat(axis = var_1991, interleave = full_mask_37_interleave_0, values = (context_mask_51_cast_fp16, fill_16_to_fp16))[name = string("full_mask_37_cast_fp16")]; + tensor input_183_cast_fp16 = mul(x = hidden_states_213_cast_fp16, y = full_mask_37_cast_fp16)[name = string("input_183_cast_fp16")]; + string hidden_states_215_pad_type_0 = const()[name = string("hidden_states_215_pad_type_0"), val = string("valid")]; + tensor hidden_states_215_dilations_0 = const()[name = string("hidden_states_215_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_215_strides_0 = const()[name = string("hidden_states_215_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_0 = const()[name = string("hidden_states_215_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_215_groups_0 = const()[name = string("hidden_states_215_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115330240))))[name = string("audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115330816)))]; + tensor hidden_states_215_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_215_dilations_0, groups = hidden_states_215_groups_0, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = hidden_states_215_strides_0, weight = audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized, x = input_183_cast_fp16)[name = string("hidden_states_215_cast_fp16")]; + tensor alpha_over_pi_55_to_fp16 = const()[name = string("alpha_over_pi_55_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331072)))]; + tensor theta_over_pi_55_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = alpha_over_pi_55_to_fp16)[name = string("theta_over_pi_55_cast_fp16")]; + tensor var_3886_cast_fp16 = round(x = theta_over_pi_55_cast_fp16)[name = string("op_3886_cast_fp16")]; + tensor reduced_55_cast_fp16 = sub(x = theta_over_pi_55_cast_fp16, y = var_3886_cast_fp16)[name = string("reduced_55_cast_fp16")]; + tensor reduced_sq_55_cast_fp16 = mul(x = reduced_55_cast_fp16, y = reduced_55_cast_fp16)[name = string("reduced_sq_55_cast_fp16")]; + tensor acc_163_mean_0_to_fp16 = const()[name = string("acc_163_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_163_variance_0_to_fp16 = const()[name = string("acc_163_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_163_gamma_0_to_fp16 = const()[name = string("acc_163_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331328)))]; + tensor acc_163_beta_0_to_fp16 = const()[name = string("acc_163_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331584)))]; + fp16 acc_163_epsilon_0_to_fp16 = const()[name = string("acc_163_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_163_cast_fp16 = batch_norm(beta = acc_163_beta_0_to_fp16, epsilon = acc_163_epsilon_0_to_fp16, gamma = acc_163_gamma_0_to_fp16, mean = acc_163_mean_0_to_fp16, variance = acc_163_variance_0_to_fp16, x = reduced_sq_55_cast_fp16)[name = string("acc_163_cast_fp16")]; + tensor var_3899_cast_fp16 = mul(x = acc_163_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3899_cast_fp16")]; + tensor c_109_to_fp16 = const()[name = string("c_109_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331840)))]; + tensor acc_165_cast_fp16 = add(x = var_3899_cast_fp16, y = c_109_to_fp16)[name = string("acc_165_cast_fp16")]; + tensor var_3901_cast_fp16 = mul(x = acc_165_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3901_cast_fp16")]; + tensor c_111_to_fp16 = const()[name = string("c_111_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115332096)))]; + tensor acc_167_cast_fp16 = add(x = var_3901_cast_fp16, y = c_111_to_fp16)[name = string("acc_167_cast_fp16")]; + tensor var_3903_cast_fp16 = mul(x = acc_167_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3903_cast_fp16")]; + tensor hidden_states_217_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = var_3903_cast_fp16)[name = string("hidden_states_217_cast_fp16")]; + string hidden_states_219_pad_type_0 = const()[name = string("hidden_states_219_pad_type_0"), val = string("valid")]; + tensor hidden_states_219_strides_0 = const()[name = string("hidden_states_219_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_0 = const()[name = string("hidden_states_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_219_dilations_0 = const()[name = string("hidden_states_219_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_219_groups_0 = const()[name = string("hidden_states_219_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115332352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115341632))))[name = string("audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342208)))]; + tensor hidden_states_219_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_219_dilations_0, groups = hidden_states_219_groups_0, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = hidden_states_219_strides_0, weight = audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")]; + tensor hidden_states_221_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = residual_cast_fp16)[name = string("hidden_states_221_cast_fp16")]; + tensor context_mask_begin_0 = const()[name = string("context_mask_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_end_0 = const()[name = string("context_mask_end_0"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_end_mask_0 = const()[name = string("context_mask_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_cast_fp16 = slice_by_index(begin = context_mask_begin_0, end = context_mask_end_0, end_mask = context_mask_end_mask_0, x = context_mask_47_cast_fp16)[name = string("context_mask_cast_fp16")]; + tensor alpha_over_pi_to_fp16 = const()[name = string("alpha_over_pi_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342464)))]; + tensor theta_over_pi_cast_fp16 = mul(x = hidden_states_221_cast_fp16, y = alpha_over_pi_to_fp16)[name = string("theta_over_pi_cast_fp16")]; + tensor var_3945_cast_fp16 = round(x = theta_over_pi_cast_fp16)[name = string("op_3945_cast_fp16")]; + tensor reduced_cast_fp16 = sub(x = theta_over_pi_cast_fp16, y = var_3945_cast_fp16)[name = string("reduced_cast_fp16")]; + tensor reduced_sq_cast_fp16 = mul(x = reduced_cast_fp16, y = reduced_cast_fp16)[name = string("reduced_sq_cast_fp16")]; + tensor acc_169_mean_0_to_fp16 = const()[name = string("acc_169_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_169_variance_0_to_fp16 = const()[name = string("acc_169_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_169_gamma_0_to_fp16 = const()[name = string("acc_169_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342720)))]; + tensor acc_169_beta_0_to_fp16 = const()[name = string("acc_169_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342976)))]; + fp16 acc_169_epsilon_0_to_fp16 = const()[name = string("acc_169_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_169_cast_fp16 = batch_norm(beta = acc_169_beta_0_to_fp16, epsilon = acc_169_epsilon_0_to_fp16, gamma = acc_169_gamma_0_to_fp16, mean = acc_169_mean_0_to_fp16, variance = acc_169_variance_0_to_fp16, x = reduced_sq_cast_fp16)[name = string("acc_169_cast_fp16")]; + tensor var_3958_cast_fp16 = mul(x = acc_169_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_3958_cast_fp16")]; + tensor c_113_to_fp16 = const()[name = string("c_113_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343232)))]; + tensor acc_171_cast_fp16 = add(x = var_3958_cast_fp16, y = c_113_to_fp16)[name = string("acc_171_cast_fp16")]; + tensor var_3960_cast_fp16 = mul(x = acc_171_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_3960_cast_fp16")]; + tensor c_to_fp16 = const()[name = string("c_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343488)))]; + tensor acc_cast_fp16 = add(x = var_3960_cast_fp16, y = c_to_fp16)[name = string("acc_cast_fp16")]; + tensor var_3962_cast_fp16 = mul(x = acc_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_3962_cast_fp16")]; + tensor hidden_states_223_cast_fp16 = add(x = hidden_states_221_cast_fp16, y = var_3962_cast_fp16)[name = string("hidden_states_223_cast_fp16")]; + bool full_mask_interleave_0 = const()[name = string("full_mask_interleave_0"), val = bool(false)]; + tensor full_mask_cast_fp16 = concat(axis = var_1991, interleave = full_mask_interleave_0, values = (context_mask_cast_fp16, fill_16_to_fp16))[name = string("full_mask_cast_fp16")]; + tensor input_cast_fp16 = mul(x = hidden_states_223_cast_fp16, y = full_mask_cast_fp16)[name = string("input_cast_fp16")]; + string hidden_states_pad_type_0 = const()[name = string("hidden_states_pad_type_0"), val = string("valid")]; + tensor hidden_states_strides_0 = const()[name = string("hidden_states_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_pad_0 = const()[name = string("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_dilations_0 = const()[name = string("hidden_states_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_groups_0 = const()[name = string("hidden_states_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_6_conv_weight_to_fp16 = const()[name = string("audio_upsampler_decoder_6_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343744)))]; + tensor audio_upsampler_decoder_6_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_6_conv_bias_to_fp16"), val = tensor([-0x1.1p-19])]; + tensor hidden_states_cast_fp16 = conv(bias = audio_upsampler_decoder_6_conv_bias_to_fp16, dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = audio_upsampler_decoder_6_conv_weight_to_fp16, x = input_cast_fp16)[name = string("hidden_states_cast_fp16")]; + fp16 var_1978_to_fp16 = const()[name = string("op_1978_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_1977_to_fp16 = const()[name = string("op_1977_to_fp16"), val = fp16(0x1p+0)]; + tensor audio = clip(alpha = var_1978_to_fp16, beta = var_1977_to_fp16, x = hidden_states_cast_fp16)[name = string("clip_16_cast_fp16")]; + } -> (audio, key_cache_updates, value_cache_updates, hidden_context_update, pre_conv_context_update); + func throughput(tensor audio_codes, tensor cache_length, tensor hidden_context, tensor hidden_context_mask, tensor key_cache, tensor key_padding_mask, tensor kv_cache_update_mask, tensor pre_conv_context, tensor qk_mask, tensor value_cache) { + tensor codes_1_begin_0 = const()[name = string("codes_1_begin_0"), val = tensor([0, 0, 0])]; + tensor codes_1_end_0 = const()[name = string("codes_1_end_0"), val = tensor([1, 1, 4])]; + tensor codes_1_end_mask_0 = const()[name = string("codes_1_end_mask_0"), val = tensor([true, false, true])]; + tensor codes_1 = slice_by_index(begin = codes_1_begin_0, end = codes_1_end_0, end_mask = codes_1_end_mask_0, x = audio_codes)[name = string("codes_1")]; + tensor var_43 = const()[name = string("op_43"), val = tensor([1, 0, 2])]; + tensor input_1_begin_0 = const()[name = string("input_1_begin_0"), val = tensor([0, 0, 0])]; + tensor input_1_end_0 = const()[name = string("input_1_end_0"), val = tensor([1, 1, 4])]; + tensor input_1_end_mask_0 = const()[name = string("input_1_end_mask_0"), val = tensor([false, true, true])]; + tensor input_1_squeeze_mask_0 = const()[name = string("input_1_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor codes_3 = transpose(perm = var_43, x = codes_1)[name = string("transpose_61")]; + tensor input_1 = slice_by_index(begin = input_1_begin_0, end = input_1_end_0, end_mask = input_1_end_mask_0, squeeze_mask = input_1_squeeze_mask_0, x = codes_3)[name = string("input_1")]; + int32 quantized_1_batch_dims_0 = const()[name = string("quantized_1_batch_dims_0"), val = int32(0)]; + bool quantized_1_validate_indices_0 = const()[name = string("quantized_1_validate_indices_0"), val = bool(false)]; + tensor weight_1_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("weight_1_to_fp16_palettized")]; + string input_1_to_int16_dtype_0 = const()[name = string("input_1_to_int16_dtype_0"), val = string("int16")]; + string cast_310_dtype_0 = const()[name = string("cast_310_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor input_1_to_int16 = cast(dtype = input_1_to_int16_dtype_0, x = input_1)[name = string("cast_20")]; + tensor cast_310 = cast(dtype = cast_310_dtype_0, x = input_1_to_int16)[name = string("cast_19")]; + tensor greater_equal_0 = greater_equal(x = cast_310, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_112 = const()[name = string("slice_by_index_112"), val = int32(2048)]; + tensor add_0 = add(x = cast_310, y = slice_by_index_112)[name = string("add_0")]; + tensor select_0 = select(a = cast_310, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_18")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_17")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(2048)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 quantized_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("quantized_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor quantized_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = quantized_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = quantized_1_batch_dims_0, indices = select_0_1, validate_indices = quantized_1_validate_indices_0, x = weight_1_to_fp16_palettized)[name = string("quantized_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor var_56 = const()[name = string("op_56"), val = tensor([0, 2, 1])]; + tensor input_3_axes_0 = const()[name = string("input_3_axes_0"), val = tensor([2])]; + tensor var_57_cast_fp16 = transpose(perm = var_56, x = quantized_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_60")]; + tensor input_3_cast_fp16 = expand_dims(axes = input_3_axes_0, x = var_57_cast_fp16)[name = string("input_3_cast_fp16")]; + string quantized_pad_type_0 = const()[name = string("quantized_pad_type_0"), val = string("valid")]; + tensor quantized_strides_0 = const()[name = string("quantized_strides_0"), val = tensor([1, 1])]; + tensor quantized_pad_0 = const()[name = string("quantized_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor quantized_dilations_0 = const()[name = string("quantized_dilations_0"), val = tensor([1, 1])]; + int32 quantized_groups_0 = const()[name = string("quantized_groups_0"), val = int32(1)]; + tensor quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656128))))[name = string("quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized")]; + tensor quantized_cast_fp16 = conv(dilations = quantized_dilations_0, groups = quantized_groups_0, pad = quantized_pad_0, pad_type = quantized_pad_type_0, strides = quantized_strides_0, weight = quantizer_quantizer_rvq_first_output_proj_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("quantized_cast_fp16")]; + tensor codes_5_begin_0 = const()[name = string("codes_5_begin_0"), val = tensor([0, 1, 0])]; + tensor codes_5_end_0 = const()[name = string("codes_5_end_0"), val = tensor([1, 16, 4])]; + tensor codes_5_end_mask_0 = const()[name = string("codes_5_end_mask_0"), val = tensor([true, true, true])]; + tensor codes_5 = slice_by_index(begin = codes_5_begin_0, end = codes_5_end_0, end_mask = codes_5_end_mask_0, x = audio_codes)[name = string("codes_5")]; + tensor var_69 = const()[name = string("op_69"), val = tensor([1, 0, 2])]; + tensor input_5_begin_0 = const()[name = string("input_5_begin_0"), val = tensor([0, 0, 0])]; + tensor input_5_end_0 = const()[name = string("input_5_end_0"), val = tensor([1, 1, 4])]; + tensor input_5_end_mask_0 = const()[name = string("input_5_end_mask_0"), val = tensor([false, true, true])]; + tensor input_5_squeeze_mask_0 = const()[name = string("input_5_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor codes = transpose(perm = var_69, x = codes_5)[name = string("transpose_59")]; + tensor input_5 = slice_by_index(begin = input_5_begin_0, end = input_5_end_0, end_mask = input_5_end_mask_0, squeeze_mask = input_5_squeeze_mask_0, x = codes)[name = string("input_5")]; + int32 quantized_3_axis_0 = const()[name = string("quantized_3_axis_0"), val = int32(0)]; + int32 quantized_3_batch_dims_0 = const()[name = string("quantized_3_batch_dims_0"), val = int32(0)]; + bool quantized_3_validate_indices_0 = const()[name = string("quantized_3_validate_indices_0"), val = bool(false)]; + tensor weight_5_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1181056))))[name = string("weight_5_to_fp16_palettized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_16")]; + tensor quantized_3_cast_fp16_cast_uint16 = gather(axis = quantized_3_axis_0, batch_dims = quantized_3_batch_dims_0, indices = input_5_to_uint16, validate_indices = quantized_3_validate_indices_0, x = weight_5_to_fp16_palettized)[name = string("quantized_3_cast_fp16_cast_uint16")]; + tensor var_110 = const()[name = string("op_110"), val = tensor([0, 2, 1])]; + tensor quantized_7_axes_0 = const()[name = string("quantized_7_axes_0"), val = tensor([2])]; + tensor var_111_cast_fp16 = transpose(perm = var_110, x = quantized_3_cast_fp16_cast_uint16)[name = string("transpose_58")]; + tensor quantized_7_cast_fp16 = expand_dims(axes = quantized_7_axes_0, x = var_111_cast_fp16)[name = string("quantized_7_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([1, 0, 0])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 4])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([false, true, true])]; + tensor input_7_squeeze_mask_0 = const()[name = string("input_7_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_7 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, squeeze_mask = input_7_squeeze_mask_0, x = codes)[name = string("input_7")]; + int32 quantized_5_axis_0 = const()[name = string("quantized_5_axis_0"), val = int32(0)]; + int32 quantized_5_batch_dims_0 = const()[name = string("quantized_5_batch_dims_0"), val = int32(0)]; + bool quantized_5_validate_indices_0 = const()[name = string("quantized_5_validate_indices_0"), val = bool(false)]; + tensor weight_7_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1181632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705984))))[name = string("weight_7_to_fp16_palettized")]; + string input_7_to_uint16_dtype_0 = const()[name = string("input_7_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_7_to_uint16 = cast(dtype = input_7_to_uint16_dtype_0, x = input_7)[name = string("cast_15")]; + tensor quantized_5_cast_fp16_cast_uint16 = gather(axis = quantized_5_axis_0, batch_dims = quantized_5_batch_dims_0, indices = input_7_to_uint16, validate_indices = quantized_5_validate_indices_0, x = weight_7_to_fp16_palettized)[name = string("quantized_5_cast_fp16_cast_uint16")]; + tensor var_122 = const()[name = string("op_122"), val = tensor([0, 2, 1])]; + tensor layer_out_1_axes_0 = const()[name = string("layer_out_1_axes_0"), val = tensor([2])]; + tensor var_123_cast_fp16 = transpose(perm = var_122, x = quantized_5_cast_fp16_cast_uint16)[name = string("transpose_57")]; + tensor layer_out_1_cast_fp16 = expand_dims(axes = layer_out_1_axes_0, x = var_123_cast_fp16)[name = string("layer_out_1_cast_fp16")]; + tensor quantized_11_cast_fp16 = add(x = quantized_7_cast_fp16, y = layer_out_1_cast_fp16)[name = string("quantized_11_cast_fp16")]; + tensor input_9_begin_0 = const()[name = string("input_9_begin_0"), val = tensor([2, 0, 0])]; + tensor input_9_end_0 = const()[name = string("input_9_end_0"), val = tensor([3, 1, 4])]; + tensor input_9_end_mask_0 = const()[name = string("input_9_end_mask_0"), val = tensor([false, true, true])]; + tensor input_9_squeeze_mask_0 = const()[name = string("input_9_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_9 = slice_by_index(begin = input_9_begin_0, end = input_9_end_0, end_mask = input_9_end_mask_0, squeeze_mask = input_9_squeeze_mask_0, x = codes)[name = string("input_9")]; + int32 quantized_9_axis_0 = const()[name = string("quantized_9_axis_0"), val = int32(0)]; + int32 quantized_9_batch_dims_0 = const()[name = string("quantized_9_batch_dims_0"), val = int32(0)]; + bool quantized_9_validate_indices_0 = const()[name = string("quantized_9_validate_indices_0"), val = bool(false)]; + tensor weight_9_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1706560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2230912))))[name = string("weight_9_to_fp16_palettized")]; + string input_9_to_uint16_dtype_0 = const()[name = string("input_9_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_9_to_uint16 = cast(dtype = input_9_to_uint16_dtype_0, x = input_9)[name = string("cast_14")]; + tensor quantized_9_cast_fp16_cast_uint16 = gather(axis = quantized_9_axis_0, batch_dims = quantized_9_batch_dims_0, indices = input_9_to_uint16, validate_indices = quantized_9_validate_indices_0, x = weight_9_to_fp16_palettized)[name = string("quantized_9_cast_fp16_cast_uint16")]; + tensor var_135 = const()[name = string("op_135"), val = tensor([0, 2, 1])]; + tensor layer_out_3_axes_0 = const()[name = string("layer_out_3_axes_0"), val = tensor([2])]; + tensor var_136_cast_fp16 = transpose(perm = var_135, x = quantized_9_cast_fp16_cast_uint16)[name = string("transpose_56")]; + tensor layer_out_3_cast_fp16 = expand_dims(axes = layer_out_3_axes_0, x = var_136_cast_fp16)[name = string("layer_out_3_cast_fp16")]; + tensor quantized_15_cast_fp16 = add(x = quantized_11_cast_fp16, y = layer_out_3_cast_fp16)[name = string("quantized_15_cast_fp16")]; + tensor input_11_begin_0 = const()[name = string("input_11_begin_0"), val = tensor([3, 0, 0])]; + tensor input_11_end_0 = const()[name = string("input_11_end_0"), val = tensor([4, 1, 4])]; + tensor input_11_end_mask_0 = const()[name = string("input_11_end_mask_0"), val = tensor([false, true, true])]; + tensor input_11_squeeze_mask_0 = const()[name = string("input_11_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_11 = slice_by_index(begin = input_11_begin_0, end = input_11_end_0, end_mask = input_11_end_mask_0, squeeze_mask = input_11_squeeze_mask_0, x = codes)[name = string("input_11")]; + int32 quantized_13_axis_0 = const()[name = string("quantized_13_axis_0"), val = int32(0)]; + int32 quantized_13_batch_dims_0 = const()[name = string("quantized_13_batch_dims_0"), val = int32(0)]; + bool quantized_13_validate_indices_0 = const()[name = string("quantized_13_validate_indices_0"), val = bool(false)]; + tensor weight_11_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2231488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2755840))))[name = string("weight_11_to_fp16_palettized")]; + string input_11_to_uint16_dtype_0 = const()[name = string("input_11_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_11_to_uint16 = cast(dtype = input_11_to_uint16_dtype_0, x = input_11)[name = string("cast_13")]; + tensor quantized_13_cast_fp16_cast_uint16 = gather(axis = quantized_13_axis_0, batch_dims = quantized_13_batch_dims_0, indices = input_11_to_uint16, validate_indices = quantized_13_validate_indices_0, x = weight_11_to_fp16_palettized)[name = string("quantized_13_cast_fp16_cast_uint16")]; + tensor var_148 = const()[name = string("op_148"), val = tensor([0, 2, 1])]; + tensor layer_out_5_axes_0 = const()[name = string("layer_out_5_axes_0"), val = tensor([2])]; + tensor var_149_cast_fp16 = transpose(perm = var_148, x = quantized_13_cast_fp16_cast_uint16)[name = string("transpose_55")]; + tensor layer_out_5_cast_fp16 = expand_dims(axes = layer_out_5_axes_0, x = var_149_cast_fp16)[name = string("layer_out_5_cast_fp16")]; + tensor quantized_19_cast_fp16 = add(x = quantized_15_cast_fp16, y = layer_out_5_cast_fp16)[name = string("quantized_19_cast_fp16")]; + tensor input_13_begin_0 = const()[name = string("input_13_begin_0"), val = tensor([4, 0, 0])]; + tensor input_13_end_0 = const()[name = string("input_13_end_0"), val = tensor([5, 1, 4])]; + tensor input_13_end_mask_0 = const()[name = string("input_13_end_mask_0"), val = tensor([false, true, true])]; + tensor input_13_squeeze_mask_0 = const()[name = string("input_13_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_13 = slice_by_index(begin = input_13_begin_0, end = input_13_end_0, end_mask = input_13_end_mask_0, squeeze_mask = input_13_squeeze_mask_0, x = codes)[name = string("input_13")]; + int32 quantized_17_axis_0 = const()[name = string("quantized_17_axis_0"), val = int32(0)]; + int32 quantized_17_batch_dims_0 = const()[name = string("quantized_17_batch_dims_0"), val = int32(0)]; + bool quantized_17_validate_indices_0 = const()[name = string("quantized_17_validate_indices_0"), val = bool(false)]; + tensor weight_13_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2756416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3280768))))[name = string("weight_13_to_fp16_palettized")]; + string input_13_to_uint16_dtype_0 = const()[name = string("input_13_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_13_to_uint16 = cast(dtype = input_13_to_uint16_dtype_0, x = input_13)[name = string("cast_12")]; + tensor quantized_17_cast_fp16_cast_uint16 = gather(axis = quantized_17_axis_0, batch_dims = quantized_17_batch_dims_0, indices = input_13_to_uint16, validate_indices = quantized_17_validate_indices_0, x = weight_13_to_fp16_palettized)[name = string("quantized_17_cast_fp16_cast_uint16")]; + tensor var_161 = const()[name = string("op_161"), val = tensor([0, 2, 1])]; + tensor layer_out_7_axes_0 = const()[name = string("layer_out_7_axes_0"), val = tensor([2])]; + tensor var_162_cast_fp16 = transpose(perm = var_161, x = quantized_17_cast_fp16_cast_uint16)[name = string("transpose_54")]; + tensor layer_out_7_cast_fp16 = expand_dims(axes = layer_out_7_axes_0, x = var_162_cast_fp16)[name = string("layer_out_7_cast_fp16")]; + tensor quantized_23_cast_fp16 = add(x = quantized_19_cast_fp16, y = layer_out_7_cast_fp16)[name = string("quantized_23_cast_fp16")]; + tensor input_15_begin_0 = const()[name = string("input_15_begin_0"), val = tensor([5, 0, 0])]; + tensor input_15_end_0 = const()[name = string("input_15_end_0"), val = tensor([6, 1, 4])]; + tensor input_15_end_mask_0 = const()[name = string("input_15_end_mask_0"), val = tensor([false, true, true])]; + tensor input_15_squeeze_mask_0 = const()[name = string("input_15_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_15 = slice_by_index(begin = input_15_begin_0, end = input_15_end_0, end_mask = input_15_end_mask_0, squeeze_mask = input_15_squeeze_mask_0, x = codes)[name = string("input_15")]; + int32 quantized_21_axis_0 = const()[name = string("quantized_21_axis_0"), val = int32(0)]; + int32 quantized_21_batch_dims_0 = const()[name = string("quantized_21_batch_dims_0"), val = int32(0)]; + bool quantized_21_validate_indices_0 = const()[name = string("quantized_21_validate_indices_0"), val = bool(false)]; + tensor weight_15_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3281344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3805696))))[name = string("weight_15_to_fp16_palettized")]; + string input_15_to_uint16_dtype_0 = const()[name = string("input_15_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_15_to_uint16 = cast(dtype = input_15_to_uint16_dtype_0, x = input_15)[name = string("cast_11")]; + tensor quantized_21_cast_fp16_cast_uint16 = gather(axis = quantized_21_axis_0, batch_dims = quantized_21_batch_dims_0, indices = input_15_to_uint16, validate_indices = quantized_21_validate_indices_0, x = weight_15_to_fp16_palettized)[name = string("quantized_21_cast_fp16_cast_uint16")]; + tensor var_174 = const()[name = string("op_174"), val = tensor([0, 2, 1])]; + tensor layer_out_9_axes_0 = const()[name = string("layer_out_9_axes_0"), val = tensor([2])]; + tensor var_175_cast_fp16 = transpose(perm = var_174, x = quantized_21_cast_fp16_cast_uint16)[name = string("transpose_53")]; + tensor layer_out_9_cast_fp16 = expand_dims(axes = layer_out_9_axes_0, x = var_175_cast_fp16)[name = string("layer_out_9_cast_fp16")]; + tensor quantized_27_cast_fp16 = add(x = quantized_23_cast_fp16, y = layer_out_9_cast_fp16)[name = string("quantized_27_cast_fp16")]; + tensor input_17_begin_0 = const()[name = string("input_17_begin_0"), val = tensor([6, 0, 0])]; + tensor input_17_end_0 = const()[name = string("input_17_end_0"), val = tensor([7, 1, 4])]; + tensor input_17_end_mask_0 = const()[name = string("input_17_end_mask_0"), val = tensor([false, true, true])]; + tensor input_17_squeeze_mask_0 = const()[name = string("input_17_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_17 = slice_by_index(begin = input_17_begin_0, end = input_17_end_0, end_mask = input_17_end_mask_0, squeeze_mask = input_17_squeeze_mask_0, x = codes)[name = string("input_17")]; + int32 quantized_25_axis_0 = const()[name = string("quantized_25_axis_0"), val = int32(0)]; + int32 quantized_25_batch_dims_0 = const()[name = string("quantized_25_batch_dims_0"), val = int32(0)]; + bool quantized_25_validate_indices_0 = const()[name = string("quantized_25_validate_indices_0"), val = bool(false)]; + tensor weight_17_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3806272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330624))))[name = string("weight_17_to_fp16_palettized")]; + string input_17_to_uint16_dtype_0 = const()[name = string("input_17_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_17_to_uint16 = cast(dtype = input_17_to_uint16_dtype_0, x = input_17)[name = string("cast_10")]; + tensor quantized_25_cast_fp16_cast_uint16 = gather(axis = quantized_25_axis_0, batch_dims = quantized_25_batch_dims_0, indices = input_17_to_uint16, validate_indices = quantized_25_validate_indices_0, x = weight_17_to_fp16_palettized)[name = string("quantized_25_cast_fp16_cast_uint16")]; + tensor var_187 = const()[name = string("op_187"), val = tensor([0, 2, 1])]; + tensor layer_out_11_axes_0 = const()[name = string("layer_out_11_axes_0"), val = tensor([2])]; + tensor var_188_cast_fp16 = transpose(perm = var_187, x = quantized_25_cast_fp16_cast_uint16)[name = string("transpose_52")]; + tensor layer_out_11_cast_fp16 = expand_dims(axes = layer_out_11_axes_0, x = var_188_cast_fp16)[name = string("layer_out_11_cast_fp16")]; + tensor quantized_31_cast_fp16 = add(x = quantized_27_cast_fp16, y = layer_out_11_cast_fp16)[name = string("quantized_31_cast_fp16")]; + tensor input_19_begin_0 = const()[name = string("input_19_begin_0"), val = tensor([7, 0, 0])]; + tensor input_19_end_0 = const()[name = string("input_19_end_0"), val = tensor([8, 1, 4])]; + tensor input_19_end_mask_0 = const()[name = string("input_19_end_mask_0"), val = tensor([false, true, true])]; + tensor input_19_squeeze_mask_0 = const()[name = string("input_19_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_19 = slice_by_index(begin = input_19_begin_0, end = input_19_end_0, end_mask = input_19_end_mask_0, squeeze_mask = input_19_squeeze_mask_0, x = codes)[name = string("input_19")]; + int32 quantized_29_axis_0 = const()[name = string("quantized_29_axis_0"), val = int32(0)]; + int32 quantized_29_batch_dims_0 = const()[name = string("quantized_29_batch_dims_0"), val = int32(0)]; + bool quantized_29_validate_indices_0 = const()[name = string("quantized_29_validate_indices_0"), val = bool(false)]; + tensor weight_19_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4331200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4855552))))[name = string("weight_19_to_fp16_palettized")]; + string input_19_to_uint16_dtype_0 = const()[name = string("input_19_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_19_to_uint16 = cast(dtype = input_19_to_uint16_dtype_0, x = input_19)[name = string("cast_9")]; + tensor quantized_29_cast_fp16_cast_uint16 = gather(axis = quantized_29_axis_0, batch_dims = quantized_29_batch_dims_0, indices = input_19_to_uint16, validate_indices = quantized_29_validate_indices_0, x = weight_19_to_fp16_palettized)[name = string("quantized_29_cast_fp16_cast_uint16")]; + tensor var_200 = const()[name = string("op_200"), val = tensor([0, 2, 1])]; + tensor layer_out_13_axes_0 = const()[name = string("layer_out_13_axes_0"), val = tensor([2])]; + tensor var_201_cast_fp16 = transpose(perm = var_200, x = quantized_29_cast_fp16_cast_uint16)[name = string("transpose_51")]; + tensor layer_out_13_cast_fp16 = expand_dims(axes = layer_out_13_axes_0, x = var_201_cast_fp16)[name = string("layer_out_13_cast_fp16")]; + tensor quantized_35_cast_fp16 = add(x = quantized_31_cast_fp16, y = layer_out_13_cast_fp16)[name = string("quantized_35_cast_fp16")]; + tensor input_21_begin_0 = const()[name = string("input_21_begin_0"), val = tensor([8, 0, 0])]; + tensor input_21_end_0 = const()[name = string("input_21_end_0"), val = tensor([9, 1, 4])]; + tensor input_21_end_mask_0 = const()[name = string("input_21_end_mask_0"), val = tensor([false, true, true])]; + tensor input_21_squeeze_mask_0 = const()[name = string("input_21_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_21 = slice_by_index(begin = input_21_begin_0, end = input_21_end_0, end_mask = input_21_end_mask_0, squeeze_mask = input_21_squeeze_mask_0, x = codes)[name = string("input_21")]; + int32 quantized_33_axis_0 = const()[name = string("quantized_33_axis_0"), val = int32(0)]; + int32 quantized_33_batch_dims_0 = const()[name = string("quantized_33_batch_dims_0"), val = int32(0)]; + bool quantized_33_validate_indices_0 = const()[name = string("quantized_33_validate_indices_0"), val = bool(false)]; + tensor weight_21_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4856128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5380480))))[name = string("weight_21_to_fp16_palettized")]; + string input_21_to_uint16_dtype_0 = const()[name = string("input_21_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_21_to_uint16 = cast(dtype = input_21_to_uint16_dtype_0, x = input_21)[name = string("cast_8")]; + tensor quantized_33_cast_fp16_cast_uint16 = gather(axis = quantized_33_axis_0, batch_dims = quantized_33_batch_dims_0, indices = input_21_to_uint16, validate_indices = quantized_33_validate_indices_0, x = weight_21_to_fp16_palettized)[name = string("quantized_33_cast_fp16_cast_uint16")]; + tensor var_213 = const()[name = string("op_213"), val = tensor([0, 2, 1])]; + tensor layer_out_15_axes_0 = const()[name = string("layer_out_15_axes_0"), val = tensor([2])]; + tensor var_214_cast_fp16 = transpose(perm = var_213, x = quantized_33_cast_fp16_cast_uint16)[name = string("transpose_50")]; + tensor layer_out_15_cast_fp16 = expand_dims(axes = layer_out_15_axes_0, x = var_214_cast_fp16)[name = string("layer_out_15_cast_fp16")]; + tensor quantized_39_cast_fp16 = add(x = quantized_35_cast_fp16, y = layer_out_15_cast_fp16)[name = string("quantized_39_cast_fp16")]; + tensor input_23_begin_0 = const()[name = string("input_23_begin_0"), val = tensor([9, 0, 0])]; + tensor input_23_end_0 = const()[name = string("input_23_end_0"), val = tensor([10, 1, 4])]; + tensor input_23_end_mask_0 = const()[name = string("input_23_end_mask_0"), val = tensor([false, true, true])]; + tensor input_23_squeeze_mask_0 = const()[name = string("input_23_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_23 = slice_by_index(begin = input_23_begin_0, end = input_23_end_0, end_mask = input_23_end_mask_0, squeeze_mask = input_23_squeeze_mask_0, x = codes)[name = string("input_23")]; + int32 quantized_37_axis_0 = const()[name = string("quantized_37_axis_0"), val = int32(0)]; + int32 quantized_37_batch_dims_0 = const()[name = string("quantized_37_batch_dims_0"), val = int32(0)]; + bool quantized_37_validate_indices_0 = const()[name = string("quantized_37_validate_indices_0"), val = bool(false)]; + tensor weight_23_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5381056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5905408))))[name = string("weight_23_to_fp16_palettized")]; + string input_23_to_uint16_dtype_0 = const()[name = string("input_23_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_23_to_uint16 = cast(dtype = input_23_to_uint16_dtype_0, x = input_23)[name = string("cast_7")]; + tensor quantized_37_cast_fp16_cast_uint16 = gather(axis = quantized_37_axis_0, batch_dims = quantized_37_batch_dims_0, indices = input_23_to_uint16, validate_indices = quantized_37_validate_indices_0, x = weight_23_to_fp16_palettized)[name = string("quantized_37_cast_fp16_cast_uint16")]; + tensor var_226 = const()[name = string("op_226"), val = tensor([0, 2, 1])]; + tensor layer_out_17_axes_0 = const()[name = string("layer_out_17_axes_0"), val = tensor([2])]; + tensor var_227_cast_fp16 = transpose(perm = var_226, x = quantized_37_cast_fp16_cast_uint16)[name = string("transpose_49")]; + tensor layer_out_17_cast_fp16 = expand_dims(axes = layer_out_17_axes_0, x = var_227_cast_fp16)[name = string("layer_out_17_cast_fp16")]; + tensor quantized_43_cast_fp16 = add(x = quantized_39_cast_fp16, y = layer_out_17_cast_fp16)[name = string("quantized_43_cast_fp16")]; + tensor input_25_begin_0 = const()[name = string("input_25_begin_0"), val = tensor([10, 0, 0])]; + tensor input_25_end_0 = const()[name = string("input_25_end_0"), val = tensor([11, 1, 4])]; + tensor input_25_end_mask_0 = const()[name = string("input_25_end_mask_0"), val = tensor([false, true, true])]; + tensor input_25_squeeze_mask_0 = const()[name = string("input_25_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_25 = slice_by_index(begin = input_25_begin_0, end = input_25_end_0, end_mask = input_25_end_mask_0, squeeze_mask = input_25_squeeze_mask_0, x = codes)[name = string("input_25")]; + int32 quantized_41_axis_0 = const()[name = string("quantized_41_axis_0"), val = int32(0)]; + int32 quantized_41_batch_dims_0 = const()[name = string("quantized_41_batch_dims_0"), val = int32(0)]; + bool quantized_41_validate_indices_0 = const()[name = string("quantized_41_validate_indices_0"), val = bool(false)]; + tensor weight_25_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5905984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6430336))))[name = string("weight_25_to_fp16_palettized")]; + string input_25_to_uint16_dtype_0 = const()[name = string("input_25_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_25_to_uint16 = cast(dtype = input_25_to_uint16_dtype_0, x = input_25)[name = string("cast_6")]; + tensor quantized_41_cast_fp16_cast_uint16 = gather(axis = quantized_41_axis_0, batch_dims = quantized_41_batch_dims_0, indices = input_25_to_uint16, validate_indices = quantized_41_validate_indices_0, x = weight_25_to_fp16_palettized)[name = string("quantized_41_cast_fp16_cast_uint16")]; + tensor var_239 = const()[name = string("op_239"), val = tensor([0, 2, 1])]; + tensor layer_out_19_axes_0 = const()[name = string("layer_out_19_axes_0"), val = tensor([2])]; + tensor var_240_cast_fp16 = transpose(perm = var_239, x = quantized_41_cast_fp16_cast_uint16)[name = string("transpose_48")]; + tensor layer_out_19_cast_fp16 = expand_dims(axes = layer_out_19_axes_0, x = var_240_cast_fp16)[name = string("layer_out_19_cast_fp16")]; + tensor quantized_47_cast_fp16 = add(x = quantized_43_cast_fp16, y = layer_out_19_cast_fp16)[name = string("quantized_47_cast_fp16")]; + tensor input_27_begin_0 = const()[name = string("input_27_begin_0"), val = tensor([11, 0, 0])]; + tensor input_27_end_0 = const()[name = string("input_27_end_0"), val = tensor([12, 1, 4])]; + tensor input_27_end_mask_0 = const()[name = string("input_27_end_mask_0"), val = tensor([false, true, true])]; + tensor input_27_squeeze_mask_0 = const()[name = string("input_27_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_27 = slice_by_index(begin = input_27_begin_0, end = input_27_end_0, end_mask = input_27_end_mask_0, squeeze_mask = input_27_squeeze_mask_0, x = codes)[name = string("input_27")]; + int32 quantized_45_axis_0 = const()[name = string("quantized_45_axis_0"), val = int32(0)]; + int32 quantized_45_batch_dims_0 = const()[name = string("quantized_45_batch_dims_0"), val = int32(0)]; + bool quantized_45_validate_indices_0 = const()[name = string("quantized_45_validate_indices_0"), val = bool(false)]; + tensor weight_27_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6430912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6955264))))[name = string("weight_27_to_fp16_palettized")]; + string input_27_to_uint16_dtype_0 = const()[name = string("input_27_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_27_to_uint16 = cast(dtype = input_27_to_uint16_dtype_0, x = input_27)[name = string("cast_5")]; + tensor quantized_45_cast_fp16_cast_uint16 = gather(axis = quantized_45_axis_0, batch_dims = quantized_45_batch_dims_0, indices = input_27_to_uint16, validate_indices = quantized_45_validate_indices_0, x = weight_27_to_fp16_palettized)[name = string("quantized_45_cast_fp16_cast_uint16")]; + tensor var_252 = const()[name = string("op_252"), val = tensor([0, 2, 1])]; + tensor layer_out_21_axes_0 = const()[name = string("layer_out_21_axes_0"), val = tensor([2])]; + tensor var_253_cast_fp16 = transpose(perm = var_252, x = quantized_45_cast_fp16_cast_uint16)[name = string("transpose_47")]; + tensor layer_out_21_cast_fp16 = expand_dims(axes = layer_out_21_axes_0, x = var_253_cast_fp16)[name = string("layer_out_21_cast_fp16")]; + tensor quantized_51_cast_fp16 = add(x = quantized_47_cast_fp16, y = layer_out_21_cast_fp16)[name = string("quantized_51_cast_fp16")]; + tensor input_29_begin_0 = const()[name = string("input_29_begin_0"), val = tensor([12, 0, 0])]; + tensor input_29_end_0 = const()[name = string("input_29_end_0"), val = tensor([13, 1, 4])]; + tensor input_29_end_mask_0 = const()[name = string("input_29_end_mask_0"), val = tensor([false, true, true])]; + tensor input_29_squeeze_mask_0 = const()[name = string("input_29_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_29 = slice_by_index(begin = input_29_begin_0, end = input_29_end_0, end_mask = input_29_end_mask_0, squeeze_mask = input_29_squeeze_mask_0, x = codes)[name = string("input_29")]; + int32 quantized_49_axis_0 = const()[name = string("quantized_49_axis_0"), val = int32(0)]; + int32 quantized_49_batch_dims_0 = const()[name = string("quantized_49_batch_dims_0"), val = int32(0)]; + bool quantized_49_validate_indices_0 = const()[name = string("quantized_49_validate_indices_0"), val = bool(false)]; + tensor weight_29_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6955840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480192))))[name = string("weight_29_to_fp16_palettized")]; + string input_29_to_uint16_dtype_0 = const()[name = string("input_29_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_29_to_uint16 = cast(dtype = input_29_to_uint16_dtype_0, x = input_29)[name = string("cast_4")]; + tensor quantized_49_cast_fp16_cast_uint16 = gather(axis = quantized_49_axis_0, batch_dims = quantized_49_batch_dims_0, indices = input_29_to_uint16, validate_indices = quantized_49_validate_indices_0, x = weight_29_to_fp16_palettized)[name = string("quantized_49_cast_fp16_cast_uint16")]; + tensor var_265 = const()[name = string("op_265"), val = tensor([0, 2, 1])]; + tensor layer_out_23_axes_0 = const()[name = string("layer_out_23_axes_0"), val = tensor([2])]; + tensor var_266_cast_fp16 = transpose(perm = var_265, x = quantized_49_cast_fp16_cast_uint16)[name = string("transpose_46")]; + tensor layer_out_23_cast_fp16 = expand_dims(axes = layer_out_23_axes_0, x = var_266_cast_fp16)[name = string("layer_out_23_cast_fp16")]; + tensor quantized_55_cast_fp16 = add(x = quantized_51_cast_fp16, y = layer_out_23_cast_fp16)[name = string("quantized_55_cast_fp16")]; + tensor input_31_begin_0 = const()[name = string("input_31_begin_0"), val = tensor([13, 0, 0])]; + tensor input_31_end_0 = const()[name = string("input_31_end_0"), val = tensor([14, 1, 4])]; + tensor input_31_end_mask_0 = const()[name = string("input_31_end_mask_0"), val = tensor([false, true, true])]; + tensor input_31_squeeze_mask_0 = const()[name = string("input_31_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_31 = slice_by_index(begin = input_31_begin_0, end = input_31_end_0, end_mask = input_31_end_mask_0, squeeze_mask = input_31_squeeze_mask_0, x = codes)[name = string("input_31")]; + int32 quantized_53_axis_0 = const()[name = string("quantized_53_axis_0"), val = int32(0)]; + int32 quantized_53_batch_dims_0 = const()[name = string("quantized_53_batch_dims_0"), val = int32(0)]; + bool quantized_53_validate_indices_0 = const()[name = string("quantized_53_validate_indices_0"), val = bool(false)]; + tensor weight_31_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8005120))))[name = string("weight_31_to_fp16_palettized")]; + string input_31_to_uint16_dtype_0 = const()[name = string("input_31_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_31_to_uint16 = cast(dtype = input_31_to_uint16_dtype_0, x = input_31)[name = string("cast_3")]; + tensor quantized_53_cast_fp16_cast_uint16 = gather(axis = quantized_53_axis_0, batch_dims = quantized_53_batch_dims_0, indices = input_31_to_uint16, validate_indices = quantized_53_validate_indices_0, x = weight_31_to_fp16_palettized)[name = string("quantized_53_cast_fp16_cast_uint16")]; + tensor var_278 = const()[name = string("op_278"), val = tensor([0, 2, 1])]; + tensor layer_out_25_axes_0 = const()[name = string("layer_out_25_axes_0"), val = tensor([2])]; + tensor var_279_cast_fp16 = transpose(perm = var_278, x = quantized_53_cast_fp16_cast_uint16)[name = string("transpose_45")]; + tensor layer_out_25_cast_fp16 = expand_dims(axes = layer_out_25_axes_0, x = var_279_cast_fp16)[name = string("layer_out_25_cast_fp16")]; + tensor quantized_59_cast_fp16 = add(x = quantized_55_cast_fp16, y = layer_out_25_cast_fp16)[name = string("quantized_59_cast_fp16")]; + tensor input_33_begin_0 = const()[name = string("input_33_begin_0"), val = tensor([14, 0, 0])]; + tensor input_33_end_0 = const()[name = string("input_33_end_0"), val = tensor([15, 1, 4])]; + tensor input_33_end_mask_0 = const()[name = string("input_33_end_mask_0"), val = tensor([false, true, true])]; + tensor input_33_squeeze_mask_0 = const()[name = string("input_33_squeeze_mask_0"), val = tensor([true, false, false])]; + tensor input_33 = slice_by_index(begin = input_33_begin_0, end = input_33_end_0, end_mask = input_33_end_mask_0, squeeze_mask = input_33_squeeze_mask_0, x = codes)[name = string("input_33")]; + int32 quantized_57_axis_0 = const()[name = string("quantized_57_axis_0"), val = int32(0)]; + int32 quantized_57_batch_dims_0 = const()[name = string("quantized_57_batch_dims_0"), val = int32(0)]; + bool quantized_57_validate_indices_0 = const()[name = string("quantized_57_validate_indices_0"), val = bool(false)]; + tensor weight_33_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8005696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8530048))))[name = string("weight_33_to_fp16_palettized")]; + string input_33_to_uint16_dtype_0 = const()[name = string("input_33_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_33_to_uint16 = cast(dtype = input_33_to_uint16_dtype_0, x = input_33)[name = string("cast_2")]; + tensor quantized_57_cast_fp16_cast_uint16 = gather(axis = quantized_57_axis_0, batch_dims = quantized_57_batch_dims_0, indices = input_33_to_uint16, validate_indices = quantized_57_validate_indices_0, x = weight_33_to_fp16_palettized)[name = string("quantized_57_cast_fp16_cast_uint16")]; + tensor var_291 = const()[name = string("op_291"), val = tensor([0, 2, 1])]; + tensor layer_out_axes_0 = const()[name = string("layer_out_axes_0"), val = tensor([2])]; + tensor var_292_cast_fp16 = transpose(perm = var_291, x = quantized_57_cast_fp16_cast_uint16)[name = string("transpose_44")]; + tensor layer_out_cast_fp16 = expand_dims(axes = layer_out_axes_0, x = var_292_cast_fp16)[name = string("layer_out_cast_fp16")]; + tensor input_35_cast_fp16 = add(x = quantized_59_cast_fp16, y = layer_out_cast_fp16)[name = string("input_35_cast_fp16")]; + string var_300_pad_type_0 = const()[name = string("op_300_pad_type_0"), val = string("valid")]; + tensor var_300_strides_0 = const()[name = string("op_300_strides_0"), val = tensor([1, 1])]; + tensor var_300_pad_0 = const()[name = string("op_300_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_300_dilations_0 = const()[name = string("op_300_dilations_0"), val = tensor([1, 1])]; + int32 var_300_groups_0 = const()[name = string("op_300_groups_0"), val = int32(1)]; + tensor quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8530624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8661760))))[name = string("quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized")]; + tensor var_300_cast_fp16 = conv(dilations = var_300_dilations_0, groups = var_300_groups_0, pad = var_300_pad_0, pad_type = var_300_pad_type_0, strides = var_300_strides_0, weight = quantizer_quantizer_rvq_rest_output_proj_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor pre_conv_context_update = add(x = quantized_cast_fp16, y = var_300_cast_fp16)[name = string("hidden_in_cast_fp16")]; + tensor var_316_axes_0 = const()[name = string("op_316_axes_0"), val = tensor([1])]; + tensor var_316 = expand_dims(axes = var_316_axes_0, x = cache_length)[name = string("op_316")]; + tensor var_317 = const()[name = string("op_317"), val = tensor([[0, 1, 2, 3]])]; + tensor input_37 = add(x = var_316, y = var_317)[name = string("input_37")]; + int32 var_320_axis_0 = const()[name = string("op_320_axis_0"), val = int32(0)]; + int32 var_320_batch_dims_0 = const()[name = string("op_320_batch_dims_0"), val = int32(0)]; + bool var_320_validate_indices_0 = const()[name = string("op_320_validate_indices_0"), val = bool(false)]; + tensor rope_rope_cos_to_fp16 = const()[name = string("rope_rope_cos_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8662336)))]; + string input_37_to_uint16_dtype_0 = const()[name = string("input_37_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_37_to_uint16 = cast(dtype = input_37_to_uint16_dtype_0, x = input_37)[name = string("cast_1")]; + tensor var_320_cast_fp16_cast_uint16 = gather(axis = var_320_axis_0, batch_dims = var_320_batch_dims_0, indices = input_37_to_uint16, validate_indices = var_320_validate_indices_0, x = rope_rope_cos_to_fp16)[name = string("op_320_cast_fp16_cast_uint16")]; + tensor obj_7_perm_0 = const()[name = string("obj_7_perm_0"), val = tensor([0, 2, 1])]; + int32 var_322_axis_0 = const()[name = string("op_322_axis_0"), val = int32(0)]; + int32 var_322_batch_dims_0 = const()[name = string("op_322_batch_dims_0"), val = int32(0)]; + bool var_322_validate_indices_0 = const()[name = string("op_322_validate_indices_0"), val = bool(false)]; + tensor rope_rope_sin_to_fp16 = const()[name = string("rope_rope_sin_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9186688)))]; + tensor var_322_cast_fp16_cast_uint16 = gather(axis = var_322_axis_0, batch_dims = var_322_batch_dims_0, indices = input_37_to_uint16, validate_indices = var_322_validate_indices_0, x = rope_rope_sin_to_fp16)[name = string("op_322_cast_fp16_cast_uint16")]; + tensor obj_9_perm_0 = const()[name = string("obj_9_perm_0"), val = tensor([0, 2, 1])]; + int32 var_333 = const()[name = string("op_333"), val = int32(-2)]; + int32 var_338 = const()[name = string("op_338"), val = int32(3)]; + int32 var_343 = const()[name = string("op_343"), val = int32(-1)]; + int32 var_344 = const()[name = string("op_344"), val = int32(1)]; + tensor tile_0 = const()[name = string("tile_0"), val = tensor([1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024])]; + int32 var_367_axis_0 = const()[name = string("op_367_axis_0"), val = int32(1)]; + tensor var_367_cast_fp16_0, tensor var_367_cast_fp16_1, tensor var_367_cast_fp16_2, tensor var_367_cast_fp16_3, tensor var_367_cast_fp16_4, tensor var_367_cast_fp16_5, tensor var_367_cast_fp16_6, tensor var_367_cast_fp16_7 = split(axis = var_367_axis_0, split_sizes = tile_0, x = key_cache)[name = string("op_367_cast_fp16")]; + tensor tile_1 = const()[name = string("tile_1"), val = tensor([1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024])]; + int32 var_376_axis_0 = const()[name = string("op_376_axis_0"), val = int32(1)]; + tensor var_376_cast_fp16_0, tensor var_376_cast_fp16_1, tensor var_376_cast_fp16_2, tensor var_376_cast_fp16_3, tensor var_376_cast_fp16_4, tensor var_376_cast_fp16_5, tensor var_376_cast_fp16_6, tensor var_376_cast_fp16_7 = split(axis = var_376_axis_0, split_sizes = tile_1, x = value_cache)[name = string("op_376_cast_fp16")]; + bool input_39_interleave_0 = const()[name = string("input_39_interleave_0"), val = bool(false)]; + tensor input_39_cast_fp16 = concat(axis = var_343, interleave = input_39_interleave_0, values = (pre_conv_context, pre_conv_context_update))[name = string("input_39_cast_fp16")]; + string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("valid")]; + tensor input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor([1, 1])]; + tensor input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor([1, 1])]; + int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(1)]; + tensor pre_transformer_pre_conv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9711040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11283968))))[name = string("pre_transformer_pre_conv_conv_weight_to_fp16_palettized")]; + tensor pre_transformer_pre_conv_conv_bias_to_fp16 = const()[name = string("pre_transformer_pre_conv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11284544)))]; + tensor input_41_cast_fp16 = conv(bias = pre_transformer_pre_conv_conv_bias_to_fp16, dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = pre_transformer_pre_conv_conv_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + string inputs_1_pad_type_0 = const()[name = string("inputs_1_pad_type_0"), val = string("valid")]; + tensor inputs_1_strides_0 = const()[name = string("inputs_1_strides_0"), val = tensor([1, 1])]; + tensor inputs_1_pad_0 = const()[name = string("inputs_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor inputs_1_dilations_0 = const()[name = string("inputs_1_dilations_0"), val = tensor([1, 1])]; + int32 inputs_1_groups_0 = const()[name = string("inputs_1_groups_0"), val = int32(1)]; + tensor pre_transformer_input_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11286656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11811008))))[name = string("pre_transformer_input_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_input_proj_bias_to_fp16 = const()[name = string("pre_transformer_input_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11811584)))]; + tensor inputs_1_cast_fp16 = conv(bias = pre_transformer_input_proj_bias_to_fp16, dilations = inputs_1_dilations_0, groups = inputs_1_groups_0, pad = inputs_1_pad_0, pad_type = inputs_1_pad_type_0, strides = inputs_1_strides_0, weight = pre_transformer_input_proj_weight_to_fp16_palettized, x = input_41_cast_fp16)[name = string("inputs_1_cast_fp16")]; + tensor inputs_sq_1_cast_fp16 = mul(x = inputs_1_cast_fp16, y = inputs_1_cast_fp16)[name = string("inputs_sq_1_cast_fp16")]; + tensor variance_1_axes_0 = const()[name = string("variance_1_axes_0"), val = tensor([1])]; + bool variance_1_keep_dims_0 = const()[name = string("variance_1_keep_dims_0"), val = bool(true)]; + tensor variance_1_cast_fp16 = reduce_mean(axes = variance_1_axes_0, keep_dims = variance_1_keep_dims_0, x = inputs_sq_1_cast_fp16)[name = string("variance_1_cast_fp16")]; + fp16 var_411_to_fp16 = const()[name = string("op_411_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_412_cast_fp16 = add(x = variance_1_cast_fp16, y = var_411_to_fp16)[name = string("op_412_cast_fp16")]; + fp32 var_413_epsilon_0 = const()[name = string("op_413_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_413_cast_fp16 = rsqrt(epsilon = var_413_epsilon_0, x = var_412_cast_fp16)[name = string("op_413_cast_fp16")]; + tensor hidden_states_1_cast_fp16 = mul(x = inputs_1_cast_fp16, y = var_413_cast_fp16)[name = string("hidden_states_1_cast_fp16")]; + tensor w_1_to_fp16 = const()[name = string("w_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11812672)))]; + tensor obj_1_cast_fp16 = mul(x = w_1_to_fp16, y = hidden_states_1_cast_fp16)[name = string("obj_1_cast_fp16")]; + string query_1_pad_type_0 = const()[name = string("query_1_pad_type_0"), val = string("valid")]; + tensor query_1_strides_0 = const()[name = string("query_1_strides_0"), val = tensor([1, 1])]; + tensor query_1_pad_0 = const()[name = string("query_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_1_dilations_0 = const()[name = string("query_1_dilations_0"), val = tensor([1, 1])]; + int32 query_1_groups_0 = const()[name = string("query_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11813760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12338112))))[name = string("pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = string("pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12338688)))]; + tensor query_1_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_1_dilations_0, groups = query_1_groups_0, pad = query_1_pad_0, pad_type = query_1_pad_type_0, strides = query_1_strides_0, weight = pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("query_1_cast_fp16")]; + string key_1_pad_type_0 = const()[name = string("key_1_pad_type_0"), val = string("valid")]; + tensor key_1_strides_0 = const()[name = string("key_1_strides_0"), val = tensor([1, 1])]; + tensor key_1_pad_0 = const()[name = string("key_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_1_dilations_0 = const()[name = string("key_1_dilations_0"), val = tensor([1, 1])]; + int32 key_1_groups_0 = const()[name = string("key_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12340800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12865152))))[name = string("pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_1_cast_fp16 = conv(dilations = key_1_dilations_0, groups = key_1_groups_0, pad = key_1_pad_0, pad_type = key_1_pad_type_0, strides = key_1_strides_0, weight = pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("key_1_cast_fp16")]; + string obj_15_pad_type_0 = const()[name = string("obj_15_pad_type_0"), val = string("valid")]; + tensor obj_15_strides_0 = const()[name = string("obj_15_strides_0"), val = tensor([1, 1])]; + tensor obj_15_pad_0 = const()[name = string("obj_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_15_dilations_0 = const()[name = string("obj_15_dilations_0"), val = tensor([1, 1])]; + int32 obj_15_groups_0 = const()[name = string("obj_15_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12865728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13390080))))[name = string("pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_15_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_15_dilations_0, groups = obj_15_groups_0, pad = obj_15_pad_0, pad_type = obj_15_pad_type_0, strides = obj_15_strides_0, weight = pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = string("obj_15_cast_fp16")]; + tensor var_451 = const()[name = string("op_451"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_1_cast_fp16 = reshape(shape = var_451, x = query_1_cast_fp16)[name = string("mh_q_1_cast_fp16")]; + tensor var_453 = const()[name = string("op_453"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_1_cast_fp16 = reshape(shape = var_453, x = key_1_cast_fp16)[name = string("mh_k_1_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor obj_7_cast_fp16 = transpose(perm = obj_7_perm_0, x = var_320_cast_fp16_cast_uint16)[name = string("transpose_43")]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = obj_7_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor obj_9_cast_fp16 = transpose(perm = obj_9_perm_0, x = var_322_cast_fp16_cast_uint16)[name = string("transpose_42")]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = obj_9_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor var_457_cast_fp16 = mul(x = mh_q_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_457_cast_fp16")]; + tensor var_462_begin_0 = const()[name = string("op_462_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_462_end_0 = const()[name = string("op_462_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_462_end_mask_0 = const()[name = string("op_462_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_462_cast_fp16 = slice_by_index(begin = var_462_begin_0, end = var_462_end_0, end_mask = var_462_end_mask_0, x = mh_q_1_cast_fp16)[name = string("op_462_cast_fp16")]; + tensor var_468_begin_0 = const()[name = string("op_468_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_468_end_0 = const()[name = string("op_468_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_468_end_mask_0 = const()[name = string("op_468_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_468_cast_fp16 = slice_by_index(begin = var_468_begin_0, end = var_468_end_0, end_mask = var_468_end_mask_0, x = mh_q_1_cast_fp16)[name = string("op_468_cast_fp16")]; + fp16 const_33_promoted_to_fp16 = const()[name = string("const_33_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_470_cast_fp16 = mul(x = var_468_cast_fp16, y = const_33_promoted_to_fp16)[name = string("op_470_cast_fp16")]; + bool var_472_interleave_0 = const()[name = string("op_472_interleave_0"), val = bool(false)]; + tensor var_472_cast_fp16 = concat(axis = var_333, interleave = var_472_interleave_0, values = (var_470_cast_fp16, var_462_cast_fp16))[name = string("op_472_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_472_cast_fp16, y = sin_1_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor mh_q_3_cast_fp16 = add(x = var_457_cast_fp16, y = var_473_cast_fp16)[name = string("mh_q_3_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = mh_k_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor var_480_begin_0 = const()[name = string("op_480_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_480_end_0 = const()[name = string("op_480_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_480_end_mask_0 = const()[name = string("op_480_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_480_cast_fp16 = slice_by_index(begin = var_480_begin_0, end = var_480_end_0, end_mask = var_480_end_mask_0, x = mh_k_1_cast_fp16)[name = string("op_480_cast_fp16")]; + tensor var_486_begin_0 = const()[name = string("op_486_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_486_end_0 = const()[name = string("op_486_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_486_end_mask_0 = const()[name = string("op_486_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_486_cast_fp16 = slice_by_index(begin = var_486_begin_0, end = var_486_end_0, end_mask = var_486_end_mask_0, x = mh_k_1_cast_fp16)[name = string("op_486_cast_fp16")]; + fp16 const_36_promoted_to_fp16 = const()[name = string("const_36_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_488_cast_fp16 = mul(x = var_486_cast_fp16, y = const_36_promoted_to_fp16)[name = string("op_488_cast_fp16")]; + bool var_490_interleave_0 = const()[name = string("op_490_interleave_0"), val = bool(false)]; + tensor var_490_cast_fp16 = concat(axis = var_333, interleave = var_490_interleave_0, values = (var_488_cast_fp16, var_480_cast_fp16))[name = string("op_490_cast_fp16")]; + tensor var_491_cast_fp16 = mul(x = var_490_cast_fp16, y = sin_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor mh_k_3_cast_fp16 = add(x = var_475_cast_fp16, y = var_491_cast_fp16)[name = string("mh_k_3_cast_fp16")]; + tensor var_495 = const()[name = string("op_495"), val = tensor([1, 1024, 1, 4])]; + tensor obj_13_cast_fp16 = reshape(shape = var_495, x = mh_k_3_cast_fp16)[name = string("obj_13_cast_fp16")]; + tensor var_498_axes_0 = const()[name = string("op_498_axes_0"), val = tensor([1])]; + bool var_498_keep_dims_0 = const()[name = string("op_498_keep_dims_0"), val = bool(false)]; + tensor var_498_cast_fp16 = reduce_sum(axes = var_498_axes_0, keep_dims = var_498_keep_dims_0, x = kv_cache_update_mask)[name = string("op_498_cast_fp16")]; + fp16 var_332_to_fp16 = const()[name = string("op_332_to_fp16"), val = fp16(0x1p+0)]; + tensor var_499_cast_fp16 = sub(x = var_332_to_fp16, y = var_498_cast_fp16)[name = string("op_499_cast_fp16")]; + tensor var_501_axes_0 = const()[name = string("op_501_axes_0"), val = tensor([1])]; + tensor var_501_cast_fp16 = expand_dims(axes = var_501_axes_0, x = var_499_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_axes_0 = const()[name = string("op_502_axes_0"), val = tensor([2])]; + tensor var_502_cast_fp16 = expand_dims(axes = var_502_axes_0, x = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_5 = const()[name = string("concat_5"), val = tensor([1, 4, 1024])]; + tensor transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = obj_13_cast_fp16)[name = string("transpose_41")]; + tensor reshape_1_cast_fp16 = reshape(shape = concat_5, x = transpose_1_cast_fp16)[name = string("reshape_1_cast_fp16")]; + bool matmul_0_transpose_x_1 = const()[name = string("matmul_0_transpose_x_1"), val = bool(true)]; + bool matmul_0_transpose_y_1 = const()[name = string("matmul_0_transpose_y_1"), val = bool(false)]; + tensor matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_1, transpose_y = matmul_0_transpose_y_1, x = kv_cache_update_mask, y = reshape_1_cast_fp16)[name = string("matmul_0_cast_fp16")]; + tensor concat_9 = const()[name = string("concat_9"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_2_cast_fp16 = reshape(shape = concat_9, x = matmul_0_cast_fp16)[name = string("reshape_2_cast_fp16")]; + tensor key_scatter_1_perm_0 = const()[name = string("key_scatter_1_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_3_perm_0 = const()[name = string("transpose_3_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_15 = const()[name = string("concat_15"), val = tensor([1, 4, 1024])]; + tensor transpose_3_cast_fp16 = transpose(perm = transpose_3_perm_0, x = obj_15_cast_fp16)[name = string("transpose_40")]; + tensor reshape_4_cast_fp16 = reshape(shape = concat_15, x = transpose_3_cast_fp16)[name = string("reshape_4_cast_fp16")]; + bool matmul_1_transpose_x_1 = const()[name = string("matmul_1_transpose_x_1"), val = bool(true)]; + bool matmul_1_transpose_y_1 = const()[name = string("matmul_1_transpose_y_1"), val = bool(false)]; + tensor matmul_1_cast_fp16 = matmul(transpose_x = matmul_1_transpose_x_1, transpose_y = matmul_1_transpose_y_1, x = kv_cache_update_mask, y = reshape_4_cast_fp16)[name = string("matmul_1_cast_fp16")]; + tensor concat_19 = const()[name = string("concat_19"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_5_cast_fp16 = reshape(shape = concat_19, x = matmul_1_cast_fp16)[name = string("reshape_5_cast_fp16")]; + tensor value_scatter_1_perm_0 = const()[name = string("value_scatter_1_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_508_cast_fp16 = mul(x = var_367_cast_fp16_0, y = var_502_cast_fp16)[name = string("op_508_cast_fp16")]; + tensor key_scatter_1_cast_fp16 = transpose(perm = key_scatter_1_perm_0, x = reshape_2_cast_fp16)[name = string("transpose_39")]; + tensor key_3_cast_fp16 = add(x = var_508_cast_fp16, y = key_scatter_1_cast_fp16)[name = string("key_3_cast_fp16")]; + tensor var_510_cast_fp16 = mul(x = var_376_cast_fp16_0, y = var_502_cast_fp16)[name = string("op_510_cast_fp16")]; + tensor value_scatter_1_cast_fp16 = transpose(perm = value_scatter_1_perm_0, x = reshape_5_cast_fp16)[name = string("transpose_38")]; + tensor value_1_cast_fp16 = add(x = var_510_cast_fp16, y = value_scatter_1_cast_fp16)[name = string("value_1_cast_fp16")]; + fp16 var_516_to_fp16 = const()[name = string("op_516_to_fp16"), val = fp16(0x1p-3)]; + tensor var_517_cast_fp16 = mul(x = mh_q_3_cast_fp16, y = var_516_to_fp16)[name = string("op_517_cast_fp16")]; + tensor var_520 = const()[name = string("op_520"), val = tensor([1, 16, 64, 80])]; + tensor var_521_cast_fp16 = reshape(shape = var_520, x = key_3_cast_fp16)[name = string("op_521_cast_fp16")]; + bool mh_w_1_transpose_x_0 = const()[name = string("mh_w_1_transpose_x_0"), val = bool(true)]; + bool mh_w_1_transpose_y_0 = const()[name = string("mh_w_1_transpose_y_0"), val = bool(false)]; + tensor mh_w_1_cast_fp16 = matmul(transpose_x = mh_w_1_transpose_x_0, transpose_y = mh_w_1_transpose_y_0, x = var_517_cast_fp16, y = var_521_cast_fp16)[name = string("mh_w_1_cast_fp16")]; + tensor var_525_axes_0 = const()[name = string("op_525_axes_0"), val = tensor([1])]; + tensor var_525_cast_fp16 = expand_dims(axes = var_525_axes_0, x = key_padding_mask)[name = string("op_525_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([2])]; + tensor var_526_cast_fp16 = expand_dims(axes = var_526_axes_0, x = var_525_cast_fp16)[name = string("op_526_cast_fp16")]; + tensor mh_w_3_cast_fp16 = add(x = mh_w_1_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_3_cast_fp16")]; + tensor qk_mask_3_axes_0 = const()[name = string("qk_mask_3_axes_0"), val = tensor([1])]; + tensor qk_mask_3_cast_fp16 = expand_dims(axes = qk_mask_3_axes_0, x = qk_mask)[name = string("qk_mask_3_cast_fp16")]; + tensor mh_w_5_cast_fp16 = add(x = mh_w_3_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_5_cast_fp16")]; + tensor var_531_cast_fp16 = softmax(axis = var_338, x = mh_w_5_cast_fp16)[name = string("op_531_cast_fp16")]; + tensor var_532 = const()[name = string("op_532"), val = tensor([1, 16, 64, 80])]; + tensor var_533_cast_fp16 = reshape(shape = var_532, x = value_1_cast_fp16)[name = string("op_533_cast_fp16")]; + bool attn_1_transpose_x_0 = const()[name = string("attn_1_transpose_x_0"), val = bool(false)]; + bool attn_1_transpose_y_0 = const()[name = string("attn_1_transpose_y_0"), val = bool(true)]; + tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_533_cast_fp16, y = var_531_cast_fp16)[name = string("attn_1_cast_fp16")]; + tensor var_536 = const()[name = string("op_536"), val = tensor([1, -1, 1, 4])]; + tensor input_43_cast_fp16 = reshape(shape = var_536, x = attn_1_cast_fp16)[name = string("input_43_cast_fp16")]; + string obj_11_pad_type_0 = const()[name = string("obj_11_pad_type_0"), val = string("valid")]; + tensor obj_11_strides_0 = const()[name = string("obj_11_strides_0"), val = tensor([1, 1])]; + tensor obj_11_pad_0 = const()[name = string("obj_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_11_dilations_0 = const()[name = string("obj_11_dilations_0"), val = tensor([1, 1])]; + int32 obj_11_groups_0 = const()[name = string("obj_11_groups_0"), val = int32(1)]; + tensor op_552_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13390656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13915008))))[name = string("op_552_weight_0_to_fp16_palettized")]; + tensor var_552_bias_0_to_fp16 = const()[name = string("op_552_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13915584)))]; + tensor var_552_cast_fp16 = conv(bias = var_552_bias_0_to_fp16, dilations = obj_11_dilations_0, groups = obj_11_groups_0, pad = obj_11_pad_0, pad_type = obj_11_pad_type_0, strides = obj_11_strides_0, weight = op_552_weight_0_to_fp16_palettized, x = input_43_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor inputs_3_cast_fp16 = add(x = inputs_1_cast_fp16, y = var_552_cast_fp16)[name = string("inputs_3_cast_fp16")]; + tensor inputs_sq_3_cast_fp16 = mul(x = inputs_3_cast_fp16, y = inputs_3_cast_fp16)[name = string("inputs_sq_3_cast_fp16")]; + tensor variance_3_axes_0 = const()[name = string("variance_3_axes_0"), val = tensor([1])]; + bool variance_3_keep_dims_0 = const()[name = string("variance_3_keep_dims_0"), val = bool(true)]; + tensor variance_3_cast_fp16 = reduce_mean(axes = variance_3_axes_0, keep_dims = variance_3_keep_dims_0, x = inputs_sq_3_cast_fp16)[name = string("variance_3_cast_fp16")]; + fp16 var_558_to_fp16 = const()[name = string("op_558_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_559_cast_fp16 = add(x = variance_3_cast_fp16, y = var_558_to_fp16)[name = string("op_559_cast_fp16")]; + fp32 var_560_epsilon_0 = const()[name = string("op_560_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_560_cast_fp16 = rsqrt(epsilon = var_560_epsilon_0, x = var_559_cast_fp16)[name = string("op_560_cast_fp16")]; + tensor hidden_states_3_cast_fp16 = mul(x = inputs_3_cast_fp16, y = var_560_cast_fp16)[name = string("hidden_states_3_cast_fp16")]; + tensor w_3_to_fp16 = const()[name = string("w_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13916672)))]; + tensor input_45_cast_fp16 = mul(x = w_3_to_fp16, y = hidden_states_3_cast_fp16)[name = string("input_45_cast_fp16")]; + string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")]; + tensor input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor([1, 1])]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor([1, 1])]; + int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13917760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14442112))))[name = string("pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_47_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = pre_transformer_layers_0_mlp_fc3_weight_to_fp16_palettized, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor gate_1_cast_fp16 = silu(x = input_47_cast_fp16)[name = string("gate_1_cast_fp16")]; + string up_1_pad_type_0 = const()[name = string("up_1_pad_type_0"), val = string("valid")]; + tensor up_1_strides_0 = const()[name = string("up_1_strides_0"), val = tensor([1, 1])]; + tensor up_1_pad_0 = const()[name = string("up_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_1_dilations_0 = const()[name = string("up_1_dilations_0"), val = tensor([1, 1])]; + int32 up_1_groups_0 = const()[name = string("up_1_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14442688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14967040))))[name = string("pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_1_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_1_dilations_0, groups = up_1_groups_0, pad = up_1_pad_0, pad_type = up_1_pad_type_0, strides = up_1_strides_0, weight = pre_transformer_layers_0_mlp_fc1_weight_to_fp16_palettized, x = input_45_cast_fp16)[name = string("up_1_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = gate_1_cast_fp16, y = up_1_cast_fp16)[name = string("input_49_cast_fp16")]; + string hidden_states_5_pad_type_0 = const()[name = string("hidden_states_5_pad_type_0"), val = string("valid")]; + tensor hidden_states_5_strides_0 = const()[name = string("hidden_states_5_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_0 = const()[name = string("hidden_states_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_5_dilations_0 = const()[name = string("hidden_states_5_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_5_groups_0 = const()[name = string("hidden_states_5_groups_0"), val = int32(1)]; + tensor op_594_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14967616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15491968))))[name = string("op_594_weight_0_to_fp16_palettized")]; + tensor var_594_bias_0_to_fp16 = const()[name = string("op_594_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15492544)))]; + tensor var_594_cast_fp16 = conv(bias = var_594_bias_0_to_fp16, dilations = hidden_states_5_dilations_0, groups = hidden_states_5_groups_0, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = hidden_states_5_strides_0, weight = op_594_weight_0_to_fp16_palettized, x = input_49_cast_fp16)[name = string("op_594_cast_fp16")]; + tensor inputs_5_cast_fp16 = add(x = inputs_3_cast_fp16, y = var_594_cast_fp16)[name = string("inputs_5_cast_fp16")]; + tensor inputs_sq_5_cast_fp16 = mul(x = inputs_5_cast_fp16, y = inputs_5_cast_fp16)[name = string("inputs_sq_5_cast_fp16")]; + tensor variance_5_axes_0 = const()[name = string("variance_5_axes_0"), val = tensor([1])]; + bool variance_5_keep_dims_0 = const()[name = string("variance_5_keep_dims_0"), val = bool(true)]; + tensor variance_5_cast_fp16 = reduce_mean(axes = variance_5_axes_0, keep_dims = variance_5_keep_dims_0, x = inputs_sq_5_cast_fp16)[name = string("variance_5_cast_fp16")]; + fp16 var_610_to_fp16 = const()[name = string("op_610_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_611_cast_fp16 = add(x = variance_5_cast_fp16, y = var_610_to_fp16)[name = string("op_611_cast_fp16")]; + fp32 var_612_epsilon_0 = const()[name = string("op_612_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_612_cast_fp16 = rsqrt(epsilon = var_612_epsilon_0, x = var_611_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor hidden_states_7_cast_fp16 = mul(x = inputs_5_cast_fp16, y = var_612_cast_fp16)[name = string("hidden_states_7_cast_fp16")]; + tensor w_5_to_fp16 = const()[name = string("w_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15493632)))]; + tensor obj_17_cast_fp16 = mul(x = w_5_to_fp16, y = hidden_states_7_cast_fp16)[name = string("obj_17_cast_fp16")]; + string query_5_pad_type_0 = const()[name = string("query_5_pad_type_0"), val = string("valid")]; + tensor query_5_strides_0 = const()[name = string("query_5_strides_0"), val = tensor([1, 1])]; + tensor query_5_pad_0 = const()[name = string("query_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_5_dilations_0 = const()[name = string("query_5_dilations_0"), val = tensor([1, 1])]; + int32 query_5_groups_0 = const()[name = string("query_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15494720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16019072))))[name = string("pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_5_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_5_dilations_0, groups = query_5_groups_0, pad = query_5_pad_0, pad_type = query_5_pad_type_0, strides = query_5_strides_0, weight = pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized, x = obj_17_cast_fp16)[name = string("query_5_cast_fp16")]; + string key_5_pad_type_0 = const()[name = string("key_5_pad_type_0"), val = string("valid")]; + tensor key_5_strides_0 = const()[name = string("key_5_strides_0"), val = tensor([1, 1])]; + tensor key_5_pad_0 = const()[name = string("key_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_5_dilations_0 = const()[name = string("key_5_dilations_0"), val = tensor([1, 1])]; + int32 key_5_groups_0 = const()[name = string("key_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16019648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16544000))))[name = string("pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_5_cast_fp16 = conv(dilations = key_5_dilations_0, groups = key_5_groups_0, pad = key_5_pad_0, pad_type = key_5_pad_type_0, strides = key_5_strides_0, weight = pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized, x = obj_17_cast_fp16)[name = string("key_5_cast_fp16")]; + string obj_27_pad_type_0 = const()[name = string("obj_27_pad_type_0"), val = string("valid")]; + tensor obj_27_strides_0 = const()[name = string("obj_27_strides_0"), val = tensor([1, 1])]; + tensor obj_27_pad_0 = const()[name = string("obj_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_27_dilations_0 = const()[name = string("obj_27_dilations_0"), val = tensor([1, 1])]; + int32 obj_27_groups_0 = const()[name = string("obj_27_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16544576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17068928))))[name = string("pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_27_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_27_dilations_0, groups = obj_27_groups_0, pad = obj_27_pad_0, pad_type = obj_27_pad_type_0, strides = obj_27_strides_0, weight = pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized, x = obj_17_cast_fp16)[name = string("obj_27_cast_fp16")]; + tensor var_650 = const()[name = string("op_650"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_7_cast_fp16 = reshape(shape = var_650, x = query_5_cast_fp16)[name = string("mh_q_7_cast_fp16")]; + tensor var_652 = const()[name = string("op_652"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_5_cast_fp16 = reshape(shape = var_652, x = key_5_cast_fp16)[name = string("mh_k_5_cast_fp16")]; + tensor var_656_cast_fp16 = mul(x = mh_q_7_cast_fp16, y = cos_1_cast_fp16)[name = string("op_656_cast_fp16")]; + tensor var_661_begin_0 = const()[name = string("op_661_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_661_end_0 = const()[name = string("op_661_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_661_end_mask_0 = const()[name = string("op_661_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_661_cast_fp16 = slice_by_index(begin = var_661_begin_0, end = var_661_end_0, end_mask = var_661_end_mask_0, x = mh_q_7_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_667_begin_0 = const()[name = string("op_667_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_667_end_0 = const()[name = string("op_667_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_667_end_mask_0 = const()[name = string("op_667_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_667_cast_fp16 = slice_by_index(begin = var_667_begin_0, end = var_667_end_0, end_mask = var_667_end_mask_0, x = mh_q_7_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 const_52_promoted_to_fp16 = const()[name = string("const_52_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_669_cast_fp16 = mul(x = var_667_cast_fp16, y = const_52_promoted_to_fp16)[name = string("op_669_cast_fp16")]; + bool var_671_interleave_0 = const()[name = string("op_671_interleave_0"), val = bool(false)]; + tensor var_671_cast_fp16 = concat(axis = var_333, interleave = var_671_interleave_0, values = (var_669_cast_fp16, var_661_cast_fp16))[name = string("op_671_cast_fp16")]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = sin_1_cast_fp16)[name = string("op_672_cast_fp16")]; + tensor mh_q_9_cast_fp16 = add(x = var_656_cast_fp16, y = var_672_cast_fp16)[name = string("mh_q_9_cast_fp16")]; + tensor var_674_cast_fp16 = mul(x = mh_k_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_674_cast_fp16")]; + tensor var_679_begin_0 = const()[name = string("op_679_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_679_end_0 = const()[name = string("op_679_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_679_end_mask_0 = const()[name = string("op_679_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_679_cast_fp16 = slice_by_index(begin = var_679_begin_0, end = var_679_end_0, end_mask = var_679_end_mask_0, x = mh_k_5_cast_fp16)[name = string("op_679_cast_fp16")]; + tensor var_685_begin_0 = const()[name = string("op_685_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_685_end_0 = const()[name = string("op_685_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_685_end_mask_0 = const()[name = string("op_685_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_685_cast_fp16 = slice_by_index(begin = var_685_begin_0, end = var_685_end_0, end_mask = var_685_end_mask_0, x = mh_k_5_cast_fp16)[name = string("op_685_cast_fp16")]; + fp16 const_55_promoted_to_fp16 = const()[name = string("const_55_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_687_cast_fp16 = mul(x = var_685_cast_fp16, y = const_55_promoted_to_fp16)[name = string("op_687_cast_fp16")]; + bool var_689_interleave_0 = const()[name = string("op_689_interleave_0"), val = bool(false)]; + tensor var_689_cast_fp16 = concat(axis = var_333, interleave = var_689_interleave_0, values = (var_687_cast_fp16, var_679_cast_fp16))[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_689_cast_fp16, y = sin_1_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor mh_k_7_cast_fp16 = add(x = var_674_cast_fp16, y = var_690_cast_fp16)[name = string("mh_k_7_cast_fp16")]; + tensor var_694 = const()[name = string("op_694"), val = tensor([1, 1024, 1, 4])]; + tensor obj_25_cast_fp16 = reshape(shape = var_694, x = mh_k_7_cast_fp16)[name = string("obj_25_cast_fp16")]; + tensor transpose_5_perm_0 = const()[name = string("transpose_5_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_25 = const()[name = string("concat_25"), val = tensor([1, 4, 1024])]; + tensor transpose_5_cast_fp16 = transpose(perm = transpose_5_perm_0, x = obj_25_cast_fp16)[name = string("transpose_37")]; + tensor reshape_7_cast_fp16 = reshape(shape = concat_25, x = transpose_5_cast_fp16)[name = string("reshape_7_cast_fp16")]; + bool matmul_2_transpose_x_1 = const()[name = string("matmul_2_transpose_x_1"), val = bool(true)]; + bool matmul_2_transpose_y_1 = const()[name = string("matmul_2_transpose_y_1"), val = bool(false)]; + tensor matmul_2_cast_fp16 = matmul(transpose_x = matmul_2_transpose_x_1, transpose_y = matmul_2_transpose_y_1, x = kv_cache_update_mask, y = reshape_7_cast_fp16)[name = string("matmul_2_cast_fp16")]; + tensor concat_29 = const()[name = string("concat_29"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_8_cast_fp16 = reshape(shape = concat_29, x = matmul_2_cast_fp16)[name = string("reshape_8_cast_fp16")]; + tensor key_scatter_3_perm_0 = const()[name = string("key_scatter_3_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_7_perm_0 = const()[name = string("transpose_7_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_35 = const()[name = string("concat_35"), val = tensor([1, 4, 1024])]; + tensor transpose_7_cast_fp16 = transpose(perm = transpose_7_perm_0, x = obj_27_cast_fp16)[name = string("transpose_36")]; + tensor reshape_10_cast_fp16 = reshape(shape = concat_35, x = transpose_7_cast_fp16)[name = string("reshape_10_cast_fp16")]; + bool matmul_3_transpose_x_1 = const()[name = string("matmul_3_transpose_x_1"), val = bool(true)]; + bool matmul_3_transpose_y_1 = const()[name = string("matmul_3_transpose_y_1"), val = bool(false)]; + tensor matmul_3_cast_fp16 = matmul(transpose_x = matmul_3_transpose_x_1, transpose_y = matmul_3_transpose_y_1, x = kv_cache_update_mask, y = reshape_10_cast_fp16)[name = string("matmul_3_cast_fp16")]; + tensor concat_39 = const()[name = string("concat_39"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_11_cast_fp16 = reshape(shape = concat_39, x = matmul_3_cast_fp16)[name = string("reshape_11_cast_fp16")]; + tensor value_scatter_3_perm_0 = const()[name = string("value_scatter_3_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_707_cast_fp16 = mul(x = var_367_cast_fp16_1, y = var_502_cast_fp16)[name = string("op_707_cast_fp16")]; + tensor key_scatter_3_cast_fp16 = transpose(perm = key_scatter_3_perm_0, x = reshape_8_cast_fp16)[name = string("transpose_35")]; + tensor key_7_cast_fp16 = add(x = var_707_cast_fp16, y = key_scatter_3_cast_fp16)[name = string("key_7_cast_fp16")]; + tensor var_709_cast_fp16 = mul(x = var_376_cast_fp16_1, y = var_502_cast_fp16)[name = string("op_709_cast_fp16")]; + tensor value_scatter_3_cast_fp16 = transpose(perm = value_scatter_3_perm_0, x = reshape_11_cast_fp16)[name = string("transpose_34")]; + tensor value_3_cast_fp16 = add(x = var_709_cast_fp16, y = value_scatter_3_cast_fp16)[name = string("value_3_cast_fp16")]; + fp16 var_715_to_fp16 = const()[name = string("op_715_to_fp16"), val = fp16(0x1p-3)]; + tensor var_716_cast_fp16 = mul(x = mh_q_9_cast_fp16, y = var_715_to_fp16)[name = string("op_716_cast_fp16")]; + tensor var_719 = const()[name = string("op_719"), val = tensor([1, 16, 64, 80])]; + tensor var_720_cast_fp16 = reshape(shape = var_719, x = key_7_cast_fp16)[name = string("op_720_cast_fp16")]; + bool mh_w_7_transpose_x_0 = const()[name = string("mh_w_7_transpose_x_0"), val = bool(true)]; + bool mh_w_7_transpose_y_0 = const()[name = string("mh_w_7_transpose_y_0"), val = bool(false)]; + tensor mh_w_7_cast_fp16 = matmul(transpose_x = mh_w_7_transpose_x_0, transpose_y = mh_w_7_transpose_y_0, x = var_716_cast_fp16, y = var_720_cast_fp16)[name = string("mh_w_7_cast_fp16")]; + tensor mh_w_9_cast_fp16 = add(x = mh_w_7_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_9_cast_fp16")]; + tensor mh_w_11_cast_fp16 = add(x = mh_w_9_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_11_cast_fp16")]; + tensor var_730_cast_fp16 = softmax(axis = var_338, x = mh_w_11_cast_fp16)[name = string("op_730_cast_fp16")]; + tensor var_731 = const()[name = string("op_731"), val = tensor([1, 16, 64, 80])]; + tensor var_732_cast_fp16 = reshape(shape = var_731, x = value_3_cast_fp16)[name = string("op_732_cast_fp16")]; + bool attn_3_transpose_x_0 = const()[name = string("attn_3_transpose_x_0"), val = bool(false)]; + bool attn_3_transpose_y_0 = const()[name = string("attn_3_transpose_y_0"), val = bool(true)]; + tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_732_cast_fp16, y = var_730_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_735 = const()[name = string("op_735"), val = tensor([1, -1, 1, 4])]; + tensor input_51_cast_fp16 = reshape(shape = var_735, x = attn_3_cast_fp16)[name = string("input_51_cast_fp16")]; + string obj_23_pad_type_0 = const()[name = string("obj_23_pad_type_0"), val = string("valid")]; + tensor obj_23_strides_0 = const()[name = string("obj_23_strides_0"), val = tensor([1, 1])]; + tensor obj_23_pad_0 = const()[name = string("obj_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_23_dilations_0 = const()[name = string("obj_23_dilations_0"), val = tensor([1, 1])]; + int32 obj_23_groups_0 = const()[name = string("obj_23_groups_0"), val = int32(1)]; + tensor op_751_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17069504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17593856))))[name = string("op_751_weight_0_to_fp16_palettized")]; + tensor var_751_bias_0_to_fp16 = const()[name = string("op_751_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17594432)))]; + tensor var_751_cast_fp16 = conv(bias = var_751_bias_0_to_fp16, dilations = obj_23_dilations_0, groups = obj_23_groups_0, pad = obj_23_pad_0, pad_type = obj_23_pad_type_0, strides = obj_23_strides_0, weight = op_751_weight_0_to_fp16_palettized, x = input_51_cast_fp16)[name = string("op_751_cast_fp16")]; + tensor inputs_7_cast_fp16 = add(x = inputs_5_cast_fp16, y = var_751_cast_fp16)[name = string("inputs_7_cast_fp16")]; + tensor inputs_sq_7_cast_fp16 = mul(x = inputs_7_cast_fp16, y = inputs_7_cast_fp16)[name = string("inputs_sq_7_cast_fp16")]; + tensor variance_7_axes_0 = const()[name = string("variance_7_axes_0"), val = tensor([1])]; + bool variance_7_keep_dims_0 = const()[name = string("variance_7_keep_dims_0"), val = bool(true)]; + tensor variance_7_cast_fp16 = reduce_mean(axes = variance_7_axes_0, keep_dims = variance_7_keep_dims_0, x = inputs_sq_7_cast_fp16)[name = string("variance_7_cast_fp16")]; + fp16 var_757_to_fp16 = const()[name = string("op_757_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_758_cast_fp16 = add(x = variance_7_cast_fp16, y = var_757_to_fp16)[name = string("op_758_cast_fp16")]; + fp32 var_759_epsilon_0 = const()[name = string("op_759_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_759_cast_fp16 = rsqrt(epsilon = var_759_epsilon_0, x = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor hidden_states_9_cast_fp16 = mul(x = inputs_7_cast_fp16, y = var_759_cast_fp16)[name = string("hidden_states_9_cast_fp16")]; + tensor w_7_to_fp16 = const()[name = string("w_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17595520)))]; + tensor input_53_cast_fp16 = mul(x = w_7_to_fp16, y = hidden_states_9_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("valid")]; + tensor input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor([1, 1])]; + tensor input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor([1, 1])]; + int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17596608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18120960))))[name = string("pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_55_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = pre_transformer_layers_1_mlp_fc3_weight_to_fp16_palettized, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor gate_3_cast_fp16 = silu(x = input_55_cast_fp16)[name = string("gate_3_cast_fp16")]; + string up_3_pad_type_0 = const()[name = string("up_3_pad_type_0"), val = string("valid")]; + tensor up_3_strides_0 = const()[name = string("up_3_strides_0"), val = tensor([1, 1])]; + tensor up_3_pad_0 = const()[name = string("up_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_3_dilations_0 = const()[name = string("up_3_dilations_0"), val = tensor([1, 1])]; + int32 up_3_groups_0 = const()[name = string("up_3_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18121536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18645888))))[name = string("pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_3_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_3_dilations_0, groups = up_3_groups_0, pad = up_3_pad_0, pad_type = up_3_pad_type_0, strides = up_3_strides_0, weight = pre_transformer_layers_1_mlp_fc1_weight_to_fp16_palettized, x = input_53_cast_fp16)[name = string("up_3_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = gate_3_cast_fp16, y = up_3_cast_fp16)[name = string("input_57_cast_fp16")]; + string hidden_states_11_pad_type_0 = const()[name = string("hidden_states_11_pad_type_0"), val = string("valid")]; + tensor hidden_states_11_strides_0 = const()[name = string("hidden_states_11_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_0 = const()[name = string("hidden_states_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_11_dilations_0 = const()[name = string("hidden_states_11_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_11_groups_0 = const()[name = string("hidden_states_11_groups_0"), val = int32(1)]; + tensor op_793_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18646464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19170816))))[name = string("op_793_weight_0_to_fp16_palettized")]; + tensor var_793_bias_0_to_fp16 = const()[name = string("op_793_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19171392)))]; + tensor var_793_cast_fp16 = conv(bias = var_793_bias_0_to_fp16, dilations = hidden_states_11_dilations_0, groups = hidden_states_11_groups_0, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = hidden_states_11_strides_0, weight = op_793_weight_0_to_fp16_palettized, x = input_57_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor inputs_9_cast_fp16 = add(x = inputs_7_cast_fp16, y = var_793_cast_fp16)[name = string("inputs_9_cast_fp16")]; + tensor inputs_sq_9_cast_fp16 = mul(x = inputs_9_cast_fp16, y = inputs_9_cast_fp16)[name = string("inputs_sq_9_cast_fp16")]; + tensor variance_9_axes_0 = const()[name = string("variance_9_axes_0"), val = tensor([1])]; + bool variance_9_keep_dims_0 = const()[name = string("variance_9_keep_dims_0"), val = bool(true)]; + tensor variance_9_cast_fp16 = reduce_mean(axes = variance_9_axes_0, keep_dims = variance_9_keep_dims_0, x = inputs_sq_9_cast_fp16)[name = string("variance_9_cast_fp16")]; + fp16 var_809_to_fp16 = const()[name = string("op_809_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_810_cast_fp16 = add(x = variance_9_cast_fp16, y = var_809_to_fp16)[name = string("op_810_cast_fp16")]; + fp32 var_811_epsilon_0 = const()[name = string("op_811_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_811_cast_fp16 = rsqrt(epsilon = var_811_epsilon_0, x = var_810_cast_fp16)[name = string("op_811_cast_fp16")]; + tensor hidden_states_13_cast_fp16 = mul(x = inputs_9_cast_fp16, y = var_811_cast_fp16)[name = string("hidden_states_13_cast_fp16")]; + tensor w_9_to_fp16 = const()[name = string("w_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19172480)))]; + tensor obj_29_cast_fp16 = mul(x = w_9_to_fp16, y = hidden_states_13_cast_fp16)[name = string("obj_29_cast_fp16")]; + string query_9_pad_type_0 = const()[name = string("query_9_pad_type_0"), val = string("valid")]; + tensor query_9_strides_0 = const()[name = string("query_9_strides_0"), val = tensor([1, 1])]; + tensor query_9_pad_0 = const()[name = string("query_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_9_dilations_0 = const()[name = string("query_9_dilations_0"), val = tensor([1, 1])]; + int32 query_9_groups_0 = const()[name = string("query_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19173568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19697920))))[name = string("pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_9_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_9_dilations_0, groups = query_9_groups_0, pad = query_9_pad_0, pad_type = query_9_pad_type_0, strides = query_9_strides_0, weight = pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized, x = obj_29_cast_fp16)[name = string("query_9_cast_fp16")]; + string key_9_pad_type_0 = const()[name = string("key_9_pad_type_0"), val = string("valid")]; + tensor key_9_strides_0 = const()[name = string("key_9_strides_0"), val = tensor([1, 1])]; + tensor key_9_pad_0 = const()[name = string("key_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_9_dilations_0 = const()[name = string("key_9_dilations_0"), val = tensor([1, 1])]; + int32 key_9_groups_0 = const()[name = string("key_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19698496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20222848))))[name = string("pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_9_cast_fp16 = conv(dilations = key_9_dilations_0, groups = key_9_groups_0, pad = key_9_pad_0, pad_type = key_9_pad_type_0, strides = key_9_strides_0, weight = pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized, x = obj_29_cast_fp16)[name = string("key_9_cast_fp16")]; + string obj_39_pad_type_0 = const()[name = string("obj_39_pad_type_0"), val = string("valid")]; + tensor obj_39_strides_0 = const()[name = string("obj_39_strides_0"), val = tensor([1, 1])]; + tensor obj_39_pad_0 = const()[name = string("obj_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_39_dilations_0 = const()[name = string("obj_39_dilations_0"), val = tensor([1, 1])]; + int32 obj_39_groups_0 = const()[name = string("obj_39_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20223424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20747776))))[name = string("pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_39_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_39_dilations_0, groups = obj_39_groups_0, pad = obj_39_pad_0, pad_type = obj_39_pad_type_0, strides = obj_39_strides_0, weight = pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized, x = obj_29_cast_fp16)[name = string("obj_39_cast_fp16")]; + tensor var_849 = const()[name = string("op_849"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_13_cast_fp16 = reshape(shape = var_849, x = query_9_cast_fp16)[name = string("mh_q_13_cast_fp16")]; + tensor var_851 = const()[name = string("op_851"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_9_cast_fp16 = reshape(shape = var_851, x = key_9_cast_fp16)[name = string("mh_k_9_cast_fp16")]; + tensor var_855_cast_fp16 = mul(x = mh_q_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_855_cast_fp16")]; + tensor var_860_begin_0 = const()[name = string("op_860_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_860_end_0 = const()[name = string("op_860_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_860_end_mask_0 = const()[name = string("op_860_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_860_cast_fp16 = slice_by_index(begin = var_860_begin_0, end = var_860_end_0, end_mask = var_860_end_mask_0, x = mh_q_13_cast_fp16)[name = string("op_860_cast_fp16")]; + tensor var_866_begin_0 = const()[name = string("op_866_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_866_end_0 = const()[name = string("op_866_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_866_end_mask_0 = const()[name = string("op_866_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_866_cast_fp16 = slice_by_index(begin = var_866_begin_0, end = var_866_end_0, end_mask = var_866_end_mask_0, x = mh_q_13_cast_fp16)[name = string("op_866_cast_fp16")]; + fp16 const_71_promoted_to_fp16 = const()[name = string("const_71_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_868_cast_fp16 = mul(x = var_866_cast_fp16, y = const_71_promoted_to_fp16)[name = string("op_868_cast_fp16")]; + bool var_870_interleave_0 = const()[name = string("op_870_interleave_0"), val = bool(false)]; + tensor var_870_cast_fp16 = concat(axis = var_333, interleave = var_870_interleave_0, values = (var_868_cast_fp16, var_860_cast_fp16))[name = string("op_870_cast_fp16")]; + tensor var_871_cast_fp16 = mul(x = var_870_cast_fp16, y = sin_1_cast_fp16)[name = string("op_871_cast_fp16")]; + tensor mh_q_15_cast_fp16 = add(x = var_855_cast_fp16, y = var_871_cast_fp16)[name = string("mh_q_15_cast_fp16")]; + tensor var_873_cast_fp16 = mul(x = mh_k_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_873_cast_fp16")]; + tensor var_878_begin_0 = const()[name = string("op_878_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_878_end_0 = const()[name = string("op_878_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_878_end_mask_0 = const()[name = string("op_878_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_878_cast_fp16 = slice_by_index(begin = var_878_begin_0, end = var_878_end_0, end_mask = var_878_end_mask_0, x = mh_k_9_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor var_884_begin_0 = const()[name = string("op_884_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_884_end_0 = const()[name = string("op_884_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_884_end_mask_0 = const()[name = string("op_884_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_884_cast_fp16 = slice_by_index(begin = var_884_begin_0, end = var_884_end_0, end_mask = var_884_end_mask_0, x = mh_k_9_cast_fp16)[name = string("op_884_cast_fp16")]; + fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_886_cast_fp16 = mul(x = var_884_cast_fp16, y = const_74_promoted_to_fp16)[name = string("op_886_cast_fp16")]; + bool var_888_interleave_0 = const()[name = string("op_888_interleave_0"), val = bool(false)]; + tensor var_888_cast_fp16 = concat(axis = var_333, interleave = var_888_interleave_0, values = (var_886_cast_fp16, var_878_cast_fp16))[name = string("op_888_cast_fp16")]; + tensor var_889_cast_fp16 = mul(x = var_888_cast_fp16, y = sin_1_cast_fp16)[name = string("op_889_cast_fp16")]; + tensor mh_k_11_cast_fp16 = add(x = var_873_cast_fp16, y = var_889_cast_fp16)[name = string("mh_k_11_cast_fp16")]; + tensor var_893 = const()[name = string("op_893"), val = tensor([1, 1024, 1, 4])]; + tensor obj_37_cast_fp16 = reshape(shape = var_893, x = mh_k_11_cast_fp16)[name = string("obj_37_cast_fp16")]; + tensor transpose_9_perm_0 = const()[name = string("transpose_9_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_45 = const()[name = string("concat_45"), val = tensor([1, 4, 1024])]; + tensor transpose_9_cast_fp16 = transpose(perm = transpose_9_perm_0, x = obj_37_cast_fp16)[name = string("transpose_33")]; + tensor reshape_13_cast_fp16 = reshape(shape = concat_45, x = transpose_9_cast_fp16)[name = string("reshape_13_cast_fp16")]; + bool matmul_4_transpose_x_1 = const()[name = string("matmul_4_transpose_x_1"), val = bool(true)]; + bool matmul_4_transpose_y_1 = const()[name = string("matmul_4_transpose_y_1"), val = bool(false)]; + tensor matmul_4_cast_fp16 = matmul(transpose_x = matmul_4_transpose_x_1, transpose_y = matmul_4_transpose_y_1, x = kv_cache_update_mask, y = reshape_13_cast_fp16)[name = string("matmul_4_cast_fp16")]; + tensor concat_49 = const()[name = string("concat_49"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_14_cast_fp16 = reshape(shape = concat_49, x = matmul_4_cast_fp16)[name = string("reshape_14_cast_fp16")]; + tensor key_scatter_5_perm_0 = const()[name = string("key_scatter_5_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_11_perm_0 = const()[name = string("transpose_11_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_55 = const()[name = string("concat_55"), val = tensor([1, 4, 1024])]; + tensor transpose_11_cast_fp16 = transpose(perm = transpose_11_perm_0, x = obj_39_cast_fp16)[name = string("transpose_32")]; + tensor reshape_16_cast_fp16 = reshape(shape = concat_55, x = transpose_11_cast_fp16)[name = string("reshape_16_cast_fp16")]; + bool matmul_5_transpose_x_1 = const()[name = string("matmul_5_transpose_x_1"), val = bool(true)]; + bool matmul_5_transpose_y_1 = const()[name = string("matmul_5_transpose_y_1"), val = bool(false)]; + tensor matmul_5_cast_fp16 = matmul(transpose_x = matmul_5_transpose_x_1, transpose_y = matmul_5_transpose_y_1, x = kv_cache_update_mask, y = reshape_16_cast_fp16)[name = string("matmul_5_cast_fp16")]; + tensor concat_59 = const()[name = string("concat_59"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_17_cast_fp16 = reshape(shape = concat_59, x = matmul_5_cast_fp16)[name = string("reshape_17_cast_fp16")]; + tensor value_scatter_5_perm_0 = const()[name = string("value_scatter_5_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_906_cast_fp16 = mul(x = var_367_cast_fp16_2, y = var_502_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor key_scatter_5_cast_fp16 = transpose(perm = key_scatter_5_perm_0, x = reshape_14_cast_fp16)[name = string("transpose_31")]; + tensor key_11_cast_fp16 = add(x = var_906_cast_fp16, y = key_scatter_5_cast_fp16)[name = string("key_11_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_376_cast_fp16_2, y = var_502_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor value_scatter_5_cast_fp16 = transpose(perm = value_scatter_5_perm_0, x = reshape_17_cast_fp16)[name = string("transpose_30")]; + tensor value_5_cast_fp16 = add(x = var_908_cast_fp16, y = value_scatter_5_cast_fp16)[name = string("value_5_cast_fp16")]; + fp16 var_914_to_fp16 = const()[name = string("op_914_to_fp16"), val = fp16(0x1p-3)]; + tensor var_915_cast_fp16 = mul(x = mh_q_15_cast_fp16, y = var_914_to_fp16)[name = string("op_915_cast_fp16")]; + tensor var_918 = const()[name = string("op_918"), val = tensor([1, 16, 64, 80])]; + tensor var_919_cast_fp16 = reshape(shape = var_918, x = key_11_cast_fp16)[name = string("op_919_cast_fp16")]; + bool mh_w_13_transpose_x_0 = const()[name = string("mh_w_13_transpose_x_0"), val = bool(true)]; + bool mh_w_13_transpose_y_0 = const()[name = string("mh_w_13_transpose_y_0"), val = bool(false)]; + tensor mh_w_13_cast_fp16 = matmul(transpose_x = mh_w_13_transpose_x_0, transpose_y = mh_w_13_transpose_y_0, x = var_915_cast_fp16, y = var_919_cast_fp16)[name = string("mh_w_13_cast_fp16")]; + tensor mh_w_15_cast_fp16 = add(x = mh_w_13_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_15_cast_fp16")]; + tensor mh_w_17_cast_fp16 = add(x = mh_w_15_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_17_cast_fp16")]; + tensor var_929_cast_fp16 = softmax(axis = var_338, x = mh_w_17_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930 = const()[name = string("op_930"), val = tensor([1, 16, 64, 80])]; + tensor var_931_cast_fp16 = reshape(shape = var_930, x = value_5_cast_fp16)[name = string("op_931_cast_fp16")]; + bool attn_5_transpose_x_0 = const()[name = string("attn_5_transpose_x_0"), val = bool(false)]; + bool attn_5_transpose_y_0 = const()[name = string("attn_5_transpose_y_0"), val = bool(true)]; + tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_931_cast_fp16, y = var_929_cast_fp16)[name = string("attn_5_cast_fp16")]; + tensor var_934 = const()[name = string("op_934"), val = tensor([1, -1, 1, 4])]; + tensor input_59_cast_fp16 = reshape(shape = var_934, x = attn_5_cast_fp16)[name = string("input_59_cast_fp16")]; + string obj_35_pad_type_0 = const()[name = string("obj_35_pad_type_0"), val = string("valid")]; + tensor obj_35_strides_0 = const()[name = string("obj_35_strides_0"), val = tensor([1, 1])]; + tensor obj_35_pad_0 = const()[name = string("obj_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_35_dilations_0 = const()[name = string("obj_35_dilations_0"), val = tensor([1, 1])]; + int32 obj_35_groups_0 = const()[name = string("obj_35_groups_0"), val = int32(1)]; + tensor op_950_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20748352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21272704))))[name = string("op_950_weight_0_to_fp16_palettized")]; + tensor var_950_bias_0_to_fp16 = const()[name = string("op_950_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21273280)))]; + tensor var_950_cast_fp16 = conv(bias = var_950_bias_0_to_fp16, dilations = obj_35_dilations_0, groups = obj_35_groups_0, pad = obj_35_pad_0, pad_type = obj_35_pad_type_0, strides = obj_35_strides_0, weight = op_950_weight_0_to_fp16_palettized, x = input_59_cast_fp16)[name = string("op_950_cast_fp16")]; + tensor inputs_11_cast_fp16 = add(x = inputs_9_cast_fp16, y = var_950_cast_fp16)[name = string("inputs_11_cast_fp16")]; + tensor inputs_sq_11_cast_fp16 = mul(x = inputs_11_cast_fp16, y = inputs_11_cast_fp16)[name = string("inputs_sq_11_cast_fp16")]; + tensor variance_11_axes_0 = const()[name = string("variance_11_axes_0"), val = tensor([1])]; + bool variance_11_keep_dims_0 = const()[name = string("variance_11_keep_dims_0"), val = bool(true)]; + tensor variance_11_cast_fp16 = reduce_mean(axes = variance_11_axes_0, keep_dims = variance_11_keep_dims_0, x = inputs_sq_11_cast_fp16)[name = string("variance_11_cast_fp16")]; + fp16 var_956_to_fp16 = const()[name = string("op_956_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_957_cast_fp16 = add(x = variance_11_cast_fp16, y = var_956_to_fp16)[name = string("op_957_cast_fp16")]; + fp32 var_958_epsilon_0 = const()[name = string("op_958_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_958_cast_fp16 = rsqrt(epsilon = var_958_epsilon_0, x = var_957_cast_fp16)[name = string("op_958_cast_fp16")]; + tensor hidden_states_15_cast_fp16 = mul(x = inputs_11_cast_fp16, y = var_958_cast_fp16)[name = string("hidden_states_15_cast_fp16")]; + tensor w_11_to_fp16 = const()[name = string("w_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21274368)))]; + tensor input_61_cast_fp16 = mul(x = w_11_to_fp16, y = hidden_states_15_cast_fp16)[name = string("input_61_cast_fp16")]; + string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("valid")]; + tensor input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor([1, 1])]; + tensor input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor([1, 1])]; + int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21275456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21799808))))[name = string("pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_63_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = pre_transformer_layers_2_mlp_fc3_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor gate_5_cast_fp16 = silu(x = input_63_cast_fp16)[name = string("gate_5_cast_fp16")]; + string up_5_pad_type_0 = const()[name = string("up_5_pad_type_0"), val = string("valid")]; + tensor up_5_strides_0 = const()[name = string("up_5_strides_0"), val = tensor([1, 1])]; + tensor up_5_pad_0 = const()[name = string("up_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_5_dilations_0 = const()[name = string("up_5_dilations_0"), val = tensor([1, 1])]; + int32 up_5_groups_0 = const()[name = string("up_5_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21800384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22324736))))[name = string("pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_5_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_5_dilations_0, groups = up_5_groups_0, pad = up_5_pad_0, pad_type = up_5_pad_type_0, strides = up_5_strides_0, weight = pre_transformer_layers_2_mlp_fc1_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("up_5_cast_fp16")]; + tensor input_65_cast_fp16 = mul(x = gate_5_cast_fp16, y = up_5_cast_fp16)[name = string("input_65_cast_fp16")]; + string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")]; + tensor hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)]; + tensor op_992_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22325312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22849664))))[name = string("op_992_weight_0_to_fp16_palettized")]; + tensor var_992_bias_0_to_fp16 = const()[name = string("op_992_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22850240)))]; + tensor var_992_cast_fp16 = conv(bias = var_992_bias_0_to_fp16, dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = op_992_weight_0_to_fp16_palettized, x = input_65_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor inputs_13_cast_fp16 = add(x = inputs_11_cast_fp16, y = var_992_cast_fp16)[name = string("inputs_13_cast_fp16")]; + tensor inputs_sq_13_cast_fp16 = mul(x = inputs_13_cast_fp16, y = inputs_13_cast_fp16)[name = string("inputs_sq_13_cast_fp16")]; + tensor variance_13_axes_0 = const()[name = string("variance_13_axes_0"), val = tensor([1])]; + bool variance_13_keep_dims_0 = const()[name = string("variance_13_keep_dims_0"), val = bool(true)]; + tensor variance_13_cast_fp16 = reduce_mean(axes = variance_13_axes_0, keep_dims = variance_13_keep_dims_0, x = inputs_sq_13_cast_fp16)[name = string("variance_13_cast_fp16")]; + fp16 var_1008_to_fp16 = const()[name = string("op_1008_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1009_cast_fp16 = add(x = variance_13_cast_fp16, y = var_1008_to_fp16)[name = string("op_1009_cast_fp16")]; + fp32 var_1010_epsilon_0 = const()[name = string("op_1010_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1010_cast_fp16 = rsqrt(epsilon = var_1010_epsilon_0, x = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor hidden_states_19_cast_fp16 = mul(x = inputs_13_cast_fp16, y = var_1010_cast_fp16)[name = string("hidden_states_19_cast_fp16")]; + tensor w_13_to_fp16 = const()[name = string("w_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22851328)))]; + tensor obj_41_cast_fp16 = mul(x = w_13_to_fp16, y = hidden_states_19_cast_fp16)[name = string("obj_41_cast_fp16")]; + string query_13_pad_type_0 = const()[name = string("query_13_pad_type_0"), val = string("valid")]; + tensor query_13_strides_0 = const()[name = string("query_13_strides_0"), val = tensor([1, 1])]; + tensor query_13_pad_0 = const()[name = string("query_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_13_dilations_0 = const()[name = string("query_13_dilations_0"), val = tensor([1, 1])]; + int32 query_13_groups_0 = const()[name = string("query_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22852416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23376768))))[name = string("pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_13_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_13_dilations_0, groups = query_13_groups_0, pad = query_13_pad_0, pad_type = query_13_pad_type_0, strides = query_13_strides_0, weight = pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized, x = obj_41_cast_fp16)[name = string("query_13_cast_fp16")]; + string key_13_pad_type_0 = const()[name = string("key_13_pad_type_0"), val = string("valid")]; + tensor key_13_strides_0 = const()[name = string("key_13_strides_0"), val = tensor([1, 1])]; + tensor key_13_pad_0 = const()[name = string("key_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_13_dilations_0 = const()[name = string("key_13_dilations_0"), val = tensor([1, 1])]; + int32 key_13_groups_0 = const()[name = string("key_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23377344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23901696))))[name = string("pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_13_cast_fp16 = conv(dilations = key_13_dilations_0, groups = key_13_groups_0, pad = key_13_pad_0, pad_type = key_13_pad_type_0, strides = key_13_strides_0, weight = pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized, x = obj_41_cast_fp16)[name = string("key_13_cast_fp16")]; + string obj_51_pad_type_0 = const()[name = string("obj_51_pad_type_0"), val = string("valid")]; + tensor obj_51_strides_0 = const()[name = string("obj_51_strides_0"), val = tensor([1, 1])]; + tensor obj_51_pad_0 = const()[name = string("obj_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_51_dilations_0 = const()[name = string("obj_51_dilations_0"), val = tensor([1, 1])]; + int32 obj_51_groups_0 = const()[name = string("obj_51_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23902272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24426624))))[name = string("pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_51_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_51_dilations_0, groups = obj_51_groups_0, pad = obj_51_pad_0, pad_type = obj_51_pad_type_0, strides = obj_51_strides_0, weight = pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized, x = obj_41_cast_fp16)[name = string("obj_51_cast_fp16")]; + tensor var_1048 = const()[name = string("op_1048"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_19_cast_fp16 = reshape(shape = var_1048, x = query_13_cast_fp16)[name = string("mh_q_19_cast_fp16")]; + tensor var_1050 = const()[name = string("op_1050"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_13_cast_fp16 = reshape(shape = var_1050, x = key_13_cast_fp16)[name = string("mh_k_13_cast_fp16")]; + tensor var_1054_cast_fp16 = mul(x = mh_q_19_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1059_begin_0 = const()[name = string("op_1059_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1059_end_0 = const()[name = string("op_1059_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1059_end_mask_0 = const()[name = string("op_1059_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1059_cast_fp16 = slice_by_index(begin = var_1059_begin_0, end = var_1059_end_0, end_mask = var_1059_end_mask_0, x = mh_q_19_cast_fp16)[name = string("op_1059_cast_fp16")]; + tensor var_1065_begin_0 = const()[name = string("op_1065_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1065_end_0 = const()[name = string("op_1065_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1065_end_mask_0 = const()[name = string("op_1065_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1065_cast_fp16 = slice_by_index(begin = var_1065_begin_0, end = var_1065_end_0, end_mask = var_1065_end_mask_0, x = mh_q_19_cast_fp16)[name = string("op_1065_cast_fp16")]; + fp16 const_90_promoted_to_fp16 = const()[name = string("const_90_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1067_cast_fp16 = mul(x = var_1065_cast_fp16, y = const_90_promoted_to_fp16)[name = string("op_1067_cast_fp16")]; + bool var_1069_interleave_0 = const()[name = string("op_1069_interleave_0"), val = bool(false)]; + tensor var_1069_cast_fp16 = concat(axis = var_333, interleave = var_1069_interleave_0, values = (var_1067_cast_fp16, var_1059_cast_fp16))[name = string("op_1069_cast_fp16")]; + tensor var_1070_cast_fp16 = mul(x = var_1069_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1070_cast_fp16")]; + tensor mh_q_21_cast_fp16 = add(x = var_1054_cast_fp16, y = var_1070_cast_fp16)[name = string("mh_q_21_cast_fp16")]; + tensor var_1072_cast_fp16 = mul(x = mh_k_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1072_cast_fp16")]; + tensor var_1077_begin_0 = const()[name = string("op_1077_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1077_end_0 = const()[name = string("op_1077_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1077_end_mask_0 = const()[name = string("op_1077_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1077_cast_fp16 = slice_by_index(begin = var_1077_begin_0, end = var_1077_end_0, end_mask = var_1077_end_mask_0, x = mh_k_13_cast_fp16)[name = string("op_1077_cast_fp16")]; + tensor var_1083_begin_0 = const()[name = string("op_1083_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1083_end_0 = const()[name = string("op_1083_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1083_end_mask_0 = const()[name = string("op_1083_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1083_cast_fp16 = slice_by_index(begin = var_1083_begin_0, end = var_1083_end_0, end_mask = var_1083_end_mask_0, x = mh_k_13_cast_fp16)[name = string("op_1083_cast_fp16")]; + fp16 const_93_promoted_to_fp16 = const()[name = string("const_93_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1085_cast_fp16 = mul(x = var_1083_cast_fp16, y = const_93_promoted_to_fp16)[name = string("op_1085_cast_fp16")]; + bool var_1087_interleave_0 = const()[name = string("op_1087_interleave_0"), val = bool(false)]; + tensor var_1087_cast_fp16 = concat(axis = var_333, interleave = var_1087_interleave_0, values = (var_1085_cast_fp16, var_1077_cast_fp16))[name = string("op_1087_cast_fp16")]; + tensor var_1088_cast_fp16 = mul(x = var_1087_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1088_cast_fp16")]; + tensor mh_k_15_cast_fp16 = add(x = var_1072_cast_fp16, y = var_1088_cast_fp16)[name = string("mh_k_15_cast_fp16")]; + tensor var_1092 = const()[name = string("op_1092"), val = tensor([1, 1024, 1, 4])]; + tensor obj_49_cast_fp16 = reshape(shape = var_1092, x = mh_k_15_cast_fp16)[name = string("obj_49_cast_fp16")]; + tensor transpose_13_perm_0 = const()[name = string("transpose_13_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_65 = const()[name = string("concat_65"), val = tensor([1, 4, 1024])]; + tensor transpose_13_cast_fp16 = transpose(perm = transpose_13_perm_0, x = obj_49_cast_fp16)[name = string("transpose_29")]; + tensor reshape_19_cast_fp16 = reshape(shape = concat_65, x = transpose_13_cast_fp16)[name = string("reshape_19_cast_fp16")]; + bool matmul_6_transpose_x_1 = const()[name = string("matmul_6_transpose_x_1"), val = bool(true)]; + bool matmul_6_transpose_y_1 = const()[name = string("matmul_6_transpose_y_1"), val = bool(false)]; + tensor matmul_6_cast_fp16 = matmul(transpose_x = matmul_6_transpose_x_1, transpose_y = matmul_6_transpose_y_1, x = kv_cache_update_mask, y = reshape_19_cast_fp16)[name = string("matmul_6_cast_fp16")]; + tensor concat_69 = const()[name = string("concat_69"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_20_cast_fp16 = reshape(shape = concat_69, x = matmul_6_cast_fp16)[name = string("reshape_20_cast_fp16")]; + tensor key_scatter_7_perm_0 = const()[name = string("key_scatter_7_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_15_perm_0 = const()[name = string("transpose_15_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_75 = const()[name = string("concat_75"), val = tensor([1, 4, 1024])]; + tensor transpose_15_cast_fp16 = transpose(perm = transpose_15_perm_0, x = obj_51_cast_fp16)[name = string("transpose_28")]; + tensor reshape_22_cast_fp16 = reshape(shape = concat_75, x = transpose_15_cast_fp16)[name = string("reshape_22_cast_fp16")]; + bool matmul_7_transpose_x_1 = const()[name = string("matmul_7_transpose_x_1"), val = bool(true)]; + bool matmul_7_transpose_y_1 = const()[name = string("matmul_7_transpose_y_1"), val = bool(false)]; + tensor matmul_7_cast_fp16 = matmul(transpose_x = matmul_7_transpose_x_1, transpose_y = matmul_7_transpose_y_1, x = kv_cache_update_mask, y = reshape_22_cast_fp16)[name = string("matmul_7_cast_fp16")]; + tensor concat_79 = const()[name = string("concat_79"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_23_cast_fp16 = reshape(shape = concat_79, x = matmul_7_cast_fp16)[name = string("reshape_23_cast_fp16")]; + tensor value_scatter_7_perm_0 = const()[name = string("value_scatter_7_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_1105_cast_fp16 = mul(x = var_367_cast_fp16_3, y = var_502_cast_fp16)[name = string("op_1105_cast_fp16")]; + tensor key_scatter_7_cast_fp16 = transpose(perm = key_scatter_7_perm_0, x = reshape_20_cast_fp16)[name = string("transpose_27")]; + tensor key_15_cast_fp16 = add(x = var_1105_cast_fp16, y = key_scatter_7_cast_fp16)[name = string("key_15_cast_fp16")]; + tensor var_1107_cast_fp16 = mul(x = var_376_cast_fp16_3, y = var_502_cast_fp16)[name = string("op_1107_cast_fp16")]; + tensor value_scatter_7_cast_fp16 = transpose(perm = value_scatter_7_perm_0, x = reshape_23_cast_fp16)[name = string("transpose_26")]; + tensor value_7_cast_fp16 = add(x = var_1107_cast_fp16, y = value_scatter_7_cast_fp16)[name = string("value_7_cast_fp16")]; + fp16 var_1113_to_fp16 = const()[name = string("op_1113_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1114_cast_fp16 = mul(x = mh_q_21_cast_fp16, y = var_1113_to_fp16)[name = string("op_1114_cast_fp16")]; + tensor var_1117 = const()[name = string("op_1117"), val = tensor([1, 16, 64, 80])]; + tensor var_1118_cast_fp16 = reshape(shape = var_1117, x = key_15_cast_fp16)[name = string("op_1118_cast_fp16")]; + bool mh_w_19_transpose_x_0 = const()[name = string("mh_w_19_transpose_x_0"), val = bool(true)]; + bool mh_w_19_transpose_y_0 = const()[name = string("mh_w_19_transpose_y_0"), val = bool(false)]; + tensor mh_w_19_cast_fp16 = matmul(transpose_x = mh_w_19_transpose_x_0, transpose_y = mh_w_19_transpose_y_0, x = var_1114_cast_fp16, y = var_1118_cast_fp16)[name = string("mh_w_19_cast_fp16")]; + tensor mh_w_21_cast_fp16 = add(x = mh_w_19_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_21_cast_fp16")]; + tensor mh_w_23_cast_fp16 = add(x = mh_w_21_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_23_cast_fp16")]; + tensor var_1128_cast_fp16 = softmax(axis = var_338, x = mh_w_23_cast_fp16)[name = string("op_1128_cast_fp16")]; + tensor var_1129 = const()[name = string("op_1129"), val = tensor([1, 16, 64, 80])]; + tensor var_1130_cast_fp16 = reshape(shape = var_1129, x = value_7_cast_fp16)[name = string("op_1130_cast_fp16")]; + bool attn_7_transpose_x_0 = const()[name = string("attn_7_transpose_x_0"), val = bool(false)]; + bool attn_7_transpose_y_0 = const()[name = string("attn_7_transpose_y_0"), val = bool(true)]; + tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_1130_cast_fp16, y = var_1128_cast_fp16)[name = string("attn_7_cast_fp16")]; + tensor var_1133 = const()[name = string("op_1133"), val = tensor([1, -1, 1, 4])]; + tensor input_67_cast_fp16 = reshape(shape = var_1133, x = attn_7_cast_fp16)[name = string("input_67_cast_fp16")]; + string obj_47_pad_type_0 = const()[name = string("obj_47_pad_type_0"), val = string("valid")]; + tensor obj_47_strides_0 = const()[name = string("obj_47_strides_0"), val = tensor([1, 1])]; + tensor obj_47_pad_0 = const()[name = string("obj_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_47_dilations_0 = const()[name = string("obj_47_dilations_0"), val = tensor([1, 1])]; + int32 obj_47_groups_0 = const()[name = string("obj_47_groups_0"), val = int32(1)]; + tensor op_1149_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24427200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24951552))))[name = string("op_1149_weight_0_to_fp16_palettized")]; + tensor var_1149_bias_0_to_fp16 = const()[name = string("op_1149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24952128)))]; + tensor var_1149_cast_fp16 = conv(bias = var_1149_bias_0_to_fp16, dilations = obj_47_dilations_0, groups = obj_47_groups_0, pad = obj_47_pad_0, pad_type = obj_47_pad_type_0, strides = obj_47_strides_0, weight = op_1149_weight_0_to_fp16_palettized, x = input_67_cast_fp16)[name = string("op_1149_cast_fp16")]; + tensor inputs_15_cast_fp16 = add(x = inputs_13_cast_fp16, y = var_1149_cast_fp16)[name = string("inputs_15_cast_fp16")]; + tensor inputs_sq_15_cast_fp16 = mul(x = inputs_15_cast_fp16, y = inputs_15_cast_fp16)[name = string("inputs_sq_15_cast_fp16")]; + tensor variance_15_axes_0 = const()[name = string("variance_15_axes_0"), val = tensor([1])]; + bool variance_15_keep_dims_0 = const()[name = string("variance_15_keep_dims_0"), val = bool(true)]; + tensor variance_15_cast_fp16 = reduce_mean(axes = variance_15_axes_0, keep_dims = variance_15_keep_dims_0, x = inputs_sq_15_cast_fp16)[name = string("variance_15_cast_fp16")]; + fp16 var_1155_to_fp16 = const()[name = string("op_1155_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1156_cast_fp16 = add(x = variance_15_cast_fp16, y = var_1155_to_fp16)[name = string("op_1156_cast_fp16")]; + fp32 var_1157_epsilon_0 = const()[name = string("op_1157_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1157_cast_fp16 = rsqrt(epsilon = var_1157_epsilon_0, x = var_1156_cast_fp16)[name = string("op_1157_cast_fp16")]; + tensor hidden_states_21_cast_fp16 = mul(x = inputs_15_cast_fp16, y = var_1157_cast_fp16)[name = string("hidden_states_21_cast_fp16")]; + tensor w_15_to_fp16 = const()[name = string("w_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24953216)))]; + tensor input_69_cast_fp16 = mul(x = w_15_to_fp16, y = hidden_states_21_cast_fp16)[name = string("input_69_cast_fp16")]; + string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("valid")]; + tensor input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor([1, 1])]; + tensor input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor([1, 1])]; + int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24954304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25478656))))[name = string("pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_71_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = pre_transformer_layers_3_mlp_fc3_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor gate_7_cast_fp16 = silu(x = input_71_cast_fp16)[name = string("gate_7_cast_fp16")]; + string up_7_pad_type_0 = const()[name = string("up_7_pad_type_0"), val = string("valid")]; + tensor up_7_strides_0 = const()[name = string("up_7_strides_0"), val = tensor([1, 1])]; + tensor up_7_pad_0 = const()[name = string("up_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_7_dilations_0 = const()[name = string("up_7_dilations_0"), val = tensor([1, 1])]; + int32 up_7_groups_0 = const()[name = string("up_7_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25479232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26003584))))[name = string("pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_7_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_7_dilations_0, groups = up_7_groups_0, pad = up_7_pad_0, pad_type = up_7_pad_type_0, strides = up_7_strides_0, weight = pre_transformer_layers_3_mlp_fc1_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("up_7_cast_fp16")]; + tensor input_73_cast_fp16 = mul(x = gate_7_cast_fp16, y = up_7_cast_fp16)[name = string("input_73_cast_fp16")]; + string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")]; + tensor hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)]; + tensor op_1191_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26004160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26528512))))[name = string("op_1191_weight_0_to_fp16_palettized")]; + tensor var_1191_bias_0_to_fp16 = const()[name = string("op_1191_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26529088)))]; + tensor var_1191_cast_fp16 = conv(bias = var_1191_bias_0_to_fp16, dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = op_1191_weight_0_to_fp16_palettized, x = input_73_cast_fp16)[name = string("op_1191_cast_fp16")]; + tensor inputs_17_cast_fp16 = add(x = inputs_15_cast_fp16, y = var_1191_cast_fp16)[name = string("inputs_17_cast_fp16")]; + tensor inputs_sq_17_cast_fp16 = mul(x = inputs_17_cast_fp16, y = inputs_17_cast_fp16)[name = string("inputs_sq_17_cast_fp16")]; + tensor variance_17_axes_0 = const()[name = string("variance_17_axes_0"), val = tensor([1])]; + bool variance_17_keep_dims_0 = const()[name = string("variance_17_keep_dims_0"), val = bool(true)]; + tensor variance_17_cast_fp16 = reduce_mean(axes = variance_17_axes_0, keep_dims = variance_17_keep_dims_0, x = inputs_sq_17_cast_fp16)[name = string("variance_17_cast_fp16")]; + fp16 var_1207_to_fp16 = const()[name = string("op_1207_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1208_cast_fp16 = add(x = variance_17_cast_fp16, y = var_1207_to_fp16)[name = string("op_1208_cast_fp16")]; + fp32 var_1209_epsilon_0 = const()[name = string("op_1209_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1209_cast_fp16 = rsqrt(epsilon = var_1209_epsilon_0, x = var_1208_cast_fp16)[name = string("op_1209_cast_fp16")]; + tensor hidden_states_25_cast_fp16 = mul(x = inputs_17_cast_fp16, y = var_1209_cast_fp16)[name = string("hidden_states_25_cast_fp16")]; + tensor w_17_to_fp16 = const()[name = string("w_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26530176)))]; + tensor obj_53_cast_fp16 = mul(x = w_17_to_fp16, y = hidden_states_25_cast_fp16)[name = string("obj_53_cast_fp16")]; + string query_17_pad_type_0 = const()[name = string("query_17_pad_type_0"), val = string("valid")]; + tensor query_17_strides_0 = const()[name = string("query_17_strides_0"), val = tensor([1, 1])]; + tensor query_17_pad_0 = const()[name = string("query_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_17_dilations_0 = const()[name = string("query_17_dilations_0"), val = tensor([1, 1])]; + int32 query_17_groups_0 = const()[name = string("query_17_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26531264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27055616))))[name = string("pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_17_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_17_dilations_0, groups = query_17_groups_0, pad = query_17_pad_0, pad_type = query_17_pad_type_0, strides = query_17_strides_0, weight = pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized, x = obj_53_cast_fp16)[name = string("query_17_cast_fp16")]; + string key_17_pad_type_0 = const()[name = string("key_17_pad_type_0"), val = string("valid")]; + tensor key_17_strides_0 = const()[name = string("key_17_strides_0"), val = tensor([1, 1])]; + tensor key_17_pad_0 = const()[name = string("key_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_17_dilations_0 = const()[name = string("key_17_dilations_0"), val = tensor([1, 1])]; + int32 key_17_groups_0 = const()[name = string("key_17_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27056192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27580544))))[name = string("pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_17_cast_fp16 = conv(dilations = key_17_dilations_0, groups = key_17_groups_0, pad = key_17_pad_0, pad_type = key_17_pad_type_0, strides = key_17_strides_0, weight = pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized, x = obj_53_cast_fp16)[name = string("key_17_cast_fp16")]; + string obj_63_pad_type_0 = const()[name = string("obj_63_pad_type_0"), val = string("valid")]; + tensor obj_63_strides_0 = const()[name = string("obj_63_strides_0"), val = tensor([1, 1])]; + tensor obj_63_pad_0 = const()[name = string("obj_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_63_dilations_0 = const()[name = string("obj_63_dilations_0"), val = tensor([1, 1])]; + int32 obj_63_groups_0 = const()[name = string("obj_63_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27581120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28105472))))[name = string("pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_63_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_63_dilations_0, groups = obj_63_groups_0, pad = obj_63_pad_0, pad_type = obj_63_pad_type_0, strides = obj_63_strides_0, weight = pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized, x = obj_53_cast_fp16)[name = string("obj_63_cast_fp16")]; + tensor var_1247 = const()[name = string("op_1247"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_25_cast_fp16 = reshape(shape = var_1247, x = query_17_cast_fp16)[name = string("mh_q_25_cast_fp16")]; + tensor var_1249 = const()[name = string("op_1249"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_17_cast_fp16 = reshape(shape = var_1249, x = key_17_cast_fp16)[name = string("mh_k_17_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = mh_q_25_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1258_begin_0 = const()[name = string("op_1258_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1258_end_0 = const()[name = string("op_1258_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1258_end_mask_0 = const()[name = string("op_1258_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1258_cast_fp16 = slice_by_index(begin = var_1258_begin_0, end = var_1258_end_0, end_mask = var_1258_end_mask_0, x = mh_q_25_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1264_begin_0 = const()[name = string("op_1264_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1264_end_0 = const()[name = string("op_1264_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1264_end_mask_0 = const()[name = string("op_1264_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1264_cast_fp16 = slice_by_index(begin = var_1264_begin_0, end = var_1264_end_0, end_mask = var_1264_end_mask_0, x = mh_q_25_cast_fp16)[name = string("op_1264_cast_fp16")]; + fp16 const_109_promoted_to_fp16 = const()[name = string("const_109_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1266_cast_fp16 = mul(x = var_1264_cast_fp16, y = const_109_promoted_to_fp16)[name = string("op_1266_cast_fp16")]; + bool var_1268_interleave_0 = const()[name = string("op_1268_interleave_0"), val = bool(false)]; + tensor var_1268_cast_fp16 = concat(axis = var_333, interleave = var_1268_interleave_0, values = (var_1266_cast_fp16, var_1258_cast_fp16))[name = string("op_1268_cast_fp16")]; + tensor var_1269_cast_fp16 = mul(x = var_1268_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1269_cast_fp16")]; + tensor mh_q_27_cast_fp16 = add(x = var_1253_cast_fp16, y = var_1269_cast_fp16)[name = string("mh_q_27_cast_fp16")]; + tensor var_1271_cast_fp16 = mul(x = mh_k_17_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1271_cast_fp16")]; + tensor var_1276_begin_0 = const()[name = string("op_1276_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1276_end_0 = const()[name = string("op_1276_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1276_end_mask_0 = const()[name = string("op_1276_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1276_cast_fp16 = slice_by_index(begin = var_1276_begin_0, end = var_1276_end_0, end_mask = var_1276_end_mask_0, x = mh_k_17_cast_fp16)[name = string("op_1276_cast_fp16")]; + tensor var_1282_begin_0 = const()[name = string("op_1282_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1282_end_0 = const()[name = string("op_1282_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1282_end_mask_0 = const()[name = string("op_1282_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1282_cast_fp16 = slice_by_index(begin = var_1282_begin_0, end = var_1282_end_0, end_mask = var_1282_end_mask_0, x = mh_k_17_cast_fp16)[name = string("op_1282_cast_fp16")]; + fp16 const_112_promoted_to_fp16 = const()[name = string("const_112_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1284_cast_fp16 = mul(x = var_1282_cast_fp16, y = const_112_promoted_to_fp16)[name = string("op_1284_cast_fp16")]; + bool var_1286_interleave_0 = const()[name = string("op_1286_interleave_0"), val = bool(false)]; + tensor var_1286_cast_fp16 = concat(axis = var_333, interleave = var_1286_interleave_0, values = (var_1284_cast_fp16, var_1276_cast_fp16))[name = string("op_1286_cast_fp16")]; + tensor var_1287_cast_fp16 = mul(x = var_1286_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1287_cast_fp16")]; + tensor mh_k_19_cast_fp16 = add(x = var_1271_cast_fp16, y = var_1287_cast_fp16)[name = string("mh_k_19_cast_fp16")]; + tensor var_1291 = const()[name = string("op_1291"), val = tensor([1, 1024, 1, 4])]; + tensor obj_61_cast_fp16 = reshape(shape = var_1291, x = mh_k_19_cast_fp16)[name = string("obj_61_cast_fp16")]; + tensor transpose_17_perm_0 = const()[name = string("transpose_17_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_85 = const()[name = string("concat_85"), val = tensor([1, 4, 1024])]; + tensor transpose_17_cast_fp16 = transpose(perm = transpose_17_perm_0, x = obj_61_cast_fp16)[name = string("transpose_25")]; + tensor reshape_25_cast_fp16 = reshape(shape = concat_85, x = transpose_17_cast_fp16)[name = string("reshape_25_cast_fp16")]; + bool matmul_8_transpose_x_1 = const()[name = string("matmul_8_transpose_x_1"), val = bool(true)]; + bool matmul_8_transpose_y_1 = const()[name = string("matmul_8_transpose_y_1"), val = bool(false)]; + tensor matmul_8_cast_fp16 = matmul(transpose_x = matmul_8_transpose_x_1, transpose_y = matmul_8_transpose_y_1, x = kv_cache_update_mask, y = reshape_25_cast_fp16)[name = string("matmul_8_cast_fp16")]; + tensor concat_89 = const()[name = string("concat_89"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_26_cast_fp16 = reshape(shape = concat_89, x = matmul_8_cast_fp16)[name = string("reshape_26_cast_fp16")]; + tensor key_scatter_9_perm_0 = const()[name = string("key_scatter_9_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_19_perm_0 = const()[name = string("transpose_19_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_95 = const()[name = string("concat_95"), val = tensor([1, 4, 1024])]; + tensor transpose_19_cast_fp16 = transpose(perm = transpose_19_perm_0, x = obj_63_cast_fp16)[name = string("transpose_24")]; + tensor reshape_28_cast_fp16 = reshape(shape = concat_95, x = transpose_19_cast_fp16)[name = string("reshape_28_cast_fp16")]; + bool matmul_9_transpose_x_1 = const()[name = string("matmul_9_transpose_x_1"), val = bool(true)]; + bool matmul_9_transpose_y_1 = const()[name = string("matmul_9_transpose_y_1"), val = bool(false)]; + tensor matmul_9_cast_fp16 = matmul(transpose_x = matmul_9_transpose_x_1, transpose_y = matmul_9_transpose_y_1, x = kv_cache_update_mask, y = reshape_28_cast_fp16)[name = string("matmul_9_cast_fp16")]; + tensor concat_99 = const()[name = string("concat_99"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_29_cast_fp16 = reshape(shape = concat_99, x = matmul_9_cast_fp16)[name = string("reshape_29_cast_fp16")]; + tensor value_scatter_9_perm_0 = const()[name = string("value_scatter_9_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_1304_cast_fp16 = mul(x = var_367_cast_fp16_4, y = var_502_cast_fp16)[name = string("op_1304_cast_fp16")]; + tensor key_scatter_9_cast_fp16 = transpose(perm = key_scatter_9_perm_0, x = reshape_26_cast_fp16)[name = string("transpose_23")]; + tensor key_19_cast_fp16 = add(x = var_1304_cast_fp16, y = key_scatter_9_cast_fp16)[name = string("key_19_cast_fp16")]; + tensor var_1306_cast_fp16 = mul(x = var_376_cast_fp16_4, y = var_502_cast_fp16)[name = string("op_1306_cast_fp16")]; + tensor value_scatter_9_cast_fp16 = transpose(perm = value_scatter_9_perm_0, x = reshape_29_cast_fp16)[name = string("transpose_22")]; + tensor value_9_cast_fp16 = add(x = var_1306_cast_fp16, y = value_scatter_9_cast_fp16)[name = string("value_9_cast_fp16")]; + fp16 var_1312_to_fp16 = const()[name = string("op_1312_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1313_cast_fp16 = mul(x = mh_q_27_cast_fp16, y = var_1312_to_fp16)[name = string("op_1313_cast_fp16")]; + tensor var_1316 = const()[name = string("op_1316"), val = tensor([1, 16, 64, 80])]; + tensor var_1317_cast_fp16 = reshape(shape = var_1316, x = key_19_cast_fp16)[name = string("op_1317_cast_fp16")]; + bool mh_w_25_transpose_x_0 = const()[name = string("mh_w_25_transpose_x_0"), val = bool(true)]; + bool mh_w_25_transpose_y_0 = const()[name = string("mh_w_25_transpose_y_0"), val = bool(false)]; + tensor mh_w_25_cast_fp16 = matmul(transpose_x = mh_w_25_transpose_x_0, transpose_y = mh_w_25_transpose_y_0, x = var_1313_cast_fp16, y = var_1317_cast_fp16)[name = string("mh_w_25_cast_fp16")]; + tensor mh_w_27_cast_fp16 = add(x = mh_w_25_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_27_cast_fp16")]; + tensor mh_w_29_cast_fp16 = add(x = mh_w_27_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_29_cast_fp16")]; + tensor var_1327_cast_fp16 = softmax(axis = var_338, x = mh_w_29_cast_fp16)[name = string("op_1327_cast_fp16")]; + tensor var_1328 = const()[name = string("op_1328"), val = tensor([1, 16, 64, 80])]; + tensor var_1329_cast_fp16 = reshape(shape = var_1328, x = value_9_cast_fp16)[name = string("op_1329_cast_fp16")]; + bool attn_9_transpose_x_0 = const()[name = string("attn_9_transpose_x_0"), val = bool(false)]; + bool attn_9_transpose_y_0 = const()[name = string("attn_9_transpose_y_0"), val = bool(true)]; + tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_1329_cast_fp16, y = var_1327_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor var_1332 = const()[name = string("op_1332"), val = tensor([1, -1, 1, 4])]; + tensor input_75_cast_fp16 = reshape(shape = var_1332, x = attn_9_cast_fp16)[name = string("input_75_cast_fp16")]; + string obj_59_pad_type_0 = const()[name = string("obj_59_pad_type_0"), val = string("valid")]; + tensor obj_59_strides_0 = const()[name = string("obj_59_strides_0"), val = tensor([1, 1])]; + tensor obj_59_pad_0 = const()[name = string("obj_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_59_dilations_0 = const()[name = string("obj_59_dilations_0"), val = tensor([1, 1])]; + int32 obj_59_groups_0 = const()[name = string("obj_59_groups_0"), val = int32(1)]; + tensor op_1348_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28106048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28630400))))[name = string("op_1348_weight_0_to_fp16_palettized")]; + tensor var_1348_bias_0_to_fp16 = const()[name = string("op_1348_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28630976)))]; + tensor var_1348_cast_fp16 = conv(bias = var_1348_bias_0_to_fp16, dilations = obj_59_dilations_0, groups = obj_59_groups_0, pad = obj_59_pad_0, pad_type = obj_59_pad_type_0, strides = obj_59_strides_0, weight = op_1348_weight_0_to_fp16_palettized, x = input_75_cast_fp16)[name = string("op_1348_cast_fp16")]; + tensor inputs_19_cast_fp16 = add(x = inputs_17_cast_fp16, y = var_1348_cast_fp16)[name = string("inputs_19_cast_fp16")]; + tensor inputs_sq_19_cast_fp16 = mul(x = inputs_19_cast_fp16, y = inputs_19_cast_fp16)[name = string("inputs_sq_19_cast_fp16")]; + tensor variance_19_axes_0 = const()[name = string("variance_19_axes_0"), val = tensor([1])]; + bool variance_19_keep_dims_0 = const()[name = string("variance_19_keep_dims_0"), val = bool(true)]; + tensor variance_19_cast_fp16 = reduce_mean(axes = variance_19_axes_0, keep_dims = variance_19_keep_dims_0, x = inputs_sq_19_cast_fp16)[name = string("variance_19_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1355_cast_fp16 = add(x = variance_19_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor hidden_states_27_cast_fp16 = mul(x = inputs_19_cast_fp16, y = var_1356_cast_fp16)[name = string("hidden_states_27_cast_fp16")]; + tensor w_19_to_fp16 = const()[name = string("w_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28632064)))]; + tensor input_77_cast_fp16 = mul(x = w_19_to_fp16, y = hidden_states_27_cast_fp16)[name = string("input_77_cast_fp16")]; + string input_79_pad_type_0 = const()[name = string("input_79_pad_type_0"), val = string("valid")]; + tensor input_79_strides_0 = const()[name = string("input_79_strides_0"), val = tensor([1, 1])]; + tensor input_79_pad_0 = const()[name = string("input_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_79_dilations_0 = const()[name = string("input_79_dilations_0"), val = tensor([1, 1])]; + int32 input_79_groups_0 = const()[name = string("input_79_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28633152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29157504))))[name = string("pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_79_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_79_dilations_0, groups = input_79_groups_0, pad = input_79_pad_0, pad_type = input_79_pad_type_0, strides = input_79_strides_0, weight = pre_transformer_layers_4_mlp_fc3_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor gate_9_cast_fp16 = silu(x = input_79_cast_fp16)[name = string("gate_9_cast_fp16")]; + string up_9_pad_type_0 = const()[name = string("up_9_pad_type_0"), val = string("valid")]; + tensor up_9_strides_0 = const()[name = string("up_9_strides_0"), val = tensor([1, 1])]; + tensor up_9_pad_0 = const()[name = string("up_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_9_dilations_0 = const()[name = string("up_9_dilations_0"), val = tensor([1, 1])]; + int32 up_9_groups_0 = const()[name = string("up_9_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29158080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29682432))))[name = string("pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_9_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_9_dilations_0, groups = up_9_groups_0, pad = up_9_pad_0, pad_type = up_9_pad_type_0, strides = up_9_strides_0, weight = pre_transformer_layers_4_mlp_fc1_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("up_9_cast_fp16")]; + tensor input_81_cast_fp16 = mul(x = gate_9_cast_fp16, y = up_9_cast_fp16)[name = string("input_81_cast_fp16")]; + string hidden_states_29_pad_type_0 = const()[name = string("hidden_states_29_pad_type_0"), val = string("valid")]; + tensor hidden_states_29_strides_0 = const()[name = string("hidden_states_29_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_29_pad_0 = const()[name = string("hidden_states_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_29_dilations_0 = const()[name = string("hidden_states_29_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_29_groups_0 = const()[name = string("hidden_states_29_groups_0"), val = int32(1)]; + tensor op_1390_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29683008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30207360))))[name = string("op_1390_weight_0_to_fp16_palettized")]; + tensor var_1390_bias_0_to_fp16 = const()[name = string("op_1390_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30207936)))]; + tensor var_1390_cast_fp16 = conv(bias = var_1390_bias_0_to_fp16, dilations = hidden_states_29_dilations_0, groups = hidden_states_29_groups_0, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = hidden_states_29_strides_0, weight = op_1390_weight_0_to_fp16_palettized, x = input_81_cast_fp16)[name = string("op_1390_cast_fp16")]; + tensor inputs_21_cast_fp16 = add(x = inputs_19_cast_fp16, y = var_1390_cast_fp16)[name = string("inputs_21_cast_fp16")]; + tensor inputs_sq_21_cast_fp16 = mul(x = inputs_21_cast_fp16, y = inputs_21_cast_fp16)[name = string("inputs_sq_21_cast_fp16")]; + tensor variance_21_axes_0 = const()[name = string("variance_21_axes_0"), val = tensor([1])]; + bool variance_21_keep_dims_0 = const()[name = string("variance_21_keep_dims_0"), val = bool(true)]; + tensor variance_21_cast_fp16 = reduce_mean(axes = variance_21_axes_0, keep_dims = variance_21_keep_dims_0, x = inputs_sq_21_cast_fp16)[name = string("variance_21_cast_fp16")]; + fp16 var_1406_to_fp16 = const()[name = string("op_1406_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1407_cast_fp16 = add(x = variance_21_cast_fp16, y = var_1406_to_fp16)[name = string("op_1407_cast_fp16")]; + fp32 var_1408_epsilon_0 = const()[name = string("op_1408_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1408_cast_fp16 = rsqrt(epsilon = var_1408_epsilon_0, x = var_1407_cast_fp16)[name = string("op_1408_cast_fp16")]; + tensor hidden_states_31_cast_fp16 = mul(x = inputs_21_cast_fp16, y = var_1408_cast_fp16)[name = string("hidden_states_31_cast_fp16")]; + tensor w_21_to_fp16 = const()[name = string("w_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30209024)))]; + tensor obj_65_cast_fp16 = mul(x = w_21_to_fp16, y = hidden_states_31_cast_fp16)[name = string("obj_65_cast_fp16")]; + string query_21_pad_type_0 = const()[name = string("query_21_pad_type_0"), val = string("valid")]; + tensor query_21_strides_0 = const()[name = string("query_21_strides_0"), val = tensor([1, 1])]; + tensor query_21_pad_0 = const()[name = string("query_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_21_dilations_0 = const()[name = string("query_21_dilations_0"), val = tensor([1, 1])]; + int32 query_21_groups_0 = const()[name = string("query_21_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30210112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30734464))))[name = string("pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_21_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_21_dilations_0, groups = query_21_groups_0, pad = query_21_pad_0, pad_type = query_21_pad_type_0, strides = query_21_strides_0, weight = pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized, x = obj_65_cast_fp16)[name = string("query_21_cast_fp16")]; + string key_21_pad_type_0 = const()[name = string("key_21_pad_type_0"), val = string("valid")]; + tensor key_21_strides_0 = const()[name = string("key_21_strides_0"), val = tensor([1, 1])]; + tensor key_21_pad_0 = const()[name = string("key_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_21_dilations_0 = const()[name = string("key_21_dilations_0"), val = tensor([1, 1])]; + int32 key_21_groups_0 = const()[name = string("key_21_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30735040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31259392))))[name = string("pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_21_cast_fp16 = conv(dilations = key_21_dilations_0, groups = key_21_groups_0, pad = key_21_pad_0, pad_type = key_21_pad_type_0, strides = key_21_strides_0, weight = pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized, x = obj_65_cast_fp16)[name = string("key_21_cast_fp16")]; + string obj_75_pad_type_0 = const()[name = string("obj_75_pad_type_0"), val = string("valid")]; + tensor obj_75_strides_0 = const()[name = string("obj_75_strides_0"), val = tensor([1, 1])]; + tensor obj_75_pad_0 = const()[name = string("obj_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_75_dilations_0 = const()[name = string("obj_75_dilations_0"), val = tensor([1, 1])]; + int32 obj_75_groups_0 = const()[name = string("obj_75_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31259968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31784320))))[name = string("pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_75_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_75_dilations_0, groups = obj_75_groups_0, pad = obj_75_pad_0, pad_type = obj_75_pad_type_0, strides = obj_75_strides_0, weight = pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized, x = obj_65_cast_fp16)[name = string("obj_75_cast_fp16")]; + tensor var_1446 = const()[name = string("op_1446"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_31_cast_fp16 = reshape(shape = var_1446, x = query_21_cast_fp16)[name = string("mh_q_31_cast_fp16")]; + tensor var_1448 = const()[name = string("op_1448"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_21_cast_fp16 = reshape(shape = var_1448, x = key_21_cast_fp16)[name = string("mh_k_21_cast_fp16")]; + tensor var_1452_cast_fp16 = mul(x = mh_q_31_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1452_cast_fp16")]; + tensor var_1457_begin_0 = const()[name = string("op_1457_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1457_end_0 = const()[name = string("op_1457_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1457_end_mask_0 = const()[name = string("op_1457_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1457_cast_fp16 = slice_by_index(begin = var_1457_begin_0, end = var_1457_end_0, end_mask = var_1457_end_mask_0, x = mh_q_31_cast_fp16)[name = string("op_1457_cast_fp16")]; + tensor var_1463_begin_0 = const()[name = string("op_1463_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1463_end_0 = const()[name = string("op_1463_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1463_end_mask_0 = const()[name = string("op_1463_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1463_cast_fp16 = slice_by_index(begin = var_1463_begin_0, end = var_1463_end_0, end_mask = var_1463_end_mask_0, x = mh_q_31_cast_fp16)[name = string("op_1463_cast_fp16")]; + fp16 const_128_promoted_to_fp16 = const()[name = string("const_128_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1465_cast_fp16 = mul(x = var_1463_cast_fp16, y = const_128_promoted_to_fp16)[name = string("op_1465_cast_fp16")]; + bool var_1467_interleave_0 = const()[name = string("op_1467_interleave_0"), val = bool(false)]; + tensor var_1467_cast_fp16 = concat(axis = var_333, interleave = var_1467_interleave_0, values = (var_1465_cast_fp16, var_1457_cast_fp16))[name = string("op_1467_cast_fp16")]; + tensor var_1468_cast_fp16 = mul(x = var_1467_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1468_cast_fp16")]; + tensor mh_q_33_cast_fp16 = add(x = var_1452_cast_fp16, y = var_1468_cast_fp16)[name = string("mh_q_33_cast_fp16")]; + tensor var_1470_cast_fp16 = mul(x = mh_k_21_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1470_cast_fp16")]; + tensor var_1475_begin_0 = const()[name = string("op_1475_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1475_end_0 = const()[name = string("op_1475_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1475_end_mask_0 = const()[name = string("op_1475_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1475_cast_fp16 = slice_by_index(begin = var_1475_begin_0, end = var_1475_end_0, end_mask = var_1475_end_mask_0, x = mh_k_21_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor var_1481_begin_0 = const()[name = string("op_1481_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1481_end_0 = const()[name = string("op_1481_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1481_end_mask_0 = const()[name = string("op_1481_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1481_cast_fp16 = slice_by_index(begin = var_1481_begin_0, end = var_1481_end_0, end_mask = var_1481_end_mask_0, x = mh_k_21_cast_fp16)[name = string("op_1481_cast_fp16")]; + fp16 const_131_promoted_to_fp16 = const()[name = string("const_131_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1483_cast_fp16 = mul(x = var_1481_cast_fp16, y = const_131_promoted_to_fp16)[name = string("op_1483_cast_fp16")]; + bool var_1485_interleave_0 = const()[name = string("op_1485_interleave_0"), val = bool(false)]; + tensor var_1485_cast_fp16 = concat(axis = var_333, interleave = var_1485_interleave_0, values = (var_1483_cast_fp16, var_1475_cast_fp16))[name = string("op_1485_cast_fp16")]; + tensor var_1486_cast_fp16 = mul(x = var_1485_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1486_cast_fp16")]; + tensor mh_k_23_cast_fp16 = add(x = var_1470_cast_fp16, y = var_1486_cast_fp16)[name = string("mh_k_23_cast_fp16")]; + tensor var_1490 = const()[name = string("op_1490"), val = tensor([1, 1024, 1, 4])]; + tensor obj_73_cast_fp16 = reshape(shape = var_1490, x = mh_k_23_cast_fp16)[name = string("obj_73_cast_fp16")]; + tensor transpose_21_perm_0 = const()[name = string("transpose_21_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_105 = const()[name = string("concat_105"), val = tensor([1, 4, 1024])]; + tensor transpose_21_cast_fp16 = transpose(perm = transpose_21_perm_0, x = obj_73_cast_fp16)[name = string("transpose_21")]; + tensor reshape_31_cast_fp16 = reshape(shape = concat_105, x = transpose_21_cast_fp16)[name = string("reshape_31_cast_fp16")]; + bool matmul_10_transpose_x_1 = const()[name = string("matmul_10_transpose_x_1"), val = bool(true)]; + bool matmul_10_transpose_y_1 = const()[name = string("matmul_10_transpose_y_1"), val = bool(false)]; + tensor matmul_10_cast_fp16 = matmul(transpose_x = matmul_10_transpose_x_1, transpose_y = matmul_10_transpose_y_1, x = kv_cache_update_mask, y = reshape_31_cast_fp16)[name = string("matmul_10_cast_fp16")]; + tensor concat_109 = const()[name = string("concat_109"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_32_cast_fp16 = reshape(shape = concat_109, x = matmul_10_cast_fp16)[name = string("reshape_32_cast_fp16")]; + tensor key_scatter_11_perm_0 = const()[name = string("key_scatter_11_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_23_perm_0 = const()[name = string("transpose_23_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_115 = const()[name = string("concat_115"), val = tensor([1, 4, 1024])]; + tensor transpose_23_cast_fp16 = transpose(perm = transpose_23_perm_0, x = obj_75_cast_fp16)[name = string("transpose_20")]; + tensor reshape_34_cast_fp16 = reshape(shape = concat_115, x = transpose_23_cast_fp16)[name = string("reshape_34_cast_fp16")]; + bool matmul_11_transpose_x_1 = const()[name = string("matmul_11_transpose_x_1"), val = bool(true)]; + bool matmul_11_transpose_y_1 = const()[name = string("matmul_11_transpose_y_1"), val = bool(false)]; + tensor matmul_11_cast_fp16 = matmul(transpose_x = matmul_11_transpose_x_1, transpose_y = matmul_11_transpose_y_1, x = kv_cache_update_mask, y = reshape_34_cast_fp16)[name = string("matmul_11_cast_fp16")]; + tensor concat_119 = const()[name = string("concat_119"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_35_cast_fp16 = reshape(shape = concat_119, x = matmul_11_cast_fp16)[name = string("reshape_35_cast_fp16")]; + tensor value_scatter_11_perm_0 = const()[name = string("value_scatter_11_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_1503_cast_fp16 = mul(x = var_367_cast_fp16_5, y = var_502_cast_fp16)[name = string("op_1503_cast_fp16")]; + tensor key_scatter_11_cast_fp16 = transpose(perm = key_scatter_11_perm_0, x = reshape_32_cast_fp16)[name = string("transpose_19")]; + tensor key_23_cast_fp16 = add(x = var_1503_cast_fp16, y = key_scatter_11_cast_fp16)[name = string("key_23_cast_fp16")]; + tensor var_1505_cast_fp16 = mul(x = var_376_cast_fp16_5, y = var_502_cast_fp16)[name = string("op_1505_cast_fp16")]; + tensor value_scatter_11_cast_fp16 = transpose(perm = value_scatter_11_perm_0, x = reshape_35_cast_fp16)[name = string("transpose_18")]; + tensor value_11_cast_fp16 = add(x = var_1505_cast_fp16, y = value_scatter_11_cast_fp16)[name = string("value_11_cast_fp16")]; + fp16 var_1511_to_fp16 = const()[name = string("op_1511_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1512_cast_fp16 = mul(x = mh_q_33_cast_fp16, y = var_1511_to_fp16)[name = string("op_1512_cast_fp16")]; + tensor var_1515 = const()[name = string("op_1515"), val = tensor([1, 16, 64, 80])]; + tensor var_1516_cast_fp16 = reshape(shape = var_1515, x = key_23_cast_fp16)[name = string("op_1516_cast_fp16")]; + bool mh_w_31_transpose_x_0 = const()[name = string("mh_w_31_transpose_x_0"), val = bool(true)]; + bool mh_w_31_transpose_y_0 = const()[name = string("mh_w_31_transpose_y_0"), val = bool(false)]; + tensor mh_w_31_cast_fp16 = matmul(transpose_x = mh_w_31_transpose_x_0, transpose_y = mh_w_31_transpose_y_0, x = var_1512_cast_fp16, y = var_1516_cast_fp16)[name = string("mh_w_31_cast_fp16")]; + tensor mh_w_33_cast_fp16 = add(x = mh_w_31_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_33_cast_fp16")]; + tensor mh_w_35_cast_fp16 = add(x = mh_w_33_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_35_cast_fp16")]; + tensor var_1526_cast_fp16 = softmax(axis = var_338, x = mh_w_35_cast_fp16)[name = string("op_1526_cast_fp16")]; + tensor var_1527 = const()[name = string("op_1527"), val = tensor([1, 16, 64, 80])]; + tensor var_1528_cast_fp16 = reshape(shape = var_1527, x = value_11_cast_fp16)[name = string("op_1528_cast_fp16")]; + bool attn_11_transpose_x_0 = const()[name = string("attn_11_transpose_x_0"), val = bool(false)]; + bool attn_11_transpose_y_0 = const()[name = string("attn_11_transpose_y_0"), val = bool(true)]; + tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_1528_cast_fp16, y = var_1526_cast_fp16)[name = string("attn_11_cast_fp16")]; + tensor var_1531 = const()[name = string("op_1531"), val = tensor([1, -1, 1, 4])]; + tensor input_83_cast_fp16 = reshape(shape = var_1531, x = attn_11_cast_fp16)[name = string("input_83_cast_fp16")]; + string obj_71_pad_type_0 = const()[name = string("obj_71_pad_type_0"), val = string("valid")]; + tensor obj_71_strides_0 = const()[name = string("obj_71_strides_0"), val = tensor([1, 1])]; + tensor obj_71_pad_0 = const()[name = string("obj_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_71_dilations_0 = const()[name = string("obj_71_dilations_0"), val = tensor([1, 1])]; + int32 obj_71_groups_0 = const()[name = string("obj_71_groups_0"), val = int32(1)]; + tensor op_1547_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31784896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32309248))))[name = string("op_1547_weight_0_to_fp16_palettized")]; + tensor var_1547_bias_0_to_fp16 = const()[name = string("op_1547_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32309824)))]; + tensor var_1547_cast_fp16 = conv(bias = var_1547_bias_0_to_fp16, dilations = obj_71_dilations_0, groups = obj_71_groups_0, pad = obj_71_pad_0, pad_type = obj_71_pad_type_0, strides = obj_71_strides_0, weight = op_1547_weight_0_to_fp16_palettized, x = input_83_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor inputs_23_cast_fp16 = add(x = inputs_21_cast_fp16, y = var_1547_cast_fp16)[name = string("inputs_23_cast_fp16")]; + tensor inputs_sq_23_cast_fp16 = mul(x = inputs_23_cast_fp16, y = inputs_23_cast_fp16)[name = string("inputs_sq_23_cast_fp16")]; + tensor variance_23_axes_0 = const()[name = string("variance_23_axes_0"), val = tensor([1])]; + bool variance_23_keep_dims_0 = const()[name = string("variance_23_keep_dims_0"), val = bool(true)]; + tensor variance_23_cast_fp16 = reduce_mean(axes = variance_23_axes_0, keep_dims = variance_23_keep_dims_0, x = inputs_sq_23_cast_fp16)[name = string("variance_23_cast_fp16")]; + fp16 var_1553_to_fp16 = const()[name = string("op_1553_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1554_cast_fp16 = add(x = variance_23_cast_fp16, y = var_1553_to_fp16)[name = string("op_1554_cast_fp16")]; + fp32 var_1555_epsilon_0 = const()[name = string("op_1555_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1555_cast_fp16 = rsqrt(epsilon = var_1555_epsilon_0, x = var_1554_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = mul(x = inputs_23_cast_fp16, y = var_1555_cast_fp16)[name = string("hidden_states_33_cast_fp16")]; + tensor w_23_to_fp16 = const()[name = string("w_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32310912)))]; + tensor input_85_cast_fp16 = mul(x = w_23_to_fp16, y = hidden_states_33_cast_fp16)[name = string("input_85_cast_fp16")]; + string input_87_pad_type_0 = const()[name = string("input_87_pad_type_0"), val = string("valid")]; + tensor input_87_strides_0 = const()[name = string("input_87_strides_0"), val = tensor([1, 1])]; + tensor input_87_pad_0 = const()[name = string("input_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_87_dilations_0 = const()[name = string("input_87_dilations_0"), val = tensor([1, 1])]; + int32 input_87_groups_0 = const()[name = string("input_87_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32312000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32836352))))[name = string("pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_87_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = pre_transformer_layers_5_mlp_fc3_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("input_87_cast_fp16")]; + tensor gate_11_cast_fp16 = silu(x = input_87_cast_fp16)[name = string("gate_11_cast_fp16")]; + string up_11_pad_type_0 = const()[name = string("up_11_pad_type_0"), val = string("valid")]; + tensor up_11_strides_0 = const()[name = string("up_11_strides_0"), val = tensor([1, 1])]; + tensor up_11_pad_0 = const()[name = string("up_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_11_dilations_0 = const()[name = string("up_11_dilations_0"), val = tensor([1, 1])]; + int32 up_11_groups_0 = const()[name = string("up_11_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32836928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33361280))))[name = string("pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_11_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_11_dilations_0, groups = up_11_groups_0, pad = up_11_pad_0, pad_type = up_11_pad_type_0, strides = up_11_strides_0, weight = pre_transformer_layers_5_mlp_fc1_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("up_11_cast_fp16")]; + tensor input_89_cast_fp16 = mul(x = gate_11_cast_fp16, y = up_11_cast_fp16)[name = string("input_89_cast_fp16")]; + string hidden_states_35_pad_type_0 = const()[name = string("hidden_states_35_pad_type_0"), val = string("valid")]; + tensor hidden_states_35_strides_0 = const()[name = string("hidden_states_35_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_0 = const()[name = string("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_35_dilations_0 = const()[name = string("hidden_states_35_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_35_groups_0 = const()[name = string("hidden_states_35_groups_0"), val = int32(1)]; + tensor op_1589_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33361856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33886208))))[name = string("op_1589_weight_0_to_fp16_palettized")]; + tensor var_1589_bias_0_to_fp16 = const()[name = string("op_1589_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33886784)))]; + tensor var_1589_cast_fp16 = conv(bias = var_1589_bias_0_to_fp16, dilations = hidden_states_35_dilations_0, groups = hidden_states_35_groups_0, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = hidden_states_35_strides_0, weight = op_1589_weight_0_to_fp16_palettized, x = input_89_cast_fp16)[name = string("op_1589_cast_fp16")]; + tensor inputs_25_cast_fp16 = add(x = inputs_23_cast_fp16, y = var_1589_cast_fp16)[name = string("inputs_25_cast_fp16")]; + tensor inputs_sq_25_cast_fp16 = mul(x = inputs_25_cast_fp16, y = inputs_25_cast_fp16)[name = string("inputs_sq_25_cast_fp16")]; + tensor variance_25_axes_0 = const()[name = string("variance_25_axes_0"), val = tensor([1])]; + bool variance_25_keep_dims_0 = const()[name = string("variance_25_keep_dims_0"), val = bool(true)]; + tensor variance_25_cast_fp16 = reduce_mean(axes = variance_25_axes_0, keep_dims = variance_25_keep_dims_0, x = inputs_sq_25_cast_fp16)[name = string("variance_25_cast_fp16")]; + fp16 var_1605_to_fp16 = const()[name = string("op_1605_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1606_cast_fp16 = add(x = variance_25_cast_fp16, y = var_1605_to_fp16)[name = string("op_1606_cast_fp16")]; + fp32 var_1607_epsilon_0 = const()[name = string("op_1607_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1607_cast_fp16 = rsqrt(epsilon = var_1607_epsilon_0, x = var_1606_cast_fp16)[name = string("op_1607_cast_fp16")]; + tensor hidden_states_37_cast_fp16 = mul(x = inputs_25_cast_fp16, y = var_1607_cast_fp16)[name = string("hidden_states_37_cast_fp16")]; + tensor w_25_to_fp16 = const()[name = string("w_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33887872)))]; + tensor obj_77_cast_fp16 = mul(x = w_25_to_fp16, y = hidden_states_37_cast_fp16)[name = string("obj_77_cast_fp16")]; + string query_25_pad_type_0 = const()[name = string("query_25_pad_type_0"), val = string("valid")]; + tensor query_25_strides_0 = const()[name = string("query_25_strides_0"), val = tensor([1, 1])]; + tensor query_25_pad_0 = const()[name = string("query_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_25_dilations_0 = const()[name = string("query_25_dilations_0"), val = tensor([1, 1])]; + int32 query_25_groups_0 = const()[name = string("query_25_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33888960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34413312))))[name = string("pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_25_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_25_dilations_0, groups = query_25_groups_0, pad = query_25_pad_0, pad_type = query_25_pad_type_0, strides = query_25_strides_0, weight = pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized, x = obj_77_cast_fp16)[name = string("query_25_cast_fp16")]; + string key_25_pad_type_0 = const()[name = string("key_25_pad_type_0"), val = string("valid")]; + tensor key_25_strides_0 = const()[name = string("key_25_strides_0"), val = tensor([1, 1])]; + tensor key_25_pad_0 = const()[name = string("key_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_25_dilations_0 = const()[name = string("key_25_dilations_0"), val = tensor([1, 1])]; + int32 key_25_groups_0 = const()[name = string("key_25_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34413888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34938240))))[name = string("pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_25_cast_fp16 = conv(dilations = key_25_dilations_0, groups = key_25_groups_0, pad = key_25_pad_0, pad_type = key_25_pad_type_0, strides = key_25_strides_0, weight = pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized, x = obj_77_cast_fp16)[name = string("key_25_cast_fp16")]; + string obj_87_pad_type_0 = const()[name = string("obj_87_pad_type_0"), val = string("valid")]; + tensor obj_87_strides_0 = const()[name = string("obj_87_strides_0"), val = tensor([1, 1])]; + tensor obj_87_pad_0 = const()[name = string("obj_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_87_dilations_0 = const()[name = string("obj_87_dilations_0"), val = tensor([1, 1])]; + int32 obj_87_groups_0 = const()[name = string("obj_87_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34938816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35463168))))[name = string("pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_87_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_87_dilations_0, groups = obj_87_groups_0, pad = obj_87_pad_0, pad_type = obj_87_pad_type_0, strides = obj_87_strides_0, weight = pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized, x = obj_77_cast_fp16)[name = string("obj_87_cast_fp16")]; + tensor var_1645 = const()[name = string("op_1645"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_37_cast_fp16 = reshape(shape = var_1645, x = query_25_cast_fp16)[name = string("mh_q_37_cast_fp16")]; + tensor var_1647 = const()[name = string("op_1647"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_25_cast_fp16 = reshape(shape = var_1647, x = key_25_cast_fp16)[name = string("mh_k_25_cast_fp16")]; + tensor var_1651_cast_fp16 = mul(x = mh_q_37_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1651_cast_fp16")]; + tensor var_1656_begin_0 = const()[name = string("op_1656_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1656_end_0 = const()[name = string("op_1656_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1656_end_mask_0 = const()[name = string("op_1656_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1656_cast_fp16 = slice_by_index(begin = var_1656_begin_0, end = var_1656_end_0, end_mask = var_1656_end_mask_0, x = mh_q_37_cast_fp16)[name = string("op_1656_cast_fp16")]; + tensor var_1662_begin_0 = const()[name = string("op_1662_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1662_end_0 = const()[name = string("op_1662_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1662_end_mask_0 = const()[name = string("op_1662_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1662_cast_fp16 = slice_by_index(begin = var_1662_begin_0, end = var_1662_end_0, end_mask = var_1662_end_mask_0, x = mh_q_37_cast_fp16)[name = string("op_1662_cast_fp16")]; + fp16 const_147_promoted_to_fp16 = const()[name = string("const_147_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1664_cast_fp16 = mul(x = var_1662_cast_fp16, y = const_147_promoted_to_fp16)[name = string("op_1664_cast_fp16")]; + bool var_1666_interleave_0 = const()[name = string("op_1666_interleave_0"), val = bool(false)]; + tensor var_1666_cast_fp16 = concat(axis = var_333, interleave = var_1666_interleave_0, values = (var_1664_cast_fp16, var_1656_cast_fp16))[name = string("op_1666_cast_fp16")]; + tensor var_1667_cast_fp16 = mul(x = var_1666_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1667_cast_fp16")]; + tensor mh_q_39_cast_fp16 = add(x = var_1651_cast_fp16, y = var_1667_cast_fp16)[name = string("mh_q_39_cast_fp16")]; + tensor var_1669_cast_fp16 = mul(x = mh_k_25_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1669_cast_fp16")]; + tensor var_1674_begin_0 = const()[name = string("op_1674_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1674_end_0 = const()[name = string("op_1674_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1674_end_mask_0 = const()[name = string("op_1674_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1674_cast_fp16 = slice_by_index(begin = var_1674_begin_0, end = var_1674_end_0, end_mask = var_1674_end_mask_0, x = mh_k_25_cast_fp16)[name = string("op_1674_cast_fp16")]; + tensor var_1680_begin_0 = const()[name = string("op_1680_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1680_end_0 = const()[name = string("op_1680_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1680_end_mask_0 = const()[name = string("op_1680_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1680_cast_fp16 = slice_by_index(begin = var_1680_begin_0, end = var_1680_end_0, end_mask = var_1680_end_mask_0, x = mh_k_25_cast_fp16)[name = string("op_1680_cast_fp16")]; + fp16 const_150_promoted_to_fp16 = const()[name = string("const_150_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1682_cast_fp16 = mul(x = var_1680_cast_fp16, y = const_150_promoted_to_fp16)[name = string("op_1682_cast_fp16")]; + bool var_1684_interleave_0 = const()[name = string("op_1684_interleave_0"), val = bool(false)]; + tensor var_1684_cast_fp16 = concat(axis = var_333, interleave = var_1684_interleave_0, values = (var_1682_cast_fp16, var_1674_cast_fp16))[name = string("op_1684_cast_fp16")]; + tensor var_1685_cast_fp16 = mul(x = var_1684_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1685_cast_fp16")]; + tensor mh_k_27_cast_fp16 = add(x = var_1669_cast_fp16, y = var_1685_cast_fp16)[name = string("mh_k_27_cast_fp16")]; + tensor var_1689 = const()[name = string("op_1689"), val = tensor([1, 1024, 1, 4])]; + tensor obj_85_cast_fp16 = reshape(shape = var_1689, x = mh_k_27_cast_fp16)[name = string("obj_85_cast_fp16")]; + tensor transpose_25_perm_0 = const()[name = string("transpose_25_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_125 = const()[name = string("concat_125"), val = tensor([1, 4, 1024])]; + tensor transpose_25_cast_fp16 = transpose(perm = transpose_25_perm_0, x = obj_85_cast_fp16)[name = string("transpose_17")]; + tensor reshape_37_cast_fp16 = reshape(shape = concat_125, x = transpose_25_cast_fp16)[name = string("reshape_37_cast_fp16")]; + bool matmul_12_transpose_x_1 = const()[name = string("matmul_12_transpose_x_1"), val = bool(true)]; + bool matmul_12_transpose_y_1 = const()[name = string("matmul_12_transpose_y_1"), val = bool(false)]; + tensor matmul_12_cast_fp16 = matmul(transpose_x = matmul_12_transpose_x_1, transpose_y = matmul_12_transpose_y_1, x = kv_cache_update_mask, y = reshape_37_cast_fp16)[name = string("matmul_12_cast_fp16")]; + tensor concat_129 = const()[name = string("concat_129"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_38_cast_fp16 = reshape(shape = concat_129, x = matmul_12_cast_fp16)[name = string("reshape_38_cast_fp16")]; + tensor key_scatter_13_perm_0 = const()[name = string("key_scatter_13_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_27_perm_0 = const()[name = string("transpose_27_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_135 = const()[name = string("concat_135"), val = tensor([1, 4, 1024])]; + tensor transpose_27_cast_fp16 = transpose(perm = transpose_27_perm_0, x = obj_87_cast_fp16)[name = string("transpose_16")]; + tensor reshape_40_cast_fp16 = reshape(shape = concat_135, x = transpose_27_cast_fp16)[name = string("reshape_40_cast_fp16")]; + bool matmul_13_transpose_x_1 = const()[name = string("matmul_13_transpose_x_1"), val = bool(true)]; + bool matmul_13_transpose_y_1 = const()[name = string("matmul_13_transpose_y_1"), val = bool(false)]; + tensor matmul_13_cast_fp16 = matmul(transpose_x = matmul_13_transpose_x_1, transpose_y = matmul_13_transpose_y_1, x = kv_cache_update_mask, y = reshape_40_cast_fp16)[name = string("matmul_13_cast_fp16")]; + tensor concat_139 = const()[name = string("concat_139"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_41_cast_fp16 = reshape(shape = concat_139, x = matmul_13_cast_fp16)[name = string("reshape_41_cast_fp16")]; + tensor value_scatter_13_perm_0 = const()[name = string("value_scatter_13_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_1702_cast_fp16 = mul(x = var_367_cast_fp16_6, y = var_502_cast_fp16)[name = string("op_1702_cast_fp16")]; + tensor key_scatter_13_cast_fp16 = transpose(perm = key_scatter_13_perm_0, x = reshape_38_cast_fp16)[name = string("transpose_15")]; + tensor key_27_cast_fp16 = add(x = var_1702_cast_fp16, y = key_scatter_13_cast_fp16)[name = string("key_27_cast_fp16")]; + tensor var_1704_cast_fp16 = mul(x = var_376_cast_fp16_6, y = var_502_cast_fp16)[name = string("op_1704_cast_fp16")]; + tensor value_scatter_13_cast_fp16 = transpose(perm = value_scatter_13_perm_0, x = reshape_41_cast_fp16)[name = string("transpose_14")]; + tensor value_13_cast_fp16 = add(x = var_1704_cast_fp16, y = value_scatter_13_cast_fp16)[name = string("value_13_cast_fp16")]; + fp16 var_1710_to_fp16 = const()[name = string("op_1710_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1711_cast_fp16 = mul(x = mh_q_39_cast_fp16, y = var_1710_to_fp16)[name = string("op_1711_cast_fp16")]; + tensor var_1714 = const()[name = string("op_1714"), val = tensor([1, 16, 64, 80])]; + tensor var_1715_cast_fp16 = reshape(shape = var_1714, x = key_27_cast_fp16)[name = string("op_1715_cast_fp16")]; + bool mh_w_37_transpose_x_0 = const()[name = string("mh_w_37_transpose_x_0"), val = bool(true)]; + bool mh_w_37_transpose_y_0 = const()[name = string("mh_w_37_transpose_y_0"), val = bool(false)]; + tensor mh_w_37_cast_fp16 = matmul(transpose_x = mh_w_37_transpose_x_0, transpose_y = mh_w_37_transpose_y_0, x = var_1711_cast_fp16, y = var_1715_cast_fp16)[name = string("mh_w_37_cast_fp16")]; + tensor mh_w_39_cast_fp16 = add(x = mh_w_37_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_39_cast_fp16")]; + tensor mh_w_41_cast_fp16 = add(x = mh_w_39_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_41_cast_fp16")]; + tensor var_1725_cast_fp16 = softmax(axis = var_338, x = mh_w_41_cast_fp16)[name = string("op_1725_cast_fp16")]; + tensor var_1726 = const()[name = string("op_1726"), val = tensor([1, 16, 64, 80])]; + tensor var_1727_cast_fp16 = reshape(shape = var_1726, x = value_13_cast_fp16)[name = string("op_1727_cast_fp16")]; + bool attn_13_transpose_x_0 = const()[name = string("attn_13_transpose_x_0"), val = bool(false)]; + bool attn_13_transpose_y_0 = const()[name = string("attn_13_transpose_y_0"), val = bool(true)]; + tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1727_cast_fp16, y = var_1725_cast_fp16)[name = string("attn_13_cast_fp16")]; + tensor var_1730 = const()[name = string("op_1730"), val = tensor([1, -1, 1, 4])]; + tensor input_91_cast_fp16 = reshape(shape = var_1730, x = attn_13_cast_fp16)[name = string("input_91_cast_fp16")]; + string obj_83_pad_type_0 = const()[name = string("obj_83_pad_type_0"), val = string("valid")]; + tensor obj_83_strides_0 = const()[name = string("obj_83_strides_0"), val = tensor([1, 1])]; + tensor obj_83_pad_0 = const()[name = string("obj_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_83_dilations_0 = const()[name = string("obj_83_dilations_0"), val = tensor([1, 1])]; + int32 obj_83_groups_0 = const()[name = string("obj_83_groups_0"), val = int32(1)]; + tensor op_1746_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35463744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35988096))))[name = string("op_1746_weight_0_to_fp16_palettized")]; + tensor var_1746_bias_0_to_fp16 = const()[name = string("op_1746_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35988672)))]; + tensor var_1746_cast_fp16 = conv(bias = var_1746_bias_0_to_fp16, dilations = obj_83_dilations_0, groups = obj_83_groups_0, pad = obj_83_pad_0, pad_type = obj_83_pad_type_0, strides = obj_83_strides_0, weight = op_1746_weight_0_to_fp16_palettized, x = input_91_cast_fp16)[name = string("op_1746_cast_fp16")]; + tensor inputs_27_cast_fp16 = add(x = inputs_25_cast_fp16, y = var_1746_cast_fp16)[name = string("inputs_27_cast_fp16")]; + tensor inputs_sq_27_cast_fp16 = mul(x = inputs_27_cast_fp16, y = inputs_27_cast_fp16)[name = string("inputs_sq_27_cast_fp16")]; + tensor variance_27_axes_0 = const()[name = string("variance_27_axes_0"), val = tensor([1])]; + bool variance_27_keep_dims_0 = const()[name = string("variance_27_keep_dims_0"), val = bool(true)]; + tensor variance_27_cast_fp16 = reduce_mean(axes = variance_27_axes_0, keep_dims = variance_27_keep_dims_0, x = inputs_sq_27_cast_fp16)[name = string("variance_27_cast_fp16")]; + fp16 var_1752_to_fp16 = const()[name = string("op_1752_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1753_cast_fp16 = add(x = variance_27_cast_fp16, y = var_1752_to_fp16)[name = string("op_1753_cast_fp16")]; + fp32 var_1754_epsilon_0 = const()[name = string("op_1754_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1754_cast_fp16 = rsqrt(epsilon = var_1754_epsilon_0, x = var_1753_cast_fp16)[name = string("op_1754_cast_fp16")]; + tensor hidden_states_39_cast_fp16 = mul(x = inputs_27_cast_fp16, y = var_1754_cast_fp16)[name = string("hidden_states_39_cast_fp16")]; + tensor w_27_to_fp16 = const()[name = string("w_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35989760)))]; + tensor input_93_cast_fp16 = mul(x = w_27_to_fp16, y = hidden_states_39_cast_fp16)[name = string("input_93_cast_fp16")]; + string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("valid")]; + tensor input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor([1, 1])]; + tensor input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor([1, 1])]; + int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35990848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36515200))))[name = string("pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_95_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = pre_transformer_layers_6_mlp_fc3_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor gate_13_cast_fp16 = silu(x = input_95_cast_fp16)[name = string("gate_13_cast_fp16")]; + string up_13_pad_type_0 = const()[name = string("up_13_pad_type_0"), val = string("valid")]; + tensor up_13_strides_0 = const()[name = string("up_13_strides_0"), val = tensor([1, 1])]; + tensor up_13_pad_0 = const()[name = string("up_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_13_dilations_0 = const()[name = string("up_13_dilations_0"), val = tensor([1, 1])]; + int32 up_13_groups_0 = const()[name = string("up_13_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36515776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37040128))))[name = string("pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_13_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_13_dilations_0, groups = up_13_groups_0, pad = up_13_pad_0, pad_type = up_13_pad_type_0, strides = up_13_strides_0, weight = pre_transformer_layers_6_mlp_fc1_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("up_13_cast_fp16")]; + tensor input_97_cast_fp16 = mul(x = gate_13_cast_fp16, y = up_13_cast_fp16)[name = string("input_97_cast_fp16")]; + string hidden_states_41_pad_type_0 = const()[name = string("hidden_states_41_pad_type_0"), val = string("valid")]; + tensor hidden_states_41_strides_0 = const()[name = string("hidden_states_41_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_41_pad_0 = const()[name = string("hidden_states_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_41_dilations_0 = const()[name = string("hidden_states_41_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_41_groups_0 = const()[name = string("hidden_states_41_groups_0"), val = int32(1)]; + tensor op_1788_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37040704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37565056))))[name = string("op_1788_weight_0_to_fp16_palettized")]; + tensor var_1788_bias_0_to_fp16 = const()[name = string("op_1788_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37565632)))]; + tensor var_1788_cast_fp16 = conv(bias = var_1788_bias_0_to_fp16, dilations = hidden_states_41_dilations_0, groups = hidden_states_41_groups_0, pad = hidden_states_41_pad_0, pad_type = hidden_states_41_pad_type_0, strides = hidden_states_41_strides_0, weight = op_1788_weight_0_to_fp16_palettized, x = input_97_cast_fp16)[name = string("op_1788_cast_fp16")]; + tensor inputs_29_cast_fp16 = add(x = inputs_27_cast_fp16, y = var_1788_cast_fp16)[name = string("inputs_29_cast_fp16")]; + tensor inputs_sq_29_cast_fp16 = mul(x = inputs_29_cast_fp16, y = inputs_29_cast_fp16)[name = string("inputs_sq_29_cast_fp16")]; + tensor variance_29_axes_0 = const()[name = string("variance_29_axes_0"), val = tensor([1])]; + bool variance_29_keep_dims_0 = const()[name = string("variance_29_keep_dims_0"), val = bool(true)]; + tensor variance_29_cast_fp16 = reduce_mean(axes = variance_29_axes_0, keep_dims = variance_29_keep_dims_0, x = inputs_sq_29_cast_fp16)[name = string("variance_29_cast_fp16")]; + fp16 var_1804_to_fp16 = const()[name = string("op_1804_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1805_cast_fp16 = add(x = variance_29_cast_fp16, y = var_1804_to_fp16)[name = string("op_1805_cast_fp16")]; + fp32 var_1806_epsilon_0 = const()[name = string("op_1806_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1806_cast_fp16 = rsqrt(epsilon = var_1806_epsilon_0, x = var_1805_cast_fp16)[name = string("op_1806_cast_fp16")]; + tensor hidden_states_43_cast_fp16 = mul(x = inputs_29_cast_fp16, y = var_1806_cast_fp16)[name = string("hidden_states_43_cast_fp16")]; + tensor w_29_to_fp16 = const()[name = string("w_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37566720)))]; + tensor obj_89_cast_fp16 = mul(x = w_29_to_fp16, y = hidden_states_43_cast_fp16)[name = string("obj_89_cast_fp16")]; + string query_29_pad_type_0 = const()[name = string("query_29_pad_type_0"), val = string("valid")]; + tensor query_29_strides_0 = const()[name = string("query_29_strides_0"), val = tensor([1, 1])]; + tensor query_29_pad_0 = const()[name = string("query_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_29_dilations_0 = const()[name = string("query_29_dilations_0"), val = tensor([1, 1])]; + int32 query_29_groups_0 = const()[name = string("query_29_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37567808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38092160))))[name = string("pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized")]; + tensor query_29_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = query_29_dilations_0, groups = query_29_groups_0, pad = query_29_pad_0, pad_type = query_29_pad_type_0, strides = query_29_strides_0, weight = pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized, x = obj_89_cast_fp16)[name = string("query_29_cast_fp16")]; + string key_29_pad_type_0 = const()[name = string("key_29_pad_type_0"), val = string("valid")]; + tensor key_29_strides_0 = const()[name = string("key_29_strides_0"), val = tensor([1, 1])]; + tensor key_29_pad_0 = const()[name = string("key_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_29_dilations_0 = const()[name = string("key_29_dilations_0"), val = tensor([1, 1])]; + int32 key_29_groups_0 = const()[name = string("key_29_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38092736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38617088))))[name = string("pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized")]; + tensor key_29_cast_fp16 = conv(dilations = key_29_dilations_0, groups = key_29_groups_0, pad = key_29_pad_0, pad_type = key_29_pad_type_0, strides = key_29_strides_0, weight = pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized, x = obj_89_cast_fp16)[name = string("key_29_cast_fp16")]; + string obj_pad_type_0 = const()[name = string("obj_pad_type_0"), val = string("valid")]; + tensor obj_strides_0 = const()[name = string("obj_strides_0"), val = tensor([1, 1])]; + tensor obj_pad_0 = const()[name = string("obj_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_dilations_0 = const()[name = string("obj_dilations_0"), val = tensor([1, 1])]; + int32 obj_groups_0 = const()[name = string("obj_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38617664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39142016))))[name = string("pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized")]; + tensor obj_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = obj_dilations_0, groups = obj_groups_0, pad = obj_pad_0, pad_type = obj_pad_type_0, strides = obj_strides_0, weight = pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized, x = obj_89_cast_fp16)[name = string("obj_cast_fp16")]; + tensor var_1844 = const()[name = string("op_1844"), val = tensor([1, 16, 64, -1])]; + tensor mh_q_43_cast_fp16 = reshape(shape = var_1844, x = query_29_cast_fp16)[name = string("mh_q_43_cast_fp16")]; + tensor var_1846 = const()[name = string("op_1846"), val = tensor([1, 16, 64, -1])]; + tensor mh_k_29_cast_fp16 = reshape(shape = var_1846, x = key_29_cast_fp16)[name = string("mh_k_29_cast_fp16")]; + tensor var_1850_cast_fp16 = mul(x = mh_q_43_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor var_1855_begin_0 = const()[name = string("op_1855_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1855_end_0 = const()[name = string("op_1855_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1855_end_mask_0 = const()[name = string("op_1855_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1855_cast_fp16 = slice_by_index(begin = var_1855_begin_0, end = var_1855_end_0, end_mask = var_1855_end_mask_0, x = mh_q_43_cast_fp16)[name = string("op_1855_cast_fp16")]; + tensor var_1861_begin_0 = const()[name = string("op_1861_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1861_end_0 = const()[name = string("op_1861_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1861_end_mask_0 = const()[name = string("op_1861_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1861_cast_fp16 = slice_by_index(begin = var_1861_begin_0, end = var_1861_end_0, end_mask = var_1861_end_mask_0, x = mh_q_43_cast_fp16)[name = string("op_1861_cast_fp16")]; + fp16 const_166_promoted_to_fp16 = const()[name = string("const_166_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1863_cast_fp16 = mul(x = var_1861_cast_fp16, y = const_166_promoted_to_fp16)[name = string("op_1863_cast_fp16")]; + bool var_1865_interleave_0 = const()[name = string("op_1865_interleave_0"), val = bool(false)]; + tensor var_1865_cast_fp16 = concat(axis = var_333, interleave = var_1865_interleave_0, values = (var_1863_cast_fp16, var_1855_cast_fp16))[name = string("op_1865_cast_fp16")]; + tensor var_1866_cast_fp16 = mul(x = var_1865_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1866_cast_fp16")]; + tensor mh_q_45_cast_fp16 = add(x = var_1850_cast_fp16, y = var_1866_cast_fp16)[name = string("mh_q_45_cast_fp16")]; + tensor var_1868_cast_fp16 = mul(x = mh_k_29_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1868_cast_fp16")]; + tensor var_1873_begin_0 = const()[name = string("op_1873_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1873_end_0 = const()[name = string("op_1873_end_0"), val = tensor([1, 16, 32, 4])]; + tensor var_1873_end_mask_0 = const()[name = string("op_1873_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1873_cast_fp16 = slice_by_index(begin = var_1873_begin_0, end = var_1873_end_0, end_mask = var_1873_end_mask_0, x = mh_k_29_cast_fp16)[name = string("op_1873_cast_fp16")]; + tensor var_1879_begin_0 = const()[name = string("op_1879_begin_0"), val = tensor([0, 0, 32, 0])]; + tensor var_1879_end_0 = const()[name = string("op_1879_end_0"), val = tensor([1, 16, 64, 4])]; + tensor var_1879_end_mask_0 = const()[name = string("op_1879_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1879_cast_fp16 = slice_by_index(begin = var_1879_begin_0, end = var_1879_end_0, end_mask = var_1879_end_mask_0, x = mh_k_29_cast_fp16)[name = string("op_1879_cast_fp16")]; + fp16 const_169_promoted_to_fp16 = const()[name = string("const_169_promoted_to_fp16"), val = fp16(-0x1p+0)]; + tensor var_1881_cast_fp16 = mul(x = var_1879_cast_fp16, y = const_169_promoted_to_fp16)[name = string("op_1881_cast_fp16")]; + bool var_1883_interleave_0 = const()[name = string("op_1883_interleave_0"), val = bool(false)]; + tensor var_1883_cast_fp16 = concat(axis = var_333, interleave = var_1883_interleave_0, values = (var_1881_cast_fp16, var_1873_cast_fp16))[name = string("op_1883_cast_fp16")]; + tensor var_1884_cast_fp16 = mul(x = var_1883_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1884_cast_fp16")]; + tensor mh_k_cast_fp16 = add(x = var_1868_cast_fp16, y = var_1884_cast_fp16)[name = string("mh_k_cast_fp16")]; + tensor var_1888 = const()[name = string("op_1888"), val = tensor([1, 1024, 1, 4])]; + tensor obj_97_cast_fp16 = reshape(shape = var_1888, x = mh_k_cast_fp16)[name = string("obj_97_cast_fp16")]; + tensor transpose_29_perm_0 = const()[name = string("transpose_29_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_145 = const()[name = string("concat_145"), val = tensor([1, 4, 1024])]; + tensor transpose_29_cast_fp16 = transpose(perm = transpose_29_perm_0, x = obj_97_cast_fp16)[name = string("transpose_13")]; + tensor reshape_43_cast_fp16 = reshape(shape = concat_145, x = transpose_29_cast_fp16)[name = string("reshape_43_cast_fp16")]; + bool matmul_14_transpose_x_1 = const()[name = string("matmul_14_transpose_x_1"), val = bool(true)]; + bool matmul_14_transpose_y_1 = const()[name = string("matmul_14_transpose_y_1"), val = bool(false)]; + tensor matmul_14_cast_fp16 = matmul(transpose_x = matmul_14_transpose_x_1, transpose_y = matmul_14_transpose_y_1, x = kv_cache_update_mask, y = reshape_43_cast_fp16)[name = string("matmul_14_cast_fp16")]; + tensor concat_149 = const()[name = string("concat_149"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_44_cast_fp16 = reshape(shape = concat_149, x = matmul_14_cast_fp16)[name = string("reshape_44_cast_fp16")]; + tensor key_scatter_perm_0 = const()[name = string("key_scatter_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor transpose_31_perm_0 = const()[name = string("transpose_31_perm_0"), val = tensor([0, 3, 1, 2])]; + tensor concat_155 = const()[name = string("concat_155"), val = tensor([1, 4, 1024])]; + tensor transpose_31_cast_fp16 = transpose(perm = transpose_31_perm_0, x = obj_cast_fp16)[name = string("transpose_12")]; + tensor reshape_46_cast_fp16 = reshape(shape = concat_155, x = transpose_31_cast_fp16)[name = string("reshape_46_cast_fp16")]; + bool matmul_15_transpose_x_1 = const()[name = string("matmul_15_transpose_x_1"), val = bool(true)]; + bool matmul_15_transpose_y_1 = const()[name = string("matmul_15_transpose_y_1"), val = bool(false)]; + tensor matmul_15_cast_fp16 = matmul(transpose_x = matmul_15_transpose_x_1, transpose_y = matmul_15_transpose_y_1, x = kv_cache_update_mask, y = reshape_46_cast_fp16)[name = string("matmul_15_cast_fp16")]; + tensor concat_159 = const()[name = string("concat_159"), val = tensor([1, 80, 1024, 1])]; + tensor reshape_47_cast_fp16 = reshape(shape = concat_159, x = matmul_15_cast_fp16)[name = string("reshape_47_cast_fp16")]; + tensor value_scatter_perm_0 = const()[name = string("value_scatter_perm_0"), val = tensor([0, 2, 3, 1])]; + tensor var_1901_cast_fp16 = mul(x = var_367_cast_fp16_7, y = var_502_cast_fp16)[name = string("op_1901_cast_fp16")]; + tensor key_scatter_cast_fp16 = transpose(perm = key_scatter_perm_0, x = reshape_44_cast_fp16)[name = string("transpose_11")]; + tensor key_cast_fp16 = add(x = var_1901_cast_fp16, y = key_scatter_cast_fp16)[name = string("key_cast_fp16")]; + tensor var_1903_cast_fp16 = mul(x = var_376_cast_fp16_7, y = var_502_cast_fp16)[name = string("op_1903_cast_fp16")]; + tensor value_scatter_cast_fp16 = transpose(perm = value_scatter_perm_0, x = reshape_47_cast_fp16)[name = string("transpose_10")]; + tensor value_cast_fp16 = add(x = var_1903_cast_fp16, y = value_scatter_cast_fp16)[name = string("value_cast_fp16")]; + fp16 var_1909_to_fp16 = const()[name = string("op_1909_to_fp16"), val = fp16(0x1p-3)]; + tensor var_1910_cast_fp16 = mul(x = mh_q_45_cast_fp16, y = var_1909_to_fp16)[name = string("op_1910_cast_fp16")]; + tensor var_1913 = const()[name = string("op_1913"), val = tensor([1, 16, 64, 80])]; + tensor var_1914_cast_fp16 = reshape(shape = var_1913, x = key_cast_fp16)[name = string("op_1914_cast_fp16")]; + bool mh_w_43_transpose_x_0 = const()[name = string("mh_w_43_transpose_x_0"), val = bool(true)]; + bool mh_w_43_transpose_y_0 = const()[name = string("mh_w_43_transpose_y_0"), val = bool(false)]; + tensor mh_w_43_cast_fp16 = matmul(transpose_x = mh_w_43_transpose_x_0, transpose_y = mh_w_43_transpose_y_0, x = var_1910_cast_fp16, y = var_1914_cast_fp16)[name = string("mh_w_43_cast_fp16")]; + tensor mh_w_45_cast_fp16 = add(x = mh_w_43_cast_fp16, y = var_526_cast_fp16)[name = string("mh_w_45_cast_fp16")]; + tensor mh_w_cast_fp16 = add(x = mh_w_45_cast_fp16, y = qk_mask_3_cast_fp16)[name = string("mh_w_cast_fp16")]; + tensor var_1924_cast_fp16 = softmax(axis = var_338, x = mh_w_cast_fp16)[name = string("op_1924_cast_fp16")]; + tensor var_1925 = const()[name = string("op_1925"), val = tensor([1, 16, 64, 80])]; + tensor var_1926_cast_fp16 = reshape(shape = var_1925, x = value_cast_fp16)[name = string("op_1926_cast_fp16")]; + bool attn_transpose_x_0 = const()[name = string("attn_transpose_x_0"), val = bool(false)]; + bool attn_transpose_y_0 = const()[name = string("attn_transpose_y_0"), val = bool(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_1926_cast_fp16, y = var_1924_cast_fp16)[name = string("attn_cast_fp16")]; + tensor var_1929 = const()[name = string("op_1929"), val = tensor([1, -1, 1, 4])]; + tensor input_99_cast_fp16 = reshape(shape = var_1929, x = attn_cast_fp16)[name = string("input_99_cast_fp16")]; + string obj_95_pad_type_0 = const()[name = string("obj_95_pad_type_0"), val = string("valid")]; + tensor obj_95_strides_0 = const()[name = string("obj_95_strides_0"), val = tensor([1, 1])]; + tensor obj_95_pad_0 = const()[name = string("obj_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor obj_95_dilations_0 = const()[name = string("obj_95_dilations_0"), val = tensor([1, 1])]; + int32 obj_95_groups_0 = const()[name = string("obj_95_groups_0"), val = int32(1)]; + tensor op_1945_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39142592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39666944))))[name = string("op_1945_weight_0_to_fp16_palettized")]; + tensor var_1945_bias_0_to_fp16 = const()[name = string("op_1945_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39667520)))]; + tensor var_1945_cast_fp16 = conv(bias = var_1945_bias_0_to_fp16, dilations = obj_95_dilations_0, groups = obj_95_groups_0, pad = obj_95_pad_0, pad_type = obj_95_pad_type_0, strides = obj_95_strides_0, weight = op_1945_weight_0_to_fp16_palettized, x = input_99_cast_fp16)[name = string("op_1945_cast_fp16")]; + tensor inputs_31_cast_fp16 = add(x = inputs_29_cast_fp16, y = var_1945_cast_fp16)[name = string("inputs_31_cast_fp16")]; + tensor inputs_sq_31_cast_fp16 = mul(x = inputs_31_cast_fp16, y = inputs_31_cast_fp16)[name = string("inputs_sq_31_cast_fp16")]; + tensor variance_31_axes_0 = const()[name = string("variance_31_axes_0"), val = tensor([1])]; + bool variance_31_keep_dims_0 = const()[name = string("variance_31_keep_dims_0"), val = bool(true)]; + tensor variance_31_cast_fp16 = reduce_mean(axes = variance_31_axes_0, keep_dims = variance_31_keep_dims_0, x = inputs_sq_31_cast_fp16)[name = string("variance_31_cast_fp16")]; + fp16 var_1951_to_fp16 = const()[name = string("op_1951_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1952_cast_fp16 = add(x = variance_31_cast_fp16, y = var_1951_to_fp16)[name = string("op_1952_cast_fp16")]; + fp32 var_1953_epsilon_0 = const()[name = string("op_1953_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1953_cast_fp16 = rsqrt(epsilon = var_1953_epsilon_0, x = var_1952_cast_fp16)[name = string("op_1953_cast_fp16")]; + tensor hidden_states_45_cast_fp16 = mul(x = inputs_31_cast_fp16, y = var_1953_cast_fp16)[name = string("hidden_states_45_cast_fp16")]; + tensor w_31_to_fp16 = const()[name = string("w_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39668608)))]; + tensor input_101_cast_fp16 = mul(x = w_31_to_fp16, y = hidden_states_45_cast_fp16)[name = string("input_101_cast_fp16")]; + string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")]; + tensor input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor([1, 1])]; + tensor input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor([1, 1])]; + int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39669696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40194048))))[name = string("pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized")]; + tensor input_103_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = pre_transformer_layers_7_mlp_fc3_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor gate_cast_fp16 = silu(x = input_103_cast_fp16)[name = string("gate_cast_fp16")]; + string up_pad_type_0 = const()[name = string("up_pad_type_0"), val = string("valid")]; + tensor up_strides_0 = const()[name = string("up_strides_0"), val = tensor([1, 1])]; + tensor up_pad_0 = const()[name = string("up_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_dilations_0 = const()[name = string("up_dilations_0"), val = tensor([1, 1])]; + int32 up_groups_0 = const()[name = string("up_groups_0"), val = int32(1)]; + tensor pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40194624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40718976))))[name = string("pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized")]; + tensor up_cast_fp16 = conv(bias = pre_transformer_layers_0_self_attn_q_proj_bias_to_fp16, dilations = up_dilations_0, groups = up_groups_0, pad = up_pad_0, pad_type = up_pad_type_0, strides = up_strides_0, weight = pre_transformer_layers_7_mlp_fc1_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = string("up_cast_fp16")]; + tensor input_105_cast_fp16 = mul(x = gate_cast_fp16, y = up_cast_fp16)[name = string("input_105_cast_fp16")]; + string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")]; + tensor hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)]; + tensor op_1987_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40719552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41243904))))[name = string("op_1987_weight_0_to_fp16_palettized")]; + tensor var_1987_bias_0_to_fp16 = const()[name = string("op_1987_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41244480)))]; + tensor var_1987_cast_fp16 = conv(bias = var_1987_bias_0_to_fp16, dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = op_1987_weight_0_to_fp16_palettized, x = input_105_cast_fp16)[name = string("op_1987_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = inputs_31_cast_fp16, y = var_1987_cast_fp16)[name = string("inputs_cast_fp16")]; + tensor inputs_sq_cast_fp16 = mul(x = inputs_cast_fp16, y = inputs_cast_fp16)[name = string("inputs_sq_cast_fp16")]; + tensor variance_axes_0 = const()[name = string("variance_axes_0"), val = tensor([1])]; + bool variance_keep_dims_0 = const()[name = string("variance_keep_dims_0"), val = bool(true)]; + tensor variance_cast_fp16 = reduce_mean(axes = variance_axes_0, keep_dims = variance_keep_dims_0, x = inputs_sq_cast_fp16)[name = string("variance_cast_fp16")]; + fp16 var_1997_to_fp16 = const()[name = string("op_1997_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_1998_cast_fp16 = add(x = variance_cast_fp16, y = var_1997_to_fp16)[name = string("op_1998_cast_fp16")]; + fp32 var_1999_epsilon_0 = const()[name = string("op_1999_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1999_cast_fp16 = rsqrt(epsilon = var_1999_epsilon_0, x = var_1998_cast_fp16)[name = string("op_1999_cast_fp16")]; + tensor hidden_states_49_cast_fp16 = mul(x = inputs_cast_fp16, y = var_1999_cast_fp16)[name = string("hidden_states_49_cast_fp16")]; + tensor w_to_fp16 = const()[name = string("w_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41245568)))]; + tensor input_107_cast_fp16 = mul(x = w_to_fp16, y = hidden_states_49_cast_fp16)[name = string("input_107_cast_fp16")]; + string new_hiddens_pad_type_0 = const()[name = string("new_hiddens_pad_type_0"), val = string("valid")]; + tensor new_hiddens_strides_0 = const()[name = string("new_hiddens_strides_0"), val = tensor([1, 1])]; + tensor new_hiddens_pad_0 = const()[name = string("new_hiddens_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor new_hiddens_dilations_0 = const()[name = string("new_hiddens_dilations_0"), val = tensor([1, 1])]; + int32 new_hiddens_groups_0 = const()[name = string("new_hiddens_groups_0"), val = int32(1)]; + tensor pre_transformer_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41246656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41771008))))[name = string("pre_transformer_output_proj_weight_to_fp16_palettized")]; + tensor pre_transformer_output_proj_bias_to_fp16 = const()[name = string("pre_transformer_output_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41771584)))]; + tensor hidden_context_update = conv(bias = pre_transformer_output_proj_bias_to_fp16, dilations = new_hiddens_dilations_0, groups = new_hiddens_groups_0, pad = new_hiddens_pad_0, pad_type = new_hiddens_pad_type_0, strides = new_hiddens_strides_0, weight = pre_transformer_output_proj_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("new_hiddens_cast_fp16")]; + bool var_2012_interleave_0 = const()[name = string("op_2012_interleave_0"), val = bool(false)]; + tensor key_cache_updates = concat(axis = var_344, interleave = var_2012_interleave_0, values = (obj_13_cast_fp16, obj_25_cast_fp16, obj_37_cast_fp16, obj_49_cast_fp16, obj_61_cast_fp16, obj_73_cast_fp16, obj_85_cast_fp16, obj_97_cast_fp16))[name = string("op_2012_cast_fp16")]; + bool var_2014_interleave_0 = const()[name = string("op_2014_interleave_0"), val = bool(false)]; + tensor value_cache_updates = concat(axis = var_344, interleave = var_2014_interleave_0, values = (obj_15_cast_fp16, obj_27_cast_fp16, obj_39_cast_fp16, obj_51_cast_fp16, obj_63_cast_fp16, obj_75_cast_fp16, obj_87_cast_fp16, obj_cast_fp16))[name = string("op_2014_cast_fp16")]; + int32 var_2020 = const()[name = string("op_2020"), val = int32(-1)]; + bool hidden_states_51_interleave_0 = const()[name = string("hidden_states_51_interleave_0"), val = bool(false)]; + tensor hidden_states_51_cast_fp16 = concat(axis = var_2020, interleave = hidden_states_51_interleave_0, values = (hidden_context, hidden_context_update))[name = string("hidden_states_51_cast_fp16")]; + int32 var_2037 = const()[name = string("op_2037"), val = int32(-1)]; + string sub_pixels_1_pad_type_0 = const()[name = string("sub_pixels_1_pad_type_0"), val = string("valid")]; + tensor sub_pixels_1_strides_0 = const()[name = string("sub_pixels_1_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_1_pad_0 = const()[name = string("sub_pixels_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_1_dilations_0 = const()[name = string("sub_pixels_1_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_1_groups_0 = const()[name = string("sub_pixels_1_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41773696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43870912))))[name = string("audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43871488)))]; + tensor sub_pixels_1_cast_fp16 = conv(bias = audio_upsampler_upsample_0_0_conv_bias_to_fp16, dilations = sub_pixels_1_dilations_0, groups = sub_pixels_1_groups_0, pad = sub_pixels_1_pad_0, pad_type = sub_pixels_1_pad_type_0, strides = sub_pixels_1_strides_0, weight = audio_upsampler_upsample_0_0_conv_weight_to_fp16_palettized, x = hidden_states_51_cast_fp16)[name = string("sub_pixels_1_cast_fp16")]; + tensor var_2088 = const()[name = string("op_2088"), val = tensor([1, 2, 1024, 12])]; + tensor sub_pixels_3_cast_fp16 = reshape(shape = var_2088, x = sub_pixels_1_cast_fp16)[name = string("sub_pixels_3_cast_fp16")]; + tensor var_2090 = const()[name = string("op_2090"), val = tensor([0, 2, 3, 1])]; + tensor var_2095 = const()[name = string("op_2095"), val = tensor([1, 1024, 1, 24])]; + tensor sub_pixels_5_cast_fp16 = transpose(perm = var_2090, x = sub_pixels_3_cast_fp16)[name = string("transpose_9")]; + tensor hidden_states_53_cast_fp16 = reshape(shape = var_2095, x = sub_pixels_5_cast_fp16)[name = string("hidden_states_53_cast_fp16")]; + tensor var_2100 = const()[name = string("op_2100"), val = tensor([1, 1, 8, 1])]; + tensor var_2101_cast_fp16 = reshape(shape = var_2100, x = hidden_context_mask)[name = string("op_2101_cast_fp16")]; + tensor spread_1_reps_0 = const()[name = string("spread_1_reps_0"), val = tensor([1, 1, 1, 2])]; + tensor spread_1_cast_fp16 = tile(reps = spread_1_reps_0, x = var_2101_cast_fp16)[name = string("spread_1_cast_fp16")]; + tensor var_2107 = const()[name = string("op_2107"), val = tensor([1, 1, 1, 16])]; + tensor context_mask_1_cast_fp16 = reshape(shape = var_2107, x = spread_1_cast_fp16)[name = string("context_mask_1_cast_fp16")]; + bool full_mask_1_interleave_0 = const()[name = string("full_mask_1_interleave_0"), val = bool(false)]; + tensor fill_0_to_fp16 = const()[name = string("fill_0_to_fp16"), val = tensor([[[[0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0]]]])]; + tensor full_mask_1_cast_fp16 = concat(axis = var_2037, interleave = full_mask_1_interleave_0, values = (context_mask_1_cast_fp16, fill_0_to_fp16))[name = string("full_mask_1_cast_fp16")]; + tensor hidden_states_55_cast_fp16 = mul(x = hidden_states_53_cast_fp16, y = full_mask_1_cast_fp16)[name = string("hidden_states_55_cast_fp16")]; + tensor input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 6, 0])]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("constant")]; + fp16 const_179_to_fp16 = const()[name = string("const_179_to_fp16"), val = fp16(0x0p+0)]; + tensor input_109_cast_fp16 = pad(constant_val = const_179_to_fp16, mode = input_109_mode_0, pad = input_109_pad_0, x = hidden_states_55_cast_fp16)[name = string("input_109_cast_fp16")]; + string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")]; + int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1024)]; + tensor hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor([1, 1])]; + tensor audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43875648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43882880))))[name = string("audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43883456)))]; + tensor hidden_states_57_cast_fp16 = conv(bias = audio_upsampler_upsample_0_1_dwconv_conv_bias_to_fp16, dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = audio_upsampler_upsample_0_1_dwconv_conv_weight_to_fp16_palettized, x = input_109_cast_fp16)[name = string("hidden_states_57_cast_fp16")]; + tensor var_2135_axes_0 = const()[name = string("op_2135_axes_0"), val = tensor([2])]; + tensor var_2135_cast_fp16 = squeeze(axes = var_2135_axes_0, x = hidden_states_57_cast_fp16)[name = string("op_2135_cast_fp16")]; + tensor var_2136 = const()[name = string("op_2136"), val = tensor([0, 2, 1])]; + tensor hidden_states_59_axes_0 = const()[name = string("hidden_states_59_axes_0"), val = tensor([-1])]; + tensor audio_upsampler_upsample_0_1_norm_weight_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43885568)))]; + tensor audio_upsampler_upsample_0_1_norm_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43887680)))]; + fp16 var_2041_to_fp16 = const()[name = string("op_2041_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = var_2136, x = var_2135_cast_fp16)[name = string("transpose_8")]; + tensor hidden_states_59_cast_fp16 = layer_norm(axes = hidden_states_59_axes_0, beta = audio_upsampler_upsample_0_1_norm_bias_to_fp16, epsilon = var_2041_to_fp16, gamma = audio_upsampler_upsample_0_1_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("hidden_states_59_cast_fp16")]; + tensor var_2142 = const()[name = string("op_2142"), val = tensor([0, 2, 1])]; + tensor input_113_axes_0 = const()[name = string("input_113_axes_0"), val = tensor([2])]; + tensor var_2143_cast_fp16 = transpose(perm = var_2142, x = hidden_states_59_cast_fp16)[name = string("transpose_7")]; + tensor input_113_cast_fp16 = expand_dims(axes = input_113_axes_0, x = var_2143_cast_fp16)[name = string("input_113_cast_fp16")]; + string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("valid")]; + tensor input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor([1, 1])]; + tensor input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor([1, 1])]; + int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43889792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48084160))))[name = string("audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48084736)))]; + tensor input_115_cast_fp16 = conv(bias = audio_upsampler_upsample_0_1_pwconv1_bias_to_fp16, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = audio_upsampler_upsample_0_1_pwconv1_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = string("input_115_cast_fp16")]; + string input_117_mode_0 = const()[name = string("input_117_mode_0"), val = string("EXACT")]; + tensor input_117_cast_fp16 = gelu(mode = input_117_mode_0, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")]; + string hidden_states_61_pad_type_0 = const()[name = string("hidden_states_61_pad_type_0"), val = string("valid")]; + tensor hidden_states_61_strides_0 = const()[name = string("hidden_states_61_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_61_pad_0 = const()[name = string("hidden_states_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_61_dilations_0 = const()[name = string("hidden_states_61_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_61_groups_0 = const()[name = string("hidden_states_61_groups_0"), val = int32(1)]; + tensor hidden_states_63_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48092992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52287360))))[name = string("hidden_states_63_weight_0_to_fp16_palettized")]; + tensor hidden_states_63_bias_0_to_fp16 = const()[name = string("hidden_states_63_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52287936)))]; + tensor hidden_states_63_cast_fp16 = conv(bias = hidden_states_63_bias_0_to_fp16, dilations = hidden_states_61_dilations_0, groups = hidden_states_61_groups_0, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = hidden_states_61_strides_0, weight = hidden_states_63_weight_0_to_fp16_palettized, x = input_117_cast_fp16)[name = string("hidden_states_63_cast_fp16")]; + tensor hidden_states_65_cast_fp16 = add(x = hidden_states_53_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")]; + tensor input_119_begin_0 = const()[name = string("input_119_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor input_119_end_0 = const()[name = string("input_119_end_0"), val = tensor([1, 1024, 1, 24])]; + tensor input_119_end_mask_0 = const()[name = string("input_119_end_mask_0"), val = tensor([true, true, true, true])]; + tensor input_119_cast_fp16 = slice_by_index(begin = input_119_begin_0, end = input_119_end_0, end_mask = input_119_end_mask_0, x = hidden_states_65_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor context_mask_3_begin_0 = const()[name = string("context_mask_3_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor context_mask_3_end_0 = const()[name = string("context_mask_3_end_0"), val = tensor([1, 1, 1, 16])]; + tensor context_mask_3_end_mask_0 = const()[name = string("context_mask_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_3_cast_fp16 = slice_by_index(begin = context_mask_3_begin_0, end = context_mask_3_end_0, end_mask = context_mask_3_end_mask_0, x = context_mask_1_cast_fp16)[name = string("context_mask_3_cast_fp16")]; + string sub_pixels_7_pad_type_0 = const()[name = string("sub_pixels_7_pad_type_0"), val = string("valid")]; + tensor sub_pixels_7_strides_0 = const()[name = string("sub_pixels_7_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_7_pad_0 = const()[name = string("sub_pixels_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_7_dilations_0 = const()[name = string("sub_pixels_7_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_7_groups_0 = const()[name = string("sub_pixels_7_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52290048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54387264))))[name = string("audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54387840)))]; + tensor sub_pixels_7_cast_fp16 = conv(bias = audio_upsampler_upsample_1_0_conv_bias_to_fp16, dilations = sub_pixels_7_dilations_0, groups = sub_pixels_7_groups_0, pad = sub_pixels_7_pad_0, pad_type = sub_pixels_7_pad_type_0, strides = sub_pixels_7_strides_0, weight = audio_upsampler_upsample_1_0_conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("sub_pixels_7_cast_fp16")]; + tensor var_2183 = const()[name = string("op_2183"), val = tensor([1, 2, 1024, 21])]; + tensor sub_pixels_9_cast_fp16 = reshape(shape = var_2183, x = sub_pixels_7_cast_fp16)[name = string("sub_pixels_9_cast_fp16")]; + tensor var_2185 = const()[name = string("op_2185"), val = tensor([0, 2, 3, 1])]; + tensor var_2190 = const()[name = string("op_2190"), val = tensor([1, 1024, 1, 42])]; + tensor sub_pixels_11_cast_fp16 = transpose(perm = var_2185, x = sub_pixels_9_cast_fp16)[name = string("transpose_6")]; + tensor hidden_states_67_cast_fp16 = reshape(shape = var_2190, x = sub_pixels_11_cast_fp16)[name = string("hidden_states_67_cast_fp16")]; + tensor var_2195 = const()[name = string("op_2195"), val = tensor([1, 1, 13, 1])]; + tensor var_2196_cast_fp16 = reshape(shape = var_2195, x = context_mask_3_cast_fp16)[name = string("op_2196_cast_fp16")]; + tensor spread_3_reps_0 = const()[name = string("spread_3_reps_0"), val = tensor([1, 1, 1, 2])]; + tensor spread_3_cast_fp16 = tile(reps = spread_3_reps_0, x = var_2196_cast_fp16)[name = string("spread_3_cast_fp16")]; + tensor var_2202 = const()[name = string("op_2202"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_5_cast_fp16 = reshape(shape = var_2202, x = spread_3_cast_fp16)[name = string("context_mask_5_cast_fp16")]; + tensor residual_1_begin_0 = const()[name = string("residual_1_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_1_end_0 = const()[name = string("residual_1_end_0"), val = tensor([1, 1024, 1, 42])]; + tensor residual_1_end_mask_0 = const()[name = string("residual_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_1_cast_fp16 = slice_by_index(begin = residual_1_begin_0, end = residual_1_end_0, end_mask = residual_1_end_mask_0, x = hidden_states_67_cast_fp16)[name = string("residual_1_cast_fp16")]; + bool full_mask_3_interleave_0 = const()[name = string("full_mask_3_interleave_0"), val = bool(false)]; + tensor fill_1_to_fp16 = const()[name = string("fill_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115345152)))]; + tensor full_mask_3_cast_fp16 = concat(axis = var_2037, interleave = full_mask_3_interleave_0, values = (context_mask_5_cast_fp16, fill_1_to_fp16))[name = string("full_mask_3_cast_fp16")]; + tensor input_121_cast_fp16 = mul(x = hidden_states_67_cast_fp16, y = full_mask_3_cast_fp16)[name = string("input_121_cast_fp16")]; + string hidden_states_69_pad_type_0 = const()[name = string("hidden_states_69_pad_type_0"), val = string("valid")]; + int32 hidden_states_69_groups_0 = const()[name = string("hidden_states_69_groups_0"), val = int32(1024)]; + tensor hidden_states_69_strides_0 = const()[name = string("hidden_states_69_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_69_pad_0 = const()[name = string("hidden_states_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_69_dilations_0 = const()[name = string("hidden_states_69_dilations_0"), val = tensor([1, 1])]; + tensor audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54392000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115345280))))[name = string("audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54399808)))]; + tensor hidden_states_69_cast_fp16 = conv(bias = audio_upsampler_upsample_1_1_dwconv_conv_bias_to_fp16, dilations = hidden_states_69_dilations_0, groups = hidden_states_69_groups_0, pad = hidden_states_69_pad_0, pad_type = hidden_states_69_pad_type_0, strides = hidden_states_69_strides_0, weight = audio_upsampler_upsample_1_1_dwconv_conv_weight_to_fp16_palettized, x = input_121_cast_fp16)[name = string("hidden_states_69_cast_fp16")]; + tensor var_2229_axes_0 = const()[name = string("op_2229_axes_0"), val = tensor([2])]; + tensor var_2229_cast_fp16 = squeeze(axes = var_2229_axes_0, x = hidden_states_69_cast_fp16)[name = string("op_2229_cast_fp16")]; + tensor var_2230 = const()[name = string("op_2230"), val = tensor([0, 2, 1])]; + tensor hidden_states_71_axes_0 = const()[name = string("hidden_states_71_axes_0"), val = tensor([-1])]; + tensor audio_upsampler_upsample_1_1_norm_weight_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54401920)))]; + tensor audio_upsampler_upsample_1_1_norm_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54404032)))]; + tensor input_123_cast_fp16 = transpose(perm = var_2230, x = var_2229_cast_fp16)[name = string("transpose_5")]; + tensor hidden_states_71_cast_fp16 = layer_norm(axes = hidden_states_71_axes_0, beta = audio_upsampler_upsample_1_1_norm_bias_to_fp16, epsilon = var_2041_to_fp16, gamma = audio_upsampler_upsample_1_1_norm_weight_to_fp16, x = input_123_cast_fp16)[name = string("hidden_states_71_cast_fp16")]; + tensor var_2236 = const()[name = string("op_2236"), val = tensor([0, 2, 1])]; + tensor input_125_axes_0 = const()[name = string("input_125_axes_0"), val = tensor([2])]; + tensor var_2237_cast_fp16 = transpose(perm = var_2236, x = hidden_states_71_cast_fp16)[name = string("transpose_4")]; + tensor input_125_cast_fp16 = expand_dims(axes = input_125_axes_0, x = var_2237_cast_fp16)[name = string("input_125_cast_fp16")]; + string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("valid")]; + tensor input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor([1, 1])]; + tensor input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor([1, 1])]; + int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)]; + tensor audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54406144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58600512))))[name = string("audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized")]; + tensor audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16 = const()[name = string("audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58601088)))]; + tensor input_127_cast_fp16 = conv(bias = audio_upsampler_upsample_1_1_pwconv1_bias_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = audio_upsampler_upsample_1_1_pwconv1_weight_to_fp16_palettized, x = input_125_cast_fp16)[name = string("input_127_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = input_127_cast_fp16)[name = string("input_129_cast_fp16")]; + string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")]; + tensor hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)]; + tensor hidden_states_75_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58609344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62803712))))[name = string("hidden_states_75_weight_0_to_fp16_palettized")]; + tensor hidden_states_75_bias_0_to_fp16 = const()[name = string("hidden_states_75_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62804288)))]; + tensor hidden_states_75_cast_fp16 = conv(bias = hidden_states_75_bias_0_to_fp16, dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = hidden_states_75_weight_0_to_fp16_palettized, x = input_129_cast_fp16)[name = string("hidden_states_75_cast_fp16")]; + tensor hidden_states_77_cast_fp16 = add(x = residual_1_cast_fp16, y = hidden_states_75_cast_fp16)[name = string("hidden_states_77_cast_fp16")]; + tensor context_mask_7_begin_0 = const()[name = string("context_mask_7_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_7_end_0 = const()[name = string("context_mask_7_end_0"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_7_end_mask_0 = const()[name = string("context_mask_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_7_cast_fp16 = slice_by_index(begin = context_mask_7_begin_0, end = context_mask_7_end_0, end_mask = context_mask_7_end_mask_0, x = context_mask_5_cast_fp16)[name = string("context_mask_7_cast_fp16")]; + bool full_mask_5_interleave_0 = const()[name = string("full_mask_5_interleave_0"), val = bool(false)]; + tensor fill_2_to_fp16 = const()[name = string("fill_2_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115345152)))]; + tensor full_mask_5_cast_fp16 = concat(axis = var_2037, interleave = full_mask_5_interleave_0, values = (context_mask_7_cast_fp16, fill_2_to_fp16))[name = string("full_mask_5_cast_fp16")]; + tensor input_131_cast_fp16 = mul(x = hidden_states_77_cast_fp16, y = full_mask_5_cast_fp16)[name = string("input_131_cast_fp16")]; + string hidden_states_79_pad_type_0 = const()[name = string("hidden_states_79_pad_type_0"), val = string("valid")]; + tensor hidden_states_79_strides_0 = const()[name = string("hidden_states_79_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_79_pad_0 = const()[name = string("hidden_states_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_79_dilations_0 = const()[name = string("hidden_states_79_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_79_groups_0 = const()[name = string("hidden_states_79_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62806400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73816512))))[name = string("audio_upsampler_decoder_0_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_0_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73817088)))]; + tensor hidden_states_79_cast_fp16 = conv(bias = audio_upsampler_decoder_0_conv_bias_to_fp16, dilations = hidden_states_79_dilations_0, groups = hidden_states_79_groups_0, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = hidden_states_79_strides_0, weight = audio_upsampler_decoder_0_conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("hidden_states_79_cast_fp16")]; + tensor context_mask_9_begin_0 = const()[name = string("context_mask_9_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_9_end_0 = const()[name = string("context_mask_9_end_0"), val = tensor([1, 1, 1, 20])]; + tensor context_mask_9_end_mask_0 = const()[name = string("context_mask_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_9_cast_fp16 = slice_by_index(begin = context_mask_9_begin_0, end = context_mask_9_end_0, end_mask = context_mask_9_end_mask_0, x = context_mask_7_cast_fp16)[name = string("context_mask_9_cast_fp16")]; + tensor alpha_over_pi_1_to_fp16 = const()[name = string("alpha_over_pi_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73820224)))]; + tensor theta_over_pi_1_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = alpha_over_pi_1_to_fp16)[name = string("theta_over_pi_1_cast_fp16")]; + tensor var_2305_cast_fp16 = round(x = theta_over_pi_1_cast_fp16)[name = string("op_2305_cast_fp16")]; + tensor reduced_1_cast_fp16 = sub(x = theta_over_pi_1_cast_fp16, y = var_2305_cast_fp16)[name = string("reduced_1_cast_fp16")]; + tensor reduced_sq_1_cast_fp16 = mul(x = reduced_1_cast_fp16, y = reduced_1_cast_fp16)[name = string("reduced_sq_1_cast_fp16")]; + tensor acc_1_mean_0_to_fp16 = const()[name = string("acc_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73823360)))]; + tensor acc_1_variance_0_to_fp16 = const()[name = string("acc_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73826496)))]; + tensor acc_1_gamma_0_to_fp16 = const()[name = string("acc_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73829632)))]; + tensor acc_1_beta_0_to_fp16 = const()[name = string("acc_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73832768)))]; + fp16 acc_1_epsilon_0_to_fp16 = const()[name = string("acc_1_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_1_cast_fp16 = batch_norm(beta = acc_1_beta_0_to_fp16, epsilon = acc_1_epsilon_0_to_fp16, gamma = acc_1_gamma_0_to_fp16, mean = acc_1_mean_0_to_fp16, variance = acc_1_variance_0_to_fp16, x = reduced_sq_1_cast_fp16)[name = string("acc_1_cast_fp16")]; + tensor var_2318_cast_fp16 = mul(x = acc_1_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2318_cast_fp16")]; + tensor c_1_to_fp16 = const()[name = string("c_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73835904)))]; + tensor acc_3_cast_fp16 = add(x = var_2318_cast_fp16, y = c_1_to_fp16)[name = string("acc_3_cast_fp16")]; + tensor var_2320_cast_fp16 = mul(x = acc_3_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2320_cast_fp16")]; + tensor c_3_to_fp16 = const()[name = string("c_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73839040)))]; + tensor acc_5_cast_fp16 = add(x = var_2320_cast_fp16, y = c_3_to_fp16)[name = string("acc_5_cast_fp16")]; + tensor var_2322_cast_fp16 = mul(x = acc_5_cast_fp16, y = reduced_sq_1_cast_fp16)[name = string("op_2322_cast_fp16")]; + tensor hidden_states_81_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = var_2322_cast_fp16)[name = string("hidden_states_81_cast_fp16")]; + bool full_mask_7_interleave_0 = const()[name = string("full_mask_7_interleave_0"), val = bool(false)]; + tensor fill_3_to_fp16 = const()[name = string("fill_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115345152)))]; + tensor full_mask_7_cast_fp16 = concat(axis = var_2037, interleave = full_mask_7_interleave_0, values = (context_mask_9_cast_fp16, fill_3_to_fp16))[name = string("full_mask_7_cast_fp16")]; + tensor input_133_cast_fp16 = mul(x = hidden_states_81_cast_fp16, y = full_mask_7_cast_fp16)[name = string("input_133_cast_fp16")]; + string sub_pixels_13_pad_type_0 = const()[name = string("sub_pixels_13_pad_type_0"), val = string("valid")]; + tensor sub_pixels_13_strides_0 = const()[name = string("sub_pixels_13_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_13_pad_0 = const()[name = string("sub_pixels_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_13_dilations_0 = const()[name = string("sub_pixels_13_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_13_groups_0 = const()[name = string("sub_pixels_13_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73842176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92716608))))[name = string("audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92717184)))]; + tensor sub_pixels_13_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_1_conv_bias_to_fp16, dilations = sub_pixels_13_dilations_0, groups = sub_pixels_13_groups_0, pad = sub_pixels_13_pad_0, pad_type = sub_pixels_13_pad_type_0, strides = sub_pixels_13_strides_0, weight = audio_upsampler_decoder_1_block_1_conv_weight_to_fp16_palettized, x = input_133_cast_fp16)[name = string("sub_pixels_13_cast_fp16")]; + tensor var_2346 = const()[name = string("op_2346"), val = tensor([1, 8, 768, 29])]; + tensor sub_pixels_15_cast_fp16 = reshape(shape = var_2346, x = sub_pixels_13_cast_fp16)[name = string("sub_pixels_15_cast_fp16")]; + tensor var_2348 = const()[name = string("op_2348"), val = tensor([0, 2, 3, 1])]; + tensor var_2353 = const()[name = string("op_2353"), val = tensor([1, 768, 1, 232])]; + tensor sub_pixels_17_cast_fp16 = transpose(perm = var_2348, x = sub_pixels_15_cast_fp16)[name = string("transpose_3")]; + tensor hidden_states_83_cast_fp16 = reshape(shape = var_2353, x = sub_pixels_17_cast_fp16)[name = string("hidden_states_83_cast_fp16")]; + tensor newest_5_begin_0 = const()[name = string("newest_5_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_5_end_0 = const()[name = string("newest_5_end_0"), val = tensor([1, 1, 1, 14])]; + tensor newest_5_end_mask_0 = const()[name = string("newest_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_5_cast_fp16 = slice_by_index(begin = newest_5_begin_0, end = newest_5_end_0, end_mask = newest_5_end_mask_0, x = context_mask_9_cast_fp16)[name = string("newest_5_cast_fp16")]; + tensor var_2358 = const()[name = string("op_2358"), val = tensor([1, 1, 13, 1])]; + tensor var_2359_cast_fp16 = reshape(shape = var_2358, x = newest_5_cast_fp16)[name = string("op_2359_cast_fp16")]; + tensor spread_5_reps_0 = const()[name = string("spread_5_reps_0"), val = tensor([1, 1, 1, 8])]; + tensor spread_5_cast_fp16 = tile(reps = spread_5_reps_0, x = var_2359_cast_fp16)[name = string("spread_5_cast_fp16")]; + tensor var_2365 = const()[name = string("op_2365"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_11_cast_fp16 = reshape(shape = var_2365, x = spread_5_cast_fp16)[name = string("context_mask_11_cast_fp16")]; + tensor residual_3_begin_0 = const()[name = string("residual_3_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_3_end_0 = const()[name = string("residual_3_end_0"), val = tensor([1, 768, 1, 232])]; + tensor residual_3_end_mask_0 = const()[name = string("residual_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_3_cast_fp16 = slice_by_index(begin = residual_3_begin_0, end = residual_3_end_0, end_mask = residual_3_end_mask_0, x = hidden_states_83_cast_fp16)[name = string("residual_3_cast_fp16")]; + tensor alpha_over_pi_3_to_fp16 = const()[name = string("alpha_over_pi_3_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92729536)))]; + tensor theta_over_pi_3_cast_fp16 = mul(x = hidden_states_83_cast_fp16, y = alpha_over_pi_3_to_fp16)[name = string("theta_over_pi_3_cast_fp16")]; + tensor var_2388_cast_fp16 = round(x = theta_over_pi_3_cast_fp16)[name = string("op_2388_cast_fp16")]; + tensor reduced_3_cast_fp16 = sub(x = theta_over_pi_3_cast_fp16, y = var_2388_cast_fp16)[name = string("reduced_3_cast_fp16")]; + tensor reduced_sq_3_cast_fp16 = mul(x = reduced_3_cast_fp16, y = reduced_3_cast_fp16)[name = string("reduced_sq_3_cast_fp16")]; + tensor acc_7_mean_0_to_fp16 = const()[name = string("acc_7_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92731136)))]; + tensor acc_7_variance_0_to_fp16 = const()[name = string("acc_7_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92732736)))]; + tensor acc_7_gamma_0_to_fp16 = const()[name = string("acc_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92734336)))]; + tensor acc_7_beta_0_to_fp16 = const()[name = string("acc_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92735936)))]; + fp16 acc_7_epsilon_0_to_fp16 = const()[name = string("acc_7_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_7_cast_fp16 = batch_norm(beta = acc_7_beta_0_to_fp16, epsilon = acc_7_epsilon_0_to_fp16, gamma = acc_7_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_3_cast_fp16)[name = string("acc_7_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = acc_7_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor c_5_to_fp16 = const()[name = string("c_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92737536)))]; + tensor acc_9_cast_fp16 = add(x = var_2401_cast_fp16, y = c_5_to_fp16)[name = string("acc_9_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = acc_9_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor c_7_to_fp16 = const()[name = string("c_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92739136)))]; + tensor acc_11_cast_fp16 = add(x = var_2403_cast_fp16, y = c_7_to_fp16)[name = string("acc_11_cast_fp16")]; + tensor var_2405_cast_fp16 = mul(x = acc_11_cast_fp16, y = reduced_sq_3_cast_fp16)[name = string("op_2405_cast_fp16")]; + tensor hidden_states_85_cast_fp16 = add(x = hidden_states_83_cast_fp16, y = var_2405_cast_fp16)[name = string("hidden_states_85_cast_fp16")]; + bool full_mask_9_interleave_0 = const()[name = string("full_mask_9_interleave_0"), val = bool(false)]; + tensor fill_4_to_fp16 = const()[name = string("fill_4_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115345856)))]; + tensor full_mask_9_cast_fp16 = concat(axis = var_2037, interleave = full_mask_9_interleave_0, values = (context_mask_11_cast_fp16, fill_4_to_fp16))[name = string("full_mask_9_cast_fp16")]; + tensor input_135_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = full_mask_9_cast_fp16)[name = string("input_135_cast_fp16")]; + string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")]; + tensor hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92740864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96869696))))[name = string("audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96870272)))]; + tensor hidden_states_87_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = audio_upsampler_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("hidden_states_87_cast_fp16")]; + tensor alpha_over_pi_5_to_fp16 = const()[name = string("alpha_over_pi_5_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96871872)))]; + tensor theta_over_pi_5_cast_fp16 = mul(x = hidden_states_87_cast_fp16, y = alpha_over_pi_5_to_fp16)[name = string("theta_over_pi_5_cast_fp16")]; + tensor var_2442_cast_fp16 = round(x = theta_over_pi_5_cast_fp16)[name = string("op_2442_cast_fp16")]; + tensor reduced_5_cast_fp16 = sub(x = theta_over_pi_5_cast_fp16, y = var_2442_cast_fp16)[name = string("reduced_5_cast_fp16")]; + tensor reduced_sq_5_cast_fp16 = mul(x = reduced_5_cast_fp16, y = reduced_5_cast_fp16)[name = string("reduced_sq_5_cast_fp16")]; + tensor acc_13_gamma_0_to_fp16 = const()[name = string("acc_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96873472)))]; + tensor acc_13_beta_0_to_fp16 = const()[name = string("acc_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96875072)))]; + fp16 acc_13_epsilon_0_to_fp16 = const()[name = string("acc_13_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_13_cast_fp16 = batch_norm(beta = acc_13_beta_0_to_fp16, epsilon = acc_13_epsilon_0_to_fp16, gamma = acc_13_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_5_cast_fp16)[name = string("acc_13_cast_fp16")]; + tensor var_2455_cast_fp16 = mul(x = acc_13_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2455_cast_fp16")]; + tensor c_9_to_fp16 = const()[name = string("c_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96876672)))]; + tensor acc_15_cast_fp16 = add(x = var_2455_cast_fp16, y = c_9_to_fp16)[name = string("acc_15_cast_fp16")]; + tensor var_2457_cast_fp16 = mul(x = acc_15_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2457_cast_fp16")]; + tensor c_11_to_fp16 = const()[name = string("c_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96878272)))]; + tensor acc_17_cast_fp16 = add(x = var_2457_cast_fp16, y = c_11_to_fp16)[name = string("acc_17_cast_fp16")]; + tensor var_2459_cast_fp16 = mul(x = acc_17_cast_fp16, y = reduced_sq_5_cast_fp16)[name = string("op_2459_cast_fp16")]; + tensor hidden_states_89_cast_fp16 = add(x = hidden_states_87_cast_fp16, y = var_2459_cast_fp16)[name = string("hidden_states_89_cast_fp16")]; + string hidden_states_91_pad_type_0 = const()[name = string("hidden_states_91_pad_type_0"), val = string("valid")]; + tensor hidden_states_91_strides_0 = const()[name = string("hidden_states_91_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_91_pad_0 = const()[name = string("hidden_states_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_91_dilations_0 = const()[name = string("hidden_states_91_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_91_groups_0 = const()[name = string("hidden_states_91_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96879872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97469760))))[name = string("audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97470336)))]; + tensor hidden_states_91_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_91_dilations_0, groups = hidden_states_91_groups_0, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = hidden_states_91_strides_0, weight = audio_upsampler_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_89_cast_fp16)[name = string("hidden_states_91_cast_fp16")]; + tensor hidden_states_93_cast_fp16 = add(x = hidden_states_91_cast_fp16, y = residual_3_cast_fp16)[name = string("hidden_states_93_cast_fp16")]; + tensor context_mask_13_begin_0 = const()[name = string("context_mask_13_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_13_end_0 = const()[name = string("context_mask_13_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_13_end_mask_0 = const()[name = string("context_mask_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_13_cast_fp16 = slice_by_index(begin = context_mask_13_begin_0, end = context_mask_13_end_0, end_mask = context_mask_13_end_mask_0, x = context_mask_11_cast_fp16)[name = string("context_mask_13_cast_fp16")]; + tensor residual_5_begin_0 = const()[name = string("residual_5_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_5_end_0 = const()[name = string("residual_5_end_0"), val = tensor([1, 768, 1, 226])]; + tensor residual_5_end_mask_0 = const()[name = string("residual_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_5_cast_fp16 = slice_by_index(begin = residual_5_begin_0, end = residual_5_end_0, end_mask = residual_5_end_mask_0, x = hidden_states_93_cast_fp16)[name = string("residual_5_cast_fp16")]; + tensor alpha_over_pi_7_to_fp16 = const()[name = string("alpha_over_pi_7_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97471936)))]; + tensor theta_over_pi_7_cast_fp16 = mul(x = hidden_states_93_cast_fp16, y = alpha_over_pi_7_to_fp16)[name = string("theta_over_pi_7_cast_fp16")]; + tensor var_2494_cast_fp16 = round(x = theta_over_pi_7_cast_fp16)[name = string("op_2494_cast_fp16")]; + tensor reduced_7_cast_fp16 = sub(x = theta_over_pi_7_cast_fp16, y = var_2494_cast_fp16)[name = string("reduced_7_cast_fp16")]; + tensor reduced_sq_7_cast_fp16 = mul(x = reduced_7_cast_fp16, y = reduced_7_cast_fp16)[name = string("reduced_sq_7_cast_fp16")]; + tensor acc_19_gamma_0_to_fp16 = const()[name = string("acc_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97473536)))]; + tensor acc_19_beta_0_to_fp16 = const()[name = string("acc_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97475136)))]; + fp16 acc_19_epsilon_0_to_fp16 = const()[name = string("acc_19_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_19_cast_fp16 = batch_norm(beta = acc_19_beta_0_to_fp16, epsilon = acc_19_epsilon_0_to_fp16, gamma = acc_19_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_7_cast_fp16)[name = string("acc_19_cast_fp16")]; + tensor var_2507_cast_fp16 = mul(x = acc_19_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2507_cast_fp16")]; + tensor c_13_to_fp16 = const()[name = string("c_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97476736)))]; + tensor acc_21_cast_fp16 = add(x = var_2507_cast_fp16, y = c_13_to_fp16)[name = string("acc_21_cast_fp16")]; + tensor var_2509_cast_fp16 = mul(x = acc_21_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2509_cast_fp16")]; + tensor c_15_to_fp16 = const()[name = string("c_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97478336)))]; + tensor acc_23_cast_fp16 = add(x = var_2509_cast_fp16, y = c_15_to_fp16)[name = string("acc_23_cast_fp16")]; + tensor var_2511_cast_fp16 = mul(x = acc_23_cast_fp16, y = reduced_sq_7_cast_fp16)[name = string("op_2511_cast_fp16")]; + tensor hidden_states_95_cast_fp16 = add(x = hidden_states_93_cast_fp16, y = var_2511_cast_fp16)[name = string("hidden_states_95_cast_fp16")]; + bool full_mask_11_interleave_0 = const()[name = string("full_mask_11_interleave_0"), val = bool(false)]; + tensor full_mask_11_cast_fp16 = concat(axis = var_2037, interleave = full_mask_11_interleave_0, values = (context_mask_13_cast_fp16, fill_4_to_fp16))[name = string("full_mask_11_cast_fp16")]; + tensor input_139_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = full_mask_11_cast_fp16)[name = string("input_139_cast_fp16")]; + string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")]; + tensor hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97479936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101608768))))[name = string("audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101609344)))]; + tensor hidden_states_97_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = audio_upsampler_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized, x = input_139_cast_fp16)[name = string("hidden_states_97_cast_fp16")]; + tensor alpha_over_pi_9_to_fp16 = const()[name = string("alpha_over_pi_9_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101610944)))]; + tensor theta_over_pi_9_cast_fp16 = mul(x = hidden_states_97_cast_fp16, y = alpha_over_pi_9_to_fp16)[name = string("theta_over_pi_9_cast_fp16")]; + tensor var_2548_cast_fp16 = round(x = theta_over_pi_9_cast_fp16)[name = string("op_2548_cast_fp16")]; + tensor reduced_9_cast_fp16 = sub(x = theta_over_pi_9_cast_fp16, y = var_2548_cast_fp16)[name = string("reduced_9_cast_fp16")]; + tensor reduced_sq_9_cast_fp16 = mul(x = reduced_9_cast_fp16, y = reduced_9_cast_fp16)[name = string("reduced_sq_9_cast_fp16")]; + tensor acc_25_gamma_0_to_fp16 = const()[name = string("acc_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101612544)))]; + tensor acc_25_beta_0_to_fp16 = const()[name = string("acc_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101614144)))]; + fp16 acc_25_epsilon_0_to_fp16 = const()[name = string("acc_25_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_25_cast_fp16 = batch_norm(beta = acc_25_beta_0_to_fp16, epsilon = acc_25_epsilon_0_to_fp16, gamma = acc_25_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_9_cast_fp16)[name = string("acc_25_cast_fp16")]; + tensor var_2561_cast_fp16 = mul(x = acc_25_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2561_cast_fp16")]; + tensor c_17_to_fp16 = const()[name = string("c_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101615744)))]; + tensor acc_27_cast_fp16 = add(x = var_2561_cast_fp16, y = c_17_to_fp16)[name = string("acc_27_cast_fp16")]; + tensor var_2563_cast_fp16 = mul(x = acc_27_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2563_cast_fp16")]; + tensor c_19_to_fp16 = const()[name = string("c_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101617344)))]; + tensor acc_29_cast_fp16 = add(x = var_2563_cast_fp16, y = c_19_to_fp16)[name = string("acc_29_cast_fp16")]; + tensor var_2565_cast_fp16 = mul(x = acc_29_cast_fp16, y = reduced_sq_9_cast_fp16)[name = string("op_2565_cast_fp16")]; + tensor hidden_states_99_cast_fp16 = add(x = hidden_states_97_cast_fp16, y = var_2565_cast_fp16)[name = string("hidden_states_99_cast_fp16")]; + string hidden_states_101_pad_type_0 = const()[name = string("hidden_states_101_pad_type_0"), val = string("valid")]; + tensor hidden_states_101_strides_0 = const()[name = string("hidden_states_101_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_101_pad_0 = const()[name = string("hidden_states_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_101_dilations_0 = const()[name = string("hidden_states_101_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_101_groups_0 = const()[name = string("hidden_states_101_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101618944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102208832))))[name = string("audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102209408)))]; + tensor hidden_states_101_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_101_dilations_0, groups = hidden_states_101_groups_0, pad = hidden_states_101_pad_0, pad_type = hidden_states_101_pad_type_0, strides = hidden_states_101_strides_0, weight = audio_upsampler_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_99_cast_fp16)[name = string("hidden_states_101_cast_fp16")]; + tensor hidden_states_103_cast_fp16 = add(x = hidden_states_101_cast_fp16, y = residual_5_cast_fp16)[name = string("hidden_states_103_cast_fp16")]; + tensor context_mask_15_begin_0 = const()[name = string("context_mask_15_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_15_end_0 = const()[name = string("context_mask_15_end_0"), val = tensor([1, 1, 1, 98])]; + tensor context_mask_15_end_mask_0 = const()[name = string("context_mask_15_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_15_cast_fp16 = slice_by_index(begin = context_mask_15_begin_0, end = context_mask_15_end_0, end_mask = context_mask_15_end_mask_0, x = context_mask_13_cast_fp16)[name = string("context_mask_15_cast_fp16")]; + tensor residual_7_begin_0 = const()[name = string("residual_7_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_7_end_0 = const()[name = string("residual_7_end_0"), val = tensor([1, 768, 1, 208])]; + tensor residual_7_end_mask_0 = const()[name = string("residual_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_7_cast_fp16 = slice_by_index(begin = residual_7_begin_0, end = residual_7_end_0, end_mask = residual_7_end_mask_0, x = hidden_states_103_cast_fp16)[name = string("residual_7_cast_fp16")]; + tensor alpha_over_pi_11_to_fp16 = const()[name = string("alpha_over_pi_11_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102211008)))]; + tensor theta_over_pi_11_cast_fp16 = mul(x = hidden_states_103_cast_fp16, y = alpha_over_pi_11_to_fp16)[name = string("theta_over_pi_11_cast_fp16")]; + tensor var_2600_cast_fp16 = round(x = theta_over_pi_11_cast_fp16)[name = string("op_2600_cast_fp16")]; + tensor reduced_11_cast_fp16 = sub(x = theta_over_pi_11_cast_fp16, y = var_2600_cast_fp16)[name = string("reduced_11_cast_fp16")]; + tensor reduced_sq_11_cast_fp16 = mul(x = reduced_11_cast_fp16, y = reduced_11_cast_fp16)[name = string("reduced_sq_11_cast_fp16")]; + tensor acc_31_gamma_0_to_fp16 = const()[name = string("acc_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102212608)))]; + tensor acc_31_beta_0_to_fp16 = const()[name = string("acc_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102214208)))]; + fp16 acc_31_epsilon_0_to_fp16 = const()[name = string("acc_31_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_31_cast_fp16 = batch_norm(beta = acc_31_beta_0_to_fp16, epsilon = acc_31_epsilon_0_to_fp16, gamma = acc_31_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_11_cast_fp16)[name = string("acc_31_cast_fp16")]; + tensor var_2613_cast_fp16 = mul(x = acc_31_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor c_21_to_fp16 = const()[name = string("c_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102215808)))]; + tensor acc_33_cast_fp16 = add(x = var_2613_cast_fp16, y = c_21_to_fp16)[name = string("acc_33_cast_fp16")]; + tensor var_2615_cast_fp16 = mul(x = acc_33_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2615_cast_fp16")]; + tensor c_23_to_fp16 = const()[name = string("c_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102217408)))]; + tensor acc_35_cast_fp16 = add(x = var_2615_cast_fp16, y = c_23_to_fp16)[name = string("acc_35_cast_fp16")]; + tensor var_2617_cast_fp16 = mul(x = acc_35_cast_fp16, y = reduced_sq_11_cast_fp16)[name = string("op_2617_cast_fp16")]; + tensor hidden_states_105_cast_fp16 = add(x = hidden_states_103_cast_fp16, y = var_2617_cast_fp16)[name = string("hidden_states_105_cast_fp16")]; + bool full_mask_13_interleave_0 = const()[name = string("full_mask_13_interleave_0"), val = bool(false)]; + tensor full_mask_13_cast_fp16 = concat(axis = var_2037, interleave = full_mask_13_interleave_0, values = (context_mask_15_cast_fp16, fill_4_to_fp16))[name = string("full_mask_13_cast_fp16")]; + tensor input_143_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = full_mask_13_cast_fp16)[name = string("input_143_cast_fp16")]; + string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")]; + tensor hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102219008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106347840))))[name = string("audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106348416)))]; + tensor hidden_states_107_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = audio_upsampler_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("hidden_states_107_cast_fp16")]; + tensor alpha_over_pi_13_to_fp16 = const()[name = string("alpha_over_pi_13_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106350016)))]; + tensor theta_over_pi_13_cast_fp16 = mul(x = hidden_states_107_cast_fp16, y = alpha_over_pi_13_to_fp16)[name = string("theta_over_pi_13_cast_fp16")]; + tensor var_2654_cast_fp16 = round(x = theta_over_pi_13_cast_fp16)[name = string("op_2654_cast_fp16")]; + tensor reduced_13_cast_fp16 = sub(x = theta_over_pi_13_cast_fp16, y = var_2654_cast_fp16)[name = string("reduced_13_cast_fp16")]; + tensor reduced_sq_13_cast_fp16 = mul(x = reduced_13_cast_fp16, y = reduced_13_cast_fp16)[name = string("reduced_sq_13_cast_fp16")]; + tensor acc_37_gamma_0_to_fp16 = const()[name = string("acc_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106351616)))]; + tensor acc_37_beta_0_to_fp16 = const()[name = string("acc_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106353216)))]; + fp16 acc_37_epsilon_0_to_fp16 = const()[name = string("acc_37_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_37_cast_fp16 = batch_norm(beta = acc_37_beta_0_to_fp16, epsilon = acc_37_epsilon_0_to_fp16, gamma = acc_37_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_13_cast_fp16)[name = string("acc_37_cast_fp16")]; + tensor var_2667_cast_fp16 = mul(x = acc_37_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2667_cast_fp16")]; + tensor c_25_to_fp16 = const()[name = string("c_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106354816)))]; + tensor acc_39_cast_fp16 = add(x = var_2667_cast_fp16, y = c_25_to_fp16)[name = string("acc_39_cast_fp16")]; + tensor var_2669_cast_fp16 = mul(x = acc_39_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2669_cast_fp16")]; + tensor c_27_to_fp16 = const()[name = string("c_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106356416)))]; + tensor acc_41_cast_fp16 = add(x = var_2669_cast_fp16, y = c_27_to_fp16)[name = string("acc_41_cast_fp16")]; + tensor var_2671_cast_fp16 = mul(x = acc_41_cast_fp16, y = reduced_sq_13_cast_fp16)[name = string("op_2671_cast_fp16")]; + tensor hidden_states_109_cast_fp16 = add(x = hidden_states_107_cast_fp16, y = var_2671_cast_fp16)[name = string("hidden_states_109_cast_fp16")]; + string hidden_states_111_pad_type_0 = const()[name = string("hidden_states_111_pad_type_0"), val = string("valid")]; + tensor hidden_states_111_strides_0 = const()[name = string("hidden_states_111_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_111_pad_0 = const()[name = string("hidden_states_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_111_dilations_0 = const()[name = string("hidden_states_111_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_111_groups_0 = const()[name = string("hidden_states_111_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106358016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106947904))))[name = string("audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106948480)))]; + tensor hidden_states_111_cast_fp16 = conv(bias = audio_upsampler_decoder_1_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_111_dilations_0, groups = hidden_states_111_groups_0, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = hidden_states_111_strides_0, weight = audio_upsampler_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_109_cast_fp16)[name = string("hidden_states_111_cast_fp16")]; + tensor hidden_states_113_cast_fp16 = add(x = hidden_states_111_cast_fp16, y = residual_7_cast_fp16)[name = string("hidden_states_113_cast_fp16")]; + tensor context_mask_19_begin_0 = const()[name = string("context_mask_19_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_19_end_0 = const()[name = string("context_mask_19_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_19_end_mask_0 = const()[name = string("context_mask_19_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_19_cast_fp16 = slice_by_index(begin = context_mask_19_begin_0, end = context_mask_19_end_0, end_mask = context_mask_19_end_mask_0, x = context_mask_11_cast_fp16)[name = string("context_mask_19_cast_fp16")]; + tensor hidden_states_115_begin_0 = const()[name = string("hidden_states_115_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor hidden_states_115_end_0 = const()[name = string("hidden_states_115_end_0"), val = tensor([1, 768, 1, 154])]; + tensor hidden_states_115_end_mask_0 = const()[name = string("hidden_states_115_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_115_cast_fp16 = slice_by_index(begin = hidden_states_115_begin_0, end = hidden_states_115_end_0, end_mask = hidden_states_115_end_mask_0, x = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")]; + tensor context_mask_21_begin_0 = const()[name = string("context_mask_21_begin_0"), val = tensor([0, 0, 0, 3])]; + tensor context_mask_21_end_0 = const()[name = string("context_mask_21_end_0"), val = tensor([1, 1, 1, 26])]; + tensor context_mask_21_end_mask_0 = const()[name = string("context_mask_21_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_21_cast_fp16 = slice_by_index(begin = context_mask_21_begin_0, end = context_mask_21_end_0, end_mask = context_mask_21_end_mask_0, x = context_mask_19_cast_fp16)[name = string("context_mask_21_cast_fp16")]; + tensor alpha_over_pi_15_to_fp16 = const()[name = string("alpha_over_pi_15_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106950080)))]; + tensor theta_over_pi_15_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = alpha_over_pi_15_to_fp16)[name = string("theta_over_pi_15_cast_fp16")]; + tensor var_2731_cast_fp16 = round(x = theta_over_pi_15_cast_fp16)[name = string("op_2731_cast_fp16")]; + tensor reduced_15_cast_fp16 = sub(x = theta_over_pi_15_cast_fp16, y = var_2731_cast_fp16)[name = string("reduced_15_cast_fp16")]; + tensor reduced_sq_15_cast_fp16 = mul(x = reduced_15_cast_fp16, y = reduced_15_cast_fp16)[name = string("reduced_sq_15_cast_fp16")]; + tensor acc_43_gamma_0_to_fp16 = const()[name = string("acc_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106951680)))]; + tensor acc_43_beta_0_to_fp16 = const()[name = string("acc_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106953280)))]; + fp16 acc_43_epsilon_0_to_fp16 = const()[name = string("acc_43_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_43_cast_fp16 = batch_norm(beta = acc_43_beta_0_to_fp16, epsilon = acc_43_epsilon_0_to_fp16, gamma = acc_43_gamma_0_to_fp16, mean = acc_7_mean_0_to_fp16, variance = acc_7_variance_0_to_fp16, x = reduced_sq_15_cast_fp16)[name = string("acc_43_cast_fp16")]; + tensor var_2744_cast_fp16 = mul(x = acc_43_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2744_cast_fp16")]; + tensor c_29_to_fp16 = const()[name = string("c_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106954880)))]; + tensor acc_45_cast_fp16 = add(x = var_2744_cast_fp16, y = c_29_to_fp16)[name = string("acc_45_cast_fp16")]; + tensor var_2746_cast_fp16 = mul(x = acc_45_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2746_cast_fp16")]; + tensor c_31_to_fp16 = const()[name = string("c_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106956480)))]; + tensor acc_47_cast_fp16 = add(x = var_2746_cast_fp16, y = c_31_to_fp16)[name = string("acc_47_cast_fp16")]; + tensor var_2748_cast_fp16 = mul(x = acc_47_cast_fp16, y = reduced_sq_15_cast_fp16)[name = string("op_2748_cast_fp16")]; + tensor hidden_states_117_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = var_2748_cast_fp16)[name = string("hidden_states_117_cast_fp16")]; + bool full_mask_15_interleave_0 = const()[name = string("full_mask_15_interleave_0"), val = bool(false)]; + tensor full_mask_15_cast_fp16 = concat(axis = var_2037, interleave = full_mask_15_interleave_0, values = (context_mask_21_cast_fp16, fill_4_to_fp16))[name = string("full_mask_15_cast_fp16")]; + tensor input_147_cast_fp16 = mul(x = hidden_states_117_cast_fp16, y = full_mask_15_cast_fp16)[name = string("input_147_cast_fp16")]; + string sub_pixels_19_pad_type_0 = const()[name = string("sub_pixels_19_pad_type_0"), val = string("valid")]; + tensor sub_pixels_19_strides_0 = const()[name = string("sub_pixels_19_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_19_pad_0 = const()[name = string("sub_pixels_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_19_dilations_0 = const()[name = string("sub_pixels_19_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_19_groups_0 = const()[name = string("sub_pixels_19_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106958080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109907264))))[name = string("audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109907840)))]; + tensor sub_pixels_19_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_1_conv_bias_to_fp16, dilations = sub_pixels_19_dilations_0, groups = sub_pixels_19_groups_0, pad = sub_pixels_19_pad_0, pad_type = sub_pixels_19_pad_type_0, strides = sub_pixels_19_strides_0, weight = audio_upsampler_decoder_2_block_1_conv_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("sub_pixels_19_cast_fp16")]; + tensor var_2772 = const()[name = string("op_2772"), val = tensor([1, 5, 384, 150])]; + tensor sub_pixels_21_cast_fp16 = reshape(shape = var_2772, x = sub_pixels_19_cast_fp16)[name = string("sub_pixels_21_cast_fp16")]; + tensor var_2774 = const()[name = string("op_2774"), val = tensor([0, 2, 3, 1])]; + tensor var_2779 = const()[name = string("op_2779"), val = tensor([1, 384, 1, 750])]; + tensor sub_pixels_23_cast_fp16 = transpose(perm = var_2774, x = sub_pixels_21_cast_fp16)[name = string("transpose_2")]; + tensor hidden_states_119_cast_fp16 = reshape(shape = var_2779, x = sub_pixels_23_cast_fp16)[name = string("hidden_states_119_cast_fp16")]; + tensor newest_9_begin_0 = const()[name = string("newest_9_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_9_end_0 = const()[name = string("newest_9_end_0"), val = tensor([1, 1, 1, 23])]; + tensor newest_9_end_mask_0 = const()[name = string("newest_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_9_cast_fp16 = slice_by_index(begin = newest_9_begin_0, end = newest_9_end_0, end_mask = newest_9_end_mask_0, x = context_mask_21_cast_fp16)[name = string("newest_9_cast_fp16")]; + tensor var_2784 = const()[name = string("op_2784"), val = tensor([1, 1, 22, 1])]; + tensor var_2785_cast_fp16 = reshape(shape = var_2784, x = newest_9_cast_fp16)[name = string("op_2785_cast_fp16")]; + tensor spread_9_reps_0 = const()[name = string("spread_9_reps_0"), val = tensor([1, 1, 1, 5])]; + tensor spread_9_cast_fp16 = tile(reps = spread_9_reps_0, x = var_2785_cast_fp16)[name = string("spread_9_cast_fp16")]; + tensor var_2791 = const()[name = string("op_2791"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_23_cast_fp16 = reshape(shape = var_2791, x = spread_9_cast_fp16)[name = string("context_mask_23_cast_fp16")]; + tensor residual_9_begin_0 = const()[name = string("residual_9_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_9_end_0 = const()[name = string("residual_9_end_0"), val = tensor([1, 384, 1, 750])]; + tensor residual_9_end_mask_0 = const()[name = string("residual_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_9_cast_fp16 = slice_by_index(begin = residual_9_begin_0, end = residual_9_end_0, end_mask = residual_9_end_mask_0, x = hidden_states_119_cast_fp16)[name = string("residual_9_cast_fp16")]; + tensor alpha_over_pi_17_to_fp16 = const()[name = string("alpha_over_pi_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109911744)))]; + tensor theta_over_pi_17_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = alpha_over_pi_17_to_fp16)[name = string("theta_over_pi_17_cast_fp16")]; + tensor var_2814_cast_fp16 = round(x = theta_over_pi_17_cast_fp16)[name = string("op_2814_cast_fp16")]; + tensor reduced_17_cast_fp16 = sub(x = theta_over_pi_17_cast_fp16, y = var_2814_cast_fp16)[name = string("reduced_17_cast_fp16")]; + tensor reduced_sq_17_cast_fp16 = mul(x = reduced_17_cast_fp16, y = reduced_17_cast_fp16)[name = string("reduced_sq_17_cast_fp16")]; + tensor acc_49_mean_0_to_fp16 = const()[name = string("acc_49_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109912576)))]; + tensor acc_49_variance_0_to_fp16 = const()[name = string("acc_49_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109913408)))]; + tensor acc_49_gamma_0_to_fp16 = const()[name = string("acc_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109914240)))]; + tensor acc_49_beta_0_to_fp16 = const()[name = string("acc_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109915072)))]; + fp16 acc_49_epsilon_0_to_fp16 = const()[name = string("acc_49_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_49_cast_fp16 = batch_norm(beta = acc_49_beta_0_to_fp16, epsilon = acc_49_epsilon_0_to_fp16, gamma = acc_49_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_17_cast_fp16)[name = string("acc_49_cast_fp16")]; + tensor var_2827_cast_fp16 = mul(x = acc_49_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2827_cast_fp16")]; + tensor c_33_to_fp16 = const()[name = string("c_33_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109915904)))]; + tensor acc_51_cast_fp16 = add(x = var_2827_cast_fp16, y = c_33_to_fp16)[name = string("acc_51_cast_fp16")]; + tensor var_2829_cast_fp16 = mul(x = acc_51_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2829_cast_fp16")]; + tensor c_35_to_fp16 = const()[name = string("c_35_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109916736)))]; + tensor acc_53_cast_fp16 = add(x = var_2829_cast_fp16, y = c_35_to_fp16)[name = string("acc_53_cast_fp16")]; + tensor var_2831_cast_fp16 = mul(x = acc_53_cast_fp16, y = reduced_sq_17_cast_fp16)[name = string("op_2831_cast_fp16")]; + tensor hidden_states_121_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = var_2831_cast_fp16)[name = string("hidden_states_121_cast_fp16")]; + bool full_mask_17_interleave_0 = const()[name = string("full_mask_17_interleave_0"), val = bool(false)]; + tensor fill_8_to_fp16 = const()[name = string("fill_8_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114085888)))]; + tensor full_mask_17_cast_fp16 = concat(axis = var_2037, interleave = full_mask_17_interleave_0, values = (context_mask_23_cast_fp16, fill_8_to_fp16))[name = string("full_mask_17_cast_fp16")]; + tensor input_149_cast_fp16 = mul(x = hidden_states_121_cast_fp16, y = full_mask_17_cast_fp16)[name = string("input_149_cast_fp16")]; + string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")]; + tensor hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109917952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110950208))))[name = string("audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110950784)))]; + tensor hidden_states_123_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = audio_upsampler_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized, x = input_149_cast_fp16)[name = string("hidden_states_123_cast_fp16")]; + tensor alpha_over_pi_19_to_fp16 = const()[name = string("alpha_over_pi_19_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110951616)))]; + tensor theta_over_pi_19_cast_fp16 = mul(x = hidden_states_123_cast_fp16, y = alpha_over_pi_19_to_fp16)[name = string("theta_over_pi_19_cast_fp16")]; + tensor var_2868_cast_fp16 = round(x = theta_over_pi_19_cast_fp16)[name = string("op_2868_cast_fp16")]; + tensor reduced_19_cast_fp16 = sub(x = theta_over_pi_19_cast_fp16, y = var_2868_cast_fp16)[name = string("reduced_19_cast_fp16")]; + tensor reduced_sq_19_cast_fp16 = mul(x = reduced_19_cast_fp16, y = reduced_19_cast_fp16)[name = string("reduced_sq_19_cast_fp16")]; + tensor acc_55_gamma_0_to_fp16 = const()[name = string("acc_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110952448)))]; + tensor acc_55_beta_0_to_fp16 = const()[name = string("acc_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110953280)))]; + fp16 acc_55_epsilon_0_to_fp16 = const()[name = string("acc_55_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_55_cast_fp16 = batch_norm(beta = acc_55_beta_0_to_fp16, epsilon = acc_55_epsilon_0_to_fp16, gamma = acc_55_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_19_cast_fp16)[name = string("acc_55_cast_fp16")]; + tensor var_2881_cast_fp16 = mul(x = acc_55_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2881_cast_fp16")]; + tensor c_37_to_fp16 = const()[name = string("c_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110954112)))]; + tensor acc_57_cast_fp16 = add(x = var_2881_cast_fp16, y = c_37_to_fp16)[name = string("acc_57_cast_fp16")]; + tensor var_2883_cast_fp16 = mul(x = acc_57_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2883_cast_fp16")]; + tensor c_39_to_fp16 = const()[name = string("c_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110954944)))]; + tensor acc_59_cast_fp16 = add(x = var_2883_cast_fp16, y = c_39_to_fp16)[name = string("acc_59_cast_fp16")]; + tensor var_2885_cast_fp16 = mul(x = acc_59_cast_fp16, y = reduced_sq_19_cast_fp16)[name = string("op_2885_cast_fp16")]; + tensor hidden_states_125_cast_fp16 = add(x = hidden_states_123_cast_fp16, y = var_2885_cast_fp16)[name = string("hidden_states_125_cast_fp16")]; + string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")]; + tensor hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110955776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111103296))))[name = string("audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111103872)))]; + tensor hidden_states_127_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = audio_upsampler_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_125_cast_fp16)[name = string("hidden_states_127_cast_fp16")]; + tensor hidden_states_129_cast_fp16 = add(x = hidden_states_127_cast_fp16, y = residual_9_cast_fp16)[name = string("hidden_states_129_cast_fp16")]; + tensor context_mask_25_begin_0 = const()[name = string("context_mask_25_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_25_end_0 = const()[name = string("context_mask_25_end_0"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_25_end_mask_0 = const()[name = string("context_mask_25_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_25_cast_fp16 = slice_by_index(begin = context_mask_25_begin_0, end = context_mask_25_end_0, end_mask = context_mask_25_end_mask_0, x = context_mask_23_cast_fp16)[name = string("context_mask_25_cast_fp16")]; + tensor residual_11_begin_0 = const()[name = string("residual_11_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_11_end_0 = const()[name = string("residual_11_end_0"), val = tensor([1, 384, 1, 744])]; + tensor residual_11_end_mask_0 = const()[name = string("residual_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_11_cast_fp16 = slice_by_index(begin = residual_11_begin_0, end = residual_11_end_0, end_mask = residual_11_end_mask_0, x = hidden_states_129_cast_fp16)[name = string("residual_11_cast_fp16")]; + tensor alpha_over_pi_21_to_fp16 = const()[name = string("alpha_over_pi_21_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111104704)))]; + tensor theta_over_pi_21_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = alpha_over_pi_21_to_fp16)[name = string("theta_over_pi_21_cast_fp16")]; + tensor var_2920_cast_fp16 = round(x = theta_over_pi_21_cast_fp16)[name = string("op_2920_cast_fp16")]; + tensor reduced_21_cast_fp16 = sub(x = theta_over_pi_21_cast_fp16, y = var_2920_cast_fp16)[name = string("reduced_21_cast_fp16")]; + tensor reduced_sq_21_cast_fp16 = mul(x = reduced_21_cast_fp16, y = reduced_21_cast_fp16)[name = string("reduced_sq_21_cast_fp16")]; + tensor acc_61_gamma_0_to_fp16 = const()[name = string("acc_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111105536)))]; + tensor acc_61_beta_0_to_fp16 = const()[name = string("acc_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111106368)))]; + fp16 acc_61_epsilon_0_to_fp16 = const()[name = string("acc_61_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_61_cast_fp16 = batch_norm(beta = acc_61_beta_0_to_fp16, epsilon = acc_61_epsilon_0_to_fp16, gamma = acc_61_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_21_cast_fp16)[name = string("acc_61_cast_fp16")]; + tensor var_2933_cast_fp16 = mul(x = acc_61_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2933_cast_fp16")]; + tensor c_41_to_fp16 = const()[name = string("c_41_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111107200)))]; + tensor acc_63_cast_fp16 = add(x = var_2933_cast_fp16, y = c_41_to_fp16)[name = string("acc_63_cast_fp16")]; + tensor var_2935_cast_fp16 = mul(x = acc_63_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2935_cast_fp16")]; + tensor c_43_to_fp16 = const()[name = string("c_43_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111108032)))]; + tensor acc_65_cast_fp16 = add(x = var_2935_cast_fp16, y = c_43_to_fp16)[name = string("acc_65_cast_fp16")]; + tensor var_2937_cast_fp16 = mul(x = acc_65_cast_fp16, y = reduced_sq_21_cast_fp16)[name = string("op_2937_cast_fp16")]; + tensor hidden_states_131_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = var_2937_cast_fp16)[name = string("hidden_states_131_cast_fp16")]; + bool full_mask_19_interleave_0 = const()[name = string("full_mask_19_interleave_0"), val = bool(false)]; + tensor full_mask_19_cast_fp16 = concat(axis = var_2037, interleave = full_mask_19_interleave_0, values = (context_mask_25_cast_fp16, fill_8_to_fp16))[name = string("full_mask_19_cast_fp16")]; + tensor input_153_cast_fp16 = mul(x = hidden_states_131_cast_fp16, y = full_mask_19_cast_fp16)[name = string("input_153_cast_fp16")]; + string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")]; + tensor hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111108864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112141120))))[name = string("audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112141696)))]; + tensor hidden_states_133_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = audio_upsampler_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized, x = input_153_cast_fp16)[name = string("hidden_states_133_cast_fp16")]; + tensor alpha_over_pi_23_to_fp16 = const()[name = string("alpha_over_pi_23_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112142528)))]; + tensor theta_over_pi_23_cast_fp16 = mul(x = hidden_states_133_cast_fp16, y = alpha_over_pi_23_to_fp16)[name = string("theta_over_pi_23_cast_fp16")]; + tensor var_2974_cast_fp16 = round(x = theta_over_pi_23_cast_fp16)[name = string("op_2974_cast_fp16")]; + tensor reduced_23_cast_fp16 = sub(x = theta_over_pi_23_cast_fp16, y = var_2974_cast_fp16)[name = string("reduced_23_cast_fp16")]; + tensor reduced_sq_23_cast_fp16 = mul(x = reduced_23_cast_fp16, y = reduced_23_cast_fp16)[name = string("reduced_sq_23_cast_fp16")]; + tensor acc_67_gamma_0_to_fp16 = const()[name = string("acc_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112143360)))]; + tensor acc_67_beta_0_to_fp16 = const()[name = string("acc_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112144192)))]; + fp16 acc_67_epsilon_0_to_fp16 = const()[name = string("acc_67_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_67_cast_fp16 = batch_norm(beta = acc_67_beta_0_to_fp16, epsilon = acc_67_epsilon_0_to_fp16, gamma = acc_67_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_23_cast_fp16)[name = string("acc_67_cast_fp16")]; + tensor var_2987_cast_fp16 = mul(x = acc_67_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2987_cast_fp16")]; + tensor c_45_to_fp16 = const()[name = string("c_45_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112145024)))]; + tensor acc_69_cast_fp16 = add(x = var_2987_cast_fp16, y = c_45_to_fp16)[name = string("acc_69_cast_fp16")]; + tensor var_2989_cast_fp16 = mul(x = acc_69_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2989_cast_fp16")]; + tensor c_47_to_fp16 = const()[name = string("c_47_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112145856)))]; + tensor acc_71_cast_fp16 = add(x = var_2989_cast_fp16, y = c_47_to_fp16)[name = string("acc_71_cast_fp16")]; + tensor var_2991_cast_fp16 = mul(x = acc_71_cast_fp16, y = reduced_sq_23_cast_fp16)[name = string("op_2991_cast_fp16")]; + tensor hidden_states_135_cast_fp16 = add(x = hidden_states_133_cast_fp16, y = var_2991_cast_fp16)[name = string("hidden_states_135_cast_fp16")]; + string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")]; + tensor hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112146688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112294208))))[name = string("audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112294784)))]; + tensor hidden_states_137_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = audio_upsampler_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_135_cast_fp16)[name = string("hidden_states_137_cast_fp16")]; + tensor hidden_states_139_cast_fp16 = add(x = hidden_states_137_cast_fp16, y = residual_11_cast_fp16)[name = string("hidden_states_139_cast_fp16")]; + tensor context_mask_27_begin_0 = const()[name = string("context_mask_27_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_27_end_0 = const()[name = string("context_mask_27_end_0"), val = tensor([1, 1, 1, 104])]; + tensor context_mask_27_end_mask_0 = const()[name = string("context_mask_27_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_27_cast_fp16 = slice_by_index(begin = context_mask_27_begin_0, end = context_mask_27_end_0, end_mask = context_mask_27_end_mask_0, x = context_mask_25_cast_fp16)[name = string("context_mask_27_cast_fp16")]; + tensor residual_13_begin_0 = const()[name = string("residual_13_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_13_end_0 = const()[name = string("residual_13_end_0"), val = tensor([1, 384, 1, 726])]; + tensor residual_13_end_mask_0 = const()[name = string("residual_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_13_cast_fp16 = slice_by_index(begin = residual_13_begin_0, end = residual_13_end_0, end_mask = residual_13_end_mask_0, x = hidden_states_139_cast_fp16)[name = string("residual_13_cast_fp16")]; + tensor alpha_over_pi_25_to_fp16 = const()[name = string("alpha_over_pi_25_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112295616)))]; + tensor theta_over_pi_25_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = alpha_over_pi_25_to_fp16)[name = string("theta_over_pi_25_cast_fp16")]; + tensor var_3026_cast_fp16 = round(x = theta_over_pi_25_cast_fp16)[name = string("op_3026_cast_fp16")]; + tensor reduced_25_cast_fp16 = sub(x = theta_over_pi_25_cast_fp16, y = var_3026_cast_fp16)[name = string("reduced_25_cast_fp16")]; + tensor reduced_sq_25_cast_fp16 = mul(x = reduced_25_cast_fp16, y = reduced_25_cast_fp16)[name = string("reduced_sq_25_cast_fp16")]; + tensor acc_73_gamma_0_to_fp16 = const()[name = string("acc_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112296448)))]; + tensor acc_73_beta_0_to_fp16 = const()[name = string("acc_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112297280)))]; + fp16 acc_73_epsilon_0_to_fp16 = const()[name = string("acc_73_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_73_cast_fp16 = batch_norm(beta = acc_73_beta_0_to_fp16, epsilon = acc_73_epsilon_0_to_fp16, gamma = acc_73_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_25_cast_fp16)[name = string("acc_73_cast_fp16")]; + tensor var_3039_cast_fp16 = mul(x = acc_73_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_3039_cast_fp16")]; + tensor c_49_to_fp16 = const()[name = string("c_49_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112298112)))]; + tensor acc_75_cast_fp16 = add(x = var_3039_cast_fp16, y = c_49_to_fp16)[name = string("acc_75_cast_fp16")]; + tensor var_3041_cast_fp16 = mul(x = acc_75_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_3041_cast_fp16")]; + tensor c_51_to_fp16 = const()[name = string("c_51_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112298944)))]; + tensor acc_77_cast_fp16 = add(x = var_3041_cast_fp16, y = c_51_to_fp16)[name = string("acc_77_cast_fp16")]; + tensor var_3043_cast_fp16 = mul(x = acc_77_cast_fp16, y = reduced_sq_25_cast_fp16)[name = string("op_3043_cast_fp16")]; + tensor hidden_states_141_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = var_3043_cast_fp16)[name = string("hidden_states_141_cast_fp16")]; + bool full_mask_21_interleave_0 = const()[name = string("full_mask_21_interleave_0"), val = bool(false)]; + tensor full_mask_21_cast_fp16 = concat(axis = var_2037, interleave = full_mask_21_interleave_0, values = (context_mask_27_cast_fp16, fill_8_to_fp16))[name = string("full_mask_21_cast_fp16")]; + tensor input_157_cast_fp16 = mul(x = hidden_states_141_cast_fp16, y = full_mask_21_cast_fp16)[name = string("input_157_cast_fp16")]; + string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")]; + tensor hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112299776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113332032))))[name = string("audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113332608)))]; + tensor hidden_states_143_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = audio_upsampler_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("hidden_states_143_cast_fp16")]; + tensor alpha_over_pi_27_to_fp16 = const()[name = string("alpha_over_pi_27_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113333440)))]; + tensor theta_over_pi_27_cast_fp16 = mul(x = hidden_states_143_cast_fp16, y = alpha_over_pi_27_to_fp16)[name = string("theta_over_pi_27_cast_fp16")]; + tensor var_3080_cast_fp16 = round(x = theta_over_pi_27_cast_fp16)[name = string("op_3080_cast_fp16")]; + tensor reduced_27_cast_fp16 = sub(x = theta_over_pi_27_cast_fp16, y = var_3080_cast_fp16)[name = string("reduced_27_cast_fp16")]; + tensor reduced_sq_27_cast_fp16 = mul(x = reduced_27_cast_fp16, y = reduced_27_cast_fp16)[name = string("reduced_sq_27_cast_fp16")]; + tensor acc_79_gamma_0_to_fp16 = const()[name = string("acc_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113334272)))]; + tensor acc_79_beta_0_to_fp16 = const()[name = string("acc_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113335104)))]; + fp16 acc_79_epsilon_0_to_fp16 = const()[name = string("acc_79_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_79_cast_fp16 = batch_norm(beta = acc_79_beta_0_to_fp16, epsilon = acc_79_epsilon_0_to_fp16, gamma = acc_79_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_27_cast_fp16)[name = string("acc_79_cast_fp16")]; + tensor var_3093_cast_fp16 = mul(x = acc_79_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3093_cast_fp16")]; + tensor c_53_to_fp16 = const()[name = string("c_53_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113335936)))]; + tensor acc_81_cast_fp16 = add(x = var_3093_cast_fp16, y = c_53_to_fp16)[name = string("acc_81_cast_fp16")]; + tensor var_3095_cast_fp16 = mul(x = acc_81_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3095_cast_fp16")]; + tensor c_55_to_fp16 = const()[name = string("c_55_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113336768)))]; + tensor acc_83_cast_fp16 = add(x = var_3095_cast_fp16, y = c_55_to_fp16)[name = string("acc_83_cast_fp16")]; + tensor var_3097_cast_fp16 = mul(x = acc_83_cast_fp16, y = reduced_sq_27_cast_fp16)[name = string("op_3097_cast_fp16")]; + tensor hidden_states_145_cast_fp16 = add(x = hidden_states_143_cast_fp16, y = var_3097_cast_fp16)[name = string("hidden_states_145_cast_fp16")]; + string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")]; + tensor hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113337600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113485120))))[name = string("audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113485696)))]; + tensor hidden_states_147_cast_fp16 = conv(bias = audio_upsampler_decoder_2_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = audio_upsampler_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_145_cast_fp16)[name = string("hidden_states_147_cast_fp16")]; + tensor hidden_states_149_cast_fp16 = add(x = hidden_states_147_cast_fp16, y = residual_13_cast_fp16)[name = string("hidden_states_149_cast_fp16")]; + tensor context_mask_31_begin_0 = const()[name = string("context_mask_31_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_31_end_0 = const()[name = string("context_mask_31_end_0"), val = tensor([1, 1, 1, 110])]; + tensor context_mask_31_end_mask_0 = const()[name = string("context_mask_31_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_31_cast_fp16 = slice_by_index(begin = context_mask_31_begin_0, end = context_mask_31_end_0, end_mask = context_mask_31_end_mask_0, x = context_mask_23_cast_fp16)[name = string("context_mask_31_cast_fp16")]; + tensor hidden_states_151_begin_0 = const()[name = string("hidden_states_151_begin_0"), val = tensor([0, 0, 0, 4])]; + tensor hidden_states_151_end_0 = const()[name = string("hidden_states_151_end_0"), val = tensor([1, 384, 1, 672])]; + tensor hidden_states_151_end_mask_0 = const()[name = string("hidden_states_151_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_151_cast_fp16 = slice_by_index(begin = hidden_states_151_begin_0, end = hidden_states_151_end_0, end_mask = hidden_states_151_end_mask_0, x = hidden_states_149_cast_fp16)[name = string("hidden_states_151_cast_fp16")]; + tensor context_mask_33_begin_0 = const()[name = string("context_mask_33_begin_0"), val = tensor([0, 0, 0, 4])]; + tensor context_mask_33_end_0 = const()[name = string("context_mask_33_end_0"), val = tensor([1, 1, 1, 32])]; + tensor context_mask_33_end_mask_0 = const()[name = string("context_mask_33_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_33_cast_fp16 = slice_by_index(begin = context_mask_33_begin_0, end = context_mask_33_end_0, end_mask = context_mask_33_end_mask_0, x = context_mask_31_cast_fp16)[name = string("context_mask_33_cast_fp16")]; + tensor alpha_over_pi_29_to_fp16 = const()[name = string("alpha_over_pi_29_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113486528)))]; + tensor theta_over_pi_29_cast_fp16 = mul(x = hidden_states_151_cast_fp16, y = alpha_over_pi_29_to_fp16)[name = string("theta_over_pi_29_cast_fp16")]; + tensor var_3157_cast_fp16 = round(x = theta_over_pi_29_cast_fp16)[name = string("op_3157_cast_fp16")]; + tensor reduced_29_cast_fp16 = sub(x = theta_over_pi_29_cast_fp16, y = var_3157_cast_fp16)[name = string("reduced_29_cast_fp16")]; + tensor reduced_sq_29_cast_fp16 = mul(x = reduced_29_cast_fp16, y = reduced_29_cast_fp16)[name = string("reduced_sq_29_cast_fp16")]; + tensor acc_85_gamma_0_to_fp16 = const()[name = string("acc_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113487360)))]; + tensor acc_85_beta_0_to_fp16 = const()[name = string("acc_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113488192)))]; + fp16 acc_85_epsilon_0_to_fp16 = const()[name = string("acc_85_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_85_cast_fp16 = batch_norm(beta = acc_85_beta_0_to_fp16, epsilon = acc_85_epsilon_0_to_fp16, gamma = acc_85_gamma_0_to_fp16, mean = acc_49_mean_0_to_fp16, variance = acc_49_variance_0_to_fp16, x = reduced_sq_29_cast_fp16)[name = string("acc_85_cast_fp16")]; + tensor var_3170_cast_fp16 = mul(x = acc_85_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3170_cast_fp16")]; + tensor c_57_to_fp16 = const()[name = string("c_57_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113489024)))]; + tensor acc_87_cast_fp16 = add(x = var_3170_cast_fp16, y = c_57_to_fp16)[name = string("acc_87_cast_fp16")]; + tensor var_3172_cast_fp16 = mul(x = acc_87_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3172_cast_fp16")]; + tensor c_59_to_fp16 = const()[name = string("c_59_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113489856)))]; + tensor acc_89_cast_fp16 = add(x = var_3172_cast_fp16, y = c_59_to_fp16)[name = string("acc_89_cast_fp16")]; + tensor var_3174_cast_fp16 = mul(x = acc_89_cast_fp16, y = reduced_sq_29_cast_fp16)[name = string("op_3174_cast_fp16")]; + tensor hidden_states_153_cast_fp16 = add(x = hidden_states_151_cast_fp16, y = var_3174_cast_fp16)[name = string("hidden_states_153_cast_fp16")]; + bool full_mask_23_interleave_0 = const()[name = string("full_mask_23_interleave_0"), val = bool(false)]; + tensor full_mask_23_cast_fp16 = concat(axis = var_2037, interleave = full_mask_23_interleave_0, values = (context_mask_33_cast_fp16, fill_8_to_fp16))[name = string("full_mask_23_cast_fp16")]; + tensor input_161_cast_fp16 = mul(x = hidden_states_153_cast_fp16, y = full_mask_23_cast_fp16)[name = string("input_161_cast_fp16")]; + string sub_pixels_25_pad_type_0 = const()[name = string("sub_pixels_25_pad_type_0"), val = string("valid")]; + tensor sub_pixels_25_strides_0 = const()[name = string("sub_pixels_25_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_25_pad_0 = const()[name = string("sub_pixels_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_25_dilations_0 = const()[name = string("sub_pixels_25_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_25_groups_0 = const()[name = string("sub_pixels_25_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113490688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114080576))))[name = string("audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114081152)))]; + tensor sub_pixels_25_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_1_conv_bias_to_fp16, dilations = sub_pixels_25_dilations_0, groups = sub_pixels_25_groups_0, pad = sub_pixels_25_pad_0, pad_type = sub_pixels_25_pad_type_0, strides = sub_pixels_25_strides_0, weight = audio_upsampler_decoder_3_block_1_conv_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("sub_pixels_25_cast_fp16")]; + tensor var_3198 = const()[name = string("op_3198"), val = tensor([1, 4, 192, 667])]; + tensor sub_pixels_27_cast_fp16 = reshape(shape = var_3198, x = sub_pixels_25_cast_fp16)[name = string("sub_pixels_27_cast_fp16")]; + tensor var_3200 = const()[name = string("op_3200"), val = tensor([0, 2, 3, 1])]; + tensor var_3205 = const()[name = string("op_3205"), val = tensor([1, 192, 1, 2668])]; + tensor sub_pixels_29_cast_fp16 = transpose(perm = var_3200, x = sub_pixels_27_cast_fp16)[name = string("transpose_1")]; + tensor hidden_states_155_cast_fp16 = reshape(shape = var_3205, x = sub_pixels_29_cast_fp16)[name = string("hidden_states_155_cast_fp16")]; + tensor newest_13_begin_0 = const()[name = string("newest_13_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_13_end_0 = const()[name = string("newest_13_end_0"), val = tensor([1, 1, 1, 28])]; + tensor newest_13_end_mask_0 = const()[name = string("newest_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_13_cast_fp16 = slice_by_index(begin = newest_13_begin_0, end = newest_13_end_0, end_mask = newest_13_end_mask_0, x = context_mask_33_cast_fp16)[name = string("newest_13_cast_fp16")]; + tensor var_3210 = const()[name = string("op_3210"), val = tensor([1, 1, 27, 1])]; + tensor var_3211_cast_fp16 = reshape(shape = var_3210, x = newest_13_cast_fp16)[name = string("op_3211_cast_fp16")]; + tensor spread_13_reps_0 = const()[name = string("spread_13_reps_0"), val = tensor([1, 1, 1, 4])]; + tensor spread_13_cast_fp16 = tile(reps = spread_13_reps_0, x = var_3211_cast_fp16)[name = string("spread_13_cast_fp16")]; + tensor var_3217 = const()[name = string("op_3217"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_35_cast_fp16 = reshape(shape = var_3217, x = spread_13_cast_fp16)[name = string("context_mask_35_cast_fp16")]; + tensor residual_15_begin_0 = const()[name = string("residual_15_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_15_end_0 = const()[name = string("residual_15_end_0"), val = tensor([1, 192, 1, 2668])]; + tensor residual_15_end_mask_0 = const()[name = string("residual_15_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_15_cast_fp16 = slice_by_index(begin = residual_15_begin_0, end = residual_15_end_0, end_mask = residual_15_end_mask_0, x = hidden_states_155_cast_fp16)[name = string("residual_15_cast_fp16")]; + tensor alpha_over_pi_31_to_fp16 = const()[name = string("alpha_over_pi_31_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114082752)))]; + tensor theta_over_pi_31_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = alpha_over_pi_31_to_fp16)[name = string("theta_over_pi_31_cast_fp16")]; + tensor var_3240_cast_fp16 = round(x = theta_over_pi_31_cast_fp16)[name = string("op_3240_cast_fp16")]; + tensor reduced_31_cast_fp16 = sub(x = theta_over_pi_31_cast_fp16, y = var_3240_cast_fp16)[name = string("reduced_31_cast_fp16")]; + tensor reduced_sq_31_cast_fp16 = mul(x = reduced_31_cast_fp16, y = reduced_31_cast_fp16)[name = string("reduced_sq_31_cast_fp16")]; + tensor acc_91_mean_0_to_fp16 = const()[name = string("acc_91_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114083200)))]; + tensor acc_91_variance_0_to_fp16 = const()[name = string("acc_91_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114083648)))]; + tensor acc_91_gamma_0_to_fp16 = const()[name = string("acc_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084096)))]; + tensor acc_91_beta_0_to_fp16 = const()[name = string("acc_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084544)))]; + fp16 acc_91_epsilon_0_to_fp16 = const()[name = string("acc_91_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_91_cast_fp16 = batch_norm(beta = acc_91_beta_0_to_fp16, epsilon = acc_91_epsilon_0_to_fp16, gamma = acc_91_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_31_cast_fp16)[name = string("acc_91_cast_fp16")]; + tensor var_3253_cast_fp16 = mul(x = acc_91_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3253_cast_fp16")]; + tensor c_61_to_fp16 = const()[name = string("c_61_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114084992)))]; + tensor acc_93_cast_fp16 = add(x = var_3253_cast_fp16, y = c_61_to_fp16)[name = string("acc_93_cast_fp16")]; + tensor var_3255_cast_fp16 = mul(x = acc_93_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3255_cast_fp16")]; + tensor c_63_to_fp16 = const()[name = string("c_63_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114085440)))]; + tensor acc_95_cast_fp16 = add(x = var_3255_cast_fp16, y = c_63_to_fp16)[name = string("acc_95_cast_fp16")]; + tensor var_3257_cast_fp16 = mul(x = acc_95_cast_fp16, y = reduced_sq_31_cast_fp16)[name = string("op_3257_cast_fp16")]; + tensor hidden_states_157_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = var_3257_cast_fp16)[name = string("hidden_states_157_cast_fp16")]; + bool full_mask_25_interleave_0 = const()[name = string("full_mask_25_interleave_0"), val = bool(false)]; + tensor fill_12_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115346176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115348800))))[name = string("fill_12_to_fp16_palettized")]; + tensor full_mask_25_cast_fp16 = concat(axis = var_2037, interleave = full_mask_25_interleave_0, values = (context_mask_35_cast_fp16, fill_12_to_fp16_palettized))[name = string("full_mask_25_cast_fp16")]; + tensor input_163_cast_fp16 = mul(x = hidden_states_157_cast_fp16, y = full_mask_25_cast_fp16)[name = string("input_163_cast_fp16")]; + string hidden_states_159_pad_type_0 = const()[name = string("hidden_states_159_pad_type_0"), val = string("valid")]; + tensor hidden_states_159_strides_0 = const()[name = string("hidden_states_159_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_0 = const()[name = string("hidden_states_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_159_dilations_0 = const()[name = string("hidden_states_159_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_159_groups_0 = const()[name = string("hidden_states_159_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114087232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114345344))))[name = string("audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114345920)))]; + tensor hidden_states_159_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_159_dilations_0, groups = hidden_states_159_groups_0, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = hidden_states_159_strides_0, weight = audio_upsampler_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized, x = input_163_cast_fp16)[name = string("hidden_states_159_cast_fp16")]; + tensor alpha_over_pi_33_to_fp16 = const()[name = string("alpha_over_pi_33_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114346368)))]; + tensor theta_over_pi_33_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = alpha_over_pi_33_to_fp16)[name = string("theta_over_pi_33_cast_fp16")]; + tensor var_3294_cast_fp16 = round(x = theta_over_pi_33_cast_fp16)[name = string("op_3294_cast_fp16")]; + tensor reduced_33_cast_fp16 = sub(x = theta_over_pi_33_cast_fp16, y = var_3294_cast_fp16)[name = string("reduced_33_cast_fp16")]; + tensor reduced_sq_33_cast_fp16 = mul(x = reduced_33_cast_fp16, y = reduced_33_cast_fp16)[name = string("reduced_sq_33_cast_fp16")]; + tensor acc_97_gamma_0_to_fp16 = const()[name = string("acc_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114346816)))]; + tensor acc_97_beta_0_to_fp16 = const()[name = string("acc_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114347264)))]; + fp16 acc_97_epsilon_0_to_fp16 = const()[name = string("acc_97_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_97_cast_fp16 = batch_norm(beta = acc_97_beta_0_to_fp16, epsilon = acc_97_epsilon_0_to_fp16, gamma = acc_97_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_33_cast_fp16)[name = string("acc_97_cast_fp16")]; + tensor var_3307_cast_fp16 = mul(x = acc_97_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3307_cast_fp16")]; + tensor c_65_to_fp16 = const()[name = string("c_65_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114347712)))]; + tensor acc_99_cast_fp16 = add(x = var_3307_cast_fp16, y = c_65_to_fp16)[name = string("acc_99_cast_fp16")]; + tensor var_3309_cast_fp16 = mul(x = acc_99_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3309_cast_fp16")]; + tensor c_67_to_fp16 = const()[name = string("c_67_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114348160)))]; + tensor acc_101_cast_fp16 = add(x = var_3309_cast_fp16, y = c_67_to_fp16)[name = string("acc_101_cast_fp16")]; + tensor var_3311_cast_fp16 = mul(x = acc_101_cast_fp16, y = reduced_sq_33_cast_fp16)[name = string("op_3311_cast_fp16")]; + tensor hidden_states_161_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = var_3311_cast_fp16)[name = string("hidden_states_161_cast_fp16")]; + string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")]; + tensor hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114348608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114385536))))[name = string("audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114386112)))]; + tensor hidden_states_163_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = audio_upsampler_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_161_cast_fp16)[name = string("hidden_states_163_cast_fp16")]; + tensor hidden_states_165_cast_fp16 = add(x = hidden_states_163_cast_fp16, y = residual_15_cast_fp16)[name = string("hidden_states_165_cast_fp16")]; + tensor context_mask_37_begin_0 = const()[name = string("context_mask_37_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_37_end_0 = const()[name = string("context_mask_37_end_0"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_37_end_mask_0 = const()[name = string("context_mask_37_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_37_cast_fp16 = slice_by_index(begin = context_mask_37_begin_0, end = context_mask_37_end_0, end_mask = context_mask_37_end_mask_0, x = context_mask_35_cast_fp16)[name = string("context_mask_37_cast_fp16")]; + tensor residual_17_begin_0 = const()[name = string("residual_17_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_17_end_0 = const()[name = string("residual_17_end_0"), val = tensor([1, 192, 1, 2662])]; + tensor residual_17_end_mask_0 = const()[name = string("residual_17_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_17_cast_fp16 = slice_by_index(begin = residual_17_begin_0, end = residual_17_end_0, end_mask = residual_17_end_mask_0, x = hidden_states_165_cast_fp16)[name = string("residual_17_cast_fp16")]; + tensor alpha_over_pi_35_to_fp16 = const()[name = string("alpha_over_pi_35_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114386560)))]; + tensor theta_over_pi_35_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = alpha_over_pi_35_to_fp16)[name = string("theta_over_pi_35_cast_fp16")]; + tensor var_3346_cast_fp16 = round(x = theta_over_pi_35_cast_fp16)[name = string("op_3346_cast_fp16")]; + tensor reduced_35_cast_fp16 = sub(x = theta_over_pi_35_cast_fp16, y = var_3346_cast_fp16)[name = string("reduced_35_cast_fp16")]; + tensor reduced_sq_35_cast_fp16 = mul(x = reduced_35_cast_fp16, y = reduced_35_cast_fp16)[name = string("reduced_sq_35_cast_fp16")]; + tensor acc_103_gamma_0_to_fp16 = const()[name = string("acc_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387008)))]; + tensor acc_103_beta_0_to_fp16 = const()[name = string("acc_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387456)))]; + fp16 acc_103_epsilon_0_to_fp16 = const()[name = string("acc_103_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_103_cast_fp16 = batch_norm(beta = acc_103_beta_0_to_fp16, epsilon = acc_103_epsilon_0_to_fp16, gamma = acc_103_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_35_cast_fp16)[name = string("acc_103_cast_fp16")]; + tensor var_3359_cast_fp16 = mul(x = acc_103_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3359_cast_fp16")]; + tensor c_69_to_fp16 = const()[name = string("c_69_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114387904)))]; + tensor acc_105_cast_fp16 = add(x = var_3359_cast_fp16, y = c_69_to_fp16)[name = string("acc_105_cast_fp16")]; + tensor var_3361_cast_fp16 = mul(x = acc_105_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3361_cast_fp16")]; + tensor c_71_to_fp16 = const()[name = string("c_71_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114388352)))]; + tensor acc_107_cast_fp16 = add(x = var_3361_cast_fp16, y = c_71_to_fp16)[name = string("acc_107_cast_fp16")]; + tensor var_3363_cast_fp16 = mul(x = acc_107_cast_fp16, y = reduced_sq_35_cast_fp16)[name = string("op_3363_cast_fp16")]; + tensor hidden_states_167_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = var_3363_cast_fp16)[name = string("hidden_states_167_cast_fp16")]; + bool full_mask_27_interleave_0 = const()[name = string("full_mask_27_interleave_0"), val = bool(false)]; + tensor full_mask_27_cast_fp16 = concat(axis = var_2037, interleave = full_mask_27_interleave_0, values = (context_mask_37_cast_fp16, fill_12_to_fp16_palettized))[name = string("full_mask_27_cast_fp16")]; + tensor input_167_cast_fp16 = mul(x = hidden_states_167_cast_fp16, y = full_mask_27_cast_fp16)[name = string("input_167_cast_fp16")]; + string hidden_states_169_pad_type_0 = const()[name = string("hidden_states_169_pad_type_0"), val = string("valid")]; + tensor hidden_states_169_dilations_0 = const()[name = string("hidden_states_169_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_169_strides_0 = const()[name = string("hidden_states_169_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_169_pad_0 = const()[name = string("hidden_states_169_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_169_groups_0 = const()[name = string("hidden_states_169_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114388800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114646912))))[name = string("audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114647488)))]; + tensor hidden_states_169_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_169_dilations_0, groups = hidden_states_169_groups_0, pad = hidden_states_169_pad_0, pad_type = hidden_states_169_pad_type_0, strides = hidden_states_169_strides_0, weight = audio_upsampler_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized, x = input_167_cast_fp16)[name = string("hidden_states_169_cast_fp16")]; + tensor alpha_over_pi_37_to_fp16 = const()[name = string("alpha_over_pi_37_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114647936)))]; + tensor theta_over_pi_37_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = alpha_over_pi_37_to_fp16)[name = string("theta_over_pi_37_cast_fp16")]; + tensor var_3400_cast_fp16 = round(x = theta_over_pi_37_cast_fp16)[name = string("op_3400_cast_fp16")]; + tensor reduced_37_cast_fp16 = sub(x = theta_over_pi_37_cast_fp16, y = var_3400_cast_fp16)[name = string("reduced_37_cast_fp16")]; + tensor reduced_sq_37_cast_fp16 = mul(x = reduced_37_cast_fp16, y = reduced_37_cast_fp16)[name = string("reduced_sq_37_cast_fp16")]; + tensor acc_109_gamma_0_to_fp16 = const()[name = string("acc_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114648384)))]; + tensor acc_109_beta_0_to_fp16 = const()[name = string("acc_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114648832)))]; + fp16 acc_109_epsilon_0_to_fp16 = const()[name = string("acc_109_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_109_cast_fp16 = batch_norm(beta = acc_109_beta_0_to_fp16, epsilon = acc_109_epsilon_0_to_fp16, gamma = acc_109_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_37_cast_fp16)[name = string("acc_109_cast_fp16")]; + tensor var_3413_cast_fp16 = mul(x = acc_109_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3413_cast_fp16")]; + tensor c_73_to_fp16 = const()[name = string("c_73_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114649280)))]; + tensor acc_111_cast_fp16 = add(x = var_3413_cast_fp16, y = c_73_to_fp16)[name = string("acc_111_cast_fp16")]; + tensor var_3415_cast_fp16 = mul(x = acc_111_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3415_cast_fp16")]; + tensor c_75_to_fp16 = const()[name = string("c_75_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114649728)))]; + tensor acc_113_cast_fp16 = add(x = var_3415_cast_fp16, y = c_75_to_fp16)[name = string("acc_113_cast_fp16")]; + tensor var_3417_cast_fp16 = mul(x = acc_113_cast_fp16, y = reduced_sq_37_cast_fp16)[name = string("op_3417_cast_fp16")]; + tensor hidden_states_171_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = var_3417_cast_fp16)[name = string("hidden_states_171_cast_fp16")]; + string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")]; + tensor hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114650176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114687104))))[name = string("audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114687680)))]; + tensor hidden_states_173_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = audio_upsampler_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_171_cast_fp16)[name = string("hidden_states_173_cast_fp16")]; + tensor hidden_states_175_cast_fp16 = add(x = hidden_states_173_cast_fp16, y = residual_17_cast_fp16)[name = string("hidden_states_175_cast_fp16")]; + tensor context_mask_39_begin_0 = const()[name = string("context_mask_39_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_39_end_0 = const()[name = string("context_mask_39_end_0"), val = tensor([1, 1, 1, 102])]; + tensor context_mask_39_end_mask_0 = const()[name = string("context_mask_39_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_39_cast_fp16 = slice_by_index(begin = context_mask_39_begin_0, end = context_mask_39_end_0, end_mask = context_mask_39_end_mask_0, x = context_mask_37_cast_fp16)[name = string("context_mask_39_cast_fp16")]; + tensor residual_19_begin_0 = const()[name = string("residual_19_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_19_end_0 = const()[name = string("residual_19_end_0"), val = tensor([1, 192, 1, 2644])]; + tensor residual_19_end_mask_0 = const()[name = string("residual_19_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_19_cast_fp16 = slice_by_index(begin = residual_19_begin_0, end = residual_19_end_0, end_mask = residual_19_end_mask_0, x = hidden_states_175_cast_fp16)[name = string("residual_19_cast_fp16")]; + tensor alpha_over_pi_39_to_fp16 = const()[name = string("alpha_over_pi_39_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114688128)))]; + tensor theta_over_pi_39_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = alpha_over_pi_39_to_fp16)[name = string("theta_over_pi_39_cast_fp16")]; + tensor var_3452_cast_fp16 = round(x = theta_over_pi_39_cast_fp16)[name = string("op_3452_cast_fp16")]; + tensor reduced_39_cast_fp16 = sub(x = theta_over_pi_39_cast_fp16, y = var_3452_cast_fp16)[name = string("reduced_39_cast_fp16")]; + tensor reduced_sq_39_cast_fp16 = mul(x = reduced_39_cast_fp16, y = reduced_39_cast_fp16)[name = string("reduced_sq_39_cast_fp16")]; + tensor acc_115_gamma_0_to_fp16 = const()[name = string("acc_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114688576)))]; + tensor acc_115_beta_0_to_fp16 = const()[name = string("acc_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689024)))]; + fp16 acc_115_epsilon_0_to_fp16 = const()[name = string("acc_115_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_115_cast_fp16 = batch_norm(beta = acc_115_beta_0_to_fp16, epsilon = acc_115_epsilon_0_to_fp16, gamma = acc_115_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_39_cast_fp16)[name = string("acc_115_cast_fp16")]; + tensor var_3465_cast_fp16 = mul(x = acc_115_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3465_cast_fp16")]; + tensor c_77_to_fp16 = const()[name = string("c_77_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689472)))]; + tensor acc_117_cast_fp16 = add(x = var_3465_cast_fp16, y = c_77_to_fp16)[name = string("acc_117_cast_fp16")]; + tensor var_3467_cast_fp16 = mul(x = acc_117_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3467_cast_fp16")]; + tensor c_79_to_fp16 = const()[name = string("c_79_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114689920)))]; + tensor acc_119_cast_fp16 = add(x = var_3467_cast_fp16, y = c_79_to_fp16)[name = string("acc_119_cast_fp16")]; + tensor var_3469_cast_fp16 = mul(x = acc_119_cast_fp16, y = reduced_sq_39_cast_fp16)[name = string("op_3469_cast_fp16")]; + tensor hidden_states_177_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = var_3469_cast_fp16)[name = string("hidden_states_177_cast_fp16")]; + bool full_mask_29_interleave_0 = const()[name = string("full_mask_29_interleave_0"), val = bool(false)]; + tensor full_mask_29_cast_fp16 = concat(axis = var_2037, interleave = full_mask_29_interleave_0, values = (context_mask_39_cast_fp16, fill_12_to_fp16_palettized))[name = string("full_mask_29_cast_fp16")]; + tensor input_171_cast_fp16 = mul(x = hidden_states_177_cast_fp16, y = full_mask_29_cast_fp16)[name = string("input_171_cast_fp16")]; + string hidden_states_179_pad_type_0 = const()[name = string("hidden_states_179_pad_type_0"), val = string("valid")]; + tensor hidden_states_179_dilations_0 = const()[name = string("hidden_states_179_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_179_strides_0 = const()[name = string("hidden_states_179_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_179_pad_0 = const()[name = string("hidden_states_179_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_179_groups_0 = const()[name = string("hidden_states_179_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114690368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114948480))))[name = string("audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949056)))]; + tensor hidden_states_179_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_179_dilations_0, groups = hidden_states_179_groups_0, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = hidden_states_179_strides_0, weight = audio_upsampler_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized, x = input_171_cast_fp16)[name = string("hidden_states_179_cast_fp16")]; + tensor alpha_over_pi_41_to_fp16 = const()[name = string("alpha_over_pi_41_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949504)))]; + tensor theta_over_pi_41_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = alpha_over_pi_41_to_fp16)[name = string("theta_over_pi_41_cast_fp16")]; + tensor var_3506_cast_fp16 = round(x = theta_over_pi_41_cast_fp16)[name = string("op_3506_cast_fp16")]; + tensor reduced_41_cast_fp16 = sub(x = theta_over_pi_41_cast_fp16, y = var_3506_cast_fp16)[name = string("reduced_41_cast_fp16")]; + tensor reduced_sq_41_cast_fp16 = mul(x = reduced_41_cast_fp16, y = reduced_41_cast_fp16)[name = string("reduced_sq_41_cast_fp16")]; + tensor acc_121_gamma_0_to_fp16 = const()[name = string("acc_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114949952)))]; + tensor acc_121_beta_0_to_fp16 = const()[name = string("acc_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114950400)))]; + fp16 acc_121_epsilon_0_to_fp16 = const()[name = string("acc_121_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_121_cast_fp16 = batch_norm(beta = acc_121_beta_0_to_fp16, epsilon = acc_121_epsilon_0_to_fp16, gamma = acc_121_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_41_cast_fp16)[name = string("acc_121_cast_fp16")]; + tensor var_3519_cast_fp16 = mul(x = acc_121_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3519_cast_fp16")]; + tensor c_81_to_fp16 = const()[name = string("c_81_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114950848)))]; + tensor acc_123_cast_fp16 = add(x = var_3519_cast_fp16, y = c_81_to_fp16)[name = string("acc_123_cast_fp16")]; + tensor var_3521_cast_fp16 = mul(x = acc_123_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3521_cast_fp16")]; + tensor c_83_to_fp16 = const()[name = string("c_83_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114951296)))]; + tensor acc_125_cast_fp16 = add(x = var_3521_cast_fp16, y = c_83_to_fp16)[name = string("acc_125_cast_fp16")]; + tensor var_3523_cast_fp16 = mul(x = acc_125_cast_fp16, y = reduced_sq_41_cast_fp16)[name = string("op_3523_cast_fp16")]; + tensor hidden_states_181_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = var_3523_cast_fp16)[name = string("hidden_states_181_cast_fp16")]; + string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")]; + tensor hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114951744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114988672))))[name = string("audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114989248)))]; + tensor hidden_states_183_cast_fp16 = conv(bias = audio_upsampler_decoder_3_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = audio_upsampler_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_181_cast_fp16)[name = string("hidden_states_183_cast_fp16")]; + tensor hidden_states_185_cast_fp16 = add(x = hidden_states_183_cast_fp16, y = residual_19_cast_fp16)[name = string("hidden_states_185_cast_fp16")]; + tensor context_mask_43_begin_0 = const()[name = string("context_mask_43_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_43_end_0 = const()[name = string("context_mask_43_end_0"), val = tensor([1, 1, 1, 108])]; + tensor context_mask_43_end_mask_0 = const()[name = string("context_mask_43_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_43_cast_fp16 = slice_by_index(begin = context_mask_43_begin_0, end = context_mask_43_end_0, end_mask = context_mask_43_end_mask_0, x = context_mask_35_cast_fp16)[name = string("context_mask_43_cast_fp16")]; + tensor hidden_states_187_begin_0 = const()[name = string("hidden_states_187_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor hidden_states_187_end_0 = const()[name = string("hidden_states_187_end_0"), val = tensor([1, 192, 1, 2590])]; + tensor hidden_states_187_end_mask_0 = const()[name = string("hidden_states_187_end_mask_0"), val = tensor([true, true, true, true])]; + tensor hidden_states_187_cast_fp16 = slice_by_index(begin = hidden_states_187_begin_0, end = hidden_states_187_end_0, end_mask = hidden_states_187_end_mask_0, x = hidden_states_185_cast_fp16)[name = string("hidden_states_187_cast_fp16")]; + tensor context_mask_45_begin_0 = const()[name = string("context_mask_45_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor context_mask_45_end_0 = const()[name = string("context_mask_45_end_0"), val = tensor([1, 1, 1, 30])]; + tensor context_mask_45_end_mask_0 = const()[name = string("context_mask_45_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_45_cast_fp16 = slice_by_index(begin = context_mask_45_begin_0, end = context_mask_45_end_0, end_mask = context_mask_45_end_mask_0, x = context_mask_43_cast_fp16)[name = string("context_mask_45_cast_fp16")]; + tensor alpha_over_pi_43_to_fp16 = const()[name = string("alpha_over_pi_43_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114989696)))]; + tensor theta_over_pi_43_cast_fp16 = mul(x = hidden_states_187_cast_fp16, y = alpha_over_pi_43_to_fp16)[name = string("theta_over_pi_43_cast_fp16")]; + tensor var_3583_cast_fp16 = round(x = theta_over_pi_43_cast_fp16)[name = string("op_3583_cast_fp16")]; + tensor reduced_43_cast_fp16 = sub(x = theta_over_pi_43_cast_fp16, y = var_3583_cast_fp16)[name = string("reduced_43_cast_fp16")]; + tensor reduced_sq_43_cast_fp16 = mul(x = reduced_43_cast_fp16, y = reduced_43_cast_fp16)[name = string("reduced_sq_43_cast_fp16")]; + tensor acc_127_gamma_0_to_fp16 = const()[name = string("acc_127_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114990144)))]; + tensor acc_127_beta_0_to_fp16 = const()[name = string("acc_127_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114990592)))]; + fp16 acc_127_epsilon_0_to_fp16 = const()[name = string("acc_127_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_127_cast_fp16 = batch_norm(beta = acc_127_beta_0_to_fp16, epsilon = acc_127_epsilon_0_to_fp16, gamma = acc_127_gamma_0_to_fp16, mean = acc_91_mean_0_to_fp16, variance = acc_91_variance_0_to_fp16, x = reduced_sq_43_cast_fp16)[name = string("acc_127_cast_fp16")]; + tensor var_3596_cast_fp16 = mul(x = acc_127_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3596_cast_fp16")]; + tensor c_85_to_fp16 = const()[name = string("c_85_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991040)))]; + tensor acc_129_cast_fp16 = add(x = var_3596_cast_fp16, y = c_85_to_fp16)[name = string("acc_129_cast_fp16")]; + tensor var_3598_cast_fp16 = mul(x = acc_129_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3598_cast_fp16")]; + tensor c_87_to_fp16 = const()[name = string("c_87_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991488)))]; + tensor acc_131_cast_fp16 = add(x = var_3598_cast_fp16, y = c_87_to_fp16)[name = string("acc_131_cast_fp16")]; + tensor var_3600_cast_fp16 = mul(x = acc_131_cast_fp16, y = reduced_sq_43_cast_fp16)[name = string("op_3600_cast_fp16")]; + tensor hidden_states_189_cast_fp16 = add(x = hidden_states_187_cast_fp16, y = var_3600_cast_fp16)[name = string("hidden_states_189_cast_fp16")]; + bool full_mask_31_interleave_0 = const()[name = string("full_mask_31_interleave_0"), val = bool(false)]; + tensor full_mask_31_cast_fp16 = concat(axis = var_2037, interleave = full_mask_31_interleave_0, values = (context_mask_45_cast_fp16, fill_12_to_fp16_palettized))[name = string("full_mask_31_cast_fp16")]; + tensor input_175_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = full_mask_31_cast_fp16)[name = string("input_175_cast_fp16")]; + string sub_pixels_31_pad_type_0 = const()[name = string("sub_pixels_31_pad_type_0"), val = string("valid")]; + tensor sub_pixels_31_strides_0 = const()[name = string("sub_pixels_31_strides_0"), val = tensor([1, 1])]; + tensor sub_pixels_31_pad_0 = const()[name = string("sub_pixels_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor sub_pixels_31_dilations_0 = const()[name = string("sub_pixels_31_dilations_0"), val = tensor([1, 1])]; + int32 sub_pixels_31_groups_0 = const()[name = string("sub_pixels_31_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114991936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115102592))))[name = string("audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115103168)))]; + tensor sub_pixels_31_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_1_conv_bias_to_fp16, dilations = sub_pixels_31_dilations_0, groups = sub_pixels_31_groups_0, pad = sub_pixels_31_pad_0, pad_type = sub_pixels_31_pad_type_0, strides = sub_pixels_31_strides_0, weight = audio_upsampler_decoder_4_block_1_conv_weight_to_fp16_palettized, x = input_175_cast_fp16)[name = string("sub_pixels_31_cast_fp16")]; + tensor var_3624 = const()[name = string("op_3624"), val = tensor([1, 3, 96, 2588])]; + tensor sub_pixels_33_cast_fp16 = reshape(shape = var_3624, x = sub_pixels_31_cast_fp16)[name = string("sub_pixels_33_cast_fp16")]; + tensor var_3626 = const()[name = string("op_3626"), val = tensor([0, 2, 3, 1])]; + tensor var_3631 = const()[name = string("op_3631"), val = tensor([1, 96, 1, 7764])]; + tensor sub_pixels_cast_fp16 = transpose(perm = var_3626, x = sub_pixels_33_cast_fp16)[name = string("transpose_0")]; + tensor hidden_states_191_cast_fp16 = reshape(shape = var_3631, x = sub_pixels_cast_fp16)[name = string("hidden_states_191_cast_fp16")]; + tensor newest_17_begin_0 = const()[name = string("newest_17_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor newest_17_end_0 = const()[name = string("newest_17_end_0"), val = tensor([1, 1, 1, 29])]; + tensor newest_17_end_mask_0 = const()[name = string("newest_17_end_mask_0"), val = tensor([true, true, true, true])]; + tensor newest_17_cast_fp16 = slice_by_index(begin = newest_17_begin_0, end = newest_17_end_0, end_mask = newest_17_end_mask_0, x = context_mask_45_cast_fp16)[name = string("newest_17_cast_fp16")]; + tensor var_3636 = const()[name = string("op_3636"), val = tensor([1, 1, 28, 1])]; + tensor var_3637_cast_fp16 = reshape(shape = var_3636, x = newest_17_cast_fp16)[name = string("op_3637_cast_fp16")]; + tensor spread_17_reps_0 = const()[name = string("spread_17_reps_0"), val = tensor([1, 1, 1, 3])]; + tensor spread_17_cast_fp16 = tile(reps = spread_17_reps_0, x = var_3637_cast_fp16)[name = string("spread_17_cast_fp16")]; + tensor var_3643 = const()[name = string("op_3643"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_47_cast_fp16 = reshape(shape = var_3643, x = spread_17_cast_fp16)[name = string("context_mask_47_cast_fp16")]; + tensor residual_21_begin_0 = const()[name = string("residual_21_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor residual_21_end_0 = const()[name = string("residual_21_end_0"), val = tensor([1, 96, 1, 7764])]; + tensor residual_21_end_mask_0 = const()[name = string("residual_21_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_21_cast_fp16 = slice_by_index(begin = residual_21_begin_0, end = residual_21_end_0, end_mask = residual_21_end_mask_0, x = hidden_states_191_cast_fp16)[name = string("residual_21_cast_fp16")]; + tensor alpha_over_pi_45_to_fp16 = const()[name = string("alpha_over_pi_45_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115103808)))]; + tensor theta_over_pi_45_cast_fp16 = mul(x = hidden_states_191_cast_fp16, y = alpha_over_pi_45_to_fp16)[name = string("theta_over_pi_45_cast_fp16")]; + tensor var_3666_cast_fp16 = round(x = theta_over_pi_45_cast_fp16)[name = string("op_3666_cast_fp16")]; + tensor reduced_45_cast_fp16 = sub(x = theta_over_pi_45_cast_fp16, y = var_3666_cast_fp16)[name = string("reduced_45_cast_fp16")]; + tensor reduced_sq_45_cast_fp16 = mul(x = reduced_45_cast_fp16, y = reduced_45_cast_fp16)[name = string("reduced_sq_45_cast_fp16")]; + tensor acc_133_mean_0_to_fp16 = const()[name = string("acc_133_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_133_variance_0_to_fp16 = const()[name = string("acc_133_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_133_gamma_0_to_fp16 = const()[name = string("acc_133_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104576)))]; + tensor acc_133_beta_0_to_fp16 = const()[name = string("acc_133_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104832)))]; + fp16 acc_133_epsilon_0_to_fp16 = const()[name = string("acc_133_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_133_cast_fp16 = batch_norm(beta = acc_133_beta_0_to_fp16, epsilon = acc_133_epsilon_0_to_fp16, gamma = acc_133_gamma_0_to_fp16, mean = acc_133_mean_0_to_fp16, variance = acc_133_variance_0_to_fp16, x = reduced_sq_45_cast_fp16)[name = string("acc_133_cast_fp16")]; + tensor var_3679_cast_fp16 = mul(x = acc_133_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3679_cast_fp16")]; + tensor c_89_to_fp16 = const()[name = string("c_89_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115105088)))]; + tensor acc_135_cast_fp16 = add(x = var_3679_cast_fp16, y = c_89_to_fp16)[name = string("acc_135_cast_fp16")]; + tensor var_3681_cast_fp16 = mul(x = acc_135_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3681_cast_fp16")]; + tensor c_91_to_fp16 = const()[name = string("c_91_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115105344)))]; + tensor acc_137_cast_fp16 = add(x = var_3681_cast_fp16, y = c_91_to_fp16)[name = string("acc_137_cast_fp16")]; + tensor var_3683_cast_fp16 = mul(x = acc_137_cast_fp16, y = reduced_sq_45_cast_fp16)[name = string("op_3683_cast_fp16")]; + tensor hidden_states_193_cast_fp16 = add(x = hidden_states_191_cast_fp16, y = var_3683_cast_fp16)[name = string("hidden_states_193_cast_fp16")]; + bool full_mask_33_interleave_0 = const()[name = string("full_mask_33_interleave_0"), val = bool(false)]; + tensor fill_16_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115349376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115348800))))[name = string("fill_16_to_fp16_palettized")]; + tensor full_mask_33_cast_fp16 = concat(axis = var_2037, interleave = full_mask_33_interleave_0, values = (context_mask_47_cast_fp16, fill_16_to_fp16_palettized))[name = string("full_mask_33_cast_fp16")]; + tensor input_177_cast_fp16 = mul(x = hidden_states_193_cast_fp16, y = full_mask_33_cast_fp16)[name = string("input_177_cast_fp16")]; + string hidden_states_195_pad_type_0 = const()[name = string("hidden_states_195_pad_type_0"), val = string("valid")]; + tensor hidden_states_195_strides_0 = const()[name = string("hidden_states_195_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_0 = const()[name = string("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_195_dilations_0 = const()[name = string("hidden_states_195_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_195_groups_0 = const()[name = string("hidden_states_195_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115109504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174080))))[name = string("audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174656)))]; + tensor hidden_states_195_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_2_conv1_conv_bias_to_fp16, dilations = hidden_states_195_dilations_0, groups = hidden_states_195_groups_0, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = hidden_states_195_strides_0, weight = audio_upsampler_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized, x = input_177_cast_fp16)[name = string("hidden_states_195_cast_fp16")]; + tensor alpha_over_pi_47_to_fp16 = const()[name = string("alpha_over_pi_47_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115174912)))]; + tensor theta_over_pi_47_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = alpha_over_pi_47_to_fp16)[name = string("theta_over_pi_47_cast_fp16")]; + tensor var_3720_cast_fp16 = round(x = theta_over_pi_47_cast_fp16)[name = string("op_3720_cast_fp16")]; + tensor reduced_47_cast_fp16 = sub(x = theta_over_pi_47_cast_fp16, y = var_3720_cast_fp16)[name = string("reduced_47_cast_fp16")]; + tensor reduced_sq_47_cast_fp16 = mul(x = reduced_47_cast_fp16, y = reduced_47_cast_fp16)[name = string("reduced_sq_47_cast_fp16")]; + tensor acc_139_mean_0_to_fp16 = const()[name = string("acc_139_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_139_variance_0_to_fp16 = const()[name = string("acc_139_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_139_gamma_0_to_fp16 = const()[name = string("acc_139_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175168)))]; + tensor acc_139_beta_0_to_fp16 = const()[name = string("acc_139_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175424)))]; + fp16 acc_139_epsilon_0_to_fp16 = const()[name = string("acc_139_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_139_cast_fp16 = batch_norm(beta = acc_139_beta_0_to_fp16, epsilon = acc_139_epsilon_0_to_fp16, gamma = acc_139_gamma_0_to_fp16, mean = acc_139_mean_0_to_fp16, variance = acc_139_variance_0_to_fp16, x = reduced_sq_47_cast_fp16)[name = string("acc_139_cast_fp16")]; + tensor var_3733_cast_fp16 = mul(x = acc_139_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3733_cast_fp16")]; + tensor c_93_to_fp16 = const()[name = string("c_93_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175680)))]; + tensor acc_141_cast_fp16 = add(x = var_3733_cast_fp16, y = c_93_to_fp16)[name = string("acc_141_cast_fp16")]; + tensor var_3735_cast_fp16 = mul(x = acc_141_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3735_cast_fp16")]; + tensor c_95_to_fp16 = const()[name = string("c_95_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115175936)))]; + tensor acc_143_cast_fp16 = add(x = var_3735_cast_fp16, y = c_95_to_fp16)[name = string("acc_143_cast_fp16")]; + tensor var_3737_cast_fp16 = mul(x = acc_143_cast_fp16, y = reduced_sq_47_cast_fp16)[name = string("op_3737_cast_fp16")]; + tensor hidden_states_197_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = var_3737_cast_fp16)[name = string("hidden_states_197_cast_fp16")]; + string hidden_states_199_pad_type_0 = const()[name = string("hidden_states_199_pad_type_0"), val = string("valid")]; + tensor hidden_states_199_strides_0 = const()[name = string("hidden_states_199_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_0 = const()[name = string("hidden_states_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_199_dilations_0 = const()[name = string("hidden_states_199_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_199_groups_0 = const()[name = string("hidden_states_199_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115176192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115185472))))[name = string("audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186048)))]; + tensor hidden_states_199_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_2_conv2_conv_bias_to_fp16, dilations = hidden_states_199_dilations_0, groups = hidden_states_199_groups_0, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = hidden_states_199_strides_0, weight = audio_upsampler_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")]; + tensor hidden_states_201_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = residual_21_cast_fp16)[name = string("hidden_states_201_cast_fp16")]; + tensor context_mask_49_begin_0 = const()[name = string("context_mask_49_begin_0"), val = tensor([0, 0, 0, 6])]; + tensor context_mask_49_end_0 = const()[name = string("context_mask_49_end_0"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_49_end_mask_0 = const()[name = string("context_mask_49_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_49_cast_fp16 = slice_by_index(begin = context_mask_49_begin_0, end = context_mask_49_end_0, end_mask = context_mask_49_end_mask_0, x = context_mask_47_cast_fp16)[name = string("context_mask_49_cast_fp16")]; + tensor residual_23_begin_0 = const()[name = string("residual_23_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor residual_23_end_0 = const()[name = string("residual_23_end_0"), val = tensor([1, 96, 1, 7758])]; + tensor residual_23_end_mask_0 = const()[name = string("residual_23_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_23_cast_fp16 = slice_by_index(begin = residual_23_begin_0, end = residual_23_end_0, end_mask = residual_23_end_mask_0, x = hidden_states_201_cast_fp16)[name = string("residual_23_cast_fp16")]; + tensor alpha_over_pi_49_to_fp16 = const()[name = string("alpha_over_pi_49_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186304)))]; + tensor theta_over_pi_49_cast_fp16 = mul(x = hidden_states_201_cast_fp16, y = alpha_over_pi_49_to_fp16)[name = string("theta_over_pi_49_cast_fp16")]; + tensor var_3772_cast_fp16 = round(x = theta_over_pi_49_cast_fp16)[name = string("op_3772_cast_fp16")]; + tensor reduced_49_cast_fp16 = sub(x = theta_over_pi_49_cast_fp16, y = var_3772_cast_fp16)[name = string("reduced_49_cast_fp16")]; + tensor reduced_sq_49_cast_fp16 = mul(x = reduced_49_cast_fp16, y = reduced_49_cast_fp16)[name = string("reduced_sq_49_cast_fp16")]; + tensor acc_145_mean_0_to_fp16 = const()[name = string("acc_145_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_145_variance_0_to_fp16 = const()[name = string("acc_145_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_145_gamma_0_to_fp16 = const()[name = string("acc_145_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186560)))]; + tensor acc_145_beta_0_to_fp16 = const()[name = string("acc_145_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115186816)))]; + fp16 acc_145_epsilon_0_to_fp16 = const()[name = string("acc_145_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_145_cast_fp16 = batch_norm(beta = acc_145_beta_0_to_fp16, epsilon = acc_145_epsilon_0_to_fp16, gamma = acc_145_gamma_0_to_fp16, mean = acc_145_mean_0_to_fp16, variance = acc_145_variance_0_to_fp16, x = reduced_sq_49_cast_fp16)[name = string("acc_145_cast_fp16")]; + tensor var_3785_cast_fp16 = mul(x = acc_145_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3785_cast_fp16")]; + tensor c_97_to_fp16 = const()[name = string("c_97_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187072)))]; + tensor acc_147_cast_fp16 = add(x = var_3785_cast_fp16, y = c_97_to_fp16)[name = string("acc_147_cast_fp16")]; + tensor var_3787_cast_fp16 = mul(x = acc_147_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3787_cast_fp16")]; + tensor c_99_to_fp16 = const()[name = string("c_99_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187328)))]; + tensor acc_149_cast_fp16 = add(x = var_3787_cast_fp16, y = c_99_to_fp16)[name = string("acc_149_cast_fp16")]; + tensor var_3789_cast_fp16 = mul(x = acc_149_cast_fp16, y = reduced_sq_49_cast_fp16)[name = string("op_3789_cast_fp16")]; + tensor hidden_states_203_cast_fp16 = add(x = hidden_states_201_cast_fp16, y = var_3789_cast_fp16)[name = string("hidden_states_203_cast_fp16")]; + bool full_mask_35_interleave_0 = const()[name = string("full_mask_35_interleave_0"), val = bool(false)]; + tensor full_mask_35_cast_fp16 = concat(axis = var_2037, interleave = full_mask_35_interleave_0, values = (context_mask_49_cast_fp16, fill_16_to_fp16_palettized))[name = string("full_mask_35_cast_fp16")]; + tensor input_181_cast_fp16 = mul(x = hidden_states_203_cast_fp16, y = full_mask_35_cast_fp16)[name = string("input_181_cast_fp16")]; + string hidden_states_205_pad_type_0 = const()[name = string("hidden_states_205_pad_type_0"), val = string("valid")]; + tensor hidden_states_205_dilations_0 = const()[name = string("hidden_states_205_dilations_0"), val = tensor([1, 3])]; + tensor hidden_states_205_strides_0 = const()[name = string("hidden_states_205_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_0 = const()[name = string("hidden_states_205_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_205_groups_0 = const()[name = string("hidden_states_205_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115187584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252160))))[name = string("audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252736)))]; + tensor hidden_states_205_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_3_conv1_conv_bias_to_fp16, dilations = hidden_states_205_dilations_0, groups = hidden_states_205_groups_0, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = hidden_states_205_strides_0, weight = audio_upsampler_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("hidden_states_205_cast_fp16")]; + tensor alpha_over_pi_51_to_fp16 = const()[name = string("alpha_over_pi_51_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115252992)))]; + tensor theta_over_pi_51_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = alpha_over_pi_51_to_fp16)[name = string("theta_over_pi_51_cast_fp16")]; + tensor var_3826_cast_fp16 = round(x = theta_over_pi_51_cast_fp16)[name = string("op_3826_cast_fp16")]; + tensor reduced_51_cast_fp16 = sub(x = theta_over_pi_51_cast_fp16, y = var_3826_cast_fp16)[name = string("reduced_51_cast_fp16")]; + tensor reduced_sq_51_cast_fp16 = mul(x = reduced_51_cast_fp16, y = reduced_51_cast_fp16)[name = string("reduced_sq_51_cast_fp16")]; + tensor acc_151_mean_0_to_fp16 = const()[name = string("acc_151_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_151_variance_0_to_fp16 = const()[name = string("acc_151_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_151_gamma_0_to_fp16 = const()[name = string("acc_151_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253248)))]; + tensor acc_151_beta_0_to_fp16 = const()[name = string("acc_151_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253504)))]; + fp16 acc_151_epsilon_0_to_fp16 = const()[name = string("acc_151_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_151_cast_fp16 = batch_norm(beta = acc_151_beta_0_to_fp16, epsilon = acc_151_epsilon_0_to_fp16, gamma = acc_151_gamma_0_to_fp16, mean = acc_151_mean_0_to_fp16, variance = acc_151_variance_0_to_fp16, x = reduced_sq_51_cast_fp16)[name = string("acc_151_cast_fp16")]; + tensor var_3839_cast_fp16 = mul(x = acc_151_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3839_cast_fp16")]; + tensor c_101_to_fp16 = const()[name = string("c_101_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115253760)))]; + tensor acc_153_cast_fp16 = add(x = var_3839_cast_fp16, y = c_101_to_fp16)[name = string("acc_153_cast_fp16")]; + tensor var_3841_cast_fp16 = mul(x = acc_153_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3841_cast_fp16")]; + tensor c_103_to_fp16 = const()[name = string("c_103_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115254016)))]; + tensor acc_155_cast_fp16 = add(x = var_3841_cast_fp16, y = c_103_to_fp16)[name = string("acc_155_cast_fp16")]; + tensor var_3843_cast_fp16 = mul(x = acc_155_cast_fp16, y = reduced_sq_51_cast_fp16)[name = string("op_3843_cast_fp16")]; + tensor hidden_states_207_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = var_3843_cast_fp16)[name = string("hidden_states_207_cast_fp16")]; + string hidden_states_209_pad_type_0 = const()[name = string("hidden_states_209_pad_type_0"), val = string("valid")]; + tensor hidden_states_209_strides_0 = const()[name = string("hidden_states_209_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_209_pad_0 = const()[name = string("hidden_states_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_209_dilations_0 = const()[name = string("hidden_states_209_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_209_groups_0 = const()[name = string("hidden_states_209_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115254272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115357120))))[name = string("audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264128)))]; + tensor hidden_states_209_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_3_conv2_conv_bias_to_fp16, dilations = hidden_states_209_dilations_0, groups = hidden_states_209_groups_0, pad = hidden_states_209_pad_0, pad_type = hidden_states_209_pad_type_0, strides = hidden_states_209_strides_0, weight = audio_upsampler_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")]; + tensor hidden_states_211_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = residual_23_cast_fp16)[name = string("hidden_states_211_cast_fp16")]; + tensor context_mask_51_begin_0 = const()[name = string("context_mask_51_begin_0"), val = tensor([0, 0, 0, 18])]; + tensor context_mask_51_end_0 = const()[name = string("context_mask_51_end_0"), val = tensor([1, 1, 1, 78])]; + tensor context_mask_51_end_mask_0 = const()[name = string("context_mask_51_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_51_cast_fp16 = slice_by_index(begin = context_mask_51_begin_0, end = context_mask_51_end_0, end_mask = context_mask_51_end_mask_0, x = context_mask_49_cast_fp16)[name = string("context_mask_51_cast_fp16")]; + tensor residual_begin_0 = const()[name = string("residual_begin_0"), val = tensor([0, 0, 0, 54])]; + tensor residual_end_0 = const()[name = string("residual_end_0"), val = tensor([1, 96, 1, 7740])]; + tensor residual_end_mask_0 = const()[name = string("residual_end_mask_0"), val = tensor([true, true, true, true])]; + tensor residual_cast_fp16 = slice_by_index(begin = residual_begin_0, end = residual_end_0, end_mask = residual_end_mask_0, x = hidden_states_211_cast_fp16)[name = string("residual_cast_fp16")]; + tensor alpha_over_pi_53_to_fp16 = const()[name = string("alpha_over_pi_53_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264384)))]; + tensor theta_over_pi_53_cast_fp16 = mul(x = hidden_states_211_cast_fp16, y = alpha_over_pi_53_to_fp16)[name = string("theta_over_pi_53_cast_fp16")]; + tensor var_3878_cast_fp16 = round(x = theta_over_pi_53_cast_fp16)[name = string("op_3878_cast_fp16")]; + tensor reduced_53_cast_fp16 = sub(x = theta_over_pi_53_cast_fp16, y = var_3878_cast_fp16)[name = string("reduced_53_cast_fp16")]; + tensor reduced_sq_53_cast_fp16 = mul(x = reduced_53_cast_fp16, y = reduced_53_cast_fp16)[name = string("reduced_sq_53_cast_fp16")]; + tensor acc_157_mean_0_to_fp16 = const()[name = string("acc_157_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_157_variance_0_to_fp16 = const()[name = string("acc_157_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_157_gamma_0_to_fp16 = const()[name = string("acc_157_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264640)))]; + tensor acc_157_beta_0_to_fp16 = const()[name = string("acc_157_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115264896)))]; + fp16 acc_157_epsilon_0_to_fp16 = const()[name = string("acc_157_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_157_cast_fp16 = batch_norm(beta = acc_157_beta_0_to_fp16, epsilon = acc_157_epsilon_0_to_fp16, gamma = acc_157_gamma_0_to_fp16, mean = acc_157_mean_0_to_fp16, variance = acc_157_variance_0_to_fp16, x = reduced_sq_53_cast_fp16)[name = string("acc_157_cast_fp16")]; + tensor var_3891_cast_fp16 = mul(x = acc_157_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3891_cast_fp16")]; + tensor c_105_to_fp16 = const()[name = string("c_105_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265152)))]; + tensor acc_159_cast_fp16 = add(x = var_3891_cast_fp16, y = c_105_to_fp16)[name = string("acc_159_cast_fp16")]; + tensor var_3893_cast_fp16 = mul(x = acc_159_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3893_cast_fp16")]; + tensor c_107_to_fp16 = const()[name = string("c_107_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265408)))]; + tensor acc_161_cast_fp16 = add(x = var_3893_cast_fp16, y = c_107_to_fp16)[name = string("acc_161_cast_fp16")]; + tensor var_3895_cast_fp16 = mul(x = acc_161_cast_fp16, y = reduced_sq_53_cast_fp16)[name = string("op_3895_cast_fp16")]; + tensor hidden_states_213_cast_fp16 = add(x = hidden_states_211_cast_fp16, y = var_3895_cast_fp16)[name = string("hidden_states_213_cast_fp16")]; + bool full_mask_37_interleave_0 = const()[name = string("full_mask_37_interleave_0"), val = bool(false)]; + tensor full_mask_37_cast_fp16 = concat(axis = var_2037, interleave = full_mask_37_interleave_0, values = (context_mask_51_cast_fp16, fill_16_to_fp16_palettized))[name = string("full_mask_37_cast_fp16")]; + tensor input_185_cast_fp16 = mul(x = hidden_states_213_cast_fp16, y = full_mask_37_cast_fp16)[name = string("input_185_cast_fp16")]; + string hidden_states_215_pad_type_0 = const()[name = string("hidden_states_215_pad_type_0"), val = string("valid")]; + tensor hidden_states_215_dilations_0 = const()[name = string("hidden_states_215_dilations_0"), val = tensor([1, 9])]; + tensor hidden_states_215_strides_0 = const()[name = string("hidden_states_215_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_0 = const()[name = string("hidden_states_215_pad_0"), val = tensor([0, 0, 0, 0])]; + int32 hidden_states_215_groups_0 = const()[name = string("hidden_states_215_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115265664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115330240))))[name = string("audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115330816)))]; + tensor hidden_states_215_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_4_conv1_conv_bias_to_fp16, dilations = hidden_states_215_dilations_0, groups = hidden_states_215_groups_0, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = hidden_states_215_strides_0, weight = audio_upsampler_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("hidden_states_215_cast_fp16")]; + tensor alpha_over_pi_55_to_fp16 = const()[name = string("alpha_over_pi_55_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331072)))]; + tensor theta_over_pi_55_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = alpha_over_pi_55_to_fp16)[name = string("theta_over_pi_55_cast_fp16")]; + tensor var_3932_cast_fp16 = round(x = theta_over_pi_55_cast_fp16)[name = string("op_3932_cast_fp16")]; + tensor reduced_55_cast_fp16 = sub(x = theta_over_pi_55_cast_fp16, y = var_3932_cast_fp16)[name = string("reduced_55_cast_fp16")]; + tensor reduced_sq_55_cast_fp16 = mul(x = reduced_55_cast_fp16, y = reduced_55_cast_fp16)[name = string("reduced_sq_55_cast_fp16")]; + tensor acc_163_mean_0_to_fp16 = const()[name = string("acc_163_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_163_variance_0_to_fp16 = const()[name = string("acc_163_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_163_gamma_0_to_fp16 = const()[name = string("acc_163_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331328)))]; + tensor acc_163_beta_0_to_fp16 = const()[name = string("acc_163_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331584)))]; + fp16 acc_163_epsilon_0_to_fp16 = const()[name = string("acc_163_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_163_cast_fp16 = batch_norm(beta = acc_163_beta_0_to_fp16, epsilon = acc_163_epsilon_0_to_fp16, gamma = acc_163_gamma_0_to_fp16, mean = acc_163_mean_0_to_fp16, variance = acc_163_variance_0_to_fp16, x = reduced_sq_55_cast_fp16)[name = string("acc_163_cast_fp16")]; + tensor var_3945_cast_fp16 = mul(x = acc_163_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3945_cast_fp16")]; + tensor c_109_to_fp16 = const()[name = string("c_109_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115331840)))]; + tensor acc_165_cast_fp16 = add(x = var_3945_cast_fp16, y = c_109_to_fp16)[name = string("acc_165_cast_fp16")]; + tensor var_3947_cast_fp16 = mul(x = acc_165_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3947_cast_fp16")]; + tensor c_111_to_fp16 = const()[name = string("c_111_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115332096)))]; + tensor acc_167_cast_fp16 = add(x = var_3947_cast_fp16, y = c_111_to_fp16)[name = string("acc_167_cast_fp16")]; + tensor var_3949_cast_fp16 = mul(x = acc_167_cast_fp16, y = reduced_sq_55_cast_fp16)[name = string("op_3949_cast_fp16")]; + tensor hidden_states_217_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = var_3949_cast_fp16)[name = string("hidden_states_217_cast_fp16")]; + string hidden_states_219_pad_type_0 = const()[name = string("hidden_states_219_pad_type_0"), val = string("valid")]; + tensor hidden_states_219_strides_0 = const()[name = string("hidden_states_219_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_0 = const()[name = string("hidden_states_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_219_dilations_0 = const()[name = string("hidden_states_219_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_219_groups_0 = const()[name = string("hidden_states_219_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115332352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115357696))))[name = string("audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized")]; + tensor audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342208)))]; + tensor hidden_states_219_cast_fp16 = conv(bias = audio_upsampler_decoder_4_block_4_conv2_conv_bias_to_fp16, dilations = hidden_states_219_dilations_0, groups = hidden_states_219_groups_0, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = hidden_states_219_strides_0, weight = audio_upsampler_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")]; + tensor hidden_states_221_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = residual_cast_fp16)[name = string("hidden_states_221_cast_fp16")]; + tensor context_mask_begin_0 = const()[name = string("context_mask_begin_0"), val = tensor([0, 0, 0, 78])]; + tensor context_mask_end_0 = const()[name = string("context_mask_end_0"), val = tensor([1, 1, 1, 84])]; + tensor context_mask_end_mask_0 = const()[name = string("context_mask_end_mask_0"), val = tensor([true, true, true, true])]; + tensor context_mask_cast_fp16 = slice_by_index(begin = context_mask_begin_0, end = context_mask_end_0, end_mask = context_mask_end_mask_0, x = context_mask_47_cast_fp16)[name = string("context_mask_cast_fp16")]; + tensor alpha_over_pi_to_fp16 = const()[name = string("alpha_over_pi_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342464)))]; + tensor theta_over_pi_cast_fp16 = mul(x = hidden_states_221_cast_fp16, y = alpha_over_pi_to_fp16)[name = string("theta_over_pi_cast_fp16")]; + tensor var_3991_cast_fp16 = round(x = theta_over_pi_cast_fp16)[name = string("op_3991_cast_fp16")]; + tensor reduced_cast_fp16 = sub(x = theta_over_pi_cast_fp16, y = var_3991_cast_fp16)[name = string("reduced_cast_fp16")]; + tensor reduced_sq_cast_fp16 = mul(x = reduced_cast_fp16, y = reduced_cast_fp16)[name = string("reduced_sq_cast_fp16")]; + tensor acc_169_mean_0_to_fp16 = const()[name = string("acc_169_mean_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104064)))]; + tensor acc_169_variance_0_to_fp16 = const()[name = string("acc_169_variance_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115104320)))]; + tensor acc_169_gamma_0_to_fp16 = const()[name = string("acc_169_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342720)))]; + tensor acc_169_beta_0_to_fp16 = const()[name = string("acc_169_beta_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115342976)))]; + fp16 acc_169_epsilon_0_to_fp16 = const()[name = string("acc_169_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; + tensor acc_169_cast_fp16 = batch_norm(beta = acc_169_beta_0_to_fp16, epsilon = acc_169_epsilon_0_to_fp16, gamma = acc_169_gamma_0_to_fp16, mean = acc_169_mean_0_to_fp16, variance = acc_169_variance_0_to_fp16, x = reduced_sq_cast_fp16)[name = string("acc_169_cast_fp16")]; + tensor var_4004_cast_fp16 = mul(x = acc_169_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_4004_cast_fp16")]; + tensor c_113_to_fp16 = const()[name = string("c_113_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343232)))]; + tensor acc_171_cast_fp16 = add(x = var_4004_cast_fp16, y = c_113_to_fp16)[name = string("acc_171_cast_fp16")]; + tensor var_4006_cast_fp16 = mul(x = acc_171_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_4006_cast_fp16")]; + tensor c_to_fp16 = const()[name = string("c_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343488)))]; + tensor acc_cast_fp16 = add(x = var_4006_cast_fp16, y = c_to_fp16)[name = string("acc_cast_fp16")]; + tensor var_4008_cast_fp16 = mul(x = acc_cast_fp16, y = reduced_sq_cast_fp16)[name = string("op_4008_cast_fp16")]; + tensor hidden_states_223_cast_fp16 = add(x = hidden_states_221_cast_fp16, y = var_4008_cast_fp16)[name = string("hidden_states_223_cast_fp16")]; + bool full_mask_interleave_0 = const()[name = string("full_mask_interleave_0"), val = bool(false)]; + tensor full_mask_cast_fp16 = concat(axis = var_2037, interleave = full_mask_interleave_0, values = (context_mask_cast_fp16, fill_16_to_fp16_palettized))[name = string("full_mask_cast_fp16")]; + tensor input_cast_fp16 = mul(x = hidden_states_223_cast_fp16, y = full_mask_cast_fp16)[name = string("input_cast_fp16")]; + string hidden_states_pad_type_0 = const()[name = string("hidden_states_pad_type_0"), val = string("valid")]; + tensor hidden_states_strides_0 = const()[name = string("hidden_states_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_pad_0 = const()[name = string("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_dilations_0 = const()[name = string("hidden_states_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_groups_0 = const()[name = string("hidden_states_groups_0"), val = int32(1)]; + tensor audio_upsampler_decoder_6_conv_weight_to_fp16 = const()[name = string("audio_upsampler_decoder_6_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115343744)))]; + tensor audio_upsampler_decoder_6_conv_bias_to_fp16 = const()[name = string("audio_upsampler_decoder_6_conv_bias_to_fp16"), val = tensor([-0x1.1p-19])]; + tensor hidden_states_cast_fp16 = conv(bias = audio_upsampler_decoder_6_conv_bias_to_fp16, dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = audio_upsampler_decoder_6_conv_weight_to_fp16, x = input_cast_fp16)[name = string("hidden_states_cast_fp16")]; + fp16 var_2024_to_fp16 = const()[name = string("op_2024_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_2023_to_fp16 = const()[name = string("op_2023_to_fp16"), val = fp16(0x1p+0)]; + tensor audio = clip(alpha = var_2024_to_fp16, beta = var_2023_to_fp16, x = hidden_states_cast_fp16)[name = string("clip_16_cast_fp16")]; + } -> (audio, key_cache_updates, value_cache_updates, hidden_context_update, pre_conv_context_update); +} \ No newline at end of file diff --git a/qwen3_tts/speech_decoder/12hz-1.7b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/weights/weight.bin b/qwen3_tts/speech_decoder/12hz-1.7b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..79d225c5dd3e3011574b6d1f79e30077495ede3b --- /dev/null +++ b/qwen3_tts/speech_decoder/12hz-1.7b-customvoice/W8A16-stream-multifunction/SpeechDecoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:827114b7e98262297d55ed977405a56e3a26f431c22968e31604a3ce7d7dec50 +size 115358272