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"fixtureArrays": {
"same_upper_stride2_autopad_input_x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
"backend_qlinearconv_pointwise_u8_input_x": [255, 174, 162, 25, 203, 168, 58, 15, 59, 237, 95, 129, 0, 64, 56, 242, 153, 221, 168, 12, 166, 232, 178, 186, 195, 237, 162, 237, 188, 39, 124, 77, 80, 102, 43, 127, 230, 21, 83, 41, 40, 134, 255, 154, 92, 141, 42, 148, 247],
"ort_depthwise_per_channel_weight_zero_points_s8s8_input_x": [-8, -4, 0, 4, 8, 12, -12, 16, -16, 3, -3, 6, -6, 9, -9, 12, -12, 15, -20, -10, 0, 10, 20, 30, -30, 40, -40],
"ort_depthwise_per_channel_weight_zero_points_s8s8_input_w": [-10, -8, -6, -4, -2, 0, 2, 4, 6, 3, 5, 7, 9, 11, 13, 15, 17, 19, -20, -15, -10, -5, 0, 5, 10, 15, 20],
"dp4a_pointwise_u8s8_c8_batched_input_w": [-77, 3, 100, -100, 42, -5, 19, -64, 88, -33, 7, 125, -90, -128, 127, 0, -1, 56],
"dp4a_pointwise_s8s8_c12_signed_output_input_x": [-128, 127, 0, -1, 56, -77, 3, 100, -100, 42, -5, 19, -64, 88, -33, 7, 125, -90]
},
"cases": [
{
"name": "same_upper_stride2_autopad",
"provenance": {
"source": "ONNX Runtime's CPU provider (QLinearConv-10)",
"notes": "QLinearConv inherits Conv auto_pad semantics. This fixture exercises the exact SAME_UPPER spelling and derived asymmetric bottom/right padding; unit scales and zero points make the expected requantized values equal the integer convolution sums."
},
"attrs": { "auto_pad": "SAME_UPPER", "kernel_shape": [3, 3], "strides": [2, 2] },
"inputs": {
"x": {
"dtype": "uint8",
"shape": [1, 1, 4, 4],
"data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/same_upper_stride2_autopad_input_x" } }
},
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"w": { "dtype": "uint8", "shape": [1, 1, 3, 3], "data": { "kind": "constant", "value": 1 } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"w_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [0] } }
},
"outputs": {
"y": {
"dtype": "uint8",
"shape": [1, 1, 2, 2],
"data": { "kind": "values", "values": [54, 45, 72, 54] },
"tolerance": 0
}
}
},
{
"name": "dispatch_cliff_pointwise_u8",
"inputs": {
"x": {
"dtype": "uint8",
"shape": [1, 1, 4096, 4097],
"data": { "kind": "cycle", "values": [120, 128, 136, 144] }
},
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.1] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } },
"w": { "dtype": "uint8", "shape": [1, 1, 1, 1], "data": { "kind": "values", "values": [130] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.2] } },
"w_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.02] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [127] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 1, 4096, 4097], "tolerance": 0 } },
"attrs": {}
},
{
"name": "uint8_padding",
"attrs": { "strides": [1, 1], "pads": [1, 1, 1, 1] },
"inputs": {
"x": {
"dtype": "uint8",
"shape": [1, 1, 3, 3],
"data": { "kind": "values", "values": [128, 129, 130, 131, 132, 133, 134, 135, 136] }
},
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.5] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } },
"w": { "dtype": "uint8", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [127, 128, 129, 130] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.25] } },
"w_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.125] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 1, 4, 4] } }
},
{
"name": "int8_output",
"inputs": {
"x": {
"dtype": "int8",
"shape": [1, 1, 3, 3],
"data": { "kind": "values", "values": [-4, 3, -2, 1, 0, -1, 2, -3, 4] }
},
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.5] } },
"x_zero_point": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"w": { "dtype": "int8", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [1, -1, 2, -2] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.25] } },
"w_zero_point": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.25] } },
"y_zero_point": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [-3] } }
},
"outputs": { "y": { "dtype": "int8", "shape": [1, 1, 2, 2] } },
"attrs": {},
"provenance": {
"notes": "Signed output storage with a nonzero output zero point. The input is not a ramp, so the 2x2 kernel gives each output position its own response."
}
},
{
"name": "int8_input_uint8_output_pairwise_route",
"provenance": {
"notes": "Pairs the signed input route with the independently typed uint8 output on a compact 1-D pointwise convolution."
},
"inputs": {
"x": { "dtype": "int8", "shape": [1, 1, 1], "data": { "kind": "values", "values": [2] } },
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.5] } },
"x_zero_point": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"w": { "dtype": "int8", "shape": [1, 1, 1], "data": { "kind": "values", "values": [3] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.25] } },
"w_zero_point": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.25] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 1, 1], "tolerance": 0 } }
},
{
"name": "requant_exact_half_ties_to_even",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_U8S8_Requantize_NoBias",
"notes": "ONNX quantized convolution requantization uses ORT's RoundHalfToEven helper; pointwise accumulators +/-1 and +/-5 with y_scale=2 produce exact +/-0.5 and +/-2.5 tie values."
},
"inputs": {
"x": { "dtype": "uint8", "shape": [1, 1, 1, 1], "data": { "kind": "values", "values": [1] } },
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"w": { "dtype": "int8", "shape": [4, 1, 1, 1], "data": { "kind": "values", "values": [1, -1, 5, -5] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"w_zero_point": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [2.0] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } }
},
"outputs": {
"y": {
"dtype": "uint8",
"shape": [1, 4, 1, 1],
"tolerance": 0,
"data": { "kind": "values", "values": [128, 128, 130, 126] }
}
},
"attrs": {}
},
{
"name": "requant_huge_finite_saturates_before_i32_overflow",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_U8S8_Pointwise",
"notes": "Finite requantized outputs far outside uint8 must saturate; the scalar WebGPU requant pass currently converts to i32 before clamping."
},
"inputs": {
"x": { "dtype": "uint8", "shape": [1, 1, 1, 1], "data": { "kind": "values", "values": [1] } },
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"w": { "dtype": "int8", "shape": [2, 1, 1, 1], "data": { "kind": "values", "values": [1, -1] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"w_zero_point": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1e-20] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } }
},
"outputs": {
"y": {
"dtype": "uint8",
"shape": [1, 2, 1, 1],
"tolerance": 0,
"data": { "kind": "values", "values": [255, 0] }
}
},
"attrs": {}
},
{
"name": "requant_subnormal_y_scale_saturates",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_U8S8_Pointwise",
"notes": "A valid positive subnormal output scale can make a one-pixel convolution requantize beyond uint8; the result should saturate before any i32 conversion of an infinite f32."
},
"inputs": {
"x": { "dtype": "uint8", "shape": [1, 1, 1, 1], "data": { "kind": "values", "values": [1] } },
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"w": { "dtype": "int8", "shape": [2, 1, 1, 1], "data": { "kind": "values", "values": [1, -1] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"w_zero_point": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1e-40] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } }
},
"outputs": {
"y": {
"dtype": "uint8",
"shape": [1, 2, 1, 1],
"tolerance": 0,
"data": { "kind": "values", "values": [255, 0] }
}
},
"attrs": {}
},
{
"name": "dp4a_pointwise_requant_subnormal_y_scale_saturates",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_U8S8_Pointwise",
"notes": "Fused DP4A pointwise route with a valid positive subnormal output scale should saturate uint8 results before any i32 conversion of an infinite f32."
},
"inputs": {
"x": { "dtype": "uint8", "shape": [1, 4, 1, 1], "data": { "kind": "values", "values": [1, 0, 0, 0] } },
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"w": {
"dtype": "int8",
"shape": [2, 4, 1, 1],
"data": { "kind": "values", "values": [1, 0, 0, 0, -1, 0, 0, 0] }
},
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"w_zero_point": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1e-40] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } }
},
"outputs": {
"y": {
"dtype": "uint8",
"shape": [1, 2, 1, 1],
"tolerance": 0,
"data": { "kind": "values", "values": [255, 0] }
}
},
"attrs": {}
},
{
"name": "uint8_pointwise_subnormal_scale_ratio",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2DTest",
"notes": "Valid positive subnormal input/output scales whose ratio is exactly meaningful for scalar quantization."
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"inputs": {
"x": { "dtype": "uint8", "shape": [1, 4, 1, 1], "data": { "kind": "values", "values": [1, 0, 0, 0] } },
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1e-40] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"w": {
"dtype": "uint8",
"shape": [4, 4, 1, 1],
"data": { "kind": "values", "values": [1, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0, 4, 0, 0, 0] }
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"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1.0] } },
"w_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [1e-40] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [0] } }
},
"outputs": {
"y": {
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"shape": [1, 4, 1, 1],
"tolerance": 0,
"data": { "kind": "values", "values": [1, 2, 3, 4] }
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},
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},
{
"name": "backend_qlinearconv_pointwise_u8",
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"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2DTest",
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"inputs": {
"x": {
"dtype": "uint8",
"shape": [1, 1, 7, 7],
"data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/backend_qlinearconv_pointwise_u8_input_x" } }
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"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.00369204697] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [132] } },
"w": { "dtype": "uint8", "shape": [1, 1, 1, 1], "data": { "kind": "values", "values": [0] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.00172794575] } },
"w_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [255] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.00162681262] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [123] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 1, 7, 7], "tolerance": 0 } },
"attrs": {}
},
{
"name": "group2_depthwise_scalar_quant_u8",
"attrs": { "group": 2 },
"inputs": {
"x": {
"dtype": "uint8",
"shape": [1, 2, 2, 2],
"data": { "kind": "values", "values": [128, 129, 130, 131, 120, 124, 128, 132] }
},
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.1] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } },
"w": { "dtype": "uint8", "shape": [2, 1, 1, 1], "data": { "kind": "values", "values": [130, 126] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.2] } },
"w_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.02] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [127] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 2, 2, 2], "tolerance": 0 } }
},
{
"name": "dilated_stride2_padding_saturates_u8",
"attrs": { "strides": [2, 2], "dilations": [2, 2], "pads": [1, 1, 1, 1] },
"inputs": {
"x": {
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"shape": [1, 1, 5, 5],
"data": {
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"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.05] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } },
"w": { "dtype": "uint8", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [0, 255, 255, 0] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.04] } },
"w_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.001] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 1, 3, 3], "tolerance": 0 } }
},
{
"name": "ort_style_multi_channel_four_outputs_pad1_u8",
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"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.WithBias_2D",
"notes": "Adapts the handwritten 2D multi-channel/multi-output QLinearConv shape without optional bias."
},
"attrs": { "pads": [1, 1, 1, 1] },
"inputs": {
"x": {
"dtype": "uint8",
"shape": [1, 2, 4, 4],
"data": { "kind": "cycle", "values": [135, 140, 128, 120, 150, 110, 160, 100] }
},
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.02] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } },
"w": {
"dtype": "uint8",
"shape": [4, 2, 3, 3],
"data": { "kind": "cycle", "values": [110, 115, 105, 130, 100, 125] }
},
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.03] } },
"w_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [110] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.02] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [121] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 4, 4, 4], "tolerance": 0 } }
},
{
"name": "ort_style_group3_stride2_pad1_u8",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.WithGroup_2D",
"notes": "Covers grouped convolution with group=3 and stride=2 using symmetric padding supported by the WebGPU manifest."
},
"attrs": { "group": 3, "strides": [2, 2], "pads": [1, 1, 1, 1] },
"inputs": {
"x": {
"dtype": "uint8",
"shape": [1, 6, 5, 5],
"data": { "kind": "cycle", "values": [98, 166, 219, 195, 46, 97, 27, 211, 239, 1] }
},
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.01] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [135] } },
"w": {
"dtype": "uint8",
"shape": [6, 2, 3, 3],
"data": { "kind": "cycle", "values": [220, 111, 73, 254, 235, 151, 6, 156, 129, 204, 234, 198] }
},
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.015] } },
"w_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [110] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.04] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [121] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 6, 3, 3], "tolerance": 0 } }
},
{
"name": "ort_with_group_2d_asymmetric_pad_no_bias_u8",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.WithGroup_2D",
"notes": "Uses ORT's grouped convolution tensors and asymmetric pads without optional bias."
},
"attrs": { "group": 3, "strides": [2, 2], "pads": [0, 0, 1, 1] },
"inputs": {
"x": {
"dtype": "uint8",
"shape": [1, 6, 3, 5],
"data": {
"kind": "values",
"values": [98, 166, 219, 195, 46, 97, 27, 211, 239, 1, 28, 208, 143, 144, 215, 252, 79, 5, 154, 56, 122, 191, 94, 25, 221, 48, 37, 182, 68, 245, 210, 206, 183, 22, 163, 104, 242, 112, 161, 66, 181, 235, 117, 75, 236, 61, 115, 36, 120, 253, 165, 214, 159, 132, 11, 201, 30, 249, 89, 171, 186, 67, 225, 197, 135, 142, 241, 169, 170, 164, 178, 58, 50, 51, 200, 43, 199, 126, 222, 123, 227, 42, 3, 21, 124, 220, 24, 47, 63, 110]
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},
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.01] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [135] } },
"w": {
"dtype": "uint8",
"shape": [6, 2, 2, 2],
"data": {
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},
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.15] } },
"w_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [110] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.75] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [121] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 6, 2, 3], "tolerance": 0 } }
},
{
"name": "mixed_u8_i8_uint8_output_dilated_pad1",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_U8S8_Dilations",
"notes": "Exercises the ORT-covered mixed uint8 activation / int8 weight quantization path with dilation."
},
"attrs": { "dilations": [2, 2], "pads": [1, 1, 1, 1] },
"inputs": {
"x": {
"dtype": "uint8",
"shape": [1, 1, 5, 5],
"data": { "kind": "cycle", "values": [0, 32, 96, 127, 128, 160, 224, 255] }
},
"x_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.04] } },
"x_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } },
"w": { "dtype": "int8", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [-128, 127, -32, 96] } },
"w_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.02] } },
"w_zero_point": { "dtype": "int8", "shape": [1], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [1], "data": { "kind": "values", "values": [0.01] } },
"y_zero_point": { "dtype": "uint8", "shape": [1], "data": { "kind": "values", "values": [128] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 1, 5, 5], "tolerance": 0 } }
},
{
"name": "ort_u8s8_pointwise_multi_output",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_U8S8_Pointwise",
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},
"inputs": {
"x": {
"dtype": "uint8",
"shape": [1, 2, 2, 3],
"data": { "kind": "values", "values": [4, 15, 64, 128, 200, 255, 250, 180, 127, 80, 42, 0] }
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"x_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.05] } },
"x_zero_point": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [4] } },
"w": {
"dtype": "int8",
"shape": [3, 2, 1, 1],
"data": { "kind": "values", "values": [0, 12, -7, 31, 63, -63] }
},
"w_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.125] } },
"w_zero_point": { "dtype": "int8", "shape": [], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.55] } },
"y_zero_point": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [54] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 3, 2, 3], "tolerance": 0 } },
"attrs": {}
},
{
"name": "ort_u8s8_stride2_small",
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"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_U8S8_Strides",
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},
"attrs": { "strides": [2, 2] },
"inputs": {
"x": {
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"shape": [1, 1, 5, 5],
"data": {
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"values": [4, 18, 32, 47, 63, 79, 95, 111, 127, 143, 159, 175, 191, 207, 223, 239, 255, 231, 199, 167, 135, 103, 71, 39, 7]
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"x_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.05] } },
"x_zero_point": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [4] } },
"w": { "dtype": "int8", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [-63, -9, 17, 63] } },
"w_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.125] } },
"w_zero_point": { "dtype": "int8", "shape": [], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.31] } },
"y_zero_point": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [30] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 1, 2, 2], "tolerance": 0 } }
},
{
"name": "ort_u8s8_dilation2_small",
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"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_U8S8_Dilations",
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},
"attrs": { "dilations": [2, 2] },
"inputs": {
"x": {
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"shape": [1, 1, 4, 4],
"data": {
"kind": "values",
"values": [4, 60, 116, 172, 228, 200, 144, 88, 32, 12, 68, 124, 180, 236, 252, 96]
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},
"x_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.05] } },
"x_zero_point": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [4] } },
"w": { "dtype": "int8", "shape": [1, 1, 2, 2], "data": { "kind": "values", "values": [-3, 19, -41, 63] } },
"w_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.125] } },
"w_zero_point": { "dtype": "int8", "shape": [], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.24] } },
"y_zero_point": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [15] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 1, 2, 2], "tolerance": 0 } }
},
{
"name": "ort_u8s8_group2_small",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_U8S8_Groups",
"notes": "Adapts QLinearConvTest.Conv2D_U8S8_Groups with two groups and scalar weight quantization."
},
"attrs": { "group": 2 },
"inputs": {
"x": {
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"shape": [1, 4, 3, 3],
"data": { "kind": "cycle", "values": [4, 17, 33, 65, 97, 129, 161, 193, 225, 255] }
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"x_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.05] } },
"x_zero_point": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [4] } },
"w": {
"dtype": "int8",
"shape": [4, 2, 2, 2],
"data": { "kind": "cycle", "values": [-63, -45, -18, -1, 0, 7, 23, 49, 63] }
},
"w_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.125] } },
"w_zero_point": { "dtype": "int8", "shape": [], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.76] } },
"y_zero_point": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [88] } }
},
"outputs": { "y": { "dtype": "uint8", "shape": [1, 4, 2, 2], "tolerance": 0 } }
},
{
"name": "ort_s8s8_stride2_pad1_signed_output",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_S8S8_Strides",
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},
"attrs": { "strides": [2, 2], "pads": [1, 1, 1, 1] },
"inputs": {
"x": {
"dtype": "int8",
"shape": [1, 1, 5, 5],
"data": {
"kind": "values",
"values": [-128, -96, -64, -32, 0, 32, 64, 96, 127, 95, 63, 31, -1, -33, -65, -97, -127, -80, -40, 8, 48, 88, 120, 72, 24]
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},
"x_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.05] } },
"x_zero_point": { "dtype": "int8", "shape": [], "data": { "kind": "values", "values": [4] } },
"w": {
"dtype": "int8",
"shape": [1, 1, 3, 3],
"data": { "kind": "values", "values": [-63, -31, 0, 17, 45, 63, -12, 29, -48] }
},
"w_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.125] } },
"w_zero_point": { "dtype": "int8", "shape": [], "data": { "kind": "values", "values": [0] } },
"y_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.31] } },
"y_zero_point": { "dtype": "int8", "shape": [], "data": { "kind": "values", "values": [-30] } }
},
"outputs": { "y": { "dtype": "int8", "shape": [1, 1, 3, 3], "tolerance": 0 } }
},
{
"name": "ort_u8u8_pointwise_multi_output",
"provenance": {
"source": "onnxruntime/test/providers/cpu/nn/qlinearconv_op_test.cc",
"test": "QLinearConvTest.Conv2D_U8U8_Pointwise",
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"inputs": {
"x": {
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"shape": [1, 2, 2, 3],
"data": { "kind": "values", "values": [4, 15, 64, 128, 200, 255, 250, 180, 127, 80, 42, 0] }
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"x_scale": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [0.05] } },
"x_zero_point": { "dtype": "uint8", "shape": [], "data": { "kind": "values", "values": [4] } },
"w": {
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"name": "ort_per_channel_weight_zero_points_u8s8",
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"x": {
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{
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{
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