Kernels:
Trusted publisher
Uploaded using `kernel-builder` (batch 1/2).
Browse files- build/torch210-cu126-x86_64-linux/_ops.py +3 -3
- build/torch210-cu126-x86_64-linux/{_vllm_moe_cuda_fe2bb66.abi3.so → _vllm_moe_cuda_5fe9dbb.abi3.so} +1 -1
- build/torch210-cu126-x86_64-linux/metadata.json +6 -6
- build/torch210-cu126-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch210-cu126-x86_64-linux/symbols.json +12 -12
- build/torch210-cu128-x86_64-linux/_ops.py +3 -3
- build/torch210-cu128-x86_64-linux/{_vllm_moe_cuda_fe2bb66.abi3.so → _vllm_moe_cuda_5fe9dbb.abi3.so} +1 -1
- build/torch210-cu128-x86_64-linux/metadata.json +6 -6
- build/torch210-cu128-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch210-cu128-x86_64-linux/symbols.json +12 -12
- build/torch211-cu126-x86_64-linux/_ops.py +3 -3
- build/torch211-cu126-x86_64-linux/{_vllm_moe_cuda_fe2bb66.abi3.so → _vllm_moe_cuda_5fe9dbb.abi3.so} +1 -1
- build/torch211-cu126-x86_64-linux/metadata.json +6 -6
- build/torch211-cu126-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch211-cu126-x86_64-linux/symbols.json +12 -12
- build/torch211-cu128-x86_64-linux/_ops.py +3 -3
- build/torch211-cu128-x86_64-linux/{_vllm_moe_cuda_fe2bb66.abi3.so → _vllm_moe_cuda_5fe9dbb.abi3.so} +1 -1
- build/torch211-cu128-x86_64-linux/metadata.json +6 -6
- build/torch211-cu128-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch211-cu128-x86_64-linux/symbols.json +12 -12
- build/torch212-cu126-x86_64-linux/_ops.py +3 -3
- build/torch212-cu126-x86_64-linux/_vllm_moe_cuda_5fe9dbb.abi3.so +3 -0
- build/torch212-cu126-x86_64-linux/_vllm_moe_cuda_fe2bb66.abi3.so +0 -3
- build/torch213-cu126-x86_64-linux/_vllm_moe_cuda_fe2bb66.abi3.so +0 -3
- build/torch214-cu126-x86_64-linux/_vllm_moe_cuda_fe2bb66.abi3.so +0 -3
build/torch210-cu126-x86_64-linux/_ops.py
CHANGED
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@@ -1,9 +1,9 @@
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import torch
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from . import
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ops = torch.ops.
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def add_op_namespace_prefix(op_name: str):
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"""
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Prefix op by namespace.
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"""
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return f"
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import torch
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from . import _vllm_moe_cuda_5fe9dbb
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ops = torch.ops._vllm_moe_cuda_5fe9dbb
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def add_op_namespace_prefix(op_name: str):
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"""
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Prefix op by namespace.
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"""
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+
return f"_vllm_moe_cuda_5fe9dbb::{op_name}"
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build/torch210-cu126-x86_64-linux/{_vllm_moe_cuda_fe2bb66.abi3.so → _vllm_moe_cuda_5fe9dbb.abi3.so}
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build/torch210-cu126-x86_64-linux/metadata.json
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build/torch210-cu126-x86_64-linux/metadata.json.sigstore
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|
build/torch210-cu126-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "awq_marlin_moe_repack",
|
| 7 |
"qualname": "awq_marlin_moe_repack",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -47,7 +47,7 @@
|
|
| 47 |
{
|
| 48 |
"name": "fused_experts",
|
| 49 |
"qualname": "fused_experts",
|
| 50 |
-
"module": "
|
| 51 |
"kind": "function",
|
| 52 |
"doc": null,
|
| 53 |
"signature": {
|
|
@@ -179,7 +179,7 @@
|
|
| 179 |
{
|
| 180 |
"name": "fused_marlin_moe",
|
| 181 |
"qualname": "fused_marlin_moe",
|
| 182 |
-
"module": "
|
| 183 |
"kind": "function",
|
| 184 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- w1_scale (torch.Tensor): Scale to be used for w1.\n- w2_scale (torch.Tensor): Scale to be used for w2.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.\n- g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.\n- sort_indices1 (Optional[torch.Tensor]): The first act_order input\n permutation.\n- sort_indices2 (Optional[torch.Tensor]): The second act_order input\n permutation.\n- topk_weights (torch.Tensor): Top-k weights.\n- topk_ids (torch.Tensor): Indices of topk-k elements.\n- w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.\n- w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.\n- num_bits (bool): The number of bits in expert weights quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
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@@ -287,7 +287,7 @@
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{
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"module": "
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"kind": "function",
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"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- topk (int): The number of top-k experts to select.\n- renormalize (bool): If True, renormalize the top-k weights to sum to 1.\n- inplace (bool): If True, perform the operation in-place.\n Defaults to False.\n- activation (str): The activation function to apply after the first\n MoE layer.\n- num_expert_group: Optional[int]: additional parameter for grouped_topk\n- topk_group: Optional[int]: additional parameter for grouped_topk\n- use_grouped_topk: If True, use grouped_topk instead of fused_topk\n note: Deepseekv2 model uses grouped_topk\n- use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner\n products for w1 and w2. Defaults to False.\n- use_int8_w8a16 (bool): If True, use matmul of int8 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- global_num_experts (int): The total number of experts in the global\n expert space.\n- expert_map (Optional[torch.Tensor]): A tensor mapping expert indices\n from the global expert space to the local expert space of the expert\n parallel shard.\n- w1_scale (Optional[torch.Tensor]): Optional scale to be used for\n w1.\n- w2_scale (Optional[torch.Tensor]): Optional scale to be used for\n w2.\n- a1_scale (Optional[torch.Tensor]): Optional scale to be used for\n a1.\n- a2_scale (Optional[torch.Tensor]): Optional scale to be used for\n a2.\n- block_shape: (Optional[List[int]]): Optional block size for block-wise\n quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
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"signature": {
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@@ -449,7 +449,7 @@
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{
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"name": "fused_topk",
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"module": "
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"kind": "function",
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"doc": null,
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"signature": {
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{
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"name": "gptq_marlin_moe_repack",
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"qualname": "gptq_marlin_moe_repack",
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"module": "
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"kind": "function",
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"doc": null,
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"module": "
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"kind": "function",
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"doc": null,
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{
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| 588 |
"name": "moe_sum",
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"module": "
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| 591 |
"kind": "function",
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"doc": null,
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@@ -611,7 +611,7 @@
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{
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| 612 |
"name": "per_token_group_quant_fp8",
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| 613 |
"qualname": "per_token_group_quant_fp8",
|
| 614 |
-
"module": "
|
| 615 |
"kind": "function",
|
| 616 |
"doc": "Function to perform per-token-group quantization on an input tensor `x`.\nIt converts the tensor values into signed float8 values and returns the\nquantized tensor along with the scaling factor used for quantization.\nArgs:\n x: The input tensor with ndim >= 2.\n group_size: The group size used for quantization.\n eps: The minimum to avoid dividing zero.\n dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn`\n is supported for now.\nReturns:\n Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the\n scaling factor for quantization.",
|
| 617 |
"signature": {
|
|
@@ -653,7 +653,7 @@
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|
| 653 |
{
|
| 654 |
"name": "topk_softmax",
|
| 655 |
"qualname": "topk_softmax",
|
| 656 |
-
"module": "
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| 657 |
"kind": "function",
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| 658 |
"doc": null,
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| 659 |
"signature": {
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|
@@ -689,7 +689,7 @@
|
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| 689 |
{
|
| 690 |
"name": "w8a8_block_fp8_matmul",
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| 691 |
"qualname": "w8a8_block_fp8_matmul",
|
| 692 |
-
"module": "
|
| 693 |
"kind": "function",
|
| 694 |
"doc": "This function performs matrix multiplication with block-wise\nquantization.\nIt takes two input tensors `A` and `B` with scales `As` and `Bs`.\nThe output is returned in the specified `output_dtype`.\nArgs:\n A: The input tensor, e.g., activation.\n B: The input tensor, e.g., weight.\n As: The per-token-group quantization scale for `A`.\n Bs: The per-block quantization scale for `B`.\n block_size: The block size for per-block quantization. It should\n be 2-dim, e.g., [128, 128].\n output_dytpe: The dtype of the returned tensor.\nReturns:\n torch.Tensor: The result of matmul.",
|
| 695 |
"signature": {
|
|
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|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "awq_marlin_moe_repack",
|
| 7 |
"qualname": "awq_marlin_moe_repack",
|
| 8 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
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| 47 |
{
|
| 48 |
"name": "fused_experts",
|
| 49 |
"qualname": "fused_experts",
|
| 50 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
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| 52 |
"doc": null,
|
| 53 |
"signature": {
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| 179 |
{
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| 180 |
"name": "fused_marlin_moe",
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"qualname": "fused_marlin_moe",
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| 182 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_marlin_moe",
|
| 183 |
"kind": "function",
|
| 184 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- w1_scale (torch.Tensor): Scale to be used for w1.\n- w2_scale (torch.Tensor): Scale to be used for w2.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.\n- g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.\n- sort_indices1 (Optional[torch.Tensor]): The first act_order input\n permutation.\n- sort_indices2 (Optional[torch.Tensor]): The second act_order input\n permutation.\n- topk_weights (torch.Tensor): Top-k weights.\n- topk_ids (torch.Tensor): Indices of topk-k elements.\n- w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.\n- w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.\n- num_bits (bool): The number of bits in expert weights quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
|
| 185 |
"signature": {
|
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|
| 287 |
{
|
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"name": "fused_moe",
|
| 289 |
"qualname": "fused_moe",
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| 290 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
| 291 |
"kind": "function",
|
| 292 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- topk (int): The number of top-k experts to select.\n- renormalize (bool): If True, renormalize the top-k weights to sum to 1.\n- inplace (bool): If True, perform the operation in-place.\n Defaults to False.\n- activation (str): The activation function to apply after the first\n MoE layer.\n- num_expert_group: Optional[int]: additional parameter for grouped_topk\n- topk_group: Optional[int]: additional parameter for grouped_topk\n- use_grouped_topk: If True, use grouped_topk instead of fused_topk\n note: Deepseekv2 model uses grouped_topk\n- use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner\n products for w1 and w2. Defaults to False.\n- use_int8_w8a16 (bool): If True, use matmul of int8 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- global_num_experts (int): The total number of experts in the global\n expert space.\n- expert_map (Optional[torch.Tensor]): A tensor mapping expert indices\n from the global expert space to the local expert space of the expert\n parallel shard.\n- w1_scale (Optional[torch.Tensor]): Optional scale to be used for\n w1.\n- w2_scale (Optional[torch.Tensor]): Optional scale to be used for\n w2.\n- a1_scale (Optional[torch.Tensor]): Optional scale to be used for\n a1.\n- a2_scale (Optional[torch.Tensor]): Optional scale to be used for\n a2.\n- block_shape: (Optional[List[int]]): Optional block size for block-wise\n quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
|
| 293 |
"signature": {
|
|
|
|
| 449 |
{
|
| 450 |
"name": "fused_topk",
|
| 451 |
"qualname": "fused_topk",
|
| 452 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
| 453 |
"kind": "function",
|
| 454 |
"doc": null,
|
| 455 |
"signature": {
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|
|
|
| 485 |
{
|
| 486 |
"name": "gptq_marlin_moe_repack",
|
| 487 |
"qualname": "gptq_marlin_moe_repack",
|
| 488 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 489 |
"kind": "function",
|
| 490 |
"doc": null,
|
| 491 |
"signature": {
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|
|
|
| 527 |
{
|
| 528 |
"name": "grouped_topk",
|
| 529 |
"qualname": "grouped_topk",
|
| 530 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
| 531 |
"kind": "function",
|
| 532 |
"doc": null,
|
| 533 |
"signature": {
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| 587 |
{
|
| 588 |
"name": "moe_sum",
|
| 589 |
"qualname": "moe_sum",
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| 590 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 591 |
"kind": "function",
|
| 592 |
"doc": null,
|
| 593 |
"signature": {
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|
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| 611 |
{
|
| 612 |
"name": "per_token_group_quant_fp8",
|
| 613 |
"qualname": "per_token_group_quant_fp8",
|
| 614 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fp8_utils",
|
| 615 |
"kind": "function",
|
| 616 |
"doc": "Function to perform per-token-group quantization on an input tensor `x`.\nIt converts the tensor values into signed float8 values and returns the\nquantized tensor along with the scaling factor used for quantization.\nArgs:\n x: The input tensor with ndim >= 2.\n group_size: The group size used for quantization.\n eps: The minimum to avoid dividing zero.\n dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn`\n is supported for now.\nReturns:\n Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the\n scaling factor for quantization.",
|
| 617 |
"signature": {
|
|
|
|
| 653 |
{
|
| 654 |
"name": "topk_softmax",
|
| 655 |
"qualname": "topk_softmax",
|
| 656 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 657 |
"kind": "function",
|
| 658 |
"doc": null,
|
| 659 |
"signature": {
|
|
|
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| 689 |
{
|
| 690 |
"name": "w8a8_block_fp8_matmul",
|
| 691 |
"qualname": "w8a8_block_fp8_matmul",
|
| 692 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fp8_utils",
|
| 693 |
"kind": "function",
|
| 694 |
"doc": "This function performs matrix multiplication with block-wise\nquantization.\nIt takes two input tensors `A` and `B` with scales `As` and `Bs`.\nThe output is returned in the specified `output_dtype`.\nArgs:\n A: The input tensor, e.g., activation.\n B: The input tensor, e.g., weight.\n As: The per-token-group quantization scale for `A`.\n Bs: The per-block quantization scale for `B`.\n block_size: The block size for per-block quantization. It should\n be 2-dim, e.g., [128, 128].\n output_dytpe: The dtype of the returned tensor.\nReturns:\n torch.Tensor: The result of matmul.",
|
| 695 |
"signature": {
|
build/torch210-cu128-x86_64-linux/_ops.py
CHANGED
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@@ -1,9 +1,9 @@
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| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
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ops = torch.ops.
|
| 4 |
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| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
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|
| 9 |
-
return f"
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|
|
|
| 1 |
import torch
|
| 2 |
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from . import _vllm_moe_cuda_5fe9dbb
|
| 3 |
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ops = torch.ops._vllm_moe_cuda_5fe9dbb
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
+
return f"_vllm_moe_cuda_5fe9dbb::{op_name}"
|
build/torch210-cu128-x86_64-linux/{_vllm_moe_cuda_fe2bb66.abi3.so → _vllm_moe_cuda_5fe9dbb.abi3.so}
RENAMED
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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| 1 |
version https://git-lfs.github.com/spec/v1
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build/torch210-cu128-x86_64-linux/metadata.json
CHANGED
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@@ -1,6 +1,6 @@
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| 1 |
{
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| 3 |
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"id": "
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"version": 0,
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| 5 |
"kernels-minver": "0.14.0",
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| 6 |
"license": "Apache-2.0",
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|
@@ -28,8 +28,8 @@
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| 28 |
"algorithm": "sha256",
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| 29 |
"files": {
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| 30 |
"__init__.py": "DJzZn/dtXxUWsQBgjKQp8CQqYdJX1zctCaV6489SoTg=",
|
| 31 |
-
"_ops.py": "
|
| 32 |
-
"
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| 33 |
"configs/E=1,N=14336,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json": "iNGsE2ZeVnQEnN4A8UJ9Jv0d3hbRF2MJ9oBgjup5Szk=",
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| 34 |
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|
|
@@ -227,7 +227,7 @@
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|
| 227 |
"moe_align_block_size.py": "l461Tq7QMLbX3y1xLWF5wF1iOIlvMnB541RB0R/qM/I=",
|
| 228 |
"platforms.py": "jq0dt/8BuH+9RSs2+mRJxKHHgT+tocNwDmQuJ8DiH3Q=",
|
| 229 |
"scalar_type.py": "cnhBIZKi9kHGHhzxYGWSV8I/QlnPb3MhIor2VEj6igU=",
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"symbols.json": "
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| 231 |
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| 232 |
"utils/custom_ops.py": "U7XlQM/7PGRr5s8ia6AJi+6TsihQCiD0uMgWHe5PnVw=",
|
| 233 |
"utils/marlin_utils.py": "eaqve1NKHWVG9h307Y3T3PEYGeVziE0R4fO/5UShk1Y=",
|
|
@@ -238,11 +238,11 @@
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|
| 238 |
"provenance": {
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| 239 |
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| 240 |
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| 241 |
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"commit": "
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| 242 |
"dirty": false
|
| 243 |
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|
| 244 |
"kernel": {
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| 245 |
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"commit": "
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}
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| 1 |
{
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| 2 |
"name": "vllm-moe",
|
| 3 |
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|
| 4 |
"version": 0,
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| 5 |
"kernels-minver": "0.14.0",
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"license": "Apache-2.0",
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|
| 28 |
"algorithm": "sha256",
|
| 29 |
"files": {
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| 30 |
"__init__.py": "DJzZn/dtXxUWsQBgjKQp8CQqYdJX1zctCaV6489SoTg=",
|
| 31 |
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"_ops.py": "OZ+OvTHxDPW9aiCr60huQgvxD8ckKL+2urel/0JBLSw=",
|
| 32 |
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"_vllm_moe_cuda_5fe9dbb.abi3.so": "tZK0W/3iV3jrfq3HWbP/1gPsI7ZdtJhoW8f0/2/MNJw=",
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| 33 |
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| 34 |
"configs/E=1,N=14336,device_name=NVIDIA_A100-SXM4-80GB.json": "hH5rRN9Wtyv35azxMzyUMHWtiKgOHev5tNjIG8j6dsE=",
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| 35 |
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| 227 |
"moe_align_block_size.py": "l461Tq7QMLbX3y1xLWF5wF1iOIlvMnB541RB0R/qM/I=",
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| 228 |
"platforms.py": "jq0dt/8BuH+9RSs2+mRJxKHHgT+tocNwDmQuJ8DiH3Q=",
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| 229 |
"scalar_type.py": "cnhBIZKi9kHGHhzxYGWSV8I/QlnPb3MhIor2VEj6igU=",
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| 230 |
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"symbols.json": "xHQkwMWlg1pRmWGSz2bmggXnxkI7brpLYv+g8viCbJI=",
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"utils/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
|
| 232 |
"utils/custom_ops.py": "U7XlQM/7PGRr5s8ia6AJi+6TsihQCiD0uMgWHe5PnVw=",
|
| 233 |
"utils/marlin_utils.py": "eaqve1NKHWVG9h307Y3T3PEYGeVziE0R4fO/5UShk1Y=",
|
|
|
|
| 238 |
"provenance": {
|
| 239 |
"kernel-builder": {
|
| 240 |
"version": "0.18.0-dev0",
|
| 241 |
+
"commit": "21bdf69d97a289b9c581a0c60f63deed131647d5",
|
| 242 |
"dirty": false
|
| 243 |
},
|
| 244 |
"kernel": {
|
| 245 |
+
"commit": "5fe9dbb3d45f6f2652958c1f70e5381891fa7aa0",
|
| 246 |
"dirty": false
|
| 247 |
}
|
| 248 |
}
|
build/torch210-cu128-x86_64-linux/metadata.json.sigstore
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
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|
build/torch210-cu128-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "awq_marlin_moe_repack",
|
| 7 |
"qualname": "awq_marlin_moe_repack",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -47,7 +47,7 @@
|
|
| 47 |
{
|
| 48 |
"name": "fused_experts",
|
| 49 |
"qualname": "fused_experts",
|
| 50 |
-
"module": "
|
| 51 |
"kind": "function",
|
| 52 |
"doc": null,
|
| 53 |
"signature": {
|
|
@@ -179,7 +179,7 @@
|
|
| 179 |
{
|
| 180 |
"name": "fused_marlin_moe",
|
| 181 |
"qualname": "fused_marlin_moe",
|
| 182 |
-
"module": "
|
| 183 |
"kind": "function",
|
| 184 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- w1_scale (torch.Tensor): Scale to be used for w1.\n- w2_scale (torch.Tensor): Scale to be used for w2.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.\n- g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.\n- sort_indices1 (Optional[torch.Tensor]): The first act_order input\n permutation.\n- sort_indices2 (Optional[torch.Tensor]): The second act_order input\n permutation.\n- topk_weights (torch.Tensor): Top-k weights.\n- topk_ids (torch.Tensor): Indices of topk-k elements.\n- w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.\n- w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.\n- num_bits (bool): The number of bits in expert weights quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
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"kind": "function",
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"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- topk (int): The number of top-k experts to select.\n- renormalize (bool): If True, renormalize the top-k weights to sum to 1.\n- inplace (bool): If True, perform the operation in-place.\n Defaults to False.\n- activation (str): The activation function to apply after the first\n MoE layer.\n- num_expert_group: Optional[int]: additional parameter for grouped_topk\n- topk_group: Optional[int]: additional parameter for grouped_topk\n- use_grouped_topk: If True, use grouped_topk instead of fused_topk\n note: Deepseekv2 model uses grouped_topk\n- use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner\n products for w1 and w2. Defaults to False.\n- use_int8_w8a16 (bool): If True, use matmul of int8 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- global_num_experts (int): The total number of experts in the global\n expert space.\n- expert_map (Optional[torch.Tensor]): A tensor mapping expert indices\n from the global expert space to the local expert space of the expert\n parallel shard.\n- w1_scale (Optional[torch.Tensor]): Optional scale to be used for\n w1.\n- w2_scale (Optional[torch.Tensor]): Optional scale to be used for\n w2.\n- a1_scale (Optional[torch.Tensor]): Optional scale to be used for\n a1.\n- a2_scale (Optional[torch.Tensor]): Optional scale to be used for\n a2.\n- block_shape: (Optional[List[int]]): Optional block size for block-wise\n quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
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{
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"name": "gptq_marlin_moe_repack",
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{
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| 612 |
"name": "per_token_group_quant_fp8",
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| 613 |
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| 614 |
-
"module": "
|
| 615 |
"kind": "function",
|
| 616 |
"doc": "Function to perform per-token-group quantization on an input tensor `x`.\nIt converts the tensor values into signed float8 values and returns the\nquantized tensor along with the scaling factor used for quantization.\nArgs:\n x: The input tensor with ndim >= 2.\n group_size: The group size used for quantization.\n eps: The minimum to avoid dividing zero.\n dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn`\n is supported for now.\nReturns:\n Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the\n scaling factor for quantization.",
|
| 617 |
"signature": {
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|
@@ -653,7 +653,7 @@
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| 653 |
{
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| 654 |
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| 655 |
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| 656 |
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"module": "
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{
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| 690 |
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| 692 |
-
"module": "
|
| 693 |
"kind": "function",
|
| 694 |
"doc": "This function performs matrix multiplication with block-wise\nquantization.\nIt takes two input tensors `A` and `B` with scales `As` and `Bs`.\nThe output is returned in the specified `output_dtype`.\nArgs:\n A: The input tensor, e.g., activation.\n B: The input tensor, e.g., weight.\n As: The per-token-group quantization scale for `A`.\n Bs: The per-block quantization scale for `B`.\n block_size: The block size for per-block quantization. It should\n be 2-dim, e.g., [128, 128].\n output_dytpe: The dtype of the returned tensor.\nReturns:\n torch.Tensor: The result of matmul.",
|
| 695 |
"signature": {
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| 1 |
{
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| 2 |
"schema_version": 1,
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"module": "_vllm_moe_cuda_5fe9dbb",
|
| 4 |
"functions": [
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| 5 |
{
|
| 6 |
"name": "awq_marlin_moe_repack",
|
| 7 |
"qualname": "awq_marlin_moe_repack",
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| 8 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
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"kind": "function",
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"doc": null,
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"signature": {
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| 47 |
{
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| 48 |
"name": "fused_experts",
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+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
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"doc": null,
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"module": "_vllm_moe_cuda_5fe9dbb.fused_marlin_moe",
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"kind": "function",
|
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"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- w1_scale (torch.Tensor): Scale to be used for w1.\n- w2_scale (torch.Tensor): Scale to be used for w2.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.\n- g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.\n- sort_indices1 (Optional[torch.Tensor]): The first act_order input\n permutation.\n- sort_indices2 (Optional[torch.Tensor]): The second act_order input\n permutation.\n- topk_weights (torch.Tensor): Top-k weights.\n- topk_ids (torch.Tensor): Indices of topk-k elements.\n- w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.\n- w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.\n- num_bits (bool): The number of bits in expert weights quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
|
| 185 |
"signature": {
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{
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"name": "fused_moe",
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+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
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"kind": "function",
|
| 292 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- topk (int): The number of top-k experts to select.\n- renormalize (bool): If True, renormalize the top-k weights to sum to 1.\n- inplace (bool): If True, perform the operation in-place.\n Defaults to False.\n- activation (str): The activation function to apply after the first\n MoE layer.\n- num_expert_group: Optional[int]: additional parameter for grouped_topk\n- topk_group: Optional[int]: additional parameter for grouped_topk\n- use_grouped_topk: If True, use grouped_topk instead of fused_topk\n note: Deepseekv2 model uses grouped_topk\n- use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner\n products for w1 and w2. Defaults to False.\n- use_int8_w8a16 (bool): If True, use matmul of int8 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- global_num_experts (int): The total number of experts in the global\n expert space.\n- expert_map (Optional[torch.Tensor]): A tensor mapping expert indices\n from the global expert space to the local expert space of the expert\n parallel shard.\n- w1_scale (Optional[torch.Tensor]): Optional scale to be used for\n w1.\n- w2_scale (Optional[torch.Tensor]): Optional scale to be used for\n w2.\n- a1_scale (Optional[torch.Tensor]): Optional scale to be used for\n a1.\n- a2_scale (Optional[torch.Tensor]): Optional scale to be used for\n a2.\n- block_shape: (Optional[List[int]]): Optional block size for block-wise\n quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
|
| 293 |
"signature": {
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| 449 |
{
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"name": "fused_topk",
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"qualname": "fused_topk",
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"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
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"kind": "function",
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| 455 |
"signature": {
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| 485 |
{
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| 486 |
"name": "gptq_marlin_moe_repack",
|
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"qualname": "gptq_marlin_moe_repack",
|
| 488 |
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"module": "_vllm_moe_cuda_5fe9dbb",
|
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"kind": "function",
|
| 490 |
"doc": null,
|
| 491 |
"signature": {
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| 527 |
{
|
| 528 |
"name": "grouped_topk",
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| 529 |
"qualname": "grouped_topk",
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| 530 |
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"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
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"doc": null,
|
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"signature": {
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{
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"module": "_vllm_moe_cuda_5fe9dbb",
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"signature": {
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| 611 |
{
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| 612 |
"name": "per_token_group_quant_fp8",
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"qualname": "per_token_group_quant_fp8",
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| 614 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fp8_utils",
|
| 615 |
"kind": "function",
|
| 616 |
"doc": "Function to perform per-token-group quantization on an input tensor `x`.\nIt converts the tensor values into signed float8 values and returns the\nquantized tensor along with the scaling factor used for quantization.\nArgs:\n x: The input tensor with ndim >= 2.\n group_size: The group size used for quantization.\n eps: The minimum to avoid dividing zero.\n dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn`\n is supported for now.\nReturns:\n Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the\n scaling factor for quantization.",
|
| 617 |
"signature": {
|
|
|
|
| 653 |
{
|
| 654 |
"name": "topk_softmax",
|
| 655 |
"qualname": "topk_softmax",
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| 656 |
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"module": "_vllm_moe_cuda_5fe9dbb",
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"signature": {
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| 689 |
{
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| 690 |
"name": "w8a8_block_fp8_matmul",
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| 691 |
"qualname": "w8a8_block_fp8_matmul",
|
| 692 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fp8_utils",
|
| 693 |
"kind": "function",
|
| 694 |
"doc": "This function performs matrix multiplication with block-wise\nquantization.\nIt takes two input tensors `A` and `B` with scales `As` and `Bs`.\nThe output is returned in the specified `output_dtype`.\nArgs:\n A: The input tensor, e.g., activation.\n B: The input tensor, e.g., weight.\n As: The per-token-group quantization scale for `A`.\n Bs: The per-block quantization scale for `B`.\n block_size: The block size for per-block quantization. It should\n be 2-dim, e.g., [128, 128].\n output_dytpe: The dtype of the returned tensor.\nReturns:\n torch.Tensor: The result of matmul.",
|
| 695 |
"signature": {
|
build/torch211-cu126-x86_64-linux/_ops.py
CHANGED
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| 1 |
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|
| 2 |
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from . import
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| 3 |
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ops = torch.ops.
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| 4 |
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| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
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| 7 |
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| 9 |
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return f"
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| 1 |
import torch
|
| 2 |
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| 3 |
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ops = torch.ops._vllm_moe_cuda_5fe9dbb
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
+
return f"_vllm_moe_cuda_5fe9dbb::{op_name}"
|
build/torch211-cu126-x86_64-linux/{_vllm_moe_cuda_fe2bb66.abi3.so → _vllm_moe_cuda_5fe9dbb.abi3.so}
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build/torch211-cu126-x86_64-linux/metadata.json
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"id": "
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"_ops.py": "
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| 29 |
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| 30 |
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|
|
@@ -224,7 +224,7 @@
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| 224 |
"moe_align_block_size.py": "l461Tq7QMLbX3y1xLWF5wF1iOIlvMnB541RB0R/qM/I=",
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| 225 |
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| 230 |
"utils/marlin_utils.py": "eaqve1NKHWVG9h307Y3T3PEYGeVziE0R4fO/5UShk1Y=",
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@@ -235,11 +235,11 @@
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"_ops.py": "OZ+OvTHxDPW9aiCr60huQgvxD8ckKL+2urel/0JBLSw=",
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| 29 |
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"_vllm_moe_cuda_5fe9dbb.abi3.so": "PJdkL4oi8Z62OT1cgEjLC9VjouUIm+gsVVTMWEuuADM=",
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| 32 |
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| 224 |
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|
| 229 |
"utils/custom_ops.py": "U7XlQM/7PGRr5s8ia6AJi+6TsihQCiD0uMgWHe5PnVw=",
|
| 230 |
"utils/marlin_utils.py": "eaqve1NKHWVG9h307Y3T3PEYGeVziE0R4fO/5UShk1Y=",
|
|
|
|
| 235 |
"provenance": {
|
| 236 |
"kernel-builder": {
|
| 237 |
"version": "0.18.0-dev0",
|
| 238 |
+
"commit": "21bdf69d97a289b9c581a0c60f63deed131647d5",
|
| 239 |
"dirty": false
|
| 240 |
},
|
| 241 |
"kernel": {
|
| 242 |
+
"commit": "5fe9dbb3d45f6f2652958c1f70e5381891fa7aa0",
|
| 243 |
"dirty": false
|
| 244 |
}
|
| 245 |
}
|
build/torch211-cu126-x86_64-linux/metadata.json.sigstore
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
{"mediaType":"application/vnd.dev.sigstore.bundle.v0.3+json", 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|
build/torch211-cu126-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "awq_marlin_moe_repack",
|
| 7 |
"qualname": "awq_marlin_moe_repack",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -47,7 +47,7 @@
|
|
| 47 |
{
|
| 48 |
"name": "fused_experts",
|
| 49 |
"qualname": "fused_experts",
|
| 50 |
-
"module": "
|
| 51 |
"kind": "function",
|
| 52 |
"doc": null,
|
| 53 |
"signature": {
|
|
@@ -179,7 +179,7 @@
|
|
| 179 |
{
|
| 180 |
"name": "fused_marlin_moe",
|
| 181 |
"qualname": "fused_marlin_moe",
|
| 182 |
-
"module": "
|
| 183 |
"kind": "function",
|
| 184 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- w1_scale (torch.Tensor): Scale to be used for w1.\n- w2_scale (torch.Tensor): Scale to be used for w2.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.\n- g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.\n- sort_indices1 (Optional[torch.Tensor]): The first act_order input\n permutation.\n- sort_indices2 (Optional[torch.Tensor]): The second act_order input\n permutation.\n- topk_weights (torch.Tensor): Top-k weights.\n- topk_ids (torch.Tensor): Indices of topk-k elements.\n- w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.\n- w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.\n- num_bits (bool): The number of bits in expert weights quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
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@@ -287,7 +287,7 @@
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"module": "
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"kind": "function",
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"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- topk (int): The number of top-k experts to select.\n- renormalize (bool): If True, renormalize the top-k weights to sum to 1.\n- inplace (bool): If True, perform the operation in-place.\n Defaults to False.\n- activation (str): The activation function to apply after the first\n MoE layer.\n- num_expert_group: Optional[int]: additional parameter for grouped_topk\n- topk_group: Optional[int]: additional parameter for grouped_topk\n- use_grouped_topk: If True, use grouped_topk instead of fused_topk\n note: Deepseekv2 model uses grouped_topk\n- use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner\n products for w1 and w2. Defaults to False.\n- use_int8_w8a16 (bool): If True, use matmul of int8 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- global_num_experts (int): The total number of experts in the global\n expert space.\n- expert_map (Optional[torch.Tensor]): A tensor mapping expert indices\n from the global expert space to the local expert space of the expert\n parallel shard.\n- w1_scale (Optional[torch.Tensor]): Optional scale to be used for\n w1.\n- w2_scale (Optional[torch.Tensor]): Optional scale to be used for\n w2.\n- a1_scale (Optional[torch.Tensor]): Optional scale to be used for\n a1.\n- a2_scale (Optional[torch.Tensor]): Optional scale to be used for\n a2.\n- block_shape: (Optional[List[int]]): Optional block size for block-wise\n quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
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"signature": {
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{
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"name": "fused_topk",
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"module": "
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"kind": "function",
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"doc": null,
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{
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"name": "gptq_marlin_moe_repack",
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"qualname": "gptq_marlin_moe_repack",
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"module": "
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"kind": "function",
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"doc": null,
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{
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"qualname": "grouped_topk",
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"module": "
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"kind": "function",
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"doc": null,
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{
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| 588 |
"name": "moe_sum",
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"qualname": "moe_sum",
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"module": "
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| 591 |
"kind": "function",
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"doc": null,
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"signature": {
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@@ -611,7 +611,7 @@
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{
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| 612 |
"name": "per_token_group_quant_fp8",
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| 613 |
"qualname": "per_token_group_quant_fp8",
|
| 614 |
-
"module": "
|
| 615 |
"kind": "function",
|
| 616 |
"doc": "Function to perform per-token-group quantization on an input tensor `x`.\nIt converts the tensor values into signed float8 values and returns the\nquantized tensor along with the scaling factor used for quantization.\nArgs:\n x: The input tensor with ndim >= 2.\n group_size: The group size used for quantization.\n eps: The minimum to avoid dividing zero.\n dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn`\n is supported for now.\nReturns:\n Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the\n scaling factor for quantization.",
|
| 617 |
"signature": {
|
|
@@ -653,7 +653,7 @@
|
|
| 653 |
{
|
| 654 |
"name": "topk_softmax",
|
| 655 |
"qualname": "topk_softmax",
|
| 656 |
-
"module": "
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| 657 |
"kind": "function",
|
| 658 |
"doc": null,
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| 659 |
"signature": {
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@@ -689,7 +689,7 @@
|
|
| 689 |
{
|
| 690 |
"name": "w8a8_block_fp8_matmul",
|
| 691 |
"qualname": "w8a8_block_fp8_matmul",
|
| 692 |
-
"module": "
|
| 693 |
"kind": "function",
|
| 694 |
"doc": "This function performs matrix multiplication with block-wise\nquantization.\nIt takes two input tensors `A` and `B` with scales `As` and `Bs`.\nThe output is returned in the specified `output_dtype`.\nArgs:\n A: The input tensor, e.g., activation.\n B: The input tensor, e.g., weight.\n As: The per-token-group quantization scale for `A`.\n Bs: The per-block quantization scale for `B`.\n block_size: The block size for per-block quantization. It should\n be 2-dim, e.g., [128, 128].\n output_dytpe: The dtype of the returned tensor.\nReturns:\n torch.Tensor: The result of matmul.",
|
| 695 |
"signature": {
|
|
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|
| 1 |
{
|
| 2 |
"schema_version": 1,
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| 3 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "awq_marlin_moe_repack",
|
| 7 |
"qualname": "awq_marlin_moe_repack",
|
| 8 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
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| 47 |
{
|
| 48 |
"name": "fused_experts",
|
| 49 |
"qualname": "fused_experts",
|
| 50 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
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"kind": "function",
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"doc": null,
|
| 53 |
"signature": {
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| 179 |
{
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"name": "fused_marlin_moe",
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"qualname": "fused_marlin_moe",
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| 182 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_marlin_moe",
|
| 183 |
"kind": "function",
|
| 184 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- w1_scale (torch.Tensor): Scale to be used for w1.\n- w2_scale (torch.Tensor): Scale to be used for w2.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.\n- g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.\n- sort_indices1 (Optional[torch.Tensor]): The first act_order input\n permutation.\n- sort_indices2 (Optional[torch.Tensor]): The second act_order input\n permutation.\n- topk_weights (torch.Tensor): Top-k weights.\n- topk_ids (torch.Tensor): Indices of topk-k elements.\n- w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.\n- w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.\n- num_bits (bool): The number of bits in expert weights quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
|
| 185 |
"signature": {
|
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|
| 287 |
{
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"name": "fused_moe",
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"qualname": "fused_moe",
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| 290 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
| 291 |
"kind": "function",
|
| 292 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- topk (int): The number of top-k experts to select.\n- renormalize (bool): If True, renormalize the top-k weights to sum to 1.\n- inplace (bool): If True, perform the operation in-place.\n Defaults to False.\n- activation (str): The activation function to apply after the first\n MoE layer.\n- num_expert_group: Optional[int]: additional parameter for grouped_topk\n- topk_group: Optional[int]: additional parameter for grouped_topk\n- use_grouped_topk: If True, use grouped_topk instead of fused_topk\n note: Deepseekv2 model uses grouped_topk\n- use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner\n products for w1 and w2. Defaults to False.\n- use_int8_w8a16 (bool): If True, use matmul of int8 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- global_num_experts (int): The total number of experts in the global\n expert space.\n- expert_map (Optional[torch.Tensor]): A tensor mapping expert indices\n from the global expert space to the local expert space of the expert\n parallel shard.\n- w1_scale (Optional[torch.Tensor]): Optional scale to be used for\n w1.\n- w2_scale (Optional[torch.Tensor]): Optional scale to be used for\n w2.\n- a1_scale (Optional[torch.Tensor]): Optional scale to be used for\n a1.\n- a2_scale (Optional[torch.Tensor]): Optional scale to be used for\n a2.\n- block_shape: (Optional[List[int]]): Optional block size for block-wise\n quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
|
| 293 |
"signature": {
|
|
|
|
| 449 |
{
|
| 450 |
"name": "fused_topk",
|
| 451 |
"qualname": "fused_topk",
|
| 452 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
| 453 |
"kind": "function",
|
| 454 |
"doc": null,
|
| 455 |
"signature": {
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|
|
|
| 485 |
{
|
| 486 |
"name": "gptq_marlin_moe_repack",
|
| 487 |
"qualname": "gptq_marlin_moe_repack",
|
| 488 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 489 |
"kind": "function",
|
| 490 |
"doc": null,
|
| 491 |
"signature": {
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|
|
|
| 527 |
{
|
| 528 |
"name": "grouped_topk",
|
| 529 |
"qualname": "grouped_topk",
|
| 530 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
| 531 |
"kind": "function",
|
| 532 |
"doc": null,
|
| 533 |
"signature": {
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| 587 |
{
|
| 588 |
"name": "moe_sum",
|
| 589 |
"qualname": "moe_sum",
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| 590 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 591 |
"kind": "function",
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| 592 |
"doc": null,
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| 593 |
"signature": {
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|
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| 611 |
{
|
| 612 |
"name": "per_token_group_quant_fp8",
|
| 613 |
"qualname": "per_token_group_quant_fp8",
|
| 614 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fp8_utils",
|
| 615 |
"kind": "function",
|
| 616 |
"doc": "Function to perform per-token-group quantization on an input tensor `x`.\nIt converts the tensor values into signed float8 values and returns the\nquantized tensor along with the scaling factor used for quantization.\nArgs:\n x: The input tensor with ndim >= 2.\n group_size: The group size used for quantization.\n eps: The minimum to avoid dividing zero.\n dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn`\n is supported for now.\nReturns:\n Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the\n scaling factor for quantization.",
|
| 617 |
"signature": {
|
|
|
|
| 653 |
{
|
| 654 |
"name": "topk_softmax",
|
| 655 |
"qualname": "topk_softmax",
|
| 656 |
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"module": "_vllm_moe_cuda_5fe9dbb",
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| 657 |
"kind": "function",
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| 658 |
"doc": null,
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| 659 |
"signature": {
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|
|
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| 689 |
{
|
| 690 |
"name": "w8a8_block_fp8_matmul",
|
| 691 |
"qualname": "w8a8_block_fp8_matmul",
|
| 692 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fp8_utils",
|
| 693 |
"kind": "function",
|
| 694 |
"doc": "This function performs matrix multiplication with block-wise\nquantization.\nIt takes two input tensors `A` and `B` with scales `As` and `Bs`.\nThe output is returned in the specified `output_dtype`.\nArgs:\n A: The input tensor, e.g., activation.\n B: The input tensor, e.g., weight.\n As: The per-token-group quantization scale for `A`.\n Bs: The per-block quantization scale for `B`.\n block_size: The block size for per-block quantization. It should\n be 2-dim, e.g., [128, 128].\n output_dytpe: The dtype of the returned tensor.\nReturns:\n torch.Tensor: The result of matmul.",
|
| 695 |
"signature": {
|
build/torch211-cu128-x86_64-linux/_ops.py
CHANGED
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@@ -1,9 +1,9 @@
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|
| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
-
ops = torch.ops.
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
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|
| 9 |
-
return f"
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|
|
|
| 1 |
import torch
|
| 2 |
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from . import _vllm_moe_cuda_5fe9dbb
|
| 3 |
+
ops = torch.ops._vllm_moe_cuda_5fe9dbb
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
+
return f"_vllm_moe_cuda_5fe9dbb::{op_name}"
|
build/torch211-cu128-x86_64-linux/{_vllm_moe_cuda_fe2bb66.abi3.so → _vllm_moe_cuda_5fe9dbb.abi3.so}
RENAMED
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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build/torch211-cu128-x86_64-linux/metadata.json
CHANGED
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@@ -1,6 +1,6 @@
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{
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"name": "vllm-moe",
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| 3 |
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"id": "
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"version": 0,
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| 5 |
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| 6 |
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| 28 |
"algorithm": "sha256",
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| 29 |
"files": {
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| 30 |
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| 31 |
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"_ops.py": "
|
| 32 |
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"
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| 33 |
"configs/E=1,N=14336,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json": "iNGsE2ZeVnQEnN4A8UJ9Jv0d3hbRF2MJ9oBgjup5Szk=",
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|
|
@@ -227,7 +227,7 @@
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|
| 227 |
"moe_align_block_size.py": "l461Tq7QMLbX3y1xLWF5wF1iOIlvMnB541RB0R/qM/I=",
|
| 228 |
"platforms.py": "jq0dt/8BuH+9RSs2+mRJxKHHgT+tocNwDmQuJ8DiH3Q=",
|
| 229 |
"scalar_type.py": "cnhBIZKi9kHGHhzxYGWSV8I/QlnPb3MhIor2VEj6igU=",
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-
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"utils/custom_ops.py": "U7XlQM/7PGRr5s8ia6AJi+6TsihQCiD0uMgWHe5PnVw=",
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| 233 |
"utils/marlin_utils.py": "eaqve1NKHWVG9h307Y3T3PEYGeVziE0R4fO/5UShk1Y=",
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@@ -238,11 +238,11 @@
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| 238 |
"provenance": {
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| 240 |
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| 241 |
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| 242 |
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|
| 243 |
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| 244 |
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| 245 |
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"commit": "
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}
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| 1 |
{
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| 2 |
"name": "vllm-moe",
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| 3 |
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| 4 |
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"license": "Apache-2.0",
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| 28 |
"algorithm": "sha256",
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| 29 |
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| 30 |
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"_ops.py": "OZ+OvTHxDPW9aiCr60huQgvxD8ckKL+2urel/0JBLSw=",
|
| 32 |
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"_vllm_moe_cuda_5fe9dbb.abi3.so": "FeK5wduaDsGUryZi9zKnpvOrRI5NN7XWRLy8RyUcY/w=",
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| 34 |
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| 35 |
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| 227 |
"moe_align_block_size.py": "l461Tq7QMLbX3y1xLWF5wF1iOIlvMnB541RB0R/qM/I=",
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| 228 |
"platforms.py": "jq0dt/8BuH+9RSs2+mRJxKHHgT+tocNwDmQuJ8DiH3Q=",
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"scalar_type.py": "cnhBIZKi9kHGHhzxYGWSV8I/QlnPb3MhIor2VEj6igU=",
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"symbols.json": "xHQkwMWlg1pRmWGSz2bmggXnxkI7brpLYv+g8viCbJI=",
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|
| 232 |
"utils/custom_ops.py": "U7XlQM/7PGRr5s8ia6AJi+6TsihQCiD0uMgWHe5PnVw=",
|
| 233 |
"utils/marlin_utils.py": "eaqve1NKHWVG9h307Y3T3PEYGeVziE0R4fO/5UShk1Y=",
|
|
|
|
| 238 |
"provenance": {
|
| 239 |
"kernel-builder": {
|
| 240 |
"version": "0.18.0-dev0",
|
| 241 |
+
"commit": "21bdf69d97a289b9c581a0c60f63deed131647d5",
|
| 242 |
"dirty": false
|
| 243 |
},
|
| 244 |
"kernel": {
|
| 245 |
+
"commit": "5fe9dbb3d45f6f2652958c1f70e5381891fa7aa0",
|
| 246 |
"dirty": false
|
| 247 |
}
|
| 248 |
}
|
build/torch211-cu128-x86_64-linux/metadata.json.sigstore
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
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|
build/torch211-cu128-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "awq_marlin_moe_repack",
|
| 7 |
"qualname": "awq_marlin_moe_repack",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -47,7 +47,7 @@
|
|
| 47 |
{
|
| 48 |
"name": "fused_experts",
|
| 49 |
"qualname": "fused_experts",
|
| 50 |
-
"module": "
|
| 51 |
"kind": "function",
|
| 52 |
"doc": null,
|
| 53 |
"signature": {
|
|
@@ -179,7 +179,7 @@
|
|
| 179 |
{
|
| 180 |
"name": "fused_marlin_moe",
|
| 181 |
"qualname": "fused_marlin_moe",
|
| 182 |
-
"module": "
|
| 183 |
"kind": "function",
|
| 184 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- w1_scale (torch.Tensor): Scale to be used for w1.\n- w2_scale (torch.Tensor): Scale to be used for w2.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.\n- g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.\n- sort_indices1 (Optional[torch.Tensor]): The first act_order input\n permutation.\n- sort_indices2 (Optional[torch.Tensor]): The second act_order input\n permutation.\n- topk_weights (torch.Tensor): Top-k weights.\n- topk_ids (torch.Tensor): Indices of topk-k elements.\n- w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.\n- w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.\n- num_bits (bool): The number of bits in expert weights quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
|
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"signature": {
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@@ -287,7 +287,7 @@
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{
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"name": "fused_moe",
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"qualname": "fused_moe",
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-
"module": "
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"kind": "function",
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"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- topk (int): The number of top-k experts to select.\n- renormalize (bool): If True, renormalize the top-k weights to sum to 1.\n- inplace (bool): If True, perform the operation in-place.\n Defaults to False.\n- activation (str): The activation function to apply after the first\n MoE layer.\n- num_expert_group: Optional[int]: additional parameter for grouped_topk\n- topk_group: Optional[int]: additional parameter for grouped_topk\n- use_grouped_topk: If True, use grouped_topk instead of fused_topk\n note: Deepseekv2 model uses grouped_topk\n- use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner\n products for w1 and w2. Defaults to False.\n- use_int8_w8a16 (bool): If True, use matmul of int8 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- global_num_experts (int): The total number of experts in the global\n expert space.\n- expert_map (Optional[torch.Tensor]): A tensor mapping expert indices\n from the global expert space to the local expert space of the expert\n parallel shard.\n- w1_scale (Optional[torch.Tensor]): Optional scale to be used for\n w1.\n- w2_scale (Optional[torch.Tensor]): Optional scale to be used for\n w2.\n- a1_scale (Optional[torch.Tensor]): Optional scale to be used for\n a1.\n- a2_scale (Optional[torch.Tensor]): Optional scale to be used for\n a2.\n- block_shape: (Optional[List[int]]): Optional block size for block-wise\n quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
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"signature": {
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@@ -449,7 +449,7 @@
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{
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"name": "fused_topk",
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"qualname": "fused_topk",
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-
"module": "
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"kind": "function",
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"doc": null,
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"signature": {
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@@ -485,7 +485,7 @@
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{
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"name": "gptq_marlin_moe_repack",
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| 487 |
"qualname": "gptq_marlin_moe_repack",
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-
"module": "
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"kind": "function",
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"doc": null,
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"signature": {
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@@ -527,7 +527,7 @@
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{
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"name": "grouped_topk",
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"qualname": "grouped_topk",
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-
"module": "
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"kind": "function",
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"doc": null,
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"signature": {
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@@ -587,7 +587,7 @@
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{
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"name": "moe_sum",
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| 589 |
"qualname": "moe_sum",
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-
"module": "
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| 591 |
"kind": "function",
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"doc": null,
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"signature": {
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{
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| 612 |
"name": "per_token_group_quant_fp8",
|
| 613 |
"qualname": "per_token_group_quant_fp8",
|
| 614 |
-
"module": "
|
| 615 |
"kind": "function",
|
| 616 |
"doc": "Function to perform per-token-group quantization on an input tensor `x`.\nIt converts the tensor values into signed float8 values and returns the\nquantized tensor along with the scaling factor used for quantization.\nArgs:\n x: The input tensor with ndim >= 2.\n group_size: The group size used for quantization.\n eps: The minimum to avoid dividing zero.\n dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn`\n is supported for now.\nReturns:\n Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the\n scaling factor for quantization.",
|
| 617 |
"signature": {
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|
@@ -653,7 +653,7 @@
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| 653 |
{
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| 654 |
"name": "topk_softmax",
|
| 655 |
"qualname": "topk_softmax",
|
| 656 |
-
"module": "
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| 657 |
"kind": "function",
|
| 658 |
"doc": null,
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"signature": {
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@@ -689,7 +689,7 @@
|
|
| 689 |
{
|
| 690 |
"name": "w8a8_block_fp8_matmul",
|
| 691 |
"qualname": "w8a8_block_fp8_matmul",
|
| 692 |
-
"module": "
|
| 693 |
"kind": "function",
|
| 694 |
"doc": "This function performs matrix multiplication with block-wise\nquantization.\nIt takes two input tensors `A` and `B` with scales `As` and `Bs`.\nThe output is returned in the specified `output_dtype`.\nArgs:\n A: The input tensor, e.g., activation.\n B: The input tensor, e.g., weight.\n As: The per-token-group quantization scale for `A`.\n Bs: The per-block quantization scale for `B`.\n block_size: The block size for per-block quantization. It should\n be 2-dim, e.g., [128, 128].\n output_dytpe: The dtype of the returned tensor.\nReturns:\n torch.Tensor: The result of matmul.",
|
| 695 |
"signature": {
|
|
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|
| 1 |
{
|
| 2 |
"schema_version": 1,
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| 3 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "awq_marlin_moe_repack",
|
| 7 |
"qualname": "awq_marlin_moe_repack",
|
| 8 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
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|
| 47 |
{
|
| 48 |
"name": "fused_experts",
|
| 49 |
"qualname": "fused_experts",
|
| 50 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
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"kind": "function",
|
| 52 |
"doc": null,
|
| 53 |
"signature": {
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| 179 |
{
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"name": "fused_marlin_moe",
|
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"qualname": "fused_marlin_moe",
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| 182 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_marlin_moe",
|
| 183 |
"kind": "function",
|
| 184 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- w1_scale (torch.Tensor): Scale to be used for w1.\n- w2_scale (torch.Tensor): Scale to be used for w2.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.\n- g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.\n- sort_indices1 (Optional[torch.Tensor]): The first act_order input\n permutation.\n- sort_indices2 (Optional[torch.Tensor]): The second act_order input\n permutation.\n- topk_weights (torch.Tensor): Top-k weights.\n- topk_ids (torch.Tensor): Indices of topk-k elements.\n- w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.\n- w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.\n- num_bits (bool): The number of bits in expert weights quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
|
| 185 |
"signature": {
|
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|
| 287 |
{
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"name": "fused_moe",
|
| 289 |
"qualname": "fused_moe",
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| 290 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
| 291 |
"kind": "function",
|
| 292 |
"doc": "This function computes a Mixture of Experts (MoE) layer using two sets of\nweights, w1 and w2, and top-k gating mechanism.\n\nParameters:\n- hidden_states (torch.Tensor): The input tensor to the MoE layer.\n- w1 (torch.Tensor): The first set of expert weights.\n- w2 (torch.Tensor): The second set of expert weights.\n- gating_output (torch.Tensor): The output of the gating operation\n (before softmax).\n- topk (int): The number of top-k experts to select.\n- renormalize (bool): If True, renormalize the top-k weights to sum to 1.\n- inplace (bool): If True, perform the operation in-place.\n Defaults to False.\n- activation (str): The activation function to apply after the first\n MoE layer.\n- num_expert_group: Optional[int]: additional parameter for grouped_topk\n- topk_group: Optional[int]: additional parameter for grouped_topk\n- use_grouped_topk: If True, use grouped_topk instead of fused_topk\n note: Deepseekv2 model uses grouped_topk\n- use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner\n products for w1 and w2. Defaults to False.\n- use_int8_w8a16 (bool): If True, use matmul of int8 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16\n activation to compute the inner products for w1 and w2.\n Defaults to False.\n- global_num_experts (int): The total number of experts in the global\n expert space.\n- expert_map (Optional[torch.Tensor]): A tensor mapping expert indices\n from the global expert space to the local expert space of the expert\n parallel shard.\n- w1_scale (Optional[torch.Tensor]): Optional scale to be used for\n w1.\n- w2_scale (Optional[torch.Tensor]): Optional scale to be used for\n w2.\n- a1_scale (Optional[torch.Tensor]): Optional scale to be used for\n a1.\n- a2_scale (Optional[torch.Tensor]): Optional scale to be used for\n a2.\n- block_shape: (Optional[List[int]]): Optional block size for block-wise\n quantization.\n\nReturns:\n- torch.Tensor: The output tensor after applying the MoE layer.",
|
| 293 |
"signature": {
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|
|
|
| 449 |
{
|
| 450 |
"name": "fused_topk",
|
| 451 |
"qualname": "fused_topk",
|
| 452 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
| 453 |
"kind": "function",
|
| 454 |
"doc": null,
|
| 455 |
"signature": {
|
|
|
|
| 485 |
{
|
| 486 |
"name": "gptq_marlin_moe_repack",
|
| 487 |
"qualname": "gptq_marlin_moe_repack",
|
| 488 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 489 |
"kind": "function",
|
| 490 |
"doc": null,
|
| 491 |
"signature": {
|
|
|
|
| 527 |
{
|
| 528 |
"name": "grouped_topk",
|
| 529 |
"qualname": "grouped_topk",
|
| 530 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fused_moe",
|
| 531 |
"kind": "function",
|
| 532 |
"doc": null,
|
| 533 |
"signature": {
|
|
|
|
| 587 |
{
|
| 588 |
"name": "moe_sum",
|
| 589 |
"qualname": "moe_sum",
|
| 590 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 591 |
"kind": "function",
|
| 592 |
"doc": null,
|
| 593 |
"signature": {
|
|
|
|
| 611 |
{
|
| 612 |
"name": "per_token_group_quant_fp8",
|
| 613 |
"qualname": "per_token_group_quant_fp8",
|
| 614 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fp8_utils",
|
| 615 |
"kind": "function",
|
| 616 |
"doc": "Function to perform per-token-group quantization on an input tensor `x`.\nIt converts the tensor values into signed float8 values and returns the\nquantized tensor along with the scaling factor used for quantization.\nArgs:\n x: The input tensor with ndim >= 2.\n group_size: The group size used for quantization.\n eps: The minimum to avoid dividing zero.\n dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn`\n is supported for now.\nReturns:\n Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the\n scaling factor for quantization.",
|
| 617 |
"signature": {
|
|
|
|
| 653 |
{
|
| 654 |
"name": "topk_softmax",
|
| 655 |
"qualname": "topk_softmax",
|
| 656 |
+
"module": "_vllm_moe_cuda_5fe9dbb",
|
| 657 |
"kind": "function",
|
| 658 |
"doc": null,
|
| 659 |
"signature": {
|
|
|
|
| 689 |
{
|
| 690 |
"name": "w8a8_block_fp8_matmul",
|
| 691 |
"qualname": "w8a8_block_fp8_matmul",
|
| 692 |
+
"module": "_vllm_moe_cuda_5fe9dbb.fp8_utils",
|
| 693 |
"kind": "function",
|
| 694 |
"doc": "This function performs matrix multiplication with block-wise\nquantization.\nIt takes two input tensors `A` and `B` with scales `As` and `Bs`.\nThe output is returned in the specified `output_dtype`.\nArgs:\n A: The input tensor, e.g., activation.\n B: The input tensor, e.g., weight.\n As: The per-token-group quantization scale for `A`.\n Bs: The per-block quantization scale for `B`.\n block_size: The block size for per-block quantization. It should\n be 2-dim, e.g., [128, 128].\n output_dytpe: The dtype of the returned tensor.\nReturns:\n torch.Tensor: The result of matmul.",
|
| 695 |
"signature": {
|
build/torch212-cu126-x86_64-linux/_ops.py
CHANGED
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@@ -1,9 +1,9 @@
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|
| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
-
ops = torch.ops.
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
-
return f"
|
|
|
|
| 1 |
import torch
|
| 2 |
+
from . import _vllm_moe_cuda_5fe9dbb
|
| 3 |
+
ops = torch.ops._vllm_moe_cuda_5fe9dbb
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
+
return f"_vllm_moe_cuda_5fe9dbb::{op_name}"
|
build/torch212-cu126-x86_64-linux/_vllm_moe_cuda_5fe9dbb.abi3.so
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DELETED
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