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cleanup at::Tag::needs_fixed_stride_order (#28974)

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Signed-off-by: Boyuan Feng <boyuan@meta.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>
This commit is contained in:
Boyuan Feng 2025-11-20 02:51:36 -08:00 committed by GitHub
parent 322cb02872
commit a903d59ffa
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GPG Key ID: B5690EEEBB952194
2 changed files with 20 additions and 51 deletions
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7
csrc/cpu/torch_bindings.cpp
View File

@ -172,7 +172,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
// Quantization
#if defined(__AVX512F__) || (defined(__aarch64__) && !defined(__APPLE__)) || \
defined(__powerpc64__)
at::Tag stride_tag = at::Tag::needs_fixed_stride_order;
// Helper function to release oneDNN handlers
ops.def("release_dnnl_matmul_handler(int handler) -> ()",
&release_dnnl_matmul_handler);
@ -208,15 +207,13 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
// Compute int8 quantized tensor for given scaling factor.
ops.def(
"static_scaled_int8_quant(Tensor! out, Tensor input, Tensor scale,"
"Tensor? azp) -> ()",
{stride_tag});
"Tensor? azp) -> ()");
ops.impl("static_scaled_int8_quant", torch::kCPU, &static_scaled_int8_quant);
// Compute int8 quantized tensor and scaling factor
ops.def(
"dynamic_scaled_int8_quant(Tensor! out, Tensor input, Tensor! scale, "
"Tensor!? azp) -> ()",
{stride_tag});
"Tensor!? azp) -> ()");
ops.impl("dynamic_scaled_int8_quant", torch::kCPU,
&dynamic_scaled_int8_quant);
#endif

64
csrc/torch_bindings.cpp
View File

@ -20,18 +20,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
// vLLM custom ops
//
// The default behavior in PyTorch 2.6 was changed to "requires_contiguous",
// so we need
// to override this for many GEMMs with the following tag. Otherwise,
// torch.compile will force all input tensors to be contiguous(), which
// will break many custom ops that require column-major weight matrices.
// This was a bug and PyTorch 2.7 has since fixed this.
#if TORCH_VERSION_MAJOR == 2 && TORCH_VERSION_MINOR == 6
#define stride_tag at::Tag::needs_fixed_stride_order
#else
#define stride_tag
#endif
ops.def(
"persistent_masked_m_silu_mul_quant(Tensor input, Tensor counts, Tensor! "
"y_q, Tensor! y_s,"
@ -241,15 +229,13 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
// Quantized GEMM for AWQ.
ops.def(
"awq_gemm(Tensor _in_feats, Tensor _kernel, Tensor _scaling_factors, "
"Tensor _zeros, SymInt split_k_iters) -> Tensor",
{stride_tag});
"Tensor _zeros, SymInt split_k_iters) -> Tensor");
ops.impl("awq_gemm", torch::kCUDA, &awq_gemm);
// Dequantization for AWQ.
ops.def(
"awq_dequantize(Tensor _kernel, Tensor _scaling_factors, "
"Tensor _zeros, SymInt split_k_iters, int thx, int thy) -> Tensor",
{stride_tag});
"Tensor _zeros, SymInt split_k_iters, int thx, int thy) -> Tensor");
ops.impl("awq_dequantize", torch::kCUDA, &awq_dequantize);
// Note about marlin kernel 'workspace' arguments:
@ -271,8 +257,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
"gptq_marlin_24_gemm(Tensor a, Tensor b_q_weight, Tensor b_meta, "
"Tensor b_scales, Tensor workspace, "
"int b_q_type, "
"SymInt size_m, SymInt size_n, SymInt size_k) -> Tensor",
{stride_tag});
"SymInt size_m, SymInt size_n, SymInt size_k) -> Tensor");
// conditionally compiled so impl in source file
// Machete (Dense) Optimized Mixed Precision GEMM for Hopper.
@ -298,8 +283,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
" Tensor? channel_scales,"
" Tensor? token_scales,"
" str? schedule"
") -> Tensor",
{stride_tag});
") -> Tensor");
ops.def(
"machete_prepack_B("
" Tensor B,"
@ -319,8 +303,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
"Tensor b_scales, Tensor? global_scale, Tensor? b_zeros_or_none, Tensor? "
"g_idx_or_none, Tensor? perm_or_none, Tensor workspace, int b_q_type, "
"SymInt size_m, SymInt size_n, SymInt size_k, bool is_k_full, "
"bool use_atomic_add, bool use_fp32_reduce, bool is_zp_float) -> Tensor",
{stride_tag});
"bool use_atomic_add, bool use_fp32_reduce, bool is_zp_float) -> Tensor");
// conditionally compiled so impl registration is in source file
// gptq_marlin repack from GPTQ.
@ -346,8 +329,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
" Tensor token_scales,"
" ScalarType? out_type,"
" str? maybe_schedule"
") -> Tensor",
{stride_tag});
") -> Tensor");
// pack scales
ops.def("cutlass_pack_scale_fp8(Tensor scales) -> Tensor");
// encode and reorder weight matrix
@ -394,24 +376,21 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
ops.def(
"cutlass_scaled_fp4_mm(Tensor! out, Tensor a, Tensor b,"
" Tensor block_scale_a, Tensor block_scale_b,"
" Tensor alpha) -> ()",
{stride_tag});
" Tensor alpha) -> ()");
ops.impl("cutlass_scaled_fp4_mm", torch::kCUDA, &cutlass_scaled_fp4_mm);
// cutlass blockwise scaledgroup GEMM
ops.def(
"cutlass_blockwise_scaled_grouped_mm(Tensor! output, Tensor a, Tensor b, "
"Tensor scales_a, Tensor scales_b, "
"Tensor problem_sizes, Tensor expert_offsets) -> ()",
{stride_tag});
"Tensor problem_sizes, Tensor expert_offsets) -> ()");
// conditionally compiled so impl registration is in source file
// cutlass nvfp4 block scaled group GEMM
ops.def(
"cutlass_fp4_group_mm(Tensor! out, Tensor a, Tensor b,"
" Tensor a_blockscale, Tensor b_blockscales, Tensor alphas,"
" Tensor problem_sizes, Tensor expert_offsets, Tensor sf_offsets) -> ()",
{stride_tag});
" Tensor problem_sizes, Tensor expert_offsets, Tensor sf_offsets) -> ()");
// conditionally compiled so impl registration is in source file
// CUTLASS w8a8 GEMM, supporting symmetric per-tensor or per-row/column
@ -419,8 +398,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
ops.def(
"cutlass_scaled_mm(Tensor! out, Tensor a,"
" Tensor b, Tensor a_scales,"
" Tensor b_scales, Tensor? bias) -> ()",
{stride_tag});
" Tensor b_scales, Tensor? bias) -> ()");
ops.impl("cutlass_scaled_mm", torch::kCUDA, &cutlass_scaled_mm);
// CUTLASS w8a8 GEMM, supporting asymmetric per-tensor or per-row/column
@ -429,8 +407,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
"cutlass_scaled_mm_azp(Tensor! out, Tensor a,"
" Tensor b, Tensor a_scales,"
" Tensor b_scales, Tensor azp_adj,"
" Tensor? azp, Tensor? bias) -> ()",
{stride_tag});
" Tensor? azp, Tensor? bias) -> ()");
ops.impl("cutlass_scaled_mm_azp", torch::kCUDA, &cutlass_scaled_mm_azp);
// Check if cutlass scaled_mm is supported for CUDA devices of the given
@ -449,8 +426,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
" Tensor a_scales, Tensor b_scales, Tensor expert_offsets, "
" Tensor problem_sizes, Tensor a_strides, "
" Tensor b_strides, Tensor c_strides, bool per_act_token, "
" bool per_out_ch) -> ()",
{stride_tag});
" bool per_out_ch) -> ()");
ops.impl("cutlass_moe_mm", torch::kCUDA, &cutlass_moe_mm);
// A function that computes data required to run fused MoE with w8a8 grouped
@ -464,8 +440,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
" Tensor! problem_sizes1, Tensor! problem_sizes2, "
" Tensor! input_permutation, "
" Tensor! output_permutation, int num_experts, "
" int n, int k, Tensor? blockscale_offsets) -> ()",
{stride_tag});
" int n, int k, Tensor? blockscale_offsets) -> "
"()");
ops.impl("get_cutlass_moe_mm_data", torch::kCUDA, &get_cutlass_moe_mm_data);
// A function that computes problem sizes for each expert's multiplication
@ -476,8 +452,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
" Tensor! problem_sizes1, "
" Tensor! problem_sizes2, "
" int num_experts, int n, int k, "
" Tensor? blockscale_offsets) -> ()",
{stride_tag});
" Tensor? blockscale_offsets) -> ()");
ops.impl("get_cutlass_moe_mm_problem_sizes", torch::kCUDA,
&get_cutlass_moe_mm_problem_sizes);
@ -492,8 +467,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
" Tensor! problem_sizes2, "
" Tensor expert_num_tokens, "
" int num_local_experts, int padded_m, "
" int n, int k) -> ()",
{stride_tag});
" int n, int k) -> ()");
ops.impl("get_cutlass_pplx_moe_mm_data", torch::kCUDA,
&get_cutlass_pplx_moe_mm_data);
@ -517,8 +491,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
"cutlass_scaled_sparse_mm(Tensor! out, Tensor a,"
" Tensor bt_nzs,"
" Tensor bt_meta, Tensor a_scales,"
" Tensor b_scales, Tensor? bias) -> ()",
{stride_tag});
" Tensor b_scales, Tensor? bias) -> ()");
ops.impl("cutlass_scaled_sparse_mm", torch::kCUDA, &cutlass_scaled_sparse_mm);
// CUTLASS sparse matrix compressor
@ -567,8 +540,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
"gptq_gemm(Tensor a, Tensor b_q_weight, Tensor b_gptq_qzeros, "
"Tensor b_gptq_scales, Tensor b_g_idx, bool use_exllama, bool "
"use_v2_format, int bit) "
"-> Tensor",
{stride_tag});
"-> Tensor");
ops.impl("gptq_gemm", torch::kCUDA, &gptq_gemm);
// Post processing for GPTQ.