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[Bugfix][ROCm][Build] Fix build regression on ROCm (#21393)

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Signed-off-by: Gregory Shtrasberg <Gregory.Shtrasberg@amd.com>
This commit is contained in:
Gregory Shtrasberg
2025-07-22 23:27:41 -04:00
committed by GitHub
parent c401c64b4c
commit 3ec7170ff1
3 changed files with 16 additions and 16 deletions
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4
CMakeLists.txt
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@ -245,7 +245,6 @@ set(VLLM_EXT_SRC
"csrc/quantization/gptq/q_gemm.cu"
"csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
"csrc/quantization/fp8/common.cu"
"csrc/quantization/fp8/per_token_group_quant.cu"
"csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu"
"csrc/quantization/gguf/gguf_kernel.cu"
"csrc/quantization/activation_kernels.cu"
@ -297,7 +296,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
"csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu"
"csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
"csrc/cutlass_extensions/common.cpp"
"csrc/attention/mla/cutlass_mla_entry.cu")
"csrc/attention/mla/cutlass_mla_entry.cu"
"csrc/quantization/fp8/per_token_group_quant.cu")
set_gencode_flags_for_srcs(
SRCS "${VLLM_EXT_SRC}"

10
csrc/ops.h
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@ -287,6 +287,11 @@ void scaled_fp4_experts_quant(
torch::Tensor const& input, torch::Tensor const& input_global_scale,
torch::Tensor const& input_offset_by_experts,
torch::Tensor const& output_scale_offset_by_experts);
void per_token_group_quant_fp8(const torch::Tensor& input,
torch::Tensor& output_q, torch::Tensor& output_s,
int64_t group_size, double eps, double fp8_min,
double fp8_max, bool scale_ue8m0);
#endif
void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,
@ -297,11 +302,6 @@ void dynamic_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,
torch::Tensor& scales,
std::optional<torch::Tensor> const& azp);
void per_token_group_quant_fp8(const torch::Tensor& input,
torch::Tensor& output_q, torch::Tensor& output_s,
int64_t group_size, double eps, double fp8_min,
double fp8_max, bool scale_ue8m0);
torch::Tensor gptq_gemm(torch::Tensor a, torch::Tensor b_q_weight,
torch::Tensor b_gptq_qzeros,
torch::Tensor b_gptq_scales, torch::Tensor b_g_idx,

18
csrc/torch_bindings.cpp
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@ -601,15 +601,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
ops.impl("dynamic_scaled_int8_quant", torch::kCUDA,
&dynamic_scaled_int8_quant);
// Compute per-token-group FP8 quantized tensor and scaling factor.
ops.def(
"per_token_group_fp8_quant(Tensor input, Tensor! output_q, Tensor! "
"output_s, "
"int group_size, float eps, float fp8_min, float fp8_max, bool "
"scale_ue8m0) -> ()");
ops.impl("per_token_group_fp8_quant", torch::kCUDA,
&per_token_group_quant_fp8);
// Mamba selective scan kernel
ops.def(
"selective_scan_fwd(Tensor! u, Tensor! delta,"
@ -624,6 +615,15 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
ops.impl("selective_scan_fwd", torch::kCUDA, &selective_scan_fwd);
#ifndef USE_ROCM
// Compute per-token-group FP8 quantized tensor and scaling factor.
ops.def(
"per_token_group_fp8_quant(Tensor input, Tensor! output_q, Tensor! "
"output_s, "
"int group_size, float eps, float fp8_min, float fp8_max, bool "
"scale_ue8m0) -> ()");
ops.impl("per_token_group_fp8_quant", torch::kCUDA,
&per_token_group_quant_fp8);
// reorder weight for AllSpark Ampere W8A16 Fused Gemm kernel
ops.def(
"rearrange_kn_weight_as_n32k16_order(Tensor b_qweight, Tensor b_scales, "