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369 lines
15 KiB
Plaintext
369 lines
15 KiB
Plaintext
/*
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* Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <torch/all.h>
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#include <cuda_runtime_api.h>
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#include <cuda_runtime.h>
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#include <ATen/cuda/CUDAContext.h>
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#include <c10/cuda/CUDAGuard.h>
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#include <cuda_fp8.h>
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#include "dispatch_utils.h"
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#include "nvfp4_utils.cuh"
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#include "launch_bounds_utils.h"
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namespace vllm {
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// Use UE4M3 by default.
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template <class Type, bool UE8M0_SF = false, bool SMALL_NUM_EXPERTS = false>
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__global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
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cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
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float const* SFScale, uint32_t* out, uint32_t* SFout,
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uint32_t* input_offset_by_experts,
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uint32_t* output_scale_offset_by_experts, int n_experts,
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bool low_latency) {
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using PackedVec = PackedVec<Type>;
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static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
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(CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
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static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
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"Vec size is not matched.");
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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int colsPerRow = numCols / CVT_FP4_ELTS_PER_THREAD;
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// Each global thread processes one element
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for (int globalIdx = tid; globalIdx < numRows * colsPerRow;
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globalIdx += gridDim.x * blockDim.x) {
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// Calculate which row and column this global thread should process
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int rowIdx = globalIdx / colsPerRow;
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int colIdx = globalIdx % colsPerRow;
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int64_t inOffset = rowIdx * colsPerRow + colIdx;
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PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
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// Get the output tensor offset.
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// Same as inOffset because 8 elements are packed into one uint32_t.
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int64_t outOffset = inOffset;
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auto& out_pos = out[outOffset];
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// Find index within the experts using different strategies based on expert
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// count
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int rowIdx_in_expert = 0;
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int expert_idx = 0;
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if constexpr (SMALL_NUM_EXPERTS) {
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for (int i = 0; i < n_experts; i++) {
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uint32_t current_offset = __ldca(&input_offset_by_experts[i]);
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uint32_t next_offset = __ldca(&input_offset_by_experts[i + 1]);
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if (rowIdx >= current_offset && rowIdx < next_offset) {
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rowIdx_in_expert = rowIdx - current_offset;
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expert_idx = i;
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break;
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}
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}
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} else {
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// Load input offsets into registers first, then do the computation.
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// Local array size set to 17 because of register limit.
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uint32_t local_offsets[17];
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for (int chunk_start = 0; chunk_start < n_experts; chunk_start += 16) {
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*reinterpret_cast<int4*>(local_offsets) =
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__ldca(reinterpret_cast<const int4*>(
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&input_offset_by_experts[chunk_start]));
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*reinterpret_cast<int4*>(local_offsets + 4) =
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__ldca(reinterpret_cast<const int4*>(
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&input_offset_by_experts[chunk_start + 4]));
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*reinterpret_cast<int4*>(local_offsets + 8) =
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__ldca(reinterpret_cast<const int4*>(
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&input_offset_by_experts[chunk_start + 8]));
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*reinterpret_cast<int4*>(local_offsets + 12) =
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__ldca(reinterpret_cast<const int4*>(
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&input_offset_by_experts[chunk_start + 12]));
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local_offsets[16] = __ldca(&input_offset_by_experts[chunk_start + 16]);
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// Check against the 16 loaded offsets
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#pragma unroll
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for (int i = 0; i < 16; i++) {
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if (rowIdx >= local_offsets[i] && rowIdx < local_offsets[i + 1]) {
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rowIdx_in_expert = rowIdx - local_offsets[i];
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expert_idx = chunk_start + i;
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break;
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}
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}
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}
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}
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// Get the global scaling factor, which will be applied to the SF.
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// Note SFScale is the same as next GEMM's alpha, which is
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// (448.f / (Alpha_A / 6.f)).
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float const SFScaleVal = SFScale == nullptr ? 1.0f : SFScale[expert_idx];
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int factor = CVT_FP4_SF_VEC_SIZE * 4;
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// The actual output_scales dim is computed from the padded numCols.
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int32_t numCols_padded = (numCols + factor - 1) / factor * factor;
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int numCols_SFout = numCols_padded / CVT_FP4_SF_VEC_SIZE / 4;
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uint32_t* SFout_in_expert =
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SFout + output_scale_offset_by_experts[expert_idx] * numCols_SFout;
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auto sf_out =
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cvt_quant_to_fp4_get_sf_out_offset<uint32_t,
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CVT_FP4_NUM_THREADS_PER_SF>(
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rowIdx_in_expert, colIdx, numCols, SFout_in_expert);
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out_pos = cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(in_vec, SFScaleVal, sf_out);
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}
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}
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// Kernel for LARGE_M_TOPK = true (large m_topk optimized version)
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template <class Type, bool UE8M0_SF = false, bool SMALL_NUM_EXPERTS = false>
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__global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
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cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
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float const* SFScale, uint32_t* out, uint32_t* SFout,
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uint32_t* input_offset_by_experts,
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uint32_t* output_scale_offset_by_experts, int n_experts) {
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using PackedVec = PackedVec<Type>;
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static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
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(CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
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static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
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"Vec size is not matched.");
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extern __shared__ uint32_t shared_input_offsets[];
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// Load input offsets into shared memory.
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// If n_experts is larger than 4, use vectorized int4 to save instructions.
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// If n_experts is smaller than 4, read directly.
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if constexpr (SMALL_NUM_EXPERTS) {
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for (int i = threadIdx.x; i < n_experts + 1; i += blockDim.x) {
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shared_input_offsets[i] = input_offset_by_experts[i];
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}
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} else {
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for (int i = threadIdx.x * 4; i < n_experts; i += blockDim.x * 4) {
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*reinterpret_cast<int4*>(&shared_input_offsets[i]) =
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*reinterpret_cast<const int4*>(&input_offset_by_experts[i]);
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}
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if (threadIdx.x == 0) {
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shared_input_offsets[n_experts] = input_offset_by_experts[n_experts];
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}
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}
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__syncthreads();
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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int colsPerRow = numCols / CVT_FP4_ELTS_PER_THREAD;
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// Each global thread processes one element
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for (int globalIdx = tid; globalIdx < numRows * colsPerRow;
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globalIdx += gridDim.x * blockDim.x) {
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// Calculate which row and column this global thread should process
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int rowIdx = globalIdx / colsPerRow;
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int colIdx = globalIdx % colsPerRow;
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int64_t inOffset = rowIdx * colsPerRow + colIdx;
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PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
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int64_t outOffset = inOffset;
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auto& out_pos = out[outOffset];
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// Find expert using binary search for better performance with large m_topk
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int rowIdx_in_expert = 0;
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int expert_idx = 0;
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// Binary search through experts using shared memory
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int left = 0, right = n_experts - 1;
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while (left <= right) {
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int mid = (left + right) / 2;
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// Get offsets: shared_input_offsets[i] corresponds to
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// input_offset_by_experts[i]
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uint32_t mid_offset = shared_input_offsets[mid];
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uint32_t next_offset = shared_input_offsets[mid + 1];
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if (rowIdx >= mid_offset && rowIdx < next_offset) {
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rowIdx_in_expert = rowIdx - mid_offset;
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expert_idx = mid;
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break;
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} else if (rowIdx < mid_offset) {
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right = mid - 1;
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} else {
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left = mid + 1;
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}
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}
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float const SFScaleVal = SFScale == nullptr ? 1.0f : SFScale[expert_idx];
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int factor = CVT_FP4_SF_VEC_SIZE * 4;
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int32_t numCols_padded = (numCols + factor - 1) / factor * factor;
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int numCols_SFout = numCols_padded / CVT_FP4_SF_VEC_SIZE / 4;
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uint32_t* SFout_in_expert =
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SFout + output_scale_offset_by_experts[expert_idx] * numCols_SFout;
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auto sf_out =
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cvt_quant_to_fp4_get_sf_out_offset<uint32_t,
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CVT_FP4_NUM_THREADS_PER_SF>(
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rowIdx_in_expert, colIdx, numCols, SFout_in_expert);
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out_pos = cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(in_vec, SFScaleVal, sf_out);
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}
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}
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template <typename T>
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void quant_impl(void* output, void* output_scale, void* input,
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void* input_global_scale, void* input_offset_by_experts,
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void* output_scale_offset_by_experts, int m_topk, int k,
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int n_experts, cudaStream_t stream) {
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// TODO: this multiProcessorCount should be cached.
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int device;
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cudaGetDevice(&device);
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int multiProcessorCount;
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cudaDeviceGetAttribute(&multiProcessorCount, cudaDevAttrMultiProcessorCount,
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device);
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// Grid, Block size.
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// Each thread converts 8 values.
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int const workSizePerRow = k / ELTS_PER_THREAD;
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int const totalWorkSize = m_topk * workSizePerRow;
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dim3 block(std::min(workSizePerRow, 512));
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// Get number of blocks per SM
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int const numBlocksPerSM =
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vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
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dim3 grid(std::min(static_cast<int>((totalWorkSize + block.x - 1) / block.x),
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multiProcessorCount * numBlocksPerSM));
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while (grid.x <= multiProcessorCount && block.x > 64) {
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grid.x *= 2;
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block.x = (block.x + 1) / 2;
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}
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int const blockRepeat =
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(totalWorkSize + block.x * grid.x - 1) / (block.x * grid.x);
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if (blockRepeat > 1) {
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size_t shared_mem_size = (n_experts + 1) * sizeof(uint32_t);
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if (n_experts >= 4) {
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cvt_fp16_to_fp4<T, false, false>
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<<<grid, block, shared_mem_size, stream>>>(
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m_topk, k, reinterpret_cast<T*>(input),
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reinterpret_cast<float*>(input_global_scale),
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reinterpret_cast<uint32_t*>(output),
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reinterpret_cast<uint32_t*>(output_scale),
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reinterpret_cast<uint32_t*>(input_offset_by_experts),
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reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
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n_experts);
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} else {
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cvt_fp16_to_fp4<T, false, true><<<grid, block, shared_mem_size, stream>>>(
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m_topk, k, reinterpret_cast<T*>(input),
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reinterpret_cast<float*>(input_global_scale),
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reinterpret_cast<uint32_t*>(output),
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reinterpret_cast<uint32_t*>(output_scale),
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reinterpret_cast<uint32_t*>(input_offset_by_experts),
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reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
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n_experts);
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}
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} else {
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if (n_experts >= 16) {
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cvt_fp16_to_fp4<T, false, false><<<grid, block, 0, stream>>>(
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m_topk, k, reinterpret_cast<T*>(input),
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reinterpret_cast<float*>(input_global_scale),
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reinterpret_cast<uint32_t*>(output),
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reinterpret_cast<uint32_t*>(output_scale),
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reinterpret_cast<uint32_t*>(input_offset_by_experts),
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reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
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n_experts, /* bool low_latency */ true);
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} else {
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cvt_fp16_to_fp4<T, false, true><<<grid, block, 0, stream>>>(
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m_topk, k, reinterpret_cast<T*>(input),
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reinterpret_cast<float*>(input_global_scale),
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reinterpret_cast<uint32_t*>(output),
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reinterpret_cast<uint32_t*>(output_scale),
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reinterpret_cast<uint32_t*>(input_offset_by_experts),
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reinterpret_cast<uint32_t*>(output_scale_offset_by_experts),
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n_experts, /* bool low_latency */ true);
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}
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}
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}
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} // namespace vllm
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/*Quantization entry for fp4 experts quantization*/
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#define CHECK_TH_CUDA(x, m) TORCH_CHECK(x.is_cuda(), m, "must be a CUDA tensor")
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#define CHECK_CONTIGUOUS(x, m) \
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TORCH_CHECK(x.is_contiguous(), m, "must be contiguous")
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#define CHECK_INPUT(x, m) \
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CHECK_TH_CUDA(x, m); \
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CHECK_CONTIGUOUS(x, m);
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constexpr auto HALF = at::ScalarType::Half;
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constexpr auto BF16 = at::ScalarType::BFloat16;
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constexpr auto FLOAT = at::ScalarType::Float;
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constexpr auto INT = at::ScalarType::Int;
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constexpr auto UINT8 = at::ScalarType::Byte;
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void scaled_fp4_experts_quant_sm1xxa(
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torch::Tensor& output, torch::Tensor& output_scale,
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torch::Tensor const& input, torch::Tensor const& input_global_scale,
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torch::Tensor const& input_offset_by_experts,
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torch::Tensor const& output_scale_offset_by_experts) {
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CHECK_INPUT(output, "output must be a CUDA tensor");
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CHECK_INPUT(output_scale, "output_scale must be a CUDA tensor");
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CHECK_INPUT(input, "input must be a CUDA tensor");
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CHECK_INPUT(input_global_scale, "input_global_scale must be a CUDA tensor");
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CHECK_INPUT(input_offset_by_experts,
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"input_offset_by_experts must be a CUDA tensor");
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CHECK_INPUT(output_scale_offset_by_experts,
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"output_scale_offset_by_experts must be a CUDA tensor");
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TORCH_CHECK(output.dim() == 2);
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TORCH_CHECK(output_scale.dim() == 2);
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TORCH_CHECK(input.dim() == 2);
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TORCH_CHECK(input_global_scale.dim() == 1);
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TORCH_CHECK(input_offset_by_experts.dim() == 1);
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TORCH_CHECK(output_scale_offset_by_experts.dim() == 1);
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TORCH_CHECK(input.scalar_type() == HALF || input.scalar_type() == BF16);
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TORCH_CHECK(input_global_scale.scalar_type() == FLOAT);
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TORCH_CHECK(input_offset_by_experts.scalar_type() == INT);
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TORCH_CHECK(output_scale_offset_by_experts.scalar_type() == INT);
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// output is uint8 (two nvfp4 values are packed into one uint8)
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// output_scale is int32 (four fp8 values are packed into one int32)
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TORCH_CHECK(output.scalar_type() == UINT8);
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TORCH_CHECK(output_scale.scalar_type() == INT);
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const int BLOCK_SIZE = 16;
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auto m_topk = input.size(0);
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auto k = input.size(1);
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TORCH_CHECK(k % BLOCK_SIZE == 0, "k must be a multiple of 16");
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auto n_experts = input_global_scale.size(0);
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TORCH_CHECK(input_offset_by_experts.size(0) == n_experts + 1);
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TORCH_CHECK(output_scale_offset_by_experts.size(0) == n_experts + 1);
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TORCH_CHECK(output.size(0) == m_topk);
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TORCH_CHECK(output.size(1) == k / 2);
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int scales_k = k / BLOCK_SIZE;
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// 4 means the swizzle requirement by nvidia nvfp4.
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int padded_k = (scales_k + (4 - 1)) / 4 * 4;
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// 4 means 4 fp8 values are packed into one int32
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TORCH_CHECK(output_scale.size(1) * 4 == padded_k);
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const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
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const cudaStream_t stream =
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at::cuda::getCurrentCUDAStream(input.get_device());
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VLLM_DISPATCH_HALF_TYPES(
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input.scalar_type(), "nvfp4_experts_quant_kernel", [&] {
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using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
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vllm::quant_impl<cuda_type>(
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output.data_ptr(), output_scale.data_ptr(), input.data_ptr(),
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input_global_scale.data_ptr(), input_offset_by_experts.data_ptr(),
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output_scale_offset_by_experts.data_ptr(), m_topk, k, n_experts,
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stream);
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});
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}
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