mirror of
https://github.com/vllm-project/vllm.git
synced 2026-06-06 00:16:14 +00:00
Jee Jee Li
559d6710bf
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com> Signed-off-by: qianlihuang <91178480+qianlihuang@users.noreply.github.com> Co-authored-by: Yiliu Dong <91178480+qianlihuang@users.noreply.github.com>
224 lines
7.2 KiB
Plaintext
224 lines
7.2 KiB
Plaintext
// SPDX-License-Identifier: Apache-2.0
|
||
// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||
//
|
||
// Router GEMM: activation(T) x weight(fp32) -> fp32, H=3072, E=256, M<=32.
|
||
// Supports bf16 or fp32 activation; weight is always fp32.
|
||
// Adapted from dsv3_router_gemm_float_out.cu.
|
||
|
||
#include <cuda_bf16.h>
|
||
#include <cuda_runtime.h>
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Load helpers
|
||
// ---------------------------------------------------------------------------
|
||
|
||
// Load VPT fp32 values from the weight matrix (always fp32).
|
||
// VPT=4 when activation is fp32 (one float4 load)
|
||
// VPT=8 when activation is bf16 (two float4 loads)
|
||
template <int VPT>
|
||
__device__ __forceinline__ void load_weight(float const* ptr, float* dst);
|
||
|
||
template <>
|
||
__device__ __forceinline__ void load_weight<4>(float const* ptr, float* dst) {
|
||
float4 v = *reinterpret_cast<float4 const*>(ptr);
|
||
dst[0] = v.x;
|
||
dst[1] = v.y;
|
||
dst[2] = v.z;
|
||
dst[3] = v.w;
|
||
}
|
||
|
||
template <>
|
||
__device__ __forceinline__ void load_weight<8>(float const* ptr, float* dst) {
|
||
float4 v0 = *reinterpret_cast<float4 const*>(ptr);
|
||
float4 v1 = *reinterpret_cast<float4 const*>(ptr + 4);
|
||
dst[0] = v0.x;
|
||
dst[1] = v0.y;
|
||
dst[2] = v0.z;
|
||
dst[3] = v0.w;
|
||
dst[4] = v1.x;
|
||
dst[5] = v1.y;
|
||
dst[6] = v1.z;
|
||
dst[7] = v1.w;
|
||
}
|
||
|
||
// Load VPT activation values and convert to fp32.
|
||
template <typename T, int VPT>
|
||
__device__ __forceinline__ void load_activation(T const* ptr, float* dst);
|
||
|
||
// fp32 activation: one float4 load, no conversion needed.
|
||
template <>
|
||
__device__ __forceinline__ void load_activation<float, 4>(float const* ptr,
|
||
float* dst) {
|
||
float4 v = *reinterpret_cast<float4 const*>(ptr);
|
||
dst[0] = v.x;
|
||
dst[1] = v.y;
|
||
dst[2] = v.z;
|
||
dst[3] = v.w;
|
||
}
|
||
|
||
// bf16 activation: one uint4 load (8 × bf16) + element-wise conversion.
|
||
template <>
|
||
__device__ __forceinline__ void load_activation<__nv_bfloat16, 8>(
|
||
__nv_bfloat16 const* ptr, float* dst) {
|
||
uint4 v = *reinterpret_cast<uint4 const*>(ptr);
|
||
__nv_bfloat16 const* bf16_ptr = reinterpret_cast<__nv_bfloat16 const*>(&v);
|
||
#pragma unroll
|
||
for (int i = 0; i < 8; i++) dst[i] = __bfloat162float(bf16_ptr[i]);
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Kernel
|
||
// ---------------------------------------------------------------------------
|
||
|
||
// InputT : type of activation (float or __nv_bfloat16)
|
||
// Weight is always fp32; output is always fp32.
|
||
// VPT = 16 / sizeof(InputT): 4 for fp32, 8 for bf16
|
||
template <typename InputT, int kBlockSize, int kNumTokens, int kNumExperts,
|
||
int kHiddenDim>
|
||
__global__ __launch_bounds__(128, 1) void fp32_router_gemm_kernel(
|
||
float* out, InputT const* mat_a, float const* mat_b) {
|
||
constexpr int VPT = 16 / sizeof(InputT);
|
||
constexpr int k_elems_per_k_iteration = VPT * kBlockSize;
|
||
constexpr int k_iterations = kHiddenDim / k_elems_per_k_iteration;
|
||
constexpr int kWarpSize = 32;
|
||
constexpr int kNumWarps = kBlockSize / kWarpSize;
|
||
|
||
int const n_idx = blockIdx.x;
|
||
int const tid = threadIdx.x;
|
||
int const warpId = tid / kWarpSize;
|
||
int const laneId = tid % kWarpSize;
|
||
|
||
float acc[kNumTokens] = {};
|
||
__shared__ float sm_reduction[kNumTokens][kNumWarps];
|
||
|
||
float const* b_col = mat_b + n_idx * kHiddenDim;
|
||
|
||
int k_bases[k_iterations];
|
||
#pragma unroll
|
||
for (int ki = 0; ki < k_iterations; ki++) {
|
||
k_bases[ki] = ki * k_elems_per_k_iteration + tid * VPT;
|
||
}
|
||
|
||
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
|
||
asm volatile("griddepcontrol.wait;");
|
||
#endif
|
||
|
||
for (int ki = 0; ki < k_iterations; ki++) {
|
||
int const k_base = k_bases[ki];
|
||
|
||
float b_float[VPT];
|
||
load_weight<VPT>(b_col + k_base, b_float);
|
||
|
||
#pragma unroll
|
||
for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
|
||
float a_float[VPT];
|
||
load_activation<InputT, VPT>(mat_a + m_idx * kHiddenDim + k_base,
|
||
a_float);
|
||
#pragma unroll
|
||
for (int k = 0; k < VPT; k++) {
|
||
acc[m_idx] += a_float[k] * b_float[k];
|
||
}
|
||
}
|
||
}
|
||
|
||
// Warp-level butterfly reduction
|
||
#pragma unroll
|
||
for (int m = 0; m < kNumTokens; m++) {
|
||
float sum = acc[m];
|
||
sum += __shfl_xor_sync(0xffffffff, sum, 16);
|
||
sum += __shfl_xor_sync(0xffffffff, sum, 8);
|
||
sum += __shfl_xor_sync(0xffffffff, sum, 4);
|
||
sum += __shfl_xor_sync(0xffffffff, sum, 2);
|
||
sum += __shfl_xor_sync(0xffffffff, sum, 1);
|
||
if (laneId == 0) sm_reduction[m][warpId] = sum;
|
||
}
|
||
|
||
__syncthreads();
|
||
|
||
if (tid == 0) {
|
||
#pragma unroll
|
||
for (int m = 0; m < kNumTokens; m++) {
|
||
float final_sum = 0.0f;
|
||
#pragma unroll
|
||
for (int w = 0; w < kNumWarps; w++) final_sum += sm_reduction[m][w];
|
||
out[m * kNumExperts + n_idx] = final_sum;
|
||
}
|
||
}
|
||
|
||
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
|
||
asm volatile("griddepcontrol.launch_dependents;");
|
||
#endif
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Launcher
|
||
// ---------------------------------------------------------------------------
|
||
|
||
template <typename InputT, int kNumTokens, int kNumExperts, int kHiddenDim>
|
||
void invokeFp32RouterGemm(float* output, InputT const* mat_a,
|
||
float const* mat_b, cudaStream_t stream) {
|
||
constexpr int kBlockSize = 128;
|
||
cudaLaunchConfig_t config;
|
||
config.gridDim = kNumExperts;
|
||
config.blockDim = kBlockSize;
|
||
config.dynamicSmemBytes = 0;
|
||
config.stream = stream;
|
||
cudaLaunchAttribute attrs[1];
|
||
attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
|
||
attrs[0].val.programmaticStreamSerializationAllowed = 1;
|
||
config.numAttrs = 1;
|
||
config.attrs = attrs;
|
||
cudaLaunchKernelEx(&config,
|
||
fp32_router_gemm_kernel<InputT, kBlockSize, kNumTokens,
|
||
kNumExperts, kHiddenDim>,
|
||
output, mat_a, mat_b);
|
||
}
|
||
|
||
// ---------------------------------------------------------------------------
|
||
// Explicit instantiations: M=1..32, E=256, H=3072, for both input types
|
||
// ---------------------------------------------------------------------------
|
||
|
||
#define INSTANTIATE(T, M) \
|
||
template void invokeFp32RouterGemm<T, M, 256, 3072>( \
|
||
float*, T const*, float const*, cudaStream_t);
|
||
|
||
#define INSTANTIATE_ALL(T) \
|
||
INSTANTIATE(T, 1) \
|
||
INSTANTIATE(T, 2) \
|
||
INSTANTIATE(T, 3) \
|
||
INSTANTIATE(T, 4) \
|
||
INSTANTIATE(T, 5) \
|
||
INSTANTIATE(T, 6) \
|
||
INSTANTIATE(T, 7) \
|
||
INSTANTIATE(T, 8) \
|
||
INSTANTIATE(T, 9) \
|
||
INSTANTIATE(T, 10) \
|
||
INSTANTIATE(T, 11) \
|
||
INSTANTIATE(T, 12) \
|
||
INSTANTIATE(T, 13) \
|
||
INSTANTIATE(T, 14) \
|
||
INSTANTIATE(T, 15) \
|
||
INSTANTIATE(T, 16) \
|
||
INSTANTIATE(T, 17) \
|
||
INSTANTIATE(T, 18) \
|
||
INSTANTIATE(T, 19) \
|
||
INSTANTIATE(T, 20) \
|
||
INSTANTIATE(T, 21) \
|
||
INSTANTIATE(T, 22) \
|
||
INSTANTIATE(T, 23) \
|
||
INSTANTIATE(T, 24) \
|
||
INSTANTIATE(T, 25) \
|
||
INSTANTIATE(T, 26) \
|
||
INSTANTIATE(T, 27) \
|
||
INSTANTIATE(T, 28) \
|
||
INSTANTIATE(T, 29) \
|
||
INSTANTIATE(T, 30) \
|
||
INSTANTIATE(T, 31) \
|
||
INSTANTIATE(T, 32)
|
||
|
||
INSTANTIATE_ALL(float)
|
||
INSTANTIATE_ALL(__nv_bfloat16)
|
||
|
||
#undef INSTANTIATE_ALL
|
||
#undef INSTANTIATE
|