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[Perf] Support topk softmax fused kernel for broader num_experts (#22211)

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Signed-off-by: Shixian Cui <shixian@amazon.com>
Co-authored-by: Shixian Cui <shixian@amazon.com>
This commit is contained in:
shixianc 2025-08-12 21:34:47 -07:00 committed by GitHub
parent 77a6bf07ae
commit 4c558cf62e
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GPG Key ID: B5690EEEBB952194
2 changed files with 46 additions and 33 deletions
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77
csrc/moe/topk_softmax_kernels.cu
View File

@ -188,7 +188,9 @@ __launch_bounds__(TPB) __global__ void moeTopK(
It fuses the softmax, max and argmax into a single kernel.
Limitations:
1) This implementation is intended for when the number of experts is a small power of 2.
1) This implementation is optimized for when the number of experts is a small power of 2.
Additionally it also supports when number of experts is multiple of 64 which is still
faster than the computing softmax and topK separately (only tested on CUDA yet).
2) This implementation assumes k is small, but will work for any k.
*/
@ -198,8 +200,6 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
int* source_rows, const int k, const int start_expert, const int end_expert)
{
// We begin by enforcing compile time assertions and setting up compile time constants.
static_assert(VPT == (VPT & -VPT), "VPT must be power of 2");
static_assert(NUM_EXPERTS == (NUM_EXPERTS & -NUM_EXPERTS), "NUM_EXPERTS must be power of 2");
static_assert(BYTES_PER_LDG == (BYTES_PER_LDG & -BYTES_PER_LDG), "BYTES_PER_LDG must be power of 2");
static_assert(BYTES_PER_LDG <= 16, "BYTES_PER_LDG must be leq 16");
@ -407,12 +407,10 @@ struct TopkConstants
};
} // namespace detail
template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, typename IndType>
template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, int MAX_BYTES_PER_LDG, typename IndType>
void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, float* output, IndType* indices,
int* source_row, const int num_rows, const int k, const int start_expert, const int end_expert, cudaStream_t stream)
{
static constexpr std::size_t MAX_BYTES_PER_LDG = 16;
static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(float) * EXPERTS);
using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM>;
static constexpr int VPT = Constants::VPT;
@ -425,21 +423,12 @@ void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, f
input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert);
}
#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB) \
switch (warpSize) { \
case 32: \
topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32>( \
gating_output, nullptr, topk_weights, topk_indices, \
token_expert_indices, num_tokens, topk, 0, num_experts, stream); \
break; \
case 64: \
topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 64>( \
gating_output, nullptr, topk_weights, topk_indices, \
token_expert_indices, num_tokens, topk, 0, num_experts, stream); \
break; \
default: \
TORCH_CHECK(false, "Unsupported warp size: ", warpSize); \
}
#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES) \
static_assert(WARP_SIZE == 32 || WARP_SIZE == 64, \
"Unsupported warp size. Only 32 and 64 are supported."); \
topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, WARP_SIZE, MAX_BYTES>( \
gating_output, nullptr, topk_weights, topk_indices, \
token_expert_indices, num_tokens, topk, 0, num_experts, stream);
template <typename IndType>
void topkGatingSoftmaxKernelLauncher(
@ -453,38 +442,62 @@ void topkGatingSoftmaxKernelLauncher(
const int topk,
cudaStream_t stream) {
static constexpr int WARPS_PER_TB = 4;
auto warpSize = WARP_SIZE;
static constexpr int BYTES_PER_LDG_POWER_OF_2 = 16;
static constexpr int BYTES_PER_LDG_MULTIPLE_64 = 8;
switch (num_experts) {
case 1:
LAUNCH_SOFTMAX(1, WARPS_PER_TB);
LAUNCH_SOFTMAX(1, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
break;
case 2:
LAUNCH_SOFTMAX(2, WARPS_PER_TB);
LAUNCH_SOFTMAX(2, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
break;
case 4:
LAUNCH_SOFTMAX(4, WARPS_PER_TB);
LAUNCH_SOFTMAX(4, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
break;
case 8:
LAUNCH_SOFTMAX(8, WARPS_PER_TB);
LAUNCH_SOFTMAX(8, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
break;
case 16:
LAUNCH_SOFTMAX(16, WARPS_PER_TB);
LAUNCH_SOFTMAX(16, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
break;
case 32:
LAUNCH_SOFTMAX(32, WARPS_PER_TB);
LAUNCH_SOFTMAX(32, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
break;
case 64:
LAUNCH_SOFTMAX(64, WARPS_PER_TB);
LAUNCH_SOFTMAX(64, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
break;
case 128:
LAUNCH_SOFTMAX(128, WARPS_PER_TB);
LAUNCH_SOFTMAX(128, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
break;
case 256:
LAUNCH_SOFTMAX(256, WARPS_PER_TB);
LAUNCH_SOFTMAX(256, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
break;
case 512:
LAUNCH_SOFTMAX(512, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
break;
// (CUDA only) support multiples of 64 when num_experts is not power of 2.
// ROCm uses WARP_SIZE 64 so 8 bytes loading won't fit for some of num_experts,
// alternatively we can test 4 bytes loading and enable it in future.
#ifndef USE_ROCM
case 192:
LAUNCH_SOFTMAX(192, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
break;
case 320:
LAUNCH_SOFTMAX(320, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
break;
case 384:
LAUNCH_SOFTMAX(384, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
break;
case 448:
LAUNCH_SOFTMAX(448, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
break;
case 576:
LAUNCH_SOFTMAX(576, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
break;
#endif
default: {
TORCH_CHECK(softmax_workspace != nullptr,
"softmax_workspace must be provided for num_experts that are not a power of 2.");
"softmax_workspace must be provided for num_experts that are not a power of 2 or multiple of 64.");
static constexpr int TPB = 256;
moeSoftmax<TPB><<<num_tokens, TPB, 0, stream>>>(
gating_output, nullptr, softmax_workspace, num_experts);

2
tests/kernels/moe/test_moe.py
View File

@ -36,7 +36,7 @@ from vllm.model_executor.models.mixtral import MixtralMoE
from vllm.platforms import current_platform
from vllm.scalar_type import ScalarType, scalar_types
NUM_EXPERTS = [8, 64]
NUM_EXPERTS = [8, 64, 192]
EP_SIZE = [1, 4]
TOP_KS = [2, 6]