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[Kernel] Integrate CUTLASS MoE kernel with PPLX (#18762)

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Signed-off-by: ElizaWszola <ewszola@redhat.com>
Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>
Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
This commit is contained in:
ElizaWszola
2025-06-07 03:26:11 +02:00
committed by GitHub
parent 6e0cd10f72
commit 84166fee97
26 changed files with 918 additions and 409 deletions
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42
csrc/quantization/cutlass_w8a8/moe/moe_data.cu
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@@ -7,7 +7,7 @@
constexpr uint64_t THREADS_PER_EXPERT = 512;
__global__ void compute_problem_sizes(const int* __restrict__ topk_ids,
__global__ void compute_problem_sizes(const uint32_t* __restrict__ topk_ids,
int32_t* problem_sizes1,
int32_t* problem_sizes2,
int32_t* atomic_buffer,
@@ -62,7 +62,7 @@ __global__ void compute_expert_blockscale_offsets(
}
}
__global__ void compute_arg_sorts(const int* __restrict__ topk_ids,
__global__ void compute_arg_sorts(const uint32_t* __restrict__ topk_ids,
const int32_t* __restrict__ expert_offsets,
int32_t* input_permutation,
int32_t* output_permutation,
@@ -103,7 +103,7 @@ void get_cutlass_moe_mm_data_caller(
int num_threads = min(THREADS_PER_EXPERT, topk_ids.numel());
compute_problem_sizes<<<num_experts, num_threads, 0, stream>>>(
static_cast<const int32_t*>(topk_ids.data_ptr()),
static_cast<const uint32_t*>(topk_ids.data_ptr()),
static_cast<int32_t*>(problem_sizes1.data_ptr()),
static_cast<int32_t*>(problem_sizes2.data_ptr()),
static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(), n, k);
@@ -120,10 +120,44 @@ void get_cutlass_moe_mm_data_caller(
static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts);
}
compute_arg_sorts<<<num_experts, num_threads, 0, stream>>>(
static_cast<const int32_t*>(topk_ids.data_ptr()),
static_cast<const uint32_t*>(topk_ids.data_ptr()),
static_cast<const int32_t*>(expert_offsets.data_ptr()),
static_cast<int32_t*>(input_permutation.data_ptr()),
static_cast<int32_t*>(output_permutation.data_ptr()),
static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(),
topk_ids.size(1));
}
__global__ void compute_pplx_data(int32_t* expert_offsets,
int32_t* problem_sizes1,
int32_t* problem_sizes2,
const int32_t* __restrict__ expert_num_tokens,
const int padded_m, const int n,
const int k) {
int expert_idx = threadIdx.x;
expert_offsets[expert_idx] = expert_idx * padded_m;
problem_sizes1[expert_idx * 3] = expert_num_tokens[expert_idx];
problem_sizes1[expert_idx * 3 + 1] = 2 * n;
problem_sizes1[expert_idx * 3 + 2] = k;
problem_sizes2[expert_idx * 3] = expert_num_tokens[expert_idx];
problem_sizes2[expert_idx * 3 + 1] = k;
problem_sizes2[expert_idx * 3 + 2] = n;
}
void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
torch::Tensor& problem_sizes1,
torch::Tensor& problem_sizes2,
const torch::Tensor& expert_num_tokens,
const int64_t num_local_experts,
const int64_t padded_m,
const int64_t n, const int64_t k) {
auto stream = at::cuda::getCurrentCUDAStream(expert_offsets.device().index());
compute_pplx_data<<<1, num_local_experts, 0, stream>>>(
static_cast<int32_t*>(expert_offsets.data_ptr()),
static_cast<int32_t*>(problem_sizes1.data_ptr()),
static_cast<int32_t*>(problem_sizes2.data_ptr()),
static_cast<const int32_t*>(expert_num_tokens.data_ptr()), padded_m, n,
k);
}