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[Bugfix] Fix persistent_masked_m_silu_mul_quant tests #28366

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Merged
merged 2 commits into from
Nov 10, 2025
Merged

[Bugfix] Fix persistent_masked_m_silu_mul_quant tests #28366

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7a340e7
fallback to 1-warp config in edge case
Nov 9, 2025
e7c5900
fixers
Nov 10, 2025
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15 changes: 10 additions & 5 deletions csrc/quantization/activation_kernels.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -578,11 +578,13 @@ void persistent_masked_m_silu_mul_quant(

// This kernel currently only supports H % 128 == 0 and assumes a
// fixed GROUP_SIZE of 128.
static constexpr int GROUP_SIZE = 128;

TORCH_CHECK(input.dtype() == torch::kBFloat16);
TORCH_CHECK(y_q.dtype() == torch::kFloat8_e4m3fn ||
y_q.dtype() == torch::kFloat8_e4m3fnuz);
TORCH_CHECK(y_s.dtype() == torch::kFloat32);
TORCH_CHECK(input.size(-1) % 256 == 0);
TORCH_CHECK(input.size(-1) % (GROUP_SIZE * 2) == 0);

using Idx_t = int64_t;

Expand All @@ -601,8 +603,6 @@ void persistent_masked_m_silu_mul_quant(

Idx_t stride_counts_e = tokens_per_expert.stride(0);

static constexpr int GROUP_SIZE = 128;

const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

#define KERNEL(BLOCK_COUNT, USE_UE8M0, THREAD_COUNT, STAGES) \
Expand All @@ -628,21 +628,26 @@ void persistent_masked_m_silu_mul_quant(

static constexpr int SILU_V2_BLOCK_COUNT = 132 * 32;

int const NUM_GROUPS = H / GROUP_SIZE;
if (!use_ue8m0) {
if (H >= 4096) {
if (H >= 4096 && (NUM_GROUPS % 8 == 0)) {
/* 8 warps config */
static constexpr int NUM_STAGES = 4;
static constexpr int THREAD_COUNT = 256;
KERNEL(SILU_V2_BLOCK_COUNT, false, THREAD_COUNT, NUM_STAGES);
} else {
/* 1 warp config */
static constexpr int THREAD_COUNT = 32;
KERNEL(SILU_V2_BLOCK_COUNT, false, THREAD_COUNT, 2);
}
} else {
if (H >= 4096) {
if (H >= 4096 && (NUM_GROUPS % 8 == 0)) {
/* 8 warps config */
static constexpr int NUM_STAGES = 4;
static constexpr int THREAD_COUNT = 256;
KERNEL(SILU_V2_BLOCK_COUNT, true, THREAD_COUNT, NUM_STAGES);
} else {
/* 1 warp config */
static constexpr int THREAD_COUNT = 32;
KERNEL(SILU_V2_BLOCK_COUNT, true, THREAD_COUNT, 2);
}
Expand Down
5 changes: 4 additions & 1 deletion tests/kernels/moe/test_silu_mul_fp8_quant_deep_gemm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
(8, 16, 128 * 2, fp8_dtype),
(8, 16, 128 * 3, fp8_dtype),
(8, 64, 7168, fp8_dtype),
(8, 128, 128 * 33, fp8_dtype),
(8, 128, 7168, fp8_dtype),
(8, 512, 7168, fp8_dtype),
(8, 1024, 7168, fp8_dtype),
Expand Down Expand Up @@ -54,8 +55,10 @@ def test_silu_mul_fp8_quant_deep_gemm(E, T, H, fp8_type):
)

# Run the SiLU V2 kernel
# TODO (varun): use_e8m0 is set to false as the reference impl does
# not handle that case.
y_q, y_s = persistent_masked_m_silu_mul_quant(
y, tokens_per_expert, group_size=group_size
y, tokens_per_expert, group_size=group_size, use_ue8m0=False
)

torch.cuda.synchronize()
Expand Down
3 changes: 2 additions & 1 deletion vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -100,6 +100,7 @@ def persistent_masked_m_silu_mul_quant(
tokens_per_expert: torch.Tensor, # (E,) number of valid tokens per expert
num_parallel_tokens=16,
group_size: int = 128,
use_ue8m0: bool | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Quantize silu(y[..., :H]) * y[..., H:] to FP8 with group per-token scales
y has shape (E, T, 2*H). The first half of the last dimension is
Expand Down Expand Up @@ -164,7 +165,7 @@ def persistent_masked_m_silu_mul_quant(
device=y.device,
)

use_ue8m0 = is_deep_gemm_e8m0_used()
use_ue8m0 = use_ue8m0 if use_ue8m0 is not None else is_deep_gemm_e8m0_used()

cuda_arch = current_platform.get_device_capability(
device_id=y.device.index
Expand Down