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Silu v2 (#25074)

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Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: elvircrn <elvircrn@gmail.com>
Signed-off-by: Elvir Crnčević <elvircrn@gmail.com>
Co-authored-by: mgoin <mgoin64@gmail.com>
Co-authored-by: Varun Sundar Rabindranath <varunsundar08@gmail.com>
This commit is contained in:
Elvir Crnčević
2025-10-10 17:19:53 +02:00
committed by GitHub
parent ae9d0e7da5
commit 7b03584de8
6 changed files with 519 additions and 405 deletions
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11
tests/kernels/moe/test_silu_mul_fp8_quant_deep_gemm.py
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@ -5,7 +5,7 @@ import pytest
import torch
from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
silu_mul_fp8_quant_deep_gemm_cuda,
persistent_masked_m_silu_mul_quant,
)
from vllm.platforms import current_platform
from vllm.utils import cdiv
@ -50,15 +50,15 @@ def test_silu_mul_fp8_quant_deep_gemm(E, T, H, fp8_type):
# Input tensor of shape (E, T, 2*H)
y = torch.randn((E, T, 2 * H), dtype=torch.bfloat16, device="cuda")
tokens_per_expert = torch.randint(
low=T // 2,
low=0,
high=T,
size=(E,),
dtype=torch.int32,
device="cuda",
)
# Run the Triton kernel
y_q, y_s = silu_mul_fp8_quant_deep_gemm_cuda(
# Run the SiLU V2 kernel
y_q, y_s = persistent_masked_m_silu_mul_quant(
y, tokens_per_expert, group_size=group_size
)
@ -115,10 +115,11 @@ def test_silu_mul_fp8_quant_deep_gemm(E, T, H, fp8_type):
y_se = y_s[e].float()
y_qe = y_q[e].float()
torch.testing.assert_close(y_se[:nt], ref_s[:nt], atol=1e-4, rtol=1e-2)
torch.testing.assert_close(
y_qe[:nt].to(torch.float32),
ref_q[:nt].to(torch.float32),
atol=2,
rtol=2e-1,
)
torch.testing.assert_close(y_se[:nt], ref_s[:nt], atol=1e-4, rtol=1e-2)