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[Bugfix] Fix CI moe kernel failure (#22556)

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Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
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Jee Jee Li 2025-08-09 15:03:29 +08:00 committed by GitHub
parent 7920e9b1c5
commit 0edc0cd52b
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1 changed files with 141 additions and 63 deletions
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204
tests/kernels/moe/test_gpt_oss_triton_kernels.py
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@ -5,6 +5,15 @@ from dataclasses import dataclass, fields
import pytest
import torch
import torch.nn.functional as F
from vllm.utils import has_triton_kernels
if not has_triton_kernels():
pytest.skip(
"triton_kernels not found, skipping all related tests",
allow_module_level=True,
)
import triton_kernels.swiglu
from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig
from triton_kernels.numerics import InFlexData
@ -65,7 +74,7 @@ def init_compute_data(M, K, N, E, a_dtype: str, w_dtype: str, num_warps: int):
dtype_dict = {
"bf16": torch.bfloat16,
"fp8_e4m3": torch.float8_e4m3fn,
"fp8_e5m2": torch.float8_e5m2
"fp8_e5m2": torch.float8_e5m2,
}
x = x.to(dtype_dict[a_dtype]).to(torch.bfloat16)
@ -97,12 +106,18 @@ def init_compute_data(M, K, N, E, a_dtype: str, w_dtype: str, num_warps: int):
x_pad = w1_bottom_pad
w1_tri = F.pad(w1_tri, (0, w1_right_pad, 0, w1_bottom_pad, 0, 0),
mode="constant",
value=0)
w2_tri = F.pad(w2_tri, (0, w2_right_pad, 0, w2_bottom_pad, 0, 0),
mode="constant",
value=0)
w1_tri = F.pad(
w1_tri,
(0, w1_right_pad, 0, w1_bottom_pad, 0, 0),
mode="constant",
value=0,
)
w2_tri = F.pad(
w2_tri,
(0, w2_right_pad, 0, w2_bottom_pad, 0, 0),
mode="constant",
value=0,
)
w1_bias_tri = F.pad(w1_bias_tri, (0, w1_right_pad, 0, 0),
mode="constant",
@ -127,13 +142,19 @@ def init_compute_data(M, K, N, E, a_dtype: str, w_dtype: str, num_warps: int):
w1_tri = convert_layout(wrap_torch_tensor(w1_tri, FP4), w_layout,
**w_layout_opts)
w1_scale_tri = convert_layout(wrap_torch_tensor(w1_scale_tri),
w_scale_layout, **w_scale_layout_opts)
w1_scale_tri = convert_layout(
wrap_torch_tensor(w1_scale_tri),
w_scale_layout,
**w_scale_layout_opts,
)
w2_tri = convert_layout(wrap_torch_tensor(w2_tri, FP4), w_layout,
**w_layout_opts)
w2_scale_tri = convert_layout(wrap_torch_tensor(w2_scale_tri),
w_scale_layout, **w_scale_layout_opts)
w2_scale_tri = convert_layout(
wrap_torch_tensor(w2_scale_tri),
w_scale_layout,
**w_scale_layout_opts,
)
pc1 = PrecisionConfig(weight_scale=w1_scale_tri,
flex_ctx=FlexCtx(rhs_data=InFlexData()))
@ -149,8 +170,22 @@ def init_compute_data(M, K, N, E, a_dtype: str, w_dtype: str, num_warps: int):
w1 = w1.transpose(-1, -2).contiguous()
w2 = w2.transpose(-1, -2).contiguous()
return (x, w1, w1_bias, w2, w2_bias, exp_data, x_tri, w1_tri, w2_tri,
exp_data_tri, w1_bias_tri, w2_bias_tri, pc1, pc2)
return (
x,
w1,
w1_bias,
w2,
w2_bias,
exp_data,
x_tri,
w1_tri,
w2_tri,
exp_data_tri,
w1_bias_tri,
w2_bias_tri,
pc1,
pc2,
)
@dataclass
@ -184,13 +219,14 @@ def swiglu(x, alpha: float = 1.702, limit: float = 1.0):
def oai_moe_forward(
hidden_states: torch.Tensor, # (M, K)
w1: torch.Tensor, # (E, 2N)
w1_bias: torch.Tensor, # (E, 2N, K)
w2: torch.Tensor, # (E, K, N)
w2_bias: torch.Tensor, # (E, N)
gating_output: torch.Tensor, # (M, E)
topk: int):
hidden_states: torch.Tensor, # (M, K)
w1: torch.Tensor, # (E, 2N)
w1_bias: torch.Tensor, # (E, 2N, K)
w2: torch.Tensor, # (E, K, N)
w2_bias: torch.Tensor, # (E, N)
gating_output: torch.Tensor, # (M, E)
topk: int,
):
# model.py 309:330, assuming gating and norm
t = hidden_states
experts = torch.topk(gating_output, k=topk, dim=-1, sorted=True)
@ -240,10 +276,22 @@ def test_equiv(num_token, a_dtype, w_dtype, tp):
N = ModelConfig.intermediate_size // tp
topk = ModelConfig.experts_per_token
x, w1, w1_bias, w2, w2_bias, exp_data, \
x_tri, w1_tri, w2_tri, exp_data_tri, w1_bias_tri,\
w2_bias_tri, pc1, pc2 = init_compute_data(
M, K, N, E, a_dtype, w_dtype, num_warps=8)
(
x,
w1,
w1_bias,
w2,
w2_bias,
exp_data,
x_tri,
w1_tri,
w2_tri,
exp_data_tri,
w1_bias_tri,
w2_bias_tri,
pc1,
pc2,
) = init_compute_data(M, K, N, E, a_dtype, w_dtype, num_warps=8)
out_triton_monolithic = triton_kernel_moe_forward(
hidden_states=x_tri,
@ -255,33 +303,46 @@ def test_equiv(num_token, a_dtype, w_dtype, tp):
w1_bias=w1_bias_tri,
w2_bias=w2_bias_tri,
w1_precision=pc1,
w2_precision=pc2)
w2_precision=pc2,
)
out_triton_monolithic = out_triton_monolithic[..., :K]
out_ref = oai_moe_forward(hidden_states=x,
w1=w1,
w1_bias=w1_bias,
w2=w2,
w2_bias=w2_bias,
gating_output=exp_data,
topk=topk)
out_ref = oai_moe_forward(
hidden_states=x,
w1=w1,
w1_bias=w1_bias,
w2=w2,
w2_bias=w2_bias,
gating_output=exp_data,
topk=topk,
)
assert_close(ref=out_ref,
tri=out_triton_monolithic,
maxtol=0.025,
rmstol=0.005)
def batched_moe(a: torch.Tensor, w1, w2, gating_output: torch.Tensor,
topk: int, renormalize: bool, w1_bias: torch.Tensor,
w2_bias: torch.Tensor, w1_precision: PrecisionConfig,
w2_precision: PrecisionConfig) -> torch.Tensor:
def batched_moe(
a: torch.Tensor,
w1,
w2,
gating_output: torch.Tensor,
topk: int,
renormalize: bool,
w1_bias: torch.Tensor,
w2_bias: torch.Tensor,
w1_precision: PrecisionConfig,
w2_precision: PrecisionConfig,
) -> torch.Tensor:
max_num_tokens = round_up(a.shape[0], 64)
fused_experts = FusedMoEModularKernel(
BatchedPrepareAndFinalize(max_num_tokens,
num_dispatchers=1,
num_local_experts=w1.shape[0],
rank=0),
BatchedPrepareAndFinalize(
max_num_tokens,
num_dispatchers=1,
num_local_experts=w1.shape[0],
rank=0,
),
BatchedOAITritonExperts(
None,
max_num_tokens=max_num_tokens,
@ -327,30 +388,46 @@ def test_triton_kernel_batched_moe(num_token, a_dtype, w_dtype, ep):
N = ModelConfig.intermediate_size
topk = ModelConfig.experts_per_token
x, w1, w1_bias, w2, w2_bias, exp_data, \
x_tri, w1_tri, w2_tri, exp_data_tri, w1_bias_tri, \
w2_bias_tri, pc1, pc2 = init_compute_data(
M, K, N, E, a_dtype, w_dtype, num_warps=4)
(
x,
w1,
w1_bias,
w2,
w2_bias,
exp_data,
x_tri,
w1_tri,
w2_tri,
exp_data_tri,
w1_bias_tri,
w2_bias_tri,
pc1,
pc2,
) = init_compute_data(M, K, N, E, a_dtype, w_dtype, num_warps=4)
out_tri = batched_moe(a=x_tri,
w1=w1_tri,
w2=w2_tri,
gating_output=exp_data_tri,
topk=topk,
renormalize=True,
w1_bias=w1_bias_tri,
w2_bias=w2_bias_tri,
w1_precision=pc1,
w2_precision=pc2)
out_tri = batched_moe(
a=x_tri,
w1=w1_tri,
w2=w2_tri,
gating_output=exp_data_tri,
topk=topk,
renormalize=True,
w1_bias=w1_bias_tri,
w2_bias=w2_bias_tri,
w1_precision=pc1,
w2_precision=pc2,
)
out_tri = out_tri[..., :K]
out_ref = oai_moe_forward(hidden_states=x,
w1=w1,
w1_bias=w1_bias,
w2=w2,
w2_bias=w2_bias,
gating_output=exp_data,
topk=topk)
out_ref = oai_moe_forward(
hidden_states=x,
w1=w1,
w1_bias=w1_bias,
w2=w2,
w2_bias=w2_bias,
gating_output=exp_data,
topk=topk,
)
assert_close(ref=out_ref, tri=out_tri, maxtol=0.025, rmstol=0.005)
@ -370,6 +447,7 @@ def test_unit_shuffle():
out = triton_kernels.swiglu.swiglu_torch(
out,
alpha=1.702,
precision_config=triton_kernels.swiglu.PrecisionConfig(limit=1.0))
precision_config=triton_kernels.swiglu.PrecisionConfig(limit=1.0),
)
assert_close(ref=out_ref, tri=out)
assert_close(ref=out_ref, tri=out)