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[Kernels][Bugfix] Use torch op for all kernels in FusedMoE forward. Add additional testing for cudagraphs. (#19717)

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Signed-off-by: Bill Nell <bnell@redhat.com>
This commit is contained in:
bnellnm
2025-06-25 02:22:58 -04:00
committed by GitHub
parent f59fc60fb3
commit 015fab8c2f
14 changed files with 379 additions and 238 deletions
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288
tests/kernels/moe/test_moe.py
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@@ -4,6 +4,9 @@
Run `pytest tests/kernels/test_moe.py`.
"""
import functools
from typing import Callable, Optional, Union
import pytest
import torch
from torch.nn import Parameter
@@ -14,6 +17,7 @@ from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
import vllm.model_executor.layers.fused_moe # noqa
from tests.kernels.utils import opcheck, stack_and_dev, torch_moe
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.forward_context import set_forward_context
from vllm.model_executor.layers.fused_moe import fused_moe
from vllm.model_executor.layers.fused_moe.fused_moe import (
fused_topk, modular_triton_fused_moe)
@@ -40,7 +44,76 @@ vllm_config.scheduler_config.max_num_seqs = 128
vllm_config.scheduler_config.max_model_len = 8192
@pytest.mark.parametrize("m", [1, 33, 64, 222, 1024 * 128])
def run_moe_test(
baseline: Union[Callable, torch.Tensor],
moe_fn: Callable,
a: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
score: torch.Tensor,
topk: int,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
padding: bool = False,
use_compile: bool = False,
use_cudagraph: bool = False,
atol: float = 2e-2,
rtol: float = 0,
) -> torch.Tensor:
if isinstance(baseline, torch.Tensor):
baseline_output = baseline
else:
baseline_output = baseline(a,
w1,
w2,
score,
topk,
global_num_experts=global_num_experts,
expert_map=expert_map)
# Pad the weight if moe padding is enabled
if padding:
w1 = F.pad(w1, (0, 128), "constant", 0)[..., 0:-128]
w2 = F.pad(w2, (0, 128), "constant", 0)[..., 0:-128]
if use_compile:
moe_fn = torch.compile(moe_fn, backend="inductor", fullgraph=True)
torch._dynamo.mark_dynamic(a, 0)
torch._dynamo.mark_dynamic(score, 0)
test_output = moe_fn(a,
w1,
w2,
score,
topk,
global_num_experts=global_num_experts,
expert_map=expert_map)
if use_cudagraph:
test_output.fill_(0)
stream = torch.cuda.Stream()
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph, stream=stream):
test_output = moe_fn(a,
w1,
w2,
score,
topk,
global_num_experts=global_num_experts,
expert_map=expert_map)
torch.cuda.synchronize()
graph.replay()
torch.cuda.synchronize()
torch.testing.assert_close(test_output,
baseline_output,
atol=atol,
rtol=rtol)
return baseline_output
@pytest.mark.parametrize("m", [1, 33, 64, 222, 32768, 40000])
@pytest.mark.parametrize("n", [128, 1024, 2048])
@pytest.mark.parametrize("k", [128, 511, 1024])
@pytest.mark.parametrize("e", NUM_EXPERTS)
@@ -48,6 +121,7 @@ vllm_config.scheduler_config.max_model_len = 8192
@pytest.mark.parametrize("ep_size", EP_SIZE)
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("padding", [True, False])
@pytest.mark.parametrize("chunk_size", [8192])
def test_fused_moe(
m: int,
n: int,
@@ -57,7 +131,17 @@ def test_fused_moe(
ep_size: int,
dtype: torch.dtype,
padding: bool,
chunk_size: int,
monkeypatch,
):
current_platform.seed_everything(7)
monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(chunk_size))
#
# Setup test data
#
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
@@ -77,58 +161,70 @@ def test_fused_moe(
else:
e_map = None
m_fused_moe = modular_triton_fused_moe(use_fp8_w8a8=False,
use_int8_w8a8=False,
use_int8_w8a16=False,
use_int4_w4a16=False,
per_channel_quant=False,
block_shape=None)
#
# Setup test functions
#
m_fused_moe_fn = modular_triton_fused_moe(use_fp8_w8a8=False,
use_int8_w8a8=False,
use_int8_w8a16=False,
use_int4_w4a16=False,
per_channel_quant=False,
block_shape=None)
def m_fused_moe(
a: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
score: torch.Tensor,
topk: int,
global_num_experts: int = -1,
expert_map: Optional[torch.Tensor] = None,
) -> torch.Tensor:
topk_weights, topk_ids, _ = fused_topk(a, score, topk, False)
return m_fused_moe_fn(a,
w1,
w2,
topk_weights,
topk_ids,
global_num_experts=global_num_experts,
expert_map=expert_map)
fused_moe_fn = functools.partial(fused_moe, renormalize=False)
#
# Run tests
#
runner = functools.partial(
run_moe_test,
a=a,
w1=w1,
w2=w2,
score=score,
topk=topk,
global_num_experts=e,
expert_map=e_map,
padding=padding,
)
# Note: for now use_compile will error out if the problem size is
# large enough to trigger chunking. I'm leaving the flag and
# setup code in case we are able to revisit this later.
use_compile = False
use_cudagraph = (n >= 1024 and k >= 1024
and current_platform.is_cuda_alike())
with set_current_vllm_config(vllm_config):
torch_output = torch_moe(a, w1, w2, score, topk, e_map)
iterative_output = iterative_moe(a,
w1,
w2,
score,
topk,
global_num_experts=e,
expert_map=e_map,
renormalize=False)
# Pad the weight if moe padding is enabled
if padding:
w1 = F.pad(w1, (0, 128), "constant", 0)[..., 0:-128]
torch.cuda.empty_cache()
w2 = F.pad(w2, (0, 128), "constant", 0)[..., 0:-128]
torch.cuda.empty_cache()
triton_output = fused_moe(a,
w1,
w2,
score,
topk,
global_num_experts=e,
expert_map=e_map,
renormalize=False)
topk_weights, topk_ids, _ = fused_topk(a, score, topk, False)
m_triton_output = m_fused_moe(a,
w1,
w2,
topk_weights,
topk_ids,
global_num_experts=e,
expert_map=e_map)
torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
torch.testing.assert_close(m_triton_output,
torch_output,
atol=2e-2,
rtol=0)
torch.testing.assert_close(iterative_output,
torch_output,
atol=2e-2,
rtol=0)
baseline_output = runner(torch_moe, iterative_moe)
runner(baseline_output,
fused_moe_fn,
use_compile=use_compile,
use_cudagraph=use_cudagraph)
runner(baseline_output,
m_fused_moe,
use_compile=use_compile,
use_cudagraph=use_cudagraph)
@pytest.mark.parametrize("m", [1, 32, 222])
@@ -238,7 +334,12 @@ def test_fused_moe_wn16(m: int, n: int, k: int, e: int, topk: int,
w1_zp=w1_qzeros if has_zp else None,
w2_zp=w2_qzeros if has_zp else None,
block_shape=[0, group_size])
torch_output = torch_moe(a, w1_ref, w2_ref, score, topk, e_map)
torch_output = torch_moe(a,
w1_ref,
w2_ref,
score,
topk,
expert_map=e_map)
torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
@@ -265,45 +366,51 @@ def test_mixtral_moe(dtype: torch.dtype, padding: bool, use_rocm_aiter: bool,
pytest.skip("AITER ROCm test skip for float32")
# Instantiate our and huggingface's MoE blocks
config = MixtralConfig()
hf_moe = MixtralSparseMoeBlock(config).to(dtype).to("cuda")
vllm_moe = MixtralMoE(
num_experts=config.num_local_experts,
top_k=config.num_experts_per_tok,
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
params_dtype=dtype,
tp_size=1,
dp_size=1,
).cuda()
vllm_config.compilation_config.static_forward_context = dict()
with (set_current_vllm_config(vllm_config),
set_forward_context(None, vllm_config)):
config = MixtralConfig()
hf_moe = MixtralSparseMoeBlock(config).to(dtype).to("cuda")
vllm_moe = MixtralMoE(
num_experts=config.num_local_experts,
top_k=config.num_experts_per_tok,
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
params_dtype=dtype,
tp_size=1,
dp_size=1,
).cuda()
# Load the weights
vllm_moe.gate.weight.data[:] = hf_moe.gate.weight.data
for i in range(config.num_local_experts):
weights = (hf_moe.experts[i].w1.weight.data,
hf_moe.experts[i].w3.weight.data)
vllm_moe.experts.w13_weight[i][:] = torch.cat(weights, dim=0)
vllm_moe.experts.w2_weight[i][:] = hf_moe.experts[i].w2.weight.data
# Load the weights
vllm_moe.gate.weight.data[:] = hf_moe.gate.weight.data
for i in range(config.num_local_experts):
weights = (hf_moe.experts[i].w1.weight.data,
hf_moe.experts[i].w3.weight.data)
vllm_moe.experts.w13_weight[i][:] = torch.cat(weights, dim=0)
vllm_moe.experts.w2_weight[i][:] = hf_moe.experts[i].w2.weight.data
# Generate input batch of dimensions [batch_size, seq_len, hidden_dim]
hf_inputs = torch.randn((1, 64, config.hidden_size)).to(dtype).to("cuda")
# vLLM uses 1D query [num_tokens, hidden_dim]
vllm_inputs = hf_inputs.flatten(0, 1)
# Generate input batch of dimensions [batch_size, seq_len, hidden_dim]
hf_inputs = torch.randn(
(1, 64, config.hidden_size)).to(dtype).to("cuda")
# vLLM uses 1D query [num_tokens, hidden_dim]
vllm_inputs = hf_inputs.flatten(0, 1)
# Pad the weight if moe padding is enabled
if padding:
vllm_moe.experts.w13_weight = Parameter(F.pad(
vllm_moe.experts.w13_weight, (0, 128), "constant", 0)[..., 0:-128],
requires_grad=False)
torch.cuda.empty_cache()
vllm_moe.experts.w2_weight = Parameter(F.pad(
vllm_moe.experts.w2_weight, (0, 128), "constant", 0)[..., 0:-128],
requires_grad=False)
torch.cuda.empty_cache()
# Pad the weight if moe padding is enabled
if padding:
vllm_moe.experts.w13_weight = Parameter(F.pad(
vllm_moe.experts.w13_weight, (0, 128), "constant", 0)[...,
0:-128],
requires_grad=False)
torch.cuda.empty_cache()
vllm_moe.experts.w2_weight = Parameter(F.pad(
vllm_moe.experts.w2_weight, (0, 128), "constant", 0)[...,
0:-128],
requires_grad=False)
torch.cuda.empty_cache()
# Run forward passes for both MoE blocks
hf_states, _ = hf_moe.forward(hf_inputs)
vllm_states = vllm_moe.forward(vllm_inputs)
# Run forward passes for both MoE blocks
hf_states, _ = hf_moe.forward(hf_inputs)
vllm_states = vllm_moe.forward(vllm_inputs)
mixtral_moe_tol = {
torch.float32: 1e-3,
@@ -546,7 +653,12 @@ def test_fused_marlin_moe(
topk_weights, topk_ids, _ = fused_topk(a, score, topk, False)
with set_current_vllm_config(vllm_config):
torch_output = torch_moe(a, w_ref1, w_ref2, score, topk, e_map)
torch_output = torch_moe(a,
w_ref1,
w_ref2,
score,
topk,
expert_map=e_map)
marlin_output = torch.ops.vllm.fused_marlin_moe(
a,