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[Kernel] DeepEP dispatch-combine kernel integration (#18434)

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Signed-off-by: Varun <vsundarr@redhat.com>
Co-authored-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
This commit is contained in:
Varun Sundar Rabindranath
2025-06-03 15:30:02 -04:00
committed by GitHub
parent 01eee40536
commit fa98d77773
23 changed files with 1950 additions and 122 deletions
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tests/kernels/moe/test_deepep_deepgemm_moe.py Normal file
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# SPDX-License-Identifier: Apache-2.0
"""
Test DeepEP + DeepGEMM integration
"""
import dataclasses
import importlib
from typing import Optional
import pytest
import torch.distributed
from torch.distributed import ProcessGroup
from typing_extensions import ParamSpec
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
from vllm.model_executor.layers.fused_moe.modular_kernel import (
FusedMoEModularKernel)
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
per_token_group_quant_fp8)
from vllm.platforms import current_platform
from .deepep_utils import ProcessGroupInfo, parallel_launch
has_deep_ep = importlib.util.find_spec("deep_ep") is not None
try:
import deep_gemm
has_deep_gemm = True
except ImportError:
has_deep_gemm = False
if has_deep_ep:
from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import ( # noqa: E501
DeepEPHTPrepareAndFinalize)
from .deepep_utils import DeepEPHTArgs, make_deepep_a2a
if has_deep_gemm:
from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
DeepGemmExperts)
requires_deep_ep = pytest.mark.skipif(
not has_deep_ep,
reason="Requires deep_ep kernels",
)
requires_deep_gemm = pytest.mark.skipif(
not has_deep_gemm,
reason="Requires deep_gemm kernels",
)
P = ParamSpec("P")
def per_block_cast_to_fp8(
x: torch.Tensor,
block_size_n: int = 128) -> tuple[torch.Tensor, torch.Tensor]:
assert x.dim() == 2
m, n = x.shape
x_padded = torch.zeros(
(deep_gemm.ceil_div(m, 128) * 128,
deep_gemm.ceil_div(n, block_size_n) * block_size_n),
dtype=x.dtype,
device=x.device)
x_padded[:m, :n] = x
x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, block_size_n)
x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
x_scaled_sub = x_scaled.view_as(x_padded)[:m, :n].contiguous()
scales = (x_amax / 448.0).view(x_view.size(0), x_view.size(2))
return x_scaled_sub, scales
def make_block_quant_fp8_weights(
e: int,
n: int,
k: int,
block_size: list[int],
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Return weights w1, w2, w1q, w2q, w1_scale, w2_scale
"""
dtype = torch.bfloat16
fp8_info = torch.finfo(torch.float8_e4m3fn)
fp8_max, fp8_min = fp8_info.max, fp8_info.min
w1_bf16 = torch.randn((e, 2 * n, k), dtype=dtype) / 10
w1_bf16 = w1_bf16.clamp(min=fp8_min, max=fp8_max).to(dtype=dtype)
w2_bf16 = torch.randn((e, k, n), dtype=dtype) / 10
w2_bf16 = w2_bf16.clamp(min=fp8_min, max=fp8_max).to(dtype=dtype)
block_n, block_k = block_size[0], block_size[1]
n_tiles_w1 = ((2 * n) + block_n - 1) // block_n
k_tiles_w1 = (k + block_k - 1) // block_k
n_tiles_w2 = (k + block_n - 1) // block_n
k_tiles_w2 = (n + block_k - 1) // block_k
w1 = torch.empty_like(w1_bf16, dtype=torch.float8_e4m3fn)
w2 = torch.empty_like(w2_bf16, dtype=torch.float8_e4m3fn)
w1_s = torch.empty((e, n_tiles_w1, k_tiles_w1),
device="cuda",
dtype=torch.float32)
w2_s = torch.empty((e, n_tiles_w2, k_tiles_w2),
device="cuda",
dtype=torch.float32)
assert w1_s.shape == (e, (2 * n + 127) // 128, (k + 127) // 128)
assert (w2.shape[-2] + block_n - 1) // block_n == w2_s.shape[-2]
for i in range(e):
w1[i], w1_s[i] = per_block_cast_to_fp8(w1_bf16[i])
w2[i], w2_s[i] = per_block_cast_to_fp8(w2_bf16[i])
return w1, w2, w1_s, w2_s
@dataclasses.dataclass
class TestConfig:
topk: int
m: int
k: int
n: int
num_experts: int
block_size: list[int]
@dataclasses.dataclass
class TestTensors:
rank_tokens: torch.Tensor # all ranks make this many tokens
rank_token_scales: Optional[torch.Tensor]
topk: torch.Tensor
topk_weights: torch.Tensor
config: TestConfig
@staticmethod
def make(config: TestConfig, rank) -> "TestTensors":
dtype = torch.bfloat16
topk, m, k, block_size = (config.topk, config.m, config.k,
config.block_size)
fp8_info = torch.finfo(torch.float8_e4m3fn)
fp8_max, fp8_min = fp8_info.max, fp8_info.min
rank_tokens = torch.randn(
(m, k), device=torch.cuda.current_device(), dtype=dtype) / 10.0
rank_tokens = rank_tokens.clamp(min=fp8_min, max=fp8_max)
block_k = block_size[1]
_, rank_token_scales = per_token_group_quant_fp8(rank_tokens, block_k)
topk_ids = torch.randint(
low=0,
high=config.num_experts,
size=(m, topk),
device=torch.cuda.current_device()).to(dtype=torch.int64)
topk_weights = torch.randn(topk_ids.shape,
dtype=torch.float32,
device=torch.cuda.current_device())
return TestTensors(rank_tokens=rank_tokens,
rank_token_scales=rank_token_scales,
topk=topk_ids,
topk_weights=topk_weights,
config=config)
def make_modular_kernel(pg: ProcessGroup, pgi: ProcessGroupInfo, dp_size: int,
num_local_experts: int, q_dtype: Optional[torch.dtype],
block_shape: list[int]) -> FusedMoEModularKernel:
a2a: DeepEPHTPrepareAndFinalize = make_deepep_a2a(
pg=pg,
pgi=pgi,
dp_size=dp_size,
deepep_ht_args=DeepEPHTArgs(num_local_experts=num_local_experts),
deepep_ll_args=None,
q_dtype=q_dtype,
block_shape=block_shape)
fused_experts = DeepGemmExperts()
mk = FusedMoEModularKernel(prepare_finalize=a2a,
fused_experts=fused_experts)
return mk
def deep_ep_moe_impl(pg: ProcessGroup, pgi: ProcessGroupInfo, dp_size: int,
test_tensors: TestTensors, w1: torch.Tensor,
w2: torch.Tensor, w1_scale: Optional[torch.Tensor],
w2_scale: Optional[torch.Tensor],
num_experts: int) -> torch.Tensor:
num_local_experts = w1.size(0)
def build_expert_map():
num_local_experts = w1.size(0)
expert_map = torch.full((num_experts, ),
fill_value=-1,
dtype=torch.int32)
s = pgi.rank * num_local_experts
e = s + num_local_experts
expert_map[s:e] = torch.tensor(list(range(num_local_experts)))
return expert_map.to(device=torch.cuda.current_device(),
dtype=torch.int32)
q_dtype = torch.float8_e4m3fn
# Make modular kernel
mk: FusedMoEModularKernel = make_modular_kernel(
pg, pgi, dp_size, num_local_experts, q_dtype,
test_tensors.config.block_size)
a1_scale = test_tensors.rank_token_scales
out = mk.forward(hidden_states=test_tensors.rank_tokens,
w1=w1,
w2=w2,
topk_weights=test_tensors.topk_weights,
topk_ids=test_tensors.topk,
inplace=False,
activation="silu",
global_num_experts=num_experts,
expert_map=build_expert_map(),
w1_scale=w1_scale,
w2_scale=w2_scale,
w1_zp=None,
w2_zp=None,
a1_scale=a1_scale,
a2_scale=None,
apply_router_weight_on_input=False)
return out
def triton_impl(a: torch.Tensor, topk_ids: torch.Tensor,
topk_weights: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor,
w1_scale: torch.Tensor, w2_scale: torch.Tensor,
a1_scale: torch.Tensor, block_shape: list[int]):
return fused_experts(
hidden_states=a,
w1=w1,
w2=w2,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=False,
use_fp8_w8a8=True,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a1_scale,
block_shape=block_shape,
# Make sure this is set to False so we
# dont end up comparing the same implementation.
allow_deep_gemm=False)
def _deep_ep_moe(
pgi: ProcessGroupInfo,
dp_size: int,
config: TestConfig,
w1: torch.Tensor,
w2: torch.Tensor,
w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
):
current_platform.seed_everything(pgi.rank)
w1 = w1.to(device=torch.cuda.current_device())
w2 = w2.to(device=torch.cuda.current_device())
w1_scale = w1_scale.to(device=torch.cuda.current_device())
w2_scale = w2_scale.to(device=torch.cuda.current_device())
pg = torch.distributed.new_group(list(range(pgi.world_size)))
test_tensors = TestTensors.make(config, pgi.rank)
block_shape = [
w1.size(1) // w1_scale.size(1),
w1.size(2) // w1_scale.size(2)
]
with set_current_vllm_config(VllmConfig()):
# Reference
triton_moe = triton_impl(a=test_tensors.rank_tokens,
topk_ids=test_tensors.topk,
topk_weights=test_tensors.topk_weights,
w1=w1,
w2=w2,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=test_tensors.rank_token_scales,
block_shape=block_shape)
# Slice experts for this rank.
num_local_experts = config.num_experts // pgi.world_size
e_start = num_local_experts * pgi.rank
e_end = e_start + num_local_experts
w1_ep = w1[e_start:e_end]
w2_ep = w2[e_start:e_end]
w1_scale_ep = w1_scale[e_start:e_end]
w2_scale_ep = w2_scale[e_start:e_end]
deepep_moe = deep_ep_moe_impl(
pg,
pgi,
dp_size,
test_tensors,
w1_ep,
w2_ep,
w1_scale_ep,
w2_scale_ep,
config.num_experts,
)
torch.testing.assert_close(
triton_moe,
deepep_moe,
atol=6e-2,
rtol=6e-2,
)
MNKs = [
(8, 128, 128),
(8, 128, 512),
(8, 512, 512),
(3, 1024, 2048),
(32, 128, 1024),
(45, 512, 2048),
(64, 1024, 1024),
(129, 128, 256),
(129, 1024, 2048),
(222, 1024, 2048),
]
@pytest.mark.parametrize("mnk", MNKs)
@pytest.mark.parametrize("num_experts", [32])
@pytest.mark.parametrize("topk", [2, 6])
@pytest.mark.parametrize("world_dp_size", [(2, 1)])
@requires_deep_ep
@requires_deep_gemm
def test_deep_ep_moe(mnk: tuple[int, int, int], num_experts: int, topk: int,
world_dp_size: tuple[int, int]):
m, n, k = mnk
current_platform.seed_everything(7)
if topk > num_experts:
pytest.skip(f"Skipping test: topk={topk} > E={num_experts}")
block_m = deep_gemm.get_m_alignment_for_contiguous_layout()
block_size = [block_m, block_m]
world_size, dp_size = world_dp_size
config = TestConfig(
topk=topk,
m=m,
k=k,
n=n,
num_experts=num_experts,
block_size=block_size,
)
w1, w2, w1_scale, w2_scale = make_block_quant_fp8_weights(
num_experts, n, k, block_size)
parallel_launch(world_size, _deep_ep_moe, dp_size, config, w1, w2,
w1_scale, w2_scale)