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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_moe.py Normal file
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# SPDX-License-Identifier: Apache-2.0
"""
Test deepep dispatch-combine logic
"""
import dataclasses
import importlib
from typing import Optional, Union
import pytest
import torch.distributed
from torch.distributed import ProcessGroup
from vllm import _custom_ops as ops
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe import TritonExperts
from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
BatchedTritonExperts)
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
if has_deep_ep:
from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import ( # noqa: E501
DeepEPHTPrepareAndFinalize)
from vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize import ( # noqa: E501
DeepEPLLPrepareAndFinalize)
from .deepep_utils import DeepEPHTArgs, DeepEPLLArgs, make_deepep_a2a
requires_deep_ep = pytest.mark.skipif(
not has_deep_ep,
reason="Requires deep_ep kernels",
)
MAX_TOKENS_PER_RANK = 64
def make_weights(
e, n, k, dtype
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Return weights w1, w2, w1_scale, w2_scale
"""
if dtype in [torch.float16, torch.bfloat16]:
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
return w1, w2, None, None
# per-out-channel weight quantization
assert dtype == torch.float8_e4m3fn
w1 = torch.empty((e, 2 * n, k), device="cuda", dtype=torch.float16)
w2 = torch.empty((e, k, n), device="cuda", dtype=torch.float16)
n_b_scales = 2 * n
k_b_scales = k
w1_q = torch.empty_like(w1, dtype=dtype)
w2_q = torch.empty_like(w2, dtype=dtype)
w1_scale = torch.empty((e, n_b_scales, 1),
device="cuda",
dtype=torch.float32)
w2_scale = torch.empty((e, k_b_scales, 1),
device="cuda",
dtype=torch.float32)
for expert in range(e):
w1_q[expert], w1_scale[expert] = ops.scaled_fp8_quant(
w1[expert], use_per_token_if_dynamic=True)
w2_q[expert], w2_scale[expert] = ops.scaled_fp8_quant(
w2[expert], use_per_token_if_dynamic=True)
return w1_q, w2_q, w1_scale, w2_scale
@dataclasses.dataclass
class TestConfig:
dtype: torch.dtype
topk: int
m: int
k: int
n: int
num_experts: 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, low_latency_mode: bool) -> "TestTensors":
# TODO (varun) - check that float16 works ?
assert config.dtype in [torch.bfloat16, torch.float8_e4m3fn]
token_dtype = (torch.bfloat16 if config.dtype == torch.float8_e4m3fn
else config.dtype)
rank_tokens = torch.randn(
(config.m, config.k), device="cuda", dtype=token_dtype) / 10
rank_token_scales = None
if config.dtype == torch.float8_e4m3fn:
# low_latency_mode kernels dont support per-token quant.
_, rank_token_scales = ops.scaled_fp8_quant(
rank_tokens, use_per_token_if_dynamic=not low_latency_mode)
topk = torch.randint(low=0,
high=config.num_experts,
size=(config.m, config.topk),
device="cuda").to(dtype=torch.int64)
topk_weights = torch.randn(topk.shape,
dtype=torch.float32,
device="cuda")
return TestTensors(rank_tokens=rank_tokens,
rank_token_scales=rank_token_scales,
topk=topk,
topk_weights=topk_weights,
config=config)
def make_modular_kernel(pg: ProcessGroup, pgi: ProcessGroupInfo,
low_latency_mode: bool, hidden_size: int, dp_size: int,
num_experts: int, num_local_experts: int,
q_dtype: Optional[torch.dtype],
use_fp8_dispatch: bool) -> FusedMoEModularKernel:
is_quantized = q_dtype is not None
ht_args: Optional[DeepEPHTArgs] = None
ll_args: Optional[DeepEPLLArgs] = None
if low_latency_mode:
ll_args = DeepEPLLArgs(max_tokens_per_rank=MAX_TOKENS_PER_RANK,
hidden_size=hidden_size,
num_experts=num_experts,
use_fp8_dispatch=use_fp8_dispatch)
else:
assert not use_fp8_dispatch, (
"FP8 Dispatch is valid only for low-latency kernels")
ht_args = DeepEPHTArgs(num_local_experts=num_local_experts)
a2a : Union[DeepEPHTPrepareAndFinalize, DeepEPLLPrepareAndFinalize] = \
make_deepep_a2a(pg = pg,
pgi = pgi,
dp_size = dp_size,
q_dtype = q_dtype,
block_shape = None,
deepep_ht_args = ht_args,
deepep_ll_args = ll_args)
if low_latency_mode:
fused_experts = BatchedTritonExperts(
max_num_tokens=MAX_TOKENS_PER_RANK,
world_size=pgi.world_size,
dp_size=dp_size,
use_fp8_w8a8=is_quantized,
use_int8_w8a8=False,
use_int8_w8a16=False,
use_int4_w4a16=False)
else:
fused_experts = TritonExperts(use_fp8_w8a8=is_quantized,
use_int8_w8a8=False,
use_int8_w8a16=False,
use_int4_w4a16=False,
per_channel_quant=False)
mk = FusedMoEModularKernel(prepare_finalize=a2a,
fused_experts=fused_experts)
return mk
def deep_ep_moe_impl(pg: ProcessGroup, pgi: ProcessGroupInfo,
low_latency_mode: bool, 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,
use_fp8_dispatch: bool) -> 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)
hidden_size = test_tensors.rank_tokens.size(1)
is_quantized = w1.dtype == torch.float8_e4m3fn
q_dtype = None
if is_quantized:
q_dtype = torch.float8_e4m3fn
# Make modular kernel
mk: FusedMoEModularKernel = make_modular_kernel(pg, pgi, low_latency_mode,
hidden_size, dp_size,
num_experts,
num_local_experts, q_dtype,
use_fp8_dispatch)
out_hidden_states = torch.empty_like(test_tensors.rank_tokens)
total_num_tokens = test_tensors.rank_tokens.size(0)
def process_chunk(chunk_start, chunk_end, skip_result_store=False):
rank_tokens_chunk = test_tensors.rank_tokens[chunk_start:chunk_end]
topk_weights_chunk = test_tensors.topk_weights[chunk_start:chunk_end]
topk_chunk = test_tensors.topk[chunk_start:chunk_end]
rank_token_scales_chunk = test_tensors.rank_token_scales
if rank_token_scales_chunk is not None and rank_token_scales_chunk.size(
0) == total_num_tokens:
# per act token
rank_token_scales_chunk = rank_token_scales_chunk[
chunk_start:chunk_end]
out = mk.forward(hidden_states=rank_tokens_chunk,
w1=w1,
w2=w2,
topk_weights=topk_weights_chunk,
topk_ids=topk_chunk,
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=rank_token_scales_chunk,
a2_scale=None,
apply_router_weight_on_input=False)
if not skip_result_store:
out_hidden_states[chunk_start:chunk_end, :].copy_(
out, non_blocking=True)
max_num_tokens_per_dp = (MAX_TOKENS_PER_RANK
if low_latency_mode else total_num_tokens)
for chunk_start_ in range(0, total_num_tokens, max_num_tokens_per_dp):
chunk_start = chunk_start_
chunk_end = min(chunk_start + max_num_tokens_per_dp, total_num_tokens)
# clamp start and end
chunk_start = min(chunk_start, total_num_tokens - 1)
chunk_end = min(chunk_end, total_num_tokens)
process_chunk(chunk_start,
chunk_end,
skip_result_store=chunk_start_ >= total_num_tokens)
return out_hidden_states
def torch_moe_impl(test_tensors: TestTensors, w1: torch.Tensor,
w2: torch.Tensor, w1_scale: Optional[torch.Tensor],
w2_scale: Optional[torch.Tensor], using_fp8_dispatch: bool):
a, topk_ids, topk_weights = (test_tensors.rank_tokens, test_tensors.topk,
test_tensors.topk_weights)
if using_fp8_dispatch:
# The DeepEP implementation is requested to dispatch using FP8.
# For numerical stability for testing, emulate the fp8 dispatch by
# blockwise quant and de-quant.
a = test_tensors.rank_tokens
aq, aq_scale = per_token_group_quant_fp8(a, 128)
a = (aq.view(-1, 128).to(torch.float32) * aq_scale.view(-1, 1)).view(
a.shape).to(a.dtype)
is_quantized = w1.dtype == torch.float8_e4m3fn
a_dtype = a.dtype
if is_quantized:
w1 = w1.to(dtype=torch.float32) * w1_scale
w2 = w2.to(dtype=torch.float32) * w2_scale
a = a.to(dtype=torch.float32)
m, _ = a.shape
topk = topk_ids.size(1)
out = torch.zeros_like(a)
for i in range(m):
a_i = a[i]
o_i = out[i]
for j in range(topk):
e = topk_ids[i][j]
e_w = topk_weights[i][j]
w1_e = w1[e]
w2_e = w2[e]
o_i += (SiluAndMul()
(a_i @ w1_e.transpose(0, 1)) @ w2_e.transpose(0, 1)) * e_w
if is_quantized:
out = out.to(dtype=a_dtype)
return out
def _deep_ep_moe(
pgi: ProcessGroupInfo,
low_latency_mode: bool,
dp_size: int,
config: TestConfig,
w1: torch.Tensor,
w2: torch.Tensor,
w1_scale: Optional[torch.Tensor],
w2_scale: Optional[torch.Tensor],
use_fp8_dispatch: bool,
):
if not low_latency_mode:
assert not use_fp8_dispatch, (
"FP8 dispatch interface is available only in low-latency mode")
is_quantized = w1.dtype == torch.float8_e4m3fn
w1 = w1.to(device=torch.cuda.current_device())
w2 = w2.to(device=torch.cuda.current_device())
if is_quantized:
w1_scale = w1_scale.to( # type: ignore
device=torch.cuda.current_device())
w2_scale = w2_scale.to( # type: ignore
device=torch.cuda.current_device())
pg = torch.distributed.new_group(list(range(pgi.world_size)))
test_tensors = TestTensors.make(config, low_latency_mode)
with set_current_vllm_config(VllmConfig()):
# Reference
torch_combined = torch_moe_impl(test_tensors, w1, w2, w1_scale,
w2_scale, use_fp8_dispatch)
# Splice 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, w2_scale_ep = None, None
if is_quantized:
w1_scale_ep = w1_scale[e_start:e_end] # type: ignore
w2_scale_ep = w2_scale[e_start:e_end] # type: ignore
deepep_combined = deep_ep_moe_impl(
pg,
pgi,
low_latency_mode,
dp_size,
test_tensors,
w1_ep,
w2_ep,
w1_scale_ep,
w2_scale_ep,
config.num_experts,
use_fp8_dispatch,
)
torch.testing.assert_close(
torch_combined,
deepep_combined,
atol=6e-2,
rtol=6e-2,
)
MNKs = [
(1, 128, 128),
(2, 128, 512),
(3, 1024, 2048),
(32, 128, 1024),
(45, 512, 2048),
(64, 1024, 1024),
(222, 1024, 2048),
]
DTYPES = [torch.bfloat16, torch.float8_e4m3fn]
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("mnk", MNKs)
@pytest.mark.parametrize("num_experts", [32])
@pytest.mark.parametrize("topk", [6])
@pytest.mark.parametrize("world_dp_size", [(2, 1)])
@requires_deep_ep
def test_deep_ep_moe(dtype: torch.dtype, mnk: tuple[int, int, int],
num_experts: int, topk: int, world_dp_size: tuple[int,
int]):
low_latency_mode = False
use_fp8_dispatch = False
m, n, k = mnk
current_platform.seed_everything(7)
world_size, dp_size = world_dp_size
config = TestConfig(dtype=dtype,
topk=topk,
m=m,
k=k,
n=n,
num_experts=num_experts)
w1, w2, w1_scale, w2_scale = make_weights(num_experts, n, k, dtype)
parallel_launch(world_size, _deep_ep_moe, low_latency_mode, dp_size,
config, w1, w2, w1_scale, w2_scale, use_fp8_dispatch)
MNKs = [
(1, 128, 2560),
(2, 128, 2560),
(3, 1024, 2560),
(32, 128, 2560),
(45, 512, 2560),
(64, 1024, 2560),
(222, 1024, 2560),
]
DTYPES = [torch.float8_e4m3fn, torch.bfloat16]
USE_FP8_DISPATCH = [True, False]
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("mnk", MNKs)
@pytest.mark.parametrize("num_experts", [32])
@pytest.mark.parametrize("topk", [6])
@pytest.mark.parametrize("world_dp_size", [(2, 1)])
@pytest.mark.parametrize("use_fp8_dispatch", USE_FP8_DISPATCH)
@requires_deep_ep
def test_low_latency_deep_ep_moe(dtype: torch.dtype, mnk: tuple[int, int, int],
num_experts: int, topk: int,
world_dp_size: tuple[int, int],
use_fp8_dispatch: bool):
low_latency_mode = True
m, n, k = mnk
if (low_latency_mode
and k not in DeepEPLLPrepareAndFinalize.SUPPORTED_HIDDEN_SIZES):
pytest.skip(
f"Skipping test as hidden size {k} is not in list of supported "
f"hidden sizes {DeepEPLLPrepareAndFinalize.SUPPORTED_HIDDEN_SIZES}"
)
current_platform.seed_everything(7)
world_size, dp_size = world_dp_size
config = TestConfig(dtype=dtype,
topk=topk,
m=m,
k=k,
n=n,
num_experts=num_experts)
w1, w2, w1_scale, w2_scale = make_weights(num_experts, n, k, dtype)
parallel_launch(world_size, _deep_ep_moe, low_latency_mode, dp_size,
config, w1, w2, w1_scale, w2_scale, use_fp8_dispatch)