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Support piecewise cuda graph for dsv3 fp4 (#15531)

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This commit is contained in:
Ke Bao
2025-12-21 14:50:32 +08:00
committed by GitHub
parent 6014365564
commit 8fe3e37468
7 changed files with 148 additions and 16 deletions
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4
python/sglang/srt/layers/attention/trtllm_mla_backend.py
View File

@@ -12,6 +12,7 @@ import torch
import triton
import triton.language as tl
from sglang.srt.compilation.piecewise_context_manager import is_in_piecewise_cuda_graph
from sglang.srt.layers.attention.flashinfer_mla_backend import (
FlashInferMLAAttnBackend,
FlashInferMLAMultiStepDraftBackend,
@@ -582,10 +583,11 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
):
# For extend batch with prefix length > 0, fallback to ragged kernel implemented in flashinfer MLA backend
# when chunked prefix cache is disabled.
# Also fallback to flashinfer MLA backend when in piecewise cuda graph, since it only supports MLA forward mode.
has_prefix = any(forward_batch.extend_prefix_lens_cpu)
fallback_to_flashinfer_impl = (
self.disable_chunked_prefix_cache and has_prefix
)
) or is_in_piecewise_cuda_graph()
if fallback_to_flashinfer_impl:
super().init_forward_metadata(forward_batch)

74
python/sglang/srt/layers/moe/fused_moe_triton/layer.py
View File

@@ -41,7 +41,13 @@ from sglang.srt.layers.moe.token_dispatcher.standard import (
StandardDispatcher,
StandardDispatchOutput,
)
from sglang.srt.layers.moe.topk import StandardTopKOutput, TopKOutput, TopKOutputChecker
from sglang.srt.layers.moe.topk import (
BypassedTopKOutput,
StandardTopKOutput,
TopKConfig,
TopKOutput,
TopKOutputChecker,
)
from sglang.srt.layers.moe.utils import RoutingMethodType
from sglang.srt.layers.quantization.base_config import (
FusedMoEMethodBase,
@@ -1210,16 +1216,21 @@ class FlashInferFP4MoE(FusedMoE):
return hs_fp4, hs_sf
def forward(self, hidden_states: torch.Tensor, topk_output: TopKOutput):
assert TopKOutputChecker.format_is_bypassed(
topk_output
), "Only bypassed topk output is supported for flashinfer fp4 moe"
if is_in_piecewise_cuda_graph():
assert TopKOutputChecker.format_is_standard(
topk_output
), "Only standard topk output is supported for piecewise cuda graph"
return torch.ops.sglang.moe_forward_piecewise_cuda_graph_impl(
hidden_states,
topk_output.topk_weights,
topk_output.topk_ids,
topk_output.router_logits,
self.layer_id,
return (
torch.ops.sglang.flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl(
hidden_states,
topk_output.router_logits,
topk_output.topk_config.top_k,
topk_output.topk_config.topk_group,
topk_output.topk_config.num_expert_group,
topk_output.topk_config.correction_bias,
self.layer_id,
)
)
else:
return self.forward_impl(hidden_states, topk_output)
@@ -1343,9 +1354,52 @@ def moe_forward_piecewise_cuda_graph_impl_fake(
return torch.empty_like(hidden_states)
def flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl(
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
top_k: int,
topk_group: Optional[int],
num_expert_group: Optional[int],
correction_bias: Optional[torch.Tensor],
layer_id: int,
) -> torch.Tensor:
topk_output = BypassedTopKOutput(
hidden_states=hidden_states,
router_logits=router_logits,
topk_config=TopKConfig(
top_k=top_k,
topk_group=topk_group,
num_expert_group=num_expert_group,
correction_bias=correction_bias,
),
)
forward_context = get_forward_context()
moe_layer = forward_context.moe_layers[layer_id]
return moe_layer.forward_impl(hidden_states, topk_output)
def flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl_fake(
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
top_k: int,
topk_group: Optional[int],
num_expert_group: Optional[int],
correction_bias: Optional[torch.Tensor],
layer_id: int,
) -> torch.Tensor:
return torch.empty_like(hidden_states)
direct_register_custom_op(
op_name="moe_forward_piecewise_cuda_graph_impl",
op_func=moe_forward_piecewise_cuda_graph_impl,
mutates_args=[],
fake_impl=moe_forward_piecewise_cuda_graph_impl_fake,
)
direct_register_custom_op(
op_name="flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl",
op_func=flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl,
mutates_args=[],
fake_impl=flashinfer_fp4_moe_forward_piecewise_cuda_graph_impl_fake,
)

3
python/sglang/srt/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a4_nvfp4.py
View File

@@ -17,7 +17,6 @@ from sglang.srt.layers.quantization.compressed_tensors.schemes import (
)
from sglang.srt.layers.quantization.modelopt_quant import (
FLASHINFER_FP4_GEMM_BACKEND,
_sglang_fp4_gemm,
enable_flashinfer_fp4_gemm,
fp4_quantize,
)
@@ -154,7 +153,7 @@ class CompressedTensorsW4A4Fp4(CompressedTensorsScheme):
w = layer.weight_packed.T
w_blockscale = layer.weight_scale.T
out = _sglang_fp4_gemm(
out = torch.ops.sglang.fp4_gemm(
x_fp4,
w,
x_blockscale,

2
python/sglang/srt/layers/quantization/modelopt_quant.py
View File

@@ -1229,7 +1229,7 @@ class ModelOptFp4LinearMethod(LinearMethodBase):
backend = (
FLASHINFER_FP4_GEMM_BACKEND if FLASHINFER_FP4_GEMM_BACKEND else "cutlass"
)
out = _sglang_fp4_gemm(
out = torch.ops.sglang.fp4_gemm(
x_fp4,
w,
x_scale_interleaved,

12
python/sglang/srt/models/deepseek_v2.py
View File

@@ -20,6 +20,7 @@ from __future__ import annotations
import concurrent.futures
import logging
import os
from contextlib import nullcontext
from enum import IntEnum, auto
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
@@ -400,6 +401,9 @@ def handle_attention_fa4(attn, forward_batch):
def handle_attention_trtllm_mla(attn, forward_batch):
if is_in_piecewise_cuda_graph():
return AttnForwardMethod.MLA
sum_extend_prefix_lens = _get_sum_extend_prefix_lens(forward_batch)
if forward_batch.forward_mode.is_extend_without_speculative() and (
not attn.disable_chunked_prefix_cache or sum_extend_prefix_lens == 0
@@ -3188,7 +3192,13 @@ class DeepseekV2Model(nn.Module):
normal_end_layer = normal_start_layer = 0
aux_hidden_states = []
for i in range(normal_start_layer, normal_end_layer):
with get_global_expert_distribution_recorder().with_current_layer(i):
# NOTE: torch dynamo does not support graph break in context manager
ctx = (
nullcontext()
if get_global_server_args().enable_piecewise_cuda_graph
else get_global_expert_distribution_recorder().with_current_layer(i)
)
with ctx:
if i in self.layers_to_capture:
aux_hidden_states.append(hidden_states + residual)
layer = self.layers[i]

2
test/srt/run_suite.py
View File

@@ -163,7 +163,7 @@ suites = {
TestFile("test_disaggregation_dp_attention.py", 155),
],
"per-commit-4-gpu-b200-stage-b": [
TestFile("test_deepseek_v3_fp4_4gpu.py", 1800), # Stage B test
TestFile("test_deepseek_v3_fp4_4gpu.py", 2000), # Stage B test
],
"per-commit-4-gpu-b200": [
TestFile("test_flash_attention_4.py", 90),

67
test/srt/test_deepseek_v3_fp4_4gpu.py
View File

@@ -176,5 +176,72 @@ class TestDeepseekV3FP4MTP(CustomTestCase):
self.assertGreater(speed, 150)
class TestDeepseekV3FP4PiecewiseCudaGraph(CustomTestCase):
@classmethod
def setUpClass(cls):
cls.model = FULL_DEEPSEEK_V3_FP4_MODEL_PATH
cls.base_url = DEFAULT_URL_FOR_TEST
other_args = [
"--tp",
"4",
"--attention-backend",
"trtllm_mla",
"--moe-runner-backend",
"flashinfer_trtllm",
"--quantization",
"modelopt_fp4",
"--enable-piecewise-cuda-graph",
"--kv-cache-dtype",
"fp8_e4m3",
"--model-loader-extra-config",
'{"enable_multithread_load": true,"num_threads": 64}',
]
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=SERVER_LAUNCH_TIMEOUT,
other_args=other_args,
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_a_gsm8k(
self,
):
args = SimpleNamespace(
num_shots=8,
data_path=None,
num_questions=1319,
parallel=1319,
max_new_tokens=512,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
)
metrics = run_eval_few_shot_gsm8k(args)
print(f"{metrics=}")
if is_in_ci():
write_github_step_summary(
f"### test_gsm8k (deepseek-v3-fp4)\n" f'{metrics["accuracy"]=:.3f}\n'
)
self.assertGreater(metrics["accuracy"], 0.935)
def test_bs_1_speed(self):
args = BenchArgs(port=int(self.base_url.split(":")[-1]), max_new_tokens=2048)
_, speed = send_one_prompt(args)
print(f"{speed=:.2f}")
if is_in_ci():
write_github_step_summary(
f"### test_bs_1_speed (deepseek-v3-fp4)\n" f"{speed=:.2f} token/s\n"
)
self.assertGreater(speed, 120)
if __name__ == "__main__":
unittest.main()