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Add FlashInfer allreduce RMSNorm Quant fusion (#21069)

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Signed-off-by: ilmarkov <imarkov@redhat.com>
Signed-off-by: ilmarkov <markovilya197@gmail.com>
Co-authored-by: ilmarkov <imarkov@redhat.com>
This commit is contained in:
Ilya Markov
2025-07-31 22:58:38 +02:00
committed by GitHub
parent 2dff2e21d9
commit 6e672daf62
5 changed files with 606 additions and 68 deletions
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126
tests/compile/test_fusion_all_reduce.py
View File

@ -7,22 +7,26 @@ import torch
import vllm.envs as envs
from vllm.compilation.collective_fusion import AllReduceFusionPass
from vllm.compilation.fix_functionalization import FixFunctionalizationPass
from vllm.compilation.noop_elimination import NoOpEliminationPass
from vllm.config import (CompilationConfig, CompilationLevel, DeviceConfig,
ModelConfig, PassConfig, VllmConfig)
from vllm.distributed import tensor_model_parallel_all_reduce
from vllm.distributed.parallel_state import (init_distributed_environment,
initialize_model_parallel)
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
GroupShape, QuantFP8)
from vllm.platforms import current_platform
from vllm.utils import update_environment_variables
from ..utils import multi_gpu_test
from ..utils import has_module_attribute, multi_gpu_test
from .backend import TestBackend
class TestAllReduceRMSNormModel(torch.nn.Module):
def __init__(self, hidden_size=16, eps=1e-6):
def __init__(self, hidden_size=16, token_num=16, eps=1e-6):
super().__init__()
self.hidden_size = hidden_size
self.eps = eps
@ -43,7 +47,7 @@ class TestAllReduceRMSNormModel(torch.nn.Module):
class TestAllReduceFusedAddRMSNormModel(torch.nn.Module):
def __init__(self, hidden_size=16, eps=1e-6):
def __init__(self, hidden_size=16, token_num=16, eps=1e-6):
super().__init__()
self.hidden_size = hidden_size
self.eps = eps
@ -62,24 +66,101 @@ class TestAllReduceFusedAddRMSNormModel(torch.nn.Module):
return [torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default]
class TestAllReduceFusedAddRMSNormStaticQuantFP8Model(torch.nn.Module):
def __init__(self, hidden_size=16, token_num=16, eps=1e-6):
super().__init__()
self.hidden_size = hidden_size
self.eps = eps
self.norm = RMSNorm(hidden_size, eps)
self.quant_fp8 = QuantFP8(static=True,
group_shape=GroupShape.PER_TENSOR)
self.scale = torch.rand(1, dtype=torch.float32)
self.output = torch.empty((token_num, hidden_size),
dtype=torch.float32)
def forward(self, hidden_states, residual):
view = hidden_states.reshape(-1, self.hidden_size)
all_reduce = tensor_model_parallel_all_reduce(view)
norm_output, residual_output = self.norm(all_reduce, residual)
torch.ops._C.static_scaled_fp8_quant(self.output,
norm_output.contiguous(),
self.scale)
return self.output, residual_output
def ops_in_model_after(self):
return [torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default]
def ops_in_model_before(self):
return [
torch.ops.vllm.all_reduce.default,
torch.ops._C.static_scaled_fp8_quant.default
]
class TestAllReduceFusedAddRMSNormStaticQuantFP4Model(torch.nn.Module):
def __init__(self, hidden_size=16, token_num=16, eps=1e-6):
super().__init__()
self.hidden_size = hidden_size
self.eps = eps
self.norm = RMSNorm(hidden_size, eps)
self.scale = torch.rand(1, dtype=torch.float32)
self.output = torch.empty((token_num, hidden_size),
dtype=torch.float32)
round_up = lambda x, y: (x + y - 1) // y * y
rounded_m = round_up(token_num, 128)
scale_n = hidden_size // 16
rounded_n = round_up(scale_n, 4)
self.output_scale = torch.empty((rounded_m, rounded_n // 4),
dtype=torch.int32)
def forward(self, hidden_states, residual):
view = hidden_states.reshape(-1, self.hidden_size)
all_reduce = tensor_model_parallel_all_reduce(view)
norm_output, residual_output = self.norm(all_reduce, residual)
norm_output = norm_output.reshape(-1, norm_output.shape[-1])
torch.ops._C.scaled_fp4_quant(self.output, norm_output,
self.output_scale, self.scale)
return self.output, residual_output, self.output_scale
def ops_in_model_after(self):
return [torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default]
def ops_in_model_before(self):
return [
torch.ops.vllm.all_reduce.default,
torch.ops._C.scaled_fp4_quant.default
]
@multi_gpu_test(num_gpus=2)
@pytest.mark.parametrize(
"test_model",
[TestAllReduceRMSNormModel, TestAllReduceFusedAddRMSNormModel])
@pytest.mark.parametrize("test_model", [
TestAllReduceRMSNormModel,
TestAllReduceFusedAddRMSNormModel,
TestAllReduceFusedAddRMSNormStaticQuantFP8Model,
TestAllReduceFusedAddRMSNormStaticQuantFP4Model,
])
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("seq_len", [8])
@pytest.mark.parametrize("hidden_size", [4096])
@pytest.mark.parametrize("hidden_size", [16])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"],
reason="Only test on CUDA")
@pytest.mark.skipif(not find_spec("flashinfer"),
reason="flashinfer is not installed")
@pytest.mark.skipif(not current_platform.is_device_capability(100),
reason="Only test on SM100")
@pytest.mark.skipif(
not find_spec("flashinfer")
or not has_module_attribute("flashinfer.comm", "trtllm_allreduce_fusion"),
reason="flashinfer is not found or flashinfer "
"is not compiled with trtllm_allreduce_fusion")
def test_all_reduce_fusion_pass_replace(test_model: torch.nn.Module,
batch_size: int, seq_len: int,
hidden_size: int, dtype: torch.dtype):
num_processes = 2
if (test_model == TestAllReduceFusedAddRMSNormStaticQuantFP4Model
and not current_platform.has_device_capability(100)):
pytest.skip("Skip as nvfp4 is only supported on "
"devices with compute capability 10.0 (Blackwell)")
def run_torch_spawn(fn, nprocs):
torch.multiprocessing.spawn(fn,
@ -113,12 +194,11 @@ def all_reduce_fusion_pass_on_test_model(local_rank: int, world_size: int,
init_distributed_environment()
initialize_model_parallel(tensor_model_parallel_size=world_size)
vllm_config = VllmConfig(
compilation_config=CompilationConfig(level=CompilationLevel.PIECEWISE,
custom_ops=["+rms_norm"],
compile_sizes=[2, 4, 8]))
vllm_config = VllmConfig(compilation_config=CompilationConfig(
level=CompilationLevel.PIECEWISE,
custom_ops=["+rms_norm", "+quant_fp8"]))
vllm_config.compilation_config.pass_config = PassConfig(
enable_fi_allreduce_fusion=True)
enable_fi_allreduce_fusion=True, enable_noop=True)
vllm_config.device_config = DeviceConfig(device=torch.device("cuda"))
# this is a fake model name to construct the model config
@ -130,14 +210,16 @@ def all_reduce_fusion_pass_on_test_model(local_rank: int, world_size: int,
seed=42)
all_reduce_fusion_pass = AllReduceFusionPass(vllm_config)
backend = TestBackend(all_reduce_fusion_pass)
noop_pass = NoOpEliminationPass(vllm_config)
func_pass = FixFunctionalizationPass(vllm_config)
model = test_model_cls(hidden_size)
backend = TestBackend(all_reduce_fusion_pass, noop_pass, func_pass)
hidden_states = torch.randn((batch_size * seq_len, hidden_size),
requires_grad=False)
residual = torch.randn((batch_size * seq_len, hidden_size),
requires_grad=False)
token_num = batch_size * seq_len
model = test_model_cls(hidden_size, token_num)
hidden_states = torch.randn((token_num, hidden_size), requires_grad=False)
residual = torch.randn((token_num, hidden_size), requires_grad=False)
compiled_model = torch.compile(model, backend=backend)
compiled_model(hidden_states, residual)

12
tests/utils.py
View File

@ -4,6 +4,7 @@
import asyncio
import copy
import functools
import importlib
import os
import signal
import subprocess
@ -974,3 +975,14 @@ def get_client_text_logprob_generations(
return [(text_generations, text,
(None if x.logprobs is None else x.logprobs.top_logprobs))
for completion in completions for x in completion.choices]
def has_module_attribute(module_name, attribute_name):
"""
Helper function to check if a module has a specific attribute.
"""
try:
module = importlib.import_module(module_name)
return hasattr(module, attribute_name)
except ImportError:
return False