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[Feature] Support sequence parallelism for static fp8 quantization (#19181)

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Signed-off-by: cascade812 <cascade812@outlook.com>
This commit is contained in:
cascade
2025-06-23 13:09:02 -07:00
committed by GitHub
parent d0132f025d
commit e6327c9b3e
7 changed files with 531 additions and 195 deletions
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161
tests/compile/test_sequence_parallelism.py
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@@ -6,7 +6,9 @@ import torch
import vllm.envs as envs
from vllm.compilation.fix_functionalization import FixFunctionalizationPass
from vllm.compilation.fusion import FusionPass
from vllm.compilation.fx_utils import find_auto_fn, find_auto_fn_maybe, is_func
from vllm.compilation.noop_elimination import NoOpEliminationPass
from vllm.compilation.sequence_parallelism import SequenceParallelismPass
from vllm.config import (CompilationConfig, DeviceConfig, ModelConfig,
PassConfig, VllmConfig)
@@ -14,12 +16,15 @@ 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 (
Fp8LinearOp)
from vllm.platforms import current_platform
from vllm.utils import update_environment_variables
from ..utils import multi_gpu_test
from .backend import TestBackend
FP8_DTYPE = current_platform.fp8_dtype()
prompts = [
"Hello, my name is",
"The president of the United States is",
@@ -30,13 +35,16 @@ prompts = [
class TestModel(torch.nn.Module):
def __init__(self, hidden_size=16, intermediate_size=32):
def __init__(self,
hidden_size=16,
intermediate_size=32,
vllm_config: VllmConfig = None):
super().__init__()
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.gate_proj = torch.nn.Parameter(
torch.empty((intermediate_size, hidden_size)))
self.norm = RMSNorm(hidden_size, 1e-05)
self.norm = RMSNorm(intermediate_size, 1e-05)
# Initialize weights
torch.nn.init.normal_(self.gate_proj, std=0.02)
@@ -79,32 +87,138 @@ class TestModel(torch.nn.Module):
return [torch.ops._C.fused_add_rms_norm.default]
class TestQuantModel(torch.nn.Module):
def __init__(self,
hidden_size=16,
intermediate_size=32,
vllm_config: VllmConfig = None):
super().__init__()
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.vllm_config = vllm_config
self.gate_proj = torch.nn.Parameter(torch.empty(
(intermediate_size, hidden_size)),
requires_grad=False)
self.norm = RMSNorm(intermediate_size, 1e-05)
# Initialize weights
torch.nn.init.normal_(self.gate_proj, std=0.02)
self.fp8_linear = Fp8LinearOp(cutlass_fp8_supported=True,
use_per_token_if_dynamic=False)
self.scale = torch.rand(1, dtype=torch.float32)
# Create a weight that is compatible with torch._scaled_mm,
# which expects a column-major layout.
self.w = torch.rand(hidden_size,
intermediate_size).to(dtype=FP8_DTYPE).t()
self.wscale = torch.rand(1, dtype=torch.float32)
def forward(self, hidden_states, residual):
"""
Forward pass implementing the operations in the FX graph
Args:
hidden_states: Input tensor
residual: Residual tensor from previous layer
Returns:
Tuple containing the output tensor
"""
# Reshape input
view = hidden_states.reshape(-1, self.hidden_size)
#matrix multiplication
permute = self.gate_proj.permute(1, 0)
mm = torch.mm(view, permute)
# Tensor parallel all-reduce
all_reduce = tensor_model_parallel_all_reduce(mm)
# layer normalization
norm_output, residual_output = self.norm(all_reduce, residual)
# for static input quantization
# self.fp8_linear is initialized with use_per_token_if_dynamic=False
fp8_linear_result = self.fp8_linear.apply(norm_output,
self.w,
self.wscale,
input_scale=self.scale.to(
norm_output.device))
return fp8_linear_result, residual_output
def ops_in_model_before(self):
ops_to_remove = [torch.ops.vllm.all_reduce.default
] # Always removed by SP
# The following are only removed if fusion happens
if self.vllm_config and self.vllm_config.compilation_config \
.pass_config.enable_fusion:
ops_to_remove.extend([
torch.ops._C.fused_add_rms_norm.default,
torch.ops._C.static_scaled_fp8_quant.default,
])
return ops_to_remove
def ops_in_model_after(self):
ops_to_add = [
torch.ops.vllm.reduce_scatter.default,
torch.ops.vllm.all_gather.default
]
# The following is only added if fusion happens
if self.vllm_config and self.vllm_config.compilation_config \
.pass_config.enable_fusion:
ops_to_add.append(
torch.ops._C.fused_add_rms_norm_static_fp8_quant.default)
return ops_to_add
def ops_in_model(self):
if self.vllm_config and self.vllm_config.compilation_config \
.pass_config.enable_fusion:
# If fusion happens, the fused op is the one
# we check for (de)functionalization
return [torch.ops._C.fused_add_rms_norm_static_fp8_quant.default
] # noqa: E501
else:
# If no fusion, the original ops are checked
return [
torch.ops._C.fused_add_rms_norm.default,
# TODO functionalization pass does not handle this yet
# torch.ops._C.static_scaled_fp8_quant.default,
]
@multi_gpu_test(num_gpus=2)
@pytest.mark.parametrize("test_model_cls", [TestModel, TestQuantModel])
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("seq_len", [16])
@pytest.mark.parametrize("hidden_size", [16])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("enable_fusion", [True, False])
@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"],
reason="Only test on CUDA")
def test_sequence_parallelism_pass(batch_size: int, seq_len: int,
hidden_size: int, dtype: torch.dtype):
def test_sequence_parallelism_pass(test_model_cls: type[torch.nn.Module],
batch_size: int, seq_len: int,
hidden_size: int, dtype: torch.dtype,
enable_fusion: bool):
num_processes = 2
def run_torch_spawn(fn, nprocs):
# need to use torch.mp.spawn otherwise will have problems with
# torch.distributed and cuda
torch.multiprocessing.spawn(fn,
args=(num_processes, batch_size, seq_len,
hidden_size, dtype),
args=(num_processes, test_model_cls,
batch_size, seq_len, hidden_size,
dtype, enable_fusion),
nprocs=nprocs)
run_torch_spawn(sequence_parallelism_pass_on_test_model, num_processes)
def sequence_parallelism_pass_on_test_model(local_rank: int, world_size: int,
batch_size: int, seq_len: int,
hidden_size: int,
dtype: torch.dtype):
def sequence_parallelism_pass_on_test_model(
local_rank: int, world_size: int,
test_model_cls: type[torch.nn.Module], batch_size: int, seq_len: int,
hidden_size: int, dtype: torch.dtype, enable_fusion: bool):
current_platform.seed_everything(0)
device = torch.device(f"cuda:{local_rank}")
@@ -127,26 +241,39 @@ def sequence_parallelism_pass_on_test_model(local_rank: int, world_size: int,
# configure vllm config for SequenceParallelismPass
vllm_config = VllmConfig()
vllm_config.compilation_config = CompilationConfig(pass_config=PassConfig(
enable_sequence_parallelism=True))
enable_sequence_parallelism=True,
enable_fusion=enable_fusion,
enable_noop=True)) # NoOp needed for fusion
vllm_config.device_config = DeviceConfig(device=torch.device("cuda"))
# this is a fake model name to construct the model config
# in the vllm_config, it's not really used.
model = "nm-testing/TinyLlama-1.1B-Chat-v1.0-FP8-e2e"
vllm_config.model_config = ModelConfig(model=model,
model_name = "nm-testing/TinyLlama-1.1B-Chat-v1.0-FP8-e2e"
vllm_config.model_config = ModelConfig(model=model_name,
task="auto",
tokenizer=model,
tokenizer=model_name,
tokenizer_mode="auto",
trust_remote_code=True,
dtype=dtype,
seed=42)
sequence_parallelism_pass = SequenceParallelismPass(vllm_config)
backend_no_func = TestBackend(sequence_parallelism_pass)
noop_pass = NoOpEliminationPass(vllm_config)
func_pass = FixFunctionalizationPass(vllm_config)
backend_func = TestBackend(sequence_parallelism_pass, func_pass)
model = TestModel(hidden_size, hidden_size * 2)
passes_for_backend = [noop_pass, sequence_parallelism_pass]
if enable_fusion:
fusion_pass = FusionPass.instance(vllm_config)
passes_for_backend.append(fusion_pass)
backend_no_func = TestBackend(*passes_for_backend)
backend_func = TestBackend(*passes_for_backend, func_pass)
model = test_model_cls(hidden_size,
hidden_size * 2,
vllm_config=vllm_config)
hidden_states = torch.randn((batch_size * seq_len, hidden_size),
dtype=dtype)
residual = torch.randn((batch_size * seq_len, hidden_size), dtype=dtype)