233 lines
9.2 KiB
Python
233 lines
9.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from importlib.util import find_spec
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import pytest
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import torch
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import vllm.envs as envs
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from vllm.compilation.collective_fusion import AllReduceFusionPass
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from vllm.compilation.fix_functionalization import FixFunctionalizationPass
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from vllm.compilation.noop_elimination import NoOpEliminationPass
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from vllm.config import (CompilationConfig, CompilationLevel, DeviceConfig,
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ModelConfig, PassConfig, VllmConfig)
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from vllm.distributed import tensor_model_parallel_all_reduce
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from vllm.distributed.parallel_state import (init_distributed_environment,
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initialize_model_parallel)
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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GroupShape, QuantFP8)
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from vllm.platforms import current_platform
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from vllm.utils import update_environment_variables
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from ..utils import has_module_attribute, multi_gpu_test
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from .backend import TestBackend
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class TestAllReduceRMSNormModel(torch.nn.Module):
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def __init__(self, hidden_size=16, token_num=16, eps=1e-6):
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super().__init__()
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self.hidden_size = hidden_size
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self.eps = eps
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self.norm = RMSNorm(hidden_size, eps)
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def forward(self, hidden_states, residual):
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view = hidden_states.reshape(-1, self.hidden_size)
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all_reduce = tensor_model_parallel_all_reduce(view)
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norm = self.norm(all_reduce)
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return norm
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def ops_in_model_before(self):
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return [torch.ops.vllm.all_reduce.default]
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def ops_in_model_after(self):
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return [torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default]
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class TestAllReduceFusedAddRMSNormModel(torch.nn.Module):
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def __init__(self, hidden_size=16, token_num=16, eps=1e-6):
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super().__init__()
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self.hidden_size = hidden_size
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self.eps = eps
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self.norm = RMSNorm(hidden_size, eps)
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def forward(self, hidden_states, residual):
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view = hidden_states.reshape(-1, self.hidden_size)
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all_reduce = tensor_model_parallel_all_reduce(view)
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norm, _ = self.norm(all_reduce, residual)
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return norm
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def ops_in_model_before(self):
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return [torch.ops.vllm.all_reduce.default]
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def ops_in_model_after(self):
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return [torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default]
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class TestAllReduceFusedAddRMSNormStaticQuantFP8Model(torch.nn.Module):
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def __init__(self, hidden_size=16, token_num=16, eps=1e-6):
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super().__init__()
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self.hidden_size = hidden_size
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self.eps = eps
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self.norm = RMSNorm(hidden_size, eps)
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self.quant_fp8 = QuantFP8(static=True,
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group_shape=GroupShape.PER_TENSOR)
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self.scale = torch.rand(1, dtype=torch.float32)
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self.output = torch.empty((token_num, hidden_size),
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dtype=torch.float32)
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def forward(self, hidden_states, residual):
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view = hidden_states.reshape(-1, self.hidden_size)
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all_reduce = tensor_model_parallel_all_reduce(view)
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norm_output, residual_output = self.norm(all_reduce, residual)
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torch.ops._C.static_scaled_fp8_quant(self.output,
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norm_output.contiguous(),
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self.scale)
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return self.output, residual_output
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def ops_in_model_after(self):
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return [torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default]
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def ops_in_model_before(self):
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return [
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torch.ops.vllm.all_reduce.default,
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torch.ops._C.static_scaled_fp8_quant.default
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]
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class TestAllReduceFusedAddRMSNormStaticQuantFP4Model(torch.nn.Module):
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def __init__(self, hidden_size=16, token_num=16, eps=1e-6):
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super().__init__()
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self.hidden_size = hidden_size
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self.eps = eps
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self.norm = RMSNorm(hidden_size, eps)
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self.scale = torch.rand(1, dtype=torch.float32)
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self.output = torch.empty((token_num, hidden_size),
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dtype=torch.float32)
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round_up = lambda x, y: (x + y - 1) // y * y
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rounded_m = round_up(token_num, 128)
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scale_n = hidden_size // 16
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rounded_n = round_up(scale_n, 4)
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self.output_scale = torch.empty((rounded_m, rounded_n // 4),
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dtype=torch.int32)
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def forward(self, hidden_states, residual):
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view = hidden_states.reshape(-1, self.hidden_size)
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all_reduce = tensor_model_parallel_all_reduce(view)
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norm_output, residual_output = self.norm(all_reduce, residual)
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norm_output = norm_output.reshape(-1, norm_output.shape[-1])
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torch.ops._C.scaled_fp4_quant(self.output, norm_output,
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self.output_scale, self.scale)
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return self.output, residual_output, self.output_scale
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def ops_in_model_after(self):
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return [torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default]
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def ops_in_model_before(self):
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return [
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torch.ops.vllm.all_reduce.default,
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torch.ops._C.scaled_fp4_quant.default
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]
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@multi_gpu_test(num_gpus=2)
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@pytest.mark.parametrize(
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"test_model",
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[
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TestAllReduceRMSNormModel,
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TestAllReduceFusedAddRMSNormModel,
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TestAllReduceFusedAddRMSNormStaticQuantFP8Model,
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# TODO: Enable with torch==2.8.0
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# TestAllReduceFusedAddRMSNormStaticQuantFP4Model,
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])
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@pytest.mark.parametrize("batch_size", [8])
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@pytest.mark.parametrize("seq_len", [8])
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@pytest.mark.parametrize("hidden_size", [16])
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@pytest.mark.parametrize("dtype", [torch.bfloat16])
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@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"],
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reason="Only test on CUDA")
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@pytest.mark.skipif(
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not find_spec("flashinfer")
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or not has_module_attribute("flashinfer.comm", "trtllm_allreduce_fusion"),
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reason="flashinfer is not found or flashinfer "
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"is not compiled with trtllm_allreduce_fusion")
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def test_all_reduce_fusion_pass_replace(test_model: torch.nn.Module,
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batch_size: int, seq_len: int,
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hidden_size: int, dtype: torch.dtype):
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num_processes = 2
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if (test_model == TestAllReduceFusedAddRMSNormStaticQuantFP4Model
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and not current_platform.has_device_capability(100)):
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pytest.skip("Skip as nvfp4 is only supported on "
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"devices with compute capability 10.0 (Blackwell)")
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def run_torch_spawn(fn, nprocs):
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torch.multiprocessing.spawn(fn,
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args=(num_processes, test_model,
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batch_size, seq_len, hidden_size,
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dtype),
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nprocs=nprocs)
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run_torch_spawn(all_reduce_fusion_pass_on_test_model, num_processes)
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def all_reduce_fusion_pass_on_test_model(local_rank: int, world_size: int,
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test_model_cls: torch.nn.Module,
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batch_size: int, seq_len: int,
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hidden_size: int, dtype: torch.dtype):
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current_platform.seed_everything(0)
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device = torch.device(f"cuda:{local_rank}")
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torch.cuda.set_device(device)
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torch.set_default_device(device)
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torch.set_default_dtype(dtype)
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update_environment_variables({
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'RANK': str(local_rank),
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'LOCAL_RANK': str(local_rank),
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'WORLD_SIZE': str(world_size),
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'MASTER_ADDR': 'localhost',
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'MASTER_PORT': '12345',
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})
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init_distributed_environment()
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initialize_model_parallel(tensor_model_parallel_size=world_size)
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vllm_config = VllmConfig(compilation_config=CompilationConfig(
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level=CompilationLevel.PIECEWISE,
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custom_ops=["+rms_norm", "+quant_fp8"]))
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vllm_config.compilation_config.pass_config = PassConfig(
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enable_fi_allreduce_fusion=True, enable_noop=True)
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vllm_config.device_config = DeviceConfig(device=torch.device("cuda"))
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# this is a fake model name to construct the model config
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# in the vllm_config, it's not really used.
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model_name = "nm-testing/TinyLlama-1.1B-Chat-v1.0-FP8-e2e"
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vllm_config.model_config = ModelConfig(model=model_name,
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trust_remote_code=True,
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dtype=dtype,
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seed=42)
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all_reduce_fusion_pass = AllReduceFusionPass(vllm_config)
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noop_pass = NoOpEliminationPass(vllm_config)
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func_pass = FixFunctionalizationPass(vllm_config)
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backend = TestBackend(all_reduce_fusion_pass, noop_pass, func_pass)
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token_num = batch_size * seq_len
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model = test_model_cls(hidden_size, token_num)
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hidden_states = torch.randn((token_num, hidden_size), requires_grad=False)
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residual = torch.randn((token_num, hidden_size), requires_grad=False)
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compiled_model = torch.compile(model, backend=backend)
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compiled_model(hidden_states, residual)
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backend.check_before_ops(model.ops_in_model_before(), fully_replaced=False)
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backend.check_after_ops(model.ops_in_model_after())
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del all_reduce_fusion_pass
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