[Feature] support sequence parallelism using compilation pass (#16155)

Signed-off-by: cascade812 <cascade812@outlook.com> Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
2025-04-27 06:29:35 -07:00
parent ed7a29d9f8
commit 690fe019f0
21 changed files with 1072 additions and 44 deletions
--- a/tests/compile/test_sequence_parallelism.py
+++ b/tests/compile/test_sequence_parallelism.py
@@ -0,0 +1,190 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import torch
+
+import vllm.envs as envs
+from vllm.compilation.fix_functionalization import FixFunctionalizationPass
+from vllm.compilation.fx_utils import (find_auto_fn, find_auto_fn_maybe,
+                                       find_specified_fn,
+                                       find_specified_fn_maybe, is_func)
+from vllm.compilation.sequence_parallelism import SequenceParallelismPass
+from vllm.config import (CompilationConfig, DeviceConfig, ModelConfig,
+                         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.platforms import current_platform
+from vllm.utils import update_environment_variables
+
+from ..utils import multi_gpu_test
+from .backend import TestBackend
+
+OPS_IN_MODEL_BEFORE = [
+    torch.ops.vllm.all_reduce.default,
+]
+
+OPS_IN_MODEL_AFTER = [
+    torch.ops.vllm.reduce_scatter.default,
+    torch.ops.vllm.all_gather.default,
+]
+
+OPS_IN_MODEL = [torch.ops._C.fused_add_rms_norm.default]
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+
+class TestModel(torch.nn.Module):
+
+    def __init__(self, hidden_size=16, intermediate_size=32):
+        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)
+        # Initialize weights
+        torch.nn.init.normal_(self.gate_proj, std=0.02)
+
+    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)
+
+        return norm_output, residual_output
+
+
+@multi_gpu_test(num_gpus=2)
+@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.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):
+    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),
+                                    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):
+    current_platform.seed_everything(0)
+
+    device = torch.device(f"cuda:{local_rank}")
+    torch.cuda.set_device(device)
+    torch.set_default_device(device)
+    torch.set_default_dtype(dtype)
+
+    update_environment_variables({
+        'RANK': str(local_rank),
+        'LOCAL_RANK': str(local_rank),
+        'WORLD_SIZE': str(world_size),
+        'MASTER_ADDR': 'localhost',
+        'MASTER_PORT': '12345',
+    })
+
+    # initialize distributed
+    init_distributed_environment()
+    initialize_model_parallel(tensor_model_parallel_size=world_size)
+
+    # configure vllm config for SequenceParallelismPass
+    vllm_config = VllmConfig()
+    vllm_config.compilation_config = CompilationConfig(
+        pass_config=CompilationConfig.PassConfig(
+            enable_sequence_parallelism=True, ), )
+    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,
+                                           task="auto",
+                                           tokenizer=model,
+                                           tokenizer_mode="auto",
+                                           trust_remote_code=True,
+                                           dtype=dtype,
+                                           seed=42)
+
+    sequence_parallelism_pass = SequenceParallelismPass(vllm_config)
+    backend_no_func = TestBackend(sequence_parallelism_pass)
+    func_pass = FixFunctionalizationPass(vllm_config)
+    backend_func = TestBackend(sequence_parallelism_pass, func_pass)
+
+    model = TestModel(hidden_size, hidden_size * 2)
+    hidden_states = torch.randn((batch_size * seq_len, hidden_size),
+                                dtype=dtype)
+    residual = torch.randn((batch_size * seq_len, hidden_size), dtype=dtype)
+
+    compiled_model_no_func = torch.compile(model, backend=backend_no_func)
+    compiled_model_no_func(hidden_states, residual)
+    compiled_model_func = torch.compile(model, backend=backend_func)
+    compiled_model_func(hidden_states, residual)
+
+    # Check substitution worked
+    pre_nodes = backend_no_func.graph_pre_pass.nodes
+    post_nodes = backend_no_func.graph_post_pass.nodes
+
+    # In pre-nodes, all reduce should be there,
+    # reduce scatter and all gather should not
+    for op in OPS_IN_MODEL_BEFORE:
+        find_specified_fn(pre_nodes, op)
+    for op in OPS_IN_MODEL_AFTER:
+        assert find_specified_fn_maybe(pre_nodes, op) is None
+
+    # In post-nodes, reduce scatter and all gather should be there,
+    # all reduce should not
+    for op in OPS_IN_MODEL_AFTER:
+        find_specified_fn(post_nodes, op)
+    for op in OPS_IN_MODEL_BEFORE:
+        assert find_specified_fn_maybe(post_nodes, op) is None
+
+    # check if the functionalization pass is applied
+    for op in OPS_IN_MODEL:
+        find_auto_fn(backend_no_func.graph_post_pass.nodes, op)
+        assert find_auto_fn_maybe(backend_func.graph_post_pass.nodes,
+                                  op) is None  # noqa: E501
+
+    # make sure the ops were all de-functionalized
+    found = dict()
+    for node in backend_func.graph_post_pass.nodes:
+        for op in OPS_IN_MODEL:
+            if is_func(node, op):
+                found[op] = True
+    assert all(found[op] for op in OPS_IN_MODEL)