[Feature]Add async tensor parallelism using compilation pass

.buildkite/test-pipeline.yaml

@@ -309,6 +309,7 @@ steps:
     - pytest -v -s compile/test_pass_manager.py
     - pytest -v -s compile/test_fusion.py
     - pytest -v -s compile/test_sequence_parallelism.py
+    - pytest -v -s compile/test_async_tp.py
 
 - label: PyTorch Fullgraph Smoke Test # 9min
   mirror_hardwares: [amdexperimental, amdproduction]

tests/compile/backend.py

@@ -5,6 +5,8 @@
 
 from torch import fx
 
+from vllm.compilation.fx_utils import (find_specified_fn,
+                                       find_specified_fn_maybe)
 from vllm.compilation.inductor_pass import InductorPass
 from vllm.config import get_current_vllm_config
 
@@ -44,3 +46,19 @@ def post_pass(self, graph: fx.Graph):
         self.graph_post_pass = deepcopy(graph)
         # assign by reference, will reflect the final state of the graph
         self.final_graph = graph
+
+    def check_before_ops(self, ops,
+                         find_fn=find_specified_fn, \
+                         find_fn_maybe=find_specified_fn_maybe, \
+                        ops_fully_replaced=True):
+        for op in ops:
+            find_fn(self.graph_pre_pass.nodes, op)
+            if ops_fully_replaced:
+                assert find_fn_maybe(self.graph_post_pass.nodes, op) is None
+
+    def check_after_ops(self, ops,
+                        find_fn=find_specified_fn, \
+                        find_fn_maybe=find_specified_fn_maybe):
+        for op in ops:
+            find_fn(self.graph_post_pass.nodes, op)
+            assert find_fn_maybe(self.graph_pre_pass.nodes, op) is None

tests/compile/test_async_tp.py

@@ -0,0 +1,253 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+
+import pytest
+import torch
+
+import vllm.envs as envs
+from vllm.compilation.collective_fusion import AsyncTPPass
+from vllm.config import (CompilationConfig, DeviceConfig, ModelConfig,
+                         PassConfig, VllmConfig)
+from vllm.distributed import (tensor_model_parallel_all_gather,
+                              tensor_model_parallel_reduce_scatter)
+from vllm.distributed.parallel_state import (init_distributed_environment,
+                                             initialize_model_parallel)
+from vllm.platforms import current_platform
+from vllm.utils import update_environment_variables
+
+from ..models.registry import HF_EXAMPLE_MODELS
+from ..utils import (compare_two_settings, create_new_process_for_each_test,
+                     multi_gpu_test)
+from .backend import TestBackend
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+
+class TestMMRSModel(torch.nn.Module):
+
+    def __init__(self, hidden_size=16):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.gate_proj = torch.nn.Parameter(torch.empty(
+            (self.hidden_size * 2, hidden_size)),
+                                            requires_grad=False)
+        # Initialize weights
+        torch.nn.init.normal_(self.gate_proj, std=0.02)
+
+    def forward(self, hidden_states):
+        """
+        Forward pass implementing the mm + reduce scatter in the FX graph
+
+        """
+        # Reshape input
+        view = hidden_states.reshape(-1, self.hidden_size)
+
+        # matrix multiplication
+        permute = self.gate_proj.permute(1, 0)
+        mm = torch.mm(view, permute)
+        reduce_scatter = tensor_model_parallel_reduce_scatter(mm, dim=0)
+        return reduce_scatter
+
+    def ops_in_model_before(self):
+        return [torch.ops.vllm.reduce_scatter.default]
+
+    def ops_in_model_after(self):
+        return [torch.ops.symm_mem.fused_matmul_reduce_scatter.default]
+
+
+class TestAGMMModel(torch.nn.Module):
+
+    def __init__(self, hidden_size=16):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.weight = torch.nn.Parameter(torch.empty(
+            (hidden_size, hidden_size)),
+                                         requires_grad=False)
+        # Initialize weights
+        torch.nn.init.normal_(self.weight, std=0.02)
+
+    def forward(self, hidden_states):
+        """
+        Forward pass implementing the mm + all gather in the FX graph
+        """
+        # Reshape input
+        view = hidden_states.reshape(-1, self.hidden_size)
+        all_gather = tensor_model_parallel_all_gather(view, dim=0)
+        permute = self.weight.permute(1, 0)
+        mm = torch.mm(all_gather, permute)
+        return mm
+
+    def ops_in_model_before(self):
+        return [torch.ops.vllm.all_gather.default]
+
+    def ops_in_model_after(self):
+        return [torch.ops.symm_mem.fused_all_gather_matmul.default]
+
+
+@multi_gpu_test(num_gpus=2)
+@pytest.mark.parametrize("test_model", [TestMMRSModel, TestAGMMModel])
+@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_async_tp_pass_replace(test_model: str, 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, test_model,
+                                          batch_size, seq_len, hidden_size,
+                                          dtype),
+                                    nprocs=nprocs)
+
+    run_torch_spawn(async_tp_pass_on_test_model, num_processes)
+
+
+def async_tp_pass_on_test_model(local_rank: int, world_size: int,
+                                test_model_cls: torch.nn.Module,
+                                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=PassConfig(
+        enable_async_tp=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_name = "nm-testing/TinyLlama-1.1B-Chat-v1.0-FP8-e2e"
+    vllm_config.model_config = ModelConfig(model=model_name,
+                                           task="auto",
+                                           tokenizer=model_name,
+                                           tokenizer_mode="auto",
+                                           trust_remote_code=True,
+                                           dtype=dtype,
+                                           seed=42)
+
+    async_tp_pass = AsyncTPPass(vllm_config)
+    backend = TestBackend(async_tp_pass)
+
+    model = test_model_cls(hidden_size)
+
+    hidden_states = torch.randn((batch_size * seq_len, hidden_size),
+                                dtype=dtype,
+                                requires_grad=False)
+
+    compiled_model = torch.compile(model, backend=backend)
+    compiled_model(hidden_states)
+
+    # In pre-nodes, all gather or reduce scatter should exist,
+    # fused_matmul_reduce_scatter or fused_all_gather_matmul should not
+    backend.check_before_ops(model.ops_in_model_before(),
+                             ops_fully_replaced=False)
+
+    # In post-nodes, fused_matmul_reduce_scatter or \
+    # fused_all_gather_matmul should exist
+    backend.check_after_ops(model.ops_in_model_after())
+
+
+@create_new_process_for_each_test()
+@pytest.mark.parametrize("model_id", ["meta-llama/Llama-3.2-1B-Instruct"])
+@pytest.mark.parametrize("tp_size", [2])
+@pytest.mark.parametrize("async_tp_enabled", [True])
+@pytest.mark.parametrize("distributed_backend", ["mp"])
+@pytest.mark.parametrize("eager_mode", [False, True])
+def test_async_tp_pass_correctness(
+    model_id: str,
+    tp_size: int,
+    async_tp_enabled: bool,
+    distributed_backend: str,
+    eager_mode: bool,
+    num_gpus_available: int,
+):
+    model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
+    model_info.check_transformers_version(on_fail="skip")
+
+    # trust_remote_code = model_info.trust_remote_code
+    # tokenizer_mode = model_info.tokenizer_mode
+    # hf_overrides = model_info.hf_overrides
+    model_info.check_available_online(on_fail="skip")
+
+    pp_size = 1
+    if num_gpus_available < tp_size:
+        pytest.skip(f"Need at least {tp_size} x {pp_size} GPUs")
+
+    common_args = [
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "float16",
+        "--max-model-len",
+        "2048",
+        "--max-num-seqs",
+        "8",
+    ]
+    if eager_mode:
+        common_args.append("--enforce-eager")
+
+    compilation_config = {
+        'level': 3,
+        'compile_sizes': [2, 4, 8],
+        'splitting_ops': [],
+        'pass_config': {
+            'enable_async_tp': async_tp_enabled
+        },
+    }
+
+    async_tp_env = tp_env = {
+        "VLLM_USE_V1": "1",
+    }
+
+    aysnc_tp_args = [
+        *common_args,
+        "--tensor-parallel-size",
+        str(tp_size),
+        "--distributed-executor-backend",
+        distributed_backend,
+        "--compilation_config",
+        json.dumps(compilation_config),
+    ]
+
+    tp_args = [
+        *common_args,
+        "--tensor-parallel-size",
+        str(tp_size),
+        "--distributed-executor-backend",
+        "mp",
+    ]
+
+    compare_two_settings(model_id,
+                         aysnc_tp_args,
+                         tp_args,
+                         async_tp_env,
+                         tp_env,
+                         method="generate")

tests/compile/test_fusion.py

@@ -29,6 +29,10 @@ def __init__(self, hidden_size: int, eps: float, static: bool,
         self.cutlass_fp8_enabled = cutlass_fp8_enabled
         self.norm = [RMSNorm(hidden_size, eps) for _ in range(3)]
         self.wscale = [torch.rand(1, dtype=torch.float32) for _ in range(2)]
+        self.key = QuantKey(dtype=FP8_DTYPE,
+                            static=static,
+                            per_tensor=static,
+                            symmetric=True)
         if static:
             self.scale = [torch.rand(1, dtype=torch.float32) for _ in range(2)]
         else:
@@ -59,6 +63,15 @@ def forward(self, x):
         y3, resid = self.norm[2](x3, resid)  # use resid here
         return y3
 
+    def ops_in_model_before(self):
+        return [QUANT_OPS[self.key]]
+
+    def ops_in_model_after(self):
+        return [
+            FUSED_OPS[FusedRMSQuantKey(self.key, False)],
+            FUSED_OPS[FusedRMSQuantKey(self.key, True)]
+        ]
+
 
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
 @pytest.mark.parametrize("hidden_size", [64, 3392, 4096])
@@ -107,25 +120,10 @@ def test_fusion_rmsnorm_quant(dtype, hidden_size, num_tokens, eps, static,
 
         torch.testing.assert_close(result, result2, atol=ATOL, rtol=RTOL)
 
-        # Check substitution worked
-        pre_nodes = backend.graph_pre_pass.nodes
-        post_nodes = backend.graph_post_pass.nodes
-
-        # static is per-tensor, dynamic is per-token
-        key = QuantKey(dtype=FP8_DTYPE,
-                       static=static,
-                       per_tensor=static,
-                       symmetric=True)
-        rms_quant = FUSED_OPS[FusedRMSQuantKey(key, False)]
-        add_rms_quant = FUSED_OPS[FusedRMSQuantKey(key, True)]
-        fp8_quant = QUANT_OPS[key]
-
         # In pre-nodes, fp8 quant should be there and fused kernels should not
-        assert find_auto_fn_maybe(pre_nodes, rms_quant) is None
-        assert find_auto_fn_maybe(pre_nodes, add_rms_quant) is None
-        find_auto_fn(pre_nodes, fp8_quant)
+        backend.check_before_ops(model.ops_in_model_before(), find_auto_fn,
+                                 find_auto_fn_maybe)
 
         # In post-nodes, fused kernels should be there and fp8 quant should not
-        find_auto_fn(post_nodes, rms_quant)
-        find_auto_fn(post_nodes, add_rms_quant)
-        assert find_auto_fn_maybe(post_nodes, fp8_quant) is None
+        backend.check_before_ops(model.ops_in_model_after(), find_auto_fn,
+                                 find_auto_fn_maybe)