Integration SM100 FlashInfer fused allreduce RMSNorm (#20691)

Signed-off-by: ilmarkov <imarkov@redhat.com>
Co-authored-by: ilmarkov <imarkov@redhat.com>

tests/compile/test_fusion_all_reduce.py

@@ -0,0 +1,152 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from importlib.util import find_spec
+
+import pytest
+import torch
+
+import vllm.envs as envs
+from vllm.compilation.collective_fusion import AllReduceFusionPass
+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.platforms import current_platform
+from vllm.utils import update_environment_variables
+
+from ..utils import multi_gpu_test
+from .backend import TestBackend
+
+
+class TestAllReduceRMSNormModel(torch.nn.Module):
+
+    def __init__(self, hidden_size=16, eps=1e-6):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.eps = eps
+        self.norm = RMSNorm(hidden_size, eps)
+
+    def forward(self, hidden_states, residual):
+        view = hidden_states.reshape(-1, self.hidden_size)
+        all_reduce = tensor_model_parallel_all_reduce(view)
+        norm = self.norm(all_reduce)
+        return norm
+
+    def ops_in_model_before(self):
+        return [torch.ops.vllm.all_reduce.default]
+
+    def ops_in_model_after(self):
+        return [torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default]
+
+
+class TestAllReduceFusedAddRMSNormModel(torch.nn.Module):
+
+    def __init__(self, hidden_size=16, eps=1e-6):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.eps = eps
+        self.norm = RMSNorm(hidden_size, eps)
+
+    def forward(self, hidden_states, residual):
+        view = hidden_states.reshape(-1, self.hidden_size)
+        all_reduce = tensor_model_parallel_all_reduce(view)
+        norm, _ = self.norm(all_reduce, residual)
+        return norm
+
+    def ops_in_model_before(self):
+        return [torch.ops.vllm.all_reduce.default]
+
+    def ops_in_model_after(self):
+        return [torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default]
+
+
+@multi_gpu_test(num_gpus=2)
+@pytest.mark.parametrize(
+    "test_model",
+    [TestAllReduceRMSNormModel, TestAllReduceFusedAddRMSNormModel])
+@pytest.mark.parametrize("batch_size", [8])
+@pytest.mark.parametrize("seq_len", [8])
+@pytest.mark.parametrize("hidden_size", [4096])
+@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")
+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
+
+    def run_torch_spawn(fn, nprocs):
+        torch.multiprocessing.spawn(fn,
+                                    args=(num_processes, test_model,
+                                          batch_size, seq_len, hidden_size,
+                                          dtype),
+                                    nprocs=nprocs)
+
+    run_torch_spawn(all_reduce_fusion_pass_on_test_model, num_processes)
+
+
+def all_reduce_fusion_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',
+    })
+
+    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.compilation_config.pass_config = PassConfig(
+        enable_fi_allreduce_fusion=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)
+
+    all_reduce_fusion_pass = AllReduceFusionPass(
+        vllm_config, vllm_config.compilation_config.pass_config.
+        fi_allreduce_fusion_max_token_num)
+    backend = TestBackend(all_reduce_fusion_pass)
+
+    model = test_model_cls(hidden_size)
+
+    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)
+
+    compiled_model = torch.compile(model, backend=backend)
+    compiled_model(hidden_states, residual)
+
+    backend.check_before_ops(model.ops_in_model_before(), fully_replaced=False)
+    backend.check_after_ops(model.ops_in_model_after())
+    del all_reduce_fusion_pass

vllm/compilation/collective_fusion.py

@@ -1,23 +1,39 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from importlib.util import find_spec
 from typing import Optional
 
 import torch
 import torch._inductor.pattern_matcher as pm
 import torch.fx as fx
+from torch._higher_order_ops.auto_functionalize import auto_functionalized
 from torch._inductor.pattern_matcher import PatternMatcherPass
 from torch.distributed._symmetric_memory import enable_symm_mem_for_group
 
 from vllm.config import VllmConfig
-from vllm.distributed import get_tp_group
+from vllm.distributed import get_tp_group, tensor_model_parallel_all_reduce
 from vllm.distributed.parallel_state import (
-    get_tensor_model_parallel_world_size)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.logger import init_logger
+from vllm.utils import direct_register_custom_op
 
 from .vllm_inductor_pass import VllmInductorPass
 
+if find_spec("flashinfer"):
+    import flashinfer.comm as flashinfer_comm
+
+    flashinfer_comm = (flashinfer_comm if hasattr(
+        flashinfer_comm, "trtllm_allreduce_fusion") else None)
+else:
+    flashinfer_comm = None
+from vllm.platforms import current_platform
+
 logger = init_logger(__name__)
 
+ALLREDUCE_OP = torch.ops.vllm.all_reduce.default
+RMS_OP = torch.ops._C.rms_norm.default
+RMS_ADD_OP = torch.ops._C.fused_add_rms_norm.default
+
 
 class BasePattern:
 
@@ -43,7 +59,8 @@ class GEMMReduceScatterPattern(BasePattern):
                 mm,
                 dim=0,
                 world_size=self.tp_size,
-                group_name=self.tp.unique_name)
+                group_name=self.tp.unique_name,
+            )
             return reduce_scatter
 
         def replacement(mul: torch.Tensor, mm_weight: torch.Tensor):
@@ -79,7 +96,8 @@ class AllGatherGEMMPattern(BasePattern):
                 x,
                 dim=0,
                 world_size=self.tp_size,
-                group_name=self.tp.unique_name)
+                group_name=self.tp.unique_name,
+            )
 
             return torch.ops.aten.mm.default(all_gather, weight)
 
@@ -125,3 +143,333 @@ class AsyncTPPass(VllmInductorPass):
         logger.debug("Replaced %s patterns", count)
         self.dump_graph(graph, "after_async_tp_pass")
         self.end_and_log()
+
+
+if flashinfer_comm is not None:
+    _FI_WORKSPACE_TENSOR = None
+
+    MiB = 1024 * 1024
+    # Max size of the input tensor per world size
+    # to use flashinfer fused allreduce
+    _FI_MAX_SIZES = {
+        2: MiB,  # 1MB
+        4: MiB,  # 1MB
+        6: MiB // 2,  # 512KB
+        8: MiB // 2,  # 512KB
+    }
+
+    def call_trtllm_fused_allreduce_norm(
+        allreduce_in: torch.Tensor,
+        residual: torch.Tensor,
+        rms_gamma: torch.Tensor,
+        rms_eps: float,
+        world_rank: int,
+        world_size: int,
+        launch_with_pdl: bool,
+        trigger_completion_at_end: bool,
+        fp32_acc: bool,
+        max_token_num: int,
+        norm_out: Optional[torch.Tensor] = None,
+    ) -> None:
+        use_flashinfer = allreduce_in.shape[0] * allreduce_in.shape[
+            1] * allreduce_in.element_size() <= min(
+                _FI_MAX_SIZES[world_size],
+                max_token_num * allreduce_in.shape[0] *
+                allreduce_in.element_size(),
+            )
+        if use_flashinfer:
+            assert (_FI_WORKSPACE_TENSOR is not None
+                    ), "Flashinfer must be enabled when using flashinfer"
+            if norm_out is None:
+                norm_out = allreduce_in
+                residual_out = residual
+            else:
+                # return residual_out as allreduce_out with zeroed residual_in
+                # as flashinfer does not support rms_norm
+                # and allreduce_out together
+                residual_out = allreduce_in
+            # For the sizes that are smaller than the max size,
+            # we only use flashinfer one shot allreduce
+            flashinfer_comm.trtllm_allreduce_fusion(
+                allreduce_in=allreduce_in,
+                token_num=allreduce_in.shape[0],
+                residual_in=residual,
+                residual_out=residual_out,
+                norm_out=norm_out,
+                rms_gamma=rms_gamma,
+                rms_eps=rms_eps,
+                world_rank=world_rank,
+                world_size=world_size,
+                hidden_dim=allreduce_in.shape[-1],
+                workspace_ptrs=_FI_WORKSPACE_TENSOR,
+                launch_with_pdl=launch_with_pdl,
+                use_oneshot=True,
+                trigger_completion_at_end=trigger_completion_at_end,
+                fp32_acc=fp32_acc,
+                pattern_code=flashinfer_comm.AllReduceFusionPattern.
+                kARResidualRMSNorm,
+                allreduce_out=None,
+                quant_out=None,
+                scale_out=None,
+                layout_code=None,
+                scale_factor=None,
+            )
+        else:
+            allreduce_out = tensor_model_parallel_all_reduce(allreduce_in)
+            if norm_out is None:
+                torch.ops._C.fused_add_rms_norm(allreduce_out, residual,
+                                                rms_gamma, rms_eps)
+            else:
+                torch.ops._C.rms_norm(norm_out, allreduce_out, rms_gamma,
+                                      rms_eps)
+            allreduce_in.copy_(allreduce_out)
+
+    def call_trtllm_fused_allreduce_norm_fake(
+        allreduce_in: torch.Tensor,
+        residual: torch.Tensor,
+        rms_gamma: torch.Tensor,
+        rms_eps: float,
+        world_rank: int,
+        world_size: int,
+        launch_with_pdl: bool,
+        trigger_completion_at_end: bool,
+        fp32_acc: bool,
+        max_token_num: int,
+        norm_out: Optional[torch.Tensor] = None,
+    ) -> None:
+        pass
+
+    direct_register_custom_op(
+        op_name="flashinfer_trtllm_fused_allreduce_norm",
+        op_func=call_trtllm_fused_allreduce_norm,
+        mutates_args=[
+            "allreduce_in",
+            "residual",
+            "norm_out",
+        ],
+        fake_impl=call_trtllm_fused_allreduce_norm_fake,
+        dispatch_key=current_platform.dispatch_key,
+    )
+    flashinfer_trtllm_fused_allreduce_norm = (
+        torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default)
+
+
+class FlashInferFusedAllReduceParams:
+    """Parameters for FlashInfer fused allreduce operations."""
+
+    def __init__(
+        self,
+        rank: int,
+        world_size: int,
+        use_fp32_lamport: bool = False,
+        max_token_num: int = 1024,
+    ):
+        self.rank = rank
+        self.world_size = world_size
+        self.use_fp32_lamport = use_fp32_lamport
+        self.trigger_completion_at_end = True
+        self.launch_with_pdl = True
+        self.fp32_acc = True
+        self.use_oneshot = False
+        self.max_token_num = max_token_num
+
+    def get_trtllm_fused_allreduce_kwargs(self):
+        return {
+            "world_rank": self.rank,
+            "world_size": self.world_size,
+            "launch_with_pdl": self.launch_with_pdl,
+            "trigger_completion_at_end": self.trigger_completion_at_end,
+            "fp32_acc": self.fp32_acc,
+            "max_token_num": self.max_token_num,
+        }
+
+
+class AllReduceRMSNORMPattern(BasePattern):
+
+    def __init__(
+        self,
+        epsilon: float,
+        dtype: torch.dtype,
+        device: str,
+        allreduce_params: FlashInferFusedAllReduceParams,
+    ):
+        super().__init__(dtype, device)
+        self.epsilon = epsilon
+        self.allreduce_params = allreduce_params
+
+    def get_inputs(self):
+        input = torch.empty([1, 8, 4], device=self.device, dtype=self.dtype)
+        rms_result = torch.empty([1, 8, 4],
+                                 device=self.device,
+                                 dtype=self.dtype)
+        weight = torch.empty([4], device=self.device, dtype=self.dtype)
+
+        return [input, rms_result, weight]
+
+    def register(self, pm_pass: PatternMatcherPass):
+
+        def pattern(input: torch.Tensor, rms_result: torch.Tensor,
+                    weight: torch.Tensor):
+            all_reduce_output = tensor_model_parallel_all_reduce(input)
+            rms = auto_functionalized(
+                RMS_OP,
+                result=rms_result,
+                input=all_reduce_output,
+                weight=weight,
+                epsilon=self.epsilon,
+            )
+            return rms[1], all_reduce_output
+
+        def replacement(input: torch.Tensor, rms_result: torch.Tensor,
+                        weight: torch.Tensor):
+            residual = torch.zeros_like(input)
+            allreduce = auto_functionalized(
+                torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default,
+                allreduce_in=input,
+                residual=residual,
+                norm_out=rms_result,
+                rms_gamma=weight,
+                rms_eps=self.epsilon,
+                **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
+            )
+
+            return allreduce[3], allreduce[1]
+
+        pm.register_replacement(pattern, replacement, self.get_inputs(),
+                                pm.fwd_only, pm_pass)
+
+
+class AllReduceFusedAddRMSNormPattern(BasePattern):
+
+    def __init__(
+        self,
+        epsilon: float,
+        dtype: torch.dtype,
+        device: str,
+        allreduce_params: FlashInferFusedAllReduceParams,
+    ):
+        super().__init__(dtype, device)
+        self.epsilon = epsilon
+        self.allreduce_params = allreduce_params
+
+    def get_inputs(self):
+        input = torch.empty([4, 4], device=self.device, dtype=self.dtype)
+        residual = torch.empty([4, 4], device=self.device, dtype=self.dtype)
+        weight = torch.empty([4, 4], device=self.device, dtype=self.dtype)
+        return [
+            residual,
+            input,
+            weight,
+        ]
+
+    def register(self, pm_pass: PatternMatcherPass):
+
+        def pattern(residual: torch.Tensor, input: torch.Tensor,
+                    weight: torch.Tensor):
+            all_reduce_output = tensor_model_parallel_all_reduce(input)
+            rms = auto_functionalized(
+                RMS_ADD_OP,
+                input=all_reduce_output,
+                residual=residual,
+                weight=weight,
+                epsilon=self.epsilon,
+            )
+            return rms[1], rms[2]
+
+        def replacement(residual: torch.Tensor, input: torch.Tensor,
+                        weight: torch.Tensor):
+            allreduce = auto_functionalized(
+                torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default,
+                allreduce_in=input,
+                residual=residual,
+                rms_gamma=weight,
+                rms_eps=self.epsilon,
+                norm_out=None,
+                **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
+            )
+            return allreduce[1], allreduce[2]
+
+        pm.register_replacement(pattern, replacement, self.get_inputs(),
+                                pm.fwd_only, pm_pass)
+
+
+class AllReduceFusionPass(VllmInductorPass):
+
+    def __init__(self, config: VllmConfig, max_token_num: int):
+        super().__init__(config)
+        self.disabled = True
+        self.tp_size = get_tensor_model_parallel_world_size()
+        if self.tp_size <= 1:
+            return
+        self.patterns: PatternMatcherPass = PatternMatcherPass(
+            pass_name="all_reduce_fusion_pass")
+        if config.model_config is None:
+            return
+        self.hidden_dim = config.model_config.get_hidden_size()
+        self.group = get_tp_group().device_group
+        rank = get_tensor_model_parallel_rank()
+        use_fp32_lamport = self.model_dtype == torch.float32
+        if flashinfer_comm is None:
+            logger.warning(
+                "Flashinfer is not installed, skipping allreduce fusion pass")
+            return
+        # Check if the world size is supported
+        if self.tp_size not in _FI_MAX_SIZES:
+            logger.warning(
+                "Flashinfer allreduce fusion is not "
+                "supported for world size %s",
+                self.tp_size,
+            )
+            return
+
+        self.ipc_handles, workspace_tensor = (
+            flashinfer_comm.trtllm_create_ipc_workspace_for_all_reduce_fusion(
+                tp_rank=rank,
+                tp_size=self.tp_size,
+                max_token_num=max_token_num,
+                hidden_dim=self.hidden_dim,
+                group=self.group,
+                use_fp32_lamport=use_fp32_lamport,
+            ))
+
+        global _FI_WORKSPACE_TENSOR
+        _FI_WORKSPACE_TENSOR = workspace_tensor
+        self.allreduce_params = FlashInferFusedAllReduceParams(
+            rank=rank,
+            world_size=self.tp_size,
+            use_fp32_lamport=use_fp32_lamport,
+            max_token_num=max_token_num,
+        )
+
+        for epsilon in [1e-5, 1e-6]:
+            AllReduceRMSNORMPattern(
+                epsilon,
+                self.model_dtype,
+                self.device,
+                self.allreduce_params,
+            ).register(self.patterns)
+            AllReduceFusedAddRMSNormPattern(
+                epsilon,
+                self.model_dtype,
+                self.device,
+                self.allreduce_params,
+            ).register(self.patterns)
+
+        self.disabled = False
+
+    def __call__(self, graph: fx.Graph):
+        if self.disabled:
+            return
+        self.begin()
+        self.dump_graph(graph, "before_all_reduce_fusion_pass")
+        count = self.patterns.apply(graph)
+        logger.debug("Replaced %s patterns", count)
+        self.dump_graph(graph, "after_all_reduce_fusion_pass")
+        self.end_and_log()
+
+    def __del__(self):
+        if self.disabled:
+            return
+        if flashinfer_comm is not None:
+            flashinfer_comm.trtllm_destroy_ipc_workspace(
+                self.ipc_handles, self.group)

vllm/compilation/pass_manager.py

@@ -7,7 +7,7 @@ from vllm.config import VllmConfig
 from vllm.logger import init_logger
 
 from .activation_quant_fusion import ActivationQuantFusionPass
-from .collective_fusion import AsyncTPPass
+from .collective_fusion import AllReduceFusionPass, AsyncTPPass
 from .fix_functionalization import FixFunctionalizationPass
 from .fusion import FusionPass
 from .fusion_attn import AttnFusionPass
@@ -62,7 +62,11 @@ class PostGradPassManager(CustomGraphPass):
 
         if self.pass_config.enable_attn_fusion:
             self.passes += [AttnFusionPass(config)]
-
+        if self.pass_config.enable_fi_allreduce_fusion:
+            self.passes += [
+                AllReduceFusionPass(
+                    config, self.pass_config.fi_allreduce_fusion_max_token_num)
+            ]
         self.fix_functionalization = FixFunctionalizationPass(config)
 
     def add(self, pass_: InductorPass):

vllm/config.py

@@ -3962,6 +3962,10 @@ class PassConfig:
     """Whether to enable sequence parallelism."""
     enable_async_tp: bool = False
     """Whether to enable async TP."""
+    enable_fi_allreduce_fusion: bool = False
+    """Whether to enable flashinfer allreduce fusion."""
+    fi_allreduce_fusion_max_token_num: int = 1024
+    """Max number of tokens to used in flashinfer allreduce fusion."""
 
     # TODO(luka) better pass enabling system.