[WIP] Support torch compile based pass manager framework

python/sglang/compilation/collective_fusion.py

@@ -0,0 +1,385 @@
+import logging
+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_v2
+from torch._inductor.pattern_matcher import PatternMatcherPass
+from torch.distributed._symmetric_memory import enable_symm_mem_for_group
+
+from sglang.srt.configs.compilation_config import CompilationConfig
+from sglang.srt.configs.device_config import DeviceConfig
+from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.distributed import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    get_tp_group,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.utils import (
+    direct_register_custom_op,
+    is_cpu,
+    is_cuda,
+    is_flashinfer_available,
+    is_hip,
+    is_npu,
+    is_xpu,
+)
+
+_is_cuda = is_cuda()
+_is_flashinfer_available = is_flashinfer_available()
+
+
+if _is_cuda:
+    if _is_flashinfer_available:
+        from flashinfer.norm import fused_add_rmsnorm
+    else:
+        from sgl_kernel import fused_add_rmsnorm
+
+from .inductor_pass import enable_fake_mode
+from .sglang_inductor_pass import SglangInductorPass, SglangPatternMatcherPass
+
+if find_spec("flashinfer"):
+    try:
+        import flashinfer.comm as flashinfer_comm
+
+        flashinfer_comm = (
+            flashinfer_comm
+            if hasattr(flashinfer_comm, "trtllm_allreduce_fusion")
+            else None
+        )
+    except ImportError:
+        flashinfer_comm = None
+else:
+    flashinfer_comm = None
+
+logger = logging.getLogger(__name__)
+
+
+class BasePattern:
+
+    def __init__(self, dtype: torch.dtype, device: str):
+        self.dtype = dtype
+        self.device = device
+        self.tp = get_tp_group()
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+
+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: 64 * MiB,  # 64MB
+        4: MiB,  # 1MB
+        6: MiB // 2,  # 512KB
+        8: MiB // 2,  # 512KB
+    }
+
+    # opt for a more conservative default value
+    # when world size is not in _FI_MAX_SIZES
+    _DEFAULT_FI_MAX_SIZE = MiB // 2
+
+    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,
+        pattern_code: int,
+        fuse_rms_quant: bool,
+        norm_out: Optional[torch.Tensor] = None,
+        quant_out: Optional[torch.Tensor] = None,
+        scale_out: Optional[torch.Tensor] = None,
+        scale_factor: Optional[torch.Tensor] = None,
+    ) -> None:
+        num_tokens, hidden_size = allreduce_in.shape
+        element_size = allreduce_in.element_size()
+        current_tensor_size = num_tokens * hidden_size * element_size
+        max_fusion_size = max_token_num * hidden_size * element_size
+        use_flashinfer = current_tensor_size <= min(
+            _FI_MAX_SIZES.get(world_size, _DEFAULT_FI_MAX_SIZE),
+            max_fusion_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=pattern_code,
+                allreduce_out=None,
+                quant_out=quant_out,
+                scale_out=scale_out,
+                # in sglang we only support swizzled layout
+                layout_code=flashinfer_comm.QuantizationSFLayout.SWIZZLED_128x4,
+                scale_factor=scale_factor,
+            )
+        else:
+            # TODO
+            pass
+
+    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,
+        pattern_code: int,
+        fuse_rms_quant: bool,
+        norm_out: Optional[torch.Tensor] = None,
+        quant_out: Optional[torch.Tensor] = None,
+        scale_out: Optional[torch.Tensor] = None,
+        scale_factor: Optional[torch.Tensor] = None,
+    ) -> None:
+        pass
+
+    direct_register_custom_op(
+        op_name="flashinfer_allreduce_residual_rmsnorm",
+        op_func=call_trtllm_fused_allreduce_norm,
+        mutates_args=[
+            "allreduce_in",
+            "residual",
+            "norm_out",
+            "quant_out",
+            "scale_out",
+        ],
+        fake_impl=call_trtllm_fused_allreduce_norm_fake,
+    )
+    flashinfer_allreduce_residual_rmsnorm = (
+        torch.ops.sglang.flashinfer_allreduce_residual_rmsnorm.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,
+        fuse_rms_quant: bool = False,
+    ):
+        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
+        self.fuse_rms_quant = fuse_rms_quant
+
+    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,
+            "fuse_rms_quant": self.fuse_rms_quant,
+        }
+
+
+class AllReduceFusedAddRMSNormPattern(BasePattern):
+    """
+    This pattern replaces the allreduce + rms norm (with residual)
+    with fused flashinfer implementation.
+    Applies to o_proj + rmsnorm after attn and mlp + rmsnorm before attn.
+    """
+
+    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):
+            allreduce_output = tensor_model_parallel_all_reduce(input)
+            rms = auto_functionalized_v2(
+                torch.ops.sgl_kernel.fused_add_rmsnorm.default,
+                input=allreduce_output,
+                residual=residual,
+                weight=weight,
+                epsilon=self.epsilon,
+            )
+            # input, residual
+            return rms[1], rms[2]
+
+        def replacement(
+            residual: torch.Tensor, input: torch.Tensor, weight: torch.Tensor
+        ):
+            allreduce = auto_functionalized_v2(
+                flashinfer_trtllm_fused_allreduce_norm,
+                allreduce_in=input,
+                residual=residual,
+                norm_out=None,
+                quant_out=None,
+                scale_out=None,
+                rms_gamma=weight,
+                rms_eps=self.epsilon,
+                pattern_code=flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNorm,
+                **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(),
+            )
+            # allreduce_in, residual
+            return allreduce[1], allreduce[2]
+
+        pm.register_replacement(
+            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
+        )
+
+
+class AllReduceFusionPass(SglangPatternMatcherPass):
+    # TODO(yuan-luo): replace with SglangConfig
+    def __init__(
+        self,
+        compilation_config: CompilationConfig,
+        model_config: ModelConfig,
+        device_config: DeviceConfig,
+    ):
+        super().__init__(compilation_config, model_config, device_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 model_config is None:
+            return
+        self.hidden_dim = 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 or comm module not found, "
+                "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
+        max_num_token = min(
+            _FI_MAX_SIZES.get(self.tp_size, _DEFAULT_FI_MAX_SIZE)
+            // (self.hidden_dim * self.tp_size * (4 if use_fp32_lamport else 2)),
+            compilation_config.pass_config.fi_allreduce_fusion_max_token_num,
+        )
+        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_num_token,
+                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_num_token,
+            # fuse rms norm static fp8 quant fused op
+            # in fallback path, when we don't use flashinfer
+            fuse_rms_quant=compilation_config.pass_config.enable_fusion,
+        )
+
+        self.register_patterns()
+
+    @enable_fake_mode
+    def register_patterns(self):
+        for epsilon in [1e-5, 1e-6]:
+            AllReduceFusedAddRMSNormPattern(
+                epsilon,
+                self.model_dtype,
+                self.device,
+                self.allreduce_params,
+            ).register(self.patterns)
+
+            # WARNING: This is a hack to clear the pattern matcher cache
+            # and allow multiple values of epsilon.
+            torch._inductor.pattern_matcher._seen_patterns.clear()
+
+        self.disabled = False
+
+    def __call__(self, graph: fx.Graph):
+        if self.disabled:
+            logger.debug("AllReduceFusionPass disabled")
+            return
+
+        self.matched_count = self.patterns.apply(graph)
+        logger.debug("Replaced %s patterns", self.matched_count)
+
+    def __del__(self):
+        if getattr(self, "disabled", True):
+            return
+        if flashinfer_comm is not None:
+            flashinfer_comm.trtllm_destroy_ipc_workspace_for_all_reduce(
+                self.ipc_handles, self.group
+            )