# 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, tensor_model_parallel_all_reduce from vllm.distributed.parallel_state import ( get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) from vllm.logger import init_logger from vllm.platforms import current_platform from vllm.utils import direct_register_custom_op from .vllm_inductor_pass import VllmInductorPass FP8_DTYPE = current_platform.fp8_dtype() 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 = 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 STATIC_FP8_QUANT_OP = torch.ops._C.static_scaled_fp8_quant.default STATIC_FP4_QUANT_OP = torch.ops._C.scaled_fp4_quant.default 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() class GEMMReduceScatterPattern(BasePattern): def get_inputs(self): mul = torch.empty([16, 4], device=self.device, dtype=self.dtype) mm_weight = torch.empty([4, 4], device=self.device, dtype=self.dtype) return [mul, mm_weight] def register(self, pm_pass: PatternMatcherPass): def pattern(mul: torch.Tensor, mm_weight: torch.Tensor): mm = torch.ops.aten.mm.default(mul, mm_weight) reduce_scatter = torch.ops.vllm.reduce_scatter.default( mm, dim=0, world_size=self.tp_size, group_name=self.tp.unique_name, ) return reduce_scatter def replacement(mul: torch.Tensor, mm_weight: torch.Tensor): gemm_rs = torch.ops.symm_mem.fused_matmul_reduce_scatter( mul, mm_weight, "avg", scatter_dim=0, group_name=self.tp.device_group.group_name, ) return gemm_rs pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass) class AllGatherGEMMPattern(BasePattern): def get_inputs(self): x = torch.empty([4, 4], device=self.device, dtype=self.dtype) weight = torch.empty([4, 4], device=self.device, dtype=self.dtype) return [x, weight] def register(self, pm_pass: PatternMatcherPass): def pattern( x: torch.Tensor, weight: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: all_gather = torch.ops.vllm.all_gather.default( x, dim=0, world_size=self.tp_size, group_name=self.tp.unique_name, ) return torch.ops.aten.mm.default(all_gather, weight) def replacement( x: torch.Tensor, weight: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: ag_output, mm_outputs = torch.ops.symm_mem.fused_all_gather_matmul( x, [weight], gather_dim=0, group_name=self.tp.device_group.group_name, ) return mm_outputs pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass) class ScaledMMReduceScatterPattern(BasePattern): def get_inputs(self): input = torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE) mm_weight = torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE).contiguous().transpose(0, 1) scale_a = torch.empty([16, 1], device=self.device, dtype=torch.float32) scale_b = torch.empty([1, 16], device=self.device, dtype=torch.float32) return [input, mm_weight, scale_a, scale_b] def register(self, pm_pass: PatternMatcherPass): def pattern(input: torch.Tensor, mat2: torch.Tensor, scale_a: torch.Tensor, scale_b: torch.Tensor) -> torch.Tensor: scaled_mm = torch.ops.aten._scaled_mm.default(input, mat2=mat2, scale_a=scale_a, scale_b=scale_b, bias=None, scale_result=None, out_dtype=self.dtype) reduce_scatter = torch.ops.vllm.reduce_scatter.default( scaled_mm, dim=0, world_size=self.tp_size, group_name=self.tp.unique_name) return reduce_scatter def replacement(input: torch.Tensor, mat2: torch.Tensor, scale_a: torch.Tensor, scale_b: torch.Tensor) -> torch.Tensor: gemm_rs = torch.ops.symm_mem.fused_scaled_matmul_reduce_scatter( input, mat2, scale_a, scale_b, "avg", scatter_dim=0, out_dtype=self.dtype, group_name=self.tp.device_group.group_name, ) return gemm_rs pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass) class AllGatherScaledMMPattern(BasePattern): def get_inputs(self): x = torch.empty([8, 16], device=self.device, dtype=FP8_DTYPE) weight = torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE).contiguous().transpose(0, 1) s1 = x.shape[0] * self.tp_size scale_a = torch.empty([s1, 1], device=self.device, dtype=torch.float32) scale_b = torch.empty([1, 16], device=self.device, dtype=torch.float32) return [x, weight, scale_a, scale_b] def register(self, pm_pass: PatternMatcherPass): def pattern( x: torch.Tensor, weight: torch.Tensor, scale_a: torch.Tensor, scale_b: torch.Tensor, ) -> torch.Tensor: all_gather = torch.ops.vllm.all_gather.default( x, dim=0, world_size=self.tp_size, group_name=self.tp.unique_name) return torch.ops.aten._scaled_mm.default(all_gather, mat2=weight, scale_a=scale_a, scale_b=scale_b, bias=None, scale_result=None, out_dtype=self.dtype) def replacement(x: torch.Tensor, weight: torch.Tensor, scale_a: torch.Tensor, scale_b: torch.Tensor) -> torch.Tensor: ag_output, mm_outputs = torch.ops.symm_mem.fused_all_gather_scaled_matmul( # noqa x, [weight], scale_a, [scale_b], gather_dim=0, biases=[None], result_scales=[None], out_dtypes=[self.dtype], use_fast_accum=[False], group_name=self.tp.device_group.group_name, ) return mm_outputs pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass) class CutlassScaledMMReduceScatterPattern(BasePattern): def get_inputs(self): input = torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE) mm_weight = torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE).contiguous().transpose(0, 1) scale_a = torch.empty([16, 1], device=self.device, dtype=torch.float32) scale_b = torch.empty([1, 16], device=self.device, dtype=torch.float32) cutlass_mm_output = torch.empty([16, 16], device=self.device, dtype=self.dtype) return [input, mm_weight, scale_a, scale_b, cutlass_mm_output] def register(self, pm_pass: PatternMatcherPass): def pattern(input: torch.Tensor, weight: torch.Tensor, scale_a: torch.Tensor, scale_b: torch.Tensor, cutlass_mm_output: torch.Tensor) -> torch.Tensor: cutlass_scaled_mm = torch.ops.higher_order.auto_functionalized( torch.ops._C.cutlass_scaled_mm.default, out=cutlass_mm_output, a=input, b=weight, a_scales=scale_a, b_scales=scale_b, bias=None) reduce_scatter = torch.ops.vllm.reduce_scatter.default( cutlass_scaled_mm[1], dim=0, world_size=self.tp_size, group_name=self.tp.unique_name) return reduce_scatter def replacement(input: torch.Tensor, mat2: torch.Tensor, scale_a: torch.Tensor, scale_b: torch.Tensor, cutlass_mm_output: torch.Tensor) -> torch.Tensor: gemm_rs = torch.ops.symm_mem.fused_scaled_matmul_reduce_scatter( input, mat2, scale_a, scale_b, "avg", scatter_dim=0, out_dtype=self.dtype, group_name=self.tp.device_group.group_name, ) return gemm_rs pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass) class AllGatherCutlassScaledMMPattern(BasePattern): def get_inputs(self): x = torch.empty([8, 16], device=self.device, dtype=FP8_DTYPE) weight = torch.empty([16, 16], device=self.device, dtype=FP8_DTYPE).contiguous().transpose(0, 1) s1 = x.shape[0] * self.tp_size scale_a = torch.empty([s1, 1], device=self.device, dtype=torch.float32) scale_b = torch.empty([1, 16], device=self.device, dtype=torch.float32) s2 = weight.shape[1] output = torch.empty([s1, s2], device=self.device, dtype=self.dtype) return [x, weight, scale_a, scale_b, output] def register(self, pm_pass: PatternMatcherPass): def pattern( x: torch.Tensor, weight: torch.Tensor, scale_a: torch.Tensor, scale_b: torch.Tensor, output: torch.Tensor, ) -> torch.Tensor: all_gather = torch.ops.vllm.all_gather.default( x, dim=0, world_size=self.tp_size, group_name=self.tp.unique_name) cutlass_scaled_mm = torch.ops.higher_order.auto_functionalized( torch.ops._C.cutlass_scaled_mm.default, out=output, a=all_gather, b=weight, a_scales=scale_a, b_scales=scale_b, bias=None) return cutlass_scaled_mm[1] def replacement(x: torch.Tensor, weight: torch.Tensor, scale_a: torch.Tensor, scale_b: torch.Tensor, output: torch.Tensor) -> torch.Tensor: ag_output, mm_outputs = torch.ops.symm_mem.fused_all_gather_scaled_matmul( # noqa x, [weight], scale_a, [scale_b], gather_dim=0, biases=[None], result_scales=[None], out_dtypes=[self.dtype], use_fast_accum=[False], group_name=self.tp.device_group.group_name, ) return mm_outputs pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass) class AsyncTPPass(VllmInductorPass): def __init__(self, config: VllmConfig): super().__init__(config) # Enable symmetric memory for the TP process group enable_symm_mem_for_group(get_tp_group().device_group.group_name) self.patterns: PatternMatcherPass = PatternMatcherPass( pass_name="async_tp_pass") GEMMReduceScatterPattern(self.model_dtype, self.device).register(self.patterns) AllGatherGEMMPattern(self.model_dtype, self.device).register(self.patterns) # These fusions are enabled only for bfloat16 models because # `scaled_mm` or `cutlass_scaled_mm` with per-token (row-wise) scaling # only supports bfloat16 as the output dtype. if self.model_dtype == torch.bfloat16: ScaledMMReduceScatterPattern(self.model_dtype, self.device).register(self.patterns) AllGatherScaledMMPattern(self.model_dtype, self.device).register(self.patterns) CutlassScaledMMReduceScatterPattern( self.model_dtype, self.device).register(self.patterns) AllGatherCutlassScaledMMPattern( self.model_dtype, self.device).register(self.patterns) def is_applicable_for_shape(self, shape: Optional[int]) -> bool: # only do replace for specific shapes tp_size = get_tensor_model_parallel_world_size() return shape is not None and shape % tp_size == 0 def __call__(self, graph: fx.Graph): self.begin() self.dump_graph(graph, "before_async_tp_pass") count = self.patterns.apply(graph) logger.debug("Replaced %s patterns with async TP pass.", 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: 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 vllm we only support swizzled layout layout_code=flashinfer_comm.FP4QuantizationSFLayout.SWIZZLED, scale_factor=scale_factor, ) else: allreduce_out = tensor_model_parallel_all_reduce(allreduce_in) if (scale_factor is not None and scale_out is None and fuse_rms_quant): # Do fused rms norm static fp8 quant fused op if norm_out is None: torch.ops._C.fused_add_rms_norm_static_fp8_quant( quant_out, allreduce_out, residual, rms_gamma, scale_factor, rms_eps) else: torch.ops._C.rms_norm_static_fp8_quant( quant_out, allreduce_out, rms_gamma, scale_factor, rms_eps) else: if norm_out is None: torch.ops._C.fused_add_rms_norm(allreduce_out, residual, rms_gamma, rms_eps) norm_out = allreduce_out else: torch.ops._C.rms_norm(norm_out, allreduce_out, rms_gamma, rms_eps) if scale_factor is not None: if scale_out is not None: torch.ops._C.scaled_fp4_quant(quant_out, norm_out, scale_out, scale_factor) else: torch.ops._C.static_scaled_fp8_quant( quant_out, norm_out, scale_factor) if scale_factor is None or norm_out is not None: # we need to return allreduce outpput # in cases of non quant fused AR + RMS norm # and fused AR + RMS norm + quant without fused add 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, 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_trtllm_fused_allreduce_norm", 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, 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, 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 AllReduceRMSNormPattern(BasePattern): """ This pattern replaces the allreduce + rms norm (without residual) with fused flashinfer implementation. Applies to allreduce + rmsnorm before attn in the first Transformer block. """ 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): allreduce_output = tensor_model_parallel_all_reduce(input) rms = auto_functionalized( RMS_OP, result=rms_result, input=allreduce_output, weight=weight, epsilon=self.epsilon, ) # rms_result, allreduce_output return rms[1], allreduce_output def replacement(input: torch.Tensor, rms_result: torch.Tensor, weight: torch.Tensor): residual = torch.zeros_like(input) allreduce = auto_functionalized( flashinfer_trtllm_fused_allreduce_norm, allreduce_in=input, residual=residual, norm_out=rms_result, 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(), ) # rms_result, allreduce_in return allreduce[3], allreduce[1] pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass) 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( RMS_ADD_OP, 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( 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 AllReduceFusedRMSNormStaticQuantFP8Pattern(BasePattern): """ This pattern replaces the allreduce + rms norm (without residual) + static fp8 quant with fused flashinfer implementation. Applies to allreduce + rmsnorm + quant before attn in the first Transformer block. """ 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 self.quant_dtype = torch.float8_e4m3fn def register(self, pm_pass: PatternMatcherPass): def get_inputs(): input = torch.zeros([1, 8, 4], device=self.device, dtype=self.dtype) rmsnorm_result = torch.empty([1, 8, 4], device=self.device, dtype=self.dtype) quant_result = torch.empty([1, 8, 4], device=self.device, dtype=self.quant_dtype) weight = torch.empty([4], device=self.device, dtype=self.dtype) scale = torch.tensor(1.0, device=self.device, dtype=torch.float32) return [input, rmsnorm_result, quant_result, weight, scale] def pattern( input: torch.Tensor, rmsnorm_result: torch.Tensor, quant_result: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor, ): all_reduce = tensor_model_parallel_all_reduce(input) rmsnorm_out_tuple = auto_functionalized(RMS_OP, result=rmsnorm_result, input=all_reduce, weight=weight, epsilon=self.epsilon) quant_out_tuple = auto_functionalized(STATIC_FP8_QUANT_OP, result=quant_result, input=rmsnorm_out_tuple[1], scale=scale) # quant_out, allreduce_output return quant_out_tuple[1], all_reduce def replacement(input: torch.Tensor, result_rms: torch.Tensor, quant_result: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor): residual = torch.zeros_like(input) allreduce = auto_functionalized( flashinfer_trtllm_fused_allreduce_norm, allreduce_in=input, residual=residual, norm_out=result_rms, quant_out=quant_result, scale_out=None, rms_gamma=weight, rms_eps=self.epsilon, pattern_code=flashinfer_comm.AllReduceFusionPattern. kARResidualRMSNormFP8Quant, # we don't use norm_out afterwards scale_factor=scale, **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(), ) # quant_out, allreduce_output return allreduce[4], allreduce[1] pm.register_replacement(pattern, replacement, get_inputs(), pm.fwd_only, pm_pass) class AllReduceFusedAddRMSNormStaticQuantFP8Pattern(BasePattern): """ This pattern replaces the allreduce + rms norm (with residual) + static fp8 quant with fused flashinfer implementation. Applies to o_proj + rmsnorm after attn + quant and mlp + rmsnorm + quant 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 self.quant_dtype = torch.float8_e4m3fn def register(self, pm_pass: PatternMatcherPass): def get_inputs(): 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) quant_result = torch.empty([4, 4], device=self.device, dtype=self.quant_dtype) scale = torch.empty([1, 1], device=self.device, dtype=torch.float32) return [ quant_result, residual, input, weight, scale, ] def pattern( quant_result: torch.Tensor, residual: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor, ): allreduce_output = tensor_model_parallel_all_reduce(input) fused_add_rmsnorm_out_tuple = \ auto_functionalized( RMS_ADD_OP, input=allreduce_output, residual=residual, weight=weight, epsilon=self.epsilon) quant_out_tuple = auto_functionalized( STATIC_FP8_QUANT_OP, result=quant_result, input=fused_add_rmsnorm_out_tuple[1], scale=scale) # quant_out, allreduce_output return quant_out_tuple[1], fused_add_rmsnorm_out_tuple[2] def replacement(quant_result: torch.Tensor, residual: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor): allreduce = auto_functionalized( flashinfer_trtllm_fused_allreduce_norm, allreduce_in=input, residual=residual, norm_out=None, quant_out=quant_result, scale_out=None, rms_gamma=weight, rms_eps=self.epsilon, pattern_code=flashinfer_comm.AllReduceFusionPattern. kARResidualRMSNormFP8Quant, # we don't use norm_out afterwards scale_factor=scale, **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(), ) # # quant_out, rms_norm_residual return allreduce[4], allreduce[2] pm.register_replacement(pattern, replacement, get_inputs(), pm.fwd_only, pm_pass) class AllReduceFusedRMSNormStaticQuantNVFP4Pattern(BasePattern): """ This pattern replaces the allreduce + rms norm (without residual) + static nvfp4 quant with fused flashinfer implementation. Applies to allreduce + rmsnorm + quant before attn in the first Transformer block. """ 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 register(self, pm_pass: PatternMatcherPass): def get_inputs(): input = torch.empty([1, 16, 16], device=self.device, dtype=self.dtype) rmsnorm_result = torch.empty([1, 16, 16], device=self.device, dtype=self.dtype) quant_result = torch.empty((16, 8), device=self.device, dtype=torch.uint8) input_global_scale = torch.empty([1, 1], device=self.device, dtype=torch.float32) weight = torch.empty([16], device=self.device, dtype=self.dtype) output_scale = torch.empty([128, 4], device=self.device, dtype=torch.int32) return [ input, rmsnorm_result, quant_result, weight, input_global_scale, output_scale ] def pattern( input: torch.Tensor, rmsnorm_result: torch.Tensor, quant_result: torch.Tensor, weight: torch.Tensor, input_global_scale: torch.Tensor, output_scale: torch.Tensor, ): all_reduce = tensor_model_parallel_all_reduce(input) rmsnorm_out_tuple = auto_functionalized(RMS_OP, result=rmsnorm_result, input=all_reduce, weight=weight, epsilon=self.epsilon) quant_out_tuple = auto_functionalized( STATIC_FP4_QUANT_OP, output=quant_result, input=rmsnorm_out_tuple[1], output_scale=output_scale, input_scale=input_global_scale) # quant_out, allreduce_output, output_scale return quant_out_tuple[1], all_reduce, quant_out_tuple[2] def replacement(input: torch.Tensor, result_rms: torch.Tensor, quant_result: torch.Tensor, weight: torch.Tensor, input_global_scale: torch.Tensor, output_scale: torch.Tensor): residual = torch.zeros_like(input) allreduce = auto_functionalized( flashinfer_trtllm_fused_allreduce_norm, allreduce_in=input, residual=residual, norm_out=result_rms, quant_out=quant_result, scale_out=output_scale, rms_gamma=weight, rms_eps=self.epsilon, pattern_code=flashinfer_comm.AllReduceFusionPattern. kARResidualRMSNormFP4Quant, # we don't use norm_out afterwards scale_factor=input_global_scale, **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(), ) # quant_out, allreduce_output, output_scale return allreduce[4], allreduce[1], allreduce[5] pm.register_replacement(pattern, replacement, get_inputs(), pm.fwd_only, pm_pass) class AllReduceFusedAddRMSNormStaticQuantNVFP4Pattern(BasePattern): """ This pattern replaces the allreduce + rms norm (with residual) + static nvfp4 quant with fused flashinfer implementation. Applies to o_proj + rmsnorm after attn + quant and mlp + rmsnorm + quant 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 register(self, pm_pass: PatternMatcherPass): def get_inputs(): input = torch.empty([16, 16], device=self.device, dtype=self.dtype) residual = torch.empty([16, 16], device=self.device, dtype=self.dtype) weight = torch.empty([16, 16], device=self.device, dtype=self.dtype) quant_result = torch.empty((16, 8), device=self.device, dtype=torch.uint8) input_global_scale = torch.empty([1, 1], device=self.device, dtype=torch.float32) output_scale = torch.empty([128, 4], device=self.device, dtype=torch.int32) return [ quant_result, residual, input, output_scale, weight, input_global_scale, ] def pattern(quant_result: torch.Tensor, residual: torch.Tensor, input: torch.Tensor, output_scale: torch.Tensor, weight: torch.Tensor, input_global_scale: torch.Tensor): allreduce_output = tensor_model_parallel_all_reduce(input) fused_add_rmsnorm_out_tuple = \ auto_functionalized( RMS_ADD_OP, input=allreduce_output, residual=residual, weight=weight, epsilon=self.epsilon) quant_out_tuple = auto_functionalized( STATIC_FP4_QUANT_OP, output=quant_result, input=fused_add_rmsnorm_out_tuple[1], output_scale=output_scale, input_scale=input_global_scale) # quant_out, allreduce_output, output_scale return quant_out_tuple[1], fused_add_rmsnorm_out_tuple[ 2], quant_out_tuple[2] def replacement(quant_result: torch.Tensor, residual: torch.Tensor, input: torch.Tensor, output_scale: torch.Tensor, weight: torch.Tensor, input_global_scale: torch.Tensor): allreduce = auto_functionalized( flashinfer_trtllm_fused_allreduce_norm, allreduce_in=input, residual=residual, norm_out=None, quant_out=quant_result, scale_out=output_scale, rms_gamma=weight, rms_eps=self.epsilon, pattern_code=flashinfer_comm.AllReduceFusionPattern. kARResidualRMSNormFP4Quant, # we don't use norm_out afterwards scale_factor=input_global_scale, **self.allreduce_params.get_trtllm_fused_allreduce_kwargs(), ) # quant_out, rms_norm_residual, output_scale return allreduce[4], allreduce[2], allreduce[5] pm.register_replacement(pattern, replacement, get_inputs(), pm.fwd_only, pm_pass) class AllReduceFusionPass(VllmInductorPass): def __init__(self, config: VllmConfig): 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 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)), config.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=config.compilation_config.pass_config.enable_fusion) for epsilon in [1e-5, 1e-6]: AllReduceFusedRMSNormStaticQuantFP8Pattern( epsilon, self.model_dtype, self.device, self.allreduce_params, ).register(self.patterns) AllReduceFusedAddRMSNormStaticQuantFP8Pattern( epsilon, self.model_dtype, self.device, self.allreduce_params, ).register(self.patterns) if current_platform.has_device_capability(100): AllReduceFusedRMSNormStaticQuantNVFP4Pattern( epsilon, self.model_dtype, self.device, self.allreduce_params, ).register(self.patterns) AllReduceFusedAddRMSNormStaticQuantNVFP4Pattern( epsilon, self.model_dtype, self.device, self.allreduce_params, ).register(self.patterns) 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) # 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: 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_for_all_reduce( self.ipc_handles, self.group)