171 lines
7.3 KiB
Python
171 lines
7.3 KiB
Python
################################################################################
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# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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################################################################################
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from typing import Optional
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import torch_br
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from torch import nn
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from vllm.distributed import (get_tensor_model_parallel_world_size,
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get_tp_group, tensor_model_parallel_all_reduce)
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from vllm.distributed.parallel_state import (get_pp_group,
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get_tensor_model_parallel_rank)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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MergedColumnParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm_br import envs
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from vllm_br.utils import get_grandparent_pid
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class LlamaMlpSiluL3(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_proj = ColumnParallelLinear(input_size=hidden_size,
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output_size=intermediate_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_proj")
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self.up_proj = ColumnParallelLinear(input_size=hidden_size,
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output_size=intermediate_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.up_proj")
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self.down_proj = RowParallelLinear(intermediate_size,
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hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.down_proj")
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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate, _ = self.gate_proj(x)
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up, _ = self.up_proj(x)
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x = torch_br.supa_silumul(gate, up)
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x, _ = self.down_proj(x)
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return x
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class MergedGateUpMLPSiluL2(nn.Module):
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"""
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"""
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: bool = True,
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bias: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.intermediate_size = intermediate_size
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self.prefix = prefix
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size, [intermediate_size] * 2,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj")
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self.gate_up_proj.has_cross_weight = True
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self.down_proj = RowParallelLinear(intermediate_size,
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hidden_size,
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bias=bias,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj")
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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = SiluAndMul()
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def forward(self, x):
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if envs.VLLM_BR_USE_CPU_ALL_REDUCE != 0 and not hasattr(
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self, "grandparent_pid"):
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self.grandparent_pid = get_grandparent_pid()
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if "shared_experts" not in self.prefix:
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quant_flag = hasattr(self.gate_up_proj, "qweight")
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hidden_size = x.shape[-1]
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seq_len = x.shape[-2]
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gu_weight = self.gate_up_proj.qweight if quant_flag else self.gate_up_proj.weight
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gu_scales = self.gate_up_proj.scales if quant_flag else None
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gate_up_output = torch_br.br_fused_mlp_infer(
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x, [gu_weight],
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output_w=self.intermediate_size // self.tp_size,
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scales=[gu_scales] if gu_scales is not None else None,
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activation_mode="act_swiglu")
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down_weight = self.down_proj.qweight if quant_flag else self.down_proj.weight
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down_scales = self.down_proj.scales if quant_flag else None
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# bypass tp8 and tp4pp2 allreduce
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pp_size = get_pp_group().world_size
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all_rank = self.tp_size * pp_size
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support_types = ((16, 4), (32, 2), (32, 4))
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if all_rank <= envs.VLLM_BR_USE_FUSED_ALLREDUCE and seq_len <= envs.VLLM_BR_STATIC_MOE_DECODER_MAX_LEN and \
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(envs.VLLM_BR_DEVICE_SPC_NUM, self.tp_size) in support_types:
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tp_rank = get_tp_group().rank_in_group
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global_rank = get_tp_group().rank
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rank_i = global_rank % self.tp_size
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assert rank_i == tp_rank
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down_output = torch_br.supa_fused_linear_allreduce_opt(
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gate_up_output,
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down_weight,
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hidden_size,
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tp_rank,
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self.tp_size,
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global_rank,
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0,
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scales=down_scales)
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return down_output
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else:
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down_output = torch_br.br_fused_mlp_infer(
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gate_up_output, [down_weight],
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output_w=hidden_size,
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scales=[down_scales] if down_scales is not None else None)
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if self.tp_size > 1:
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out = down_output
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if envs.VLLM_BR_USE_CPU_ALL_REDUCE != 0 and self.tp_size >= 4 and out.shape[
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1] <= 32:
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tp_rank = get_tensor_model_parallel_rank()
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output = torch_br.supa_allreduce_pcie_infer(
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out, tp_rank, self.tp_size, self.grandparent_pid)
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else:
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output = tensor_model_parallel_all_reduce(out)
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return output
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else:
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return down_output
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else:
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return self.gate_up_proj.weight, self.down_proj.weight
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