mirror of
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238 lines
8.6 KiB
Python
238 lines
8.6 KiB
Python
"""
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# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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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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from dataclasses import dataclass
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from paddle import nn
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from paddlenlp.utils.log import logger
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from fastdeploy.model_executor.layers.utils import get_tensor
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from .cutlass_fused_moe import CutlassFusedMoeMethod
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@dataclass
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class MoEComputeParams:
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"""
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some params for computing MoE.
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it is given to different compute methods.
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"""
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global_num_experts: int = -1
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top_k: int = -1
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hidden_size: int = -1
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num_local_experts: int = -1
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moe_intermediate_size: int = -1
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tp_size: int = -1
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ep_size: int = -1
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dp_size: int = -1
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moe_quant_type: str = ""
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class FusedMoE(nn.Layer):
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"""
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FusedMoE is a layer that performs MoE (Mixture of Experts) computation.
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"""
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def __init__(
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self,
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llm_config,
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moe_intermediate_size: int = -1,
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num_experts: int = -1,
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top_k: int = -1,
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moe_use_gate_correction_bias: bool = False,
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moe_quant_type: str = "weight_only_int4",
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layer_idx: int = -1,
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gate_weight_key=None,
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gate_correction_bias_key=None,
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ffn1_expert_weight_key=None,
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ffn2_expert_weight_key=None,
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moe_ffn1_bias_keys=None,
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moe_ffn2_bias_keys=None,
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moe_ffn1_weight_scale_keys=None,
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moe_ffn2_weight_scale_keys=None,
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moe_ffn1_in_scale_keys=None,
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moe_ffn2_in_scale_keys=None,
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):
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"""
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Initialize the Moe layer with given parameters.
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Args:
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llm_config (LLMConfig): Arguments related to inference, containing
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attributes such as weight_dtype, act_dtype, mp_size, hidden_size, head_dim,
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num_attention_heads, and ffn_hidden_size.
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"""
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super().__init__()
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self.llm_config = llm_config
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self.layer_idx = layer_idx
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self.tp_size = llm_config.parallel_config.mp_size
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self.ep_size = llm_config.parallel_config.ep_size
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self.moe_use_gate_correction_bias = moe_use_gate_correction_bias
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self.hidden_size = llm_config.model_config.hidden_size
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self.moe_config = llm_config.moe_config
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self.use_offline_quant = llm_config.tmp_config.use_offline_quant
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moe_tag = self.llm_config.moe_config.moe_tag
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logger.info(f"{moe_tag}MoE is running in {moe_quant_type} mode")
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self.moe_quant_type = moe_quant_type
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self.num_experts = num_experts
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self.num_local_experts = self.num_experts // self.ep_size
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logger.info(f'''MoE config is num_experts:{num_experts},
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top_k:{top_k},
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hidden_size:{self.hidden_size},
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moe_intermediate_size:{moe_intermediate_size}''')
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logger.info(
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f"MoE is running on moe_quant_type: {self.moe_quant_type}, ep:{self.ep_size}, tp:{self.tp_size} mode"
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)
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self.moe_intermediate_size = moe_intermediate_size // self.tp_size
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self.gate_weight_key = gate_weight_key
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self.gate_correction_bias_key = gate_correction_bias_key
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self.ffn1_expert_weight_key = ffn1_expert_weight_key
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self.ffn2_expert_weight_key = ffn2_expert_weight_key
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self.ffn1_bias_key = moe_ffn1_bias_keys
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self.ffn2_bias_key = moe_ffn2_bias_keys
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if self.moe_quant_type == "w4a8":
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# below keys are only used in MoE W4A8!
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self.ffn1_expert_weight_scale_key = moe_ffn1_weight_scale_keys
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self.ffn2_expert_weight_scale_key = moe_ffn2_weight_scale_keys
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self.ffn1_expert_in_scale_key = moe_ffn1_in_scale_keys
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self.ffn2_expert_in_scale_key = moe_ffn2_in_scale_keys
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self.compute_method = CutlassFusedMoeMethod()
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self.moe_compute_params = MoEComputeParams()
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self.moe_compute_params.global_num_experts = self.num_experts
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self.moe_compute_params.top_k = top_k
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self.moe_compute_params.hidden_size = self.hidden_size
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self.moe_compute_params.num_local_experts = self.num_local_experts
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self.moe_compute_params.moe_quant_type = self.moe_quant_type
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self.moe_compute_params.moe_intermediate_size = self.moe_intermediate_size
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self.moe_compute_params.ep_size = self.ep_size
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self.moe_compute_params.tp_size = self.tp_size
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def load_gate_state_dict(self, state_dict):
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"""
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load_gate_state_dict function.
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"""
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up_gate_proj_weight = []
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up_gate_proj_weight_scale = []
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down_proj_weight = []
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down_proj_weight_scale = []
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for j in range(self.num_experts):
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up_gate_proj_weight.append(
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get_tensor(
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state_dict.pop(self.ffn1_expert_weight_key.format(j))))
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down_proj_weight.append(
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get_tensor(
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state_dict.pop(self.ffn2_expert_weight_key.format(j))))
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return up_gate_proj_weight, down_proj_weight
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def load_state_dict(self, state_dict, is_update: bool = False):
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"""
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load_state_dict function.
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"""
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# gate
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if not is_update:
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gate_weight_tensor = get_tensor(state_dict.pop(self.gate_weight_key))
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self.gate_weight = self.create_parameter(
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shape=gate_weight_tensor.shape,
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dtype="float32",
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)
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self.gate_weight.set_value(gate_weight_tensor)
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# gate_correction_bias
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if self.moe_use_gate_correction_bias:
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gate_correction_bias_tensor = get_tensor(
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state_dict.pop(self.gate_correction_bias_key))
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self.gate_correction_bias = self.create_parameter(
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shape=gate_correction_bias_tensor.shape,
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dtype="float32",
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)
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self.gate_correction_bias.set_value(gate_correction_bias_tensor)
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else:
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self.gate_correction_bias = None
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up_gate_proj_weight, down_proj_weight = self.load_gate_state_dict(
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state_dict)
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weight1_scale = None
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weight2_scale = None
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ffn1_in_scale = None
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ffn2_in_scale = None
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if self.moe_quant_type == "w4a8":
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weight1_scale = []
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weight2_scale = []
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ffn1_in_scale = []
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ffn2_in_scale = []
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for j in range(self.num_experts):
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weight1_scale.append(
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get_tensor(
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state_dict.pop(
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self.ffn1_expert_weight_scale_key.format(
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self.layer_idx, j))))
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weight2_scale.append(
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get_tensor(
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state_dict.pop(
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self.ffn2_expert_weight_scale_key.format(
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self.layer_idx, j))))
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ffn1_in_scale.append(
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get_tensor(
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state_dict.pop(
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self.ffn1_expert_in_scale_key.format(
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self.layer_idx, j))))
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ffn2_in_scale.append(
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get_tensor(
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state_dict.pop(
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self.ffn2_expert_in_scale_key.format(
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self.layer_idx, j))))
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# other weight is with compute_method
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# different method may have different way to create weights
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self.compute_method.create_weights(self, self.moe_compute_params,
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up_gate_proj_weight,
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down_proj_weight, None, None,
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weight1_scale, weight2_scale,
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ffn1_in_scale, ffn2_in_scale)
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def forward(self, x, **kwargs):
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"""
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Defines the forward computation of the moe layer.
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Args:
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x (Tensor): Input tensor to the moe layer.
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Returns:
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Tensor: Output tensor.
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"""
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out = self.compute_method.apply(self, self.moe_compute_params, x)
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if self.tp_size > 1:
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from fastdeploy.distributed.communication_op import \
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tensor_model_parallel_all_reduce
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tensor_model_parallel_all_reduce(out)
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return out
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