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* [Model] tp+ep support v1_loader * fix * fix mtp_linear * fix mtp_linear * fix * fix * fix v0 loader * fix * Add get_tensor for EP * fix linear weight_loader * fix typo * fix
739 lines
31 KiB
Python
739 lines
31 KiB
Python
"""
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# Copyright (c) 2025 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 functools import partial
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from typing import Callable, Optional
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import paddle
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from paddle import nn
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from paddleformers.utils.log import logger
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from fastdeploy import envs
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from fastdeploy.distributed.communication import (
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tensor_model_parallel_all_reduce,
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tensor_model_parallel_all_reduce_custom,
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)
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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from fastdeploy.model_executor.layers.moe.routing_indices_cache import (
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save_routing_to_buffer,
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)
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from fastdeploy.model_executor.layers.utils import get_tensor
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from fastdeploy.model_executor.utils import h2d_copy, slice_fn
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from fastdeploy.platforms import current_platform
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from fastdeploy.worker.experts_manager import RedundantExpertManger
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try:
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from fastdeploy.model_executor.ops.gpu import noaux_tc, noaux_tc_redundant
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except:
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logger.warning("import noaux_tc Failed!")
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import numpy as np
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def get_moe_method():
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"""
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return moe method based on device platform
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"""
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if current_platform.is_cuda() or current_platform.is_iluvatar():
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from .fused_moe_cutlass_backend import CutlassMoEMethod
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return CutlassMoEMethod(None)
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elif current_platform.is_xpu():
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from fastdeploy.model_executor.layers.backends import XPUMoEMethod
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return XPUMoEMethod(None)
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elif current_platform.is_gcu():
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from fastdeploy.model_executor.layers.backends import GCUFusedMoeMethod
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return GCUFusedMoeMethod(None)
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elif current_platform.is_intel_hpu():
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from fastdeploy.model_executor.layers.backends import HpuMoEMethod
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return HpuMoEMethod(None)
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# return HpuTensorWiseFP8MoEMethod(None)
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elif current_platform.is_maca():
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from fastdeploy.model_executor.layers.backends import (
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MetaxCutlassUnquantizedFusedMoEMethod,
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)
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return MetaxCutlassUnquantizedFusedMoEMethod(None)
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return None
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def get_moe_scores(
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gating_output: paddle.Tensor,
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n_group,
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topk_group,
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top_k,
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routed_scaling_factor,
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e_score_correction_bias,
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renormalize: bool = False,
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expert_id_to_ep_rank_array: paddle.Tensor = None,
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expert_in_rank_num_list: paddle.Tensor = None,
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tokens_per_expert_stats_list: paddle.Tensor = None,
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redundant_ep_rank_num_plus_one: int = 1,
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) -> paddle.Tensor:
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"""
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compute moe scores using e_score_correction_bias.
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"""
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scores = paddle.nn.functional.sigmoid(gating_output)
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assert e_score_correction_bias is not None, "e_score_correction_bias is none!"
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scores_with_bias = scores + e_score_correction_bias
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if expert_id_to_ep_rank_array is None:
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scores, topk_values, topk_idx = noaux_tc(
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scores,
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scores_with_bias,
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n_group if n_group > 0 else 1,
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topk_group if topk_group > 0 else 1,
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top_k,
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renormalize,
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routed_scaling_factor,
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)
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else:
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scores, topk_values, topk_idx = noaux_tc_redundant(
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scores,
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scores_with_bias,
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expert_id_to_ep_rank_array,
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expert_in_rank_num_list,
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tokens_per_expert_stats_list,
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n_group if n_group > 0 else 1,
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topk_group if topk_group > 0 else 1,
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top_k,
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renormalize,
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routed_scaling_factor,
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redundant_ep_rank_num_plus_one,
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)
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return scores, topk_values, topk_idx
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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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fd_config,
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reduce_results: bool = True,
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renormalize: bool = False,
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moe_intermediate_size: int = -1,
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num_experts: int = -1,
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expert_id_offset: int = 0,
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top_k: int = -1,
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topk_method: str = "",
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topk_group: int = -1,
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n_group: int = -1,
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routed_scaling_factor: float = 1.0,
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layer_idx: int = -1,
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moe_tag: str = "",
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gate_correction_bias=None,
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redundant_table_manger: RedundantExpertManger = None,
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weight_key_map: dict = {},
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with_bias: bool = False,
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activation="swiglu",
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model_format: Optional[str] = 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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fd_config (FDConfig): 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.fd_config = fd_config
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self.layer_idx = layer_idx
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self.reduce_results = reduce_results
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self.renormalize = renormalize
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self.tp_rank = fd_config.parallel_config.tensor_parallel_rank
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self.tp_size = fd_config.parallel_config.tensor_parallel_size
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self.ep_size = fd_config.parallel_config.expert_parallel_size
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self.ep_rank = fd_config.parallel_config.expert_parallel_rank
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self.tp_group = fd_config.parallel_config.tp_group
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# NOTE(Zhenyu Li): just supports tp_size = 1 when ep_size > 1 in MOE now.
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if self.ep_size > 1:
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self.tp_size = 1
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self.tp_rank = 0
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self.attn_tp_size = fd_config.parallel_config.tensor_parallel_size
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self.attn_tp_rank = fd_config.parallel_config.tensor_parallel_rank
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assert (self.tp_size >= 1 and self.ep_size == 1) or (
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self.tp_size == 1 and self.ep_size > 1
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), "MoE only support parallelism on TP or EP dimension."
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self.hidden_size = fd_config.model_config.hidden_size
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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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self.moe_intermediate_size = moe_intermediate_size // self.tp_size
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self.top_k = top_k
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self.weight_key_map = weight_key_map
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self.use_method = envs.FD_MOE_BACKEND.lower()
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self.moe_tag = moe_tag
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self.with_bias = with_bias
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self.activation = activation
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if self.ep_size > 1:
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expert_id_offset = expert_id_offset + self.ep_rank * self.num_local_experts
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self.expert_id_offset = expert_id_offset
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self.gate_correction_bias_key = self.weight_key_map.get("gate_correction_bias_key", None)
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if self.gate_correction_bias_key is not None:
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self.moe_use_gate_correction_bias = True
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else:
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self.moe_use_gate_correction_bias = False
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# used for deepseek_v3
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self.topk_method = topk_method
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self.topk_group = topk_group
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self.n_group = n_group
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self.routed_scaling_factor = routed_scaling_factor
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self._dtype = self._helper.get_default_dtype()
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self.weight_dtype = self._dtype
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self.is_quantized = fd_config.model_config.is_quantized and not (
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fd_config.quant_config.name() == "mix_quant" and fd_config.quant_config.moe_quant_type is None
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)
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moe_quant_config = fd_config.quant_config
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self.moe_quant_config = moe_quant_config
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self.moe_quant_type = None
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if moe_quant_config and moe_quant_config.get_quant_method(self):
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self.quant_method = moe_quant_config.get_quant_method(self)
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self.moe_quant_type = moe_quant_config.name()
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else:
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# unquantized quant_method
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self.quant_method = get_moe_method()
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assert self.quant_method is not None, "self.quant_method should not be None"
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self.redundant_table_manger = redundant_table_manger
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self.is_rearrange = False
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if self.ep_size > 1:
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self.quant_method.init_ep(self)
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self.enable_routing_replay = fd_config.routing_replay_config.enable_routing_replay
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# Merge normal and RL build model
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if gate_correction_bias is not None:
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self.gate_correction_bias = gate_correction_bias
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else:
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self.gate_correction_bias = None
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self.quant_method.create_weights(
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self,
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weight_loader=self.weight_loader,
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model_format=fd_config.model_config.model_format if model_format is None else model_format,
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num_experts=self.num_local_experts if self.ep_size > 1 else self.num_experts,
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hidden_size=self.hidden_size,
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moe_intermediate_size=self.moe_intermediate_size,
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)
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logger.info(
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f"{moe_tag}MoE config is {num_experts=}[{expert_id_offset}, {expert_id_offset + self.num_local_experts}), \
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{top_k=}, hidden_size={self.hidden_size}, {moe_intermediate_size=}, \
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, ep_size={self.ep_size}, \
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tp_size={self.tp_size}."
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)
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def weight_loader(
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self, param, loaded_weight, expert_id, shard_id: Optional[str] = None, source: Optional[str] = None
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):
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"""
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source:Avoid redundant transpose of fused weights when weight_loader is called iteratively
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"""
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if expert_id is None and shard_id is None:
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# MoE experts has been fused in disk
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self._load_fused_experts_weight(param, loaded_weight)
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return
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if hasattr(param, "SHARD_ID_TO_SHARDED_DIM"):
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SHARD_ID_TO_SHARDED_DIM = param.SHARD_ID_TO_SHARDED_DIM
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elif current_platform.is_cuda() or current_platform.is_iluvatar() or current_platform.is_maca():
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SHARD_ID_TO_SHARDED_DIM = {"gate": 1, "down": 0, "up": 1}
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else:
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SHARD_ID_TO_SHARDED_DIM = {"gate": 0, "down": 1, "up": 0}
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if not (expert_id - self.expert_id_offset >= 0 and expert_id - self.expert_id_offset < self.num_local_experts):
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return
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if not param._is_initialized():
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param.initialize()
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weight_need_transpose = getattr(param, "weight_need_transpose", False)
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if self.ep_size > 1 or weight_need_transpose:
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loaded_weight = get_tensor(loaded_weight)
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if shard_id is None:
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# 1.gate up fused in disk
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if weight_need_transpose:
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loaded_weight = loaded_weight.transpose([1, 0])
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output_size = param[expert_id - self.expert_id_offset].shape[SHARD_ID_TO_SHARDED_DIM["gate"]]
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shard_offsets = [
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# (shard_id, shard_offset, shard_size)
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("gate", 0, output_size // 2 * self.tp_size),
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("up", output_size // 2 * self.tp_size, output_size // 2 * self.tp_size),
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]
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for shard_id, shard_offset, shard_size in shard_offsets:
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loaded_weight_shard = slice_fn(
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loaded_weight, SHARD_ID_TO_SHARDED_DIM[shard_id], shard_offset, shard_offset + shard_size
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)
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self.weight_loader(param, loaded_weight_shard, expert_id, shard_id, "fused")
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else:
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if weight_need_transpose and source != "fused":
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loaded_weight = loaded_weight.transpose([1, 0])
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# 2.gate up splited in disk
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assert shard_id in ["gate", "down", "up"]
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self._load_expert_weight(
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param=param,
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expert_id=expert_id,
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loaded_weight=loaded_weight,
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shard_id=shard_id,
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shard_dim=SHARD_ID_TO_SHARDED_DIM[shard_id],
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)
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def _load_gate_up_weight(self, param, expert_id, loaded_weight, shard_id, shard_dim=None, is_sharded=False):
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if self.tp_size > 1 and not is_sharded:
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tp_shard_dim = shard_dim
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weight_dim = -1 if tp_shard_dim else 0
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size = loaded_weight.shape[weight_dim]
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block_size = size // self.tp_size
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shard_offset = self.tp_rank * block_size
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shard_size = (self.tp_rank + 1) * block_size
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loaded_weight = slice_fn(loaded_weight, tp_shard_dim, shard_offset, shard_size)
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expert_param = param[expert_id - self.expert_id_offset]
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dim = -1 if shard_dim else 0
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param_shard_size = expert_param.shape[dim] // 2
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if shard_id == "gate":
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param_shard_offset = 0
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else:
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# shard_id == "up":
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param_shard_offset = param_shard_size
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expert_param = slice_fn(
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expert_param, shard_dim, start=param_shard_offset, end=param_shard_offset + param_shard_size
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)
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if hasattr(param, "tensor_track"):
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# for dyn quant
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param.tensor_track.mark(
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start=param_shard_offset,
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end=param_shard_offset + param_shard_size,
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batch_id=expert_id - self.expert_id_offset,
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)
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# To ensure compatibility across backends, apply an extra transpose for GCU and XPU
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if expert_param.shape != loaded_weight.shape:
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loaded_weight = loaded_weight.transpose([1, 0])
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assert expert_param.shape == loaded_weight.shape, (
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f"Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({expert_param.shape})"
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)
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if expert_param.dtype != loaded_weight.dtype:
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if loaded_weight.dtype == paddle.int8 and expert_param.dtype == paddle.float8_e4m3fn:
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loaded_weight = loaded_weight.view(expert_param.dtype)
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else:
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loaded_weight = loaded_weight.cast(expert_param.dtype)
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h2d_copy(dst=expert_param, src=loaded_weight)
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def _load_down_weight(self, param, expert_id, loaded_weight, shard_id, shard_dim=None):
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if self.tp_size > 1 and shard_dim is not None:
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tp_shard_dim = shard_dim
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dim = -1 if tp_shard_dim else 0
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size = loaded_weight.shape[dim]
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block_size = size // self.tp_size
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shard_offset = self.tp_rank * block_size
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shard_size = (self.tp_rank + 1) * block_size
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loaded_weight = slice_fn(loaded_weight, tp_shard_dim, shard_offset, shard_size)
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expert_param = param[expert_id - self.expert_id_offset]
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if hasattr(param, "tensor_track"):
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# for dyn quant
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param.tensor_track.mark(start=0, batch_id=expert_id - self.expert_id_offset)
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# To ensure compatibility across backends, apply an extra transpose for GCU and XPU and opensource weight
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if expert_param.shape != loaded_weight.shape:
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loaded_weight = loaded_weight.transpose([1, 0])
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assert expert_param.shape == loaded_weight.shape, (
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f"Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({expert_param.shape})"
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)
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if expert_param.dtype != loaded_weight.dtype:
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if loaded_weight.dtype == paddle.int8 and expert_param.dtype == paddle.float8_e4m3fn:
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loaded_weight = loaded_weight.view(expert_param.dtype)
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else:
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loaded_weight = loaded_weight.cast(expert_param.dtype)
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h2d_copy(dst=expert_param, src=loaded_weight)
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def _load_fused_experts_weight(self, param, loaded_weight):
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if self.tp_size > 1:
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dim = -1
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if isinstance(loaded_weight, (np.ndarray, paddle.Tensor)):
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size = loaded_weight.shape[dim]
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else:
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size = loaded_weight.get_shape()[dim]
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block_size = size // self.tp_size
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shard_offset = self.tp_rank * block_size
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shard_size = (self.tp_rank + 1) * block_size
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loaded_weight = slice_fn(loaded_weight, dim, shard_offset, shard_size)
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assert param.shape == loaded_weight.shape, (
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f"Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({param.shape})"
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)
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h2d_copy(dst=param, src=loaded_weight)
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|
if hasattr(param, "tensor_track"):
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|
for i in range(self.num_local_experts):
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param.tensor_track.mark(start=0, batch_id=i)
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|
|
|
def _load_expert_weight(
|
|
self,
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param,
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expert_id,
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loaded_weight,
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shard_id,
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shard_dim=None,
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):
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if shard_id == "down":
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self._load_down_weight(param, expert_id, loaded_weight, shard_id, shard_dim)
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elif shard_id in ["gate", "up"]:
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self._load_gate_up_weight(param, expert_id, loaded_weight, shard_id, shard_dim)
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|
|
@classmethod
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|
def make_expert_params_mapping(
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cls,
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|
num_experts: int,
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ckpt_gate_proj_name: Optional[str] = None,
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|
ckpt_up_proj_name: Optional[str] = None,
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|
ckpt_down_proj_name: Optional[str] = None,
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ckpt_gate_up_proj_name: Optional[str] = None,
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param_gate_up_proj_name: Optional[str] = None,
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param_down_proj_name: Optional[str] = None,
|
|
ckpt_expert_key_name: str = "experts",
|
|
experts_offset: int = 0,
|
|
num_experts_start_offset: int = 0,
|
|
) -> list[tuple[str, str, int, str]]:
|
|
param_name_maping = []
|
|
|
|
if ckpt_gate_up_proj_name:
|
|
param_name_maping.append((None, ckpt_gate_up_proj_name))
|
|
if ckpt_gate_proj_name:
|
|
param_name_maping.append(("gate", ckpt_gate_proj_name))
|
|
if ckpt_down_proj_name:
|
|
param_name_maping.append(("down", ckpt_down_proj_name))
|
|
if ckpt_up_proj_name:
|
|
param_name_maping.append(("up", ckpt_up_proj_name))
|
|
|
|
return [
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
(
|
|
(
|
|
param_gate_up_proj_name
|
|
if weight_name in [ckpt_gate_proj_name, ckpt_up_proj_name, ckpt_gate_up_proj_name]
|
|
else param_down_proj_name
|
|
),
|
|
f"{ckpt_expert_key_name}.{expert_id}.{weight_name}.",
|
|
expert_id,
|
|
shard_id,
|
|
)
|
|
for expert_id in range(
|
|
experts_offset + num_experts_start_offset, experts_offset + num_experts_start_offset + num_experts
|
|
)
|
|
for shard_id, weight_name in param_name_maping
|
|
]
|
|
|
|
def load_experts_weight(
|
|
self,
|
|
state_dict: dict,
|
|
up_gate_proj_expert_weight_key: str,
|
|
down_proj_expert_weight_key: str,
|
|
is_rearrange: bool = False,
|
|
):
|
|
"""
|
|
Load experts weight from state_dict.
|
|
Args:
|
|
state_dict (dict): The state_dict of model.
|
|
up_gate_proj_expert_weight_key (str): The key of up_gate_proj expert weight.
|
|
down_proj_expert_weight_key (str): The key of down_proj expert weight.
|
|
"""
|
|
logical_expert_ids = [
|
|
i
|
|
for i in range(
|
|
self.expert_id_offset,
|
|
self.expert_id_offset + self.num_local_experts,
|
|
)
|
|
]
|
|
ep_rank_to_expert_id_list = [i for i in range(self.num_experts)]
|
|
if self.redundant_table_manger is not None and is_rearrange is True:
|
|
(
|
|
ep_rank_to_expert_id_list,
|
|
expert_id_to_ep_rank_array,
|
|
expert_in_rank_num_list,
|
|
tokens_per_expert_stats_list,
|
|
) = self.redundant_table_manger.get_ep_rank_to_expert_id_list_by_layer(self.layer_idx)
|
|
logical_expert_ids = ep_rank_to_expert_id_list[
|
|
self.expert_id_offset : self.expert_id_offset + self.num_local_experts
|
|
]
|
|
up_gate_proj_weights = []
|
|
down_proj_weights = []
|
|
if isinstance(state_dict, list):
|
|
state_dict = dict(state_dict)
|
|
is_ffn_merged = (
|
|
up_gate_proj_expert_weight_key.format(logical_expert_ids[0] if is_rearrange else self.expert_id_offset)
|
|
in state_dict
|
|
)
|
|
if is_ffn_merged:
|
|
for expert_idx in logical_expert_ids:
|
|
down_proj_expert_weight_key_name = down_proj_expert_weight_key.format(expert_idx)
|
|
up_gate_proj_expert_weight_key_name = up_gate_proj_expert_weight_key.format(expert_idx)
|
|
up_gate_proj_weights.append(
|
|
get_tensor(
|
|
(
|
|
state_dict.pop(up_gate_proj_expert_weight_key_name)
|
|
if up_gate_proj_expert_weight_key_name in state_dict
|
|
else up_gate_proj_expert_weight_key_name
|
|
),
|
|
self.fd_config.model_config.model,
|
|
)
|
|
)
|
|
down_proj_weights.append(
|
|
get_tensor(
|
|
(
|
|
state_dict.pop(down_proj_expert_weight_key_name)
|
|
if down_proj_expert_weight_key_name in state_dict
|
|
else down_proj_expert_weight_key_name
|
|
),
|
|
self.fd_config.model_config.model,
|
|
)
|
|
)
|
|
else:
|
|
gate_expert_weight_key = up_gate_proj_expert_weight_key.replace("up_gate_proj", "gate_proj")
|
|
up_expert_weight_key = up_gate_proj_expert_weight_key.replace("up_gate_proj", "up_proj")
|
|
for expert_idx in logical_expert_ids:
|
|
gate_expert_weight_key_name = gate_expert_weight_key.format(expert_idx)
|
|
up_expert_weight_key_name = up_expert_weight_key.format(expert_idx)
|
|
down_proj_expert_weight_key_name = down_proj_expert_weight_key.format(expert_idx)
|
|
gate = get_tensor(
|
|
(
|
|
state_dict.pop(gate_expert_weight_key_name)
|
|
if gate_expert_weight_key_name in state_dict
|
|
else gate_expert_weight_key_name
|
|
),
|
|
self.fd_config.model_config.model,
|
|
)
|
|
up = get_tensor(
|
|
(
|
|
state_dict.pop(up_expert_weight_key_name)
|
|
if up_expert_weight_key_name in state_dict
|
|
else up_expert_weight_key_name
|
|
),
|
|
self.fd_config.model_config.model,
|
|
)
|
|
up_gate_proj_weights.append(paddle.concat([gate, up], axis=-1))
|
|
down_proj_weights.append(
|
|
get_tensor(
|
|
(
|
|
state_dict.pop(down_proj_expert_weight_key_name)
|
|
if down_proj_expert_weight_key_name in state_dict
|
|
else down_proj_expert_weight_key_name
|
|
),
|
|
self.fd_config.model_config.model,
|
|
)
|
|
)
|
|
return up_gate_proj_weights, down_proj_weights, logical_expert_ids, ep_rank_to_expert_id_list
|
|
|
|
def extract_moe_ffn_weights(self, state_dict: dict):
|
|
"""
|
|
Extract MoE FFN weights from state dict based on weight key mapping.
|
|
|
|
Args:
|
|
state_dict (dict): Model state dictionary containing the weights.
|
|
|
|
Returns:
|
|
tuple: A tuple containing two lists:
|
|
- up_gate_proj_weights: List of tensors for first FFN layer weights
|
|
- down_proj_weights: List of tensors for second FFN layer weights
|
|
|
|
Raises:
|
|
AssertionError: If required weight keys are missing or number of weights
|
|
doesn't match number of local experts.
|
|
"""
|
|
up_gate_proj_expert_weight_key = self.weight_key_map.get("up_gate_proj_expert_weight_key", None)
|
|
down_proj_expert_weight_key = self.weight_key_map.get("down_proj_expert_weight_key", None)
|
|
assert up_gate_proj_expert_weight_key is not None, "up_gate_proj_expert_weight_key should not be none."
|
|
assert down_proj_expert_weight_key is not None, "down_proj_expert_weight_key should not be none."
|
|
|
|
up_gate_proj_weights, down_proj_weights, logical_expert_ids, ep_rank_to_expert_id_list = (
|
|
self.load_experts_weight(
|
|
state_dict,
|
|
up_gate_proj_expert_weight_key,
|
|
down_proj_expert_weight_key,
|
|
)
|
|
)
|
|
assert (
|
|
len(up_gate_proj_weights) == self.num_local_experts
|
|
), "up_gate_proj_weights length should be equal to num_local_experts."
|
|
assert (
|
|
len(down_proj_weights) == self.num_local_experts
|
|
), "down_proj_weights length should be equal to num_local_experts."
|
|
|
|
return up_gate_proj_weights, down_proj_weights, logical_expert_ids, ep_rank_to_expert_id_list
|
|
|
|
def extract_gate_correction_bias(self, gate_correction_bias_key, state_dict):
|
|
"""
|
|
extract_gate_correction_bias function.
|
|
"""
|
|
gate_correction_bias_tensor = get_tensor(state_dict.pop(gate_correction_bias_key)).astype("float32")
|
|
return gate_correction_bias_tensor
|
|
|
|
def load_state_dict(self, state_dict, is_rearrange: bool = False):
|
|
"""
|
|
load_state_dict function.
|
|
"""
|
|
if self.is_quantized or self.fd_config.model_config.is_moe_quantized:
|
|
if getattr(self.fd_config.quant_config, "is_permuted", True):
|
|
self.quant_method.process_prequanted_weights(self, state_dict, is_rearrange)
|
|
else:
|
|
self.quant_method.process_loaded_weights(self, state_dict)
|
|
else:
|
|
self.quant_method.process_loaded_weights(self, state_dict)
|
|
|
|
def forward_split_allgather(self, x: paddle.Tensor, gate: nn.Layer, topk_ids_hookfunc: Callable = None):
|
|
"""
|
|
Forward split allgather function.
|
|
"""
|
|
token_num = x.shape[0]
|
|
token_num_per_rank = (token_num + self.attn_tp_size - 1) // self.attn_tp_size
|
|
# AllGather will hang when the data shapes on multi-ranks are different!
|
|
part_x = paddle.zeros(shape=[token_num_per_rank, x.shape[1]], dtype=x.dtype)
|
|
start_offset = self.attn_tp_rank * token_num_per_rank
|
|
end_offset = (self.attn_tp_rank + 1) * token_num_per_rank
|
|
if start_offset >= token_num:
|
|
start_offset = token_num
|
|
if end_offset > token_num:
|
|
end_offset = token_num
|
|
part_x[: (end_offset - start_offset), :] = x[start_offset:end_offset, :]
|
|
out = self.quant_method.apply(self, part_x, gate, topk_ids_hookfunc=topk_ids_hookfunc)
|
|
multi_outs = paddle.zeros([token_num_per_rank * self.attn_tp_size, x.shape[1]], dtype=x.dtype)
|
|
paddle.distributed.all_gather(multi_outs, out, self.tp_group)
|
|
out = multi_outs[:token_num, :]
|
|
|
|
return out
|
|
|
|
def forward(self, x: paddle.Tensor, gate: nn.Layer, forward_meta: ForwardMeta = None):
|
|
"""
|
|
Defines the forward computation of the moe layer.
|
|
|
|
Args:
|
|
x (Tensor): Input tensor to the moe layer.
|
|
|
|
Returns:
|
|
Tensor: Output tensor.s
|
|
|
|
"""
|
|
topk_ids_hookfunc = None
|
|
if self.enable_routing_replay:
|
|
if forward_meta is not None: # forward_meta is None when execute empty_input_forward
|
|
topk_ids_hookfunc = partial(
|
|
save_routing_to_buffer,
|
|
routing_replay_table=forward_meta.routing_replay_table,
|
|
batch_id_per_token=forward_meta.batch_id_per_token,
|
|
seq_lens_decoder=forward_meta.seq_lens_decoder,
|
|
cu_seqlens_q=forward_meta.cu_seqlens_q,
|
|
layer_idx=self.layer_idx,
|
|
tp_size=self.fd_config.parallel_config.tensor_parallel_size,
|
|
ep_size=self.fd_config.parallel_config.expert_parallel_size,
|
|
tp_group=self.fd_config.parallel_config.tp_group,
|
|
)
|
|
|
|
token_num = x.shape[0]
|
|
if (
|
|
self.ep_size > 1
|
|
and self.attn_tp_size > 1
|
|
and (not self.fd_config.parallel_config.use_sequence_parallel_moe)
|
|
and token_num >= self.attn_tp_size
|
|
):
|
|
out = self.forward_split_allgather(x, gate, topk_ids_hookfunc=topk_ids_hookfunc)
|
|
elif self.fd_config.parallel_config.use_ep and self.fd_config.parallel_config.enable_chunked_moe:
|
|
out = self.forward_chunked_moe(
|
|
x,
|
|
gate,
|
|
forward_meta,
|
|
topk_ids_hookfunc=topk_ids_hookfunc,
|
|
)
|
|
else:
|
|
out = self.forward_normal(x, gate, forward_meta, topk_ids_hookfunc=topk_ids_hookfunc)
|
|
|
|
if self.reduce_results and self.tp_size > 1:
|
|
if current_platform.is_intel_hpu():
|
|
tensor_model_parallel_all_reduce_custom(out)
|
|
else:
|
|
out = tensor_model_parallel_all_reduce(out, self.tp_group)
|
|
return out
|
|
|
|
def forward_chunked_moe(
|
|
self, x: paddle.Tensor, gate: nn.Layer, forward_meta: ForwardMeta, topk_ids_hookfunc: Callable = None
|
|
):
|
|
"""
|
|
Split input to multi chunk to reduce the memory usage of moe.
|
|
|
|
Args:
|
|
x (Tensor): Input tensor to the moe layer.
|
|
|
|
Returns:
|
|
Tensor: Output tensor.s
|
|
"""
|
|
chunk_size = self.fd_config.parallel_config.chunked_moe_size
|
|
token_num = x.shape[0]
|
|
fake_x = paddle.empty(
|
|
shape=[0, self.fd_config.model_config.hidden_size],
|
|
dtype=paddle.get_default_dtype(),
|
|
)
|
|
# input size that are less than a chunk, less than the max size data or empty input
|
|
# need to be repeated until the max chunk data infer MOE finished.
|
|
if token_num > chunk_size: # chunked moe
|
|
x_split_list = paddle.tensor_split(x, forward_meta.moe_num_chunk, axis=0)
|
|
out_split_list = [None] * forward_meta.moe_num_chunk
|
|
|
|
for i in range(forward_meta.max_moe_num_chunk):
|
|
if i < forward_meta.moe_num_chunk:
|
|
out_split_list[i] = self.quant_method.apply(
|
|
self, x_split_list[i], gate, topk_ids_hookfunc=topk_ids_hookfunc
|
|
)
|
|
else:
|
|
# just need to use real data to infer max_moe_num_chunk times.
|
|
self.quant_method.apply(self, fake_x, gate, topk_ids_hookfunc=topk_ids_hookfunc)
|
|
|
|
out = paddle.concat(out_split_list, axis=0)
|
|
else:
|
|
# when only one chunk, just need to use real data to infer once.
|
|
out = self.quant_method.apply(self, x, gate, topk_ids_hookfunc=topk_ids_hookfunc)
|
|
for i in range(forward_meta.max_moe_num_chunk - 1):
|
|
self.quant_method.apply(self, fake_x, gate, topk_ids_hookfunc=topk_ids_hookfunc)
|
|
|
|
return out
|
|
|
|
def forward_normal(
|
|
self, x: paddle.Tensor, gate: nn.Layer, forward_meta: ForwardMeta, topk_ids_hookfunc: Callable = None
|
|
):
|
|
"""
|
|
Normal mode of forward.
|
|
|
|
Args:
|
|
x (Tensor): Input tensor to the moe layer.
|
|
|
|
Returns:
|
|
Tensor: Output tensor.s
|
|
|
|
"""
|
|
out = self.quant_method.apply(self, x, gate, topk_ids_hookfunc=topk_ids_hookfunc)
|
|
return out
|