mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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f1b5392e20
* [Intel HPU] Support intel hpu platform * fix some issues * apply precommit and move AttentionBackend_HPU * fix format issue * correct ops import * fix ci issue * update code in layers * fix code style issue * remove dense tp moe ep mode * fix enc_dec_block_num * fix rebase issue * rename hpu to gaudi in readme * rename ForwardMeta_HPU to HPUForwardMeta
549 lines
22 KiB
Python
549 lines
22 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 typing import Optional
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import numpy as np
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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.model_executor.layers.utils import get_tensor
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from fastdeploy.model_executor.utils import 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
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except:
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logger.warning("import noaux_tc Failed!")
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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():
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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_maca():
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from fastdeploy.model_executor.layers.backends import (
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MetaxTritonWeightOnlyMoEMethod,
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)
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return MetaxTritonWeightOnlyMoEMethod(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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raise NotImplementedError
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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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) -> 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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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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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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):
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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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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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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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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:
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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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self.quant_method = get_moe_method()
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self.redundant_table_manger = redundant_table_manger
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if self.ep_size > 1:
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self.quant_method.init_ep(self)
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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, weight_loader=self.weight_loader, model_format=fd_config.model_config.model_format
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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(self, param, loaded_weight, expert_id, shard_id: Optional[str] = None):
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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():
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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 param._is_initialized():
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param.initialize()
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if shard_id is None:
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# 1.gate up fused in disk
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weight_need_transpose = getattr(param, "weight_need_transpose", False)
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output_size = param[expert_id - self.expert_id_offset].shape[SHARD_ID_TO_SHARDED_DIM["gate"]]
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per_rank = output_size // 2
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start = self.tp_rank * per_rank
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loaded_weight_shard_gate = slice_fn(
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loaded_weight, weight_need_transpose ^ SHARD_ID_TO_SHARDED_DIM["gate"], start, start + per_rank
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)
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self._load_gate_up_weight(
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param, expert_id, loaded_weight_shard_gate, "gate", SHARD_ID_TO_SHARDED_DIM["gate"], is_sharded=True
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)
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start_up = output_size // 2 * self.tp_size + self.tp_rank * per_rank
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loaded_weight_shard_up = slice_fn(
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loaded_weight, weight_need_transpose ^ SHARD_ID_TO_SHARDED_DIM["up"], start_up, start_up + per_rank
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)
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self._load_gate_up_weight(
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param, expert_id, loaded_weight_shard_up, "up", SHARD_ID_TO_SHARDED_DIM["up"], is_sharded=True
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)
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else:
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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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weight_need_transpose = getattr(param, "weight_need_transpose", False)
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if self.tp_size > 1 and not is_sharded:
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tp_shard_dim = weight_need_transpose ^ shard_dim
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weight_dim = -1 if tp_shard_dim else 0
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if isinstance(loaded_weight, (np.ndarray, paddle.Tensor)):
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size = loaded_weight.shape[weight_dim]
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else:
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size = loaded_weight.get_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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loaded_weight = get_tensor(loaded_weight)
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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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expert_param.copy_(loaded_weight, False)
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def _load_down_weight(self, param, expert_id, loaded_weight, shard_id, shard_dim=None):
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weight_need_transpose = getattr(param, "weight_need_transpose", False)
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if self.tp_size > 1 and shard_dim is not None:
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tp_shard_dim = weight_need_transpose ^ shard_dim
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dim = -1 if tp_shard_dim else 0
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if isinstance(loaded_weight, 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, tp_shard_dim, shard_offset, shard_size)
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loaded_weight = get_tensor(loaded_weight)
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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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expert_param.copy_(loaded_weight, False)
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def _load_expert_weight(
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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,
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ckpt_expert_key_name: str = "experts",
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experts_offset: int = 0,
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num_experts_start_offset: int = 0,
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) -> list[tuple[str, str, int, str]]:
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param_name_maping = []
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if ckpt_gate_up_proj_name:
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param_name_maping.append((None, ckpt_gate_up_proj_name))
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if ckpt_gate_proj_name:
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param_name_maping.append(("gate", ckpt_gate_proj_name))
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if ckpt_down_proj_name:
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param_name_maping.append(("down", ckpt_down_proj_name))
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if ckpt_up_proj_name:
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param_name_maping.append(("up", ckpt_up_proj_name))
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return [
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# (param_name, weight_name, expert_id, shard_id)
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(
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(
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param_gate_up_proj_name
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if weight_name in [ckpt_gate_proj_name, ckpt_up_proj_name, ckpt_gate_up_proj_name]
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else param_down_proj_name
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),
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f"{ckpt_expert_key_name}.{expert_id}.{weight_name}.",
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expert_id,
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shard_id,
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)
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for expert_id in range(
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experts_offset + num_experts_start_offset, experts_offset + num_experts_start_offset + num_experts
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)
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for shard_id, weight_name in param_name_maping
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]
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def load_experts_weight(
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self,
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state_dict: dict,
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up_gate_proj_expert_weight_key: str,
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down_proj_expert_weight_key: str,
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is_rearrange: bool = False,
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):
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"""
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Load experts weight from state_dict.
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Args:
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state_dict (dict): The state_dict of model.
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up_gate_proj_expert_weight_key (str): The key of up_gate_proj expert weight.
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down_proj_expert_weight_key (str): The key of down_proj expert weight.
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"""
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logical_expert_ids = [
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i
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for i in range(
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self.expert_id_offset,
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self.expert_id_offset + self.num_local_experts,
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)
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]
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ep_rank_to_expert_id_list = [i for i in range(self.num_experts)]
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if self.redundant_table_manger is not None:
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(
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ep_rank_to_expert_id_list,
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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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) = self.redundant_table_manger.get_ep_rank_to_expert_id_list_by_layer(self.layer_idx)
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logical_expert_ids = ep_rank_to_expert_id_list[
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self.expert_id_offset : self.expert_id_offset + self.num_local_experts
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]
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up_gate_proj_weights = []
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down_proj_weights = []
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if isinstance(state_dict, list):
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state_dict = dict(state_dict)
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is_ffn_merged = (
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up_gate_proj_expert_weight_key.format(logical_expert_ids[0] if is_rearrange else self.expert_id_offset)
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in state_dict
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)
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if is_ffn_merged:
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for expert_idx in logical_expert_ids:
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down_proj_expert_weight_key_name = down_proj_expert_weight_key.format(expert_idx)
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up_gate_proj_expert_weight_key_name = up_gate_proj_expert_weight_key.format(expert_idx)
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up_gate_proj_weights.append(
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get_tensor(
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(
|
|
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.fd_config.model_config.is_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(self, x: paddle.Tensor, gate: nn.Layer):
|
|
"""
|
|
Defines the forward computation of the moe layer.
|
|
|
|
Args:
|
|
x (Tensor): Input tensor to the moe layer.
|
|
|
|
Returns:
|
|
Tensor: Output tensor.s
|
|
|
|
"""
|
|
out = self.quant_method.apply(self, x, gate)
|
|
return out
|