530 lines
20 KiB
Python
530 lines
20 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/inclusionAI/Ling/blob/master/models/modeling_bailing_moe.py
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# Copyright 2023 The vLLM team.
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# Copyright 2023 Antgroup and The HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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"""Inference-only BailingMoE model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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from itertools import islice
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from typing import Optional, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers.configuration_utils import PretrainedConfig
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from vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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class BailingAttention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.total_num_heads = config.num_attention_heads
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self.total_kv_heads = config.num_key_value_heads
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tp_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tp_size == 0
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assert self.total_kv_heads % tp_size == 0
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assert self.total_num_heads >= self.total_kv_heads
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self.num_heads = self.total_num_heads // tp_size
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self.head_dim = config.head_dim or (self.hidden_size //
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self.total_num_heads)
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self.q_size_per_rank = self.head_dim * self.num_heads
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self.num_kv_heads = self.total_kv_heads // tp_size
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self.kv_size_per_rank = self.num_kv_heads * self.head_dim
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self.scale = self.head_dim**-0.5
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self.query_key_value = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_kv_heads,
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bias=(config.use_bias or config.use_qkv_bias),
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quant_config=quant_config,
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prefix=f"{prefix}.query_key_value",
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)
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self.dense = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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self.hidden_size,
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bias=config.use_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.dense",
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scale,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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prefix=f"{prefix}.attn")
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=config.max_position_embeddings,
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base=config.rope_theta,
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is_neox_style=True,
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rope_scaling=config.rope_scaling,
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.query_key_value(hidden_states)
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q, k, v = qkv.split([
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self.q_size_per_rank, self.kv_size_per_rank, self.kv_size_per_rank
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],
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dim=-1)
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q, k = self.rotary_emb(position_ids, q, k)
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context_layer = self.attn(q, k, v)
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attn_output, _ = self.dense(context_layer)
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return attn_output
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class BailingMLP(nn.Module):
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def __init__(
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self,
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intermediate_size: int,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: Optional[bool] = True,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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config.hidden_size,
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[intermediate_size] * 2,
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bias=config.use_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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config.hidden_size,
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bias=config.use_bias,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj",
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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x, _ = self.gate_up_proj(x)
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x = self.act_fn(x)
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x, _ = self.down_proj(x)
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return x
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class BailingMoE(nn.Module):
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def __init__(
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self,
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intermediate_size: int,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: Optional[bool] = True,
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prefix: str = "",
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.tp_rank = get_tensor_model_parallel_rank()
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self.num_experts = config.num_experts
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self.top_k = config.num_experts_per_tok
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self.norm_expert_prob = config.norm_topk_prob
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self.hidden_size = config.hidden_size
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self.quant_config = quant_config
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self.num_shared_experts = config.num_shared_experts
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# Gate always runs at half / full precision for now.
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self.gate = ReplicatedLinear(self.hidden_size,
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self.num_experts,
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bias=False,
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quant_config=None)
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self.experts = FusedMoE(num_experts=self.num_experts,
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top_k=self.top_k,
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hidden_size=self.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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reduce_results=False,
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renormalize=self.norm_expert_prob,
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quant_config=quant_config,
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prefix=f"{prefix}.experts")
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if self.num_shared_experts > 0:
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intermediate_size = (config.moe_intermediate_size *
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self.num_shared_experts)
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self.shared_experts = BailingMLP(
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intermediate_size=intermediate_size,
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config=config,
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quant_config=quant_config,
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reduce_results=False,
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prefix=f"{prefix}.shared_experts")
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else:
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self.shared_experts = None
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_size = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_size)
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if self.num_shared_experts > 0:
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shared_output = self.shared_experts(hidden_states)
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = self.experts(hidden_states=hidden_states,
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router_logits=router_logits)
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if self.num_shared_experts > 0:
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final_hidden_states = final_hidden_states + shared_output
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(
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final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_size)
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class BailingMoeBlock(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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hidden_size = config.hidden_size
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intermediate_size = config.intermediate_size
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self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps)
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self.attention = BailingAttention(config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.attention")
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self.post_attention_layernorm = RMSNorm(hidden_size,
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eps=config.rms_norm_eps)
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self.mlp = BailingMoE(intermediate_size,
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config,
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quant_config,
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True,
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prefix=f"{prefix}.mlp")
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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residual: Optional[torch.Tensor],
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) -> torch.Tensor:
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.attention(
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hidden_states=hidden_states,
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position_ids=position_ids,
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)
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_torch_compile
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class BailingMoeModel(nn.Module):
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.vocab_size = config.vocab_size
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self.embed_dim = config.hidden_size
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if get_pp_group().is_first_rank or (config.tie_word_embeddings
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and get_pp_group().is_last_rank):
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self.word_embeddings = VocabParallelEmbedding(
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self.vocab_size, self.embed_dim)
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else:
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self.word_embeddings = PPMissingLayer()
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self.embedding_dropout = torch.nn.Dropout(config.embedding_dropout)
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: BailingMoeBlock(
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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),
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prefix=f"{prefix}.layers")
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size))
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.word_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for layer in islice(self.layers, self.start_layer, self.end_layer):
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hidden_states, residual = layer(
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hidden_states,
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position_ids,
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residual,
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
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"hidden_states": hidden_states,
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"residual": residual
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})
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
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return FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.num_experts,
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)
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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loaded_params: set[str] = set()
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expert_params_mapping = self.get_expert_mapping()
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for name, loaded_weight in weights:
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if self.config.norm_head and "lm_head.weight" in name:
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loaded_weight = F.normalize(loaded_weight,
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dim=0,
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p=2,
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eps=1e-7)
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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if "mlp.experts" in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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for mapping in expert_params_mapping:
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param_name, weight_name, expert_id, shard_id = mapping
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param,
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loaded_weight,
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name,
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shard_id=shard_id,
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expert_id=expert_id)
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break
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else:
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if name.endswith(".bias") and name not in params_dict:
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continue
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if name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class BailingMoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
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packed_modules_mapping = {
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"query_key_value": ["query_key_value"],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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) -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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self.max_position_embeddings = config.max_position_embeddings
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self.model = BailingMoeModel(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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if get_pp_group().is_last_rank:
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self.lm_head = (self.word_embeddings if config.tie_word_embeddings
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else ParallelLMHead(config.vocab_size,
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config.hidden_size,
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quant_config=quant_config))
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self.logits_processor = LogitsProcessor(config.vocab_size)
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else:
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self.lm_head = PPMissingLayer()
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
model_output = self.model(input_ids, positions, intermediate_tensors,
|
|
inputs_embeds)
|
|
return model_output
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(["lm_head."]
|
|
if self.config.tie_word_embeddings else None),
|
|
)
|
|
return loader.load_weights(weights)
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|