637 lines
23 KiB
Python
637 lines
23 KiB
Python
# Copyright 2023-2024 SGLang Team
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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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# Adapted from
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# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/qwen2_moe.py
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"""Inference-only Qwen2MoE model compatible with HuggingFace weights."""
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import logging
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from dataclasses import dataclass
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from enum import Enum, auto
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from typing import Any, Dict, Iterable, Optional, Tuple, 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 import PretrainedConfig
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from sglang.srt.distributed import (
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get_pp_group,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce,
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)
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from sglang.srt.layers.activation import SiluAndMul
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from sglang.srt.layers.communicator import (
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LayerCommunicator,
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LayerScatterModes,
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ScatterMode,
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)
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from sglang.srt.layers.dp_attention import (
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attn_tp_all_gather,
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attn_tp_reduce_scatter,
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dp_gather_partial,
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dp_scatter,
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get_attention_tp_rank,
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get_attention_tp_size,
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get_local_attention_dp_size,
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)
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
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from sglang.srt.layers.moe.ep_moe.layer import EPMoE, get_moe_impl_class
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.managers.expert_distribution import (
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ExpertDistributionRecorder,
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get_global_expert_distribution_recorder,
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)
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from sglang.srt.managers.expert_location import ModelConfigForExpertLocation
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.two_batch_overlap import model_forward_maybe_tbo
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from sglang.srt.utils import add_prefix, make_layers
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logger = logging.getLogger(__name__)
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class Qwen2MoeMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: 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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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=add_prefix("gate_up_proj", prefix),
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=add_prefix("down_proj", prefix),
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class Qwen2MoeSparseMoeBlock(nn.Module):
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def __init__(
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self,
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layer_id: int,
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config: PretrainedConfig,
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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.tp_size = get_tensor_model_parallel_world_size()
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self.layer_id = layer_id
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if self.tp_size > config.num_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.num_experts}."
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)
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self.experts = get_moe_impl_class()(
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layer_id=self.layer_id,
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num_experts=config.num_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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prefix=add_prefix("experts", prefix),
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)
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self.gate = ReplicatedLinear(
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config.hidden_size,
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config.num_experts,
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bias=False,
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quant_config=None,
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prefix=add_prefix("gate", prefix),
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)
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if config.shared_expert_intermediate_size > 0:
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self.shared_expert = Qwen2MoeMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.shared_expert_intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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reduce_results=False,
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prefix=add_prefix("shared_expert", prefix),
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)
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else:
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self.shared_expert = None
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self.shared_expert_gate = torch.nn.Linear(config.hidden_size, 1, bias=False)
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def forward(
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self, hidden_states: torch.Tensor, forward_batch: Optional[ForwardBatch] = None
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) -> torch.Tensor:
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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shared_output = None
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if self.shared_expert is not None:
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shared_output = self.shared_expert(hidden_states)
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if self.shared_expert_gate is not None:
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shared_output = (
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F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_output
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)
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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(
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hidden_states=hidden_states, router_logits=router_logits
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)
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if shared_output is not None:
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final_hidden_states = final_hidden_states + shared_output
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final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_dim)
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class Qwen2MoeAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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layer_id: int = 0,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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qkv_bias: int = True,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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attn_tp_rank = get_attention_tp_rank()
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attn_tp_size = get_attention_tp_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % attn_tp_size == 0
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self.num_heads = self.total_num_heads // attn_tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= attn_tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % attn_tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert attn_tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
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self.head_dim = hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=qkv_bias,
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quant_config=quant_config,
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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prefix=add_prefix("qkv_proj", prefix),
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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reduce_results=False,
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prefix=add_prefix("o_proj", prefix),
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)
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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=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, forward_batch)
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output, _ = self.o_proj(attn_output)
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return output
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class Qwen2MoeDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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qkv_bias = getattr(config, "qkv_bias", True)
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self.self_attn = Qwen2MoeAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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layer_id=layer_id,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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qkv_bias=qkv_bias,
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prefix=add_prefix("self_attn", prefix),
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)
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self.layer_id = layer_id
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self.attn_tp_size = get_attention_tp_size()
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self.attn_tp_rank = get_attention_tp_rank()
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self.local_dp_size = get_local_attention_dp_size()
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# Qwen2MoE all layers are sparse and have no nextn now
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self.is_layer_sparse = True
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is_previous_layer_sparse = True
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self.layer_scatter_modes = LayerScatterModes.init_new(
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layer_id=layer_id,
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num_layers=config.num_hidden_layers,
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is_layer_sparse=self.is_layer_sparse,
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is_previous_layer_sparse=is_previous_layer_sparse,
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)
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if self.is_layer_sparse:
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self.mlp = Qwen2MoeSparseMoeBlock(
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layer_id=layer_id,
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config=config,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix),
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)
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else:
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self.mlp = Qwen2MoeMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix),
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.layer_communicator = LayerCommunicator(
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layer_scatter_modes=self.layer_scatter_modes,
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input_layernorm=self.input_layernorm,
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post_attention_layernorm=self.post_attention_layernorm,
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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hidden_states, residual = self.layer_communicator.prepare_attn(
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hidden_states, residual, forward_batch
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)
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if hidden_states.shape[0] != 0:
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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)
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hidden_states, residual = self.layer_communicator.prepare_mlp(
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hidden_states, residual, forward_batch
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)
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hidden_states = self.mlp(hidden_states, forward_batch)
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hidden_states, residual = self.layer_communicator.postprocess_layer(
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hidden_states, residual, forward_batch
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)
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return hidden_states, residual
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class Qwen2MoeModel(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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decoder_layer_type: type[nn.Module] = Qwen2MoeDecoderLayer,
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) -> None:
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super().__init__()
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.pp_group = get_pp_group()
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if self.pp_group.is_first_rank:
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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enable_tp=not global_server_args_dict["enable_dp_attention"],
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prefix=add_prefix("embed_tokens", prefix),
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)
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else:
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self.embed_tokens = PPMissingLayer()
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# Use the provided decoder layer type or default to Qwen2MoeDecoderLayer
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decoder_layer_type = decoder_layer_type or Qwen2MoeDecoderLayer
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self.layers, self.start_layer, self.end_layer = make_layers(
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config.num_hidden_layers,
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lambda idx, prefix: decoder_layer_type(
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layer_id=idx,
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config=config,
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quant_config=quant_config,
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prefix=prefix,
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),
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pp_rank=self.pp_group.rank_in_group,
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pp_size=self.pp_group.world_size,
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prefix=add_prefix("layers", prefix),
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)
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if self.pp_group.is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer(return_tuple=True)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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pp_proxy_tensors: Optional[PPProxyTensors] = None,
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) -> Union[torch.Tensor, PPProxyTensors]:
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if self.pp_group.is_first_rank:
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if input_embeds is None:
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hidden_states = self.embed_tokens(input_ids)
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else:
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hidden_states = input_embeds
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residual = None
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else:
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assert pp_proxy_tensors is not None
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hidden_states = pp_proxy_tensors["hidden_states"]
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residual = pp_proxy_tensors["residual"]
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if forward_batch.can_run_tbo:
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hidden_states, residual = model_forward_maybe_tbo(
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layers=self.layers,
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enable_tbo=True,
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input_data_scatter_mode=ScatterMode.model_input_mode(),
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positions=positions,
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forward_batch=forward_batch,
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hidden_states=hidden_states,
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residual=residual,
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)
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else:
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for i in range(self.start_layer, self.end_layer):
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with get_global_expert_distribution_recorder().with_current_layer(i):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions, hidden_states, forward_batch, residual
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)
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if not self.pp_group.is_last_rank:
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return PPProxyTensors(
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{
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"hidden_states": hidden_states,
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"residual": residual,
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}
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)
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else:
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if hidden_states.shape[0] != 0:
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if residual is None:
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hidden_states = self.norm(hidden_states)
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else:
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class Qwen2MoeForCausalLM(nn.Module):
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|
fall_back_to_pt_during_load = False
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
self.pp_group = get_pp_group()
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.model = Qwen2MoeModel(
|
|
config, quant_config, prefix=add_prefix("model", prefix)
|
|
)
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("lm_head", prefix),
|
|
use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
|
|
)
|
|
self.logits_processor = LogitsProcessor(config)
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.model(
|
|
input_ids,
|
|
positions,
|
|
forward_batch,
|
|
input_embeds,
|
|
pp_proxy_tensors=pp_proxy_tensors,
|
|
)
|
|
if self.pp_group.is_last_rank:
|
|
return self.logits_processor(
|
|
input_ids, hidden_states, self.lm_head, forward_batch
|
|
)
|
|
else:
|
|
return hidden_states
|
|
|
|
@property
|
|
def start_layer(self):
|
|
return self.model.start_layer
|
|
|
|
@property
|
|
def end_layer(self):
|
|
return self.model.end_layer
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
MoEImpl = EPMoE if global_server_args_dict["enable_ep_moe"] else FusedMoE
|
|
|
|
expert_params_mapping = MoEImpl.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.num_experts,
|
|
)
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
for name, loaded_weight in weights:
|
|
layer_id = get_layer_id(name)
|
|
if (
|
|
layer_id is not None
|
|
and hasattr(self.model, "start_layer")
|
|
and (
|
|
layer_id < self.model.start_layer
|
|
or layer_id >= self.model.end_layer
|
|
)
|
|
):
|
|
continue
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
# Skip non-stacked layers and experts (experts handled below).
|
|
if weight_name not in name:
|
|
continue
|
|
# We have mlp.experts[0].gate_proj in the checkpoint.
|
|
# Since we handle the experts below in expert_params_mapping,
|
|
# we need to skip here BEFORE we update the name, otherwise
|
|
# name will be updated to mlp.experts[0].gate_up_proj, which
|
|
# will then be updated below in expert_params_mapping
|
|
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
|
if "mlp.experts" in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
if name in params_dict.keys():
|
|
param = params_dict[name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
else:
|
|
logger.warning(f"Parameter {name} not found in params_dict")
|
|
|
|
@classmethod
|
|
def get_model_config_for_expert_location(cls, config):
|
|
return ModelConfigForExpertLocation(
|
|
num_layers=config.num_hidden_layers,
|
|
num_logical_experts=config.num_experts,
|
|
num_groups=None,
|
|
)
|
|
|
|
|
|
EntryClass = Qwen2MoeForCausalLM
|