830 lines
30 KiB
Python
830 lines
30 KiB
Python
# Adapted from qwen2_moe.py
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# 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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"""Inference-only Qwen3MoE 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 functools import partial
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from typing import Any, Dict, Iterable, Optional, Tuple
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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 sglang.srt.distributed import (
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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parallel_state,
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split_tensor_along_last_dim,
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tensor_model_parallel_all_gather,
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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 LayerCommunicator, LayerScatterModes
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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 get_moe_impl_class
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from sglang.srt.layers.moe.ep_moe.token_dispatcher import DeepEPDispatcher
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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from sglang.srt.layers.moe.topk import select_experts
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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 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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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.expert_location_dispatch import ExpertLocationDispatchInfo
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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 (
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ForwardBatch,
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ForwardMode,
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PPProxyTensors,
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)
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.qwen2_moe import Qwen2MoeMLP as Qwen3MoeMLP
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from sglang.srt.models.qwen2_moe import Qwen2MoeModel
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from sglang.srt.two_batch_overlap import MaybeTboDeepEPDispatcher
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from sglang.srt.utils import DeepEPMode, add_prefix, is_non_idle_and_non_empty
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Qwen3MoeConfig = None
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logger = logging.getLogger(__name__)
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class Qwen3MoeSparseMoeBlock(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: Qwen3MoeConfig,
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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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num_experts=config.num_experts
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+ global_server_args_dict["ep_num_redundant_experts"],
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top_k=config.num_experts_per_tok,
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layer_id=layer_id,
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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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dict(deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]])
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if global_server_args_dict["enable_deepep_moe"]
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else {}
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),
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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 global_server_args_dict["enable_deepep_moe"]:
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# TODO: we will support tp < ep in the future
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self.ep_size = get_tensor_model_parallel_world_size()
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self.num_experts = (
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config.num_experts + global_server_args_dict["ep_num_redundant_experts"]
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)
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self.top_k = config.num_experts_per_tok
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self.renormalize = config.norm_topk_prob
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self.deepep_dispatcher = MaybeTboDeepEPDispatcher(
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group=parallel_state.get_tp_group().device_group,
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router_topk=self.top_k,
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permute_fusion=True,
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num_experts=self.num_experts,
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num_local_experts=config.num_experts // self.tp_size,
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hidden_size=config.hidden_size,
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params_dtype=config.torch_dtype,
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deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]],
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async_finish=True, # TODO
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return_recv_hook=True,
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)
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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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if not global_server_args_dict["enable_deepep_moe"]:
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return self.forward_normal(hidden_states)
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else:
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return self.forward_deepep(hidden_states, forward_batch)
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def get_moe_weights(self):
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return [
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x.data
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for name, x in self.experts.named_parameters()
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if name not in ["correction_bias"]
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]
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def forward_normal(self, hidden_states: torch.Tensor) -> 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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# 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 self.tp_size > 1:
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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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def forward_deepep(
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self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
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) -> torch.Tensor:
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forward_mode = forward_batch.forward_mode
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if is_non_idle_and_non_empty(forward_mode, 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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topk_weights, topk_idx = select_experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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top_k=self.top_k,
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use_grouped_topk=False,
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renormalize=self.renormalize,
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expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
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layer_id=self.layer_id,
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),
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)
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else:
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topk_idx = torch.full(
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(0, self.top_k), -1, dtype=torch.int, device=hidden_states.device
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)
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topk_weights = torch.empty(
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(0, self.top_k), dtype=torch.float32, device=hidden_states.device
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)
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if self.ep_size > 1:
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# TODO(ch-wan): allow users to set num_max_dispatch_tokens_per_rank value
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(
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hidden_states,
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topk_idx,
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topk_weights,
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reorder_topk_ids,
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num_recv_tokens_per_expert,
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seg_indptr,
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masked_m,
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expected_m,
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) = self.deepep_dispatcher.dispatch(
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hidden_states=hidden_states,
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topk_idx=topk_idx,
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topk_weights=topk_weights,
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forward_mode=forward_mode,
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)
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final_hidden_states = self.experts(
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hidden_states=hidden_states,
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topk_idx=topk_idx,
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topk_weights=topk_weights,
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reorder_topk_ids=reorder_topk_ids,
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seg_indptr=seg_indptr,
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masked_m=masked_m,
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expected_m=expected_m,
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num_recv_tokens_per_expert=num_recv_tokens_per_expert,
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forward_mode=forward_mode,
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)
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if self.ep_size > 1:
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final_hidden_states = self.deepep_dispatcher.combine(
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hidden_states=final_hidden_states,
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topk_idx=topk_idx,
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topk_weights=topk_weights,
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forward_mode=forward_mode,
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)
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return final_hidden_states
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def op_gate(self, state):
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if is_non_idle_and_non_empty(
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state.forward_batch.forward_mode, state.hidden_states_mlp_input
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):
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# router_logits: (num_tokens, n_experts)
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state.router_logits, _ = self.gate(state.hidden_states_mlp_input)
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else:
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state.router_logits = None
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def op_select_experts(self, state):
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router_logits = state.pop("router_logits")
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hidden_states = state.hidden_states_mlp_input
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if router_logits is not None:
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state.topk_weights_local, state.topk_idx_local = select_experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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top_k=self.top_k,
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use_grouped_topk=False,
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renormalize=self.renormalize,
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expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
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layer_id=self.layer_id,
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),
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)
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else:
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state.topk_idx_local = torch.full(
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(0, self.top_k), -1, dtype=torch.int, device=hidden_states.device
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)
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state.topk_weights_local = torch.empty(
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(0, self.top_k), dtype=torch.float32, device=hidden_states.device
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)
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def op_dispatch_a(self, state):
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if self.ep_size > 1:
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# TODO(ch-wan): allow users to set num_max_dispatch_tokens_per_rank value
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self.deepep_dispatcher.dispatch_a(
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hidden_states=state.pop("hidden_states_mlp_input"),
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topk_idx=state.pop("topk_idx_local"),
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topk_weights=state.pop("topk_weights_local"),
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forward_mode=state.forward_batch.forward_mode,
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tbo_subbatch_index=state.get("tbo_subbatch_index"),
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)
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def op_dispatch_b(self, state):
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if self.ep_size > 1:
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with get_global_expert_distribution_recorder().with_current_layer(
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self.layer_id
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):
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(
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state.hidden_states_experts_input,
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state.topk_idx_dispatched,
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state.topk_weights_dispatched,
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state.reorder_topk_ids,
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state.num_recv_tokens_per_expert,
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state.seg_indptr,
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state.masked_m,
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state.expected_m,
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) = self.deepep_dispatcher.dispatch_b(
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tbo_subbatch_index=state.get("tbo_subbatch_index"),
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)
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def op_experts(self, state):
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state.hidden_states_experts_output = self.experts(
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hidden_states=state.pop("hidden_states_experts_input"),
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topk_idx=state.topk_idx_dispatched,
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topk_weights=state.topk_weights_dispatched,
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reorder_topk_ids=state.pop("reorder_topk_ids"),
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seg_indptr=state.pop("seg_indptr"),
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masked_m=state.pop("masked_m"),
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expected_m=state.pop("expected_m"),
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num_recv_tokens_per_expert=state.pop("num_recv_tokens_per_expert"),
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forward_mode=state.forward_batch.forward_mode,
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)
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def op_combine_a(self, state):
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if self.ep_size > 1:
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self.deepep_dispatcher.combine_a(
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hidden_states=state.pop("hidden_states_experts_output"),
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topk_idx=state.pop("topk_idx_dispatched"),
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topk_weights=state.pop("topk_weights_dispatched"),
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forward_mode=state.forward_batch.forward_mode,
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tbo_subbatch_index=state.get("tbo_subbatch_index"),
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)
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def op_combine_b(self, state):
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if self.ep_size > 1:
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state.hidden_states_after_combine = self.deepep_dispatcher.combine_b(
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tbo_subbatch_index=state.get("tbo_subbatch_index"),
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)
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def op_output(self, state):
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state.hidden_states_mlp_output = state.pop("hidden_states_after_combine")
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class Qwen3MoeAttention(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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head_dim: Optional[int] = None,
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rms_norm_eps: float = 1e-06,
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attention_bias: bool = False,
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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 = head_dim or 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.tp_rank = get_tensor_model_parallel_rank()
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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=attention_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=attention_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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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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prefix=add_prefix("attn", prefix),
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)
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self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
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self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
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def _apply_qk_norm(
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self, q: torch.Tensor, k: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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q_by_head = q.reshape(-1, self.head_dim)
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q_by_head = self.q_norm(q_by_head)
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q = q_by_head.view(q.shape)
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k_by_head = k.reshape(-1, self.head_dim)
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k_by_head = self.k_norm(k_by_head)
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k = k_by_head.view(k.shape)
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return q, k
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def op_prepare(self, state):
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state.attn_intermediate_state = self.forward_prepare(
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positions=state.positions,
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hidden_states=state.pop("hidden_states_after_comm_pre_attn"),
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forward_batch=state.forward_batch,
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)
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def op_core(self, state):
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state.hidden_states_after_attn = self.forward_core(
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state.pop("attn_intermediate_state")
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)
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def forward_prepare(
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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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):
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if hidden_states.shape[0] == 0:
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return hidden_states, forward_batch, None
|
|
qkv, _ = self.qkv_proj(hidden_states)
|
|
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
|
q, k = self._apply_qk_norm(q, k)
|
|
q, k = self.rotary_emb(positions, q, k)
|
|
inner_state = q, k, v, forward_batch
|
|
return None, forward_batch, inner_state
|
|
|
|
def forward_core(self, intermediate_state):
|
|
hidden_states, forward_batch, inner_state = intermediate_state
|
|
if inner_state is None:
|
|
return hidden_states
|
|
attn_output = self.attn(*inner_state)
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
s = self.forward_prepare(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
forward_batch=forward_batch,
|
|
)
|
|
return self.forward_core(s)
|
|
|
|
|
|
class Qwen3MoeDecoderLayer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: Qwen3MoeConfig,
|
|
layer_id: int,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
rope_theta = getattr(config, "rope_theta", 10000)
|
|
rope_scaling = getattr(config, "rope_scaling", None)
|
|
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
|
head_dim = getattr(
|
|
config, "head_dim", config.hidden_size // config.num_attention_heads
|
|
)
|
|
rms_norm_eps = config.rms_norm_eps
|
|
attention_bias = config.attention_bias
|
|
self.self_attn = Qwen3MoeAttention(
|
|
hidden_size=self.hidden_size,
|
|
num_heads=config.num_attention_heads,
|
|
num_kv_heads=config.num_key_value_heads,
|
|
layer_id=layer_id,
|
|
rope_theta=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
max_position_embeddings=max_position_embeddings,
|
|
head_dim=head_dim,
|
|
rms_norm_eps=rms_norm_eps,
|
|
attention_bias=attention_bias,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("self_attn", prefix),
|
|
)
|
|
|
|
self.layer_id = layer_id
|
|
|
|
self.attn_tp_size = get_attention_tp_size()
|
|
self.attn_tp_rank = get_attention_tp_rank()
|
|
self.local_dp_size = get_local_attention_dp_size()
|
|
|
|
# Qwen3MoE all layers are sparse and have no nextn now
|
|
self.is_layer_sparse = True
|
|
is_previous_layer_sparse = True
|
|
|
|
self.layer_scatter_modes = LayerScatterModes.init_new(
|
|
layer_id=layer_id,
|
|
num_layers=config.num_hidden_layers,
|
|
is_layer_sparse=self.is_layer_sparse,
|
|
is_previous_layer_sparse=is_previous_layer_sparse,
|
|
)
|
|
|
|
if self.is_layer_sparse:
|
|
self.mlp = Qwen3MoeSparseMoeBlock(
|
|
layer_id=self.layer_id,
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("mlp", prefix),
|
|
)
|
|
else:
|
|
self.mlp = Qwen3MoeMLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("mlp", prefix),
|
|
)
|
|
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = RMSNorm(
|
|
config.hidden_size, eps=config.rms_norm_eps
|
|
)
|
|
|
|
self.layer_communicator = LayerCommunicator(
|
|
layer_scatter_modes=self.layer_scatter_modes,
|
|
input_layernorm=self.input_layernorm,
|
|
post_attention_layernorm=self.post_attention_layernorm,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
residual: Optional[torch.Tensor],
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
|
|
hidden_states, residual = self.layer_communicator.prepare_attn(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
|
|
if hidden_states.shape[0] != 0:
|
|
hidden_states = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
forward_batch=forward_batch,
|
|
)
|
|
|
|
hidden_states, residual = self.layer_communicator.prepare_mlp(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
|
|
hidden_states = self.mlp(hidden_states, forward_batch)
|
|
|
|
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
|
|
return hidden_states, residual
|
|
|
|
def op_comm_prepare_attn(
|
|
self,
|
|
state,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
residual: Optional[torch.Tensor],
|
|
tbo_subbatch_index: Optional[int] = None,
|
|
):
|
|
state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = (
|
|
self.layer_communicator.prepare_attn(hidden_states, residual, forward_batch)
|
|
)
|
|
state.update(
|
|
dict(
|
|
forward_batch=forward_batch,
|
|
positions=positions,
|
|
tbo_subbatch_index=tbo_subbatch_index,
|
|
)
|
|
)
|
|
|
|
def op_comm_prepare_mlp(self, state):
|
|
state.hidden_states_mlp_input, state.residual_after_comm_pre_mlp = (
|
|
self.layer_communicator.prepare_mlp(
|
|
state.pop("hidden_states_after_attn"),
|
|
state.pop("residual_after_input_ln"),
|
|
state.forward_batch,
|
|
)
|
|
)
|
|
|
|
def op_mlp(self, state):
|
|
hidden_states = state.pop("hidden_states_mlp_input")
|
|
state.hidden_states_mlp_output = self.mlp(
|
|
hidden_states, state.forward_batch.forward_mode
|
|
)
|
|
|
|
def op_comm_postprocess_layer(self, state):
|
|
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
|
state.pop("hidden_states_mlp_output"),
|
|
state.pop("residual_after_comm_pre_mlp"),
|
|
state.forward_batch,
|
|
)
|
|
|
|
output = dict(
|
|
positions=state.positions,
|
|
hidden_states=hidden_states,
|
|
residual=residual,
|
|
forward_batch=state.forward_batch,
|
|
tbo_subbatch_index=state.tbo_subbatch_index,
|
|
)
|
|
|
|
state.clear(
|
|
expect_keys={
|
|
"positions",
|
|
"forward_batch",
|
|
"tbo_subbatch_index",
|
|
}
|
|
)
|
|
return output
|
|
|
|
|
|
class Qwen3MoeModel(Qwen2MoeModel):
|
|
def __init__(
|
|
self,
|
|
config: Qwen3MoeConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__(
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=prefix,
|
|
decoder_layer_type=Qwen3MoeDecoderLayer,
|
|
)
|
|
|
|
|
|
class Qwen3MoeForCausalLM(nn.Module):
|
|
fall_back_to_pt_during_load = False
|
|
|
|
def __init__(
|
|
self,
|
|
config: Qwen3MoeConfig,
|
|
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 = Qwen3MoeModel(
|
|
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),
|
|
]
|
|
|
|
expert_params_mapping = get_moe_impl_class().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")
|
|
|
|
self.routed_experts_weights_of_layer = {
|
|
layer_id: layer.mlp.get_moe_weights()
|
|
for layer_id, layer in enumerate(self.model.layers)
|
|
if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock)
|
|
}
|
|
|
|
@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 = Qwen3MoeForCausalLM
|