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vllm/model_executor/models/minimax_m2.py
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550
vllm/model_executor/models/minimax_m2.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2025 The MiniMax AI team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI 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 MiniMaxM2 model."""
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from collections.abc import Iterable
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from typing import Any
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention.layer import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, ModelConfig, VllmConfig
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from vllm.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 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 (
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mamba.linear_attn import MiniMaxText01RMSNormTP
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from vllm.model_executor.layers.quantization import 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,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsPP
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from .utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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class MiniMaxM2MoE(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: QuantizationConfig | None = 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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if self.tp_size > config.num_local_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_local_experts}."
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)
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self.use_routing_bias = getattr(config, "use_routing_bias", False)
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if self.use_routing_bias:
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self.e_score_correction_bias = nn.Parameter(
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torch.empty(config.num_local_experts, dtype=torch.float32)
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)
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self.e_score_correction_bias.weight_loader = (
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MiniMaxM2MoE.ebias_weight_loader
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)
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else:
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self.e_score_correction_bias = None
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self.experts = FusedMoE(
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num_experts=config.num_local_experts,
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top_k=config.num_experts_per_tok,
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scoring_func=config.scoring_func,
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use_grouped_topk=True,
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num_expert_group=1,
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topk_group=1,
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e_score_correction_bias=self.e_score_correction_bias,
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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reduce_results=False,
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renormalize=True,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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)
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self.gate = ReplicatedLinear(
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config.hidden_size,
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config.num_local_experts,
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bias=False,
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params_dtype=torch.float32,
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quant_config=None,
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prefix=f"{prefix}.gate",
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)
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@staticmethod
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def ebias_weight_loader(param: nn.Parameter, loaded_weight: torch.Tensor) -> None:
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assert param.size() == loaded_weight.size()
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param.data.copy_(loaded_weight.to(torch.float32))
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def forward(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.to(torch.float32))
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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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final_hidden_states = final_hidden_states
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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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class MiniMaxM2Attention(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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rotary_dim: int,
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rope_parameters: dict[str, Any] | None = None,
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attn_window_size: int | None = None,
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max_position_embeddings: int = 8192,
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head_dim: int | None = None,
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rms_norm_eps: float = 1e-06,
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qkv_bias: bool = False,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = 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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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= 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 % 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 tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // 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.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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prefix=f"{prefix}.qkv_proj",
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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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prefix=f"{prefix}.o_proj",
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)
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if (
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rope_parameters is not None
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and "partial_rotary_factor" not in rope_parameters
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):
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rope_parameters["partial_rotary_factor"] = rotary_dim / self.head_dim
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self.rotary_emb = get_rope(
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self.head_dim,
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max_position=max_position_embeddings,
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rope_parameters=rope_parameters,
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)
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self.attn = Attention(
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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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per_layer_sliding_window=attn_window_size,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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)
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self.q_norm = MiniMaxText01RMSNormTP(
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self.head_dim * self.total_num_heads, eps=rms_norm_eps
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)
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self.k_norm = MiniMaxText01RMSNormTP(
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self.head_dim * self.total_num_kv_heads, eps=rms_norm_eps
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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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) -> 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 = self.q_norm(q)
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k = self.k_norm(k)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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class MiniMaxM2DecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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prefix: str,
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model_config: ModelConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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if hasattr(config, "max_model_len") and isinstance(config.max_model_len, int):
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max_position_embeddings = max(
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config.max_position_embeddings, config.max_model_len
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)
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# DecoderLayers are created with `make_layers` which passes the prefix
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# with the layer's index.
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layer_idx = int(prefix.split(sep=".")[-1])
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self.layer_idx = layer_idx
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self.self_attn = MiniMaxM2Attention(
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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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rotary_dim=config.rotary_dim,
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rope_parameters=config.rope_parameters,
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max_position_embeddings=max_position_embeddings,
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rms_norm_eps=config.rms_norm_eps,
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qkv_bias=getattr(config, "attention_bias", False),
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head_dim=getattr(config, "head_dim", None),
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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self.block_sparse_moe = MiniMaxM2MoE(
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config=config,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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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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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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residual: torch.Tensor | None,
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) -> torch.Tensor:
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# Self Attention
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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(hidden_states, residual)
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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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)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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hidden_states = self.block_sparse_moe(hidden_states)
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return hidden_states, residual
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@support_torch_compile
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class MiniMaxM2Model(nn.Module):
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fall_back_to_pt_during_load = False
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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model_config = vllm_config.model_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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if get_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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quant_config=None,
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prefix=f"{prefix}.embed_tokens",
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)
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else:
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self.embed_tokens = PPMissingLayer()
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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: MiniMaxM2DecoderLayer(
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config,
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prefix,
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model_config=model_config,
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cache_config=cache_config,
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quant_config=quant_config,
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),
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prefix=f"{prefix}.layers",
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)
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|
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if get_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()
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
|
||||
["hidden_states", "residual"], config.hidden_size
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||||
)
|
||||
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||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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|
||||
def forward(
|
||||
self,
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||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
if get_pp_group().is_first_rank:
|
||||
if inputs_embeds is not None:
|
||||
hidden_states = inputs_embeds
|
||||
else:
|
||||
hidden_states = self.embed_input_ids(input_ids)
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||||
residual = None
|
||||
else:
|
||||
assert intermediate_tensors is not None
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
residual = intermediate_tensors["residual"]
|
||||
|
||||
for layer in self.layers[self.start_layer : self.end_layer]:
|
||||
hidden_states, residual = layer(positions, hidden_states, residual)
|
||||
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors(
|
||||
{"hidden_states": hidden_states, "residual": residual}
|
||||
)
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
||||
return FusedMoE.make_expert_params_mapping(
|
||||
ckpt_gate_proj_name="w1",
|
||||
ckpt_down_proj_name="w2",
|
||||
ckpt_up_proj_name="w3",
|
||||
num_experts=self.config.num_local_experts,
|
||||
)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
]
|
||||
|
||||
# Params for weights, fp8 weight scales, fp8 activation scales
|
||||
# (param_name, weight_name, expert_id, shard_id)
|
||||
expert_params_mapping = self.get_expert_mapping()
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: set[str] = set()
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
|
||||
if spec_layer is not None:
|
||||
continue # skip spec decode layers for main model
|
||||
|
||||
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) and name not in params_dict:
|
||||
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 is_pp_missing_parameter(name, self):
|
||||
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)
|
||||
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
|
||||
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
|
||||
|
||||
# Remapping the name of FP8 kv-scale.
|
||||
name = maybe_remap_kv_scale_name(name, params_dict)
|
||||
if name is None:
|
||||
continue
|
||||
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
class MiniMaxM2ForCausalLM(nn.Module, SupportsPP):
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
if hasattr(vllm_config.model_config, "max_model_len"):
|
||||
self.config.max_model_len = vllm_config.model_config.max_model_len
|
||||
self.model = MiniMaxM2Model(
|
||||
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
||||
)
|
||||
if get_pp_group().is_last_rank:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size, config.hidden_size, quant_config=None
|
||||
)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
self.logits_processor = LogitsProcessor(config.vocab_size)
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.embed_input_ids(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
**kwargs,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
hidden_states = self.model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor | None:
|
||||
logits = self.logits_processor(self.lm_head, hidden_states)
|
||||
return logits
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
loader = AutoWeightsLoader(self)
|
||||
return loader.load_weights(weights)
|
||||
|
||||
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
||||
return self.model.get_expert_mapping()
|
||||
|
||||
|
||||
def get_spec_layer_idx_from_weight_name(
|
||||
config: PretrainedConfig, weight_name: str
|
||||
) -> int | None:
|
||||
if hasattr(config, "num_mtp_modules") and (config.num_mtp_modules > 0):
|
||||
layer_idx = config.num_hidden_layers
|
||||
for i in range(config.num_mtp_modules):
|
||||
if weight_name.startswith(f"model.layers.{layer_idx + i}."):
|
||||
return layer_idx + i
|
||||
return None
|
||||
Reference in New Issue
Block a user