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vllm/model_executor/models/glm_ocr_mtp.py
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vllm/model_executor/models/glm_ocr_mtp.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 2026 The ZhipuAI Team.
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# Copyright 2026 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 GLM-OCR MTP model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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import torch
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import torch.nn as nn
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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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.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from .glm4 import Glm4DecoderLayer, get_spec_layer_idx_from_weight_name
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from .glm4_moe_lite_mtp import (
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Glm4MoeLiteMultiTokenPredictor,
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SharedHead,
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)
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from .interfaces import SupportsPP
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from .utils import (
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is_pp_missing_parameter,
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maybe_prefix,
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)
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class GlmOcrMultiTokenPredictorLayer(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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nn.Module.__init__(self)
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config = vllm_config.speculative_config.draft_model_config.hf_config.text_config
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self.config = config
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quant_config = vllm_config.quant_config
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self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
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self.device = current_platform.device_type
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self.shared_head = SharedHead(
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config=config, prefix=prefix, quant_config=quant_config
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)
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self.mtp_block = Glm4DecoderLayer(
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vllm_config=vllm_config, prefix=prefix, config=self.config
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)
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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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previous_hidden_states: torch.Tensor,
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inputs_embeds: torch.Tensor | None = None,
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spec_step_index: int = 0,
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) -> torch.Tensor:
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assert inputs_embeds is not None
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# masking inputs at position 0, as not needed by MTP
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inputs_embeds[positions[0] == 0] = 0
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inputs_embeds = self.enorm(inputs_embeds)
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previous_hidden_states = self.hnorm(previous_hidden_states)
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hidden_states = self.eh_proj(
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torch.cat([inputs_embeds, previous_hidden_states], dim=-1)
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)
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hidden_states, residual = self.mtp_block(
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positions=positions, hidden_states=hidden_states, residual=None
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)
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hidden_states = residual + hidden_states
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return hidden_states
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class GlmOcrMultiTokenPredictor(Glm4MoeLiteMultiTokenPredictor):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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nn.Module.__init__(self)
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config = vllm_config.model_config.hf_config.text_config
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self.mtp_start_layer_idx = config.num_hidden_layers
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self.num_mtp_layers = config.num_nextn_predict_layers
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self.layers = torch.nn.ModuleDict(
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{
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str(idx): GlmOcrMultiTokenPredictorLayer(
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vllm_config=vllm_config,
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prefix=f"{prefix}.layers.{idx}",
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)
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for idx in range(
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self.mtp_start_layer_idx,
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self.mtp_start_layer_idx + self.num_mtp_layers,
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)
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}
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)
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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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)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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class GlmOcrMTP(nn.Module, SupportsPP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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self.config = vllm_config.model_config.hf_config.text_config
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quant_config = vllm_config.quant_config
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self.quant_config = quant_config
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self.model = GlmOcrMultiTokenPredictor(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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self.expert_weights = []
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self.num_layers = self.config.num_nextn_predict_layers
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for layer in self.model.layers.values():
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assert isinstance(layer, GlmOcrMultiTokenPredictorLayer)
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layer = layer.mtp_block
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assert isinstance(layer, Glm4DecoderLayer)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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spec_step_idx: int = 0,
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) -> torch.Tensor:
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hidden_states = self.model(
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input_ids, positions, hidden_states, inputs_embeds, spec_step_idx
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)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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spec_step_idx: int = 0,
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) -> torch.Tensor | None:
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return self.model.compute_logits(hidden_states, spec_step_idx)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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(".gate_up_proj", ".gate_proj", 0),
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(".gate_up_proj", ".up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if name == "lm_head.weight":
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spec_layer = self.model.mtp_start_layer_idx
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name = f"model.layers.{spec_layer}.shared_head.head.weight"
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elif name == "model.embed_tokens.weight":
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spec_layer = self.model.mtp_start_layer_idx
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else:
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spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
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if spec_layer is None:
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continue
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name = self._rewrite_spec_layer_name(spec_layer, name)
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if self.quant_config is not None and (
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scale_name := self.quant_config.get_cache_scale(name)
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):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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loaded_weight = (
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loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
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)
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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if "scale" in name or "zero_point" in name:
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# Remapping the name of FP8 kv-scale or zero point.
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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# Some checkpoints include weight scale tensors for the
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# LM head even when the quantized head isn't built. Skip
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# them if the model does not expose a matching parameter
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# to avoid KeyError during load.
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if name.endswith(".weight_scale") and name not in params_dict:
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continue
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# According to DeepSeek-V3 Technical Report, MTP modules
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# shares embedding layer. We only load the first weights.
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if (
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spec_layer != self.model.mtp_start_layer_idx
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and ".layers" not in name
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):
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str:
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"""
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Rewrite the weight name to match the format of the original model.
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Add .mtp_block for modules in transformer layer block for spec layer
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and rename shared layer weights to be top level.
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"""
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name = name.replace("model.language_model.layers", "model.layers")
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spec_layer_weight_names = [
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"embed_tokens",
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"enorm",
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"hnorm",
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"eh_proj",
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"shared_head",
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]
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shared_weight_names = ["embed_tokens"]
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spec_layer_weight = False
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shared_weight = False
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for weight_name in spec_layer_weight_names:
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if weight_name in name:
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spec_layer_weight = True
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if weight_name in shared_weight_names:
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shared_weight = True
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break
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if not spec_layer_weight:
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# treat rest weights as weights for transformer layer block
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name = name.replace(
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f"model.layers.{spec_layer}.", f"model.layers.{spec_layer}.mtp_block."
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)
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elif shared_weight:
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# treat shared weights as top level weights
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name = name.replace(f"model.layers.{spec_layer}.", "model.")
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return name
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