302 lines
11 KiB
Python
302 lines
11 KiB
Python
#
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# Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
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# Adapted from vllm/model_executor/models/glm4.py
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# Copyright 2023 The vLLM team.
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#
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# This file is a part of the vllm-kunlun project.
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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-4-0414 model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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from typing import Optional, Union
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import torch
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from torch import nn
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from transformers import Glm4Config
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from vllm.attention import AttentionType
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from vllm_kunlun.ops.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, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization 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 ParallelLMHead
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
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from vllm_kunlun.models.llama import LlamaMLP as Glm4MLP
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from vllm_kunlun.models.llama import LlamaModel
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from vllm.model_executor.models.utils import AutoWeightsLoader, PPMissingLayer, maybe_prefix
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class Glm4Attention(nn.Module):
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def __init__(self,
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config: Glm4Config,
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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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max_position: int = 4096 * 32,
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head_dim: Optional[int] = None,
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qkv_bias: bool = False,
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rope_theta: float = 10000,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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rope_scaling: Optional[tuple] = None,
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prefix: str = "",
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attn_type: str = AttentionType.DECODER) -> 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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partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
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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.rotary_dim = self.head_dim
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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.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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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.rotary_dim,
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max_position=max_position,
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base=self.rope_theta,
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rope_scaling=rope_scaling,
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partial_rotary_factor=partial_rotary_factor,
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is_neox_style=False,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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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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attn_type=attn_type)
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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, 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 Glm4DecoderLayer(nn.Module):
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def __init__(
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self,
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config: Glm4Config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 1000000)
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rope_scaling = getattr(config, "rope_scaling", None)
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self.self_attn = Glm4Attention(
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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max_position=config.max_position_embeddings,
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num_kv_heads=config.num_key_value_heads,
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rope_theta=rope_theta,
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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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rope_scaling=rope_scaling,
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prefix=f"{prefix}.self_attn",
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attn_type=AttentionType.DECODER,
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)
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self.mlp = Glm4MLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_self_attn_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_mlp_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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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: Optional[torch.Tensor],
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) -> tuple[torch.Tensor, 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(
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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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hidden_states = self.post_self_attn_layernorm(hidden_states)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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hidden_states = self.post_mlp_layernorm(hidden_states)
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return hidden_states, residual
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ALL_DECODER_LAYER_TYPES = {
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"attention": Glm4DecoderLayer,
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}
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@support_torch_compile(
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dynamic_arg_dims={
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"input_ids": 0,
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"positions": -1,
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"intermediate_tensors": 0,
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"inputs_embeds": 0,
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})
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class Glm4Model(LlamaModel):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__(vllm_config=vllm_config,
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prefix=prefix,
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layer_type=Glm4DecoderLayer)
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class Glm4ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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def __init__(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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quant_config = vllm_config.quant_config
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lora_config = vllm_config.lora_config
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self.config = config
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self.lora_config = lora_config
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self.quant_config = quant_config
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self.model = Glm4Model(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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if get_pp_group().is_last_rank:
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if config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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self.lm_head = ParallelLMHead(config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(
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prefix, "lm_head"))
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else:
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self.lm_head = PPMissingLayer()
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(input_ids, positions, intermediate_tensors,
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inputs_embeds)
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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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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(
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self,
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skip_prefixes=(["lm_head."]
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if self.config.tie_word_embeddings else None),
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)
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return loader.load_weights(weights)
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