479 lines
18 KiB
Python
479 lines
18 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/zai-org/ChatGLM2-6B
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"""Inference-only ChatGLM model compatible with THUDM weights."""
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import json
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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 torch.nn import LayerNorm
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from vllm.attention 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.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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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 (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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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.transformers_utils.configs import ChatGLMConfig
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from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
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from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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class GLMAttention(nn.Module):
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def __init__(
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self,
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config: ChatGLMConfig,
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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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):
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super().__init__()
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_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.multi_query_attention = config.multi_query_attention
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self.total_num_kv_heads = (config.multi_query_group_num
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if config.multi_query_attention else
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config.num_attention_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 = config.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.query_key_value = QKVParallelLinear(
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self.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=config.add_bias_linear or config.add_qkv_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.query_key_value",
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)
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self.dense = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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config.hidden_size,
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bias=config.add_bias_linear,
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quant_config=quant_config,
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prefix=f"{prefix}.dense",
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)
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# https://huggingface.co/zai-org/chatglm3-6b-32k/blob/e210410255278dd9d74463cf396ba559c0ef801c/modeling_chatglm.py#L141
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rope_ratio = getattr(config, "rope_ratio", 1.0)
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max_positions = getattr(config, "seq_length", 8192)
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# NOTE: zai-org/cogagent-9b-20241220 uses original_rope=False,
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# which is equivalent to is_neox_style=True
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is_neox_style = not config.original_rope
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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 // 2,
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max_position=max_positions,
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base=10000 * rope_ratio,
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is_neox_style=is_neox_style,
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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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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.query_key_value(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(position_ids, q, k)
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context_layer = self.attn(q, k, v)
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attn_output, _ = self.dense(context_layer)
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return attn_output
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class GLMMLP(nn.Module):
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"""MLP.
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MLP will take the input with h hidden state, project it to 4*h
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hidden dimension, perform nonlinear transformation, and project the
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state back into h hidden dimension.
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"""
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def __init__(
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self,
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config: ChatGLMConfig,
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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.add_bias = config.add_bias_linear
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# Project to 4h.
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self.dense_h_to_4h = MergedColumnParallelLinear(
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config.hidden_size,
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[config.ffn_hidden_size] * 2,
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bias=config.add_bias_linear,
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quant_config=quant_config,
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prefix=f"{prefix}.dense_h_to_4h",
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)
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self.activation_func = SiluAndMul()
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# Project back to h.
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self.dense_4h_to_h = RowParallelLinear(
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config.ffn_hidden_size,
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config.hidden_size,
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bias=config.add_bias_linear,
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quant_config=quant_config,
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prefix=f"{prefix}.dense_4h_to_h",
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)
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def forward(self, hidden_states):
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# [s, b, 4hp]
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intermediate_parallel, _ = self.dense_h_to_4h(hidden_states)
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intermediate_parallel = self.activation_func(intermediate_parallel)
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# [s, b, h]
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output, _ = self.dense_4h_to_h(intermediate_parallel)
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return output
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class GLMBlock(nn.Module):
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"""A single transformer layer.
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Transformer layer takes input with size [s, b, h] and returns an
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output of the same size.
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"""
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def __init__(
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self,
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config: ChatGLMConfig,
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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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):
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super().__init__()
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self.apply_residual_connection_post_layernorm = (
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config.apply_residual_connection_post_layernorm)
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self.fp32_residual_connection = config.fp32_residual_connection
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layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
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# Layernorm on the input data.
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self.input_layernorm = layer_norm_func(config.hidden_size,
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eps=config.layernorm_epsilon)
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# Self attention.
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self.self_attention = GLMAttention(config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.self_attention")
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self.hidden_dropout = config.hidden_dropout
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# Layernorm on the attention output
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self.post_attention_layernorm = layer_norm_func(
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config.hidden_size, eps=config.layernorm_epsilon)
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# MLP
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self.mlp = GLMMLP(config, quant_config, prefix=f"{prefix}.mlp")
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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) -> torch.Tensor:
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# hidden_states: [num_tokens, h]
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# Layer norm at the beginning of the transformer layer.
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layernorm_output = self.input_layernorm(hidden_states)
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# Self attention.
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attention_output = self.self_attention(
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hidden_states=layernorm_output,
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position_ids=position_ids,
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)
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# Residual connection.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = hidden_states
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layernorm_input = residual + attention_output
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# Layer norm post the self attention.
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layernorm_output = self.post_attention_layernorm(layernorm_input)
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# Second residual connection.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = layernorm_input
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output = self.mlp(layernorm_output) + residual
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return output
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class GLMTransformer(nn.Module):
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"""Transformer class."""
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def __init__(
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self,
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config: ChatGLMConfig,
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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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):
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super().__init__()
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self.post_layer_norm = config.post_layer_norm
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# Number of layers.
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self.num_layers = config.num_layers
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# Transformer layers.
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self.start_layer, self.end_layer, self.layers = make_layers(
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self.num_layers,
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lambda prefix: GLMBlock(
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config, cache_config, quant_config, prefix=prefix),
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prefix=f"{prefix}.layers",
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)
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if self.post_layer_norm:
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layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
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# Final layer norm before output.
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self.final_layernorm = layer_norm_func(
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config.hidden_size, eps=config.layernorm_epsilon)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(["hidden_states"],
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config.hidden_size))
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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) -> Union[torch.Tensor, IntermediateTensors]:
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for layer in self.layers[self.start_layer:self.end_layer]:
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hidden_states = layer(hidden_states=hidden_states,
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position_ids=position_ids)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": hidden_states})
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# Final layer norm.
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if self.post_layer_norm:
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states
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@support_torch_compile
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class ChatGLMModel(nn.Module, SupportsQuant):
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packed_modules_mapping = {
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"linear_proj.merged_proj":
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["linear_proj.gate_proj", "linear_proj.dense_h_to_4h"]
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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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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.embedding = VocabParallelEmbedding(config.padded_vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.embedding")
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self.num_layers = config.num_layers
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self.multi_query_group_num = config.multi_query_group_num
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self.kv_channels = config.kv_channels
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self.encoder = GLMTransformer(config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.encoder")
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self.output_layer = ParallelLMHead(config.padded_vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.output_layer")
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self.make_empty_intermediate_tensors = (
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self.encoder.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.embedding(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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**kwargs: object,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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# Run encoder.
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hidden_states = self.encoder(
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hidden_states=hidden_states,
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position_ids=positions,
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)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str,
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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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("linear_proj.merged_proj", "linear_proj.gate_proj", 0),
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("linear_proj.merged_proj", "linear_proj.dense_h_to_4h", 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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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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if "rotary_pos_emb.inv_freq" in name:
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continue
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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 = getattr(param, "weight_loader",
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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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class ChatGLMBaseModel(nn.Module):
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hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_substr={".word_embeddings": ""}, )
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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transformer_type: type[ChatGLMModel] = ChatGLMModel,
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) -> None:
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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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multimodal_config = vllm_config.model_config.multimodal_config
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self.config = config
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self.lora_config = lora_config
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self.multimodal_config = multimodal_config
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self.quant_config = quant_config
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self.max_position_embeddings = getattr(config, "max_sequence_length",
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8192)
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self.transformer = transformer_type(vllm_config=vllm_config,
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prefix=maybe_prefix(
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prefix, "transformer"))
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if self.config.tie_word_embeddings:
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self.transformer.output_layer.weight = (
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self.transformer.embedding.weight)
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self.lm_head = self.transformer.output_layer
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self.logits_processor = LogitsProcessor(config.padded_vocab_size)
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self.make_empty_intermediate_tensors = (
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self.transformer.make_empty_intermediate_tensors)
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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, torch.Tensor]]):
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
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class ChatGLMForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
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SupportsQuant):
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packed_modules_mapping = {
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"query_key_value": ["query_key_value"],
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"dense_h_to_4h": ["dense_h_to_4h"]
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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config = vllm_config.model_config.hf_config
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if hasattr(config, "vision_config"):
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hf_overrides = {"architectures": ["GLM4VForCausalLM"]}
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raise RuntimeError(
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"The configuration of this model indicates that it supports "
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"vision inputs, but you instantiated the text-only version "
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"of this model. Please use the vision model by setting "
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f"`--hf-overrides '{json.dumps(hf_overrides)}'`")
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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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.transformer(input_ids, positions,
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intermediate_tensors, inputs_embeds)
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return hidden_states
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