[Minor] Remove unused config files (#3039)

vllm/model_executor/models/baichuan.py

@@ -23,6 +23,7 @@ from typing import List, Optional, Tuple
 
 import torch
 from torch import nn
+from transformers import PretrainedConfig
 
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -42,7 +43,6 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.weight_utils import (default_weight_loader,
                                               hf_model_weights_iterator)
 from vllm.sequence import SamplerOutput
-from vllm.transformers_utils.configs.baichuan import BaiChuanConfig
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -186,7 +186,7 @@ class BaiChuanAttention(nn.Module):
 class BaiChuanDecoderLayer(nn.Module):
 
     def __init__(self,
-                 config: BaiChuanConfig,
+                 config: PretrainedConfig,
                  position_embedding: str,
                  linear_method: Optional[LinearMethodBase] = None):
         super().__init__()
@@ -245,7 +245,7 @@ class BaiChuanDecoderLayer(nn.Module):
 class BaiChuanModel(nn.Module):
 
     def __init__(self,
-                 config: BaiChuanConfig,
+                 config: PretrainedConfig,
                  position_embedding: str,
                  linear_method: Optional[LinearMethodBase] = None):
         super().__init__()

vllm/model_executor/models/olmo.py

@@ -61,7 +61,9 @@ from vllm.model_executor.weight_utils import (
     hf_model_weights_iterator,
 )
 from vllm.sequence import SamplerOutput
-from vllm.transformers_utils.configs.olmo import OLMoConfig
+
+# this model must need this dependency
+from hf_olmo import OLMoConfig
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 

vllm/model_executor/models/qwen.py

@@ -8,6 +8,7 @@ from typing import Any, Dict, List, Optional, Tuple
 
 import torch
 from torch import nn
+from transformers import PretrainedConfig
 
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -27,7 +28,6 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.weight_utils import (default_weight_loader,
                                               hf_model_weights_iterator)
 from vllm.sequence import SamplerOutput
-from vllm.transformers_utils.configs.qwen import QWenConfig
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -127,7 +127,7 @@ class QWenBlock(nn.Module):
 
     def __init__(
         self,
-        config: QWenConfig,
+        config: PretrainedConfig,
         linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
@@ -179,7 +179,7 @@ class QWenModel(nn.Module):
 
     def __init__(
         self,
-        config: QWenConfig,
+        config: PretrainedConfig,
         linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
@@ -222,7 +222,7 @@ class QWenLMHeadModel(nn.Module):
 
     def __init__(
         self,
-        config: QWenConfig,
+        config: PretrainedConfig,
         linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()

vllm/transformers_utils/config.py

@@ -5,10 +5,8 @@ from transformers import AutoConfig, PretrainedConfig
 from vllm.transformers_utils.configs import *
 
 _CONFIG_REGISTRY = {
-    "baichuan": BaiChuanConfig,
     "chatglm": ChatGLMConfig,
     "mpt": MPTConfig,
-    "qwen": QWenConfig,
     "RefinedWeb": RWConfig,  # For tiiuae/falcon-40b(-instruct)
     "RefinedWebModel": RWConfig,  # For tiiuae/falcon-7b(-instruct)
 }

vllm/transformers_utils/configs/__init__.py

-from vllm.transformers_utils.configs.baichuan import BaiChuanConfig
 from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
 from vllm.transformers_utils.configs.mpt import MPTConfig
-from vllm.transformers_utils.configs.olmo import OLMoConfig
-from vllm.transformers_utils.configs.qwen import QWenConfig
 # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
 # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
 # `FalconConfig` class from the official HuggingFace transformers library.
 from vllm.transformers_utils.configs.falcon import RWConfig
 
 __all__ = [
-    "BaiChuanConfig",
     "ChatGLMConfig",
     "MPTConfig",
-    "OLMoConfig",
-    "QWenConfig",
     "RWConfig",
 ]

vllm/transformers_utils/configs/baichuan.py

-# coding=utf-8
-# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from transformers.configuration_utils import PretrainedConfig
-
-
-class BaiChuanConfig(PretrainedConfig):
-    model_type = "baichuan"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=64000,
-        hidden_size=4096,
-        intermediate_size=11008,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        hidden_act="silu",
-        max_position_embeddings=4096,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=0,
-        bos_token_id=1,
-        eos_token_id=2,
-        tie_word_embeddings=False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )

vllm/transformers_utils/configs/olmo.py

-# coding=utf-8
-# adapted from https://github.com/allenai/OLMo/blob/v0.2.4/hf_olmo/configuration_olmo.py
-"""OLMo configuration"""
-from transformers import PretrainedConfig
-
-
-class OLMoConfig(PretrainedConfig):
-    model_type = 'olmo'
-    attribute_map = {
-        'num_attention_heads': 'n_heads',
-        'hidden_size': 'd_model',
-        'num_hidden_layers': 'n_layers',
-    }
-
-    # Note that the defaults for these attributes are equivalent to the base GPT2 model.
-    def __init__(
-        self,
-        d_model=768,
-        n_heads=12,
-        n_layers=12,
-        mlp_ratio=4,
-        mlp_hidden_size=None,
-        activation_type="swiglu",
-        block_type="sequential",
-        block_group_size=1,
-        alibi=False,
-        alibi_bias_max=8.0,
-        rope=False,
-        rope_full_precision=True,
-        multi_query_attention=False,
-        attention_layer_norm=False,
-        layer_norm_type="default",
-        layer_norm_with_affine=True,
-        attention_layer_norm_with_affine=True,
-        max_sequence_length=1024,
-        include_bias=True,
-        bias_for_layer_norm=None,
-        scale_logits=False,
-        vocab_size=50257,
-        embedding_size=50304,
-        weight_tying=True,
-        eos_token_id=50256,
-        pad_token_id=50256,
-        **kwargs,
-    ):
-        self.d_model = d_model
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.mlp_ratio = mlp_ratio
-        self.mlp_hidden_size = mlp_hidden_size
-        self.activation_type = activation_type
-        self.block_type = block_type
-        self.block_group_size = block_group_size
-        self.alibi = alibi
-        self.alibi_bias_max = alibi_bias_max
-        self.rope = rope
-        self.rope_full_precision = rope_full_precision
-        self.multi_query_attention = multi_query_attention
-        self.attention_layer_norm = attention_layer_norm
-        self.layer_norm_type = layer_norm_type
-        self.layer_norm_with_affine = layer_norm_with_affine
-        self.attention_layer_norm_with_affine = attention_layer_norm_with_affine
-        self.max_sequence_length = max_sequence_length
-        self.include_bias = include_bias
-        self.bias_for_layer_norm = bias_for_layer_norm
-        self.scale_logits = scale_logits
-        self.vocab_size = vocab_size
-        self.embedding_size = embedding_size
-        self.weight_tying = weight_tying
-        self.eos_token_id = eos_token_id
-        self.pad_token_id = pad_token_id
-        super().__init__(**kwargs)

vllm/transformers_utils/configs/qwen.py

-# Copyright (c) Alibaba Cloud.
-# LICENSE: https://huggingface.co/Qwen/Qwen-7B/blob/main/LICENSE
-
-from transformers import PretrainedConfig
-
-
-class QWenConfig(PretrainedConfig):
-    model_type = "qwen"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=151936,
-        hidden_size=4096,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        emb_dropout_prob=0.0,
-        attn_dropout_prob=0.0,
-        layer_norm_epsilon=1e-6,
-        initializer_range=0.02,
-        max_position_embeddings=8192,
-        scale_attn_weights=True,
-        use_cache=True,
-        bf16=False,
-        fp16=False,
-        fp32=False,
-        kv_channels=128,
-        rotary_pct=1.0,
-        rotary_emb_base=10000,
-        use_dynamic_ntk=True,
-        use_logn_attn=True,
-        use_flash_attn="auto",
-        intermediate_size=22016,
-        no_bias=True,
-        tie_word_embeddings=False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.emb_dropout_prob = emb_dropout_prob
-        self.attn_dropout_prob = attn_dropout_prob
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-        self.scale_attn_weights = scale_attn_weights
-        self.use_cache = use_cache
-        self.max_position_embeddings = max_position_embeddings
-        self.bf16 = bf16
-        self.fp16 = fp16
-        self.fp32 = fp32
-        self.kv_channels = kv_channels
-        self.rotary_pct = rotary_pct
-        self.rotary_emb_base = rotary_emb_base
-        self.use_dynamic_ntk = use_dynamic_ntk
-        self.use_logn_attn = use_logn_attn
-        self.use_flash_attn = use_flash_attn
-        self.no_bias = no_bias
-        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)