Bump transformers version for Llama 3.1 hotfix and patch Chameleon (#6690)

requirements-common.txt

@@ -6,7 +6,7 @@ numpy < 2.0.0
 requests
 tqdm
 py-cpuinfo
-transformers >= 4.42.4  # Required for Gemma 2 and for additional chat template parameters.
+transformers >= 4.43.1  # Required for Chameleon and Llama 3.1 hotfox.
 tokenizers >= 0.19.1  # Required for Llama 3.
 fastapi
 aiohttp

tests/test_config.py

@@ -64,9 +64,8 @@ def test_get_sliding_window():
 
 
 def test_rope_customization():
-    TEST_ROPE_SCALING = {"type": "dynamic", "factor": 2.0}
+    TEST_ROPE_SCALING = {"rope_type": "dynamic", "factor": 2.0}
     TEST_ROPE_THETA = 16_000_000.0
-    LONGCHAT_ROPE_SCALING = {"type": "linear", "factor": 8.0}
 
     llama_model_config = ModelConfig(
         "meta-llama/Meta-Llama-3-8B-Instruct",
@@ -96,27 +95,29 @@ def test_rope_customization():
                    None) == TEST_ROPE_THETA
     assert llama_model_config.max_model_len == 16384
 
-    longchat_model_config = ModelConfig(
+    # TODO: add these back when the rope configs are fixed
-        "lmsys/longchat-13b-16k",
+    # LONGCHAT_ROPE_SCALING = {"rope_type": "linear", "factor": 8.0}
-        "lmsys/longchat-13b-16k",
+    # longchat_model_config = ModelConfig(
-        tokenizer_mode="auto",
+    #     "lmsys/longchat-13b-16k",
-        trust_remote_code=False,
+    #     "lmsys/longchat-13b-16k",
-        dtype="float16",
+    #     tokenizer_mode="auto",
-        seed=0,
+    #     trust_remote_code=False,
-    )
+    #     dtype="float16",
-    assert getattr(longchat_model_config.hf_config, "rope_scaling",
+    #     seed=0,
-                   None) == LONGCHAT_ROPE_SCALING
+    # )
-    assert longchat_model_config.max_model_len == 16384
+    # assert getattr(longchat_model_config.hf_config, "rope_scaling",
+    #                None) == LONGCHAT_ROPE_SCALING
+    # assert longchat_model_config.max_model_len == 16384
 
-    longchat_model_config = ModelConfig(
+    # longchat_model_config = ModelConfig(
-        "lmsys/longchat-13b-16k",
+    #     "lmsys/longchat-13b-16k",
-        "lmsys/longchat-13b-16k",
+    #     "lmsys/longchat-13b-16k",
-        tokenizer_mode="auto",
+    #     tokenizer_mode="auto",
-        trust_remote_code=False,
+    #     trust_remote_code=False,
-        dtype="float16",
+    #     dtype="float16",
-        seed=0,
+    #     seed=0,
-        rope_scaling=TEST_ROPE_SCALING,
+    #     rope_scaling=TEST_ROPE_SCALING,
-    )
+    # )
-    assert getattr(longchat_model_config.hf_config, "rope_scaling",
+    # assert getattr(longchat_model_config.hf_config, "rope_scaling",
-                   None) == TEST_ROPE_SCALING
+    #                None) == TEST_ROPE_SCALING
-    assert longchat_model_config.max_model_len == 4096
+    # assert longchat_model_config.max_model_len == 4096

vllm/model_executor/models/__init__.py

@@ -16,8 +16,6 @@ _GENERATION_MODELS = {
     "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"),  # baichuan-7b
     "BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"),  # baichuan-13b
     "BloomForCausalLM": ("bloom", "BloomForCausalLM"),
-    #TODO(ywang96): remove this when huggingface fixes the model repo
-    "ChameleonForCausalLM": ("chameleon", "ChameleonForConditionalGeneration"),
     "ChameleonForConditionalGeneration":
     ("chameleon", "ChameleonForConditionalGeneration"),
     "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),

vllm/model_executor/models/chameleon.py

@@ -6,6 +6,7 @@ import torch
 import torch.nn.functional as F
 from PIL import Image
 from torch import nn
+from transformers import ChameleonConfig, ChameleonVQVAEConfig
 
 from vllm.attention import Attention, AttentionMetadata
 from vllm.config import CacheConfig, MultiModalConfig
@@ -30,8 +31,6 @@ from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.image import (cached_get_tokenizer,
                                    repeat_and_pad_image_tokens)
 from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData
-from vllm.transformers_utils.configs import (ChameleonConfig,
-                                             ChameleonVQVAEConfig)
 from vllm.utils import print_warning_once
 
 from .interfaces import SupportsVision

vllm/transformers_utils/config.py

@@ -5,10 +5,10 @@ from transformers import GenerationConfig, PretrainedConfig
 
 from vllm.envs import VLLM_USE_MODELSCOPE
 from vllm.logger import init_logger
-from vllm.transformers_utils.configs import (ChameleonConfig, ChatGLMConfig,
+from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
-                                             DbrxConfig, JAISConfig,
+                                             JAISConfig, MedusaConfig,
-                                             MedusaConfig, MLPSpeculatorConfig,
+                                             MLPSpeculatorConfig, MPTConfig,
-                                             MPTConfig, RWConfig)
+                                             RWConfig)
 
 if VLLM_USE_MODELSCOPE:
     from modelscope import AutoConfig
@@ -18,7 +18,6 @@ else:
 logger = init_logger(__name__)
 
 _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
-    "chameleon": ChameleonConfig,
     "chatglm": ChatGLMConfig,
     "dbrx": DbrxConfig,
     "mpt": MPTConfig,

vllm/transformers_utils/configs/__init__.py

@@ -1,5 +1,3 @@
-from vllm.transformers_utils.configs.chameleon import (ChameleonConfig,
-                                                       ChameleonVQVAEConfig)
 from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
 from vllm.transformers_utils.configs.dbrx import DbrxConfig
 # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
@@ -12,8 +10,6 @@ from vllm.transformers_utils.configs.mlp_speculator import MLPSpeculatorConfig
 from vllm.transformers_utils.configs.mpt import MPTConfig
 
 __all__ = [
-    "ChameleonConfig",
-    "ChameleonVQVAEConfig",
     "ChatGLMConfig",
     "DbrxConfig",
     "MPTConfig",

vllm/transformers_utils/configs/chameleon.py

@@ -1,138 +0,0 @@
-from typing import List, Optional
-
-from transformers import PretrainedConfig
-
-
-#TODO (ywang96): Remove this file and import it from
-# transformers once the new release with Chameleon support
-# is available.
-class ChameleonConfig(PretrainedConfig):
-    model_type = "chameleon"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=65536,
-        hidden_size=4096,
-        intermediate_size=11008,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=32,
-        hidden_act="silu",
-        max_position_embeddings=4096,
-        initializer_range=0.02,
-        rms_norm_eps=1e-05,
-        use_cache=True,
-        pad_token_id=None,
-        bos_token_id=1,
-        eos_token_id=2,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        attention_bias=False,
-        attention_dropout=0.0,
-        model_parallel_size=1,
-        swin_norm=False,
-        vq_config=None,
-        vocabulary_map=None,
-        mlp_bias=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.mlp_bias = mlp_bias
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self._rope_scaling_validation()
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        self.model_parallel_size = model_parallel_size
-        self.swin_norm = swin_norm
-
-        if vq_config is None:
-            vq_config = {}
-
-        self.vq_config = ChameleonVQVAEConfig(**vq_config)
-
-        self.vocabulary_map = vocabulary_map
-
-        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,
-        )
-
-    def _rope_scaling_validation(self):
-        """
-        Validate the `rope_scaling` configuration.
-        """
-        if self.rope_scaling is None:
-            return
-
-        if not isinstance(self.rope_scaling,
-                          dict) or len(self.rope_scaling) != 2:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with with two fields, "
-                f"`type` and `factor`, got {self.rope_scaling}")
-        rope_scaling_type = self.rope_scaling.get("type", None)
-        rope_scaling_factor = self.rope_scaling.get("factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in [
-                "linear", "dynamic"
-        ]:
-            raise ValueError(
-                "`rope_scaling`'s type field must be one of ['linear', "
-                f"'dynamic'], got {rope_scaling_type}")
-        if rope_scaling_factor is None or not isinstance(
-                rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(
-                "`rope_scaling`'s factor field must be a float > 1, "
-                f"got {rope_scaling_factor}")
-
-
-class ChameleonVQVAEConfig(PretrainedConfig):
-
-    model_type = "chameleon_vqgan"
-
-    def __init__(
-        self,
-        embed_dim: int = 256,
-        num_embeddings: int = 8192,
-        double_latent: bool = False,
-        latent_channels: int = 256,
-        resolution: int = 512,
-        in_channels: int = 3,
-        base_channels: int = 128,
-        channel_multiplier: List[int] = [1, 1, 2, 2, 4],  #noqa
-        num_res_blocks: int = 2,
-        attn_resolutions: Optional[List[int]] = None,
-        dropout: float = 0.0,
-        attn_type: str = "vanilla",
-        initializer_range=0.02,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.embed_dim = embed_dim
-        self.num_embeddings = num_embeddings
-        self.double_latent = double_latent
-        self.latent_channels = latent_channels
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.base_channels = base_channels
-        self.channel_multiplier = channel_multiplier
-        self.num_res_blocks = num_res_blocks
-        self.attn_resolutions = attn_resolutions
-        self.dropout = dropout
-        self.attn_type = attn_type
-        self.initializer_range = initializer_range