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

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Roger Wang 2024-07-23 13:47:48 -07:00 committed by GitHub
parent 507ef787d8
commit 1bedf210e3
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7 changed files with 32 additions and 177 deletions
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2
requirements-common.txt
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@ -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

51
tests/test_config.py
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@ -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(
"lmsys/longchat-13b-16k",
"lmsys/longchat-13b-16k",
tokenizer_mode="auto",
trust_remote_code=False,
dtype="float16",
seed=0,
)
assert getattr(longchat_model_config.hf_config, "rope_scaling",
None) == LONGCHAT_ROPE_SCALING
assert longchat_model_config.max_model_len == 16384
# TODO: add these back when the rope configs are fixed
# LONGCHAT_ROPE_SCALING = {"rope_type": "linear", "factor": 8.0}
# longchat_model_config = ModelConfig(
# "lmsys/longchat-13b-16k",
# "lmsys/longchat-13b-16k",
# tokenizer_mode="auto",
# trust_remote_code=False,
# dtype="float16",
# seed=0,
# )
# 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(
"lmsys/longchat-13b-16k",
"lmsys/longchat-13b-16k",
tokenizer_mode="auto",
trust_remote_code=False,
dtype="float16",
seed=0,
rope_scaling=TEST_ROPE_SCALING,
)
assert getattr(longchat_model_config.hf_config, "rope_scaling",
None) == TEST_ROPE_SCALING
assert longchat_model_config.max_model_len == 4096
# longchat_model_config = ModelConfig(
# "lmsys/longchat-13b-16k",
# "lmsys/longchat-13b-16k",
# tokenizer_mode="auto",
# trust_remote_code=False,
# dtype="float16",
# seed=0,
# rope_scaling=TEST_ROPE_SCALING,
# )
# assert getattr(longchat_model_config.hf_config, "rope_scaling",
# None) == TEST_ROPE_SCALING
# assert longchat_model_config.max_model_len == 4096

2
vllm/model_executor/models/__init__.py
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@ -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"),

3
vllm/model_executor/models/chameleon.py
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@ -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

9
vllm/transformers_utils/config.py
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@ -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,
DbrxConfig, JAISConfig,
MedusaConfig, MLPSpeculatorConfig,
MPTConfig, RWConfig)
from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
JAISConfig, MedusaConfig,
MLPSpeculatorConfig, MPTConfig,
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,

4
vllm/transformers_utils/configs/__init__.py
View File

@ -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",

138
vllm/transformers_utils/configs/chameleon.py
View File

@ -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