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model_adapter.py
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model_adapter.py
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"""Model adapter registration."""
import math
import os
import re
import sys
from typing import Dict, List, Optional
import warnings
if sys.version_info >= (3, 9):
from functools import cache
else:
from functools import lru_cache as cache
import psutil
import torch
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
LlamaTokenizer,
LlamaForCausalLM,
T5Tokenizer,
)
from fastchat.constants import CPU_ISA
from fastchat.conversation import Conversation, get_conv_template
from fastchat.model.compression import load_compress_model
from fastchat.model.llama_condense_monkey_patch import replace_llama_with_condense
from fastchat.model.model_chatglm import generate_stream_chatglm
from fastchat.model.model_codet5p import generate_stream_codet5p
from fastchat.model.model_falcon import generate_stream_falcon
from fastchat.model.model_yuan2 import generate_stream_yuan2
from fastchat.model.model_exllama import generate_stream_exllama
from fastchat.model.model_xfastertransformer import generate_stream_xft
from fastchat.model.model_cllm import generate_stream_cllm
from fastchat.model.monkey_patch_non_inplace import (
replace_llama_attn_with_non_inplace_operations,
)
from fastchat.modules.awq import AWQConfig, load_awq_quantized
from fastchat.modules.exllama import ExllamaConfig, load_exllama_model
from fastchat.modules.xfastertransformer import load_xft_model, XftConfig
from fastchat.modules.gptq import GptqConfig, load_gptq_quantized
from fastchat.utils import get_gpu_memory
# Check an environment variable to check if we should be sharing Peft model
# weights. When false we treat all Peft models as separate.
peft_share_base_weights = (
os.environ.get("PEFT_SHARE_BASE_WEIGHTS", "false").lower() == "true"
)
ANTHROPIC_MODEL_LIST = (
"claude-1",
"claude-2",
"claude-2.0",
"claude-2.1",
"claude-3-haiku-20240307",
"claude-3-haiku-20240307-vertex",
"claude-3-sonnet-20240229",
"claude-3-sonnet-20240229-vertex",
"claude-3-5-sonnet-20240620",
"claude-3-opus-20240229",
"claude-instant-1",
"claude-instant-1.2",
)
OPENAI_MODEL_LIST = (
"gpt-3.5-turbo",
"gpt-3.5-turbo-0301",
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-1106",
"gpt-3.5-turbo-0125",
"gpt-4",
"gpt-4-0314",
"gpt-4-0613",
"gpt-4-turbo",
"gpt-4-1106-preview",
"gpt-4-0125-preview",
"gpt-4-turbo-browsing",
"gpt-4-turbo-2024-04-09",
"gpt2-chatbot",
"im-also-a-good-gpt2-chatbot",
"im-a-good-gpt2-chatbot",
"gpt-4o-mini-2024-07-18",
"gpt-4o-2024-05-13",
"gpt-4o-2024-08-06",
"chatgpt-4o-latest-20240903",
"chatgpt-4o-latest",
"o1-preview",
"o1-mini",
)
class BaseModelAdapter:
"""The base and the default model adapter."""
use_fast_tokenizer = True
def match(self, model_path: str):
return True
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
revision = from_pretrained_kwargs.get("revision", "main")
try:
tokenizer = AutoTokenizer.from_pretrained(
model_path,
use_fast=self.use_fast_tokenizer,
revision=revision,
trust_remote_code=True,
)
except TypeError:
tokenizer = AutoTokenizer.from_pretrained(
model_path, use_fast=False, revision=revision, trust_remote_code=True
)
try:
model = AutoModelForCausalLM.from_pretrained(
model_path,
low_cpu_mem_usage=True,
trust_remote_code=True,
**from_pretrained_kwargs,
)
except NameError:
model = AutoModel.from_pretrained(
model_path,
low_cpu_mem_usage=True,
trust_remote_code=True,
**from_pretrained_kwargs,
)
return model, tokenizer
def load_compress_model(self, model_path, device, torch_dtype, revision="main"):
return load_compress_model(
model_path,
device,
torch_dtype,
use_fast=self.use_fast_tokenizer,
revision=revision,
)
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("one_shot")
# A global registry for all model adapters
# TODO (lmzheng): make it a priority queue.
model_adapters: List[BaseModelAdapter] = []
def register_model_adapter(cls):
"""Register a model adapter."""
model_adapters.append(cls())
@cache
def get_model_adapter(model_path: str) -> BaseModelAdapter:
"""Get a model adapter for a model_path."""
model_path_basename = os.path.basename(os.path.normpath(model_path))
# Try the basename of model_path at first
for adapter in model_adapters:
if adapter.match(model_path_basename) and type(adapter) != BaseModelAdapter:
return adapter
# Then try the full path
for adapter in model_adapters:
if adapter.match(model_path):
return adapter
raise ValueError(f"No valid model adapter for {model_path}")
def raise_warning_for_incompatible_cpu_offloading_configuration(
device: str, load_8bit: bool, cpu_offloading: bool
):
if cpu_offloading:
if not load_8bit:
warnings.warn(
"The cpu-offloading feature can only be used while also using 8-bit-quantization.\n"
"Use '--load-8bit' to enable 8-bit-quantization\n"
"Continuing without cpu-offloading enabled\n"
)
return False
if not "linux" in sys.platform:
warnings.warn(
"CPU-offloading is only supported on linux-systems due to the limited compatability with the bitsandbytes-package\n"
"Continuing without cpu-offloading enabled\n"
)
return False
if device != "cuda":
warnings.warn(
"CPU-offloading is only enabled when using CUDA-devices\n"
"Continuing without cpu-offloading enabled\n"
)
return False
return cpu_offloading
def load_model(
model_path: str,
device: str = "cuda",
num_gpus: int = 1,
max_gpu_memory: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
load_8bit: bool = False,
cpu_offloading: bool = False,
gptq_config: Optional[GptqConfig] = None,
awq_config: Optional[AWQConfig] = None,
exllama_config: Optional[ExllamaConfig] = None,
xft_config: Optional[XftConfig] = None,
revision: str = "main",
debug: bool = False,
):
"""Load a model from Hugging Face."""
import accelerate
# get model adapter
adapter = get_model_adapter(model_path)
# Handle device mapping
cpu_offloading = raise_warning_for_incompatible_cpu_offloading_configuration(
device, load_8bit, cpu_offloading
)
if device == "cpu":
kwargs = {"torch_dtype": torch.float32}
if CPU_ISA in ["avx512_bf16", "amx"]:
try:
import intel_extension_for_pytorch as ipex
kwargs = {"torch_dtype": torch.bfloat16}
except ImportError:
warnings.warn(
"Intel Extension for PyTorch is not installed, it can be installed to accelerate cpu inference"
)
elif device == "cuda":
kwargs = {"torch_dtype": torch.float16}
if num_gpus != 1:
kwargs["device_map"] = "auto"
if max_gpu_memory is None:
kwargs[
"device_map"
] = "sequential" # This is important for not the same VRAM sizes
available_gpu_memory = get_gpu_memory(num_gpus)
kwargs["max_memory"] = {
i: str(int(available_gpu_memory[i] * 0.85)) + "GiB"
for i in range(num_gpus)
}
else:
kwargs["max_memory"] = {i: max_gpu_memory for i in range(num_gpus)}
elif device == "mps":
kwargs = {"torch_dtype": torch.float16}
import transformers
version = tuple(int(v) for v in transformers.__version__.split("."))
if version < (4, 35, 0):
# NOTE: Recent transformers library seems to fix the mps issue, also
# it has made some changes causing compatibility issues with our
# original patch. So we only apply the patch for older versions.
# Avoid bugs in mps backend by not using in-place operations.
replace_llama_attn_with_non_inplace_operations()
elif device == "xpu":
kwargs = {"torch_dtype": torch.bfloat16}
# Try to load ipex, while it looks unused, it links into torch for xpu support
try:
import intel_extension_for_pytorch as ipex
except ImportError:
warnings.warn(
"Intel Extension for PyTorch is not installed, but is required for xpu inference."
)
elif device == "npu":
kwargs = {"torch_dtype": torch.float16}
# Try to load ipex, while it looks unused, it links into torch for xpu support
try:
import torch_npu
except ImportError:
warnings.warn("Ascend Extension for PyTorch is not installed.")
else:
raise ValueError(f"Invalid device: {device}")
if cpu_offloading:
# raises an error on incompatible platforms
from transformers import BitsAndBytesConfig
if "max_memory" in kwargs:
kwargs["max_memory"]["cpu"] = (
str(math.floor(psutil.virtual_memory().available / 2**20)) + "Mib"
)
kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_8bit_fp32_cpu_offload=cpu_offloading
)
kwargs["load_in_8bit"] = load_8bit
elif load_8bit:
if num_gpus != 1:
warnings.warn(
"8-bit quantization is not supported for multi-gpu inference."
)
else:
model, tokenizer = adapter.load_compress_model(
model_path=model_path,
device=device,
torch_dtype=kwargs["torch_dtype"],
revision=revision,
)
if debug:
print(model)
return model, tokenizer
elif awq_config and awq_config.wbits < 16:
assert (
awq_config.wbits == 4
), "Currently we only support 4-bit inference for AWQ."
model, tokenizer = load_awq_quantized(model_path, awq_config, device)
if num_gpus != 1:
device_map = accelerate.infer_auto_device_map(
model,
max_memory=kwargs["max_memory"],
no_split_module_classes=[
"OPTDecoderLayer",
"LlamaDecoderLayer",
"BloomBlock",
"MPTBlock",
"DecoderLayer",
],
)
model = accelerate.dispatch_model(
model, device_map=device_map, offload_buffers=True
)
else:
model.to(device)
return model, tokenizer
elif gptq_config and gptq_config.wbits < 16:
model, tokenizer = load_gptq_quantized(model_path, gptq_config)
if num_gpus != 1:
device_map = accelerate.infer_auto_device_map(
model,
max_memory=kwargs["max_memory"],
no_split_module_classes=["LlamaDecoderLayer"],
)
model = accelerate.dispatch_model(
model, device_map=device_map, offload_buffers=True
)
else:
model.to(device)
return model, tokenizer
elif exllama_config:
model, tokenizer = load_exllama_model(model_path, exllama_config)
return model, tokenizer
elif xft_config:
model, tokenizer = load_xft_model(model_path, xft_config)
return model, tokenizer
kwargs["revision"] = revision
if dtype is not None: # Overwrite dtype if it is provided in the arguments.
kwargs["torch_dtype"] = dtype
if os.environ.get("FASTCHAT_USE_MODELSCOPE", "False").lower() == "true":
# download model from ModelScope hub,
# lazy import so that modelscope is not required for normal use.
try:
from modelscope.hub.snapshot_download import snapshot_download
if not os.path.exists(model_path):
model_path = snapshot_download(model_id=model_path, revision=revision)
except ImportError as e:
warnings.warn(
"Use model from www.modelscope.cn need pip install modelscope"
)
raise e
# Load model
model, tokenizer = adapter.load_model(model_path, kwargs)
if (
device == "cpu"
and kwargs["torch_dtype"] is torch.bfloat16
and CPU_ISA is not None
):
model = ipex.optimize(model, dtype=kwargs["torch_dtype"])
if (device == "cuda" and num_gpus == 1 and not cpu_offloading) or device in (
"mps",
"xpu",
"npu",
):
model.to(device)
if device == "xpu":
model = torch.xpu.optimize(model, dtype=kwargs["torch_dtype"], inplace=True)
if debug:
print(model)
return model, tokenizer
def get_conversation_template(model_path: str) -> Conversation:
"""Get the default conversation template."""
adapter = get_model_adapter(model_path)
return adapter.get_default_conv_template(model_path)
def get_generate_stream_function(model: torch.nn.Module, model_path: str):
"""Get the generate_stream function for inference."""
from fastchat.serve.inference import generate_stream
model_type = str(type(model)).lower()
is_peft = "peft" in model_type
is_chatglm = "chatglm" in model_type
is_falcon = "rwforcausallm" in model_type
is_codet5p = "codet5p" in model_type
is_exllama = "exllama" in model_type
is_xft = "xft" in model_type
is_yuan = "yuan" in model_type
is_cllm = "consistency-llm" in model_path.lower()
if is_chatglm:
return generate_stream_chatglm
elif is_falcon:
return generate_stream_falcon
elif is_codet5p:
return generate_stream_codet5p
elif is_exllama:
return generate_stream_exllama
elif is_xft:
return generate_stream_xft
elif is_yuan:
return generate_stream_yuan2
elif is_cllm:
return generate_stream_cllm
elif peft_share_base_weights and is_peft:
# Return a curried stream function that loads the right adapter
# according to the model_name available in this context. This ensures
# the right weights are available.
@torch.inference_mode()
def generate_stream_peft(
model,
tokenizer,
params: Dict,
device: str,
context_len: int,
stream_interval: int = 2,
judge_sent_end: bool = False,
):
model.set_adapter(model_path)
base_model_type = str(type(model.base_model.model))
is_chatglm = "chatglm" in base_model_type
is_falcon = "rwforcausallm" in base_model_type
is_codet5p = "codet5p" in base_model_type
is_exllama = "exllama" in base_model_type
is_xft = "xft" in base_model_type
is_yuan = "yuan" in base_model_type
is_cllm = "consistency-llm" in model_path.lower()
generate_stream_function = generate_stream
if is_chatglm:
generate_stream_function = generate_stream_chatglm
elif is_falcon:
generate_stream_function = generate_stream_falcon
elif is_codet5p:
generate_stream_function = generate_stream_codet5p
elif is_exllama:
generate_stream_function = generate_stream_exllama
elif is_xft:
generate_stream_function = generate_stream_xft
elif is_yuan:
generate_stream_function = generate_stream_yuan2
elif is_cllm:
generate_stream_function = generate_stream_cllm
for x in generate_stream_function(
model,
tokenizer,
params,
device,
context_len,
stream_interval,
judge_sent_end,
):
yield x
return generate_stream_peft
else:
return generate_stream
def add_model_args(parser):
parser.add_argument(
"--model-path",
type=str,
default="lmsys/vicuna-7b-v1.5",
help="The path to the weights. This can be a local folder or a Hugging Face repo ID.",
)
parser.add_argument(
"--revision",
type=str,
default="main",
help="Hugging Face Hub model revision identifier",
)
parser.add_argument(
"--device",
type=str,
choices=["cpu", "cuda", "mps", "xpu", "npu"],
default="cuda",
help="The device type",
)
parser.add_argument(
"--gpus",
type=str,
default=None,
help="A single GPU like 1 or multiple GPUs like 0,2",
)
parser.add_argument("--num-gpus", type=int, default=1)
parser.add_argument(
"--max-gpu-memory",
type=str,
help="The maximum memory per GPU for storing model weights. Use a string like '13Gib'",
)
parser.add_argument(
"--dtype",
type=str,
choices=["float32", "float16", "bfloat16"],
help="Override the default dtype. If not set, it will use float16 on GPU and float32 on CPU.",
default=None,
)
parser.add_argument(
"--load-8bit", action="store_true", help="Use 8-bit quantization"
)
parser.add_argument(
"--cpu-offloading",
action="store_true",
help="Only when using 8-bit quantization: Offload excess weights to the CPU that don't fit on the GPU",
)
parser.add_argument(
"--gptq-ckpt",
type=str,
default=None,
help="Used for GPTQ. The path to the local GPTQ checkpoint.",
)
parser.add_argument(
"--gptq-wbits",
type=int,
default=16,
choices=[2, 3, 4, 8, 16],
help="Used for GPTQ. #bits to use for quantization",
)
parser.add_argument(
"--gptq-groupsize",
type=int,
default=-1,
help="Used for GPTQ. Groupsize to use for quantization; default uses full row.",
)
parser.add_argument(
"--gptq-act-order",
action="store_true",
help="Used for GPTQ. Whether to apply the activation order GPTQ heuristic",
)
parser.add_argument(
"--awq-ckpt",
type=str,
default=None,
help="Used for AWQ. Load quantized model. The path to the local AWQ checkpoint.",
)
parser.add_argument(
"--awq-wbits",
type=int,
default=16,
choices=[4, 16],
help="Used for AWQ. #bits to use for AWQ quantization",
)
parser.add_argument(
"--awq-groupsize",
type=int,
default=-1,
help="Used for AWQ. Groupsize to use for AWQ quantization; default uses full row.",
)
parser.add_argument(
"--enable-exllama",
action="store_true",
help="Used for exllamabv2. Enable exllamaV2 inference framework.",
)
parser.add_argument(
"--exllama-max-seq-len",
type=int,
default=4096,
help="Used for exllamabv2. Max sequence length to use for exllamav2 framework; default 4096 sequence length.",
)
parser.add_argument(
"--exllama-gpu-split",
type=str,
default=None,
help="Used for exllamabv2. Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7",
)
parser.add_argument(
"--exllama-cache-8bit",
action="store_true",
help="Used for exllamabv2. Use 8-bit cache to save VRAM.",
)
parser.add_argument(
"--enable-xft",
action="store_true",
help="Used for xFasterTransformer Enable xFasterTransformer inference framework.",
)
parser.add_argument(
"--xft-max-seq-len",
type=int,
default=4096,
help="Used for xFasterTransformer. Max sequence length to use for xFasterTransformer framework; default 4096 sequence length.",
)
parser.add_argument(
"--xft-dtype",
type=str,
choices=["fp16", "bf16", "int8", "bf16_fp16", "bf16_int8"],
help="Override the default dtype. If not set, it will use bfloat16 for first token and float16 next tokens on CPU.",
default=None,
)
def remove_parent_directory_name(model_path):
"""Remove parent directory name."""
if model_path[-1] == "/":
model_path = model_path[:-1]
return model_path.split("/")[-1]
peft_model_cache = {}
class PeftModelAdapter:
"""Loads any "peft" model and it's base model."""
def match(self, model_path: str):
"""Accepts any model path with "peft" in the name"""
if os.path.exists(os.path.join(model_path, "adapter_config.json")):
return True
return "peft" in model_path.lower()
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
"""Loads the base model then the (peft) adapter weights"""
from peft import PeftConfig, PeftModel
config = PeftConfig.from_pretrained(model_path)
base_model_path = config.base_model_name_or_path
if "peft" in base_model_path:
raise ValueError(
f"PeftModelAdapter cannot load a base model with 'peft' in the name: {config.base_model_name_or_path}"
)
# Basic proof of concept for loading peft adapters that share the base
# weights. This is pretty messy because Peft re-writes the underlying
# base model and internally stores a map of adapter layers.
# So, to make this work we:
# 1. Cache the first peft model loaded for a given base models.
# 2. Call `load_model` for any follow on Peft models.
# 3. Make sure we load the adapters by the model_path. Why? This is
# what's accessible during inference time.
# 4. In get_generate_stream_function, make sure we load the right
# adapter before doing inference. This *should* be safe when calls
# are blocked the same semaphore.
if peft_share_base_weights:
if base_model_path in peft_model_cache:
model, tokenizer = peft_model_cache[base_model_path]
# Super important: make sure we use model_path as the
# `adapter_name`.
model.load_adapter(model_path, adapter_name=model_path)
else:
base_adapter = get_model_adapter(base_model_path)
base_model, tokenizer = base_adapter.load_model(
base_model_path, from_pretrained_kwargs
)
# Super important: make sure we use model_path as the
# `adapter_name`.
model = PeftModel.from_pretrained(
base_model, model_path, adapter_name=model_path
)
peft_model_cache[base_model_path] = (model, tokenizer)
return model, tokenizer
# In the normal case, load up the base model weights again.
base_adapter = get_model_adapter(base_model_path)
base_model, tokenizer = base_adapter.load_model(
base_model_path, from_pretrained_kwargs
)
model = PeftModel.from_pretrained(base_model, model_path)
return model, tokenizer
def get_default_conv_template(self, model_path: str) -> Conversation:
"""Uses the conv template of the base model"""
from peft import PeftConfig, PeftModel
config = PeftConfig.from_pretrained(model_path)
if "peft" in config.base_model_name_or_path:
raise ValueError(
f"PeftModelAdapter cannot load a base model with 'peft' in the name: {config.base_model_name_or_path}"
)
base_model_path = config.base_model_name_or_path
base_adapter = get_model_adapter(base_model_path)
return base_adapter.get_default_conv_template(config.base_model_name_or_path)
class VicunaAdapter(BaseModelAdapter):
"Model adapter for Vicuna models (e.g., lmsys/vicuna-7b-v1.5)" ""
use_fast_tokenizer = False
def match(self, model_path: str):
return "vicuna" in model_path.lower()
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
revision = from_pretrained_kwargs.get("revision", "main")
tokenizer = AutoTokenizer.from_pretrained(
model_path, use_fast=self.use_fast_tokenizer, revision=revision
)
model = AutoModelForCausalLM.from_pretrained(
model_path,
low_cpu_mem_usage=True,
**from_pretrained_kwargs,
)
self.raise_warning_for_old_weights(model)
return model, tokenizer
def get_default_conv_template(self, model_path: str) -> Conversation:
if "v0" in remove_parent_directory_name(model_path):
return get_conv_template("one_shot")
return get_conv_template("vicuna_v1.1")
def raise_warning_for_old_weights(self, model):
if isinstance(model, LlamaForCausalLM) and model.model.vocab_size > 32000:
warnings.warn(
"\nYou are probably using the old Vicuna-v0 model, "
"which will generate unexpected results with the "
"current fastchat.\nYou can try one of the following methods:\n"
"1. Upgrade your weights to the new Vicuna-v1.3: https://github.com/lm-sys/FastChat#vicuna-weights.\n"
"2. Use the old conversation template by `python3 -m fastchat.serve.cli --model-path /path/to/vicuna-v0 --conv-template one_shot`\n"
"3. Downgrade fschat to fschat==0.1.10 (Not recommended).\n"
)
class AiroborosAdapter(BaseModelAdapter):
"""The model adapter for jondurbin/airoboros-*"""
def match(self, model_path: str):
if re.search(r"airoboros|spicyboros", model_path, re.I):
return True
return False
def get_default_conv_template(self, model_path: str) -> Conversation:
if "-3." in model_path or "-3p" in model_path:
return get_conv_template("airoboros_v3")
if "spicyboros" in model_path or re.search(r"-(2\.[2-9]+)", model_path):
return get_conv_template("airoboros_v2")
return get_conv_template("airoboros_v1")
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
if "mpt" not in model_path.lower():
return super().load_model(model_path, from_pretrained_kwargs)
model = AutoModelForCausalLM.from_pretrained(
model_path,
low_cpu_mem_usage=True,
trust_remote_code=True,
max_seq_len=8192,
**from_pretrained_kwargs,
)
tokenizer = AutoTokenizer.from_pretrained(
model_path, trust_remote_code=True, use_fast=True
)
return model, tokenizer
class LongChatAdapter(BaseModelAdapter):
"Model adapter for LongChat models (e.g., lmsys/longchat-7b-16k)."
use_fast_tokenizer = False
def match(self, model_path: str):
return "longchat" in model_path.lower()
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
revision = from_pretrained_kwargs.get("revision", "main")
# Apply monkey patch, TODO(Dacheng): Add flash attention support
config = AutoConfig.from_pretrained(model_path, revision=revision)
replace_llama_with_condense(config.rope_scaling["factor"])
tokenizer = AutoTokenizer.from_pretrained(
model_path, use_fast=self.use_fast_tokenizer, revision=revision
)
model = AutoModelForCausalLM.from_pretrained(
model_path,
low_cpu_mem_usage=True,
**from_pretrained_kwargs,
)
return model, tokenizer
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("vicuna_v1.1")
class GoogleT5Adapter(BaseModelAdapter):
"""The model adapter for google/Flan based models, such as Salesforce/codet5p-6b, lmsys/fastchat-t5-3b-v1.0, flan-t5-*, flan-ul2"""
def match(self, model_path: str):
return any(
model_str in model_path.lower()
for model_str in ["flan-", "fastchat-t5", "codet5p"]
)
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
revision = from_pretrained_kwargs.get("revision", "main")
tokenizer = T5Tokenizer.from_pretrained(model_path, revision=revision)
model = AutoModelForSeq2SeqLM.from_pretrained(
model_path,
low_cpu_mem_usage=True,
trust_remote_code=True,
**from_pretrained_kwargs,
)
return model, tokenizer
class KoalaAdapter(BaseModelAdapter):
"""The model adapter for Koala"""
use_fast_tokenizer = False
def match(self, model_path: str):
return "koala" in model_path.lower()
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("koala_v1")
class AlpacaAdapter(BaseModelAdapter):
"""The model adapter for Alpaca"""
use_fast_tokenizer = False
def match(self, model_path: str):
return "alpaca" in model_path.lower()
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("alpaca")
class ChatGLMAdapter(BaseModelAdapter):
"""The model adapter for THUDM/chatglm-6b, THUDM/chatglm2-6b"""
def match(self, model_path: str):
return "chatglm" in model_path.lower()
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
revision = from_pretrained_kwargs.get("revision", "main")
if "chatglm3" in model_path.lower():
tokenizer = AutoTokenizer.from_pretrained(
model_path,
encode_special_tokens=True,
trust_remote_code=True,
revision=revision,
)
else:
tokenizer = AutoTokenizer.from_pretrained(
model_path, trust_remote_code=True, revision=revision
)
model = AutoModel.from_pretrained(
model_path, trust_remote_code=True, **from_pretrained_kwargs
)
return model, tokenizer
def get_default_conv_template(self, model_path: str) -> Conversation:
model_path = model_path.lower()
if "chatglm2" in model_path.lower():
return get_conv_template("chatglm2")
if "chatglm3" in model_path.lower():
return get_conv_template("chatglm3")
return get_conv_template("chatglm")
class CodeGeexAdapter(BaseModelAdapter):
"""The model adapter for THUDM/codegeex-6b, THUDM/codegeex2-6b"""
def match(self, model_path: str):
return "codegeex" in model_path.lower()
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
revision = from_pretrained_kwargs.get("revision", "main")
tokenizer = AutoTokenizer.from_pretrained(
model_path, trust_remote_code=True, revision=revision
)
model = AutoModel.from_pretrained(
model_path, trust_remote_code=True, **from_pretrained_kwargs
)
return model, tokenizer
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("codegeex")
class DollyV2Adapter(BaseModelAdapter):
"""The model adapter for databricks/dolly-v2-12b"""
def match(self, model_path: str):
return "dolly-v2" in model_path.lower()
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
revision = from_pretrained_kwargs.get("revision", "main")
tokenizer = AutoTokenizer.from_pretrained(model_path, revision=revision)
model = AutoModelForCausalLM.from_pretrained(
model_path,
low_cpu_mem_usage=True,
**from_pretrained_kwargs,
)
# 50277 means "### End"
tokenizer.eos_token_id = 50277
model.config.eos_token_id = tokenizer.eos_token_id
model.config.pad_token_id = tokenizer.pad_token_id
return model, tokenizer
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("dolly_v2")
class OasstPythiaAdapter(BaseModelAdapter):
"""The model adapter for OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"""
def match(self, model_path: str):
model_path = model_path.lower()
return "oasst" in model_path and "pythia" in model_path
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("oasst_pythia")
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
model, tokenizer = super().load_model(model_path, from_pretrained_kwargs)
model.config.eos_token_id = tokenizer.eos_token_id
model.config.pad_token_id = tokenizer.pad_token_id
return model, tokenizer
class OasstLLaMAAdapter(BaseModelAdapter):
"""The model adapter for OpenAssistant/oasst-sft-7-llama-30b"""
use_fast_tokenizer = False
def match(self, model_path: str):
model_path = model_path.lower()
if "openassistant-sft-7-llama-30b-hf" in model_path:
return True
return "oasst" in model_path and "pythia" not in model_path
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("oasst_llama")
class OpenChat35Adapter(BaseModelAdapter):
"""The model adapter for OpenChat 3.5 (e.g. openchat/openchat_3.5)"""
def match(self, model_path: str):
if "openchat" in model_path.lower() and "3.5" in model_path.lower():
return True
elif "starling-lm" in model_path.lower():
return True
return False
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("openchat_3.5")
class TenyxChatAdapter(BaseModelAdapter):
"""The model adapter for TenyxChat (e.g. tenyx/TenyxChat-7B-v1)"""
def match(self, model_path: str):
return "tenyxchat" in model_path.lower()
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("tenyxchat")
class PythiaAdapter(BaseModelAdapter):
"""The model adapter for any EleutherAI/pythia model"""
def match(self, model_path: str):
return "pythia" in model_path.lower()
def load_model(self, model_path: str, from_pretrained_kwargs: dict):
model, tokenizer = super().load_model(model_path, from_pretrained_kwargs)
model.config.eos_token_id = tokenizer.eos_token_id
model.config.pad_token_id = tokenizer.pad_token_id
return model, tokenizer
class StableLMAdapter(BaseModelAdapter):
"""The model adapter for StabilityAI/stablelm-tuned-alpha-7b"""
def match(self, model_path: str):
return "stablelm" in model_path.lower()
def get_default_conv_template(self, model_path: str) -> Conversation:
return get_conv_template("stablelm")