File size: 6,167 Bytes
bc55b34 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
# Copyright 2024 the LlamaFactory team.
#
# 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.
import json
import os
from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Sequence, Tuple
from ..chat import ChatModel
from ..data import Role
from ..extras.constants import PEFT_METHODS
from ..extras.misc import torch_gc
from ..extras.packages import is_gradio_available
from .common import QUANTIZATION_BITS, get_save_dir
from .locales import ALERTS
if TYPE_CHECKING:
from ..chat import BaseEngine
from .manager import Manager
if is_gradio_available():
import gradio as gr
class WebChatModel(ChatModel):
def __init__(self, manager: "Manager", demo_mode: bool = False, lazy_init: bool = True) -> None:
self.manager = manager
self.demo_mode = demo_mode
self.engine: Optional["BaseEngine"] = None
if not lazy_init: # read arguments from command line
super().__init__()
if demo_mode and os.environ.get("DEMO_MODEL") and os.environ.get("DEMO_TEMPLATE"): # load demo model
model_name_or_path = os.environ.get("DEMO_MODEL")
template = os.environ.get("DEMO_TEMPLATE")
infer_backend = os.environ.get("DEMO_BACKEND", "huggingface")
super().__init__(
dict(model_name_or_path=model_name_or_path, template=template, infer_backend=infer_backend)
)
@property
def loaded(self) -> bool:
return self.engine is not None
def load_model(self, data) -> Generator[str, None, None]:
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path")
finetuning_type, checkpoint_path = get("top.finetuning_type"), get("top.checkpoint_path")
error = ""
if self.loaded:
error = ALERTS["err_exists"][lang]
elif not model_name:
error = ALERTS["err_no_model"][lang]
elif not model_path:
error = ALERTS["err_no_path"][lang]
elif self.demo_mode:
error = ALERTS["err_demo"][lang]
if error:
gr.Warning(error)
yield error
return
if get("top.quantization_bit") in QUANTIZATION_BITS:
quantization_bit = int(get("top.quantization_bit"))
else:
quantization_bit = None
yield ALERTS["info_loading"][lang]
args = dict(
model_name_or_path=model_path,
finetuning_type=finetuning_type,
quantization_bit=quantization_bit,
quantization_method=get("top.quantization_method"),
template=get("top.template"),
flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
use_unsloth=(get("top.booster") == "unsloth"),
rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
infer_backend=get("infer.infer_backend"),
infer_dtype=get("infer.infer_dtype"),
)
if checkpoint_path:
if finetuning_type in PEFT_METHODS: # list
args["adapter_name_or_path"] = ",".join(
[get_save_dir(model_name, finetuning_type, adapter) for adapter in checkpoint_path]
)
else: # str
args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, checkpoint_path)
super().__init__(args)
yield ALERTS["info_loaded"][lang]
def unload_model(self, data) -> Generator[str, None, None]:
lang = data[self.manager.get_elem_by_id("top.lang")]
if self.demo_mode:
gr.Warning(ALERTS["err_demo"][lang])
yield ALERTS["err_demo"][lang]
return
yield ALERTS["info_unloading"][lang]
self.engine = None
torch_gc()
yield ALERTS["info_unloaded"][lang]
def append(
self,
chatbot: List[List[Optional[str]]],
messages: Sequence[Dict[str, str]],
role: str,
query: str,
) -> Tuple[List[List[Optional[str]]], List[Dict[str, str]], str]:
return chatbot + [[query, None]], messages + [{"role": role, "content": query}], ""
def stream(
self,
chatbot: List[List[Optional[str]]],
messages: Sequence[Dict[str, str]],
system: str,
tools: str,
image: Optional[Any],
video: Optional[Any],
max_new_tokens: int,
top_p: float,
temperature: float,
) -> Generator[Tuple[List[List[Optional[str]]], List[Dict[str, str]]], None, None]:
chatbot[-1][1] = ""
response = ""
for new_text in self.stream_chat(
messages, system, tools, image, video, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
):
response += new_text
if tools:
result = self.engine.template.extract_tool(response)
else:
result = response
if isinstance(result, list):
tool_calls = [{"name": tool[0], "arguments": json.loads(tool[1])} for tool in result]
tool_calls = json.dumps(tool_calls, indent=4, ensure_ascii=False)
output_messages = messages + [{"role": Role.FUNCTION.value, "content": tool_calls}]
bot_text = "```json\n" + tool_calls + "\n```"
else:
output_messages = messages + [{"role": Role.ASSISTANT.value, "content": result}]
bot_text = result
chatbot[-1][1] = bot_text
yield chatbot, output_messages
|