File size: 6,285 Bytes
2807ff7 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | import gc
import json
import torch
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.generation.logits_process import LogitsProcessor
from typing import Union, Tuple
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(
self, input_ids: torch.LongTensor, scores: torch.FloatTensor
) -> torch.FloatTensor:
if torch.isnan(scores).any() or torch.isinf(scores).any():
scores.zero_()
scores[..., 5] = 5e4
return scores
def process_response(output: str, use_tool: bool = False) -> Union[str, dict]:
content = ""
for response in output.split("<|assistant|>"):
metadata, content = response.split("\n", maxsplit=1)
if not metadata.strip():
content = content.strip()
content = content.replace("[[训练时间]]", "2023年")
else:
if use_tool:
content = "\n".join(content.split("\n")[1:-1])
def tool_call(**kwargs):
return kwargs
parameters = eval(content)
content = {
"name": metadata.strip(),
"arguments": json.dumps(parameters, ensure_ascii=False)
}
else:
content = {
"name": metadata.strip(),
"content": content
}
return content
@torch.inference_mode()
def generate_stream_chatglm3(model: PreTrainedModel, tokenizer: PreTrainedTokenizer, params: dict):
messages = params["messages"]
tools = params["tools"]
temperature = float(params.get("temperature", 1.0))
repetition_penalty = float(params.get("repetition_penalty", 1.0))
top_p = float(params.get("top_p", 1.0))
max_new_tokens = int(params.get("max_tokens", 256))
echo = params.get("echo", True)
messages = process_chatglm_messages(messages, tools=tools)
query, role = messages[-1]["content"], messages[-1]["role"]
inputs = tokenizer.build_chat_input(query, history=messages[:-1], role=role)
inputs = inputs.to(model.device)
input_echo_len = len(inputs["input_ids"][0])
if input_echo_len >= model.config.seq_length:
print(f"Input length larger than {model.config.seq_length}")
eos_token_id = [
tokenizer.eos_token_id,
tokenizer.get_command("<|user|>"),
tokenizer.get_command("<|observation|>")
]
gen_kwargs = {
"max_new_tokens": max_new_tokens,
"do_sample": True if temperature > 1e-5 else False,
"top_p": top_p,
"repetition_penalty": repetition_penalty,
"logits_processor": [InvalidScoreLogitsProcessor()],
}
if temperature > 1e-5:
gen_kwargs["temperature"] = temperature
total_len = 0
for total_ids in model.stream_generate(**inputs, eos_token_id=eos_token_id, **gen_kwargs):
total_ids = total_ids.tolist()[0]
total_len = len(total_ids)
if echo:
output_ids = total_ids[:-1]
else:
output_ids = total_ids[input_echo_len:-1]
response = tokenizer.decode(output_ids)
if response and response[-1] != "�":
response, stop_found = apply_stopping_strings(response, ["<|observation|>"])
yield {
"text": response,
"usage": {
"prompt_tokens": input_echo_len,
"completion_tokens": total_len - input_echo_len,
"total_tokens": total_len,
},
"finish_reason": "function_call" if stop_found else None,
}
if stop_found:
break
# Only last stream result contains finish_reason, we set finish_reason as stop
ret = {
"text": response,
"usage": {
"prompt_tokens": input_echo_len,
"completion_tokens": total_len - input_echo_len,
"total_tokens": total_len,
},
"finish_reason": "stop",
}
yield ret
gc.collect()
torch.cuda.empty_cache()
def process_chatglm_messages(messages, tools=None):
_messages = messages
messages = []
msg_has_sys = False
if tools:
messages.append(
{
"role": "system",
"content": "Answer the following questions as best as you can. You have access to the following tools:",
"tools": tools
}
)
msg_has_sys = True
for m in _messages:
role, content, func_call = m.role, m.content, m.function_call
if role == "function":
messages.append(
{
"role": "observation",
"content": content
}
)
elif role == "assistant" and func_call is not None:
for response in content.split("<|assistant|>"):
metadata, sub_content = response.split("\n", maxsplit=1)
messages.append(
{
"role": role,
"metadata": metadata,
"content": sub_content.strip()
}
)
else:
if role == "system" and msg_has_sys:
msg_has_sys = False
continue
messages.append({"role": role, "content": content})
return messages
def generate_chatglm3(model: PreTrainedModel, tokenizer: PreTrainedTokenizer, params: dict):
for response in generate_stream_chatglm3(model, tokenizer, params):
pass
return response
def apply_stopping_strings(reply, stop_strings) -> Tuple[str, bool]:
stop_found = False
for string in stop_strings:
idx = reply.find(string)
if idx != -1:
reply = reply[:idx]
stop_found = True
break
if not stop_found:
# If something like "\nYo" is generated just before "\nYou: is completed, trim it
for string in stop_strings:
for j in range(len(string) - 1, 0, -1):
if reply[-j:] == string[:j]:
reply = reply[:-j]
break
else:
continue
break
return reply, stop_found
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