b4752c1e62
Metadata is generated via a template's module. This requires a single iteration through the template. If a template tries to access a passed variable that doesn't exist, it will error. Therefore, generate the metadata at runtime to prevent these errors from happening. To optimize further, cache the metadata after the first generation to prevent the expensive call of making a template module. Signed-off-by: kingbri <bdashore3@proton.me>
473 lines
16 KiB
Python
473 lines
16 KiB
Python
"""Chat completion utilities for OAI server."""
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import asyncio
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import pathlib
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from asyncio import CancelledError
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from copy import deepcopy
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from typing import List, Optional
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import json
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from fastapi import HTTPException, Request
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from jinja2 import TemplateError
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from loguru import logger
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from common import model
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from common.networking import (
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get_generator_error,
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handle_request_disconnect,
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handle_request_error,
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request_disconnect_loop,
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)
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from common.utils import unwrap
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from endpoints.OAI.types.chat_completion import (
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ChatCompletionLogprobs,
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ChatCompletionLogprob,
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ChatCompletionMessage,
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ChatCompletionRequest,
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ChatCompletionRespChoice,
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ChatCompletionStreamChunk,
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ChatCompletionResponse,
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ChatCompletionStreamChoice,
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)
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from endpoints.OAI.types.common import UsageStats
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from endpoints.OAI.utils.completion import _stream_collector
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from endpoints.OAI.types.tools import ToolCall
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def _create_response(
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request_id: str, generations: List[dict], model_name: Optional[str]
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):
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"""Create a chat completion response from the provided text."""
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prompt_tokens = unwrap(generations[-1].get("prompt_tokens"), 0)
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completion_tokens = unwrap(generations[-1].get("generated_tokens"), 0)
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choices = []
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for index, generation in enumerate(generations):
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message = ChatCompletionMessage(
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role="assistant", content=unwrap(generation.get("text"), "")
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)
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tool_calls = generation["tool_calls"]
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if tool_calls:
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message.tool_calls = postprocess_tool_call(tool_calls)
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logprob_response = None
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token_probs = unwrap(generation.get("token_probs"), {})
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if token_probs:
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logprobs = unwrap(generation.get("logprobs"), [])
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collected_token_probs = []
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for index, token in enumerate(token_probs.keys()):
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top_logprobs = [
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ChatCompletionLogprob(token=token, logprob=logprob)
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for token, logprob in logprobs[index].items()
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]
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collected_token_probs.append(
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ChatCompletionLogprob(
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token=token,
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logprob=token_probs[token],
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top_logprobs=top_logprobs,
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)
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)
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logprob_response = ChatCompletionLogprobs(content=collected_token_probs)
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choice = ChatCompletionRespChoice(
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index=index,
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finish_reason=generation.get("finish_reason"),
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stop_str=generation.get("stop_str"),
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message=message,
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logprobs=logprob_response,
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)
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choices.append(choice)
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response = ChatCompletionResponse(
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id=f"chatcmpl-{request_id}",
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choices=choices,
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model=unwrap(model_name, ""),
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usage=UsageStats(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens,
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),
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)
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return response
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def _create_stream_chunk(
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request_id: str,
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generation: Optional[dict] = None,
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model_name: Optional[str] = None,
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is_usage_chunk: bool = False,
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):
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"""Create a chat completion stream chunk from the provided text."""
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index = generation.get("index")
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choices = []
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usage_stats = None
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if is_usage_chunk:
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prompt_tokens = unwrap(generation.get("prompt_tokens"), 0)
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completion_tokens = unwrap(generation.get("generated_tokens"), 0)
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usage_stats = UsageStats(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens,
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)
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elif "finish_reason" in generation:
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choice = ChatCompletionStreamChoice(
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index=index,
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finish_reason=generation.get("finish_reason"),
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)
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# lets check if we have tool calls since we are at the end of the generation
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if "tool_calls" in generation:
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tool_calls = generation["tool_calls"]
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message = ChatCompletionMessage(
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tool_calls=postprocess_tool_call(tool_calls)
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)
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choice.delta = message
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choices.append(choice)
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else:
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message = ChatCompletionMessage(
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role="assistant", content=unwrap(generation.get("text"), "")
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)
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logprob_response = None
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token_probs = unwrap(generation.get("token_probs"), {})
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if token_probs:
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logprobs = unwrap(generation.get("logprobs"), {})
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top_logprobs = [
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ChatCompletionLogprob(token=token, logprob=logprob)
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for token, logprob in logprobs.items()
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]
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generated_token = next(iter(token_probs))
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token_prob_response = ChatCompletionLogprob(
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token=generated_token,
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logprob=token_probs[generated_token],
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top_logprobs=top_logprobs,
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)
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logprob_response = ChatCompletionLogprobs(content=[token_prob_response])
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choice = ChatCompletionStreamChoice(
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index=index,
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delta=message,
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logprobs=logprob_response,
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)
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choices.append(choice)
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chunk = ChatCompletionStreamChunk(
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id=f"chatcmpl-{request_id}",
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choices=choices,
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model=unwrap(model_name, ""),
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usage=usage_stats,
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)
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return chunk
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def _append_template_metadata(data: ChatCompletionRequest):
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"""Adding metadata is a one-time process."""
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template_metadata = model.container.prompt_template.extract_metadata(
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data.template_vars
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)
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# Stop strings
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if isinstance(data.stop, str):
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data.stop = [data.stop] + template_metadata.stop_strings
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else:
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data.stop += template_metadata.stop_strings
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# Tool call start strings
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if template_metadata.tool_starts:
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if data.tool_call_start is None:
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data.tool_call_start = template_metadata.tool_starts
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# Append to stop strings to halt for a tool call generation
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data.stop.extend(template_metadata.tool_starts)
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def format_prompt_with_template(
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data: ChatCompletionRequest, tool_precursor: Optional[str] = None
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):
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"""
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Compile the prompt and get any additional stop strings from the template.
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Template stop strings can be overriden by sampler overrides if force is true.
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"""
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try:
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special_tokens_dict = model.container.get_special_tokens(
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unwrap(data.add_bos_token, True),
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unwrap(data.ban_eos_token, False),
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)
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# Deal with list in messages.content
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# Just replace the content list with the very first text message
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for message in data.messages:
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if message["role"] == "user" and isinstance(message["content"], list):
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message["content"] = next(
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(
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content["text"]
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for content in message["content"]
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if content["type"] == "text"
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),
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"",
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)
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if "tool_calls" in message:
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message["tool_calls_json"] = json.dumps(message["tool_calls"], indent=2)
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# Overwrite any protected vars with their values
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data.template_vars.update(
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{
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"messages": data.messages,
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"add_generation_prompt": data.add_generation_prompt,
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"tools_json": json.dumps(data.model_dump()["tools"], indent=2),
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"functions_json": json.dumps(data.functions, indent=2),
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"tool_precursor": tool_precursor,
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**special_tokens_dict,
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}
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)
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prompt = model.container.prompt_template.render(data.template_vars)
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# Append response prefix if present
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if data.response_prefix:
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if data.add_generation_prompt:
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prompt += data.response_prefix
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else:
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logger.warning(
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"Could not add response prefix because "
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"add_generation_prompt is False"
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)
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# Removes the starting BOS token if present
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# This is to prevent add_bos_token from adding multiple bos tokens
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bos_token = special_tokens_dict.get("bos_token")
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if bos_token and prompt.startswith(bos_token):
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prompt = prompt.removeprefix(bos_token)
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# Add template metadata
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_append_template_metadata(data)
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return prompt
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except KeyError as exc:
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error_message = handle_request_error(
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"Could not find a Conversation from prompt template "
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f"'{model.container.prompt_template.name}'. "
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"Check your spelling?",
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).error.message
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raise HTTPException(400, error_message) from exc
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except TemplateError as exc:
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error_message = handle_request_error(f"TemplateError: {str(exc)}").error.message
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raise HTTPException(400, error_message) from exc
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async def stream_generate_chat_completion(
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prompt: str, data: ChatCompletionRequest, request: Request, model_path: pathlib.Path
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):
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"""Generator for the generation process."""
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abort_event = asyncio.Event()
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gen_queue = asyncio.Queue()
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gen_tasks: List[asyncio.Task] = []
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disconnect_task = asyncio.create_task(request_disconnect_loop(request))
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try:
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logger.info(f"Received chat completion streaming request {request.state.id}")
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gen_params = data.to_gen_params()
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for n in range(0, data.n):
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if n > 0:
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task_gen_params = deepcopy(gen_params)
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else:
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task_gen_params = gen_params
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gen_task = asyncio.create_task(
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_stream_collector(
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n,
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gen_queue,
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prompt,
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request.state.id,
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abort_event,
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**task_gen_params,
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)
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)
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gen_tasks.append(gen_task)
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# We need to keep track of the text generated so we can resume the tool calls
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current_generation_text = ""
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# Consumer loop
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while True:
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if disconnect_task.done():
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abort_event.set()
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handle_request_disconnect(
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f"Chat completion generation {request.state.id} cancelled by user."
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)
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generation = await gen_queue.get()
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# lets only append the text if we need it for tool calls later
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if data.tool_call_start and "text" in generation:
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current_generation_text += generation["text"]
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# check if we are running a tool model, and that we are at stop
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if data.tool_call_start and "stop_str" in generation:
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generations = await generate_tool_calls(
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data,
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[generation],
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request,
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current_generations=current_generation_text,
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)
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generation = generations[0] # We only have one generation in this case
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# Stream collector will push an exception to the queue if it fails
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if isinstance(generation, Exception):
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raise generation
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response = _create_stream_chunk(
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request.state.id, generation, model_path.name
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)
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yield response.model_dump_json()
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# Check if all tasks are completed
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if all(task.done() for task in gen_tasks) and gen_queue.empty():
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# Send a usage chunk
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if data.stream_options and data.stream_options.include_usage:
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usage_chunk = _create_stream_chunk(
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request.state.id,
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generation,
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model_path.name,
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is_usage_chunk=True,
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)
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yield usage_chunk.model_dump_json()
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logger.info(
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f"Finished chat completion streaming request {request.state.id}"
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)
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yield "[DONE]"
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break
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except CancelledError:
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# Get out if the request gets disconnected
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if not disconnect_task.done():
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abort_event.set()
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handle_request_disconnect("Chat completion generation cancelled by user.")
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except Exception:
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yield get_generator_error(
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"Chat completion aborted. Please check the server console."
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)
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async def generate_chat_completion(
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prompt: str, data: ChatCompletionRequest, request: Request, model_path: pathlib.Path
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):
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gen_tasks: List[asyncio.Task] = []
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gen_params = data.to_gen_params()
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try:
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for n in range(0, data.n):
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# Deepcopy gen params above the first index
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# to ensure nested structures aren't shared
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if n > 0:
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task_gen_params = deepcopy(gen_params)
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else:
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task_gen_params = gen_params
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gen_tasks.append(
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asyncio.create_task(
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model.container.generate(
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prompt, request.state.id, **task_gen_params
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)
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)
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)
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generations = await asyncio.gather(*gen_tasks)
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# Let's not waste our time if we arn't running a tool model
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if data.tool_call_start:
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generations = await generate_tool_calls(data, generations, request)
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response = _create_response(request.state.id, generations, model_path.name)
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logger.info(f"Finished chat completion request {request.state.id}")
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return response
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except Exception as exc:
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error_message = handle_request_error(
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f"Chat completion {request.state.id} aborted. "
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"Maybe the model was unloaded? "
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"Please check the server console."
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).error.message
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# Server error if there's a generation exception
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raise HTTPException(503, error_message) from exc
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async def generate_tool_calls(
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data: ChatCompletionRequest,
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generations: List[str],
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request: Request,
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current_generations: str = None,
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):
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gen_tasks: List[asyncio.Task] = []
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tool_idx: List[int] = []
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# Copy to make sure the parent JSON schema doesn't get modified
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# FIXME: May not be necessary depending on how the codebase evolves
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tool_data = deepcopy(data)
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tool_data.json_schema = tool_data.tool_call_schema
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gen_params = tool_data.to_gen_params()
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for idx, gen in enumerate(generations):
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if gen["stop_str"] in tool_data.tool_call_start:
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if "text" in gen:
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# non streaming, all generations will have the text they generated
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pre_tool_prompt = format_prompt_with_template(data, gen["text"])
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elif current_generations is not None:
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# streaming, we wont have text in the generation,
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# we'll have to use the current_generations
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pre_tool_prompt = format_prompt_with_template(data, current_generations)
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gen_tasks.append(
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asyncio.create_task(
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model.container.generate(
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pre_tool_prompt, request.state.id, **gen_params
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)
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)
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)
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tool_idx.append(idx)
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tool_calls = await asyncio.gather(*gen_tasks)
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for outer_idx in range(0, len(tool_idx)):
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gen_idx = tool_idx[outer_idx]
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generations[gen_idx]["tool_calls"] = tool_calls[outer_idx]["text"]
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return generations
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def postprocess_tool_call(call_str: str) -> List[ToolCall]:
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tool_calls = json.loads(call_str)
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for tool_call in tool_calls:
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tool_call["function"]["arguments"] = json.dumps(
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tool_call["function"]["arguments"]
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)
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return [ToolCall(**tool_call) for tool_call in tool_calls]
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