llama-stack/llama_toolchain/inference/inference.py
2024-07-23 08:32:33 -07:00

159 lines
5.5 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import AsyncGenerator
from llama_models.llama3_1.api.datatypes import StopReason
from .api.config import InlineImplConfig
from .api.datatypes import (
ChatCompletionResponseEvent,
ChatCompletionResponseEventType,
ToolCallDelta,
ToolCallParseStatus,
)
from .api.endpoints import (
ChatCompletionRequest,
ChatCompletionResponseStreamChunk,
CompletionRequest,
Inference,
)
from .model_parallel import LlamaModelParallelGenerator
class InferenceImpl(Inference):
def __init__(self, config: InlineImplConfig) -> None:
self.config = config
async def initialize(self) -> None:
self.generator = LlamaModelParallelGenerator(self.config)
self.generator.start()
async def shutdown(self) -> None:
self.generator.stop()
async def completion(self, request: CompletionRequest) -> AsyncGenerator:
raise NotImplementedError()
async def chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.start,
delta="",
)
)
tokens = []
logprobs = []
stop_reason = None
buffer = ""
ipython = False
for token_result in self.generator.chat_completion(
messages=request.messages,
temperature=request.sampling_params.temperature,
top_p=request.sampling_params.top_p,
max_gen_len=request.sampling_params.max_tokens,
logprobs=request.logprobs,
):
buffer += token_result.text
tokens.append(token_result.token)
if not ipython and buffer.startswith("<|python_tag|>"):
ipython = True
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.started,
),
)
)
buffer = buffer[len("<|python_tag|>") :]
continue
if not request.stream:
if request.logprobs:
logprobs.append(token_result.logprob)
continue
if token_result.text == "<|eot_id|>":
stop_reason = StopReason.end_of_turn
text = ""
elif token_result.text == "<|eom_id|>":
stop_reason = StopReason.end_of_message
text = ""
else:
text = token_result.text
if ipython:
delta = ToolCallDelta(
content=text,
parse_status=ToolCallParseStatus.in_progress,
)
else:
delta = text
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=delta,
stop_reason=stop_reason,
)
)
if stop_reason is None:
stop_reason = StopReason.out_of_tokens
# TODO(ashwin): parse tool calls separately here and report errors?
# if someone breaks the iteration before coming here we are toast
message = self.generator.formatter.decode_assistant_message(tokens, stop_reason)
if request.stream:
parsed_tool_calls = len(message.tool_calls) > 0
if ipython and not parsed_tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.failure,
),
stop_reason=stop_reason,
)
)
for tool_call in message.tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content=tool_call,
parse_status=ToolCallParseStatus.success,
),
stop_reason=stop_reason,
)
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.complete,
delta="",
stop_reason=stop_reason,
)
)
# TODO(ashwin): what else do we need to send out here when everything finishes?
else:
yield ChatCompletionResponse(
content=message.content,
tool_calls=message.tool_calls,
stop_reason=stop_reason,
logprobs=logprobs if request.logprobs else None,
)