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122 lines
3.5 KiB
Python
122 lines
3.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from .datatypes import * # noqa: F403
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from typing import Optional, Protocol
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# this dependency is annoying and we need a forked up version anyway
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from llama_models.schema_utils import webmethod
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@json_schema_type
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class CompletionRequest(BaseModel):
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model: str
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content: InterleavedTextAttachment
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sampling_params: Optional[SamplingParams] = SamplingParams()
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stream: Optional[bool] = False
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logprobs: Optional[LogProbConfig] = None
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quantization_config: Optional[QuantizationConfig] = None
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@json_schema_type
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class CompletionResponse(BaseModel):
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completion_message: CompletionMessage
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logprobs: Optional[List[TokenLogProbs]] = None
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@json_schema_type
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class CompletionResponseStreamChunk(BaseModel):
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"""streamed completion response."""
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delta: str
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stop_reason: Optional[StopReason] = None
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logprobs: Optional[List[TokenLogProbs]] = None
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@json_schema_type
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class BatchCompletionRequest(BaseModel):
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model: str
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content_batch: List[InterleavedTextAttachment]
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sampling_params: Optional[SamplingParams] = SamplingParams()
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logprobs: Optional[LogProbConfig] = None
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quantization_config: Optional[QuantizationConfig] = None
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@json_schema_type
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class BatchCompletionResponse(BaseModel):
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completion_message_batch: List[CompletionMessage]
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@json_schema_type
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class ChatCompletionRequest(BaseModel):
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model: str
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messages: List[Message]
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sampling_params: Optional[SamplingParams] = SamplingParams()
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# zero-shot tool definitions as input to the model
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available_tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
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stream: Optional[bool] = False
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logprobs: Optional[LogProbConfig] = None
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quantization_config: Optional[QuantizationConfig] = None
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@json_schema_type
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class ChatCompletionResponseStreamChunk(BaseModel):
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"""SSE-stream of these events."""
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event: ChatCompletionResponseEvent
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@json_schema_type
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class ChatCompletionResponse(BaseModel):
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completion_message: CompletionMessage
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logprobs: Optional[List[TokenLogProbs]] = None
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@json_schema_type
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class BatchChatCompletionRequest(BaseModel):
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model: str
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messages_batch: List[List[Message]]
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sampling_params: Optional[SamplingParams] = SamplingParams()
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# zero-shot tool definitions as input to the model
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available_tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
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logprobs: Optional[LogProbConfig] = None
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quantization_config: Optional[QuantizationConfig] = None
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@json_schema_type
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class BatchChatCompletionResponse(BaseModel):
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completion_message_batch: List[CompletionMessage]
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class Inference(Protocol):
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@webmethod(route="/inference/completion")
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async def completion(
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self,
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request: CompletionRequest,
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) -> Union[CompletionResponse, CompletionResponseStreamChunk]: ...
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@webmethod(route="/inference/chat_completion")
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async def chat_completion(
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self,
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request: ChatCompletionRequest,
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) -> Union[ChatCompletionResponse, ChatCompletionResponseStreamChunk]: ...
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@webmethod(route="/inference/batch_completion")
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async def batch_completion(
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self,
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request: BatchCompletionRequest,
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) -> BatchCompletionResponse: ...
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@webmethod(route="/inference/batch_chat_completion")
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async def batch_chat_completion(
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self,
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request: BatchChatCompletionRequest,
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) -> BatchChatCompletionResponse: ...
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