llama-stack-mirror/llama_stack/apis/inference/inference.py

# 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 enum import Enum

from typing import (
    Any,
    AsyncIterator,
    Dict,
    List,
    Literal,
    Optional,
    Protocol,
    runtime_checkable,
    Union,
)

from llama_models.llama3.api.datatypes import (
    BuiltinTool,
    SamplingParams,
    StopReason,
    ToolCall,
    ToolDefinition,
    ToolPromptFormat,
)

from llama_models.schema_utils import json_schema_type, register_schema, webmethod

from pydantic import BaseModel, Field, field_validator
from typing_extensions import Annotated

from llama_stack.apis.common.content_types import InterleavedContent

from llama_stack.apis.models import Model

from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol


class LogProbConfig(BaseModel):
    top_k: Optional[int] = 0


@json_schema_type
class QuantizationType(Enum):
    bf16 = "bf16"
    fp8 = "fp8"
    int4 = "int4"


@json_schema_type
class Fp8QuantizationConfig(BaseModel):
    type: Literal["fp8"] = "fp8"


@json_schema_type
class Bf16QuantizationConfig(BaseModel):
    type: Literal["bf16"] = "bf16"


@json_schema_type
class Int4QuantizationConfig(BaseModel):
    type: Literal["int4"] = "int4"
    scheme: Optional[str] = "int4_weight_int8_dynamic_activation"


QuantizationConfig = Annotated[
    Union[Bf16QuantizationConfig, Fp8QuantizationConfig, Int4QuantizationConfig],
    Field(discriminator="type"),
]


@json_schema_type
class UserMessage(BaseModel):
    role: Literal["user"] = "user"
    content: InterleavedContent
    context: Optional[InterleavedContent] = None


@json_schema_type
class SystemMessage(BaseModel):
    role: Literal["system"] = "system"
    content: InterleavedContent


@json_schema_type
class ToolResponseMessage(BaseModel):
    role: Literal["ipython"] = "ipython"
    # it was nice to re-use the ToolResponse type, but having all messages
    # have a `content` type makes things nicer too
    call_id: str
    tool_name: Union[BuiltinTool, str]
    content: InterleavedContent


@json_schema_type
class CompletionMessage(BaseModel):
    role: Literal["assistant"] = "assistant"
    content: InterleavedContent
    stop_reason: StopReason
    tool_calls: List[ToolCall] = Field(default_factory=list)


Message = register_schema(
    Annotated[
        Union[
            UserMessage,
            SystemMessage,
            ToolResponseMessage,
            CompletionMessage,
        ],
        Field(discriminator="role"),
    ],
    name="Message",
)


@json_schema_type
class ToolResponse(BaseModel):
    call_id: str
    tool_name: Union[BuiltinTool, str]
    content: InterleavedContent

    @field_validator("tool_name", mode="before")
    @classmethod
    def validate_field(cls, v):
        if isinstance(v, str):
            try:
                return BuiltinTool(v)
            except ValueError:
                return v
        return v


@json_schema_type
class ToolChoice(Enum):
    auto = "auto"
    required = "required"


@json_schema_type
class TokenLogProbs(BaseModel):
    logprobs_by_token: Dict[str, float]


@json_schema_type
class ChatCompletionResponseEventType(Enum):
    start = "start"
    complete = "complete"
    progress = "progress"


@json_schema_type
class ToolCallParseStatus(Enum):
    started = "started"
    in_progress = "in_progress"
    failure = "failure"
    success = "success"


@json_schema_type
class ToolCallDelta(BaseModel):
    content: Union[str, ToolCall]
    parse_status: ToolCallParseStatus


@json_schema_type
class ChatCompletionResponseEvent(BaseModel):
    """Chat completion response event."""

    event_type: ChatCompletionResponseEventType
    delta: Union[str, ToolCallDelta]
    logprobs: Optional[List[TokenLogProbs]] = None
    stop_reason: Optional[StopReason] = None


class ResponseFormatType(Enum):
    json_schema = "json_schema"
    grammar = "grammar"


class JsonSchemaResponseFormat(BaseModel):
    type: Literal[ResponseFormatType.json_schema.value] = (
        ResponseFormatType.json_schema.value
    )
    json_schema: Dict[str, Any]


class GrammarResponseFormat(BaseModel):
    type: Literal[ResponseFormatType.grammar.value] = ResponseFormatType.grammar.value
    bnf: Dict[str, Any]


ResponseFormat = register_schema(
    Annotated[
        Union[JsonSchemaResponseFormat, GrammarResponseFormat],
        Field(discriminator="type"),
    ],
    name="ResponseFormat",
)


@json_schema_type
class CompletionRequest(BaseModel):
    model: str
    content: InterleavedContent
    sampling_params: Optional[SamplingParams] = SamplingParams()
    response_format: Optional[ResponseFormat] = None

    stream: Optional[bool] = False
    logprobs: Optional[LogProbConfig] = None


@json_schema_type
class CompletionResponse(BaseModel):
    """Completion response."""

    content: str
    stop_reason: StopReason
    logprobs: Optional[List[TokenLogProbs]] = None


@json_schema_type
class CompletionResponseStreamChunk(BaseModel):
    """streamed completion response."""

    delta: str
    stop_reason: Optional[StopReason] = None
    logprobs: Optional[List[TokenLogProbs]] = None


@json_schema_type
class BatchCompletionRequest(BaseModel):
    model: str
    content_batch: List[InterleavedContent]
    sampling_params: Optional[SamplingParams] = SamplingParams()
    response_format: Optional[ResponseFormat] = None
    logprobs: Optional[LogProbConfig] = None


@json_schema_type
class BatchCompletionResponse(BaseModel):
    """Batch completion response."""

    batch: List[CompletionResponse]


@json_schema_type
class ChatCompletionRequest(BaseModel):
    model: str
    messages: List[Message]
    sampling_params: Optional[SamplingParams] = SamplingParams()

    # zero-shot tool definitions as input to the model
    tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
    tool_choice: Optional[ToolChoice] = Field(default=ToolChoice.auto)
    tool_prompt_format: Optional[ToolPromptFormat] = Field(
        default=ToolPromptFormat.json
    )
    response_format: Optional[ResponseFormat] = None

    stream: Optional[bool] = False
    logprobs: Optional[LogProbConfig] = None


@json_schema_type
class ChatCompletionResponseStreamChunk(BaseModel):
    """SSE-stream of these events."""

    event: ChatCompletionResponseEvent


@json_schema_type
class ChatCompletionResponse(BaseModel):
    """Chat completion response."""

    completion_message: CompletionMessage
    logprobs: Optional[List[TokenLogProbs]] = None


@json_schema_type
class BatchChatCompletionRequest(BaseModel):
    model: str
    messages_batch: List[List[Message]]
    sampling_params: Optional[SamplingParams] = SamplingParams()

    # zero-shot tool definitions as input to the model
    tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
    tool_choice: Optional[ToolChoice] = Field(default=ToolChoice.auto)
    tool_prompt_format: Optional[ToolPromptFormat] = Field(
        default=ToolPromptFormat.json
    )
    logprobs: Optional[LogProbConfig] = None


@json_schema_type
class BatchChatCompletionResponse(BaseModel):
    batch: List[ChatCompletionResponse]


@json_schema_type
class EmbeddingsResponse(BaseModel):
    embeddings: List[List[float]]


class ModelStore(Protocol):
    def get_model(self, identifier: str) -> Model: ...


@runtime_checkable
@trace_protocol
class Inference(Protocol):
    model_store: ModelStore

    @webmethod(route="/inference/completion")
    async def completion(
        self,
        model_id: str,
        content: InterleavedContent,
        sampling_params: Optional[SamplingParams] = SamplingParams(),
        response_format: Optional[ResponseFormat] = None,
        stream: Optional[bool] = False,
        logprobs: Optional[LogProbConfig] = None,
    ) -> Union[CompletionResponse, AsyncIterator[CompletionResponseStreamChunk]]: ...

    @webmethod(route="/inference/chat-completion")
    async def chat_completion(
        self,
        model_id: str,
        messages: List[Message],
        sampling_params: Optional[SamplingParams] = SamplingParams(),
        # zero-shot tool definitions as input to the model
        tools: Optional[List[ToolDefinition]] = None,
        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
        response_format: Optional[ResponseFormat] = None,
        stream: Optional[bool] = False,
        logprobs: Optional[LogProbConfig] = None,
    ) -> Union[
        ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]
    ]: ...

    @webmethod(route="/inference/embeddings")
    async def embeddings(
        self,
        model_id: str,
        contents: List[InterleavedContent],
    ) -> EmbeddingsResponse: ...