641 lines
24 KiB
Python
641 lines
24 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
import json
|
|
from collections.abc import AsyncGenerator, Callable, Mapping
|
|
from functools import partial
|
|
from typing import Any, Final, Literal, TypeAlias, cast
|
|
|
|
import torch
|
|
from fastapi import Request
|
|
from typing_extensions import assert_never
|
|
|
|
from vllm.engine.protocol import EngineClient
|
|
from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
|
|
from vllm.entrypoints.logger import RequestLogger
|
|
from vllm.entrypoints.openai.engine.protocol import ErrorResponse, UsageInfo
|
|
from vllm.entrypoints.openai.engine.serving import OpenAIServing, ServeContext
|
|
from vllm.entrypoints.openai.models.serving import OpenAIServingModels
|
|
from vllm.entrypoints.pooling.embed.protocol import (
|
|
EmbeddingBytesResponse,
|
|
EmbeddingChatRequest,
|
|
EmbeddingCompletionRequest,
|
|
EmbeddingRequest,
|
|
EmbeddingResponse,
|
|
EmbeddingResponseData,
|
|
)
|
|
from vllm.entrypoints.pooling.utils import (
|
|
encode_pooling_bytes,
|
|
encode_pooling_output_base64,
|
|
encode_pooling_output_float,
|
|
)
|
|
from vllm.inputs.data import ProcessorInputs, TokensPrompt, token_inputs
|
|
from vllm.logger import init_logger
|
|
from vllm.outputs import PoolingOutput, PoolingRequestOutput
|
|
from vllm.pooling_params import PoolingParams
|
|
from vllm.utils.async_utils import merge_async_iterators
|
|
from vllm.utils.collection_utils import chunk_list
|
|
from vllm.utils.serial_utils import EmbedDType, Endianness
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
EmbeddingServeContext: TypeAlias = ServeContext[EmbeddingRequest]
|
|
|
|
|
|
class OpenAIServingEmbedding(OpenAIServing):
|
|
request_id_prefix = "embd"
|
|
|
|
def __init__(
|
|
self,
|
|
engine_client: EngineClient,
|
|
models: OpenAIServingModels,
|
|
*,
|
|
request_logger: RequestLogger | None,
|
|
chat_template: str | None,
|
|
chat_template_content_format: ChatTemplateContentFormatOption,
|
|
trust_request_chat_template: bool = False,
|
|
log_error_stack: bool = False,
|
|
) -> None:
|
|
super().__init__(
|
|
engine_client=engine_client,
|
|
models=models,
|
|
request_logger=request_logger,
|
|
log_error_stack=log_error_stack,
|
|
)
|
|
|
|
self.chat_template = chat_template
|
|
self.chat_template_content_format: Final = chat_template_content_format
|
|
self.trust_request_chat_template = trust_request_chat_template
|
|
|
|
pooler_config = self.model_config.pooler_config
|
|
assert pooler_config is not None
|
|
self.pooler_config = pooler_config
|
|
|
|
async def _preprocess(
|
|
self,
|
|
ctx: EmbeddingServeContext,
|
|
) -> ErrorResponse | None:
|
|
try:
|
|
ctx.lora_request = self._maybe_get_adapters(ctx.request)
|
|
|
|
if isinstance(ctx.request, EmbeddingChatRequest):
|
|
error_check_ret = self._validate_chat_template(
|
|
request_chat_template=ctx.request.chat_template,
|
|
chat_template_kwargs=ctx.request.chat_template_kwargs,
|
|
trust_request_chat_template=self.trust_request_chat_template,
|
|
)
|
|
if error_check_ret is not None:
|
|
return error_check_ret
|
|
|
|
_, ctx.engine_prompts = await self._preprocess_chat(
|
|
ctx.request,
|
|
ctx.request.messages,
|
|
default_template=self.chat_template,
|
|
default_template_content_format=self.chat_template_content_format,
|
|
default_template_kwargs=None,
|
|
)
|
|
elif isinstance(ctx.request, EmbeddingCompletionRequest):
|
|
ctx.engine_prompts = await self._preprocess_completion(
|
|
ctx.request,
|
|
prompt_input=ctx.request.input,
|
|
prompt_embeds=None,
|
|
)
|
|
else:
|
|
return self.create_error_response("Invalid classification request type")
|
|
|
|
return None
|
|
except (ValueError, TypeError) as e:
|
|
logger.exception("Error in preprocessing prompt inputs")
|
|
return self.create_error_response(str(e))
|
|
|
|
def request_output_to_embed_json_response(
|
|
self,
|
|
final_res_batch: list[PoolingRequestOutput],
|
|
request_id: str,
|
|
created_time: int,
|
|
model_name: str,
|
|
encoding_format: Literal["float", "base64"],
|
|
embed_dtype: EmbedDType,
|
|
endianness: Endianness,
|
|
) -> EmbeddingResponse:
|
|
encode_fn = cast(
|
|
Callable[[PoolingRequestOutput], list[float] | str],
|
|
(
|
|
encode_pooling_output_float
|
|
if encoding_format == "float"
|
|
else partial(
|
|
encode_pooling_output_base64,
|
|
embed_dtype=embed_dtype,
|
|
endianness=endianness,
|
|
)
|
|
),
|
|
)
|
|
|
|
items: list[EmbeddingResponseData] = []
|
|
num_prompt_tokens = 0
|
|
|
|
for idx, final_res in enumerate(final_res_batch):
|
|
item = EmbeddingResponseData(
|
|
index=idx,
|
|
embedding=encode_fn(final_res),
|
|
)
|
|
prompt_token_ids = final_res.prompt_token_ids
|
|
|
|
items.append(item)
|
|
num_prompt_tokens += len(prompt_token_ids)
|
|
|
|
usage = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
total_tokens=num_prompt_tokens,
|
|
)
|
|
|
|
return EmbeddingResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
data=items,
|
|
usage=usage,
|
|
)
|
|
|
|
def request_output_to_embed_bytes_response(
|
|
self,
|
|
final_res_batch: list[PoolingRequestOutput],
|
|
request_id: str,
|
|
created_time: int,
|
|
model_name: str,
|
|
encoding_format: Literal["bytes", "bytes_only"],
|
|
embed_dtype: EmbedDType,
|
|
endianness: Endianness,
|
|
) -> EmbeddingBytesResponse:
|
|
content, items, usage = encode_pooling_bytes(
|
|
pooling_outputs=final_res_batch,
|
|
embed_dtype=embed_dtype,
|
|
endianness=endianness,
|
|
)
|
|
|
|
headers = (
|
|
None
|
|
if encoding_format == "bytes_only"
|
|
else {
|
|
"metadata": json.dumps(
|
|
{
|
|
"id": request_id,
|
|
"created": created_time,
|
|
"model": model_name,
|
|
"data": items,
|
|
"usage": usage,
|
|
}
|
|
)
|
|
}
|
|
)
|
|
|
|
return EmbeddingBytesResponse(content=content, headers=headers)
|
|
|
|
def _build_response(
|
|
self,
|
|
ctx: EmbeddingServeContext,
|
|
) -> EmbeddingResponse | EmbeddingBytesResponse | ErrorResponse:
|
|
encoding_format = ctx.request.encoding_format
|
|
embed_dtype = ctx.request.embed_dtype
|
|
endianness = ctx.request.endianness
|
|
|
|
if encoding_format == "float" or encoding_format == "base64":
|
|
return self.request_output_to_embed_json_response(
|
|
ctx.final_res_batch,
|
|
ctx.request_id,
|
|
ctx.created_time,
|
|
ctx.model_name,
|
|
encoding_format,
|
|
embed_dtype,
|
|
endianness,
|
|
)
|
|
|
|
if encoding_format == "bytes" or encoding_format == "bytes_only":
|
|
return self.request_output_to_embed_bytes_response(
|
|
ctx.final_res_batch,
|
|
ctx.request_id,
|
|
ctx.created_time,
|
|
ctx.model_name,
|
|
encoding_format,
|
|
embed_dtype,
|
|
endianness,
|
|
)
|
|
|
|
assert_never(encoding_format)
|
|
|
|
def _get_max_position_embeddings(self) -> int:
|
|
"""Get the model's effective maximum sequence length for chunking."""
|
|
return self.model_config.max_model_len
|
|
|
|
def _should_use_chunked_processing(self, request) -> bool:
|
|
"""Check if chunked processing should be used for this request."""
|
|
return (
|
|
isinstance(request, (EmbeddingCompletionRequest, EmbeddingChatRequest))
|
|
and self.pooler_config.enable_chunked_processing
|
|
)
|
|
|
|
async def _process_chunked_request(
|
|
self,
|
|
ctx: EmbeddingServeContext,
|
|
token_ids: list[int],
|
|
pooling_params: PoolingParams,
|
|
trace_headers: Mapping[str, str] | None,
|
|
prompt_idx: int,
|
|
) -> list[AsyncGenerator[PoolingRequestOutput, None]]:
|
|
"""Process a single prompt using chunked processing."""
|
|
generators: list[AsyncGenerator[PoolingRequestOutput, None]] = []
|
|
|
|
# Split into chunks using max_position_embeddings
|
|
max_pos_embeddings = self._get_max_position_embeddings()
|
|
# Process all chunks for MEAN aggregation
|
|
for chunk_idx, chunk_tokens in enumerate(
|
|
chunk_list(token_ids, max_pos_embeddings)
|
|
):
|
|
# Create a request ID for this chunk
|
|
chunk_request_id = f"{ctx.request_id}-prompt-{prompt_idx}-chunk-{chunk_idx}"
|
|
|
|
# Create engine prompt for this chunk
|
|
chunk_engine_prompt = token_inputs(chunk_tokens)
|
|
|
|
# Log the chunk
|
|
self._log_inputs(
|
|
chunk_request_id,
|
|
chunk_engine_prompt,
|
|
params=pooling_params,
|
|
lora_request=ctx.lora_request,
|
|
)
|
|
|
|
# Create generator for this chunk and wrap it to return indices
|
|
original_generator = self.engine_client.encode(
|
|
chunk_engine_prompt,
|
|
pooling_params,
|
|
chunk_request_id,
|
|
lora_request=ctx.lora_request,
|
|
trace_headers=trace_headers,
|
|
priority=ctx.request.priority,
|
|
)
|
|
|
|
generators.append(original_generator)
|
|
|
|
return generators
|
|
|
|
def _validate_input(
|
|
self,
|
|
request: object,
|
|
input_ids: list[int],
|
|
input_text: str,
|
|
) -> TokensPrompt:
|
|
"""Override to support chunked processing for embedding requests."""
|
|
token_num = len(input_ids)
|
|
|
|
# Note: EmbeddingRequest doesn't have max_tokens
|
|
if isinstance(request, (EmbeddingCompletionRequest, EmbeddingChatRequest)):
|
|
# Check if chunked processing is enabled for pooling models
|
|
enable_chunked = self._should_use_chunked_processing(request)
|
|
|
|
# Use max_position_embeddings for chunked processing decisions
|
|
max_pos_embeddings = self._get_max_position_embeddings()
|
|
|
|
# Determine the effective max length for validation
|
|
if self.pooler_config.max_embed_len:
|
|
# Use max_embed_len for validation instead of max_model_len
|
|
length_type = "maximum embedding input length"
|
|
max_length_value = self.pooler_config.max_embed_len
|
|
else:
|
|
# Fall back to max_model_len validation (original behavior)
|
|
length_type = "maximum context length"
|
|
max_length_value = self.model_config.max_model_len
|
|
|
|
validation_error_msg = (
|
|
"This model's {length_type} is {max_length_value} tokens. "
|
|
"However, you requested {token_num} tokens in the input for "
|
|
"embedding generation. Please reduce the length of the input."
|
|
)
|
|
|
|
chunked_processing_error_msg = (
|
|
"This model's {length_type} is {max_length_value} tokens. "
|
|
"However, you requested {token_num} tokens in the input for "
|
|
"embedding generation. Please reduce the length of the input "
|
|
"or enable chunked processing."
|
|
)
|
|
|
|
# Check if input exceeds max length
|
|
if token_num > max_length_value:
|
|
raise ValueError(
|
|
validation_error_msg.format(
|
|
length_type=length_type,
|
|
max_length_value=max_length_value,
|
|
token_num=token_num,
|
|
)
|
|
)
|
|
|
|
# Check for chunked processing
|
|
# when exceeding max_position_embeddings
|
|
if token_num > max_pos_embeddings:
|
|
if enable_chunked:
|
|
# Allow long inputs when chunked processing is enabled
|
|
logger.info(
|
|
"Input length %s exceeds max_position_embeddings "
|
|
"%s, will use chunked processing",
|
|
token_num,
|
|
max_pos_embeddings,
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
chunked_processing_error_msg.format(
|
|
length_type="maximum position embeddings length",
|
|
max_length_value=max_pos_embeddings,
|
|
token_num=token_num,
|
|
)
|
|
)
|
|
|
|
return TokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
|
|
|
|
# For other request types, use the parent's implementation
|
|
return super()._validate_input(request, input_ids, input_text)
|
|
|
|
async def _create_single_prompt_generator(
|
|
self,
|
|
ctx: EmbeddingServeContext,
|
|
engine_prompt: ProcessorInputs,
|
|
pooling_params: PoolingParams,
|
|
trace_headers: Mapping[str, str] | None,
|
|
prompt_index: int,
|
|
) -> AsyncGenerator[PoolingRequestOutput, None]:
|
|
"""Create a generator for a single prompt using standard processing."""
|
|
request_id_item = f"{ctx.request_id}-{prompt_index}"
|
|
|
|
self._log_inputs(
|
|
request_id_item,
|
|
engine_prompt,
|
|
params=pooling_params,
|
|
lora_request=ctx.lora_request,
|
|
)
|
|
|
|
# Return the original generator without wrapping
|
|
return self.engine_client.encode(
|
|
engine_prompt,
|
|
pooling_params,
|
|
request_id_item,
|
|
lora_request=ctx.lora_request,
|
|
trace_headers=trace_headers,
|
|
priority=ctx.request.priority,
|
|
)
|
|
|
|
async def _prepare_generators(
|
|
self,
|
|
ctx: EmbeddingServeContext,
|
|
) -> ErrorResponse | None:
|
|
"""Override to support chunked processing."""
|
|
# Check if we should use chunked processing
|
|
use_chunked = self._should_use_chunked_processing(ctx.request)
|
|
|
|
# If no chunked processing needed, delegate to parent class
|
|
if not use_chunked:
|
|
return await super()._prepare_generators(ctx)
|
|
|
|
# Custom logic for chunked processing
|
|
generators: list[AsyncGenerator[PoolingRequestOutput, None]] = []
|
|
|
|
try:
|
|
trace_headers = (
|
|
None
|
|
if ctx.raw_request is None
|
|
else await self._get_trace_headers(ctx.raw_request.headers)
|
|
)
|
|
|
|
pooling_params = self._create_pooling_params(ctx)
|
|
if isinstance(pooling_params, ErrorResponse):
|
|
return pooling_params
|
|
|
|
if ctx.engine_prompts is None:
|
|
return self.create_error_response("Engine prompts not available")
|
|
|
|
max_pos_embeddings = self._get_max_position_embeddings()
|
|
|
|
for i, engine_prompt in enumerate(ctx.engine_prompts):
|
|
# Check if this specific prompt needs chunked processing
|
|
if "prompt_token_ids" in engine_prompt:
|
|
prompt_token_ids = engine_prompt["prompt_token_ids"] # type: ignore[typeddict-item]
|
|
|
|
if len(prompt_token_ids) > max_pos_embeddings:
|
|
# Use chunked processing for this prompt
|
|
chunk_generators = await self._process_chunked_request(
|
|
ctx,
|
|
prompt_token_ids,
|
|
pooling_params,
|
|
trace_headers,
|
|
i,
|
|
)
|
|
generators.extend(chunk_generators)
|
|
continue
|
|
|
|
# Normal processing for short prompts or non-token prompts
|
|
generator = await self._create_single_prompt_generator(
|
|
ctx, engine_prompt, pooling_params, trace_headers, i
|
|
)
|
|
generators.append(generator)
|
|
|
|
ctx.result_generator = merge_async_iterators(*generators)
|
|
|
|
return None
|
|
|
|
except Exception as e:
|
|
return self.create_error_response(e)
|
|
|
|
async def _collect_batch(
|
|
self,
|
|
ctx: EmbeddingServeContext,
|
|
) -> ErrorResponse | None:
|
|
"""Collect and aggregate batch results
|
|
with support for chunked processing.
|
|
|
|
For chunked requests, performs online aggregation to
|
|
minimize memory usage.
|
|
For regular requests, collects results normally.
|
|
"""
|
|
try:
|
|
if ctx.engine_prompts is None:
|
|
return self.create_error_response("Engine prompts not available")
|
|
|
|
# Check if we used chunked processing
|
|
use_chunked = self._should_use_chunked_processing(ctx.request)
|
|
|
|
if not use_chunked:
|
|
return await super()._collect_batch(ctx=ctx)
|
|
|
|
if ctx.result_generator is None:
|
|
return self.create_error_response("Result generator not available")
|
|
|
|
# Online aggregation for chunked requests to
|
|
# minimize memory usage
|
|
# Track aggregation state for each prompt
|
|
prompt_aggregators: dict[int, dict[str, Any]] = {}
|
|
short_prompts_results: dict[int, PoolingRequestOutput] = {}
|
|
|
|
async for result_idx, result in ctx.result_generator:
|
|
if "-chunk-" in result.request_id:
|
|
# Extract prompt_idx from chunked request_id
|
|
parts = result.request_id.split("-")
|
|
try:
|
|
prompt_idx = int(parts[parts.index("prompt") + 1])
|
|
except (ValueError, IndexError):
|
|
# Fallback: extract from result_idx if parsing fails
|
|
prompt_idx = result_idx
|
|
|
|
# Initialize aggregator for this prompt if needed
|
|
if prompt_idx not in prompt_aggregators:
|
|
prompt_aggregators[prompt_idx] = {
|
|
"weighted_sum": None,
|
|
"total_weight": 0,
|
|
"chunk_count": 0,
|
|
"request_id": result.request_id.split("-chunk-")[0],
|
|
}
|
|
|
|
aggregator = prompt_aggregators[prompt_idx]
|
|
|
|
# MEAN pooling with online weighted averaging
|
|
# Ensure result is PoolingRequestOutput
|
|
# for embedding processing
|
|
if not isinstance(result, PoolingRequestOutput):
|
|
return self.create_error_response(
|
|
f"Expected PoolingRequestOutput for "
|
|
f"chunked embedding, got "
|
|
f"{type(result).__name__}"
|
|
)
|
|
|
|
# Handle both PoolingOutput and
|
|
# EmbeddingOutput types
|
|
if hasattr(result.outputs, "data"):
|
|
# PoolingOutput case
|
|
embedding_data = result.outputs.data
|
|
elif hasattr(result.outputs, "embedding"):
|
|
# EmbeddingOutput case -
|
|
# convert embedding list to tensor
|
|
embedding_data = result.outputs.embedding
|
|
else:
|
|
return self.create_error_response(
|
|
f"Unsupported output type: {type(result.outputs).__name__}"
|
|
)
|
|
|
|
if not isinstance(embedding_data, torch.Tensor):
|
|
embedding_data = torch.tensor(
|
|
embedding_data, dtype=torch.float32
|
|
)
|
|
|
|
if result.prompt_token_ids is None:
|
|
return self.create_error_response(
|
|
"prompt_token_ids cannot be None for chunked processing"
|
|
)
|
|
weight = len(result.prompt_token_ids)
|
|
|
|
weighted_embedding = embedding_data.to(dtype=torch.float32) * weight
|
|
|
|
if aggregator["weighted_sum"] is None:
|
|
# First chunk
|
|
aggregator["weighted_sum"] = weighted_embedding
|
|
else:
|
|
# Accumulate
|
|
aggregator["weighted_sum"] += weighted_embedding
|
|
|
|
aggregator["total_weight"] += weight
|
|
aggregator["chunk_count"] += 1
|
|
else:
|
|
# Non-chunked result - extract prompt_idx from request_id
|
|
parts = result.request_id.split("-")
|
|
try:
|
|
# Last part should be prompt index
|
|
prompt_idx = int(parts[-1])
|
|
except (ValueError, IndexError):
|
|
prompt_idx = result_idx # Fallback to result_idx
|
|
|
|
short_prompts_results[prompt_idx] = result
|
|
|
|
# Finalize aggregated results
|
|
final_res_batch: list[PoolingRequestOutput] = []
|
|
num_prompts = len(ctx.engine_prompts)
|
|
|
|
for prompt_idx in range(num_prompts):
|
|
if prompt_idx in prompt_aggregators:
|
|
# Finalize MEAN aggregation for this chunked prompt
|
|
aggregator = prompt_aggregators[prompt_idx]
|
|
|
|
weighted_sum = aggregator["weighted_sum"]
|
|
total_weight = aggregator["total_weight"]
|
|
|
|
if (
|
|
weighted_sum is not None
|
|
and isinstance(weighted_sum, torch.Tensor)
|
|
and isinstance(total_weight, (int, float))
|
|
and total_weight > 0
|
|
):
|
|
# Compute final mean embedding
|
|
final_embedding = weighted_sum / total_weight
|
|
|
|
# Create a PoolingRequestOutput
|
|
# for the aggregated result
|
|
pooling_output_data = PoolingOutput(data=final_embedding)
|
|
|
|
# Get original prompt token IDs for this prompt
|
|
original_prompt = ctx.engine_prompts[prompt_idx]
|
|
if "prompt_token_ids" not in original_prompt:
|
|
return self.create_error_response(
|
|
f"Chunked prompt {prompt_idx} does not contain "
|
|
"token IDs"
|
|
)
|
|
|
|
original_token_ids = original_prompt["prompt_token_ids"] # type: ignore[typeddict-item]
|
|
|
|
pooling_request_output = PoolingRequestOutput(
|
|
request_id=aggregator["request_id"],
|
|
prompt_token_ids=original_token_ids,
|
|
outputs=pooling_output_data,
|
|
num_cached_tokens=0,
|
|
finished=True,
|
|
)
|
|
|
|
final_res_batch.append(pooling_request_output)
|
|
else:
|
|
return self.create_error_response(
|
|
f"Failed to aggregate chunks for prompt {prompt_idx}"
|
|
)
|
|
elif prompt_idx in short_prompts_results:
|
|
final_res_batch.append(short_prompts_results[prompt_idx])
|
|
else:
|
|
return self.create_error_response(
|
|
f"Result not found for prompt {prompt_idx}"
|
|
)
|
|
|
|
ctx.final_res_batch = final_res_batch
|
|
|
|
return None
|
|
|
|
except Exception as e:
|
|
return self.create_error_response(e)
|
|
|
|
async def create_embedding(
|
|
self,
|
|
request: EmbeddingRequest,
|
|
raw_request: Request | None = None,
|
|
) -> EmbeddingResponse | ErrorResponse:
|
|
"""
|
|
Embedding API similar to OpenAI's API.
|
|
|
|
See https://platform.openai.com/docs/api-reference/embeddings/create
|
|
for the API specification. This API mimics the OpenAI Embedding API.
|
|
"""
|
|
model_name = self.models.model_name()
|
|
request_id = (
|
|
f"{self.request_id_prefix}-"
|
|
f"{self._base_request_id(raw_request, request.request_id)}"
|
|
)
|
|
|
|
ctx = EmbeddingServeContext(
|
|
request=request,
|
|
raw_request=raw_request,
|
|
model_name=model_name,
|
|
request_id=request_id,
|
|
)
|
|
|
|
return await self.handle(ctx) # type: ignore[return-value]
|