mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-09-26 20:41:53 +08:00
c86945ef49
* support pool * update pooling * add pooler_config and check * update * support AutoWeightsLoader load weight * fix * update * delete print * update pre-commit * fix * fix xpu * fix ModelRegistry->model_registry * fix Copilot review * fix pooler.py * delete StepPooler * fix abstract * fix default_loader_v1 * fix Pre Commit * support torch qwen3 dense * add test and fix torch-qwen * fix * fix * adapter ci: * fix review * fix pooling_params.py * fix * fix tasks.py 2025 * fix print and logger * Modefy ModelRegistry and delete AutoWeightsLoader * fix logger * fix test_embedding * fix ci bug * ernie4_5 model_registry * fix test * support Qwen3-Embedding-0.6B tp=1 load * fix extra code * fix * delete fix vocab_size * delete prepare_params_dict * fix:
551 lines
18 KiB
Python
551 lines
18 KiB
Python
"""
|
|
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License"
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""
|
|
|
|
from abc import ABC, abstractmethod
|
|
from collections.abc import Mapping, Set
|
|
from dataclasses import dataclass
|
|
from enum import IntEnum
|
|
from itertools import groupby
|
|
from typing import Callable, Optional, TypeVar, Union, cast
|
|
|
|
import paddle
|
|
import paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
|
|
from fastdeploy.config import FDConfig, ModelConfig, PoolerConfig
|
|
from fastdeploy.engine.tasks import PoolingTask
|
|
from fastdeploy.model_executor.layers.pool.metadata import (
|
|
PoolingCursor,
|
|
PoolingMetadata,
|
|
PoolingParams,
|
|
)
|
|
from fastdeploy.model_executor.models.adapters import _load_st_projector
|
|
from fastdeploy.output.pooler import PoolerOutput, PoolingSequenceGroupOutput
|
|
from fastdeploy.utils import get_logger
|
|
|
|
logger = get_logger("pooler", "pooler.log")
|
|
|
|
PoolingFn = Callable[
|
|
[Union[paddle.Tensor, list[paddle.Tensor]], PoolingMetadata], Union[paddle.Tensor, list[paddle.Tensor]]
|
|
]
|
|
ClassifierFn = Callable[[paddle.Tensor], paddle.Tensor]
|
|
|
|
|
|
class PoolingType(IntEnum):
|
|
"""Enumeration for different types of pooling methods."""
|
|
|
|
LAST = 0
|
|
ALL = 1
|
|
CLS = 2
|
|
STEP = 3
|
|
MEAN = 4
|
|
|
|
|
|
_T = TypeVar("_T", paddle.Tensor, list[paddle.Tensor])
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class ResolvedPoolingConfig:
|
|
pooling_type: PoolingType
|
|
task: PoolingTask
|
|
|
|
@classmethod
|
|
def from_config(
|
|
cls,
|
|
task: PoolingTask,
|
|
pooler_config: PoolerConfig,
|
|
) -> "ResolvedPoolingConfig":
|
|
assert pooler_config.pooling_type is not None
|
|
return cls(task=task, pooling_type=PoolingType[pooler_config.pooling_type])
|
|
|
|
|
|
def get_pooling_params(pooling_metadata: PoolingMetadata) -> list[PoolingParams]:
|
|
pooling_params = pooling_metadata.pooling_params
|
|
return pooling_params
|
|
|
|
|
|
def get_tasks(pooling_metadata: PoolingMetadata) -> list[PoolingTask]:
|
|
pooling_params = get_pooling_params(pooling_metadata)
|
|
|
|
tasks: list[PoolingTask] = [task for pooling_param in pooling_params if (task := pooling_param.task) is not None]
|
|
assert len(pooling_params) == len(tasks)
|
|
|
|
return tasks
|
|
|
|
|
|
def get_prompt_token_ids(pooling_metadata: PoolingMetadata) -> list[paddle.Tensor]:
|
|
assert (
|
|
pooling_metadata.prompt_token_ids is not None
|
|
), "Please set `requires_token_ids=True` in `get_pooling_updates`"
|
|
|
|
return [pooling_metadata.prompt_token_ids[i, :num] for i, num in enumerate(pooling_metadata.prompt_lens)]
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class PoolingParamsUpdate:
|
|
requires_token_ids: bool = False
|
|
"""Set this flag to enable `get_prompt_token_ids` for your pooler."""
|
|
|
|
def apply(self, params: PoolingParams) -> None:
|
|
params.requires_token_ids = self.requires_token_ids
|
|
|
|
|
|
class Pooler(nn.Layer, ABC):
|
|
"""The interface required for all poolers used in pooling models in FastDeploy."""
|
|
|
|
@staticmethod
|
|
def for_encode(pooler_config: PoolerConfig, model_config: Optional["ModelConfig"] = None):
|
|
if pooler_config.pooling_type == "STEP":
|
|
return StepPooler()
|
|
|
|
resolved_config = ResolvedPoolingConfig(task="encode", pooling_type=PoolingType.ALL)
|
|
return SimplePooler.from_config(resolved_config, model_config)
|
|
|
|
@staticmethod
|
|
def for_embed(pooler_config: PoolerConfig, model_config: Optional["ModelConfig"] = None):
|
|
resolved_config = ResolvedPoolingConfig.from_config(
|
|
task="embed",
|
|
pooler_config=pooler_config,
|
|
)
|
|
return SimplePooler.from_config(resolved_config, model_config)
|
|
|
|
@staticmethod
|
|
def for_classify(
|
|
pooler_config: PoolerConfig,
|
|
classify: Optional[ClassifierFn],
|
|
):
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
"""Determine which pooling tasks are supported."""
|
|
raise NotImplementedError
|
|
|
|
def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
|
|
"""
|
|
Construct the updated pooling parameters to use for a supported task.
|
|
"""
|
|
return PoolingParamsUpdate()
|
|
|
|
@abstractmethod
|
|
def forward(
|
|
self,
|
|
hidden_states: Union[list[paddle.Tensor], paddle.Tensor],
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> PoolerOutput:
|
|
raise NotImplementedError
|
|
|
|
|
|
class BasePoolerActication(nn.Layer, ABC):
|
|
|
|
@abstractmethod
|
|
def forward(self, pooled_data: _T) -> _T:
|
|
# shape:
|
|
# classify (& score) -> (batch_size, num_classes)
|
|
# embed -> (batch_size, embedding_dim) or list(embedding_dim)
|
|
# (batch_size, dimensions) or list(dimensions) if using MRL
|
|
raise NotImplementedError
|
|
|
|
|
|
class PoolerActivation(BasePoolerActication):
|
|
|
|
@staticmethod
|
|
def wraps(module: nn.Layer):
|
|
if isinstance(module, nn.Identity):
|
|
return PoolerIdentity()
|
|
if isinstance(module, (nn.Sigmoid, nn.Softmax)):
|
|
return PoolerClassify()
|
|
|
|
return LambdaPoolerActivation(module)
|
|
|
|
@abstractmethod
|
|
def forward_chunk(self, pooled_data: paddle.Tensor) -> paddle.Tensor:
|
|
raise NotImplementedError
|
|
|
|
def forward(self, pooled_data: _T) -> _T:
|
|
if isinstance(pooled_data, list):
|
|
return [self.forward_chunk(data) for data in pooled_data]
|
|
|
|
return self.forward_chunk(pooled_data)
|
|
|
|
|
|
class PoolerIdentity(PoolerActivation):
|
|
|
|
def forward_chunk(self, pooled_data: paddle.Tensor) -> paddle.Tensor:
|
|
return pooled_data
|
|
|
|
|
|
class PoolerClassify(PoolerActivation):
|
|
|
|
def __init__(self, *, static_num_labels: bool = True) -> None:
|
|
super().__init__()
|
|
|
|
if static_num_labels:
|
|
fd_config = FDConfig()
|
|
self.num_labels = getattr(fd_config.model_config, "num_labels", 0)
|
|
if self.num_labels == 0:
|
|
logger.warning(
|
|
"num_labels should be > 0 for classification"
|
|
"models, falling back to softmax. "
|
|
"Please check if the configuration is correct."
|
|
)
|
|
else:
|
|
self.num_labels = None
|
|
|
|
def forward_chunk(self, pooled_data: paddle.Tensor) -> paddle.Tensor:
|
|
num_labels = self.num_labels if self.num_labels is not None else pooled_data.shape[-1]
|
|
if num_labels < 2:
|
|
return F.sigmoid(pooled_data.astype("float32")).astype(pooled_data.dtype)
|
|
|
|
return F.softmax(pooled_data.astype("float32"), axis=-1).astype(pooled_data.dtype)
|
|
|
|
|
|
class LambdaPoolerActivation(PoolerActivation):
|
|
|
|
def __init__(self, fn: Callable[[paddle.Tensor], paddle.Tensor]):
|
|
super().__init__()
|
|
|
|
self.fn = fn
|
|
|
|
def forward_chunk(self, pooled_data: paddle.Tensor) -> paddle.Tensor:
|
|
return self.fn(pooled_data)
|
|
|
|
|
|
class PoolerHead(nn.Layer):
|
|
|
|
def __init__(self, activation: PoolerActivation) -> None:
|
|
super().__init__()
|
|
self.activation = activation
|
|
|
|
def forward(self, pooled_data: Union[list[paddle.Tensor], paddle.Tensor], pooling_metadata: PoolingMetadata):
|
|
|
|
return self.activation(pooled_data)
|
|
|
|
|
|
class EmbeddingPoolerHead(PoolerHead):
|
|
|
|
def __init__(self, model_config: Optional["ModelConfig"] = None) -> None:
|
|
super().__init__(activation=PoolerNormalize())
|
|
|
|
self.projector = _load_st_projector(model_config)
|
|
|
|
def forward(self, pooled_data: Union[list[paddle.Tensor], paddle.Tensor], pooling_metadata: PoolingMetadata):
|
|
|
|
if isinstance(pooled_data, list):
|
|
pooled_data = paddle.stack(pooled_data)
|
|
# pooled_data shape: [batchsize, hidden_dimension]
|
|
|
|
# Apply ST projector
|
|
if self.projector is not None:
|
|
projector = cast(nn.Layer, self.projector)
|
|
|
|
def _proj(x: paddle.Tensor) -> paddle.Tensor:
|
|
orig_dtype = x.dtype
|
|
y = projector(x.astype("float32"))
|
|
return y.astype(orig_dtype)
|
|
|
|
pooled_data = _proj(pooled_data)
|
|
# pooled_data shape: [batchsize, embedding_dimension]
|
|
|
|
pooling_params = get_pooling_params(pooling_metadata)
|
|
|
|
# for matryoshka representation
|
|
dimensions_list = [pooling_param.dimensions for pooling_param in pooling_params]
|
|
if any(d is not None for d in dimensions_list):
|
|
# change the output dimension
|
|
assert len(pooled_data) == len(dimensions_list)
|
|
if len(set(dimensions_list)) == 1 and not isinstance(pooled_data, list):
|
|
# if all dimensions are the same
|
|
d = dimensions_list[0]
|
|
pooled_data = pooled_data[..., :d]
|
|
else:
|
|
pooled_data = [vecs if d is None else vecs[..., :d] for vecs, d in zip(pooled_data, dimensions_list)]
|
|
# for normalize
|
|
flags = [p.normalize for p in pooling_params]
|
|
if len(set(flags)) == 1:
|
|
if flags[0]:
|
|
pooled_data = self.activation(pooled_data)
|
|
else:
|
|
pooled_data = [self.activation(vecs) if f else vecs for vecs, f in zip(pooled_data, flags)]
|
|
|
|
# pooled_data shape: [batchsize, embedding_dimension]
|
|
return pooled_data
|
|
|
|
|
|
class RewardPoolerHead(PoolerHead):
|
|
|
|
def __init__(self, model_config: Optional["ModelConfig"] = None) -> None:
|
|
super().__init__(activation=PoolerClassify(static_num_labels=False))
|
|
self.model_config = model_config
|
|
|
|
def forward(self, pooled_data: Union[list[paddle.Tensor], paddle.Tensor], pooling_metadata: PoolingMetadata):
|
|
pooling_params = get_pooling_params(pooling_metadata)
|
|
|
|
# for softmax
|
|
flags = [p.softmax for p in pooling_params]
|
|
if len(set(flags)) == 1:
|
|
if flags[0]:
|
|
pooled_data = self.activation(pooled_data)
|
|
else:
|
|
pooled_data = [self.activation(vecs) if f else vecs for vecs, f in zip(pooled_data, flags)]
|
|
|
|
return pooled_data
|
|
|
|
|
|
def build_output(
|
|
all_data: Union[paddle.Tensor, list[paddle.Tensor]],
|
|
) -> PoolerOutput:
|
|
# Pooling models D2H & synchronize occurs here
|
|
if isinstance(all_data, list):
|
|
all_data = [d.cpu() for d in all_data]
|
|
else:
|
|
all_data = all_data.cpu()
|
|
|
|
all_outputs = [PoolingSequenceGroupOutput(data) for data in all_data]
|
|
return PoolerOutput(outputs=all_outputs)
|
|
|
|
|
|
class PoolingMethod(nn.Layer, ABC):
|
|
|
|
@staticmethod
|
|
def from_pooling_type(pooling_type: PoolingType) -> "PoolingMethod":
|
|
if pooling_type == PoolingType.LAST:
|
|
return LastPool()
|
|
if pooling_type == PoolingType.ALL:
|
|
return AllPool()
|
|
if pooling_type == PoolingType.CLS:
|
|
return CLSPool()
|
|
if pooling_type == PoolingType.MEAN:
|
|
return MeanPool()
|
|
raise NotImplementedError(f"Unsupported method: {pooling_type}")
|
|
|
|
|
|
class LastPool(PoolingMethod):
|
|
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
return {"encode", "embed", "classify", "score"}
|
|
|
|
def forward_all(
|
|
self,
|
|
hidden_states: paddle.Tensor,
|
|
pooling_cursor: PoolingCursor,
|
|
) -> Union[list[paddle.Tensor], paddle.Tensor]:
|
|
return hidden_states[pooling_cursor.last_token_indices_gpu]
|
|
|
|
|
|
class AllPool(PoolingMethod):
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
return {"encode"}
|
|
|
|
def forward_all(
|
|
self,
|
|
hidden_states: paddle.Tensor,
|
|
pooling_cursor: PoolingCursor,
|
|
) -> Union[list[paddle.Tensor], paddle.Tensor]:
|
|
|
|
assert not pooling_cursor.is_partial_prefill(), "partial prefill not supported with ALL pooling"
|
|
|
|
hidden_states_lst = list(hidden_states.split(pooling_cursor.num_scheduled_tokens_cpu.tolist()))
|
|
return [hidden_states_lst[i] for i in pooling_cursor.index]
|
|
|
|
|
|
class MeanPool(PoolingMethod):
|
|
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
return {"encode", "embed", "classify", "score"}
|
|
|
|
def forward_all(
|
|
self,
|
|
hidden_states: paddle.Tensor,
|
|
pooling_cursor: PoolingCursor,
|
|
) -> Union[list[paddle.Tensor], paddle.Tensor]:
|
|
|
|
assert not pooling_cursor.is_partial_prefill(), "partial prefill not supported with MEAN pooling"
|
|
|
|
if hidden_states.place.is_gpu_place():
|
|
prompt_lens = pooling_cursor.prompt_lens_cpu.cuda()
|
|
else:
|
|
prompt_lens = pooling_cursor.prompt_lens_cpu
|
|
|
|
# Use float32 for paddle.cumsum in MeanPool,
|
|
# otherwise precision will be lost significantly.
|
|
cumsum = paddle.cumsum(hidden_states.astype("float32"), axis=0)
|
|
|
|
start_indices = pooling_cursor.first_token_indices_gpu
|
|
end_indices = pooling_cursor.last_token_indices_gpu
|
|
return (cumsum[end_indices] - cumsum[start_indices] + hidden_states[start_indices]) / prompt_lens.unsqueeze(1)
|
|
|
|
|
|
class CLSPool(PoolingMethod):
|
|
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
return {"encode", "embed", "classify", "score"}
|
|
|
|
def forward_all(
|
|
self,
|
|
hidden_states: paddle.Tensor,
|
|
pooling_cursor: PoolingCursor,
|
|
) -> Union[list[paddle.Tensor], paddle.Tensor]:
|
|
assert not pooling_cursor.is_partial_prefill(), "partial prefill not supported with CLS pooling"
|
|
|
|
return hidden_states[pooling_cursor.first_token_indices_gpu]
|
|
|
|
|
|
class StepPooler(Pooler):
|
|
def __init__(
|
|
self,
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.pooling = AllPool()
|
|
self.head = RewardPoolerHead()
|
|
|
|
def extract_states(
|
|
self,
|
|
hidden_states: Union[paddle.Tensor, list[paddle.Tensor]],
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> Union[list[paddle.Tensor], paddle.Tensor]:
|
|
pooled_data_lst = self.pooling(hidden_states, pooling_metadata)
|
|
prompt_token_ids = get_prompt_token_ids(pooling_metadata)
|
|
|
|
pooled_data = list[paddle.Tensor]()
|
|
|
|
pooling_params = get_pooling_params(pooling_metadata)
|
|
|
|
for data, token_id, pooling_param in zip(pooled_data_lst, prompt_token_ids, pooling_params):
|
|
step_tag_id = pooling_param.step_tag_id
|
|
returned_token_ids = pooling_param.returned_token_ids
|
|
|
|
if returned_token_ids is not None and len(returned_token_ids) > 0:
|
|
data = data[:, returned_token_ids]
|
|
|
|
if step_tag_id is not None:
|
|
data = data[token_id == step_tag_id]
|
|
pooled_data.append(data)
|
|
|
|
return pooled_data
|
|
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
return {"encode"}
|
|
|
|
def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
|
|
return PoolingParamsUpdate(requires_token_ids=True)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Union[paddle.Tensor, list[paddle.Tensor]],
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> PoolerOutput:
|
|
pooled_data = self.extract_states(hidden_states, pooling_metadata)
|
|
pooled_data = self.head(pooled_data, pooling_metadata)
|
|
return build_output(pooled_data)
|
|
|
|
|
|
class SimplePooler(Pooler):
|
|
"""A layer that pools specific information from hidden states.
|
|
|
|
This layer does the following:
|
|
1. Extracts specific tokens or aggregates data based on pooling method.
|
|
2. Normalizes output if specified.
|
|
3. Returns structured results as `PoolerOutput`.
|
|
"""
|
|
|
|
@classmethod
|
|
def from_config(
|
|
cls,
|
|
pooler_config: ResolvedPoolingConfig,
|
|
model_config: Optional["ModelConfig"] = None,
|
|
) -> "SimplePooler":
|
|
pooling = PoolingMethod.from_pooling_type(pooler_config.pooling_type)
|
|
if pooler_config.task == "embed":
|
|
head = EmbeddingPoolerHead(model_config)
|
|
elif pooler_config.task == "encode":
|
|
head = RewardPoolerHead(model_config)
|
|
else:
|
|
raise NotImplementedError(f"Unknown task: {pooler_config.task}")
|
|
return cls(pooling, head)
|
|
|
|
def __init__(self, pooling: PoolingMethod, head: PoolerHead) -> None:
|
|
super().__init__()
|
|
|
|
self.pooling = pooling
|
|
self.head = head
|
|
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
return self.pooling.get_supported_tasks()
|
|
|
|
def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
|
|
return self.pooling.get_pooling_updates(task)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Union[paddle.Tensor, list[paddle.Tensor]],
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> PoolerOutput:
|
|
pooled_data = self.pooling(hidden_states, pooling_metadata)
|
|
pooled_data = self.head(pooled_data, pooling_metadata)
|
|
return build_output(pooled_data)
|
|
|
|
|
|
class PoolerNormalize(PoolerActivation):
|
|
def forward_chunk(self, pooled_data: paddle.Tensor) -> paddle.Tensor:
|
|
x = F.normalize(pooled_data.astype("float32"), p=2, axis=-1)
|
|
return x.astype(pooled_data.dtype)
|
|
|
|
|
|
class DispatchPooler(Pooler):
|
|
"""Dispatches calls to a sub-pooler based on the pooling task."""
|
|
|
|
def __init__(self, poolers_by_task: Mapping[PoolingTask, Pooler]) -> None:
|
|
super().__init__()
|
|
|
|
for task, pooler in poolers_by_task.items():
|
|
if task not in pooler.get_supported_tasks():
|
|
raise ValueError(
|
|
f"{pooler=} does not support {task=}. " f"Supported tasks: {pooler.get_supported_tasks()}"
|
|
)
|
|
|
|
self.poolers_by_task = poolers_by_task
|
|
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
return set(self.poolers_by_task)
|
|
|
|
def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
|
|
return self.poolers_by_task[task].get_pooling_updates(task)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Union[paddle.Tensor, list[paddle.Tensor]],
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> PoolerOutput:
|
|
poolers_by_task = self.poolers_by_task
|
|
|
|
outputs = list[PoolingSequenceGroupOutput]()
|
|
offset = 0
|
|
for task, group in groupby(get_tasks(pooling_metadata)):
|
|
if not (pooler := poolers_by_task.get(task)):
|
|
raise ValueError(f"Unsupported task: {task} " f"Supported tasks: {self.get_supported_tasks()}")
|
|
|
|
num_items = len(list(group))
|
|
group_output: PoolerOutput = pooler(
|
|
hidden_states,
|
|
pooling_metadata[offset : offset + num_items],
|
|
)
|
|
outputs.extend(group_output.outputs)
|
|
offset += num_items
|
|
|
|
return PoolerOutput(outputs)
|