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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. 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.
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from .download_utils import ppdiffusers_bos_download
from .utils import (
FASTDEPLOY_MODEL_NAME,
FASTDEPLOY_WEIGHTS_NAME,
is_fastdeploy_available,
is_paddle_available,
logging,
)
if is_paddle_available():
import paddle
if is_fastdeploy_available():
import fastdeploy as fd
def fdtensor2pdtensor(fdtensor: fd.C.FDTensor):
dltensor = fdtensor.to_dlpack()
pdtensor = paddle.utils.dlpack.from_dlpack(dltensor)
return pdtensor
def pdtensor2fdtensor(pdtensor: paddle.Tensor, name: str = "", share_with_raw_ptr=False):
if not share_with_raw_ptr:
dltensor = paddle.utils.dlpack.to_dlpack(pdtensor)
return fd.C.FDTensor.from_dlpack(name, dltensor)
else:
return fd.C.FDTensor.from_external_data(
name,
pdtensor.data_ptr(),
pdtensor.shape,
pdtensor.dtype.name,
str(pdtensor.place),
int(pdtensor.place.gpu_device_id()),
)
logger = logging.get_logger(__name__)
class FastDeployRuntimeModel:
def __init__(self, model=None, **kwargs):
logger.info("`ppdiffusers.FastDeployRuntimeModel` is experimental and might change in the future.")
self.model = model
self.model_save_dir = kwargs.get("model_save_dir", None)
self.latest_model_name = kwargs.get("latest_model_name", "inference.pdmodel")
self.latest_params_name = kwargs.get("latest_params_name", "inference.pdiparams")
def zero_copy_infer(self, prebinded_inputs: dict, prebinded_outputs: dict, share_with_raw_ptr=True, **kwargs):
"""
Execute inference without copying data from cpu to gpu.
Arguments:
kwargs (`dict(name, paddle.Tensor)`):
An input map from name to tensor.
Return:
List of output tensor.
"""
for inputs_name, inputs_tensor in prebinded_inputs.items():
input_fdtensor = pdtensor2fdtensor(inputs_tensor, inputs_name, share_with_raw_ptr=share_with_raw_ptr)
self.model.bind_input_tensor(inputs_name, input_fdtensor)
for outputs_name, outputs_tensor in prebinded_outputs.items():
output_fdtensor = pdtensor2fdtensor(outputs_tensor, outputs_name, share_with_raw_ptr=share_with_raw_ptr)
self.model.bind_output_tensor(outputs_name, output_fdtensor)
self.model.zero_copy_infer()
def __call__(self, **kwargs):
inputs = {k: np.array(v) for k, v in kwargs.items()}
return self.model.infer(inputs)
@staticmethod
def load_model(
model_path: Union[str, Path],
params_path: Union[str, Path],
runtime_options: Optional["fd.RuntimeOption"] = None,
):
"""
Loads an FastDeploy Inference Model with fastdeploy.RuntimeOption
Arguments:
model_path (`str` or `Path`):
Model path from which to load
params_path (`str` or `Path`):
Params path from which to load
runtime_options (fd.RuntimeOption, *optional*):
The RuntimeOption of fastdeploy to initialize the fastdeploy runtime. Default setting
the device to cpu and the backend to paddle inference
"""
option = runtime_options
if option is None or not isinstance(runtime_options, fd.RuntimeOption):
logger.info("No fastdeploy.RuntimeOption specified, using CPU device and paddle inference backend.")
option = fd.RuntimeOption()
option.use_paddle_backend()
option.use_cpu()
option.set_model_path(model_path, params_path)
return fd.Runtime(option)
def _save_pretrained(
self,
save_directory: Union[str, Path],
model_file_name: Optional[str] = None,
params_file_name: Optional[str] = None,
**kwargs
):
"""
Save a model and its configuration file to a directory, so that it can be re-loaded using the
[`~FastDeployRuntimeModel.from_pretrained`] class method. It will always save the
latest_model_name.
Arguments:
save_directory (`str` or `Path`):
Directory where to save the model file.
model_file_name(`str`, *optional*):
Overwrites the default model file name from `"inference.pdmodel"` to `model_file_name`. This allows you to save the
model with a different name.
params_file_name(`str`, *optional*):
Overwrites the default model file name from `"inference.pdiparams"` to `params_file_name`. This allows you to save the
model with a different name.
"""
model_file_name = model_file_name if model_file_name is not None else FASTDEPLOY_MODEL_NAME
params_file_name = params_file_name if params_file_name is not None else FASTDEPLOY_WEIGHTS_NAME
src_model_path = self.model_save_dir.joinpath(self.latest_model_name)
dst_model_path = Path(save_directory).joinpath(model_file_name)
src_params_path = self.model_save_dir.joinpath(self.latest_params_name)
dst_params_path = Path(save_directory).joinpath(params_file_name)
try:
shutil.copyfile(src_model_path, dst_model_path)
shutil.copyfile(src_params_path, dst_params_path)
except shutil.SameFileError:
pass
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
**kwargs,
):
"""
Save a model to a directory, so that it can be re-loaded using the [`~FastDeployRuntimeModel.from_pretrained`] class
method.:
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
os.makedirs(save_directory, exist_ok=True)
# saving model weights/files
self._save_pretrained(save_directory, **kwargs)
@classmethod
def _from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, Path],
cache_dir: Optional[str] = None,
model_file_name: Optional[str] = None,
params_file_name: Optional[str] = None,
runtime_options: Optional["fd.RuntimeOption"] = None,
**kwargs,
):
"""
Load a model from a directory or the BOS.
Arguments:
pretrained_model_name_or_path (`str` or `Path`):
Directory from which to load
cache_dir (`Union[str, Path]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
model_file_name (`str`):
Overwrites the default model file name from `"inference.pdmodel"` to `file_name`. This allows you to load
different model files from the same repository or directory.
params_file_name (`str`):
Overwrites the default params file name from `"inference.pdiparams"` to `file_name`. This allows you to load
different model files from the same repository or directory.
runtime_options (`fastdeploy.RuntimeOption`, *optional*):
The RuntimeOption of fastdeploy.
kwargs (`Dict`, *optional*):
kwargs will be passed to the model during initialization
"""
model_file_name = model_file_name if model_file_name is not None else FASTDEPLOY_MODEL_NAME
params_file_name = params_file_name if params_file_name is not None else FASTDEPLOY_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(pretrained_model_name_or_path):
model = FastDeployRuntimeModel.load_model(
os.path.join(pretrained_model_name_or_path, model_file_name),
os.path.join(pretrained_model_name_or_path, params_file_name),
runtime_options=runtime_options,
)
kwargs["model_save_dir"] = Path(pretrained_model_name_or_path)
# load model from hub
else:
# download model
model_cache_path = ppdiffusers_bos_download(
pretrained_model_name_or_path=pretrained_model_name_or_path,
filename=model_file_name,
cache_dir=cache_dir,
)
# download params
params_cache_path = ppdiffusers_bos_download(
pretrained_model_name_or_path=pretrained_model_name_or_path,
filename=params_file_name,
cache_dir=cache_dir,
)
kwargs["model_save_dir"] = Path(model_cache_path).parent
kwargs["latest_model_name"] = Path(model_cache_path).name
kwargs["latest_params_name"] = Path(params_cache_path).name
model = FastDeployRuntimeModel.load_model(
model_cache_path, params_cache_path, runtime_options=runtime_options
)
return cls(model=model, **kwargs)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, Path],
cache_dir: Optional[str] = None,
model_file_name: Optional[str] = None,
params_file_name: Optional[str] = None,
runtime_options: Optional["fd.RuntimeOption"] = None,
**model_kwargs,
):
return cls._from_pretrained(
pretrained_model_name_or_path=pretrained_model_name_or_path,
cache_dir=cache_dir,
model_file_name=model_file_name,
params_file_name=params_file_name,
runtime_options=runtime_options,
**model_kwargs,
)
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