Rename fluid API to static API (#1193)

demo/auto_compression/README.md

@@ -61,7 +61,7 @@ image = paddle.static.data(
 train_loader = paddle.io.DataLoader(train_dataset, feed_list=[image], batch_size=32, return_list=False)
 # 开始自动压缩
 ac = AutoCompression(
-    model_dir="./MobileNetV1_infer/",
+    model_dir="./MobileNetV1_infer",
     model_filename="inference.pdmodel",
     params_filename="inference.pdiparams",
     save_dir="output",
@@ -76,6 +76,7 @@ ac.compress()
 - DataLoader传入的数据集是待压缩模型所用的数据集，DataLoader继承自`paddle.io.DataLoader`。
 - 如无需验证自动化压缩过程中模型的精度，`eval_callback`可不传入function，程序会自动根据损失来选择最优模型。
 - 自动化压缩Config中定义量化、蒸馏、剪枝等压缩算法会合并执行，压缩策略有：量化+蒸馏，剪枝+蒸馏等等。
+- 如果要压缩的模型参数是存储在各自分离的文件中，需要先通过[convert.py](./convert.py) 脚本将其保存成一个单独的二进制文件。
 
 ## 应用示例
 

demo/auto_compression/convert.py

+import argparse
+import paddle
+from ..utility import add_arguments, print_arguments
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+
+# yapf: disable
+add_arg('model_dir',                   str,    None,         "inference model directory.")
+add_arg('save_dir',                    str,    None,         "directory to save compressed model.")
+add_arg('model_filename',              str,    None,         "inference model filename.")
+# yapf: enable
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    print_arguments(args)
+    paddle.enable_static()
+    place = fluid.CPUPlace()
+    exe = paddle.static.Executor(paddle.CPUPlace())
+
+    [inference_program, feed_target_names,
+     fetch_targets] = paddle.fluid.io.load_inference_model(
+         dirname=args.model_dir,
+         executor=exe,
+         model_filename=args.model_filename,
+         params_filename=None)
+
+    feed_vars = [
+        inference_program.global_block().var(name) for name in feed_target_names
+    ]
+    paddle.static.save_inference_model(
+        args.save_dir,
+        executor=exe,
+        model_filename='model.pdmodel',
+        params_filename='model.pdiparams',
+        feed_vars=feed_vars,
+        fetch_vars=fetch_targets)

demo/auto_compression/detection/configs/ppyoloe_l_qat_dis.yaml

@@ -3,7 +3,7 @@ Global:
   reader_config: configs/yolo_reader.yml
   input_list: ['image', 'scale_factor']
   Evaluation: True
-  model_dir: ./ppyoloe_crn_l_300e_coco/
+  model_dir: ./ppyoloe_crn_l_300e_coco
   model_filename: model.pdmodel
   params_filename: model.pdiparams
 

demo/auto_compression/detection/configs/ssd_mbv1_voc_qat_dis.yaml

@@ -2,7 +2,7 @@ Global:
   reader_config: configs/ssd_reader.yml
   input_list: ['image', 'scale_factor', 'im_shape']
   Evaluation: True
-  model_dir: ./ssd_mobilenet_v1_300_120e_voc/
+  model_dir: ./ssd_mobilenet_v1_300_120e_voc
   model_filename: model.pdmodel
   params_filename: model.pdiparams
 

demo/auto_compression/detection/configs/tinypose_qat_dis.yaml

@@ -2,7 +2,7 @@ Global:
   reader_config: configs/tinypose_reader.yml
   input_list: ['image']
   Evaluation: False
-  model_dir: ./tinypose_128x96/
+  model_dir: ./tinypose_128x96
   model_filename: model.pdmodel
   params_filename: model.pdiparams
 

demo/auto_compression/detection/configs/yolov3_mbv1_qat_dis.yaml

@@ -2,7 +2,7 @@ Global:
   reader_config: configs/yolo_reader.yml
   input_list: ['image', 'im_shape', 'scale_factor']
   Evaluation: True
-  model_dir: ./yolov3_mobilenet_v1_270e_coco/  # Model Link: https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_270e_coco.tar
+  model_dir: ./yolov3_mobilenet_v1_270e_coco  # Model Link: https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_270e_coco.tar
   model_filename: model.pdmodel
   params_filename: model.pdiparams
 

demo/auto_compression/detection/configs/yolov5s_qat_dis.yaml

@@ -4,7 +4,7 @@ Global:
   input_list: {'image': 'x2paddle_images'}
   Evaluation: True
   arch: 'YOLOv5'
-  model_dir: ./yolov5s_infer/
+  model_dir: ./yolov5s_infer
   model_filename: model.pdmodel
   params_filename: model.pdiparams
 

demo/auto_compression/nlp/README.md

@@ -109,7 +109,7 @@ tar -zxvf afqmc.tar
 export CUDA_VISIBLE_DEVICES=0
 python run.py \
     --model_type='ppminilm' \
-    --model_dir='./afqmc/' \
+    --model_dir='./afqmc' \
     --model_filename='inference.pdmodel' \
     --params_filename='inference.pdiparams' \
     --dataset='clue' \
@@ -147,7 +147,7 @@ TrainConfig:
 
 ```yaml
 Distillation:
-  teacher_model_dir: ./afqmc/
+  teacher_model_dir: ./afqmc
   teacher_model_filename: inference.pdmodel
   teacher_params_filename: inference.pdiparams
 ```

demo/auto_compression/nlp/run.sh

 export FLAGS_cudnn_deterministic=True
 python run.py \
     --model_type='ppminilm' \
-    --model_dir='./all_original_models/AFQMC' \
-    --model_filename='infer.pdmodel' \
-    --params_filename='infer.pdiparams' \
+    --model_dir='./afqmc' \
+    --model_filename='inference.pdmodel' \
+    --params_filename='inference.pdiparams' \
     --dataset='clue' \
     --save_dir='./save_afqmc_pruned/' \
     --batch_size=16 \
     --max_seq_length=128 \
     --task_name='afqmc' \
-    --config_path='./configs/afqmc.yaml' 
+    --config_path='./configs/pp-minilm/auto/afqmc.yaml' 
 
 

paddleslim/auto_compression/compressor.py

@@ -35,7 +35,7 @@ from .strategy_config import TrainConfig, ProgramInfo, merge_config
 from .auto_strategy import prepare_strategy, get_final_quant_config, create_strategy_config, create_train_config
 from .config_helpers import load_config, extract_strategy_config, extract_train_config
 from .utils.predict import with_variable_shape
-from .utils import get_feed_vars, wrap_dataloader
+from .utils import get_feed_vars, wrap_dataloader, load_inference_model
 
 _logger = get_logger(__name__, level=logging.INFO)
 
@@ -64,14 +64,10 @@ class AutoCompression:
 
         Args:
             model_dir(str): The path of inference model that will be compressed, and
-                the model and params that saved by ``paddle.static.io.save_inference_model``
+                the model and params that saved by ``paddle.static.save_inference_model``
                 are under the path.
-            model_filename(str, optional):  The name of model file. If parameters
-                are saved in separate files, set it as 'None'. Default: 'None'.
-            params_filename(str, optional): The name of params file.
-                When all parameters are saved in a single file, set it
-                as filename. If parameters are saved in separate files,
-                set it as 'None'. Default : 'None'.
+            model_filename(str):  The name of model file. 
+            params_filename(str): The name of params file.
             save_dir(str): The path to save compressed model. The models in this directory will be overwrited
                 after calling 'compress()' function.
             train_data_loader(Python Generator, Paddle.io.DataLoader): The
@@ -121,12 +117,19 @@ class AutoCompression:
             deploy_hardware(str, optional): The hardware you want to deploy. Default: 'gpu'.
         """
         self.model_dir = model_dir
+
         if model_filename == 'None':
             model_filename = None
         self.model_filename = model_filename
         if params_filename == 'None':
             params_filename = None
         self.params_filename = params_filename
+
+        if params_filename is None and model_filename is not None:
+            raise NotImplementedError(
+                "NOT SUPPORT parameters saved in separate files. Please convert it to single binary file first."
+            )
+
         self.final_dir = save_dir
         if not os.path.exists(self.final_dir):
             os.makedirs(self.final_dir)
@@ -241,12 +244,9 @@ class AutoCompression:
         ], f'Type of input_shapes should be in [dict, tuple or list] but got {type(input_shapes)}.'
         paddle.enable_static()
         exe = paddle.static.Executor(paddle.CPUPlace())
-        [inference_program, feed_target_names, fetch_targets] = (
-            paddle.static.load_inference_model(
-                model_dir,
-                exe,
-                model_filename=model_filename,
-                params_filename=params_filename))
+        [inference_program, feed_target_names,
+         fetch_targets] = (load_inference_model(model_dir, exe, model_filename,
+                                                params_filename))
 
         if type(input_shapes) in [list, tuple]:
             assert len(
@@ -307,10 +307,10 @@ class AutoCompression:
         return exe, places
 
     def _get_model_type(self, exe, model_dir, model_filename, params_filename):
-        [inference_program, _, _]= paddle.fluid.io.load_inference_model( \
-            dirname=model_dir, \
+        [inference_program, _, _]= (load_inference_model( \
+            model_dir, \
             model_filename=model_filename, params_filename=params_filename,
-            executor=exe)
+            executor=exe))
         _, _, model_type = get_patterns(inference_program)
         _logger.info(f"Detect model type: {model_type}")
         return model_type
@@ -575,10 +575,23 @@ class AutoCompression:
         final_model_path = os.path.join(self.final_dir)
         if not os.path.exists(final_model_path):
             os.makedirs(final_model_path)
+
+        tmp_model_file = ".".join([tmp_model_path, "pdmodel"])
+        if not os.path.exists(tmp_model_file):
             tmp_model_file = os.path.join(tmp_model_path, self.model_filename)
+
+        tmp_params_file = ".".join([tmp_model_path, "pdiparams"])
+        if not os.path.exists(tmp_params_file):
             tmp_params_file = os.path.join(tmp_model_path, self.params_filename)
+
+        if self.model_filename is None:
+            self.model_filename = "infer.pdmodel"
+        if self.params_filename is None:
+            self.params_filename = "infer.pdiparams"
+
         final_model_file = os.path.join(final_model_path, self.model_filename)
         final_params_file = os.path.join(final_model_path, self.params_filename)
+
         if paddle.distributed.get_rank() == 0:
             shutil.move(tmp_model_file, final_model_file)
             shutil.move(tmp_params_file, final_params_file)
@@ -656,8 +669,8 @@ class AutoCompression:
                 model_dir = os.path.join(
                     self.tmp_dir, 'strategy_{}'.format(str(strategy_idx)))
 
-            [inference_program, feed_target_names, fetch_targets]= paddle.fluid.io.load_inference_model( \
-                dirname=model_dir, \
+            [inference_program, feed_target_names, fetch_targets]= load_inference_model( \
+                model_dir, \
                 model_filename=self.model_filename, params_filename=self.params_filename,
                 executor=self._exe)
 
@@ -694,7 +707,7 @@ class AutoCompression:
     def _start_train(self, train_program_info, test_program_info, strategy,
                      train_config):
         best_metric = -1.0
-        total_epochs = self.train_config.epochs if self.train_config.epochs else 100
+        total_epochs = train_config.epochs if train_config.epochs else 100
         total_train_iter = 0
         for epoch_id in range(total_epochs):
             for batch_id, data in enumerate(self.train_dataloader()):
@@ -716,8 +729,8 @@ class AutoCompression:
                             total_train_iter, epoch_id, batch_id,
                             np_probs_float))
                 total_train_iter += 1
-                if total_train_iter % int(self.train_config.eval_iter
-                                          ) == 0 and total_train_iter != 0:
+                if total_train_iter % int(
+                        train_config.eval_iter) == 0 and total_train_iter != 0:
                     if self.eval_function is not None:
 
                         # GMP pruner step 3: update params before summrizing sparsity, saving model or evaluation. 
@@ -755,7 +768,7 @@ class AutoCompression:
                         _logger.warning(
                             "Not set eval function, so unable to test accuracy performance."
                         )
-                if self.train_config.train_iter and total_train_iter >= self.train_config.train_iter:
+                if train_config.train_iter and total_train_iter >= train_config.train_iter:
                     break
 
         if 'unstructure' in self._strategy or train_config.sparse_model:
@@ -784,11 +797,16 @@ class AutoCompression:
                                  'strategy_{}'.format(str(strategy_idx + 1)))
         if not os.path.exists(model_dir):
             os.makedirs(model_dir)
-        paddle.fluid.io.save_inference_model(
-            dirname=str(model_dir),
-            feeded_var_names=test_program_info.feed_target_names,
-            target_vars=test_program_info.fetch_targets,
+        feed_vars = [
+            test_program.global_block().var(name)
+            for name in test_program_info.feed_target_names
+        ]
+
+        paddle.static.save_inference_model(
+            path_prefix=str(model_dir),
+            feed_vars=feed_vars,
+            fetch_vars=test_program_info.fetch_targets,
             executor=self._exe,
-            main_program=test_program,
+            program=test_program,
             model_filename=self.model_filename,
             params_filename=self.params_filename)

paddleslim/auto_compression/create_compressed_program.py

@@ -23,6 +23,7 @@ from ..dist import *
 from ..common.recover_program import recover_inference_program, _remove_fetch_node
 from ..common import get_logger
 from .strategy_config import ProgramInfo
+from .utils import load_inference_model
 
 _logger = get_logger(__name__, level=logging.INFO)
 __all__ = [
@@ -151,20 +152,21 @@ def _load_program_and_merge(executor,
                             feed_target_names=None):
     scope = paddle.static.global_scope()
     new_scope = paddle.static.Scope()
+
     if params_filename == 'None':
         params_filename = None
-    try:
+
+    if params_filename is None and model_filename is not None:
+        raise NotImplementedError(
+            "NOT SUPPORT parameters saved in separate files. Please convert it to single binary file first."
+        )
+
     with paddle.static.scope_guard(new_scope):
-            [teacher_program, teacher_feed_target_names, teacher_fetch_targets]= paddle.fluid.io.load_inference_model( \
-                dirname=model_dir, \
+        [teacher_program, teacher_feed_target_names, teacher_fetch_targets]= (load_inference_model( \
+            model_dir, \
             model_filename=model_filename, \
             params_filename=params_filename, \
-                executor=executor)
-    except:
-        with paddle.static.scope_guard(new_scope):
-            [teacher_program, teacher_feed_target_names, teacher_fetch_targets]= paddle.static.load_inference_model( \
-                path_prefix=model_dir, \
-                executor=executor)
+            executor=executor))
 
     _remove_fetch_node(teacher_program)
 
@@ -209,9 +211,9 @@ def build_distill_program(executor,
     """build distill program with infermodel"""
     startup_program = paddle.static.Program()
     if train_program_info is None:
-        [train_program, feed_target_names, fetch_targets]= paddle.static.load_inference_model( \
+        [train_program, feed_target_names, fetch_targets]= (load_inference_model( \
             path_prefix=config["model_dir"] if "model_dir" in config else config["model_path_prefix"], \
-            executor=executor)
+            executor=executor))
         train_program = recover_inference_program(train_program)
     else:
         train_program = train_program_info.program
@@ -497,7 +499,7 @@ def remove_unused_var_nodes(program):
     Returns:
         program(paddle.static.Program): The sparse model.
     '''
-    from paddle.fluid import core
+    from paddle.framework import core
     from paddle.fluid.framework import IrGraph
     graph = IrGraph(core.Graph(program.desc), for_test=True)
     removed_nodes = set()

paddleslim/auto_compression/utils/__init__.py

@@ -16,6 +16,9 @@ from __future__ import absolute_import
 from .predict import predict_compressed_model
 from .dataloader import *
 from . import dataloader
+from .load_model import *
+from . import load_model
 
 __all__ = ["predict_compressed_model"]
 __all__ += dataloader.__all__
+__all__ += load_model.__all__

paddleslim/auto_compression/utils/fake_ptq.py

 import paddle
 from paddle.fluid.framework import IrGraph
-from paddle.fluid import core
+from paddle.framework import core
 from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass, AddQuantDequantPass, QuantizationFreezePass
 
 try:
@@ -11,6 +11,8 @@ except:
     TRANSFORM_PASS_OP_TYPES = QuantizationTransformPass._supported_quantizable_op_type
     QUANT_DEQUANT_PASS_OP_TYPES = AddQuantDequantPass._supported_quantizable_op_type
 
+from .load_model import load_inference_model
+
 
 def post_quant_fake(executor,
                     model_dir,
@@ -51,7 +53,7 @@ def post_quant_fake(executor,
         for op_type in _quantizable_op_type:
             assert op_type in _support_quantize_op_type, \
                 op_type + " is not supported for quantization."
-    _program, _feed_list, _fetch_list = paddle.fluid.io.load_inference_model(
+    _program, _feed_list, _fetch_list = load_inference_model(
         model_dir,
         executor,
         model_filename=model_filename,
@@ -108,12 +110,13 @@ def post_quant_fake(executor,
 
     _program = graph.to_program()
 
-    paddle.fluid.io.save_inference_model(
-        dirname=save_model_path,
+    feed_vars = [_program.global_block().var(name) for name in _feed_list]
+    paddle.static.save_inference_model(
+        path_prefix=save_model_path,
         model_filename=model_filename,
         params_filename=params_filename,
-        feeded_var_names=_feed_list,
-        target_vars=_fetch_list,
+        feed_vars=feed_vars,
+        fetch_vars=_fetch_list,
         executor=executor,
-        main_program=_program)
+        program=_program)
     print("The quantized model is saved in: " + save_model_path)

paddleslim/auto_compression/utils/load_model.py

+#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 paddle
+
+__all__ = ['load_inference_model']
+
+
+def load_inference_model(path_prefix,
+                         executor,
+                         model_filename=None,
+                         params_filename=None):
+    if model_filename is not None and params_filename is not None:
+        [inference_program, feed_target_names, fetch_targets] = (
+            paddle.static.load_inference_model(
+                path_prefix=path_prefix,
+                executor=executor,
+                model_filename=model_filename,
+                params_filename=params_filename))
+    else:
+        [inference_program, feed_target_names, fetch_targets] = (
+            paddle.static.load_inference_model(
+                path_prefix=path_prefix, executor=executor))
+
+    return [inference_program, feed_target_names, fetch_targets]

paddleslim/auto_compression/utils/predict.py

@@ -4,6 +4,7 @@ import paddle
 from ...analysis import TableLatencyPredictor
 from .prune_model import get_sparse_model, get_prune_model
 from .fake_ptq import post_quant_fake
+from .load_model import load_inference_model
 
 
 def with_variable_shape(model_dir, model_filename=None, params_filename=None):
@@ -18,12 +19,11 @@ def with_variable_shape(model_dir, model_filename=None, params_filename=None):
     """
     paddle.enable_static()
     exe = paddle.static.Executor(paddle.CPUPlace())
-    [inference_program, feed_target_names, fetch_targets] = (
-        paddle.fluid.io.load_inference_model(
+    inference_program, feed_target_names, fetch_targets = load_inference_model(
         model_dir,
         exe,
         model_filename=model_filename,
-            params_filename=params_filename))
+        params_filename=params_filename)
     for var_ in inference_program.list_vars():
         if var_.name in feed_target_names:
             if var_.shape.count(-1) > 1:

paddleslim/auto_compression/utils/prune_model.py

@@ -2,10 +2,10 @@ import os
 import time
 import numpy as np
 import paddle
-import paddle.fluid as fluid
 import paddle.static as static
 from ...prune import Pruner
 from ...core import GraphWrapper
+from .load_model import load_inference_model
 __all__ = ["get_sparse_model", "get_prune_model"]
 
 
@@ -37,12 +37,8 @@ def get_sparse_model(executor, places, model_file, param_file, ratio,
     startup_prog = static.Program()
     executor.run(startup_prog)
 
-    [inference_program, feed_target_names, fetch_targets] = (
-        fluid.io.load_inference_model(
-            folder,
-            executor,
-            model_filename=model_name,
-            params_filename=param_name))
+    inference_program, feed_target_names, fetch_targets = load_inference_model(
+        folder, executor, model_filename=model_name, params_filename=param_name)
     thresholds = {}
 
     graph = GraphWrapper(inference_program)
@@ -87,12 +83,15 @@ def get_sparse_model(executor, places, model_file, param_file, ratio,
             paddle.static.global_scope().find_var(name).get_tensor().set(
                 array, paddle.CPUPlace())
 
-    fluid.io.save_inference_model(
+    feed_vars = [
+        inference_program.global_block().var(name) for name in feed_target_names
+    ]
+    static.save_inference_model(
         save_path,
-        feeded_var_names=feed_target_names,
-        target_vars=fetch_targets,
+        feed_vars=feed_vars,
+        fetch_vars=fetch_targets,
         executor=executor,
-        main_program=inference_program,
+        program=inference_program,
         model_filename=model_name,
         params_filename=param_name)
     print("The pruned model is saved in: ", save_path)
@@ -127,12 +126,8 @@ def get_prune_model(executor, places, model_file, param_file, ratio, save_path):
     scope = static.global_scope()
     executor.run(startup_prog)
 
-    [inference_program, feed_target_names, fetch_targets] = (
-        fluid.io.load_inference_model(
-            folder,
-            executor,
-            model_filename=model_name,
-            params_filename=param_name))
+    inference_program, feed_target_names, fetch_targets = load_inference_model(
+        folder, executor, model_filename=model_name, params_filename=param_name)
 
     prune_params = []
     graph = GraphWrapper(inference_program)
@@ -162,11 +157,14 @@ def get_prune_model(executor, places, model_file, param_file, ratio, save_path):
         param_backup=None,
         param_shape_backup=None)
 
-    fluid.io.save_inference_model(
+    feed_vars = [
+        main_program.global_block().var(name) for name in feed_target_names
+    ]
+    static.save_inference_model(
         save_path,
-        feeded_var_names=feed_target_names,
-        target_vars=fetch_targets,
+        feed_vars=feed_vars,
+        fetch_vars=fetch_targets,
         executor=executor,
-        main_program=main_program,
+        program=main_program,
         model_filename=model_name,
         params_filename=param_name)

tests/act/test_act_api.py

 import sys
 import os
-sys.path.append("../")
+sys.path.append("../../")
 import unittest
 import tempfile
 import paddle
 import unittest
 import numpy as np
-from static_case import StaticCase
 from paddle.io import Dataset
 from paddleslim.auto_compression import AutoCompression
 from paddleslim.auto_compression.config_helpers import load_config