optimization train config (#1187)

paddleslim/auto_compression/auto_strategy.py

@@ -46,12 +46,13 @@ default_hpo_config = {
 
 # default quant config, can be used by ptq&hpo and qat&distillation
 default_quant_config = {
-    'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul', 'matmul'],
+    'quantize_op_types':
+    ['conv2d', 'depthwise_conv2d', 'mul', 'matmul', 'matmul_v2'],
     'weight_bits': 8,
     'activation_bits': 8,
     "is_full_quantize": False,
-    "activation_quantize_type": 'range_abs_max',
-    "weight_quantize_type": 'abs_max',
+    "activation_quantize_type": 'moving_average_abs_max',
+    "weight_quantize_type": 'channel_wise_abs_max',
     "not_quant_pattern": ["skip_quant"],
 }
 
@@ -60,10 +61,12 @@ DefaultTrainConfig = {
     "epochs": 1,
     "eval_iter": 500,
     "learning_rate": 0.0001,
-    "optimizer": "Momentum",
-    "optim_args": {
+    "optimizer_builder": {
+        "optimizer": {
+            "type": "Momentum",
+        },
         "weight_decay": 4.0e-05
-    },
+    }
 }
 
 EXPERIENCE_STRATEGY_WITHOUT_LOSS = [

paddleslim/auto_compression/compressor.py

@@ -16,6 +16,7 @@ import logging
 import os
 import sys
 import numpy as np
+import copy
 import inspect
 import shutil
 from time import gmtime, strftime
@@ -28,7 +29,7 @@ from ..common import get_logger
 from ..common.patterns import get_patterns
 from ..analysis import TableLatencyPredictor
 from .create_compressed_program import build_distill_program, build_quant_program, build_prune_program, remove_unused_var_nodes
-from .strategy_config import ProgramInfo, merge_config
+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 .utils.predict import with_variable_shape
 
@@ -127,7 +128,6 @@ class AutoCompression:
         if not os.path.exists(self.final_dir):
             os.makedirs(self.final_dir)
         self.strategy_config = strategy_config
-        self.train_config = train_config
         self.train_dataloader = train_dataloader
         self.target_speedup = target_speedup
         self.eval_function = eval_callback
@@ -142,7 +142,7 @@ class AutoCompression:
         self.model_type = self._get_model_type(self._exe, model_dir,
                                                model_filename, params_filename)
 
-        if self.train_config is not None and self.train_config.use_fleet:
+        if train_config is not None and train_config.use_fleet:
             fleet.init(is_collective=True)
 
         if with_variable_shape(
@@ -173,10 +173,48 @@ class AutoCompression:
         self._strategy, self._config = self._prepare_strategy(
             self.strategy_config)
 
+        self.train_config = self._get_final_train_config(
+            train_config, self._strategy, self.model_type)
+
+    def _get_final_train_config(self, train_config, strategy_config,
+                                model_type):
         # If train_config is None, set default train_config
-        if self.train_config is None:
-            self.train_config = create_train_config(self.strategy_config,
-                                                    self.model_type)
+        if train_config is None:
+            train_config = create_train_config(strategy_config, model_type)
+
+        train_configs = [train_config]
+        for idx in range(1, len(self._strategy)):
+            if 'qat' in self._strategy[idx]:
+                ### if compress strategy more than one, the train config in the yaml set for prune
+                ### the train config for quantization is extrapolate from the yaml
+                tmp_train_config = copy.deepcopy(train_config.__dict__)
+                ### the epoch, train_iter, learning rate of quant is 10% of the prune compress
+                tmp_train_config['epochs'] = max(
+                    int(train_config.epochs * 0.1), 1)
+                if train_config.train_iter is not None:
+                    tmp_train_config['train_iter'] = int(
+                        train_config.train_iter * 0.1)
+                if isinstance(train_config.learning_rate, float):
+                    tmp_train_config[
+                        'learning_rate'] = train_config.learning_rate * 0.1
+                else:
+                    if 'learning_rate' in train_config.learning_rate:
+                        tmp_train_config['learning_rate'][
+                            'learning_rate'] = train_config.learning_rate[
+                                'learning_rate'] * 0.1
+                    else:  ### learning rate decay is PiecewiseDecay
+                        tmp_train_config['learning_rate']['values'] = list(
+                            map(lambda x: x * 0.1, train_config.learning_rate[
+                                'values']))
+                train_cfg = TrainConfig(**tmp_train_config)
+            elif 'ptq' in self._strategy[idx]:
+                train_cfg = None
+            else:
+                tmp_train_config = copy.deepcopy(train_config.__dict__)
+                train_cfg = TrainConfig(**tmp_train_config)
+
+            train_configs.append(train_cfg)
+        return train_configs
 
     def _infer_shape(self, model_dir, model_filename, params_filename,
                      input_shapes, save_path):
@@ -285,42 +323,50 @@ class AutoCompression:
                    single_teacher_distill_config is not None else \
                    multi_teacher_distill_config
 
-            ### case1: quant_config & hpo_config ==> PTQ & HPO
-            if quant_config is not None and hpo_config is not None:
-                strategy.append('ptq_hpo')
-                config.append(merge_config(quant_config, hpo_config))
-
-            ### case2: quant_config & distill config ==> QAT & Distill
-            elif quant_config is not None and self._distill_config is not None:
-                strategy.append('qat_dis')
-                config.append(merge_config(quant_config, self._distill_config))
+            only_distillation = True
 
-            ### case3: prune_config & distill config
-            elif prune_config is not None and self._distill_config is not None:
+            ### case1: prune_config & distill config
+            if prune_config is not None and self._distill_config is not None:
+                only_distillation = False
                 strategy.append('channel_prune_dis')
                 config.append(merge_config(prune_config, self._distill_config))
 
-            ### case4: asp_config & distill config
-            elif asp_config is not None and self._distill_config is not None:
+            ### case2: asp_config & distill config
+            if asp_config is not None and self._distill_config is not None:
+                only_distillation = False
                 strategy.append('asp_prune_dis')
                 config.append(merge_config(asp_config, self._distill_config))
 
-            ### case5: transformer_prune_config & distill config
-            elif transformer_prune_config is not None and self._distill_config is not None:
+            ### case3: transformer_prune_config & distill config
+            if transformer_prune_config is not None and self._distill_config is not None:
+                only_distillation = False
                 strategy.append('transformer_prune_dis')
                 config.append(
                     merge_config(transformer_prune_config,
                                  self._distill_config))
 
-            ### case6: unstructure_config & distill config
-            elif unstructure_prune_config is not None and self._distill_config is not None:
+            ### case4: unstructure_config & distill config
+            if unstructure_prune_config is not None and self._distill_config is not None:
+                only_distillation = False
                 strategy.append('unstructure_prune_dis')
                 config.append(
                     merge_config(unstructure_prune_config,
                                  self._distill_config))
 
+            ### case5: quant_config & hpo_config ==> PTQ & HPO
+            if quant_config is not None and hpo_config is not None:
+                only_distillation = False
+                strategy.append('ptq_hpo')
+                config.append(merge_config(quant_config, hpo_config))
+
+            ### case6: quant_config & distill config ==> QAT & Distill
+            if quant_config is not None and self._distill_config is not None:
+                only_distillation = False
+                strategy.append('qat_dis')
+                config.append(merge_config(quant_config, self._distill_config))
+
             ### case7: distill_config
-            elif self._distill_config is not None:
+            if only_distillation == True and self._distill_config is not None:
                 if single_teacher_distill_config is not None:
                     strategy.append('single_teacher_dis')
                     config.append(single_teacher_distill_config)
@@ -328,11 +374,18 @@ class AutoCompression:
                     strategy.append('multi_teacher_dis')
                     config.append(multi_teacher_distill_config)
 
-            ### case N: todo
-            else:
-                raise NotImplementedError(
-                    "Not Implemented {} be set at the same time now".format(
-                        strategy_c.keys()))
+        ### NOTE: keep quantation in the last step
+        idx = -1
+        if 'qat_dis' in strategy and strategy.index('qat_dis') != (
+                len(strategy) - 1):
+            idx = strategy.index('qat_dis')
+        elif 'ptq_hpo' in strategy and strategy.index('ptq_hpo') != (
+                len(strategy) - 1):
+            idx = strategy.index('ptq_hpo')
+
+        if idx != -1:
+            strategy = strategy[:idx] + strategy[idx + 1:] + [strategy[idx]]
+            config = config[:idx] + config[idx + 1:] + [config[idx]]
 
         return strategy, config
 
@@ -356,7 +409,8 @@ class AutoCompression:
         return strategy
 
     def _prepare_program(self, program, feed_target_names, fetch_targets,
-                         patterns, default_distill_node_pair, strategy, config):
+                         patterns, default_distill_node_pair, strategy, config,
+                         train_config):
         train_program = recover_inference_program(program)
         startup_program = paddle.static.Program()
         train_program_info = ProgramInfo(startup_program, train_program,
@@ -369,11 +423,11 @@ class AutoCompression:
             _logger.info(
                 "Calculating the iterations per epoch……(It will take some time)")
             # NOTE:XXX: This way of calculating the iters needs to be improved.
-            if self.train_config.epochs:
+            if train_config.epochs:
                 iters_per_epoch = len(list(self.train_dataloader()))
-                total_iters = self.train_config.epochs * iters_per_epoch
-            elif self.train_config.train_iter:
-                total_iters = self.train_config.train_iter
+                total_iters = train_config.epochs * iters_per_epoch
+            elif train_config.train_iter:
+                total_iters = train_config.train_iter
             else:
                 raise RuntimeError(
                     'train_config must has `epochs` or `train_iter` field.')
@@ -392,8 +446,8 @@ class AutoCompression:
                 self._exe, self._places, config_dict, train_program_info,
                 strategy, patterns, self.eval_dataloader)
 
-        if self.train_config.use_fleet:
-            dist_strategy = _prepare_fleet_strategy(self.train_config)
+        if train_config.use_fleet:
+            dist_strategy = _prepare_fleet_strategy(train_config)
         else:
             dist_strategy = None
 
@@ -403,7 +457,7 @@ class AutoCompression:
                 self._exe,
                 self._places,
                 config_dict,
-                self.train_config.__dict__,
+                train_config.__dict__,
                 train_program_info,
                 pruner=self._pruner,
                 dist_strategy=dist_strategy,
@@ -415,7 +469,7 @@ class AutoCompression:
             train_program_info, test_program_info, self._quant_config = build_quant_program(
                 self._exe, self._places, config_dict, train_program_info,
                 test_program_info)
-        if self.train_config.sparse_model:
+        if train_config.sparse_model:
             from ..prune.unstructured_pruner import UnstructuredPruner
             # NOTE: The initialization parameter of this pruner doesn't work, it is only used to call the 'set_static_masks' function
             self._pruner = UnstructuredPruner(
@@ -428,10 +482,10 @@ class AutoCompression:
 
         self._exe.run(train_program_info.startup_program)
 
-        if (not self.train_config.use_fleet
-            ) and self.train_config.amp_config is not None:
-            if hasattr(self.train_config.amp_config, 'use_pure_fp16'
-                       ) and self.train_config.amp_config.use_pure_fp16:
+        if (not train_config.use_fleet) and train_config.amp_config is not None:
+            if hasattr(
+                    train_config.amp_config,
+                    'use_pure_fp16') and train_config.amp_config.use_pure_fp16:
                 train_program_info.optimizer.amp_init(
                     self._places, scope=paddle.static.global_scope())
 
@@ -439,7 +493,7 @@ class AutoCompression:
             ### prune weight in scope
             self._pruner.prune_model(train_program_info.program)
 
-        if not self.train_config.use_fleet:
+        if not train_config.use_fleet:
             train_program_info = self._compiled_program(train_program_info,
                                                         strategy)
             test_program_info = self._compiled_program(test_program_info,
@@ -475,9 +529,10 @@ class AutoCompression:
     def compress(self):
         self.tmp_dir = self.create_tmp_dir(self.final_dir)
         for strategy_idx, (
-                strategy,
-                config) in enumerate(zip(self._strategy, self._config)):
-            self.single_strategy_compress(strategy, config, strategy_idx)
+                strategy, config, train_config
+        ) in enumerate(zip(self._strategy, self._config, self.train_config)):
+            self.single_strategy_compress(strategy, config, strategy_idx,
+                                          train_config)
 
         if strategy == 'ptq_hpo' and config.max_quant_count == 1 and platform.system(
         ).lower() == 'linux':
@@ -488,7 +543,8 @@ class AutoCompression:
                 quant_strategy, quant_config = self._prepare_strategy(
                     final_quant_config)
                 self.single_strategy_compress(quant_strategy[0],
-                                              quant_config[0], strategy_idx)
+                                              quant_config[0], strategy_idx,
+                                              train_config)
         tmp_model_path = os.path.join(
             self.tmp_dir, 'strategy_{}'.format(str(strategy_idx + 1)))
         final_model_path = os.path.join(self.final_dir)
@@ -507,7 +563,8 @@ class AutoCompression:
                 format(final_model_path))
         os._exit(0)
 
-    def single_strategy_compress(self, strategy, config, strategy_idx):
+    def single_strategy_compress(self, strategy, config, strategy_idx,
+                                 train_config):
         # start compress, including train/eval model
         # TODO: add the emd loss of evaluation model.
         if strategy == 'quant_post':
@@ -581,19 +638,19 @@ class AutoCompression:
 
             ### used to check whether the dataloader is right
             self.metric_before_compressed = None
-            if self.eval_function is not None and self.train_config.origin_metric is not None:
+            if self.eval_function is not None and train_config.origin_metric is not None:
                 _logger.info("start to test metric before compress")
                 metric = self.eval_function(self._exe, inference_program,
                                             feed_target_names, fetch_targets)
                 _logger.info("metric of compressed model is: {}".format(metric))
                 buf = 0.05
-                if metric < (float(self.train_config.origin_metric) - buf) or \
-                        metric > (float(self.train_config.origin_metric) + buf):
+                if metric < (float(train_config.origin_metric) - buf) or \
+                        metric > (float(train_config.origin_metric) + buf):
                     raise RuntimeError("target metric of pretrained model is {}, \
                           but now is {}, Please check the format of evaluation dataset \
                           or check the origin_metric in train_config"
                                                                      .format(\
-                          self.train_config.origin_metric, metric))
+                          train_config.origin_metric, metric))
                 self.metric_before_compressed = metric
 
             patterns, default_distill_node_pair, _ = get_patterns(
@@ -601,15 +658,16 @@ class AutoCompression:
 
             train_program_info, test_program_info = self._prepare_program(
                 inference_program, feed_target_names, fetch_targets, patterns,
-                default_distill_node_pair, strategy, config)
+                default_distill_node_pair, strategy, config, train_config)
             if 'unstructure' in self._strategy:
                 test_program_info.program._program = remove_unused_var_nodes(
                     test_program_info.program._program)
-            test_program_info = self._start_train(train_program_info,
-                                                  test_program_info, strategy)
+            test_program_info = self._start_train(
+                train_program_info, test_program_info, strategy, train_config)
             self._save_model(test_program_info, strategy, strategy_idx)
 
-    def _start_train(self, train_program_info, test_program_info, strategy):
+    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_train_iter = 0
@@ -623,10 +681,10 @@ class AutoCompression:
                 if 'unstructure' in strategy:
                     self._pruner.step()
 
-                if self.train_config.logging_iter is None:
+                if train_config.logging_iter is None:
                     logging_iter = 10
                 else:
-                    logging_iter = self.train_config.logging_iter
+                    logging_iter = train_config.logging_iter
                 if batch_id % int(logging_iter) == 0:
                     _logger.info(
                         "Total iter: {}, epoch: {}, batch: {}, loss: {}".format(
@@ -661,8 +719,8 @@ class AutoCompression:
                                         self.metric_before_compressed)
                             ) / self.metric_before_compressed <= 0.005:
                                 break
-                        if self.train_config.target_metric is not None:
-                            if metric > float(self.train_config.target_metric):
+                        if train_config.target_metric is not None:
+                            if metric > float(train_config.target_metric):
                                 break
 
                     else:
@@ -672,7 +730,7 @@ class AutoCompression:
                 if self.train_config.train_iter and total_train_iter >= self.train_config.train_iter:
                     break
 
-        if 'unstructure' in self._strategy or self.train_config.sparse_model:
+        if 'unstructure' in self._strategy or train_config.sparse_model:
             self._pruner.update_params()
 
         return test_program_info