fix ernie example to adapt develop paddle (#1644)

* fix ac

* adapt x2paddle version

* adapte develop paddle

paddleslim/auto_compression/compressor.py

@@ -241,9 +241,9 @@ 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] = load_inference_model(model_dir, exe, model_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(
@@ -451,30 +451,29 @@ class AutoCompression:
         strategy.build_strategy = build_strategy
         if train_config.recompute_config is not None:
             strategy.recompute = True
-            strategy.recompute_configs = { ** train_config.recompute_config}
+            strategy.recompute_configs = {**train_config.recompute_config}
         if train_config.sharding_config is not None:
             strategy.sharding = True
-            strategy.sharding_configs = { ** train_config.sharding_config}
+            strategy.sharding_configs = {**train_config.sharding_config}
         if train_config.amp_config is not None:
             strategy.amp = True
-            strategy.amp_configs = { ** train_config.amp_config}
+            strategy.amp_configs = {**train_config.amp_config}
         if train_config.asp_config is not None:
             strategy.asp = True
         return strategy
 
     def _prepare_program(self, program, feed_target_names, fetch_targets,
                          patterns, strategy, config, train_config):
-        train_program = recover_inference_program(program)
         startup_program = paddle.static.Program()
+        train_program = recover_inference_program(program, startup_program)
         train_program_info = ProgramInfo(startup_program, train_program,
                                          feed_target_names, fetch_targets)
 
         config_dict = config.__dict__
-        if "prune_strategy" in config_dict and config_dict[
-                "prune_strategy"] == "gmp" and config_dict[
-                    'gmp_config'] is None:
+        if "prune_strategy" in config_dict and config_dict["prune_strategy"] == "gmp" and config_dict['gmp_config'] is None:
             _logger.info(
-                "Calculating the iterations per epoch……(It will take some time)")
+                "Calculating the iterations per epoch……(It will take some time)"
+            )
             # NOTE:XXX: This way of calculating the iters needs to be improved.
             if train_config.epochs:
                 iters_per_epoch = len(list(self.train_dataloader()))
@@ -587,9 +586,8 @@ class AutoCompression:
         train_config = None
         strategy_idx = None
         self.final_metric = -1.0
-        for strategy_idx, (
-                strategy, config, train_config
-        ) in enumerate(zip(self._strategy, self._config, self.train_config)):
+        for 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)
 
@@ -815,7 +813,7 @@ class AutoCompression:
                         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. 
+                        # GMP pruner step 3: update params before summrizing sparsity, saving model or evaluation.
                         if 'unstructure' in strategy:
                             self._pruner.update_params()
 

paddleslim/auto_compression/transformer_pruner.py

@@ -296,13 +296,14 @@ class TransformerPruner:
         head_num = -1
         tmp_mha_ops = patterns['MHA$0']
         for op in tmp_mha_ops:
-            if op.type() in ['matmul', 'matmul_v2'] and (
-                    not has_trainable_var(op)) and head_num == -1:
+            if op.type() in [
+                    'matmul', 'matmul_v2'
+            ] and (not has_trainable_var(op)) and head_num == -1:
                 inp_var = op.inputs("X")
                 head_num = inp_var[0].shape()[1]
 
-        mha_weight, ffn_weight = preprocess_transformer_patterns(patterns,
-                                                                 graph)
+        mha_weight, ffn_weight = preprocess_transformer_patterns(
+            patterns, graph)
         return input_mask_op, layer_num, head_num, mha_weight, ffn_weight
 
     def _program_add_mask(self, program, patterns, layer_num, head_num,
@@ -312,7 +313,7 @@ class TransformerPruner:
         for ft in fetch_targets:
             fetch_list.append(ft.name)
         program = recover_inference_program(program)
-        block = program.global_block()
+        block = program.current_block()
         head_mask = block.create_var(
             name='head_mask',
             shape=[layer_num, head_num],
@@ -325,11 +326,12 @@ class TransformerPruner:
             1.0,
             out=head_mask,
             stop_gradient=False)
-        head_mask = unsqueeze_op(
-            block, -1,
-            unsqueeze_op(block, -1,
-                         unsqueeze_op(block, 1, head_mask, feed_num + 1),
-                         feed_num + 2), feed_num + 3)
+        head_mask = unsqueeze_op(block, -1,
+                                 unsqueeze_op(block, -1,
+                                              unsqueeze_op(
+                                                  block, 1, head_mask,
+                                                  feed_num + 1), feed_num + 2),
+                                 feed_num + 3)
 
         for pattern_name, pattern in patterns.items():
             if 'MHA' in pattern_name:
@@ -432,8 +434,7 @@ class TransformerPruner:
         index = np.reshape(
             np.take(
                 np.reshape(
-                    np.arange(
-                        0, head_num * num_per_head, dtype='int64'),
+                    np.arange(0, head_num * num_per_head, dtype='int64'),
                     (head_num, num_per_head)),
                 idx,
                 axis=0), (-1))
@@ -455,13 +456,13 @@ class TransformerPruner:
 
         for w_idx, weight_name in enumerate(qkv):
             if w_idx % 2 == 0:
-                ### reorder qkv weight 
+                ### reorder qkv weight
                 reorder_head_matrix(weight_name, qkv_index, dim=1)
             else:
-                ### reorder qkv bias 
+                ### reorder qkv bias
                 reorder_head_matrix(weight_name, qkv_index, dim=0)
 
-        ### reorder attention output weight 
+        ### reorder attention output weight
         reorder_head_matrix(attn_out[0], index, dim=0)
 
     def _reorder_neuron(self, scope, place, weight, idx):
@@ -528,8 +529,8 @@ class TransformerPruner:
         if _var is None:
             return
         param_t = _var.get_tensor()
-        pruned_ratio = [pruned_ratio[1]] if len(param_t.shape(
-        )) == 1 else pruned_ratio
+        pruned_ratio = [pruned_ratio[1]
+                        ] if len(param_t.shape()) == 1 else pruned_ratio
         origin_shape = param_t.shape()
 
         def process_qkv(qkv_param, pruned_ratio):
@@ -602,12 +603,12 @@ class TransformerPruner:
                         origin_shape = op.attr('shape')
                         pruned_shape = origin_shape
                         if len(origin_shape) == 3:
-                            pruned_shape[-1] = int(origin_shape[-1] *
-                                                   self.width_mult)
+                            pruned_shape[-1] = int(
+                                origin_shape[-1] * self.width_mult)
                             op.set_attr('shape', pruned_shape)
                         elif len(origin_shape) == 4 or len(origin_shape) == 5:
-                            pruned_shape[-2] = int(origin_shape[-2] *
-                                                   self.width_mult)
+                            pruned_shape[-2] = int(
+                                origin_shape[-2] * self.width_mult)
                             op.set_attr('shape', pruned_shape)
                         else:
                             raise IndexError

paddleslim/common/load_model.py

@@ -50,28 +50,28 @@ def load_inference_model(path_prefix,
         ), 'Please check {}, or fix params_filename parameter.'.format(
             os.path.join(path_prefix, model_name + '.pdiparams'))
         model_path_prefix = os.path.join(path_prefix, model_name)
-        [inference_program, feed_target_names, fetch_targets] = (
-            paddle.static.load_inference_model(
-                path_prefix=model_path_prefix, executor=executor))
+        [inference_program, feed_target_names,
+         fetch_targets] = (paddle.static.load_inference_model(
+             path_prefix=model_path_prefix, executor=executor))
     elif 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))
+        [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:
         model_name = '.'.join(model_filename.split('.')
                               [:-1]) if model_filename is not None else 'model'
         if os.path.exists(os.path.join(path_prefix, model_name + '.pdmodel')):
             model_path_prefix = os.path.join(path_prefix, model_name)
-            [inference_program, feed_target_names, fetch_targets] = (
-                paddle.static.load_inference_model(
-                    path_prefix=model_path_prefix, executor=executor))
+            [inference_program, feed_target_names,
+             fetch_targets] = (paddle.static.load_inference_model(
+                 path_prefix=model_path_prefix, executor=executor))
         else:
-            [inference_program, feed_target_names, fetch_targets] = (
-                paddle.static.load_inference_model(
-                    path_prefix=path_prefix, executor=executor))
+            [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]
 
@@ -125,13 +125,13 @@ def load_onnx_model(model_path,
             version = x2paddle.__version__
             v0, v1, v2 = version.split('.')
             version_sum = int(v0) * 100 + int(v1) * 10 + int(v2)
-            if version_sum < 139:
+            if version_sum != 139:
                 _logger.warning(
-                    "x2paddle>=1.3.9 is required, please use \"pip install x2paddle\"."
+                    "x2paddle==1.3.9 is required, please use \"pip install x2paddle==1.3.9\"."
                 )
-                os.system('python -m pip install -U x2paddle')
+                os.system('python -m pip install -U x2paddle==1.3.9')
         except:
-            os.system('python -m pip install -U x2paddle')
+            os.system('python -m pip install -U x2paddle==1.3.9')
         # check onnx installation and version
         try:
             pkg.require('onnx')
@@ -153,7 +153,8 @@ def load_onnx_model(model_path,
         time_info = int(time.time())
         if not disable_feedback:
             ConverterCheck(
-                task="ONNX", time_info=time_info, convert_state="Start").start()
+                task="ONNX", time_info=time_info,
+                convert_state="Start").start()
         # support distributed convert model
         model_idx = paddle.distributed.get_rank(
         ) if paddle.distributed.get_world_size() > 1 else 0

paddleslim/common/recover_program.py

@@ -41,9 +41,9 @@ def _recover_outputs_attr(program):
                 if "ReserveSpace" not in op.output_names or len(
                         op.output("ReserveSpace")) == 0:
                     reserve_space = block.create_var(
-                        name=paddle.fluid.unique_name.
-                        generate_with_ignorable_key(".".join(
-                            ["reserve_space", 'tmp'])),
+                        name=paddle.fluid.
+                        unique_name.generate_with_ignorable_key(
+                            ".".join(["reserve_space", 'tmp'])),
                         dtype=block.var(op.input("X")[0]).dtype,
                         type=paddle.framework.core.VarDesc.VarType.LOD_TENSOR,
                         persistable=False,
@@ -52,9 +52,9 @@ def _recover_outputs_attr(program):
             if op.type == 'transpose2':
                 if 'XShape' not in op.output_names:
                     xshape = block.create_var(
-                        name=paddle.fluid.unique_name.
-                        generate_with_ignorable_key(".".join(["xshape", 'tmp'
-                                                              ])),
+                        name=paddle.fluid.
+                        unique_name.generate_with_ignorable_key(
+                            ".".join(["xshape", 'tmp'])),
                         dtype=block.var(op.input("X")[0]).dtype,
                         type=paddle.framework.core.VarDesc.VarType.LOD_TENSOR,
                         shape=(0, ) + block.var(op.input("X")[0]).shape,
@@ -64,24 +64,24 @@ def _recover_outputs_attr(program):
     return program
 
 
-def _recover_param_attr(program):
+def _recover_param_attr(program, startup_program):
     """recover parameters attribute.
        Params in infermodel are stored in the form of variable, which can not be trained."""
     all_weights = [param for param in program.list_vars() \
         if param.persistable is True and param.name != 'feed' and param.name != 'fetch']
-    with paddle.static.program_guard(program):
+    with paddle.static.program_guard(program, startup_program):
         for w in all_weights:
             new_w = paddle.create_parameter(
                 shape=w.shape, dtype=w.dtype, name=w.name)
             new_w.set_value(w.get_value())
-            program.block(0).vars[w.name] = new_w
+            program.current_block().vars[w.name] = new_w
     return program
 
 
-def recover_inference_program(inference_program):
+def recover_inference_program(inference_program, startup_program=None):
     """  recover inference program to train program which can be trained. """
     _remove_fetch_node(inference_program)
-    inference_program = _recover_param_attr(inference_program)
+    inference_program = _recover_param_attr(inference_program, startup_program)
     inference_program = _recover_outputs_attr(inference_program)
     for var in inference_program.list_vars():
         var.stop_gradient = False