Clear fluid api and fix tests (#1641)

* remove fluid apis.

* fix hpo.

* fix asp.

demo/models/pvanet.py

@@ -2,10 +2,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 import paddle
-import paddle.fluid as fluid
 from paddle.nn.initializer import KaimingUniform
-import os, sys, time, math
-import numpy as np
 from collections import namedtuple
 
 BLOCK_TYPE_MCRELU = 'BLOCK_TYPE_MCRELU'
@@ -458,15 +455,24 @@ def loss(f_score, f_geo, l_score, l_geo, l_mask, class_num=1):
     abs_geo_diff = paddle.abs(geo_diff)
     l_flag = l_score >= 1
     l_flag = paddle.cast(x=l_flag, dtype="float32")
-    l_flag = fluid.layers.expand(x=l_flag, expand_times=[1, channels, 1, 1])
+    l_flag = paddle.expand(
+        x=l_flag,
+        shape=[
+            l_flag.shape[0], l_flag.shape[1] * channels, l_flag.shape[2],
+            l_flag.shape[3]
+        ])
 
     smooth_l1_sign = abs_geo_diff < l_flag
     smooth_l1_sign = paddle.cast(x=smooth_l1_sign, dtype="float32")
 
     in_loss = abs_geo_diff * abs_geo_diff * smooth_l1_sign + (
         abs_geo_diff - 0.5) * (1.0 - smooth_l1_sign)
-    l_short_edge = fluid.layers.expand(
-        x=l_short_edge, expand_times=[1, channels, 1, 1])
+    l_short_edge = paddle.expand(
+        x=l_short_edge,
+        shape=[
+            l_short_edge.shape[0], l_short_edge.shape[1] * channels,
+            l_short_edge.shape[2], l_short_edge.shape[3]
+        ])
     out_loss = l_short_edge * in_loss * l_flag
     out_loss = out_loss * l_flag
     smooth_l1_loss = paddle.mean(out_loss)

demo/quant/pact_quant_aware/train.py

@@ -18,7 +18,7 @@ from paddleslim.analysis import flops
 from paddleslim.quant import quant_aware, quant_post, convert
 import models
 from utility import add_arguments, print_arguments
-from paddle.fluid.layer_helper import LayerHelper
+from paddle.common_ops_import import LayerHelper
 quantization_model_save_dir = './quantization_models/'
 
 _logger = get_logger(__name__, level=logging.INFO)
@@ -146,8 +146,8 @@ def compress(args):
         raise ValueError("{} is not supported.".format(args.data))
 
     image_shape = [int(m) for m in image_shape.split(",")]
-    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
-                                                                     model_list)
+    assert args.model in model_list, "{} is not in lists: {}".format(
+        args.model, model_list)
     image = paddle.static.data(
         name='image', shape=[None] + image_shape, dtype='float32')
     if args.use_pact:

demo/quant/quant_embedding/net.py

@@ -15,15 +15,17 @@
 neural network for word2vec
 """
 from __future__ import print_function
-import math
-import numpy as np
 import paddle
-import paddle.fluid as fluid
+import paddle.nn.functional as F
 
 
-def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
+def skip_gram_word2vec(dict_size,
+                       embedding_size,
+                       batch_size,
+                       is_sparse=False,
+                       neg_num=5):
 
-    datas = []
+    words = []
     input_word = paddle.static.data(
         name="input_word", shape=[None, 1], dtype='int64')
     true_word = paddle.static.data(
@@ -31,14 +33,13 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
     neg_word = paddle.static.data(
         name="neg_label", shape=[None, neg_num], dtype='int64')
 
-    datas.append(input_word)
-    datas.append(true_word)
-    datas.append(neg_word)
+    words.append(input_word)
+    words.append(true_word)
+    words.append(neg_word)
 
-    py_reader = fluid.layers.create_py_reader_by_data(
-        capacity=64, feed_list=datas, name='py_reader', use_double_buffer=True)
+    py_reader = paddle.io.DataLoader.from_generator(
+        capacity=64, feed_list=words, use_double_buffer=True, iterable=False)
 
-    words = fluid.layers.read_file(py_reader)
     words[0] = paddle.reshape(words[0], [-1])
     words[1] = paddle.reshape(words[1], [-1])
     init_width = 0.5 / embedding_size
@@ -72,8 +73,7 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
         input=neg_word_reshape,
         is_sparse=is_sparse,
         size=[dict_size, embedding_size],
-        param_attr=paddle.ParamAttr(
-            name='emb_w', learning_rate=1.0))
+        param_attr=paddle.ParamAttr(name='emb_w', learning_rate=1.0))
 
     neg_emb_w_re = paddle.reshape(
         neg_emb_w, shape=[-1, neg_num, embedding_size])
@@ -81,31 +81,29 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
         input=neg_word_reshape,
         is_sparse=is_sparse,
         size=[dict_size, 1],
-        param_attr=paddle.ParamAttr(
-            name='emb_b', learning_rate=1.0))
+        param_attr=paddle.ParamAttr(name='emb_b', learning_rate=1.0))
 
     neg_emb_b_vec = paddle.reshape(neg_emb_b, shape=[-1, neg_num])
-    true_logits = paddle.add(paddle.mean(
-        paddle.multiply(input_emb, true_emb_w), keepdim=True),
-                             true_emb_b)
+    true_logits = paddle.add(
+        paddle.mean(paddle.multiply(input_emb, true_emb_w), keepdim=True),
+        true_emb_b)
     input_emb_re = paddle.reshape(input_emb, shape=[-1, 1, embedding_size])
     neg_matmul = paddle.matmul(input_emb_re, neg_emb_w_re, transpose_y=True)
     neg_matmul_re = paddle.reshape(neg_matmul, shape=[-1, neg_num])
     neg_logits = paddle.add(neg_matmul_re, neg_emb_b_vec)
     #nce loss
 
-    # TODO: replaced by paddle.tensor.creation.fill_constant_batch_size_like
-    label_ones = fluid.layers.fill_constant_batch_size_like(
-        true_logits, shape=[-1, 1], value=1.0, dtype='float32')
-    label_zeros = fluid.layers.fill_constant_batch_size_like(
-        true_logits, shape=[-1, neg_num], value=0.0, dtype='float32')
-
-    true_xent = paddle.nn.functional.binary_cross_entropy(true_logits,
-                                                          label_ones)
-    neg_xent = paddle.nn.functional.binary_cross_entropy(neg_logits,
-                                                         label_zeros)
-    cost = paddle.add(paddle.sum(true_xent, axis=1),
-                      paddle.sum(neg_xent, axis=1))
+    label_ones = paddle.full(
+        shape=[batch_size, 1], fill_value=1.0, dtype='float32')
+    label_zeros = paddle.full(
+        shape=[batch_size, neg_num], fill_value=0.0, dtype='float32')
+
+    true_xent = F.binary_cross_entropy_with_logits(
+        true_logits, label_ones, reduction='none')
+    neg_xent = F.binary_cross_entropy_with_logits(
+        neg_logits, label_zeros, reduction='none')
+    cost = paddle.add(
+        paddle.sum(true_xent, axis=1), paddle.sum(neg_xent, axis=1))
     avg_cost = paddle.mean(cost)
     return avg_cost, py_reader
 

demo/quant/quant_embedding/train.py

@@ -121,7 +121,7 @@ def convert_python_to_tensor(weight, batch_size, sample_reader):
 def train_loop(args, train_program, reader, py_reader, loss, trainer_id, weight,
                lr):
 
-    py_reader.decorate_tensor_provider(
+    py_reader.set_batch_generator(
         convert_python_to_tensor(weight, args.batch_size, reader.train()))
 
     place = paddle.CPUPlace()
@@ -213,6 +213,7 @@ def train(args):
     loss, py_reader = skip_gram_word2vec(
         word2vec_reader.dict_size,
         args.embedding_size,
+        args.batch_size,
         is_sparse=args.is_sparse,
         neg_num=args.nce_num)
 
@@ -223,7 +224,7 @@ def train(args):
 
     optimizer.minimize(loss)
 
-    # do local training 
+    # do local training
     logger.info("run local training")
     main_program = paddle.static.default_main_program()
     train_loop(args, main_program, word2vec_reader, py_reader, loss, 0,

paddleslim/auto_compression/create_compressed_program.py

@@ -78,10 +78,8 @@ def _create_optimizer(train_config):
     ### build optimizer
     optim_params = optimizer_builder['optimizer']
     optim_type = optim_params.pop('type')
-    opt = getattr(optimizer, optim_type)(learning_rate=lr,
-                                         grad_clip=grad_clip,
-                                         weight_decay=reg,
-                                         **optim_params)
+    opt = getattr(optimizer, optim_type)(
+        learning_rate=lr, grad_clip=grad_clip, weight_decay=reg, **optim_params)
     return opt, lr
 
 
@@ -160,8 +158,8 @@ def _parse_distill_loss(distill_node_pair,
     for node, loss_clas, lam in zip(distill_node_pair, distill_loss,
                                     distill_lambda):
         tmp_loss = losses.get(loss_clas, 0.0)
-        _logger.info("train config.distill_node_pair: {}".format(
-            node, loss_clas, lam))
+        _logger.info(
+            "train config.distill_node_pair: {}".format(node, loss_clas, lam))
         assert len(node) % 2 == 0, \
             "distill_node_pair config wrong, the length needs to be an even number"
         for i in range(len(node) // 2):
@@ -529,9 +527,7 @@ def build_prune_program(executor,
             original_shapes = {}
             for param in train_program_info.program.global_block(
             ).all_parameters():
-                if config[
-                        'prune_params_name'] is not None and param.name in config[
-                            'prune_params_name']:
+                if config['prune_params_name'] is not None and param.name in config['prune_params_name']:
                     params.append(param.name)
                     original_shapes[param.name] = param.shape
 
@@ -541,9 +537,8 @@ def build_prune_program(executor,
             train_program_info.program,
             paddle.static.global_scope(),
             params=params,
-            ratios=[config['pruned_ratio']] * len(params)
-            if isinstance(config['pruned_ratio'], float) else
-            config['pruned_ratio'],
+            ratios=[config['pruned_ratio']] * len(params) if isinstance(
+                config['pruned_ratio'], float) else config['pruned_ratio'],
             place=place)
         _logger.info(
             "####################channel pruning##########################")
@@ -577,8 +572,9 @@ def build_prune_program(executor,
                     pruner.add_supported_layer(param.name)
             if "teacher_" in param.name:
                 excluded_params_name.append(param.name)
-        pruner.set_excluded_layers(train_program_info.program,
-                                   excluded_params_name)
+        pruner.set_excluded_layers(
+            main_program=train_program_info.program,
+            param_names=excluded_params_name)
     elif strategy.startswith('transformer_prune'):
         from .transformer_pruner import TransformerPruner
         assert eval_dataloader is not None, "transformer_pruner must set eval_dataloader"

paddleslim/quant/post_quant_hpo.py

@@ -83,7 +83,7 @@ class QuantConfig(object):
         """QuantConfig init"""
         self.executor = executor
         self.place = place
-        self.float_infer_model_path = float_infer_model_path
+        self.float_infer_model_path = float_infer_model_path.rstrip('/')
         self.quantize_model_path = quantize_model_path
         self.algo = algo,
         self.hist_percent = hist_percent,

paddleslim/quant/reconstruction_quantization.py

@@ -25,7 +25,9 @@ from ..dist import merge
 from ..core.graph_wrapper import GraphWrapper
 from ..common import get_logger
 
-__all__ = ['ReconstructionQuantization', ]
+__all__ = [
+    'ReconstructionQuantization',
+]
 
 _logger = get_logger(
     __name__,
@@ -91,7 +93,8 @@ class ReconstructionQuantization(PostTrainingQuantization):
         batch_id = 0
         with utils.tqdm(
                 total=self._batch_nums,
-                bar_format='Preparation stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
+                bar_format=
+                'Preparation stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
                 ncols=80, ) as t:
             for data in self._data_loader():
                 self._executor.run(
@@ -111,7 +114,8 @@ class ReconstructionQuantization(PostTrainingQuantization):
         batch_id = 0
         with utils.tqdm(
                 total=self._batch_nums,
-                bar_format='Sampling stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
+                bar_format=
+                'Sampling stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
                 ncols=80, ) as t:
             for data in self._data_loader():
                 self._executor.run(
@@ -237,7 +241,7 @@ class ReconstructionQuanter(object):
                 return a batch every time.
             executor(paddle.static.Executor): The executor to load, run and save the
                 quantized model.
-            scope(fluid.Scope, optional): The scope of the program, use it to load
+            scope(static.Scope, optional): The scope of the program, use it to load
                 and save variables. If scope=None, get scope by global_scope().
             place(CPUPlace()|CUDAPlace(N)): This parameter represents
                                                     paddle run on which device.
@@ -385,8 +389,8 @@ class ReconstructionQuanter(object):
 
             with paddle.static.program_guard(tmp_program, startup_program):
                 student_var = tmp_program.global_block().var(quant_op_out_name)
-                teacher_var = tmp_program.global_block().var("teacher_" +
-                                                             quant_op_out_name)
+                teacher_var = tmp_program.global_block().var(
+                    "teacher_" + quant_op_out_name)
                 total_loss, recon_loss, round_loss = loss_function.get_loss(
                     student_var,
                     teacher_var, )
@@ -471,7 +475,8 @@ class ReconstructionQuanter(object):
             shape=weight.shape,
             dtype=weight.dtype,
             name=weight.name + ".alpha",
-            default_initializer=paddle.nn.initializer.Assign(self._alpha, ), )
+            default_initializer=paddle.nn.initializer.Assign(
+                self._alpha, ), )
 
         h_v = paddle.clip(
             paddle.nn.functional.sigmoid(v) * (ZETA - GAMMA) + GAMMA,
@@ -483,13 +488,14 @@ class ReconstructionQuanter(object):
                 dtype=weight.dtype,
                 shape=weight.shape,
                 name=weight.name + '.scale',
-                default_initializer=paddle.nn.initializer.Assign(scale, ))
+                default_initializer=paddle.nn.initializer.Assign(
+                    scale, ))
         else:
             scale_var = scale
 
         quantized_weight = _quant(weight_copy, scale_var)
-        floor_weight = (paddle.floor(quantized_weight) - quantized_weight
-                        ).detach() + quantized_weight
+        floor_weight = (paddle.floor(quantized_weight) -
+                        quantized_weight).detach() + quantized_weight
         clip_weight = paddle.clip(floor_weight + h_v, -bnt, bnt)
         w = _dequant(clip_weight, scale_var)
         return w
@@ -525,8 +531,9 @@ class ReconstructionQuanter(object):
 
     def _insert_drop_quant_dequant(self):
         for op in self._graph.ops():
-            if op.type(
-            ) in ['conv2d', 'depthwise_conv2d', 'mul', 'matmul', 'matmul_v2']:
+            if op.type() in [
+                    'conv2d', 'depthwise_conv2d', 'mul', 'matmul', 'matmul_v2'
+            ]:
                 if op.type() in ['conv2d', 'depthwise_conv2d']:
                     if op.inputs("Filter")[0].name().startswith("teacher"):
                         break
@@ -670,8 +677,8 @@ class ReconstructionQuanter(object):
                             'X': var._var,
                             'Y': op.input('Y')[0] + '.qdrop',
                         }
-                    elif _type == 'scale' and op.input('X')[
-                            0] == inputs.name + '.tmp':
+                    elif _type == 'scale' and op.input(
+                            'X')[0] == inputs.name + '.tmp':
                         _inputs = {'X': var._var}
                     else:
                         _inputs = {'X': op.input('X')[0] + '.qdrop'}
@@ -687,11 +694,13 @@ class ReconstructionQuanter(object):
                     'conv2d', 'depthwise_conv2d', 'mul', 'matmul', 'matmul_v2'
             ]:
                 continue
-            if op.type() in ['conv2d', 'depthwise_conv2d'] and op.inputs(
-                    'Filter')[0].name().startswith('teacher'):
+            if op.type() in [
+                    'conv2d', 'depthwise_conv2d'
+            ] and op.inputs('Filter')[0].name().startswith('teacher'):
                 continue
-            if op.type() in ['mul', 'matmul', 'matmul_v2'] and op.inputs('Y')[
-                    0].name().startswith('teacher'):
+            if op.type() in [
+                    'mul', 'matmul', 'matmul_v2'
+            ] and op.inputs('Y')[0].name().startswith('teacher'):
                 continue
             if func == '_soft_rounding':
                 op._op._rename_input(inputs.name, out.name + '.rounding')
@@ -964,8 +973,8 @@ class RegionBuilder(object):
             else:
                 future_ep = _find_multi_input_ep(ep)
 
-            if future_ep is None or self._depth[future_ep.idx()] - self._depth[
-                    sp.idx()] >= limit:
+            if future_ep is None or self._depth[future_ep.idx(
+            )] - self._depth[sp.idx()] >= limit:
                 return self._create_region(sp, ep)
             ep = future_ep
 

tests/test_latency_predictor.py

@@ -147,10 +147,8 @@ class ModelCase6(paddle.nn.Layer):
         x = paddle.unsqueeze(x=x, axis=[2])
         x = self.relu1(x)
         y = paddle.full(shape=x.shape, fill_value=1)
-        # x = paddle.stack([x, y], axis=3)
         x = paddle.slice(x, axes=[0], starts=[0], ends=[1])
         x = paddle.exp(x)
-        # y += paddle.fluid.layers.uniform_random(y.shape)
         y = paddle.expand(y, shape=[1, 768, 768, 2])
         x = paddle.expand(x, shape=[1, 768, 768, 2])
         out = paddle.concat([x, y])
@@ -161,8 +159,8 @@ class ModelCase6(paddle.nn.Layer):
         max_idx = paddle.argmax(
             out1.reshape((outshape[0], outshape[1], outshape[2] * outshape[3])),
             axis=-1)
-        out2 = out2.reshape(
-            (outshape[0], outshape[1], outshape[2] * outshape[3]))
+        out2 = out2.reshape((outshape[0], outshape[1],
+                             outshape[2] * outshape[3]))
         res, _ = self.lstm(out2)
         return res, max_idx
 
@@ -238,9 +236,9 @@ class TestCase2(unittest.TestCase):
 
         model_name = '.'.join(model_filename.split('.')[:-1])
         model_path_prefix = os.path.join(model_dir, model_name)
-        [inference_program, feed_target_names, fetch_targets] = (
-            paddle.static.load_inference_model(
-                path_prefix=model_path_prefix, executor=exe))
+        [inference_program, feed_target_names,
+         fetch_targets] = (paddle.static.load_inference_model(
+             path_prefix=model_path_prefix, executor=exe))
 
         if type(input_shapes) in [list, tuple]:
             assert len(