Merge branch 'bug_fix' into 'develop'

bug fix for CPU_NUM, cluster infer

See merge request !16

fleet_rec/core/trainers/cluster_trainer.py

@@ -46,6 +46,7 @@ class ClusterTrainer(TranspileTrainer):
             else:
                 self.regist_context_processor(
                     'train_pass', self.dataloader_train)
+            self.regist_context_processor('infer_pass', self.infer)
             self.regist_context_processor('terminal_pass', self.terminal)
 
     def build_strategy(self):
@@ -139,14 +140,15 @@ class ClusterTrainer(TranspileTrainer):
                     metrics = [epoch, batch_id]
                     metrics.extend(metrics_rets)
 
-                    if batch_id % 10 == 0 and batch_id != 0:
+                    if batch_id % self.fetch_period == 0 and batch_id != 0:
                         print(metrics_format.format(*metrics))
                     batch_id += 1
             except fluid.core.EOFException:
                 reader.reset()
+            self.save(epoch, "train", is_fleet=True)
 
         fleet.stop_worker()
-        context['status'] = 'terminal_pass'
+        context['status'] = 'infer_pass'
 
     def dataset_train(self, context):
         fleet.init_worker()
@@ -162,10 +164,7 @@ class ClusterTrainer(TranspileTrainer):
                                          print_period=self.fetch_period)
             self.save(i, "train", is_fleet=True)
         fleet.stop_worker()
-        context['status'] = 'terminal_pass'
-
-    def infer(self, context):
-        context['status'] = 'terminal_pass'
+        context['status'] = 'infer_pass'
 
     def terminal(self, context):
         for model in self.increment_models:

fleet_rec/core/trainers/single_trainer.py

@@ -115,55 +115,6 @@ class SingleTrainer(TranspileTrainer):
             self.save(i, "train", is_fleet=False)
         context['status'] = 'infer_pass'
 
-    def infer(self, context):
-        infer_program = fluid.Program()
-        startup_program = fluid.Program()
-        with fluid.unique_name.guard():
-            with fluid.program_guard(infer_program, startup_program):
-                self.model.infer_net()
-
-        if self.model._infer_data_loader is None:
-            context['status'] = 'terminal_pass'
-            return
-
-        reader = self._get_dataloader("Evaluate")
-
-        metrics_varnames = []
-        metrics_format = []
-
-        metrics_format.append("{}: {{}}".format("epoch"))
-        metrics_format.append("{}: {{}}".format("batch"))
-
-        for name, var in self.model.get_infer_results().items():
-            metrics_varnames.append(var.name)
-            metrics_format.append("{}: {{}}".format(name))
-
-        metrics_format = ", ".join(metrics_format)
-        self._exe.run(startup_program)
-
-        for (epoch, model_dir) in self.increment_models:
-            print("Begin to infer epoch {}, model_dir: {}".format(epoch, model_dir))
-            program = infer_program.clone()
-            fluid.io.load_persistables(self._exe, model_dir, program)
-            reader.start()
-            batch_id = 0
-            try:
-                while True:
-                    metrics_rets = self._exe.run(
-                        program=program,
-                        fetch_list=metrics_varnames)
-
-                    metrics = [epoch, batch_id]
-                    metrics.extend(metrics_rets)
-
-                    if batch_id % 2 == 0 and batch_id != 0:
-                        print(metrics_format.format(*metrics))
-                    batch_id += 1
-            except fluid.core.EOFException:
-                reader.reset()
- 
-        context['status'] = 'terminal_pass'
-
     def terminal(self, context):
         for model in self.increment_models:
             print("epoch :{}, dir: {}".format(model[0], model[1]))

fleet_rec/core/trainers/transpiler_trainer.py

@@ -36,7 +36,7 @@ class TranspileTrainer(Trainer):
     def processor_register(self):
         print("Need implement by trainer, `self.regist_context_processor('uninit', self.instance)` must be the first")
 
-    def _get_dataloader(self, state):
+    def _get_dataloader(self, state="TRAIN"):
         if state == "TRAIN":
             dataloader = self.model._data_loader
             namespace = "train.reader"
@@ -59,7 +59,7 @@ class TranspileTrainer(Trainer):
             dataloader.set_sample_generator(reader, batch_size)
         return dataloader
 
-    def _get_dataset(self, state):
+    def _get_dataset(self, state="TRAIN"):
         if state == "TRAIN":
             inputs = self.model.get_inputs()
             namespace = "train.reader"
@@ -110,11 +110,14 @@ class TranspileTrainer(Trainer):
             if not need_save(epoch_id, save_interval, False):
                 return
             
-            print("save inference model is not supported now.")
-            return
+          #  print("save inference model is not supported now.")
+          #  return
 
             feed_varnames = envs.get_global_env("save.inference.feed_varnames", None, namespace)
             fetch_varnames = envs.get_global_env("save.inference.fetch_varnames", None, namespace)
+            if feed_varnames is None or fetch_varnames is None:
+                return
+
             fetch_vars = [fluid.default_main_program().global_block().vars[varname] for varname in fetch_varnames]
             dirname = envs.get_global_env("save.inference.dirname", None, namespace)
 
@@ -122,7 +125,7 @@ class TranspileTrainer(Trainer):
             dirname = os.path.join(dirname, str(epoch_id))
 
             if is_fleet:
-                fleet.save_inference_model(dirname, feed_varnames, fetch_vars)
+                fleet.save_inference_model(self._exe, dirname, feed_varnames, fetch_vars)
             else:
                 fluid.io.save_inference_model(dirname, feed_varnames, fetch_vars, self._exe)
             self.inference_models.append((epoch_id, dirname))
@@ -167,7 +170,53 @@ class TranspileTrainer(Trainer):
         context['is_exit'] = True
 
     def infer(self, context):
-        context['is_exit'] = True
+        infer_program = fluid.Program()
+        startup_program = fluid.Program()
+        with fluid.unique_name.guard():
+            with fluid.program_guard(infer_program, startup_program):
+                self.model.infer_net()
+
+        if self.model._infer_data_loader is None:
+            context['status'] = 'terminal_pass'
+            return
+
+        reader = self._get_dataloader("Evaluate")
+
+        metrics_varnames = []
+        metrics_format = []
+
+        metrics_format.append("{}: {{}}".format("epoch"))
+        metrics_format.append("{}: {{}}".format("batch"))
+
+        for name, var in self.model.get_infer_results().items():
+            metrics_varnames.append(var.name)
+            metrics_format.append("{}: {{}}".format(name))
+
+        metrics_format = ", ".join(metrics_format)
+        self._exe.run(startup_program)
+
+        for (epoch, model_dir) in self.increment_models:
+            print("Begin to infer epoch {}, model_dir: {}".format(epoch, model_dir))
+            program = infer_program.clone()
+            fluid.io.load_persistables(self._exe, model_dir, program)
+            reader.start()
+            batch_id = 0
+            try:
+                while True:
+                    metrics_rets = self._exe.run(
+                        program=program,
+                        fetch_list=metrics_varnames)
+
+                    metrics = [epoch, batch_id]
+                    metrics.extend(metrics_rets)
+
+                    if batch_id % 2 == 0 and batch_id != 0:
+                        print(metrics_format.format(*metrics))
+                    batch_id += 1
+            except fluid.core.EOFException:
+                reader.reset()
+
+        context['status'] = 'terminal_pass'
 
     def terminal(self, context):
         print("clean up and exit")

fleet_rec/run.py

@@ -72,8 +72,11 @@ def set_runtime_envs(cluster_envs, engine_yaml):
     if cluster_envs is None:
         cluster_envs = {}
 
+    engine_extras = get_engine_extras()
+    if "train.trainer.threads" in engine_extras and "CPU_NUM" in cluster_envs:
+        cluster_envs["CPU_NUM"] = engine_extras["train.trainer.threads"]
     envs.set_runtime_environs(cluster_envs)
-    envs.set_runtime_environs(get_engine_extras())
+    envs.set_runtime_environs(engine_extras)
 
     need_print = {}
     for k, v in os.environ.items():

models/recall/word2vec/prepare_data.sh

+#! /bin/bash
+
+# download train_data
+mkdir raw_data
+wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar
+tar xvf 1-billion-word-language-modeling-benchmark-r13output.tar
+mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ raw_data/
+
+# preprocess data
+python preprocess.py --build_dict --build_dict_corpus_dir raw_data/training-monolingual.tokenized.shuffled --dict_path raw_data/test_build_dict
+python preprocess.py --filter_corpus --dict_path raw_data/test_build_dict --input_corpus_dir raw_data/training-monolingual.tokenized.shuffled --output_corpus_dir raw_data/convert_text8 --min_count 5 --downsample 0.001
+mkdir thirdparty
+mv raw_data/test_build_dict thirdparty/
+mv raw_data/test_build_dict_word_to_id_ thirdparty/
+
+python preprocess.py --data_resplit --input_corpus_dir=raw_data/convert_text8 --output_corpus_dir=train_data
+
+# download test data
+wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar
+tar xzvf test_dir.tar -C raw_data
+mv raw_data/data/test_dir test_data/
+rm -rf raw_data
+
+
+

models/recall/word2vec/preprocess.py

+# -*- coding: utf-8 -*
+import os
+import random
+import re
+import six
+import argparse
+import io
+import math
+prog = re.compile("[^a-z ]", flags=0)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Paddle Fluid word2 vector preprocess")
+    parser.add_argument(
+        '--build_dict_corpus_dir', type=str, help="The dir of corpus")
+    parser.add_argument(
+        '--input_corpus_dir', type=str, help="The dir of input corpus")
+    parser.add_argument(
+        '--output_corpus_dir', type=str, help="The dir of output corpus")
+    parser.add_argument(
+        '--dict_path',
+        type=str,
+        default='./dict',
+        help="The path of dictionary ")
+    parser.add_argument(
+        '--min_count',
+        type=int,
+        default=5,
+        help="If the word count is less then min_count, it will be removed from dict"
+    )
+    parser.add_argument(
+        '--file_nums',
+        type=int,
+        default=1024,
+        help="re-split input corpus file nums"
+    )
+    parser.add_argument(
+        '--downsample',
+        type=float,
+        default=0.001,
+        help="filter word by downsample")
+    parser.add_argument(
+        '--filter_corpus',
+        action='store_true',
+        default=False,
+        help='Filter corpus')
+    parser.add_argument(
+        '--build_dict',
+        action='store_true',
+        default=False,
+        help='Build dict from corpus')
+    parser.add_argument(
+        '--data_resplit',
+        action='store_true',
+        default=False,
+        help='re-split input corpus files')
+    return parser.parse_args()
+
+
+def text_strip(text):
+    #English Preprocess Rule
+    return prog.sub("", text.lower())
+
+
+# Shameless copy from Tensorflow https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/text_encoder.py
+# Unicode utility functions that work with Python 2 and 3
+def native_to_unicode(s):
+    if _is_unicode(s):
+        return s
+    try:
+        return _to_unicode(s)
+    except UnicodeDecodeError:
+        res = _to_unicode(s, ignore_errors=True)
+        return res
+
+
+def _is_unicode(s):
+    if six.PY2:
+        if isinstance(s, unicode):
+            return True
+    else:
+        if isinstance(s, str):
+            return True
+    return False
+
+
+def _to_unicode(s, ignore_errors=False):
+    if _is_unicode(s):
+        return s
+    error_mode = "ignore" if ignore_errors else "strict"
+    return s.decode("utf-8", errors=error_mode)
+
+
+def filter_corpus(args):
+    """
+    filter corpus and convert id.
+    """
+    word_count = dict()
+    word_to_id_ = dict()
+    word_all_count = 0
+    id_counts = []
+    word_id = 0
+    #read dict
+    with io.open(args.dict_path, 'r', encoding='utf-8') as f:
+        for line in f:
+            word, count = line.split()[0], int(line.split()[1])
+            word_count[word] = count
+            word_to_id_[word] = word_id
+            word_id += 1
+            id_counts.append(count)
+            word_all_count += count
+
+    #write word2id file
+    print("write word2id file to : " + args.dict_path + "_word_to_id_")
+    with io.open(
+            args.dict_path + "_word_to_id_", 'w+', encoding='utf-8') as fid:
+        for k, v in word_to_id_.items():
+            fid.write(k + " " + str(v) + '\n')
+    #filter corpus and convert id
+    if not os.path.exists(args.output_corpus_dir):
+        os.makedirs(args.output_corpus_dir)
+    for file in os.listdir(args.input_corpus_dir):
+        with io.open(args.output_corpus_dir + '/convert_' + file + '.csv', "w") as wf:
+            with io.open(
+                    args.input_corpus_dir + '/' + file, encoding='utf-8') as rf:
+                print(args.input_corpus_dir + '/' + file)
+                for line in rf:
+                    signal = False
+                    line = text_strip(line)
+                    words = line.split()
+                    write_line = ""
+                    for item in words:
+                        if item in word_count:
+                            idx = word_to_id_[item]
+                        else:
+                            idx = word_to_id_[native_to_unicode('<UNK>')]
+                        count_w = id_counts[idx]
+                        corpus_size = word_all_count
+                        keep_prob = (
+                            math.sqrt(count_w /
+                                      (args.downsample * corpus_size)) + 1
+                        ) * (args.downsample * corpus_size) / count_w
+                        r_value = random.random()
+                        if r_value > keep_prob:
+                            continue
+                        write_line += str(idx)
+                        write_line += ","
+                        signal = True
+                    if signal:
+                        write_line = write_line[:-1] + "\n"
+                        wf.write(_to_unicode(write_line))
+
+
+def build_dict(args):
+    """
+    proprocess the data, generate dictionary and save into dict_path.
+    :param corpus_dir: the input data dir.
+    :param dict_path: the generated dict path. the data in dict is "word count"
+    :param min_count:
+    :return:
+    """
+    # word to count
+
+    word_count = dict()
+
+    for file in os.listdir(args.build_dict_corpus_dir):
+        with io.open(
+                args.build_dict_corpus_dir + "/" + file, encoding='utf-8') as f:
+            print("build dict : ", args.build_dict_corpus_dir + "/" + file)
+            for line in f:
+                line = text_strip(line)
+                words = line.split()
+                for item in words:
+                    if item in word_count:
+                        word_count[item] = word_count[item] + 1
+                    else:
+                        word_count[item] = 1
+
+    item_to_remove = []
+    for item in word_count:
+        if word_count[item] <= args.min_count:
+            item_to_remove.append(item)
+
+    unk_sum = 0
+    for item in item_to_remove:
+        unk_sum += word_count[item]
+        del word_count[item]
+    #sort by count
+    word_count[native_to_unicode('<UNK>')] = unk_sum
+    word_count = sorted(
+        word_count.items(), key=lambda word_count: -word_count[1])
+
+    with io.open(args.dict_path, 'w+', encoding='utf-8') as f:
+        for k, v in word_count:
+            f.write(k + " " + str(v) + '\n')
+
+
+def data_split(args):
+    raw_data_dir = args.input_corpus_dir
+    new_data_dir = args.output_corpus_dir
+    if not os.path.exists(new_data_dir):
+        os.mkdir(new_data_dir)
+    files = os.listdir(raw_data_dir)
+    print(files)
+    index = 0
+    contents = []
+    for file_ in files:
+        with open(os.path.join(raw_data_dir, file_), 'r') as f:
+            contents.extend(f.readlines())
+    
+    num = int(args.file_nums)
+    lines_per_file = len(contents) / num
+    print("contents: ", str(len(contents)))
+    print("lines_per_file: ", str(lines_per_file))
+    
+    for i in range(1, num+1):
+        with open(os.path.join(new_data_dir, "part_" + str(i)), 'w') as fout:
+            data = contents[(i-1)*lines_per_file:min(i*lines_per_file,len(contents))]
+            for line in data:
+               fout.write(line)
+
+if __name__ == "__main__":
+    args = parse_args()
+    if args.build_dict:
+        build_dict(args)
+    elif args.filter_corpus:
+        filter_corpus(args)
+    elif args.data_resplit:
+        data_split(args)
+    else:
+        print(
+            "error command line, please choose --build_dict or --filter_corpus")