split ci cases

tests/st/networks/models/bert/test_bert_tdt_lossscale.py → tests/st/networks/models/bert/bert_performance/test_bert_tdt_lossscale.py

@@ -17,12 +17,8 @@
 
 import os
 import time
-
 import numpy as np
 import pytest
-from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
-from src.bert_model import BertConfig
-
 import mindspore.common.dtype as mstype
 import mindspore.dataset.engine.datasets as de
 import mindspore.dataset.transforms.c_transforms as C
@@ -35,6 +31,10 @@ from mindspore.train.callback import Callback
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager
 from mindspore.train.model import Model
 import mindspore.nn.learning_rate_schedule as lr_schedules
+from model_zoo.official.nlp.bert.src.bert_for_pre_training import BertNetworkWithLoss
+from model_zoo.official.nlp.bert.src.bert_for_pre_training import BertTrainOneStepWithLossScaleCell
+from model_zoo.official.nlp.bert.src.bert_model import BertConfig
+
 
 _current_dir = os.path.dirname(os.path.realpath(__file__))
 DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
@@ -177,74 +177,6 @@ class TimeMonitor(Callback):
         self.epoch_mseconds_list.append(epoch_mseconds)
         self.per_step_mseconds_list.append(epoch_mseconds / self.data_size)
 
-@pytest.mark.level0
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
-def test_bert_percision():
-    """test bert percision"""
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
-    ds, new_repeat_count, _ = me_de_train_dataset()
-    version = os.getenv('VERSION', 'large')
-    batch_size = 16
-    config = get_config(version=version, batch_size=batch_size)
-    netwithloss = BertNetworkWithLoss(config, True)
-    lr = BertLearningRate(decay_steps=ds.get_dataset_size()*new_repeat_count,
-                          learning_rate=5e-5, end_learning_rate=1e-9,
-                          power=10.0, warmup_steps=0)
-    decay_filter = lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower()
-    no_decay_filter = lambda x: 'layernorm' in x.name.lower() or 'bias' in x.name.lower()
-    decay_params = list(filter(decay_filter, netwithloss.trainable_params()))
-    other_params = list(filter(no_decay_filter, netwithloss.trainable_params()))
-    group_params = [{'params': decay_params, 'weight_decay': 0.01},
-                    {'params': other_params},
-                    {'order_params': netwithloss.trainable_params()}]
-    optimizer = Lamb(group_params, lr)
-    scale_window = 3
-    scale_manager = DynamicLossScaleManager(2 ** 16, 2, scale_window)
-    netwithgrads = BertTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer,
-                                                     scale_update_cell=scale_manager.get_update_cell())
-    netwithgrads.set_train(True)
-    model = Model(netwithgrads)
-    callback = ModelCallback()
-    params = netwithloss.trainable_params()
-    for param in params:
-        value = param.default_input
-        name = param.name
-        if isinstance(value, Tensor):
-            if name.split('.')[-1] in ['weight']:
-                if name.split('.')[-3] in ['cls2']:
-                    logger.info("***************** BERT param name is 1 {}".format(name))
-                    param.default_input = weight_variable(value.asnumpy().shape)
-                else:
-                    logger.info("***************** BERT param name is 2 {}".format(name))
-                    tempshape = value.asnumpy().shape
-                    shape = (tempshape[1], tempshape[0])
-                    weight_value = weight_variable(shape).asnumpy()
-                    param.default_input = Tensor(np.transpose(weight_value, [1, 0]))
-            else:
-                logger.info("***************** BERT param name is 3 {}".format(name))
-                param.default_input = weight_variable(value.asnumpy().shape)
-    model.train(new_repeat_count, ds, callbacks=callback, dataset_sink_mode=False)
-
-    # assertion occurs while the loss value, overflow state or loss_scale value is wrong
-    loss_value = np.array(callback.loss_list)
-    assert np.allclose(loss_value[0], 12.206575, 0, 0.000001)
-
-    expect_loss_value = [12.206575, 11.865044, 11.828129, 11.826707, 11.82108, 12.407423, 12.005459,
-                         12.621225, 12.222903, 12.427446]
-    print("loss value: {}".format(loss_value))
-    assert np.allclose(loss_value, expect_loss_value, 0, 0.0005)
-
-    overflow = np.array(callback.overflow_list)
-    expect_overflow = [False, False, False, True, False, False, False, True, False, False]
-    print("overflow: {}".format(overflow))
-    assert (overflow == expect_overflow).all()
-
-    loss_scale = np.array(callback.lossscale_list)
-    expect_loss_scale = [65536.0, 65536.0, 131072.0, 65536.0, 65536.0, 65536.0, 131072.0, 65536.0, 65536.0, 65536.0]
-    print("loss scale: {}".format(loss_scale))
-    assert np.allclose(loss_scale, expect_loss_scale, 0, 0)
 
 @pytest.mark.level0
 @pytest.mark.platform_arm_ascend_training
@@ -317,15 +249,14 @@ def test_bert_performance():
     assert np.allclose(loss_scale, expect_loss_scale, 0, 0)
 
     epoch_mseconds = np.array(time_monitor_callback.epoch_mseconds_list)[2]
-    expect_epoch_mseconds = 1600
+    expect_epoch_mseconds = 1400
     print("epoch mseconds: {}".format(epoch_mseconds))
     assert epoch_mseconds <= expect_epoch_mseconds + 5
 
     per_step_mseconds = np.array(time_monitor_callback.per_step_mseconds_list)[2]
-    expect_per_step_mseconds = 16
+    expect_per_step_mseconds = 14
     print("per step mseconds: {}".format(per_step_mseconds))
     assert per_step_mseconds <= expect_per_step_mseconds + 1
 
 if __name__ == '__main__':
-    test_bert_percision()
     test_bert_performance()

tests/st/networks/models/bert/bert_precision/test_bert_tdt_lossscale.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""train bert network without lossscale"""
+
+import os
+import time
+import numpy as np
+import pytest
+import mindspore.common.dtype as mstype
+import mindspore.dataset.engine.datasets as de
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore import context
+from mindspore import log as logger
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+from mindspore.nn.optim import Lamb
+from mindspore.train.callback import Callback
+from mindspore.train.loss_scale_manager import DynamicLossScaleManager
+from mindspore.train.model import Model
+import mindspore.nn.learning_rate_schedule as lr_schedules
+from model_zoo.official.nlp.bert.src.bert_for_pre_training import BertNetworkWithLoss
+from model_zoo.official.nlp.bert.src.bert_for_pre_training import BertTrainOneStepWithLossScaleCell
+from model_zoo.official.nlp.bert.src.bert_model import BertConfig
+
+
+_current_dir = os.path.dirname(os.path.realpath(__file__))
+DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
+SCHEMA_DIR = "/home/workspace/mindspore_dataset/bert/example/datasetSchema.json"
+
+
+def get_config(version='base', batch_size=1):
+    """get config"""
+    if version == 'base':
+        bert_config = BertConfig(
+            batch_size=batch_size,
+            seq_length=128,
+            vocab_size=21136,
+            hidden_size=768,
+            num_hidden_layers=2,
+            num_attention_heads=12,
+            intermediate_size=3072,
+            hidden_act="gelu",
+            hidden_dropout_prob=0.1,
+            attention_probs_dropout_prob=0.1,
+            max_position_embeddings=512,
+            type_vocab_size=2,
+            initializer_range=0.02,
+            use_relative_positions=True,
+            input_mask_from_dataset=True,
+            token_type_ids_from_dataset=True,
+            dtype=mstype.float32,
+            compute_type=mstype.float32)
+    elif version == 'large':
+        bert_config = BertConfig(
+            batch_size=batch_size,
+            seq_length=128,
+            vocab_size=21136,
+            hidden_size=1024,
+            num_hidden_layers=2,
+            num_attention_heads=16,
+            intermediate_size=4096,
+            hidden_act="gelu",
+            hidden_dropout_prob=0.0,
+            attention_probs_dropout_prob=0.0,
+            max_position_embeddings=512,
+            type_vocab_size=2,
+            initializer_range=0.02,
+            use_relative_positions=False,
+            input_mask_from_dataset=True,
+            token_type_ids_from_dataset=True,
+            dtype=mstype.float32,
+            compute_type=mstype.float16,
+            enable_fused_layernorm=False)
+    else:
+        bert_config = BertConfig(batch_size=batch_size)
+    return bert_config
+
+
+def me_de_train_dataset(sink_mode=False):
+    """test me de train dataset"""
+    # apply repeat operations
+    repeat_count = 1
+    sink_size = -1
+    batch_size = 16
+    ds = de.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
+                                                                "next_sentence_labels", "masked_lm_positions",
+                                                                "masked_lm_ids", "masked_lm_weights"], shuffle=False)
+    type_cast_op = C.TypeCast(mstype.int32)
+    new_repeat_count = repeat_count
+    if sink_mode:
+        sink_size = 100
+        new_repeat_count = 3
+    ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
+    ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
+    ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
+    ds = ds.map(input_columns="segment_ids", operations=type_cast_op)
+    ds = ds.map(input_columns="input_mask", operations=type_cast_op)
+    ds = ds.map(input_columns="input_ids", operations=type_cast_op)
+    # apply batch operations
+    ds = ds.batch(batch_size, drop_remainder=True)
+    logger.info("data size: {}".format(ds.get_dataset_size()))
+    logger.info("repeat_count: {}".format(ds.get_repeat_count()))
+    return ds, new_repeat_count, sink_size
+
+
+def weight_variable(shape):
+    """weight variable"""
+    np.random.seed(1)
+    ones = np.random.uniform(-0.1, 0.1, size=shape).astype(np.float32)
+    return Tensor(ones)
+
+
+class BertLearningRate(lr_schedules.LearningRateSchedule):
+    def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
+        super(BertLearningRate, self).__init__()
+        self.warmup_flag = False
+        if warmup_steps > 0:
+            self.warmup_flag = True
+            self.warmup_lr = lr_schedules.WarmUpLR(learning_rate, warmup_steps)
+        self.decay_lr = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
+        self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
+
+        self.greater = P.Greater()
+        self.one = Tensor(np.array([1.0]).astype(np.float32))
+        self.cast = P.Cast()
+
+    def construct(self, global_step):
+        decay_lr = self.decay_lr(global_step)
+        if self.warmup_flag:
+            is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
+            warmup_lr = self.warmup_lr(global_step)
+            lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
+        else:
+            lr = decay_lr
+        return lr
+
+
+class ModelCallback(Callback):
+    def __init__(self):
+        super(ModelCallback, self).__init__()
+        self.loss_list = []
+        self.overflow_list = []
+        self.lossscale_list = []
+
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        self.loss_list.append(cb_params.net_outputs[0].asnumpy()[0])
+        self.overflow_list.append(cb_params.net_outputs[1].asnumpy())
+        self.lossscale_list.append(cb_params.net_outputs[2].asnumpy())
+        print("epoch: {}, outputs are: {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
+
+class TimeMonitor(Callback):
+    """Time Monitor."""
+    def __init__(self, data_size):
+        super(TimeMonitor, self).__init__()
+        self.data_size = data_size
+        self.epoch_mseconds_list = []
+        self.per_step_mseconds_list = []
+    def epoch_begin(self, run_context):
+        self.epoch_time = time.time()
+
+    def epoch_end(self, run_context):
+        epoch_mseconds = (time.time() - self.epoch_time) * 1000
+        self.epoch_mseconds_list.append(epoch_mseconds)
+        self.per_step_mseconds_list.append(epoch_mseconds / self.data_size)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_bert_percision():
+    """test bert percision"""
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
+    ds, new_repeat_count, _ = me_de_train_dataset()
+    version = os.getenv('VERSION', 'large')
+    batch_size = 16
+    config = get_config(version=version, batch_size=batch_size)
+    netwithloss = BertNetworkWithLoss(config, True)
+    lr = BertLearningRate(decay_steps=ds.get_dataset_size()*new_repeat_count,
+                          learning_rate=5e-5, end_learning_rate=1e-9,
+                          power=10.0, warmup_steps=0)
+    decay_filter = lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower()
+    no_decay_filter = lambda x: 'layernorm' in x.name.lower() or 'bias' in x.name.lower()
+    decay_params = list(filter(decay_filter, netwithloss.trainable_params()))
+    other_params = list(filter(no_decay_filter, netwithloss.trainable_params()))
+    group_params = [{'params': decay_params, 'weight_decay': 0.01},
+                    {'params': other_params},
+                    {'order_params': netwithloss.trainable_params()}]
+    optimizer = Lamb(group_params, lr)
+    scale_window = 3
+    scale_manager = DynamicLossScaleManager(2 ** 16, 2, scale_window)
+    netwithgrads = BertTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer,
+                                                     scale_update_cell=scale_manager.get_update_cell())
+    netwithgrads.set_train(True)
+    model = Model(netwithgrads)
+    callback = ModelCallback()
+    params = netwithloss.trainable_params()
+    for param in params:
+        value = param.default_input
+        name = param.name
+        if isinstance(value, Tensor):
+            if name.split('.')[-1] in ['weight']:
+                if name.split('.')[-3] in ['cls2']:
+                    logger.info("***************** BERT param name is 1 {}".format(name))
+                    param.default_input = weight_variable(value.asnumpy().shape)
+                else:
+                    logger.info("***************** BERT param name is 2 {}".format(name))
+                    tempshape = value.asnumpy().shape
+                    shape = (tempshape[1], tempshape[0])
+                    weight_value = weight_variable(shape).asnumpy()
+                    param.default_input = Tensor(np.transpose(weight_value, [1, 0]))
+            else:
+                logger.info("***************** BERT param name is 3 {}".format(name))
+                param.default_input = weight_variable(value.asnumpy().shape)
+    model.train(new_repeat_count, ds, callbacks=callback, dataset_sink_mode=False)
+
+    # assertion occurs while the loss value, overflow state or loss_scale value is wrong
+    loss_value = np.array(callback.loss_list)
+    assert np.allclose(loss_value[0], 12.2065868, 0, 0.000001)
+
+    expect_loss_value = [12.2065868, 11.8651543, 11.8282356, 11.8266964, 11.8210478, 12.4073524, 12.0055466,
+                         12.6212320, 12.2229223, 12.4272099]
+    print("loss value: {}".format(loss_value))
+    assert np.allclose(loss_value, expect_loss_value, 0, 0.0005)
+
+    overflow = np.array(callback.overflow_list)
+    expect_overflow = [False, False, False, True, False, False, False, True, False, False]
+    print("overflow: {}".format(overflow))
+    assert (overflow == expect_overflow).all()
+
+    loss_scale = np.array(callback.lossscale_list)
+    expect_loss_scale = [65536.0, 65536.0, 131072.0, 65536.0, 65536.0, 65536.0, 131072.0, 65536.0, 65536.0, 65536.0]
+    print("loss scale: {}".format(loss_scale))
+    assert np.allclose(loss_scale, expect_loss_scale, 0, 0)
+
+
+if __name__ == '__main__':
+    test_bert_percision()