!2578 Add case for precision of bert network

Merge pull request !2578 from ddwolf/add_case_for_precisoin_of_bert_to_r0.5

mindspore/ccsrc/kernel/akg/akg_kernel_build.cc

@@ -276,11 +276,11 @@ bool AkgKernelBuild::CreateInputDescJson(const AnfNodePtr &anf_node, nlohmann::j
       input_desc_json[kName] = op_input_name;
       input_desc_json[kTensorName] = "input_" + std::to_string(GetInputTensorIdxInc(anf_node, real_input_index));
       auto input_shape = AnfAlgo::GetInputDeviceShape(anf_node, real_input_index);
-      if (GetInputTensorValue(anf_node, real_input_index, &input_desc_json)) {
+      if (anf_node->func_graph() != nullptr && anf_node->func_graph()->has_attr(FUNC_GRAPH_ATTR_GRAPH_KERNEL) &&
+          GetInputTensorValue(anf_node, real_input_index, &input_desc_json)) {
         MS_LOG(WARNING) << "we take input[" << real_input_index << "] of [" << anf_node->DebugString(2)
                         << "] as const tensor, shape: [" << Vector2Str(input_shape)
                         << "], value: " << input_desc_json[kValue];
-
         input_shape.clear();
       }
       if (input_shape.empty()) {

tests/st/networks/models/bert/test_bert_tdt_nockpt_lossscale.py

@@ -0,0 +1,194 @@
+# 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 pytest
+import numpy as np
+
+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.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
+from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
+from src.bert_model import BertConfig
+
+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=30522,
+            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=True,
+            input_mask_from_dataset=True,
+            token_type_ids_from_dataset=True,
+            dtype=mstype.float32,
+            compute_type=mstype.float16,
+            enable_fused_layernorm=True)
+    else:
+        bert_config = BertConfig(batch_size=batch_size)
+    return bert_config
+
+
+def me_de_train_dataset():
+    """test me de train dataset"""
+    # apply repeat operations
+    repeat_count = 1
+    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)
+    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
+    batch_size = int(os.getenv('BATCH_SIZE', '16'))
+    ds = ds.batch(batch_size, drop_remainder=True)
+    ds = ds.repeat(repeat_count)
+    return ds
+
+
+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 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)))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_bert_tdt():
+    """test bert tdt"""
+    os.system(f"rm -rf kernel_meta")
+    np.random.seed(0)
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
+    context.set_context(enable_graph_kernel=True)
+    ds = me_de_train_dataset()
+    config = get_config(version='large', batch_size=16)
+    netwithloss = BertNetworkWithLoss(config, True)
+    optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size()*ds.get_repeat_count(),
+                     start_learning_rate=5e-5, end_learning_rate=1e-9,
+                     power=10.0, warmup_steps=0, weight_decay=0.01)
+    scale_window = 3
+    scale_manager = DynamicLossScaleManager(262144, 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:
+        param.init_data()
+        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(1, 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)
+    expect_loss_value = [12.559319, 12.333815, 12.339806, 12.350235, 12.343947, 12.830965, 12.375336, 12.973715,
+                         12.57929, 12.7766905]
+    error = loss_value - expect_loss_value
+    print("loss value: {}".format(loss_value))
+    print("error value: {}".format(error))
+    assert np.allclose(loss_value, expect_loss_value, 0, 0.0005)
+
+    overflow = np.array(callback.overflow_list)
+    expect_overflow = [True, True, True, 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 = [131072.0, 65536.0, 32768.0, 16384.0, 16384.0, 16384.0, 32768.0, 16384.0, 16384.0, 16384.0]
+    print("loss scale: {}".format(loss_scale))
+    assert np.allclose(loss_scale, expect_loss_scale, 0, 0)
+
+
+if __name__ == '__main__':
+    test_bert_tdt()