Add test case and fix two bugs

1. add case to guard precision
2. fix a shape bug
3. fix a funcGraph bug

mindspore/ccsrc/device/ascend/ascend_stream_assign.cc

@@ -348,16 +348,13 @@ void AscendStreamAssign::GetProcessedStream(const NotNull<KernelGraphPtr> &graph
     uint32_t cur_stream_id = AnfAlgo::GetStreamId(cur_cnode_ptr);
 
     if (AnfAlgo::GetCNodeName(cur_cnode_ptr) == kStreamSwitchOpName) {
-      auto primitive = AnfAlgo::GetCNodePrimitive(cur_cnode_ptr);
-      MS_EXCEPTION_IF_NULL(primitive);
-      auto true_stream_id = GetValue<uint32_t>(primitive->GetAttr(kAttrTrueBranchStream));
+      auto true_stream_id = AnfAlgo::GetNodeAttr<uint32_t>(cur_cnode_ptr, kAttrTrueBranchStream);
       processed_streams_.emplace(true_stream_id);
 
-      auto value_ptr = primitive->GetAttr(kStreamNeedActivedFirst);
-      if (value_ptr == nullptr) {
+      if (!AnfAlgo::HasNodeAttr(kStreamNeedActivedFirst, cur_cnode_ptr)) {
         continue;
       }
-      auto need_active = GetValue<bool>(value_ptr);
+      auto need_active = AnfAlgo::GetNodeAttr<bool>(cur_cnode_ptr, kStreamNeedActivedFirst);
       if (need_active) {
         processed_streams_.emplace(cur_stream_id);
       }
@@ -371,20 +368,17 @@ void AscendStreamAssign::GetProcessedStream(const NotNull<KernelGraphPtr> &graph
 void AscendStreamAssign::UpdateStreamSwitch(const NotNull<KernelGraphPtr> &graph_ptr, const CNodePtr &switch_ptr,
                                             vector<CNodePtr> *orders) {
   orders->emplace_back(switch_ptr);
-  auto primitive = AnfAlgo::GetCNodePrimitive(switch_ptr);
-  MS_EXCEPTION_IF_NULL(primitive);
-  auto value_ptr = primitive->GetAttr(kStreamNeedActivedFirst);
-  if (value_ptr == nullptr) {
+  if (!AnfAlgo::HasNodeAttr(kStreamNeedActivedFirst, switch_ptr)) {
     return;
   }
 
-  auto need_active = GetValue<bool>(value_ptr);
+  auto need_active = AnfAlgo::GetNodeAttr<bool>(switch_ptr, kStreamNeedActivedFirst);
   if (!need_active) {
     return;
   }
 
   MS_EXCEPTION_IF_NULL(switch_ptr);
-  auto true_stream_id = GetValue<uint32_t>(primitive->GetAttr(kAttrTrueBranchStream));
+  auto true_stream_id = AnfAlgo::GetNodeAttr<uint32_t>(switch_ptr, kAttrTrueBranchStream);
   MS_LOG(INFO) << "Streamswtich stream id:" << AnfAlgo::GetStreamId(switch_ptr)
                << "; active stream id:" << true_stream_id;
 
@@ -677,14 +671,11 @@ void AscendStreamAssign::GetNeedActiveStreams(const NotNull<KernelGraphPtr> &gra
   for (size_t i = 0; i < cnode_ptr_list.size(); ++i) {
     cur_cnode_ptr = cnode_ptr_list[i];
     MS_EXCEPTION_IF_NULL(cur_cnode_ptr);
-    auto primitive = AnfAlgo::GetCNodePrimitive(cur_cnode_ptr);
-    MS_EXCEPTION_IF_NULL(primitive);
-    auto value_ptr = primitive->GetAttr(kStreamNeedActivedFirst);
-    if (value_ptr == nullptr) {
+    if (!AnfAlgo::HasNodeAttr(kStreamNeedActivedFirst, cur_cnode_ptr)) {
       continue;
     }
 
-    auto need_active = GetValue<bool>(value_ptr);
+    auto need_active = AnfAlgo::GetNodeAttr<bool>(cur_cnode_ptr, kStreamNeedActivedFirst);
     if (need_active) {
       auto stream_id = AnfAlgo::GetStreamId(cur_cnode_ptr);
       MS_LOG(INFO) << "Stream id:" << stream_id << " is need actived at first";

mindspore/ccsrc/kernel/akg/akg_kernel_build.cc

@@ -276,7 +276,8 @@ 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];

mindspore/ccsrc/session/anf_runtime_algorithm.cc

@@ -291,7 +291,7 @@ bool AnfRuntimeAlgorithm::HasNodeAttr(const std::string &key, const CNodePtr &no
   // graph kernel cnode.
   auto fg = AnfAlgo::GetCNodeFuncGraphPtr(node);
   MS_EXCEPTION_IF_NULL(fg);
-  return fg->has_flag(key);
+  return fg->has_attr(key);
 }
 
 size_t AnfRuntimeAlgorithm::GetInputTensorNum(const AnfNodePtr &node) {

mindspore/ccsrc/session/anf_runtime_algorithm.h

@@ -68,9 +68,14 @@ class AnfRuntimeAlgorithm {
       std::string node_debug_log = node->DebugString();
       MS_LOG(EXCEPTION) << "Only cnode has attr, but this anf is " << node_debug_log.c_str();
     }
-    auto primitive = GetCNodePrimitive(node);
-    MS_EXCEPTION_IF_NULL(primitive);
-    return GetValue<T>(primitive->GetAttr(key));
+    // single op cnode.
+    if (auto primitive = GetCNodePrimitive(node); primitive != nullptr) {
+      return GetValue<T>(primitive->GetAttr(key));
+    }
+    // graph kernel cnode.
+    auto fg = GetCNodeFuncGraphPtr(node);
+    MS_EXCEPTION_IF_NULL(fg);
+    return GetValue<T>(fg->get_attr(key));
   }
   static bool IsTupleOutput(const AnfNodePtr &anf);
   // set attr of anf node

tests/st/networks/models/bert/test_bert_graph_kernel.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 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"""
+    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()

tests/st/ops/graph_kernel/test_lamb.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.
+# ============================================================================
+
+import pytest
+import numpy as np
+import mindspore.context as context
+from mindspore import Tensor, Parameter
+from mindspore.nn import Cell
+from mindspore.nn.graph_kernels import LambUpdateWithLR, LambNextMV
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class LambNet(Cell):
+    def __init__(self, i2, i5, x6):
+        super(LambNet, self).__init__()
+        self.i2 = Parameter(i2, name='i2')
+        self.i5 = Parameter(i5, name='i5')
+        self.x6 = Parameter(x6, name='x6')
+        self.lamb_next = LambNextMV()
+        self.lamb_update = LambUpdateWithLR()
+
+    def construct(self, i1, i3, i4, i6, i7, i8, i9, ix0, ix1, ix2, ix3,
+                  x1, x2, x3, x4, x5, gy, se, my):
+        return self.lamb_next(i1, self.i2, i3, i4, self.i5, i6, i7, i8, i9, ix0,
+                              ix1, ix2, ix3), \
+               self.lamb_update(x1, x2, x3, x4, x5, self.x6, gy, se, my)
+
+def LambUpdateNumpy(x1, x2, x3, x4, x5, x6, gy, se, my):
+    trust_ratio = np.where(np.greater(x2, gy),
+                           np.where(np.greater(x1, gy), np.divide(x2, x3), se),
+                           se)
+    trust_ratio = np.maximum(np.minimum(trust_ratio, my), gy)
+    update_with_lr = trust_ratio * x4 * x5
+    next_param = x6 - np.reshape(update_with_lr, x6.shape)
+    return next_param
+
+def LambNextMVNumpy(i1, i2, i3, i4, i5, i6, i7, i8, i9, x0, x1, x2, x3):
+    m_fp32 = i5.astype(np.float32)
+    v_fp32 = i2.astype(np.float32)
+    next_m = i8 * m_fp32 + i9 * i4
+    next_v = x0 * v_fp32 + x1 * i1
+    next_mm = next_m / i6
+    next_vv = next_v / i3
+    update = next_mm / (np.sqrt(next_vv) + x3)
+    add3 = next_mm / np.sqrt(next_vv + x3) + x2 * i7
+    return add3, next_m, next_v, update
+
+
+def tensor_all(*args):
+    res = [Tensor(a) for a in args]
+    return res
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_graph_kernel_lamb():
+    shape = [1, 16]
+    oshape = [1]
+    np.random.seed(0)
+    x1 = np.random.normal(0, 1, oshape).astype(np.float32)
+    x2 = np.random.normal(0, 1, oshape).astype(np.float32)
+    x3 = np.random.normal(0, 1, oshape).astype(np.float32)
+    x4 = np.random.normal(0, 1, oshape).astype(np.float32)
+    x5 = np.random.normal(0, 1, shape).astype(np.float32)
+    x6 = np.random.normal(0, 1, shape).astype(np.float32)
+    gy = np.random.normal(0, 1, oshape).astype(np.float32)
+    se = np.random.normal(0, 1, oshape).astype(np.float32)
+    my = np.random.normal(0, 1, oshape).astype(np.float32)
+
+    tx1, tx2, tx3, tx4, tx5, tx6, tgy, tse, tmy = tensor_all(
+        x1, x2, x3, x4, x5, x6, gy, se, my)
+
+    np.random.seed(1)
+    i1 = np.abs(np.random.normal(0, 1, shape)).astype(np.float32)
+    i2 = np.abs(np.random.normal(0, 1, shape)).astype(np.float32)
+    i3 = np.abs(np.random.normal(0, 1, shape)).astype(np.float32)
+    i4 = np.random.normal(0, 1, shape).astype(np.float32)
+    i5 = np.random.normal(0, 1, shape).astype(np.float32)
+    i6 = np.abs(np.random.normal(0, 1, shape)).astype(np.float32)
+    i7 = np.random.normal(0, 1, shape).astype(np.float32)
+    i8 = np.random.normal(0, 1, shape).astype(np.float32)
+    i9 = np.random.normal(0, 1, shape).astype(np.float32)
+    ix0 = np.abs(np.random.normal(0, 1, shape)).astype(np.float32)
+    ix1 = np.abs(np.random.normal(0, 1, shape)).astype(np.float32)
+    ix2 = np.random.normal(0, 1, shape).astype(np.float32)
+    ix3 = np.ones(shape).astype(np.float32) * 1e-6
+
+    ti1, ti2, ti3, ti4, ti5, ti6, ti7, ti8, ti9, tix0, tix1, tix2, tix3 = \
+        tensor_all(i1, i2, i3, i4, i5, i6, i7, i8, i9, ix0, ix1, ix2, ix3)
+
+    context.set_context(enable_graph_kernel=True)
+
+    net = LambNet(ti2, ti5, tx6)
+    (wa3, wup), _ = net(ti1, ti3, ti4, ti6, ti7, ti8, ti9, tix0, tix1, tix2, tix3,
+                        tx1, tx2, tx3, tx4, tx5, tgy, tse, tmy)
+
+    wi2 = net.i2.data.asnumpy().copy()
+    wi5 = net.i5.data.asnumpy().copy()
+    ares = net.x6.data.asnumpy().copy()
+
+    context.set_context(enable_graph_kernel=False)
+
+    a3, a0, a1, up = LambNextMVNumpy(i1, i2, i3, i4, i5, i6, i7, i8, i9, ix0,
+                                     ix1, ix2, ix3)
+
+    np_res = LambUpdateNumpy(x1, x2, x3, x4, x5, x6, gy, se, my)
+
+    rtol = 0.0001
+    atol = 0.0001
+
+    wres = (wa3.asnumpy().copy(), wi5, wi2, wup.asnumpy().copy())
+    bres = (a3, a0, a1, up)
+
+    cmp_res = list(map(lambda x, y: np.allclose(x, y, rtol, atol),
+                       wres, bres))
+
+    assert all(cmp_res) and np.allclose(ares, np_res, rtol, atol)