Add st for dump

tests/st/dump/dump_test_utils.py

@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 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.
@@ -106,7 +106,7 @@ def generate_dump_json(dump_path, json_file_name, test_key):
     if test_key == "test_async_dump":
         data = async_dump_dict
         data["common_dump_settings"]["path"] = dump_path
-    elif test_key == "test_e2e_dump":
+    elif test_key in ("test_e2e_dump", "test_e2e_dump_trans_false"):
         data = e2e_dump_dict
         data["common_dump_settings"]["path"] = dump_path
     elif test_key == "test_async_dump_net_multi_layer_mode1":
@@ -126,6 +126,10 @@ def generate_dump_json(dump_path, json_file_name, test_key):
         data = async_dump_dict
         data["common_dump_settings"]["path"] = dump_path
         data["common_dump_settings"]["file_format"] = "bin"
+    elif test_key == "test_e2e_dump_trans_true":
+        data = e2e_dump_dict
+        data["common_dump_settings"]["path"] = dump_path
+        data["e2e_dump_settings"]["trans_flag"] = True
     else:
         raise ValueError(
             "Failed to generate dump json file. The test name value " + test_key + " is invalid.")

tests/st/dump/test_data_dump.py

@@ -1,4 +1,4 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
+# Copyright 2020-2022 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.
@@ -611,6 +611,23 @@ def test_ascend_full_dump():
     run_saved_data_dump_test('test_async_dump', 'full')
 
 
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@security_off_wrap
+def test_ascend_full_dump_kernel_by_kernel():
+    """
+    Feature: Ascend Full Dump in kernel-by-kernel (MindRT) mode
+    Description: Test Ascend full dump
+    Expectation: Tensors are stored in npy files and their statistics stored in statistic.csv
+    """
+    os.environ['GRAPH_OP_RUN'] = "1"
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    run_saved_data_dump_test('test_async_dump', 'full')
+    del os.environ['GRAPH_OP_RUN']
+
+
 @constexpr
 def construct_tensor(cst):
     return Tensor(np.array(cst))

tests/st/dump/test_dump_format.py

@@ -0,0 +1,159 @@
+# Copyright 2022 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 os
+import sys
+import tempfile
+import glob
+import shutil
+import pytest
+import numpy as np
+import mindspore as ms
+import mindspore.context as context
+import mindspore.nn as nn
+import mindspore.ops as ops
+from mindspore import Tensor
+from dump_test_utils import generate_dump_json, check_dump_structure
+from tests.security_utils import security_off_wrap
+
+
+class ConvNet(nn.Cell):
+    def __init__(self):
+        super(ConvNet, self).__init__()
+        self.conv2 = ops.Conv2D(out_channel=3, kernel_size=1)
+
+    def construct(self, x, weight):
+        return self.conv2(x, weight)
+
+
+def run_trans_flag(test_name):
+    if sys.platform != 'linux':
+        return
+    with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
+        dump_path = os.path.join(tmp_dir, test_name)
+        dump_config_path = os.path.join(tmp_dir, '{}.json'.format(test_name))
+        generate_dump_json(dump_path, dump_config_path, test_name)
+        os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
+        if os.path.isdir(dump_path):
+            shutil.rmtree(dump_path)
+        net = ConvNet()
+        tensor = Tensor(np.ones([1, 3, 3, 3]), ms.float32)
+        weight = Tensor(np.ones([3, 3, 1, 1]), ms.float32)
+        expect = net(tensor, weight)
+        check_dump_structure(dump_path, dump_config_path, 1, 1, 1)
+        dump_data_path = os.path.join(dump_path, 'rank_0', 'Net', '0', '0')
+        assert os.path.exists(dump_data_path)
+        if test_name == "test_e2e_dump_trans_true":
+            # tensor data in host format.
+            output_name = "Conv2D.Conv2D-op*.0.0.*.output.0.DefaultFormat.npy"
+            output_path = glob.glob(os.path.join(dump_data_path, output_name))[0]
+            real_path = os.path.realpath(output_path)
+            output = np.load(real_path)
+            assert output.shape == (1, 3, 3, 3)
+            assert np.array_equal(output, expect)
+        elif test_name == "test_e2e_dump_trans_false":
+            # tensor data in device format.
+            output_name = "Conv2D.Conv2D-op*.0.0.*.output.0.NC1HWC0.npy"
+            output_path = glob.glob(os.path.join(dump_data_path, output_name))[0]
+            real_path = os.path.realpath(output_path)
+            output = np.load(real_path)
+            assert output.shape == (1, 1, 3, 3, 16)
+        else:
+            # tensor data in host format.
+            output_name = "Conv2D.Conv2D-op*.*.*.*.output.0.NCHW.npy"
+            output_path = glob.glob(os.path.join(dump_data_path, output_name))[0]
+            real_path = os.path.realpath(output_path)
+            output = np.load(real_path)
+            assert output.shape == (1, 3, 3, 3)
+            assert np.array_equal(output, expect)
+        del os.environ['MINDSPORE_DUMP_CONFIG']
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@security_off_wrap
+def test_ascend_e2e_trans_true():
+    """
+    Feature: Ascend e2e dump.
+    Description: Test e2e dump in Ascend with trans_flag is configured to true.
+    Expectation: Dump files has tensor data in host format (4 dimensions).
+    """
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    run_trans_flag("test_e2e_dump_trans_true")
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@security_off_wrap
+def test_ascend_e2e_trans_false():
+    """
+    Feature: Ascend e2e dump.
+    Description: Test e2e dump in Ascend with trans_flag is configured to false.
+    Expectation: Dump files has tensor data in device format.
+    """
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    run_trans_flag("test_e2e_dump_trans_false")
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@security_off_wrap
+def test_ascend_kernel_by_kernel_trans_true():
+    """
+    Feature: Ascend kernel by kernel dump.
+    Description: Test kernel by kernel dump in Ascend with trans_flag is configured to true.
+    Expectation: Dump files has tensor data in host format (4 dimensions).
+    """
+    os.environ['GRAPH_OP_RUN'] = "1"
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    run_trans_flag("test_e2e_dump_trans_true")
+    del os.environ['GRAPH_OP_RUN']
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@security_off_wrap
+def test_ascend_kernel_by_kernel_trans_false():
+    """
+    Feature: Ascend kernel by kernel dump.
+    Description: Test kernel by kernel dump in Ascend with trans_flag is configured to false.
+    Expectation: Dump files has tensor data in device format.
+    """
+    os.environ['GRAPH_OP_RUN'] = "1"
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    run_trans_flag("test_e2e_dump_trans_false")
+    del os.environ['GRAPH_OP_RUN']
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@security_off_wrap
+def test_ascend_a_plus_m_conversion():
+    """
+    Feature: Ascend A+M dump.
+    Description: Test A+M dump in Ascend and check the format of the dump data.
+    Expectation: Dump files has tensor data in host format (4 dimensions).
+    """
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    run_trans_flag("test_async_dump_npy")

tests/st/dump/test_multi_root_graph_dump.py

@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2021-2022 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.
@@ -43,8 +43,12 @@ class NewAddNet(Cell):
         super(NewAddNet, self).__init__()
         self.add = P.AddN()
 
-    def construct(self, x, y):
+    def construct(self, b1, b2, x, y):
         z = self.add([x, y, y])
+        if b1 < b2:
+            z = self.add([x, y, y])
+        else:
+            z = self.add([x, x, y])
         return z
 
 
@@ -52,13 +56,29 @@ def train_addnet(epoch):
     net = AddNet()
     net2 = NewAddNet()
     output_list = []
+    b1 = Tensor(np.array(1).astype(np.float32))
+    b2 = Tensor(np.array(3).astype(np.float32))
     input_x = Tensor(np.ones([2, 1, 2, 1]).astype(np.float32))
     input_y = Tensor(np.ones([2, 1, 2, 1]).astype(np.float32))
     for _ in range(epoch):
         out_put = net(input_x, input_y)
-        out2 = net2(out_put, input_x)
+        out2 = net2(b1, b2, out_put, input_x)
         output_list.append(out2.asnumpy())
         input_x = input_x + input_y
+        b1 = b1+1
+    return output_list
+
+
+def check_graph_structure(dump_file_path, execution_order_path, graph_id, expect_steps):
+    dump_data_path = os.path.join(dump_file_path, graph_id)
+    assert sorted(os.listdir(dump_data_path)) == expect_steps
+    graph_history_file_path = os.path.join(
+        execution_order_path, 'ms_global_execution_order_graph_{}.csv'.format(graph_id))
+    assert path.exists(graph_history_file_path)
+    with open(graph_history_file_path) as csvfile:
+        history_graph = csv.reader(csvfile)
+        iter_list_graph = [row[0] for row in history_graph]
+    assert iter_list_graph == expect_steps
 
 
 def run_multi_root_graph_dump(device, dump_mode, test_name):
@@ -79,31 +99,24 @@ def run_multi_root_graph_dump(device, dump_mode, test_name):
         for _ in range(3):
             if not os.path.exists(dump_file_path):
                 time.sleep(2)
-        # Multi root graph script : we have 2 graphs under rank_0 dir
-        # Each graph should have 3 iteration
-        # Each graph was executed once per epoch,
-        # Graph 0 was executed in even iterations, graph one was executed in odd iterations
-        assert len(os.listdir(dump_file_path)) == 2
-        dump_path_graph_0 = os.path.join(dump_file_path, '0')
-        dump_path_graph_1 = os.path.join(dump_file_path, '1')
-        assert sorted(os.listdir(dump_path_graph_0)) == ['0', '2', '4']
-        assert sorted(os.listdir(dump_path_graph_1)) == ['1', '3', '5']
         execution_order_path = os.path.join(dump_path, 'rank_0', 'execution_order')
-        # Four files in execution_order dir.
+        # Multi root graph script: check dump data dir and graph history files and see if iteration number is matched.
-        # Two files for each graph (ms_execution_order and ms_global_execution_order)
+        if device == "GPU":
-        assert len(os.listdir(execution_order_path)) == 4
+            # In GPU, we have 4 kernel graphs folders under rank_0 dir.
-        global_exec_order_graph_0 = os.path.join(execution_order_path, 'ms_global_execution_order_graph_0.csv')
+            # In graph history dir, there are 2 files for each graph (ms_execution_order and ms_global_execution_order).
-        assert path.exists(global_exec_order_graph_0)
+            assert len(os.listdir(dump_file_path)) == 4
-        with open(global_exec_order_graph_0) as csvfile:
+            assert len(os.listdir(execution_order_path)) == 8
-            history_graph_0 = csv.reader(csvfile)
+            check_graph_structure(dump_file_path, execution_order_path, '0', ['0', '2', '4'])
-            iter_list_graph_0 = list(history_graph_0)
+            check_graph_structure(dump_file_path, execution_order_path, '1', ['1', '3', '5'])
-        assert iter_list_graph_0 == [['0'], ['2'], ['4']]
+        else:
-        global_exec_order_graph_1 = os.path.join(execution_order_path, 'ms_global_execution_order_graph_1.csv')
+            # In Ascend, we have 2 root graphs folders under rank_0 dir.
-        assert path.exists(global_exec_order_graph_1)
+            # In graph history dir, there are 4 ms_execution_order files and 2 ms_global_execution_order files.
-        with open(global_exec_order_graph_1) as csvfile:
+            # Each graph should have 3 iterations. Each graph was executed once per epoch.
-            history_graph_1 = csv.reader(csvfile)
+            # Graph 0 was executed in even iterations, graph 1 was executed in odd iterations.
-            iter_list_graph_1 = list(history_graph_1)
+            assert len(os.listdir(dump_file_path)) == 2
-        assert iter_list_graph_1 == [['1'], ['3'], ['5']]
+            assert len(os.listdir(execution_order_path)) == 6
+            check_graph_structure(dump_file_path, execution_order_path, '0', ['0', '2', '4'])
+            check_graph_structure(dump_file_path, execution_order_path, '1', ['1', '3', '5'])
 
 
 @pytest.mark.level0
@@ -154,5 +167,23 @@ def test_Ascend_async_multi_root_graph_dump():
     Expectation:
         Dump for two different graphs, graph 0 even iterations and graph 1 odd iterations.
     """
-
     run_multi_root_graph_dump("Ascend", "async_dump", "test_Ascend_async_multi_root_graph_dump")
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@security_off_wrap
+def test_ascend_multi_root_graph_dump_kernel_by_kernel():
+    """
+    Feature:
+        Multi root graph dump for Ascend kernel by kernel.
+    Description:
+        Test multi root graph dump in Ascend kernel by kernel.
+    Expectation:
+        Dump for two different graphs, graph 0 even iterations and graph 1 odd iterations.
+    """
+    os.environ['GRAPH_OP_RUN'] = "1"
+    run_multi_root_graph_dump("Ascend", "e2e_dump", "test_Ascend_e2e_multi_root_graph_dump")
+    del os.environ['GRAPH_OP_RUN']