forked from mindspore-Ecosystem/mindspore
enable runtime cache for ascend dynamic scene
This commit is contained in:
王南
parent
2d5274c415
commit
18fa59e090
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@ -100,7 +100,7 @@ mindspore/python/mindspore/version.py
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mindspore/python/mindspore/default_config.py
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mindspore/python/mindspore/device_target.py
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mindspore/python/mindspore/package_name.py
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mindspore/.commit_id
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mindspore/python/mindspore/.commit_id
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# lite test file
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mindspore/lite/test/do_test/
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@ -113,6 +113,20 @@ void IntHandler(int, siginfo_t *, void *) {
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MS_LOG(WARNING) << "Process " << this_pid << " receive KeyboardInterrupt signal.";
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(void)kill(this_pid, SIGTERM);
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}
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void AscendEnableDynamicRuntimeCache(const KernelGraph *graph) {
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const auto &node_list = graph->TopoSort(graph->get_return());
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for (auto &node : node_list) {
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auto kernel_info = node->kernel_info();
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if (!kernel_info) {
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continue;
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}
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MS_EXCEPTION_IF_NULL(kernel_info);
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auto runtime_cache = kernel_info->runtime_cache();
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MS_EXCEPTION_IF_NULL(runtime_cache);
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runtime_cache->set_valid();
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}
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}
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} // namespace
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std::vector<rtExceptionInfo> AscendKernelRuntime::task_fail_infoes_ = {};
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@ -908,6 +922,7 @@ bool AscendKernelRuntime::RunDynamicKernelAsync(const session::KernelGraph &grap
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MS_LOG(ERROR) << "GraphId:" << graph.graph_id() << " Not Found! Please generator executor first";
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return false;
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}
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AscendEnableDynamicRuntimeCache(&graph);
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auto dynamic_kernels = iter->second;
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for (const auto &dynamic_kernel : dynamic_kernels) {
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@ -0,0 +1,299 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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from mindspore import ops, nn, ParameterTuple, context, set_seed
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from mindspore.train import DatasetHelper, connect_network_with_dataset
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import mindspore.dataset as ds
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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set_seed(2)
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def _exec_preprocess(network, is_train, dataset, dataset_sink_mode, epoch_num, sink_size):
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if dataset_sink_mode and not is_train:
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dataset.__loop_size__ = 1
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dataset_helper = DatasetHelper(dataset, dataset_sink_mode, sink_size, epoch_num)
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if dataset_sink_mode:
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network = connect_network_with_dataset(network, dataset_helper)
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return dataset_helper, network
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def dynamic_shape_sink_process(network, dataset, is_train=True):
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# epoch_num=1 sink_size=1: exec one step
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dataset_sink_mode = True
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sink_size = 1
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epoch_num = 1
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dataset_helper, network = _exec_preprocess(network, is_train, dataset, dataset_sink_mode, epoch_num, sink_size)
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network.set_train(is_train)
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for inputs in dataset_helper:
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outputs = network(*inputs)
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return outputs
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def fixed_shape_process(network, dataset, is_train=True):
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network.set_train(is_train)
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for inputs in dataset.create_tuple_iterator():
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outputs = network(*inputs)
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return outputs
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def dataset_generator(data_list):
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for data in data_list:
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yield data
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class GradNetWrtX(nn.Cell):
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def __init__(self, net):
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super(GradNetWrtX, self).__init__()
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self.net = net
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self.grad_op = ops.GradOperation(get_all=True, get_by_list=True, sens_param=True)
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self.params = ParameterTuple(net.trainable_params())
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def construct(self, *inputs):
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gradient_function = self.grad_op(self.net, self.params)
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return gradient_function(*inputs)
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class LayerNormNet(nn.Cell):
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def __init__(self, last_dim):
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super(LayerNormNet, self).__init__()
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self.layernorm = nn.LayerNorm([last_dim])
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def construct(self, x):
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return self.layernorm(x)
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class Conv2dNet(nn.Cell):
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def __init__(self):
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super(Conv2dNet, self).__init__()
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self.conv = nn.Conv2d(3, 10, 4, pad_mode="valid", has_bias=False, weight_init='normal')
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def construct(self, x):
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return self.conv(x)
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class DropoutNet(nn.Cell):
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def __init__(self):
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super(DropoutNet, self).__init__()
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self.drop = nn.Dropout(0.5)
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self.relu = ops.ReLU()
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def construct(self, x):
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x = self.relu(x)
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return self.relu(self.drop(x))
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class ReduceSumNet(nn.Cell):
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def __init__(self, axis=()):
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super(ReduceSumNet, self).__init__()
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self.reduce = ops.ReduceSum()
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self.axis = axis
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def construct(self, x):
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return self.reduce(x, self.axis)
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class AddNet(nn.Cell):
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def construct(self, x, y):
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return ops.add(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_layernorm():
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"""
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Feature: Test LayerNorm and its backward. The input shape is dynamic.
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Description: The second dim of input is unknown.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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last_dim = 32
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batch_size = 16
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data_list = []
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for i in range(20, 23):
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data_list.append((np.random.rand(batch_size, i, last_dim).astype(np.float32),
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np.random.rand(batch_size, i, last_dim).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, None, last_dim], "data2": [batch_size, None, last_dim]})
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net = GradNetWrtX(LayerNormNet(last_dim))
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gradients = dynamic_shape_sink_process(net, dataset)
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gradients_cmp = fixed_shape_process(net, dataset)
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assert np.allclose(gradients[0][0].asnumpy(), gradients_cmp[0][0].asnumpy(), rtol=1.0e-4, atol=1.0e-4)
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assert np.allclose(gradients[1][0].asnumpy(), gradients_cmp[1][0].asnumpy(), rtol=1.0e-4, atol=1.0e-4)
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assert np.allclose(gradients[1][1].asnumpy(), gradients_cmp[1][1].asnumpy(), rtol=1.0e-4, atol=1.0e-4)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_conv2d():
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"""
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Feature: Test Conv2d and its backward. The input shape is dynamic.
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Description: Input dim of `H `or `W` is unknown. Conv2d's attr[pad] set to "valid".
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(220, 224):
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data_list.append((np.random.rand(batch_size, 3, i, 112).astype(np.float32),
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np.random.rand(batch_size, 10, 219, 109).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, 3, None, 112], "data2": [batch_size, 10, None, 109]})
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net = GradNetWrtX(Conv2dNet())
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gradients = dynamic_shape_sink_process(net, dataset)
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gradients_cmp = fixed_shape_process(net, dataset)
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assert np.allclose(gradients[0][0].asnumpy(), gradients_cmp[0][0].asnumpy(), rtol=1.0e-4, atol=1.0e-4)
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assert np.allclose(gradients[1][0].asnumpy(), gradients_cmp[1][0].asnumpy(), rtol=1.0e-4, atol=1.0e-4)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_dropout():
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"""
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Feature: Test Dropout and its backward.
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Description: The input shape is dynamic.
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Expectation: Dropout result is random, assert gradient shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(48, 50):
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data_list.append((np.random.rand(batch_size, i, 256).astype(np.float32),
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np.random.rand(batch_size, i, 256).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, None, 256], "data2": [batch_size, None, 256]})
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net = GradNetWrtX(DropoutNet())
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net.set_train()
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gradients = dynamic_shape_sink_process(net, dataset)
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assert gradients[0][0].shape == (batch_size, 49, 256)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_reducesum1():
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"""
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Feature: Test ReduceSum and its backward. The input shape is dynamic.
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Description: axis=(), result of reduce sum is a scalar, gradient shape is the same as input, value is all one.
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Expectation: Assert gradient shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(48, 50):
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data_list.append((np.random.rand(batch_size, i, i + 2).astype(np.float32),
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np.array(1).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, None, None], "data2": []})
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net = GradNetWrtX(ReduceSumNet())
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gradients = dynamic_shape_sink_process(net, dataset)
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assert gradients[0][0].shape == (batch_size, 49, 51)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_reducesum2():
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"""
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Feature: Test ReduceSum and its backward. The input shape is dynamic.
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Description: axis is a scalar, not tuple.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(48, 50):
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data_list.append((np.random.rand(batch_size, i, i + 2).astype(np.float32),
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np.random.rand(batch_size, i + 2).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, None, None], "data2": [batch_size, None]})
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net = GradNetWrtX(ReduceSumNet(1))
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gradients = dynamic_shape_sink_process(net, dataset)
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gradients_cmp = fixed_shape_process(net, dataset)
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assert np.allclose(gradients[0][0].asnumpy(), gradients_cmp[0][0].asnumpy(), rtol=1.0e-4, atol=1.0e-4)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_add1():
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"""
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Feature: Test Add and its backward. The input shape is dynamic.
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Description: Second input is a scalar. Shape of forward result is the same as first input.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(48, 50):
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data_list.append((np.random.rand(batch_size, i).astype(np.float32),
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np.array(1).astype(np.float32),
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np.random.rand(batch_size, i).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2", "data3"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, None], "data2": [], "data3": [batch_size, None]})
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net = GradNetWrtX(AddNet())
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gradients = dynamic_shape_sink_process(net, dataset)
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gradients_cmp = fixed_shape_process(net, dataset)
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assert np.allclose(gradients[0][0].asnumpy(), gradients_cmp[0][0].asnumpy(), rtol=1.0e-4, atol=1.0e-4)
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assert np.allclose(gradients[0][1].asnumpy(), gradients_cmp[0][1].asnumpy(), rtol=1.0e-4, atol=1.0e-4)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_add2():
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"""
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Feature: Test Add and its backward. The input shape is dynamic.
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Description: Shape of forward result is the same as first input. The axis of reduce_sum in add's bprop will be a
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empty Tensor.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(48, 50):
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data_list.append((np.random.rand(batch_size, 2, i).astype(np.float32),
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np.random.rand(2, i).astype(np.float32),
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np.random.rand(batch_size, 2, i).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2", "data3"])
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dataset.set_dynamic_columns(columns=
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{"data1": [batch_size, 2, None], "data2": [2, None], "data3": [batch_size, 2, None]})
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net = GradNetWrtX(AddNet())
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gradients = dynamic_shape_sink_process(net, dataset)
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gradients_cmp = fixed_shape_process(net, dataset)
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assert np.allclose(gradients[0][0].asnumpy(), gradients_cmp[0][0].asnumpy(), rtol=1.0e-4, atol=1.0e-4)
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assert np.allclose(gradients[0][1].asnumpy(), gradients_cmp[0][1].asnumpy(), rtol=1.0e-4, atol=1.0e-4)
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