forked from mindspore-Ecosystem/mindspore
127 lines
4.4 KiB
Python
127 lines
4.4 KiB
Python
# Copyright 2022 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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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor, context, jit
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from mindspore import dataset as ds
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from mindspore.ops import operations as P
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from mindspore.common.jit_config import JitConfig
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context.set_context(mode=ms.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", dataset_strategy="full_batch")
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def setup_function():
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context.set_auto_parallel_context(dataset_strategy="full_batch")
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class Attention(nn.Cell):
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def __init__(self):
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super(Attention, self).__init__()
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self.fc_a = nn.Dense(128, 768, activation='relu')
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self.fc_b = nn.Dense(128, 768, activation='relu')
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self.fc_c = nn.Dense(128, 768, activation='relu')
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self.fc_a.matmul.shard(((1, 1), (8, 1)))
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self.fc_b.matmul.shard(((1, 1), (8, 1)))
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self.fc_c.matmul.shard(((1, 1), (8, 1)))
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def construct(self, x):
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q = self.fc_a(x)
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k = self.fc_b(x)
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v = self.fc_c(x)
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return q, k, v
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attention = Attention()
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relu = nn.ReLU()
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@jit
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def dense_func(x, label):
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q, k, v = attention(x)
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k = P.Transpose()(k, (1, 0)) # (728, 32)
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c = relu(P.MatMul()(q, k)) # (32, 32)
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s = relu(P.MatMul()(c, v)) # (32, 768)
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s = s - label
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return P.ReduceMean()(s * s)
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optimizer_adam = nn.Adam(attention.trainable_params(), learning_rate=0.001)
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attention.set_train()
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attention.update_parameters_name("attn")
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optimizer_adam.update_parameters_name("opt")
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grad_dens_func = ms.ops.value_and_grad(dense_func, None, optimizer_adam.parameters)
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@jit
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def train_step(input_, label_):
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loss, grad = grad_dens_func(input_, label_)
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optimizer_adam(grad)
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return loss
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def test_sink():
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"""
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Feature: Function mode in auto parallel
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Description: sink mode
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Expectation: compile ok
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"""
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel",
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dataset_strategy="data_parallel", device_num=8)
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data = {"input": np.ones([16, 32, 128]).astype(np.float32), "label": np.zeros([16, 32, 768]).astype(np.float32)}
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dataset = ds.NumpySlicesDataset(data=data)
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jitconfig = JitConfig(jit_level="O1", task_sink=True)
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sink_process = ms.train.data_sink(dense_func, dataset, sink_size=4, jit_config=jitconfig)
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_ = sink_process()
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def test_no_sink():
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"""
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Feature: Function mode in auto parallel
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Description: no sink mode
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Expectation: compile ok
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"""
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", dataset_strategy="full_batch", device_num=8)
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_ = dense_func(Tensor(np.ones([32, 128]).astype(np.float32)), Tensor(np.zeros([32, 768]).astype(np.float32)))
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def test_sink_with_grad():
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"""
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Feature: Function mode in auto parallel
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Description: sink mode with grad
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Expectation: compile ok
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"""
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel",
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dataset_strategy="data_parallel", device_num=8)
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data = {"input": np.ones([16, 32, 128]).astype(np.float32), "label": np.zeros([16, 32, 768]).astype(np.float32)}
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dataset = ds.NumpySlicesDataset(data=data)
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jitconfig = JitConfig(jit_level="O1", task_sink=True)
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sink_process = ms.train.data_sink(train_step, dataset, sink_size=4, jit_config=jitconfig)
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_ = sink_process()
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def test_no_sink_with_grad():
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"""
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Feature: Function mode in auto parallel
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Description: no sink mode with grad
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Expectation: compile ok
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"""
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", dataset_strategy="full_batch", device_num=8)
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_ = train_step(Tensor(np.ones([32, 128]).astype(np.float32)), Tensor(np.zeros([32, 768]).astype(np.float32)))
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