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[DistDialect] Pir add mp precision alignment ut (PaddlePaddle#63770)
* update ut * run loss success * fix code style * tiny update * for tmp * fix interpreter probmel * update * tiny update * fix code style * fix comments * tinyfix * tinyfix * fix conflicts * update
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168 changes: 168 additions & 0 deletions
168
test/auto_parallel/pir/semi_auto_parallel_dist_to_static_mlp_pir.py
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| # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # 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 random | ||
|
|
||
| import numpy as np | ||
| from test_to_static_pir_program import DemoNet | ||
|
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| import paddle | ||
| import paddle.distributed as dist | ||
| from paddle import nn | ||
| from paddle.framework import _current_expected_place | ||
| from paddle.io import DataLoader | ||
|
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| BATCH_SIZE = 4 | ||
| BATCH_NUM = 4 | ||
| SEQ_LEN = 2 | ||
| IMAGE_SIZE = 16 | ||
| CLASS_NUM = 8 | ||
|
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|
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| def create_numpy_like_random(name): | ||
| return paddle.ParamAttr( | ||
| name=name, initializer=paddle.nn.initializer.Uniform(0, 1) | ||
| ) | ||
|
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||
|
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| class RandomDataset(paddle.io.Dataset): | ||
| def __init__(self, images, labels, num_samples, return_dict=False): | ||
| self.images = images | ||
| self.labels = labels | ||
| self.num_samples = num_samples | ||
| self.return_dict = return_dict | ||
|
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||
| def __getitem__(self, idx): | ||
| if self.return_dict: | ||
| return { | ||
| "image": self.images[idx], | ||
| "label": self.labels[idx], | ||
| } | ||
| else: | ||
| return self.images[idx], self.labels[idx] | ||
|
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| def __len__(self): | ||
| return self.num_samples | ||
|
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|
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| class TestSimpleNetForSemiAutoParallel: | ||
| def __init__(self): | ||
| self._seed = eval(os.getenv("seed")) | ||
| self._ckpt_path = os.getenv("ckpt_path") | ||
| self.mesh = dist.ProcessMesh([0, 1], dim_names=["x"]) | ||
| self._in_pir_mode = paddle.base.framework.get_flags( | ||
| "FLAGS_enable_pir_api" | ||
| )["FLAGS_enable_pir_api"] | ||
|
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| def set_random_seed(self, seed): | ||
| random.seed(seed) | ||
| np.random.seed(seed) | ||
| paddle.seed(seed) | ||
|
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| def create_data_loader(self, return_dict=False): | ||
| images = np.random.rand(BATCH_SIZE, IMAGE_SIZE).astype('float32') | ||
| labels = np.random.rand(BATCH_SIZE, CLASS_NUM).astype('float32') | ||
| dataset = RandomDataset(images, labels, BATCH_SIZE, return_dict) | ||
| loader = DataLoader(dataset, batch_size=BATCH_SIZE) | ||
| return loader | ||
|
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| def run_dy2static(self, layer, opt, dist_loader): | ||
| # create loss | ||
| loss_fn = nn.MSELoss() | ||
| # static training | ||
| dist_model = dist.to_static(layer, dist_loader, loss_fn, opt) | ||
| loss_list = [] | ||
| dist_model.train() | ||
|
|
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| if self._in_pir_mode: | ||
| mode = "train" | ||
|
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| dist_model._engine._has_prepared[mode] = True | ||
| dist_model._mode = mode | ||
| dist_model._engine._mode = mode | ||
| paddle.disable_static() | ||
| dist_model._engine._initialize(mode) | ||
| dist_model._engine._executor = paddle.static.Executor( | ||
| _current_expected_place() | ||
| ) | ||
| dist_model._engine._init_comm() | ||
|
|
||
| for epoch in range(5): | ||
| for batch_id, data in enumerate(dist_loader()): | ||
| if isinstance(data, dict): | ||
| image = data['image'] | ||
| label = data['label'] | ||
| else: | ||
| image, label = data | ||
| loss = dist_model(image, label) | ||
| loss_list.append(loss) | ||
|
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| return np.array(loss_list), dist_model | ||
|
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| def run_dynamic(self, layer, opt, dist_loader, is_recompute=False): | ||
| # create loss | ||
| loss_fn = nn.MSELoss() | ||
| loss_list = [] | ||
| for epoch in range(5): | ||
| for batch_id, data in enumerate(dist_loader()): | ||
| if isinstance(data, dict): | ||
| image = data['image'] | ||
| label = data['label'] | ||
| else: | ||
| image, label = data | ||
| if is_recompute: | ||
| image.stop_gradient = False | ||
| out = layer(image) | ||
| loss = loss_fn(out, label) | ||
| loss_list.append(loss.numpy()) | ||
| loss.backward() | ||
|
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| opt.step() | ||
| opt.clear_grad() | ||
| return np.array(loss_list) | ||
|
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| def test_mp_demo_net(self): | ||
| paddle.disable_static() | ||
| self.set_random_seed(self._seed) | ||
| data_loader = self.create_data_loader() | ||
|
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| self.set_random_seed(self._seed) | ||
| dy_layer = DemoNet(self.mesh) | ||
| dy_opt = paddle.optimizer.SGD( | ||
| learning_rate=0.1, parameters=dy_layer.parameters() | ||
| ) | ||
|
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| paddle.base.set_flags({'FLAGS_enable_pir_api': 1}) | ||
| self.set_random_seed(self._seed) | ||
| dy2static_layer = DemoNet(self.mesh) | ||
| dy2static_opt = paddle.optimizer.SGD( | ||
| learning_rate=0.1, parameters=dy2static_layer.parameters() | ||
| ) | ||
| dist_dataloader = dist.shard_dataloader( | ||
| dataloader=data_loader, | ||
| meshes=[self.mesh], | ||
| ) | ||
| dy2static_losses, dist_model = self.run_dy2static( | ||
| dy2static_layer, dy2static_opt, dist_dataloader | ||
| ) | ||
| dy_losses = self.run_dynamic(dy_layer, dy_opt, dist_dataloader) | ||
| np.testing.assert_array_equal(dy_losses, dy2static_losses) | ||
|
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| def run_test_case(self): | ||
| self.test_mp_demo_net() | ||
|
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| if __name__ == '__main__': | ||
| TestSimpleNetForSemiAutoParallel().run_test_case() |
44 changes: 44 additions & 0 deletions
44
test/auto_parallel/pir/test_semi_auto_parallel_dist_to_static_pir.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,44 @@ | ||
| # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # 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 tempfile | ||
| import unittest | ||
|
|
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| import collective.test_communication_api_base as test_base | ||
|
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|
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| class TestSemiAutoParallelStaticDecorate(test_base.CommunicationTestDistBase): | ||
| def setUp(self): | ||
| super().setUp( | ||
| num_of_devices=2, | ||
| timeout=300, | ||
| ) | ||
| self._default_envs = {"dtype": "float32", "seed": "2023"} | ||
| self._changeable_envs = {"backend": ["gpu"]} | ||
|
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| def test_mlp(self): | ||
| envs_list = test_base.gen_product_envs_list( | ||
| {"dtype": "float32", "seed": "2023"}, {"backend": ["gpu"]} | ||
| ) | ||
| for envs in envs_list: | ||
| ckpt_path_tmp = tempfile.TemporaryDirectory() | ||
| envs["ckpt_path"] = ckpt_path_tmp.name | ||
| self.run_test_case( | ||
| "semi_auto_parallel_dist_to_static_mlp_pir.py", | ||
| user_defined_envs=envs, | ||
| ) | ||
| ckpt_path_tmp.cleanup() | ||
|
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| if __name__ == "__main__": | ||
| unittest.main() |