chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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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 unittest
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import numpy as np
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import paddle
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import paddle.distributed as dist
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from paddle import nn
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from paddle.distributed import Replicate, Shard
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from paddle.io import DataLoader
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BATCH_SIZE = 4
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BATCH_NUM = 40
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IMAGE_SIZE = 16
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CLASS_NUM = 8
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np.random.seed(2024)
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paddle.seed(2024)
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class RandomDataset(paddle.io.Dataset):
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def __init__(self, images, labels, num_samples):
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self.images = images
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self.labels = labels
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self.num_samples = num_samples
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def __getitem__(self, idx):
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return self.images[idx], self.labels[idx]
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def __len__(self):
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return self.num_samples
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class DemoNet(nn.Layer):
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def __init__(self, mesh, shard=True):
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super().__init__()
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self._mesh = mesh
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self.linear_0 = nn.Linear(IMAGE_SIZE, IMAGE_SIZE, bias_attr=False)
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self.linear_1 = nn.Linear(IMAGE_SIZE, CLASS_NUM, bias_attr=False)
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self.relu_0 = nn.ReLU()
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self.relu_1 = nn.ReLU()
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self.relu_2 = nn.ReLU()
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self.shard = shard
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# shard the weights of this layer
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if self.shard:
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self.linear_0.weight = dist.shard_tensor(
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self.linear_0.weight,
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self._mesh,
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[Shard(1)],
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stop_gradient=False,
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)
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self.linear_1.weight = dist.shard_tensor(
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self.linear_1.weight,
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self._mesh,
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[Shard(0)],
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stop_gradient=False,
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)
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else:
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self.linear_0.weight = dist.shard_tensor(
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self.linear_0.weight,
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self._mesh,
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[Replicate()],
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stop_gradient=False,
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)
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self.linear_1.weight = dist.shard_tensor(
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self.linear_1.weight,
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self._mesh,
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[Replicate()],
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stop_gradient=False,
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)
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def forward(self, x):
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x.stop_gradient = False
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out = self.relu_0(x) # triggle backward partial allreduce
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out = self.linear_0(out)
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out = self.relu_1(out)
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out = self.linear_1(out)
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out = self.relu_2(out) # triggle forward partial allreduce
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out = paddle.cast(out, 'float32')
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return out
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def create_data_loader(
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batch_size=BATCH_SIZE,
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batch_num=BATCH_NUM,
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image_size=IMAGE_SIZE,
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class_num=CLASS_NUM,
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):
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nsamples = batch_size * batch_num
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images = np.random.rand(nsamples, image_size).astype('float32')
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labels = np.random.rand(nsamples, class_num).astype('float32')
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dataset = RandomDataset(images, labels, nsamples)
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loader = DataLoader(dataset, batch_size=batch_size)
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return loader
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class TestToStaticPirProgramTrain(unittest.TestCase):
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def test_to_static_program(self):
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paddle.base.set_flags({'FLAGS_enable_pir_api': 1})
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mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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layer = DemoNet(mesh)
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opt = paddle.optimizer.SGD(
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learning_rate=0.1, parameters=layer.parameters()
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)
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loss_fn = nn.MSELoss()
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loader = create_data_loader()
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dist_loader = dist.shard_dataloader(loader, meshes=[mesh])
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dist_model = dist.to_static(layer, dist_loader, loss_fn, opt)
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# dist_model.train()
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mode = "train"
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dist_model.train()
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main_program = dist_model._engine._pir_dist_main_progs["train"]
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relu_idx = 0
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matmul_idx = 0
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data_idx = 0
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matmul_grad_idx = 0
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sgd_idx = 0
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ops = main_program.global_block().ops
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backward_op_list = [
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"pd_op.sgd_",
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"pd_op.sgd_",
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"pd_op.relu_grad",
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"pd_op.all_reduce",
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"pd_op.matmul_grad",
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"pd_op.relu_grad",
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"pd_op.matmul_grad",
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"pd_op.relu_grad",
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"pd_op.cast",
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"pd_op.subtract_grad",
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"pd_op.square_grad",
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"pd_op.mean_grad",
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]
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index = -1
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for op_name in backward_op_list:
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self.assertEqual(ops[index].name(), op_name)
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index = index - 1
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for op in ops:
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# skip shadow_output
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if op.num_results() == 0:
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continue
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tensor = op.result(0)
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# while tensor's stop_gradient is true, the corresponding grad tensor is initialized.
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if not tensor.initialized():
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continue
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self.assertTrue(tensor.is_dist_dense_tensor_type())
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self.assertEqual(tensor.dist_attr().process_mesh.shape, [2])
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self.assertEqual(
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tensor.dist_attr().process_mesh.process_ids, [0, 1]
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)
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if op.name() == 'pd_op.data':
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if data_idx != 0:
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self.assertEqual(tensor.dist_attr().dims_mapping, [-1, -1])
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self.assertEqual(tensor.dist_attr().partial_dims, set())
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data_idx += 1
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elif op.name() == 'builtin.parameter':
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self.assertTrue(tensor.is_dense_tensor_type())
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self.assertTrue(tensor.is_dist_dense_tensor_type())
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self.assertTrue(tensor.is_dist_dense_tensor_type())
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self.assertEqual(tensor.dist_attr().process_mesh.shape, [2])
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self.assertEqual(
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tensor.dist_attr().process_mesh.process_ids, [0, 1]
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)
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if tensor.shape == [IMAGE_SIZE, IMAGE_SIZE]:
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self.assertEqual(tensor.dist_attr().dims_mapping, [-1, 0])
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elif tensor.shape == [IMAGE_SIZE, CLASS_NUM]:
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self.assertEqual(tensor.dist_attr().dims_mapping, [0, -1])
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self.assertEqual(tensor.dist_attr().partial_dims, set())
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if op.name() == 'pd_op.relu':
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if relu_idx == 0:
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self.assertEqual(tensor.dist_attr().dims_mapping, [-1, -1])
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self.assertEqual(tensor.dist_attr().partial_dims, set())
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self.assertEqual(
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tensor._local_shape, [BATCH_SIZE, IMAGE_SIZE]
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)
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elif relu_idx == 1:
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self.assertEqual(tensor.dist_attr().dims_mapping, [-1, 0])
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self.assertEqual(tensor.dist_attr().partial_dims, set())
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self.assertEqual(
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tensor._local_shape, [BATCH_SIZE, IMAGE_SIZE // 2]
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)
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elif relu_idx == 2:
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self.assertEqual(tensor.dist_attr().dims_mapping, [-1, -1])
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self.assertEqual(tensor.dist_attr().partial_dims, set())
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self.assertEqual(
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tensor._local_shape, [BATCH_SIZE, CLASS_NUM]
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)
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relu_idx += 1
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if op.name() == 'pd_op.matmul':
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if matmul_idx == 0:
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self.assertEqual(tensor.dist_attr().dims_mapping, [-1, 0])
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self.assertEqual(tensor.dist_attr().partial_dims, set())
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self.assertEqual(
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tensor._local_shape, [BATCH_SIZE, IMAGE_SIZE // 2]
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)
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elif matmul_idx == 1:
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self.assertEqual(tensor.dist_attr().dims_mapping, [-1, -1])
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self.assertEqual(tensor.dist_attr().partial_dims, {0})
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self.assertEqual(
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tensor._local_shape, [BATCH_SIZE, CLASS_NUM]
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)
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matmul_idx += 1
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if op.name() == 'pd_op.matmul_grad':
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if matmul_grad_idx == 0:
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self.assertEqual(tensor.dist_attr().dims_mapping, [-1, 0])
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self.assertEqual(tensor.dist_attr().partial_dims, set())
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self.assertEqual(
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tensor._local_shape, [BATCH_SIZE, CLASS_NUM]
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)
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elif matmul_grad_idx == 1:
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self.assertEqual(tensor.dist_attr().dims_mapping, [-1, -1])
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self.assertEqual(tensor.dist_attr().partial_dims, {0})
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self.assertEqual(
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tensor._local_shape, [BATCH_SIZE, IMAGE_SIZE]
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)
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matmul_grad_idx += 1
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if op.name() == 'pd_op.sgd_':
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if sgd_idx == 0:
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self.assertEqual(tensor.dist_attr().dims_mapping, [0, -1])
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self.assertEqual(tensor.dist_attr().partial_dims, set())
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self.assertEqual(
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tensor._local_shape, [IMAGE_SIZE // 2, CLASS_NUM]
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)
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elif sgd_idx == 1:
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self.assertEqual(tensor.dist_attr().dims_mapping, [-1, 0])
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self.assertEqual(tensor.dist_attr().partial_dims, set())
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self.assertEqual(
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tensor._local_shape, [IMAGE_SIZE, IMAGE_SIZE // 2]
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)
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sgd_idx += 1
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# dist_model.train()
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# for batch_id, (image, label) in enumerate(dist_loader()):
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# loss = dist_model(image, label)
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if __name__ == "__main__":
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unittest.main()
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