134 lines
4.9 KiB
Python
134 lines
4.9 KiB
Python
# Copyright The Lightning AI team.
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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 pytest
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import torch
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from lightning.pytorch import Trainer
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from lightning.pytorch.demos.boring_classes import BoringModel
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from tests_pytorch.core.test_results import spawn_launch
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from tests_pytorch.helpers.runif import RunIf
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def all_gather_ddp_spawn_fn(strategy):
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rank = strategy.local_rank
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world_size = strategy.num_processes
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tensor1 = torch.ones(8, requires_grad=True)
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tensor2 = torch.ones((8, 16, 32), requires_grad=True)
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tensor1_gathered = strategy.all_gather(tensor1, sync_grads=True)
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tensor2_gathered = strategy.all_gather(tensor2, sync_grads=True)
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tensor1_gathered = tensor1_gathered * rank
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tensor2_gathered = tensor2_gathered * rank
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tensor1_gathered.sum().backward()
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tensor2_gathered.sum().backward()
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grad1 = torch.zeros_like(tensor1.grad).fill_(torch.arange(world_size).sum().float())
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grad2 = torch.zeros_like(tensor2.grad).fill_(torch.arange(world_size).sum().float())
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assert torch.allclose(grad1, tensor1.grad)
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assert torch.allclose(grad2, tensor2.grad)
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@RunIf(skip_windows=True)
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@pytest.mark.flaky(reruns=3)
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def test_all_gather_ddp_spawn():
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spawn_launch(all_gather_ddp_spawn_fn, [torch.device("cpu")] * 3)
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@RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
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def test_all_gather_collection(tmp_path):
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class TestModel(BoringModel):
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on_train_epoch_end_called = False
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def on_train_epoch_end(self):
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losses = torch.rand(2, 2).t()
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gathered_loss = self.all_gather({
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"losses_tensor_int": losses.int(),
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"losses_tensor_float": losses,
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"losses_tensor_list": [losses, losses],
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"losses_np_ndarray": np.array([1, 2, 3]),
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"losses_bool": [True, False],
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"losses_float": [0.0, 1.0, 2.0],
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"losses_int": [0, 1, 2],
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})
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assert gathered_loss["losses_tensor_int"][0].dtype == torch.int32
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assert gathered_loss["losses_tensor_float"][0].dtype == torch.float
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assert gathered_loss["losses_np_ndarray"][0].dtype == torch.int64
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# torch.bool can't be all_gathered
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assert gathered_loss["losses_bool"][0].dtype == torch.uint8
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assert gathered_loss["losses_float"][0].dtype == torch.float
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assert gathered_loss["losses_int"][0].dtype == torch.int
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losses_numel = losses.numel()
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assert gathered_loss["losses_tensor_int"].numel() == 2 * losses_numel
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assert gathered_loss["losses_tensor_float"].numel() == 2 * losses_numel
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assert torch.stack(gathered_loss["losses_tensor_list"]).shape == (2, 2, 2, 2)
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assert gathered_loss["losses_np_ndarray"].numel() == 2 * 3
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assert torch.stack(gathered_loss["losses_bool"]).shape == (2, 2)
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assert torch.stack(gathered_loss["losses_float"]).shape == (3, 2)
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assert torch.stack(gathered_loss["losses_int"]).shape == (3, 2)
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self.on_train_epoch_end_called = True
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmp_path,
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limit_train_batches=8,
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limit_val_batches=0,
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max_epochs=1,
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log_every_n_steps=1,
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accumulate_grad_batches=2,
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accelerator="gpu",
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devices=2,
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strategy="ddp",
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enable_progress_bar=False,
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enable_model_summary=False,
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enable_checkpointing=False,
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)
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trainer.fit(model)
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assert model.on_train_epoch_end_called
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@RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
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def test_all_gather_sync_grads(tmp_path):
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class TestModel(BoringModel):
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training_step_called = False
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def training_step(self, batch, batch_idx):
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self.training_step_called = True
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tensor = torch.rand(2, 2, requires_grad=True, device=self.device)
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gathered_tensor = self.all_gather(tensor, sync_grads=True)
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assert gathered_tensor.shape == torch.Size([2, 2, 2])
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return gathered_tensor.sum()
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmp_path,
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limit_train_batches=1,
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limit_val_batches=0,
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max_epochs=1,
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accelerator="gpu",
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devices=2,
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strategy="ddp",
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enable_progress_bar=False,
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enable_model_summary=False,
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enable_checkpointing=False,
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)
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trainer.fit(model)
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assert model.training_step_called
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