232 lines
8.4 KiB
Python
232 lines
8.4 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 os
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from collections import namedtuple
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from unittest import mock
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from unittest.mock import patch
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import pytest
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import torch
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from lightning.fabric.plugins.environments import TorchElasticEnvironment
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from lightning.fabric.utilities.device_parser import _parse_gpu_ids
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from lightning.pytorch import Trainer, seed_everything
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from lightning.pytorch.accelerators import CPUAccelerator, CUDAAccelerator
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from lightning.pytorch.demos.boring_classes import BoringModel
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from lightning.pytorch.utilities.exceptions import MisconfigurationException
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import tests_pytorch.helpers.pipelines as tpipes
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from tests_pytorch.helpers.datamodules import ClassifDataModule
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from tests_pytorch.helpers.runif import RunIf
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from tests_pytorch.helpers.simple_models import ClassificationModel
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PRETEND_N_OF_GPUS = 16
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@RunIf(min_cuda_gpus=2, sklearn=True)
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def test_multi_gpu_none_backend(tmp_path):
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"""Make sure when using multiple GPUs the user can't use `accelerator = None`."""
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seed_everything(42)
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trainer_options = {
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"default_root_dir": tmp_path,
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"enable_progress_bar": False,
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"max_epochs": 1,
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"limit_train_batches": 0.2,
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"limit_val_batches": 0.2,
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"accelerator": "gpu",
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"strategy": "ddp_spawn",
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"devices": 2,
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}
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dm = ClassifDataModule()
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model = ClassificationModel()
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tpipes.run_model_test(trainer_options, model, dm)
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@RunIf(min_cuda_gpus=2)
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@pytest.mark.parametrize("devices", [1, [0], [1]])
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def test_single_gpu_model(tmp_path, devices):
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seed_everything(42)
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trainer_options = {
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"default_root_dir": tmp_path,
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"enable_progress_bar": False,
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"max_epochs": 1,
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"limit_train_batches": 0.1,
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"limit_val_batches": 0.1,
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"accelerator": "gpu",
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"devices": devices,
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"strategy": "ddp_spawn",
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}
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model)
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@pytest.mark.parametrize(
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"devices",
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[
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1,
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3,
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[1, 2],
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[0, 1],
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-1,
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"-1",
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],
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)
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def test_root_gpu_property_0_raising(mps_count_0, cuda_count_0, devices):
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"""Test that asking for a GPU when none are available will result in a MisconfigurationException."""
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with pytest.raises(MisconfigurationException, match="No supported gpu backend found!"):
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Trainer(accelerator="gpu", devices=devices, strategy="ddp")
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@mock.patch.dict(
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os.environ,
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{
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"CUDA_VISIBLE_DEVICES": "0",
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"LOCAL_RANK": "1",
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"GROUP_RANK": "1",
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"RANK": "3",
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"WORLD_SIZE": "4",
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"LOCAL_WORLD_SIZE": "2",
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"TORCHELASTIC_RUN_ID": "1",
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},
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)
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@pytest.mark.parametrize("devices", [[0, 1, 2], 2, "0,", [0, 2]])
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def test_torchelastic_gpu_parsing(cuda_count_1, devices):
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"""Ensure when using torchelastic and nproc_per_node is set to the default of 1 per GPU device that we omit
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sanitizing the gpus as only one of the GPUs is visible."""
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trainer = Trainer(accelerator="cuda", devices=devices)
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assert isinstance(trainer._accelerator_connector.cluster_environment, TorchElasticEnvironment)
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# when using gpu
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if _parse_gpu_ids(devices, include_cuda=True) is not None:
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assert isinstance(trainer.accelerator, CUDAAccelerator)
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assert trainer.num_devices == len(devices) if isinstance(devices, list) else devices
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assert trainer.device_ids == _parse_gpu_ids(devices, include_cuda=True)
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# fall back to cpu
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else:
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assert isinstance(trainer.accelerator, CPUAccelerator)
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assert trainer.num_devices == 1
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assert trainer.device_ids == [0]
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@RunIf(min_cuda_gpus=1)
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def test_single_gpu_batch_parse():
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trainer = Trainer(accelerator="gpu", devices=1)
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# non-transferrable types
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primitive_objects = [None, {}, [], 1.0, "x", [None, 2], {"x": (1, 2), "y": None}]
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for batch in primitive_objects:
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data = trainer.strategy.batch_to_device(batch, torch.device("cuda:0"))
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assert data == batch
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# batch is just a tensor
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batch = torch.rand(2, 3)
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batch = trainer.strategy.batch_to_device(batch, torch.device("cuda:0"))
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assert batch.device.index == 0
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assert batch.type() == "torch.cuda.FloatTensor"
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# tensor list
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batch = [torch.rand(2, 3), torch.rand(2, 3)]
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batch = trainer.strategy.batch_to_device(batch, torch.device("cuda:0"))
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assert batch[0].device.index == 0
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assert batch[0].type() == "torch.cuda.FloatTensor"
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assert batch[1].device.index == 0
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assert batch[1].type() == "torch.cuda.FloatTensor"
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# tensor list of lists
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batch = [[torch.rand(2, 3), torch.rand(2, 3)]]
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batch = trainer.strategy.batch_to_device(batch, torch.device("cuda:0"))
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assert batch[0][0].device.index == 0
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assert batch[0][0].type() == "torch.cuda.FloatTensor"
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assert batch[0][1].device.index == 0
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assert batch[0][1].type() == "torch.cuda.FloatTensor"
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# tensor dict
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batch = [{"a": torch.rand(2, 3), "b": torch.rand(2, 3)}]
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batch = trainer.strategy.batch_to_device(batch, torch.device("cuda:0"))
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assert batch[0]["a"].device.index == 0
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assert batch[0]["a"].type() == "torch.cuda.FloatTensor"
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assert batch[0]["b"].device.index == 0
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assert batch[0]["b"].type() == "torch.cuda.FloatTensor"
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# tuple of tensor list and list of tensor dict
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batch = ([torch.rand(2, 3) for _ in range(2)], [{"a": torch.rand(2, 3), "b": torch.rand(2, 3)} for _ in range(2)])
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batch = trainer.strategy.batch_to_device(batch, torch.device("cuda:0"))
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assert batch[0][0].device.index == 0
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assert batch[0][0].type() == "torch.cuda.FloatTensor"
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assert batch[1][0]["a"].device.index == 0
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assert batch[1][0]["a"].type() == "torch.cuda.FloatTensor"
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assert batch[1][0]["b"].device.index == 0
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assert batch[1][0]["b"].type() == "torch.cuda.FloatTensor"
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# namedtuple of tensor
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BatchType = namedtuple("BatchType", ["a", "b"])
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batch = [BatchType(a=torch.rand(2, 3), b=torch.rand(2, 3)) for _ in range(2)]
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batch = trainer.strategy.batch_to_device(batch, torch.device("cuda:0"))
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assert batch[0].a.device.index == 0
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assert batch[0].a.type() == "torch.cuda.FloatTensor"
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# non-Tensor that has `.to()` defined
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class CustomBatchType:
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def __init__(self):
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self.a = torch.rand(2, 2)
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def to(self, *args, **kwargs):
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self.a = self.a.to(*args, **kwargs)
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return self
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batch = trainer.strategy.batch_to_device(CustomBatchType(), torch.device("cuda:0"))
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assert batch.a.type() == "torch.cuda.FloatTensor"
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@RunIf(min_cuda_gpus=1)
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def test_non_blocking():
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"""Tests that non_blocking=True only gets passed on Tensor.to, but not on other objects."""
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trainer = Trainer()
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batch = torch.zeros(2, 3)
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with patch.object(batch, "to", wraps=batch.to) as mocked:
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batch = trainer.strategy.batch_to_device(batch, torch.device("cuda:0"))
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mocked.assert_called_with(torch.device("cuda", 0), non_blocking=True)
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class BatchObject:
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def to(self, *args, **kwargs):
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pass
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batch = BatchObject()
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with patch.object(batch, "to", wraps=batch.to) as mocked:
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batch = trainer.strategy.batch_to_device(batch, torch.device("cuda:0"))
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mocked.assert_called_with(torch.device("cuda", 0))
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@RunIf(min_cuda_gpus=1)
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@pytest.mark.parametrize(
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("strategy", "precision", "expected_dtype"),
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[
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("auto", "16-mixed", torch.float32),
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("auto", "16-true", torch.float16),
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pytest.param("deepspeed", "bf16-true", torch.bfloat16, marks=RunIf(deepspeed=True, bf16_cuda=True)),
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],
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)
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def test_input_tensors_cast_before_transfer_to_device(strategy, precision, expected_dtype):
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class CustomBoringModel(BoringModel):
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def transfer_batch_to_device(self, batch, *args, **kwargs):
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assert batch.dtype == expected_dtype
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return super().transfer_batch_to_device(batch, *args, **kwargs)
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model = CustomBoringModel()
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trainer = Trainer(strategy=strategy, devices=1, precision=precision, barebones=True, max_steps=2)
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trainer.fit(model)
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