wehub-resource-sync
105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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import numpy as np
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import deepspeed
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import pytest
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from deepspeed.runtime.progressive_layer_drop import ProgressiveLayerDrop
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from unit.common import DistributedTest
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from unit.simple_model import SimpleModel, PLD_SimpleModel, random_dataloader
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from deepspeed.accelerator import get_accelerator
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@pytest.mark.parametrize('theta', [0, 0.1, 0.9, 1.0])
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def test_pld_schedule(tmpdir, theta):
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gamma = 0.001
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pld_scheduler = ProgressiveLayerDrop(theta, gamma)
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for i in range(10):
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pld_scheduler.update_state(i)
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expected_theta = (1. - theta) * np.exp(-gamma * i) + theta
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actual_theta = pld_scheduler.get_theta()
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assert expected_theta == actual_theta
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@pytest.mark.parametrize('theta', [0, 0.1, 0.9, 1.0])
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class TestPLDModel(DistributedTest):
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world_size = 1
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def test_pld_model(self, theta):
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gamma = 0.001
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config_dict = {
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"train_batch_size": 1,
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"steps_per_print": 1,
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"optimizer": {
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"type": 'Adam',
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"params": {
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"lr": 0.0001
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}
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},
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"progressive_layer_drop": {
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"enabled": True,
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"theta": theta,
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"gamma": gamma
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}
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}
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if get_accelerator().is_bf16_supported():
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config_dict["bf16"] = {"enabled": True}
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elif get_accelerator().is_fp16_supported():
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config_dict["fp16"] = {"enabled": True}
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hidden_dim = 10
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model = PLD_SimpleModel(hidden_dim, empty_grad=False)
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model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters())
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data_loader = random_dataloader(model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device)
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for i, batch in enumerate(data_loader):
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loss = model(batch[0], batch[1])
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model.backward(loss)
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model.step()
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expected_theta = (1. - theta) * np.exp(-gamma * i) + theta
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actual_theta = model.get_pld_theta()
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assert expected_theta == actual_theta
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class TestNonPLDModel(DistributedTest):
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world_size = 1
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def test_non_pld_model(self):
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gamma = 0.001
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theta = 0.5
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config_dict = {
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"train_batch_size": 1,
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"steps_per_print": 1,
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"optimizer": {
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"type": 'Adam',
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"params": {
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"lr": 0.0001
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}
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},
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"progressive_layer_drop": {
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"enabled": True,
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"theta": theta,
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"gamma": gamma
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}
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}
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if get_accelerator().is_bf16_supported():
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config_dict["bf16"] = {"enabled": True}
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elif get_accelerator().is_fp16_supported():
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config_dict["fp16"] = {"enabled": True}
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hidden_dim = 10
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model = SimpleModel(hidden_dim, empty_grad=False)
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model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters())
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data_loader = random_dataloader(model=model, total_samples=1, hidden_dim=hidden_dim, device=model.device)
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for i, batch in enumerate(data_loader):
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with pytest.raises(TypeError):
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loss = model(batch[0], batch[1])
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