test_autocast.py

# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team

import pytest
import torch
from deepspeed.runtime.zero.linear import LinearModuleForZeroStage3
from deepspeed.accelerator import get_accelerator
from unit.common import DistributedTest


@pytest.mark.parametrize('half_op', [False, True])
class TestAutoCastDisable(DistributedTest):

    def test_missing_amp_autocast(self, half_op):
        hidden_dim = 4
        if half_op:
            input = torch.randn(hidden_dim).to(get_accelerator().device_name()).half()
            ds_linear = LinearModuleForZeroStage3(hidden_dim, hidden_dim).to(get_accelerator().device_name()).half()
        else:
            input = torch.randn(hidden_dim).to(get_accelerator().device_name())
            ds_linear = LinearModuleForZeroStage3(hidden_dim, hidden_dim).to(get_accelerator().device_name())

        output = ds_linear(input)
        assert output.dtype == ds_linear.weight.dtype

    def test_disable_autocast_linear(self, half_op):
        amp = get_accelerator().amp()

        hidden_dim = 4
        if half_op:
            input = torch.randn(hidden_dim).to(get_accelerator().device_name()).half()
            ds_linear = LinearModuleForZeroStage3(hidden_dim, hidden_dim).to(get_accelerator().device_name()).half()
        else:
            input = torch.randn(hidden_dim).to(get_accelerator().device_name())
            ds_linear = LinearModuleForZeroStage3(hidden_dim, hidden_dim).to(get_accelerator().device_name())

        with amp.autocast(False):
            output = ds_linear(input)
            assert output.dtype == ds_linear.weight.dtype


@pytest.mark.skipif(get_accelerator().amp() is None, reason='amp is not installed')
@pytest.mark.parametrize('half_input, half_weight', [(False, False), (False, True), (True, False), (True, True)])
class TestAutoCastEnable(DistributedTest):

    def test_autocast_linear(self, tmpdir, half_input, half_weight):
        amp = get_accelerator().amp()

        hidden_dim = 4
        input = torch.randn(hidden_dim).to(get_accelerator().device_name())
        ds_linear = LinearModuleForZeroStage3(hidden_dim, hidden_dim).to(get_accelerator().device_name())

        if half_input:
            input = input.half()

        if half_weight:
            ds_linear = ds_linear.half()

        with amp.autocast():
            output = ds_linear(input)
            assert output.dtype == torch.half or output.dtype == torch.bfloat16