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import pytest |
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import torch |
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import numpy as np |
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import megatron.core.utils as util |
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def test_divide_properly(): |
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assert util.divide(4, 2) == 2 |
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def test_divide_improperly(): |
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with pytest.raises(AssertionError): |
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util.divide(4, 5) |
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def test_global_memory_buffer(): |
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global_memory_buffer = util.GlobalMemoryBuffer() |
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obtained_tensor = global_memory_buffer.get_tensor((3, 2), torch.float32, "test_tensor") |
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expected_tensor = torch.empty((3, 2), dtype=torch.float32, device=torch.cuda.current_device()) |
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assert torch.equal(obtained_tensor, expected_tensor) |
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def test_make_viewless_tensor(): |
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inp = torch.rand((3, 4)) |
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assert(torch.equal(inp, util.make_viewless_tensor(inp, True, True))) |
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assert(torch.equal(inp, util.make_viewless_tensor(inp, True, False))) |
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def test_safely_set_viewless_tensor_data(): |
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tensor = torch.zeros((3, 4)) |
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new_data_tensor = torch.tensor(np.random.rand(3,4)) |
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util.safely_set_viewless_tensor_data(tensor, new_data_tensor) |
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assert(torch.equal(tensor, new_data_tensor)) |
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def test_assert_viewless_tensor(): |
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tensor = torch.rand((3, 4)) |
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assert(torch.equal(util.assert_viewless_tensor(tensor), tensor)) |
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input_tensor_list=[tensor, tensor, tensor] |
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output_tensor_list = util.assert_viewless_tensor(input_tensor_list) |
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for inp,out in zip(input_tensor_list, output_tensor_list): |
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assert(torch.equal(inp, out)) |
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