dmlc--dgl
Quan (Andy) Gan
3b0c0cec46
* enable sparse on windows and mac * that was stupid * let's see what's going on.. * [Sparse] Fix the import error on Mac OS. When using template functions that are defined in source files from DGL, the loader of MacOS somehow cannot find their definitions. This fix simply avoids depending on template functions from DGL headers. With this fix, the sparse tests all pass on the MAC environment. * ok this is the problem * make errors clearer * uh * test * Update __init__.py * disabling ddp on windows --------- Co-authored-by: czkkkkkk <zekucai@gmail.com>
129 行
3.1 KiB
Python
129 行
3.1 KiB
Python
import sys
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import unittest
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import backend as F
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import pytest
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import torch
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from dgl.sparse import diag, DiagMatrix, identity
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@pytest.mark.parametrize("val_shape", [(3,), (3, 2)])
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@pytest.mark.parametrize("mat_shape", [None, (3, 5), (5, 3)])
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def test_diag(val_shape, mat_shape):
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ctx = F.ctx()
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# creation
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val = torch.randn(val_shape).to(ctx)
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mat = diag(val, mat_shape)
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# val, shape attributes
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assert torch.allclose(mat.val, val)
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if mat_shape is None:
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mat_shape = (val_shape[0], val_shape[0])
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assert mat.shape == mat_shape
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val = torch.randn(val_shape).to(ctx)
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# nnz
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assert mat.nnz == val.shape[0]
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# dtype
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assert mat.dtype == val.dtype
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# device
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assert mat.device == val.device
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# as_sparse
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sp_mat = mat.to_sparse()
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# shape
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assert tuple(sp_mat.shape) == mat_shape
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# nnz
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assert sp_mat.nnz == mat.nnz
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# dtype
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assert sp_mat.dtype == mat.dtype
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# device
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assert sp_mat.device == mat.device
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# row, col, val
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edge_index = torch.arange(len(val)).to(mat.device)
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row, col = sp_mat.coo()
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val = sp_mat.val
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assert torch.allclose(row, edge_index)
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assert torch.allclose(col, edge_index)
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assert torch.allclose(val, val)
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@pytest.mark.parametrize("shape", [(3, 3), (3, 5), (5, 3)])
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@pytest.mark.parametrize("d", [None, 2])
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def test_identity(shape, d):
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ctx = F.ctx()
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# creation
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mat = identity(shape, d)
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# type
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assert isinstance(mat, DiagMatrix)
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# shape
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assert mat.shape == shape
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# val
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len_val = min(shape)
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if d is None:
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val_shape = len_val
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else:
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val_shape = (len_val, d)
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val = torch.ones(val_shape)
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assert torch.allclose(val, mat.val)
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def test_print():
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ctx = F.ctx()
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# basic
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val = torch.tensor([1.0, 1.0, 2.0]).to(ctx)
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A = diag(val)
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print(A)
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# vector-shape non zero
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val = torch.randn(3, 2).to(ctx)
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A = diag(val)
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print(A)
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@unittest.skipIf(
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F._default_context_str == "cpu",
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reason="Device conversions don't need to be tested on CPU.",
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)
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@pytest.mark.parametrize("device", ["cpu", "cuda"])
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def test_to_device(device):
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val = torch.randn(3)
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mat_shape = (3, 4)
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mat = diag(val, mat_shape)
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target_val = mat.val.to(device)
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mat2 = mat.to(device=device)
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assert mat2.shape == mat.shape
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assert torch.allclose(mat2.val, target_val)
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mat2 = getattr(mat, device)()
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assert mat2.shape == mat.shape
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assert torch.allclose(mat2.val, target_val)
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@pytest.mark.parametrize(
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"dtype", [torch.float, torch.double, torch.int, torch.long]
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)
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def test_to_dtype(dtype):
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val = torch.randn(3)
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mat_shape = (3, 4)
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mat = diag(val, mat_shape)
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target_val = mat.val.to(dtype=dtype)
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mat2 = mat.to(dtype=dtype)
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assert mat2.shape == mat.shape
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assert torch.allclose(mat2.val, target_val)
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func_name = {
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torch.float: "float",
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torch.double: "double",
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torch.int: "int",
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torch.long: "long",
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}
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mat2 = getattr(mat, func_name[dtype])()
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assert mat2.shape == mat.shape
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assert torch.allclose(mat2.val, target_val)
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