wehub-resource-sync
219 lines
6.8 KiB
Python
219 lines
6.8 KiB
Python
import warnings
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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 bspmm, diag, from_coo, val_like
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from dgl.sparse.matmul import matmul
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from .utils import (
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clone_detach_and_grad,
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dense_mask,
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rand_coo,
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rand_csc,
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rand_csr,
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rand_stride,
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sparse_matrix_to_dense,
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sparse_matrix_to_torch_sparse,
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)
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def _torch_sparse_mm(torch_A1, torch_A2):
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", category=UserWarning)
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return torch.sparse.mm(torch_A1, torch_A2)
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@pytest.mark.parametrize("create_func", [rand_coo, rand_csr, rand_csc])
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@pytest.mark.parametrize("shape", [(2, 7), (5, 2)])
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@pytest.mark.parametrize("nnz", [1, 10])
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@pytest.mark.parametrize("out_dim", [None, 10])
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def test_spmm(create_func, shape, nnz, out_dim):
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dev = F.ctx()
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A = create_func(shape, nnz, dev)
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if out_dim is not None:
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X = torch.randn(shape[1], out_dim, requires_grad=True, device=dev)
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else:
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X = torch.randn(shape[1], requires_grad=True, device=dev)
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X = rand_stride(X)
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sparse_result = matmul(A, X)
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grad = torch.randn_like(sparse_result)
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sparse_result.backward(grad)
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adj = sparse_matrix_to_dense(A)
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XX = clone_detach_and_grad(X)
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dense_result = torch.matmul(adj, XX)
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if out_dim is None:
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dense_result = dense_result.view(-1)
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dense_result.backward(grad)
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assert torch.allclose(sparse_result, dense_result, atol=1e-05)
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assert torch.allclose(X.grad, XX.grad, atol=1e-05)
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assert torch.allclose(
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dense_mask(adj.grad, A),
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sparse_matrix_to_dense(val_like(A, A.val.grad)),
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atol=1e-05,
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)
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@pytest.mark.parametrize("create_func", [rand_coo, rand_csr, rand_csc])
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@pytest.mark.parametrize("shape", [(2, 7), (5, 2)])
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@pytest.mark.parametrize("nnz", [1, 10])
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def test_bspmm(create_func, shape, nnz):
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dev = F.ctx()
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A = create_func(shape, nnz, dev, 2)
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X = torch.randn(shape[1], 10, 2, requires_grad=True, device=dev)
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X = rand_stride(X)
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sparse_result = matmul(A, X)
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grad = torch.randn_like(sparse_result)
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sparse_result.backward(grad)
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XX = clone_detach_and_grad(X)
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torch_A = A.to_dense().clone().detach().requires_grad_()
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torch_result = torch_A.permute(2, 0, 1) @ XX.permute(2, 0, 1)
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torch_result.backward(grad.permute(2, 0, 1))
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assert torch.allclose(
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sparse_result.permute(2, 0, 1), torch_result, atol=1e-05
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)
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assert torch.allclose(X.grad, XX.grad, atol=1e-05)
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assert torch.allclose(
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dense_mask(torch_A.grad, A),
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sparse_matrix_to_dense(val_like(A, A.val.grad)),
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atol=1e-05,
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)
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@pytest.mark.parametrize("create_func1", [rand_coo, rand_csr, rand_csc])
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@pytest.mark.parametrize("create_func2", [rand_coo, rand_csr, rand_csc])
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@pytest.mark.parametrize("shape_n_m", [(5, 5), (5, 6)])
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@pytest.mark.parametrize("shape_k", [3, 4])
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@pytest.mark.parametrize("nnz1", [1, 10])
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@pytest.mark.parametrize("nnz2", [1, 10])
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def test_spspmm(create_func1, create_func2, shape_n_m, shape_k, nnz1, nnz2):
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dev = F.ctx()
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shape1 = shape_n_m
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shape2 = (shape_n_m[1], shape_k)
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A1 = create_func1(shape1, nnz1, dev)
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A2 = create_func2(shape2, nnz2, dev)
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A3 = matmul(A1, A2)
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grad = torch.randn_like(A3.val)
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A3.val.backward(grad)
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torch_A1 = sparse_matrix_to_torch_sparse(A1)
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torch_A2 = sparse_matrix_to_torch_sparse(A2)
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torch_A3 = _torch_sparse_mm(torch_A1, torch_A2)
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torch_A3_grad = sparse_matrix_to_torch_sparse(A3, grad)
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torch_A3.backward(torch_A3_grad)
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with torch.no_grad():
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assert torch.allclose(A3.to_dense(), torch_A3.to_dense(), atol=1e-05)
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assert torch.allclose(
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val_like(A1, A1.val.grad).to_dense(),
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torch_A1.grad.to_dense(),
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atol=1e-05,
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)
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assert torch.allclose(
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val_like(A2, A2.val.grad).to_dense(),
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torch_A2.grad.to_dense(),
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atol=1e-05,
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)
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def test_spspmm_duplicate():
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dev = F.ctx()
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row = torch.tensor([1, 0, 0, 0, 1]).to(dev)
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col = torch.tensor([1, 1, 1, 2, 2]).to(dev)
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val = torch.randn(len(row)).to(dev)
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shape = (4, 4)
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A1 = from_coo(row, col, val, shape)
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row = torch.tensor([1, 0, 0, 1]).to(dev)
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col = torch.tensor([1, 1, 2, 2]).to(dev)
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val = torch.randn(len(row)).to(dev)
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shape = (4, 4)
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A2 = from_coo(row, col, val, shape)
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try:
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matmul(A1, A2)
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except:
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pass
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else:
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assert False, "Should raise error."
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try:
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matmul(A2, A1)
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except:
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pass
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else:
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assert False, "Should raise error."
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@pytest.mark.parametrize("create_func", [rand_coo, rand_csr, rand_csc])
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@pytest.mark.parametrize("sparse_shape", [(5, 5), (5, 6)])
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@pytest.mark.parametrize("nnz", [1, 10])
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def test_sparse_diag_mm(create_func, sparse_shape, nnz):
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dev = F.ctx()
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diag_shape = sparse_shape[1], sparse_shape[1]
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A = create_func(sparse_shape, nnz, dev)
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diag_val = torch.randn(sparse_shape[1], device=dev, requires_grad=True)
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D = diag(diag_val, diag_shape)
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B = matmul(A, D)
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grad = torch.randn_like(B.val)
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B.val.backward(grad)
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torch_A = sparse_matrix_to_torch_sparse(A)
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torch_D = sparse_matrix_to_torch_sparse(D)
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torch_B = _torch_sparse_mm(torch_A, torch_D)
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torch_B_grad = sparse_matrix_to_torch_sparse(B, grad)
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torch_B.backward(torch_B_grad)
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with torch.no_grad():
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assert torch.allclose(B.to_dense(), torch_B.to_dense(), atol=1e-05)
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assert torch.allclose(
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val_like(A, A.val.grad).to_dense(),
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torch_A.grad.to_dense(),
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atol=1e-05,
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)
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assert torch.allclose(
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diag(D.val.grad, D.shape).to_dense(),
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torch_D.grad.to_dense(),
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atol=1e-05,
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)
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@pytest.mark.parametrize("create_func", [rand_coo, rand_csr, rand_csc])
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@pytest.mark.parametrize("sparse_shape", [(5, 5), (5, 6)])
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@pytest.mark.parametrize("nnz", [1, 10])
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def test_diag_sparse_mm(create_func, sparse_shape, nnz):
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dev = F.ctx()
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diag_shape = sparse_shape[0], sparse_shape[0]
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A = create_func(sparse_shape, nnz, dev)
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diag_val = torch.randn(sparse_shape[0], device=dev, requires_grad=True)
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D = diag(diag_val, diag_shape)
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B = matmul(D, A)
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grad = torch.randn_like(B.val)
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B.val.backward(grad)
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torch_A = sparse_matrix_to_torch_sparse(A)
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torch_D = sparse_matrix_to_torch_sparse(D)
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torch_B = _torch_sparse_mm(torch_D, torch_A)
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torch_B_grad = sparse_matrix_to_torch_sparse(B, grad)
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torch_B.backward(torch_B_grad)
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with torch.no_grad():
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assert torch.allclose(B.to_dense(), torch_B.to_dense(), atol=1e-05)
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assert torch.allclose(
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val_like(A, A.val.grad).to_dense(),
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torch_A.grad.to_dense(),
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atol=1e-05,
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)
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assert torch.allclose(
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diag(D.val.grad, D.shape).to_dense(),
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torch_D.grad.to_dense(),
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atol=1e-05,
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)
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