Enable sparse tensor dense matmul op tests in eager mode.
PiperOrigin-RevId: 335670100 Change-Id: I959563a43a8955810e9a8672f127fc0af6aab5cd
This commit is contained in:
Penporn Koanantakool
TensorFlower Gardener
parent
6ae2b68fd3
commit
e665554b90
@ -97,7 +97,6 @@ class SparseTensorDenseMatMulTest(test.TestCase):
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self._testMatmul(x, y, indices_dtype=indices_dtype)
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@test_util.run_deprecated_v1
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def testBasic(self):
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np.random.seed(127) # Repeatable results
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self._testBasic(np.int32)
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@ -108,7 +107,6 @@ class SparseTensorDenseMatMulTest(test.TestCase):
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self._testBasic(np.int32, indices_dtype=np.int32)
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self._testBasic(np.float32, indices_dtype=np.int32)
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@test_util.run_deprecated_v1
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def testShapeInference(self):
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x = np.random.rand(10, 10)
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x[np.abs(x) < 0.5] = 0 # Make it sparse
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@ -116,97 +114,91 @@ class SparseTensorDenseMatMulTest(test.TestCase):
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x_indices = np.vstack(np.where(x)).astype(np.int64).T
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x_values = x[np.where(x)]
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x_shape = x.shape
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x_st = sparse_tensor.SparseTensor(x_indices, x_values, x_shape)
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result = sparse_ops.sparse_tensor_dense_matmul(x_st, y)
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self.assertEqual(result.get_shape(), (10, 20))
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x_shape_unknown = array_ops.placeholder(dtype=dtypes.int64, shape=None)
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x_st_shape_unknown = sparse_tensor.SparseTensor(x_indices, x_values,
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x_shape_unknown)
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result_left_shape_unknown = sparse_ops.sparse_tensor_dense_matmul(
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x_st_shape_unknown, y)
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self.assertEqual(result_left_shape_unknown.get_shape().as_list(),
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[None, 20])
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with ops.Graph().as_default():
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x_st = sparse_tensor.SparseTensor(x_indices, x_values, x_shape)
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result = sparse_ops.sparse_tensor_dense_matmul(x_st, y)
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self.assertEqual(result.get_shape(), (10, 20))
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x_shape_inconsistent = [10, 15]
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x_st_shape_inconsistent = sparse_tensor.SparseTensor(x_indices, x_values,
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x_shape_inconsistent)
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with self.assertRaisesRegex(ValueError, "Dimensions must be equal"):
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sparse_ops.sparse_tensor_dense_matmul(x_st_shape_inconsistent, y)
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x_shape_unknown = array_ops.placeholder(dtype=dtypes.int64, shape=None)
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x_st_shape_unknown = sparse_tensor.SparseTensor(x_indices, x_values,
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x_shape_unknown)
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result_left_shape_unknown = sparse_ops.sparse_tensor_dense_matmul(
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x_st_shape_unknown, y)
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self.assertEqual(result_left_shape_unknown.get_shape().as_list(),
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[None, 20])
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@test_util.deprecated_graph_mode_only
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x_shape_inconsistent = [10, 15]
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x_st_shape_inconsistent = sparse_tensor.SparseTensor(
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x_indices, x_values, x_shape_inconsistent)
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with self.assertRaisesRegex(ValueError, "Dimensions must be equal"):
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sparse_ops.sparse_tensor_dense_matmul(x_st_shape_inconsistent, y)
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@test_util.run_in_graph_and_eager_modes(use_gpu=False)
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def testInvalidIndicesForSparseTensorDenseMatmul(self):
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# Note: use_gpu=False because nice errors are only returned from CPU kernel.
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with self.session(use_gpu=False):
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indices = np.matrix([[1, 10]]).astype(np.int64)
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values = np.array([10]).astype(np.float32)
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shape = [3, 2]
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sparse_t = sparse_tensor.SparseTensor(indices, values, shape)
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# TODO(b/169813429): Make GPU kernel return nice errors too.
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indices = np.matrix([[1, 10]]).astype(np.int64)
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values = np.array([10]).astype(np.float32)
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shape = [3, 2]
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sparse_t = sparse_tensor.SparseTensor(indices, values, shape)
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# Test multiplying by both a small and large dense matrix, to hit
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# both cases in the kernel.
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dense_t = np.matrix([[1] * 5, [2] * 5], dtype=np.float32)
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with self.assertRaisesOpError(
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"k .10. from index.0,1. out of bounds .>=2."):
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self.evaluate(sparse_ops.sparse_tensor_dense_matmul(sparse_t, dense_t))
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dense_t = np.matrix([[1] * 500, [2] * 500], dtype=np.float32)
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with self.assertRaisesOpError(
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"k .10. from index.0,1. out of bounds .>=2."):
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self.evaluate(sparse_ops.sparse_tensor_dense_matmul(sparse_t, dense_t))
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# Test multiplying by both a small and large dense matrix, to hit
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# both cases in the kernel.
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dense_t = np.matrix([[1] * 5, [2] * 5], dtype=np.float32)
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with self.assertRaisesOpError("k .10. from index.0,1. out of bounds .>=2."):
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self.evaluate(sparse_ops.sparse_tensor_dense_matmul(sparse_t, dense_t))
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dense_t = np.matrix([[1] * 500, [2] * 500], dtype=np.float32)
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with self.assertRaisesOpError("k .10. from index.0,1. out of bounds .>=2."):
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self.evaluate(sparse_ops.sparse_tensor_dense_matmul(sparse_t, dense_t))
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# Repeat with adjoint_a, to get a different error.
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dense_t = np.matrix([[1] * 5, [2] * 5, [3] * 5], dtype=np.float32)
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with self.assertRaisesOpError(
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"m .10. from index.0,1. out of bounds .>=2."):
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self.evaluate(
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sparse_ops.sparse_tensor_dense_matmul(
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sparse_t, dense_t, adjoint_a=True))
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dense_t = np.matrix([[1] * 500, [2] * 500, [3] * 500], dtype=np.float32)
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with self.assertRaisesOpError(
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"m .10. from index.0,1. out of bounds .>=2."):
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self.evaluate(
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sparse_ops.sparse_tensor_dense_matmul(
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sparse_t, dense_t, adjoint_a=True))
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# Repeat with adjoint_a, to get a different error.
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dense_t = np.matrix([[1] * 5, [2] * 5, [3] * 5], dtype=np.float32)
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with self.assertRaisesOpError("m .10. from index.0,1. out of bounds .>=2."):
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self.evaluate(
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sparse_ops.sparse_tensor_dense_matmul(
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sparse_t, dense_t, adjoint_a=True))
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dense_t = np.matrix([[1] * 500, [2] * 500, [3] * 500], dtype=np.float32)
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with self.assertRaisesOpError("m .10. from index.0,1. out of bounds .>=2."):
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self.evaluate(
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sparse_ops.sparse_tensor_dense_matmul(
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sparse_t, dense_t, adjoint_a=True))
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@test_util.run_gpu_only
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def testInvalidIndicesForSparseTensorDenseMatmulOnGPU(self):
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# Note: use_gpu=False because nice errors are only returned from CPU kerne
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if not test.is_gpu_available():
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return
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with self.session(use_gpu=True):
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indices = np.array([[1, 10]]).astype(np.int64)
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values = np.array([10]).astype(np.float32)
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shape = [3, 2]
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sparse_t = sparse_tensor.SparseTensor(indices, values, shape)
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indices = np.array([[1, 10]]).astype(np.int64)
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values = np.array([10]).astype(np.float32)
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shape = [3, 2]
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sparse_t = sparse_tensor.SparseTensor(indices, values, shape)
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# Test multiplying by both a small and large dense matrix, to hit
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# both cases in the kernel.
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dense_t = np.matrix([[1] * 5, [2] * 5], dtype=np.float32)
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expected_t = np.array([[0] * 5, [np.nan] * 5, [0] * 5], dtype=np.float32)
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self.assertAllClose(expected_t,
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sparse_ops.sparse_tensor_dense_matmul(
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sparse_t, dense_t))
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dense_t = np.matrix([[1] * 500, [2] * 500], dtype=np.float32)
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expected_t = np.array(
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[[0] * 500, [np.nan] * 500, [0] * 500], dtype=np.float32)
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self.assertAllClose(expected_t,
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sparse_ops.sparse_tensor_dense_matmul(
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sparse_t, dense_t))
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# Test multiplying by both a small and large dense matrix, to hit
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# both cases in the kernel.
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dense_t = np.matrix([[1] * 5, [2] * 5], dtype=np.float32)
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expected_t = np.array([[0] * 5, [np.nan] * 5, [0] * 5], dtype=np.float32)
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self.assertAllClose(
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expected_t, sparse_ops.sparse_tensor_dense_matmul(sparse_t, dense_t))
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dense_t = np.matrix([[1] * 500, [2] * 500], dtype=np.float32)
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expected_t = np.array([[0] * 500, [np.nan] * 500, [0] * 500],
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dtype=np.float32)
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self.assertAllClose(
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expected_t, sparse_ops.sparse_tensor_dense_matmul(sparse_t, dense_t))
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# Repeat with adjoint_a, now the error is that the sparse index
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# is OOO w.r.t. the output. The GPU kernel can't do much here,
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# so it just doesn't accumulate.
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# Repeat with adjoint_a, now the error is that the sparse index
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# is OOO w.r.t. the output. The GPU kernel can't do much here,
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# so it just doesn't accumulate.
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dense_t = np.matrix([[1] * 5, [2] * 5, [3] * 5], dtype=np.float32)
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expected_t = np.array([[0] * 5, [0] * 5], dtype=np.float32)
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self.assertAllClose(expected_t,
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sparse_ops.sparse_tensor_dense_matmul(
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sparse_t, dense_t, adjoint_a=True))
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dense_t = np.matrix([[1] * 5, [2] * 5, [3] * 5], dtype=np.float32)
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expected_t = np.array([[0] * 5, [0] * 5], dtype=np.float32)
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self.assertAllClose(
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expected_t,
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sparse_ops.sparse_tensor_dense_matmul(
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sparse_t, dense_t, adjoint_a=True))
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dense_t = np.matrix([[1] * 500, [2] * 500, [3] * 500], dtype=np.float32)
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expected_t = np.array([[0] * 500, [0] * 500], dtype=np.float32)
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self.assertAllClose(expected_t,
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sparse_ops.sparse_tensor_dense_matmul(
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sparse_t, dense_t, adjoint_a=True))
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dense_t = np.matrix([[1] * 500, [2] * 500, [3] * 500], dtype=np.float32)
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expected_t = np.array([[0] * 500, [0] * 500], dtype=np.float32)
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self.assertAllClose(
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expected_t,
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sparse_ops.sparse_tensor_dense_matmul(
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sparse_t, dense_t, adjoint_a=True))
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# Tests setting one dimension to be a high value.
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def _testLarge(self, np_dtype):
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@ -235,7 +227,6 @@ class SparseTensorDenseMatMulTest(test.TestCase):
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self._testLarge(np.complex128)
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# Tests random sized matrices.
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@test_util.run_deprecated_v1
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def testFloatRandom(self):
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np.random.seed(127) # Repeatable results
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for _ in range(8):
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