# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np import paddle from paddle.incubate import sparse import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.framework import _test_eager_guard devices = ['cpu', 'gpu'] class TestSparseCreate(unittest.TestCase): def test_create_coo_by_tensor(self): with _test_eager_guard(): indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] values = [1, 2, 3, 4, 5] dense_shape = [3, 4] dense_indices = paddle.to_tensor(indices) dense_elements = paddle.to_tensor(values, dtype='float32') coo = paddle.incubate.sparse.sparse_coo_tensor(dense_indices, dense_elements, dense_shape, stop_gradient=False) # test the to_string.py assert np.array_equal(indices, coo.indices().numpy()) assert np.array_equal(values, coo.values().numpy()) def test_create_coo_by_np(self): with _test_eager_guard(): indices = [[0, 1, 2], [1, 2, 0]] values = [1.0, 2.0, 3.0] dense_shape = [3, 3] coo = paddle.incubate.sparse.sparse_coo_tensor( indices, values, dense_shape) assert np.array_equal(3, coo.nnz()) assert np.array_equal(indices, coo.indices().numpy()) assert np.array_equal(values, coo.values().numpy()) def test_create_csr_by_tensor(self): with _test_eager_guard(): crows = [0, 2, 3, 5] cols = [1, 3, 2, 0, 1] values = [1, 2, 3, 4, 5] dense_shape = [3, 4] dense_crows = paddle.to_tensor(crows) dense_cols = paddle.to_tensor(cols) dense_elements = paddle.to_tensor(values, dtype='float32') stop_gradient = False csr = paddle.incubate.sparse.sparse_csr_tensor( dense_crows, dense_cols, dense_elements, dense_shape, stop_gradient=stop_gradient) def test_create_csr_by_np(self): with _test_eager_guard(): crows = [0, 2, 3, 5] cols = [1, 3, 2, 0, 1] values = [1, 2, 3, 4, 5] dense_shape = [3, 4] csr = paddle.incubate.sparse.sparse_csr_tensor( crows, cols, values, dense_shape) # test the to_string.py assert np.array_equal(5, csr.nnz()) assert np.array_equal(crows, csr.crows().numpy()) assert np.array_equal(cols, csr.cols().numpy()) assert np.array_equal(values, csr.values().numpy()) def test_place(self): with _test_eager_guard(): place = core.CPUPlace() indices = [[0, 1], [0, 1]] values = [1.0, 2.0] dense_shape = [2, 2] coo = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape, place=place) assert coo.place.is_cpu_place() assert coo.values().place.is_cpu_place() assert coo.indices().place.is_cpu_place() crows = [0, 2, 3, 5] cols = [1, 3, 2, 0, 1] values = [1.0, 2.0, 3.0, 4.0, 5.0] csr = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, [3, 5], place=place) assert csr.place.is_cpu_place() assert csr.crows().place.is_cpu_place() assert csr.cols().place.is_cpu_place() assert csr.values().place.is_cpu_place() def test_dtype(self): with _test_eager_guard(): indices = [[0, 1], [0, 1]] values = [1.0, 2.0] dense_shape = [2, 2] indices = paddle.to_tensor(indices, dtype='int32') values = paddle.to_tensor(values, dtype='float32') coo = paddle.incubate.sparse.sparse_coo_tensor(indices, values, dense_shape, dtype='float64') assert coo.dtype == paddle.float64 crows = [0, 2, 3, 5] cols = [1, 3, 2, 0, 1] values = [1.0, 2.0, 3.0, 4.0, 5.0] csr = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, [3, 5], dtype='float16') assert csr.dtype == paddle.float16 def test_create_coo_no_shape(self): with _test_eager_guard(): indices = [[0, 1], [0, 1]] values = [1.0, 2.0] indices = paddle.to_tensor(indices, dtype='int32') values = paddle.to_tensor(values, dtype='float32') coo = paddle.incubate.sparse.sparse_coo_tensor(indices, values) assert [2, 2] == coo.shape class TestSparseConvert(unittest.TestCase): def test_to_sparse_coo(self): with _test_eager_guard(): x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]] indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] values = [1.0, 2.0, 3.0, 4.0, 5.0] dense_x = paddle.to_tensor(x, dtype='float32', stop_gradient=False) out = dense_x.to_sparse_coo(2) assert np.array_equal(out.indices().numpy(), indices) assert np.array_equal(out.values().numpy(), values) #test to_sparse_coo_grad backward out_grad_indices = [[0, 1], [0, 1]] out_grad_values = [2.0, 3.0] out_grad = paddle.incubate.sparse.sparse_coo_tensor( paddle.to_tensor(out_grad_indices), paddle.to_tensor(out_grad_values), shape=out.shape, stop_gradient=True) out.backward(out_grad) assert np.array_equal(dense_x.grad.numpy(), out_grad.to_dense().numpy()) def test_coo_to_dense(self): fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True}) with _test_eager_guard(): indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] values = [1.0, 2.0, 3.0, 4.0, 5.0] indices_dtypes = ['int32', 'int64'] for indices_dtype in indices_dtypes: sparse_x = paddle.incubate.sparse.sparse_coo_tensor( paddle.to_tensor(indices, dtype=indices_dtype), paddle.to_tensor(values), shape=[3, 4], stop_gradient=False) dense_tensor = sparse_x.to_dense() #test to_dense_grad backward out_grad = [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [9.0, 10.0, 11.0, 12.0]] dense_tensor.backward(paddle.to_tensor(out_grad)) #mask the out_grad by sparse_x.indices() correct_x_grad = [2.0, 4.0, 7.0, 9.0, 10.0] assert np.array_equal(correct_x_grad, sparse_x.grad.values().numpy()) paddle.device.set_device("cpu") sparse_x_cpu = paddle.incubate.sparse.sparse_coo_tensor( paddle.to_tensor(indices, dtype=indices_dtype), paddle.to_tensor(values), shape=[3, 4], stop_gradient=False) dense_tensor_cpu = sparse_x_cpu.to_dense() dense_tensor_cpu.backward(paddle.to_tensor(out_grad)) assert np.array_equal(correct_x_grad, sparse_x_cpu.grad.values().numpy()) fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False}) def test_to_sparse_csr(self): with _test_eager_guard(): x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]] crows = [0, 2, 3, 5] cols = [1, 3, 2, 0, 1] values = [1, 2, 3, 4, 5] dense_x = paddle.to_tensor(x) out = dense_x.to_sparse_csr() assert np.array_equal(out.crows().numpy(), crows) assert np.array_equal(out.cols().numpy(), cols) assert np.array_equal(out.values().numpy(), values) dense_tensor = out.to_dense() assert np.array_equal(dense_tensor.numpy(), x) def test_coo_values_grad(self): fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True}) with _test_eager_guard(): indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] values = [1.0, 2.0, 3.0, 4.0, 5.0] sparse_x = paddle.incubate.sparse.sparse_coo_tensor( paddle.to_tensor(indices), paddle.to_tensor(values), shape=[3, 4], stop_gradient=False) values_tensor = sparse_x.values() out_grad = [2.0, 3.0, 5.0, 8.0, 9.0] # test coo_values_grad values_tensor.backward(paddle.to_tensor(out_grad)) assert np.array_equal(out_grad, sparse_x.grad.values().numpy()) indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]] values = [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0], [5.0, 5.0]] sparse_x = paddle.incubate.sparse.sparse_coo_tensor( paddle.to_tensor(indices), paddle.to_tensor(values), shape=[3, 4, 2], stop_gradient=False) values_tensor = sparse_x.values() out_grad = [[2.0, 2.0], [3.0, 3.0], [5.0, 5.0], [8.0, 8.0], [9.0, 9.0]] # test coo_values_grad values_tensor.backward(paddle.to_tensor(out_grad)) assert np.array_equal(out_grad, sparse_x.grad.values().numpy()) fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False}) def test_sparse_coo_tensor_grad(self): with _test_eager_guard(): for device in devices: if device == 'cpu' or (device == 'gpu' and paddle.is_compiled_with_cuda()): paddle.device.set_device(device) indices = [[0, 1], [0, 1]] values = [1, 2] indices = paddle.to_tensor(indices, dtype='int32') values = paddle.to_tensor(values, dtype='float32', stop_gradient=False) sparse_x = paddle.incubate.sparse.sparse_coo_tensor( indices, values, shape=[2, 2], stop_gradient=False) grad_indices = [[0, 1], [1, 1]] grad_values = [2, 3] grad_indices = paddle.to_tensor(grad_indices, dtype='int32') grad_values = paddle.to_tensor(grad_values, dtype='float32') sparse_out_grad = paddle.incubate.sparse.sparse_coo_tensor( grad_indices, grad_values, shape=[2, 2]) sparse_x.backward(sparse_out_grad) correct_values_grad = [0, 3] assert np.array_equal(correct_values_grad, values.grad.numpy()) # test the non-zero values is a vector values = [[1, 1], [2, 2]] values = paddle.to_tensor(values, dtype='float32', stop_gradient=False) sparse_x = paddle.incubate.sparse.sparse_coo_tensor( indices, values, shape=[2, 2, 2], stop_gradient=False) grad_values = [[2, 2], [3, 3]] grad_values = paddle.to_tensor(grad_values, dtype='float32') sparse_out_grad = paddle.incubate.sparse.sparse_coo_tensor( grad_indices, grad_values, shape=[2, 2, 2]) sparse_x.backward(sparse_out_grad) correct_values_grad = [[0, 0], [3, 3]] assert np.array_equal(correct_values_grad, values.grad.numpy()) def test_sparse_coo_tensor_sorted(self): with _test_eager_guard(): for device in devices: if device == 'cpu' or (device == 'gpu' and paddle.is_compiled_with_cuda()): paddle.device.set_device(device) #test unsorted and duplicate indices indices = [[1, 0, 0], [0, 1, 1]] values = [1.0, 2.0, 3.0] indices = paddle.to_tensor(indices, dtype='int32') values = paddle.to_tensor(values, dtype='float32') sparse_x = paddle.incubate.sparse.sparse_coo_tensor( indices, values) indices_sorted = [[0, 1], [1, 0]] values_sorted = [5.0, 1.0] assert np.array_equal(indices_sorted, sparse_x.indices().numpy()) assert np.array_equal(values_sorted, sparse_x.values().numpy()) # test the non-zero values is a vector values = [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]] values = paddle.to_tensor(values, dtype='float32') sparse_x = paddle.incubate.sparse.sparse_coo_tensor( indices, values) values_sorted = [[5.0, 5.0], [1.0, 1.0]] assert np.array_equal(indices_sorted, sparse_x.indices().numpy()) assert np.array_equal(values_sorted, sparse_x.values().numpy()) def test_batch_csr(self): with _test_eager_guard(): def verify(dense_x): sparse_x = dense_x.to_sparse_csr() out = sparse_x.to_dense() assert np.allclose(out.numpy(), dense_x.numpy()) shape = np.random.randint(low=1, high=10, size=3) shape = list(shape) dense_x = paddle.randn(shape) dense_x = paddle.nn.functional.dropout(dense_x, p=0.5) verify(dense_x) #test batchs=1 shape[0] = 1 dense_x = paddle.randn(shape) dense_x = paddle.nn.functional.dropout(dense_x, p=0.5) verify(dense_x) shape = np.random.randint(low=2, high=10, size=3) shape = list(shape) dense_x = paddle.randn(shape) #set the 0th batch to zero dense_x[0] = 0 verify(dense_x) dense_x = paddle.randn(shape) #set the 1th batch to zero dense_x[1] = 0 verify(dense_x) dense_x = paddle.randn(shape) #set the 2th batch to zero dense_x[2] = 0 verify(dense_x) class TestCooError(unittest.TestCase): def test_small_shape(self): with _test_eager_guard(): with self.assertRaises(ValueError): indices = [[2, 3], [0, 2]] values = [1, 2] # 1. the shape too small dense_shape = [2, 2] sparse_x = paddle.incubate.sparse.sparse_coo_tensor( indices, values, shape=dense_shape) def test_same_nnz(self): with _test_eager_guard(): with self.assertRaises(ValueError): # 2. test the nnz of indices must same as nnz of values indices = [[1, 2], [1, 0]] values = [1, 2, 3] sparse_x = paddle.incubate.sparse.sparse_coo_tensor( indices, values) def test_same_dimensions(self): with _test_eager_guard(): with self.assertRaises(ValueError): indices = [[1, 2], [1, 0]] values = [1, 2, 3] shape = [2, 3, 4] sparse_x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, shape=shape) def test_indices_dtype(self): with _test_eager_guard(): with self.assertRaises(TypeError): indices = [[1.0, 2.0], [0, 1]] values = [1, 2] sparse_x = paddle.incubate.sparse.sparse_coo_tensor( indices, values) class TestCsrError(unittest.TestCase): def test_dimension1(self): with _test_eager_guard(): with self.assertRaises(ValueError): crows = [0, 1, 2, 3] cols = [0, 1, 2] values = [1, 2, 3] shape = [3] sparse_x = paddle.incubate.sparse.sparse_csr_tensor( crows, cols, values, shape) def test_dimension2(self): with _test_eager_guard(): with self.assertRaises(ValueError): crows = [0, 1, 2, 3] cols = [0, 1, 2] values = [1, 2, 3] shape = [3, 3, 3, 3] sparse_x = paddle.incubate.sparse.sparse_csr_tensor( crows, cols, values, shape) def test_same_shape1(self): with _test_eager_guard(): with self.assertRaises(ValueError): crows = [0, 1, 2, 3] cols = [0, 1, 2, 3] values = [1, 2, 3] shape = [3, 4] sparse_x = paddle.incubate.sparse.sparse_csr_tensor( crows, cols, values, shape) def test_same_shape2(self): with _test_eager_guard(): with self.assertRaises(ValueError): crows = [0, 1, 2, 3] cols = [0, 1, 2, 3] values = [1, 2, 3, 4] shape = [3, 4] sparse_x = paddle.incubate.sparse.sparse_csr_tensor( crows, cols, values, shape) def test_same_shape3(self): with _test_eager_guard(): with self.assertRaises(ValueError): crows = [0, 1, 2, 3, 0, 1, 2] cols = [0, 1, 2, 3, 0, 1, 2] values = [1, 2, 3, 4, 0, 1, 2] shape = [2, 3, 4] sparse_x = paddle.incubate.sparse.sparse_csr_tensor( crows, cols, values, shape) def test_crows_first_value(self): with _test_eager_guard(): with self.assertRaises(ValueError): crows = [1, 1, 2, 3] cols = [0, 1, 2] values = [1, 2, 3] shape = [3, 4] sparse_x = paddle.incubate.sparse.sparse_csr_tensor( crows, cols, values, shape) def test_dtype(self): with _test_eager_guard(): with self.assertRaises(TypeError): crows = [0, 1, 2, 3.0] cols = [0, 1, 2] values = [1, 2, 3] shape = [3] sparse_x = paddle.incubate.sparse.sparse_csr_tensor( crows, cols, values, shape) if __name__ == "__main__": unittest.main()