wehub-resource-sync
976 lines
36 KiB
Python
976 lines
36 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from operator import __add__, __mul__, __sub__, __truediv__
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import numpy as np
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import paddle
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from paddle.base.framework import in_pir_mode
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op_list = [__add__, __sub__, __mul__, __truediv__]
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def get_actual_res(x, y, op):
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if op == __add__:
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res = paddle.sparse.add(x, y)
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elif op == __sub__:
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res = paddle.sparse.subtract(x, y)
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elif op == __mul__:
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res = paddle.sparse.multiply(x, y)
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elif op == __truediv__:
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res = paddle.sparse.divide(x, y)
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else:
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raise ValueError("unsupported op")
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return res
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def mask_to_zero(x, mask):
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x[mask == 0] = 0
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return x
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class TestSparseElementWiseAPI(unittest.TestCase):
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"""
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test paddle.sparse.add, subtract, multiply, divide
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"""
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def setUp(self):
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np.random.seed(2022)
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self.op_list = op_list
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self.csr_shape = [8, 8]
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self.coo_shape = [4, 8, 3, 5]
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self.support_dtypes = ['float32', 'float64', 'int32', 'int64']
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def func_test_csr(self, op):
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for dtype in self.support_dtypes:
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x = np.random.randint(-255, 255, size=self.csr_shape)
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y = np.random.randint(-255, 255, size=self.csr_shape)
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mask_x = x / x
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mask_y = y / y
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mask_x[mask_x != 1] = 0
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mask_y[mask_y != 1] = 0
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x = x.astype(dtype)
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y = y.astype(dtype)
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dense_x = paddle.to_tensor(x, dtype=dtype, stop_gradient=False)
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dense_y = paddle.to_tensor(y, dtype=dtype, stop_gradient=False)
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s_dense_x = paddle.to_tensor(x, dtype=dtype, stop_gradient=False)
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s_dense_y = paddle.to_tensor(y, dtype=dtype, stop_gradient=False)
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csr_x = s_dense_x.to_sparse_csr()
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csr_y = s_dense_y.to_sparse_csr()
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actual_res = get_actual_res(csr_x, csr_y, op)
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actual_res.backward()
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expect_res = op(dense_x, dense_y)
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expect_res.backward()
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np.testing.assert_allclose(
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expect_res.numpy(),
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actual_res.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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if not (op == __truediv__ and dtype in ['int32', 'int64']):
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np.testing.assert_allclose(
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mask_to_zero(dense_x.grad.numpy(), mask_x),
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csr_x.grad.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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np.testing.assert_allclose(
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mask_to_zero(dense_y.grad.numpy(), mask_y),
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csr_y.grad.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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def func_test_coo(self, op):
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for sparse_dim in range(len(self.coo_shape) - 1, len(self.coo_shape)):
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for dtype in self.support_dtypes:
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x = np.random.randint(-255, 255, size=self.coo_shape).astype(
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dtype
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)
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y = np.random.randint(-255, 255, size=self.coo_shape).astype(
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dtype
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)
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dense_x = paddle.to_tensor(x, dtype=dtype, stop_gradient=False)
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dense_y = paddle.to_tensor(y, dtype=dtype, stop_gradient=False)
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s_dense_x = paddle.to_tensor(
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x, dtype=dtype, stop_gradient=False
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)
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s_dense_y = paddle.to_tensor(
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y, dtype=dtype, stop_gradient=False
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)
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coo_x = s_dense_x.to_sparse_coo(sparse_dim)
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coo_x.retain_grads()
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coo_y = s_dense_y.to_sparse_coo(sparse_dim)
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coo_y.retain_grads()
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actual_res = get_actual_res(coo_x, coo_y, op)
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actual_res.backward(actual_res)
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expect_res = op(dense_x, dense_y)
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expect_res.backward(expect_res)
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np.testing.assert_allclose(
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expect_res.numpy(),
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actual_res.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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np.testing.assert_allclose(coo_x.shape, coo_x.grad.shape)
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np.testing.assert_allclose(
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dense_x.grad.numpy(),
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coo_x.grad.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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np.testing.assert_allclose(coo_y.shape, coo_y.grad.shape)
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np.testing.assert_allclose(
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dense_y.grad.numpy(),
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coo_y.grad.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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def test_support_dtypes_csr(self):
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paddle.device.set_device('cpu')
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if paddle.device.get_device() == "cpu":
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for op in self.op_list:
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self.func_test_csr(op)
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def test_support_dtypes_coo(self):
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paddle.device.set_device('cpu')
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if paddle.device.get_device() == "cpu":
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for op in self.op_list:
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self.func_test_coo(op)
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def test_add_same_indices(self):
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indices_data = [[0, 1], [0, 3]]
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values1_data = [[1.0], [2.0]]
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values2_data = [[1.0], [2.0]]
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shape = [2, 4, 2]
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sp_a = paddle.sparse.sparse_coo_tensor(
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indices_data, values1_data, shape, stop_gradient=False
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)
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sp_a.retain_grads()
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sp_b = paddle.sparse.sparse_coo_tensor(
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indices_data, values2_data, shape, stop_gradient=False
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)
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sp_b.retain_grads()
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values1 = paddle.to_tensor(values1_data, stop_gradient=False)
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values2 = paddle.to_tensor(values2_data, stop_gradient=False)
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# c.values() = a.values() + b.values()
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sp_c = paddle.sparse.add(sp_a, sp_b)
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sp_c.backward()
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ref_c = values1 + values2
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ref_c.backward()
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np.testing.assert_allclose(sp_c.values().numpy(), ref_c.numpy())
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np.testing.assert_allclose(
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sp_a.grad.values().numpy(), values1.grad.numpy()
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)
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np.testing.assert_allclose(
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sp_b.grad.values().numpy(), values2.grad.numpy()
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)
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def test_add_bias(self):
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indices_data = [[0, 1], [0, 3]]
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values_data = [[1.0, 1.0], [2.0, 2.0]]
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shape = [2, 4, 2]
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sp_a = paddle.sparse.sparse_coo_tensor(
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indices_data, values_data, shape, stop_gradient=False
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)
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sp_a.retain_grads()
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bias_values = [1.0, 2.0]
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values1 = paddle.to_tensor(values_data, stop_gradient=False)
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values2 = paddle.to_tensor(bias_values, stop_gradient=False)
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values3 = paddle.to_tensor(bias_values, stop_gradient=False)
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# c.values() = a.values() + b
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sp_c = paddle.sparse.add(sp_a, values2)
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sp_c.backward()
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ref_c = values1 + values3
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ref_c.backward()
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np.testing.assert_allclose(sp_c.values().numpy(), ref_c.numpy())
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np.testing.assert_allclose(
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sp_a.grad.values().numpy(), values1.grad.numpy()
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)
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np.testing.assert_allclose(values2.grad.numpy(), values3.grad.numpy())
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class TestSparseElementWiseAPIComplex(unittest.TestCase):
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def setUp(self):
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np.random.seed(2022)
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self.op_list = op_list
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self.csr_shape = [8, 10]
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self.coo_shape = [3, 7, 2, 9]
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self.support_dtypes = ['complex64', 'complex128']
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def func_test_csr(self, op):
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for dtype in self.support_dtypes:
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x = np.vectorize(complex)(
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np.random.randint(-255, 255, size=self.csr_shape),
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np.random.randint(-255, 255, size=self.csr_shape),
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)
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y = np.vectorize(complex)(
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np.random.randint(-255, 255, size=self.csr_shape),
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np.random.randint(-255, 255, size=self.csr_shape),
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)
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mask_x = x / x
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mask_y = y / y
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mask_x[mask_x > 10000.0] = 0
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mask_y[mask_y > 10000.0] = 0
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x = x.astype(dtype)
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y = y.astype(dtype)
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dense_x = paddle.to_tensor(x, dtype=dtype, stop_gradient=False)
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dense_y = paddle.to_tensor(y, dtype=dtype, stop_gradient=False)
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s_dense_x = paddle.to_tensor(x, dtype=dtype, stop_gradient=False)
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s_dense_y = paddle.to_tensor(y, dtype=dtype, stop_gradient=False)
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csr_x = s_dense_x.to_sparse_csr()
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csr_y = s_dense_y.to_sparse_csr()
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actual_res = get_actual_res(csr_x, csr_y, op)
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actual_res.backward()
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expect_res = op(dense_x, dense_y)
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expect_res.backward()
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np.testing.assert_allclose(
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expect_res.numpy(),
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actual_res.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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np.testing.assert_allclose(
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mask_to_zero(dense_x.grad.numpy(), mask_x),
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csr_x.grad.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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np.testing.assert_allclose(
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mask_to_zero(dense_y.grad.numpy(), mask_y),
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csr_y.grad.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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def func_test_coo(self, op):
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for sparse_dim in range(len(self.coo_shape) - 1, len(self.coo_shape)):
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for dtype in self.support_dtypes:
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x = np.vectorize(complex)(
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np.random.randint(-255, 255, size=self.coo_shape),
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np.random.randint(-255, 255, size=self.coo_shape),
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)
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y = np.vectorize(complex)(
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np.random.randint(-255, 255, size=self.coo_shape),
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np.random.randint(-255, 255, size=self.coo_shape),
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)
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dense_x = paddle.to_tensor(x, dtype=dtype, stop_gradient=False)
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dense_y = paddle.to_tensor(y, dtype=dtype, stop_gradient=False)
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s_dense_x = paddle.to_tensor(
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x, dtype=dtype, stop_gradient=False
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)
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s_dense_y = paddle.to_tensor(
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y, dtype=dtype, stop_gradient=False
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)
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coo_x = s_dense_x.to_sparse_coo(sparse_dim)
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coo_x.retain_grads()
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coo_y = s_dense_y.to_sparse_coo(sparse_dim)
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coo_y.retain_grads()
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actual_res = get_actual_res(coo_x, coo_y, op)
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actual_res.backward(actual_res)
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expect_res = op(dense_x, dense_y)
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expect_res.backward(expect_res)
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np.testing.assert_allclose(
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expect_res.numpy(),
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actual_res.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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np.testing.assert_allclose(coo_x.shape, coo_x.grad.shape)
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np.testing.assert_allclose(
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dense_x.grad.numpy(),
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coo_x.grad.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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np.testing.assert_allclose(coo_y.shape, coo_y.grad.shape)
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np.testing.assert_allclose(
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dense_y.grad.numpy(),
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coo_y.grad.to_dense().numpy(),
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rtol=1e-05,
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equal_nan=True,
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)
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def test_support_dtypes_csr(self):
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paddle.device.set_device('cpu')
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if paddle.device.get_device() == "cpu":
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for op in self.op_list:
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self.func_test_csr(op)
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def test_support_dtypes_coo(self):
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paddle.device.set_device('cpu')
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if paddle.device.get_device() == "cpu":
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for op in self.op_list:
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self.func_test_coo(op)
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def test_add_same_indices(self):
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indices_data = [[0, 1], [0, 3]]
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values1_data = [[1.0 + 0.2j], [2.0 + 0.3j]]
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values2_data = [[1.0 + 0.2j], [2.0 - 0.3j]]
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shape = [2, 4, 2]
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sp_a = paddle.sparse.sparse_coo_tensor(
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indices_data, values1_data, shape, stop_gradient=False
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)
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sp_a.retain_grads()
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sp_b = paddle.sparse.sparse_coo_tensor(
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indices_data, values2_data, shape, stop_gradient=False
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)
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sp_b.retain_grads()
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values1 = paddle.to_tensor(values1_data, stop_gradient=False)
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values2 = paddle.to_tensor(values2_data, stop_gradient=False)
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# c.values() = a.values() + b.values()
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sp_c = paddle.sparse.add(sp_a, sp_b)
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sp_c.backward()
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ref_c = values1 + values2
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ref_c.backward()
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np.testing.assert_allclose(sp_c.values().numpy(), ref_c.numpy())
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np.testing.assert_allclose(
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sp_a.grad.values().numpy(), values1.grad.numpy()
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)
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np.testing.assert_allclose(
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sp_b.grad.values().numpy(), values2.grad.numpy()
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)
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def test_add_bias(self):
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indices_data = [[0, 1], [0, 3]]
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values_data = [
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[(1.0 + 0.2j), (1.0 - 0.1j)],
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[(2.0 + 0.3j), (2.0 - 0.4j)],
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]
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shape = [2, 4, 2]
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sp_a = paddle.sparse.sparse_coo_tensor(
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indices_data, values_data, shape, stop_gradient=False
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)
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sp_a.retain_grads()
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bias_values = [(1.0 + 0.1j), (2.0 - 0.1j)]
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values1 = paddle.to_tensor(values_data, stop_gradient=False)
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values2 = paddle.to_tensor(bias_values, stop_gradient=False)
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values3 = paddle.to_tensor(bias_values, stop_gradient=False)
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# c.values() = a.values() + b
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sp_c = paddle.sparse.add(sp_a, values2)
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sp_c.backward()
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ref_c = values1 + values3
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ref_c.backward()
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np.testing.assert_allclose(sp_c.values().numpy(), ref_c.numpy())
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np.testing.assert_allclose(
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sp_a.grad.values().numpy(), values1.grad.numpy()
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)
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np.testing.assert_allclose(values2.grad.numpy(), values3.grad.numpy())
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|
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class TestSparseAddStaticAPI(unittest.TestCase):
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"""
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test paddle.sparse.add
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"""
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def setUp(self):
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np.random.seed(2022)
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self.op_list = op_list
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self.coo_shape = [4, 8, 3, 5]
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self.support_dtypes = [
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'float32',
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'float64',
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'int32',
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'int64',
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'complex64',
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'complex128',
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]
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def test_coo(self):
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if in_pir_mode:
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sparse_dim = len(self.coo_shape) - 1
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op = __add__
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for dtype in self.support_dtypes:
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if 'complex' in dtype:
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x = np.vectorize(complex)(
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np.random.randint(-255, 255, size=self.coo_shape),
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np.random.randint(-255, 255, size=self.coo_shape),
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).astype(dtype)
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y = np.vectorize(complex)(
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np.random.randint(-255, 255, size=self.coo_shape),
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np.random.randint(-255, 255, size=self.coo_shape),
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).astype(dtype)
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else:
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x = np.random.randint(
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-255, 255, size=self.coo_shape
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).astype(dtype)
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y = np.random.randint(
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-255, 255, size=self.coo_shape
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).astype(dtype)
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self.dense_x = paddle.to_tensor(
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x, dtype=dtype, stop_gradient=True
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)
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self.dense_y = paddle.to_tensor(
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y, dtype=dtype, stop_gradient=True
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)
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self.expect_res = op(self.dense_x, self.dense_y)
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self.x_indices_data, self.x_values_data = (
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self.dense_x.detach().to_sparse_coo(sparse_dim).indices(),
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|
self.dense_x.detach().to_sparse_coo(sparse_dim).values(),
|
|
)
|
|
|
|
self.y_indices_data, self.y_values_data = (
|
|
self.dense_y.detach().to_sparse_coo(sparse_dim).indices(),
|
|
self.dense_y.detach().to_sparse_coo(sparse_dim).values(),
|
|
)
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_indices = paddle.static.data(
|
|
name='x_indices',
|
|
shape=self.x_indices_data.shape,
|
|
dtype=self.x_indices_data.dtype,
|
|
)
|
|
x_values = paddle.static.data(
|
|
name='x_values',
|
|
shape=self.x_values_data.shape,
|
|
dtype=self.x_values_data.dtype,
|
|
)
|
|
sp_x = paddle.sparse.sparse_coo_tensor(
|
|
x_indices,
|
|
x_values,
|
|
shape=self.dense_x.shape,
|
|
dtype=self.dense_x.dtype,
|
|
)
|
|
|
|
y_indices = paddle.static.data(
|
|
name='y_indices',
|
|
shape=self.y_indices_data.shape,
|
|
dtype=self.y_indices_data.dtype,
|
|
)
|
|
y_values = paddle.static.data(
|
|
name='y_values',
|
|
shape=self.y_values_data.shape,
|
|
dtype=self.y_values_data.dtype,
|
|
)
|
|
sp_y = paddle.sparse.sparse_coo_tensor(
|
|
y_indices,
|
|
y_values,
|
|
shape=self.dense_y.shape,
|
|
dtype=self.dense_y.dtype,
|
|
)
|
|
|
|
sp_out = paddle.sparse.add(sp_x, sp_y)
|
|
|
|
sp_dense_out = sp_out.to_dense()
|
|
|
|
sparse_exe = paddle.static.Executor()
|
|
sparse_fetch = sparse_exe.run(
|
|
feed={
|
|
'x_indices': self.x_indices_data.numpy(),
|
|
"x_values": self.x_values_data.numpy(),
|
|
'y_indices': self.y_indices_data.numpy(),
|
|
"y_values": self.y_values_data.numpy(),
|
|
},
|
|
fetch_list=[sp_dense_out],
|
|
return_numpy=True,
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
self.expect_res.numpy(), sparse_fetch[0], rtol=1e-5
|
|
)
|
|
paddle.disable_static()
|
|
|
|
def test_coo_dense(self):
|
|
# currently only support 1-D dense y input and need y.shape == x.to_dense().shape[-1]
|
|
if in_pir_mode:
|
|
sparse_dim = len(self.coo_shape) - 1
|
|
op = __add__
|
|
for dtype in self.support_dtypes:
|
|
if 'complex' in dtype:
|
|
x = np.vectorize(complex)(
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
).astype(dtype)
|
|
y = np.vectorize(complex)(
|
|
np.random.randint(-255, 255, size=self.coo_shape[-1]),
|
|
np.random.randint(-255, 255, size=self.coo_shape[-1]),
|
|
).astype(dtype)
|
|
else:
|
|
x = np.random.randint(
|
|
-255, 255, size=self.coo_shape
|
|
).astype(dtype)
|
|
y = np.random.randint(
|
|
-255, 255, size=self.coo_shape[-1]
|
|
).astype(dtype)
|
|
|
|
self.dense_x = paddle.to_tensor(
|
|
x, dtype=dtype, stop_gradient=True
|
|
)
|
|
self.dense_y = paddle.to_tensor(
|
|
y, dtype=dtype, stop_gradient=True
|
|
)
|
|
|
|
self.expect_res = op(self.dense_x, self.dense_y)
|
|
|
|
self.x_indices_data, self.x_values_data = (
|
|
self.dense_x.detach().to_sparse_coo(sparse_dim).indices(),
|
|
self.dense_x.detach().to_sparse_coo(sparse_dim).values(),
|
|
)
|
|
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_indices = paddle.static.data(
|
|
name='x_indices',
|
|
shape=self.x_indices_data.shape,
|
|
dtype=self.x_indices_data.dtype,
|
|
)
|
|
x_values = paddle.static.data(
|
|
name='x_values',
|
|
shape=self.x_values_data.shape,
|
|
dtype=self.x_values_data.dtype,
|
|
)
|
|
sp_x = paddle.sparse.sparse_coo_tensor(
|
|
x_indices,
|
|
x_values,
|
|
shape=self.dense_x.shape,
|
|
dtype=self.dense_x.dtype,
|
|
)
|
|
|
|
y = paddle.static.data(
|
|
name='y',
|
|
shape=self.dense_y.shape,
|
|
dtype=self.dense_y.dtype,
|
|
)
|
|
|
|
sp_out = paddle.sparse.add(sp_x, y)
|
|
|
|
sp_dense_out = sp_out.to_dense()
|
|
|
|
sparse_exe = paddle.static.Executor()
|
|
sparse_fetch = sparse_exe.run(
|
|
feed={
|
|
'x_indices': self.x_indices_data.numpy(),
|
|
"x_values": self.x_values_data.numpy(),
|
|
'y': self.dense_y.numpy(),
|
|
},
|
|
fetch_list=[sp_dense_out],
|
|
return_numpy=True,
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
self.expect_res.numpy(), sparse_fetch[0], rtol=1e-5
|
|
)
|
|
paddle.disable_static()
|
|
|
|
|
|
class TestSparseSubStaticAPI(unittest.TestCase):
|
|
"""
|
|
test paddle.sparse.subtract
|
|
"""
|
|
|
|
def setUp(self):
|
|
np.random.seed(2022)
|
|
self.op_list = op_list
|
|
self.coo_shape = [4, 8, 3, 5]
|
|
self.support_dtypes = [
|
|
'float32',
|
|
'float64',
|
|
'int32',
|
|
'int64',
|
|
'complex64',
|
|
'complex128',
|
|
]
|
|
|
|
def test_coo(self):
|
|
if in_pir_mode():
|
|
sparse_dim = len(self.coo_shape) - 1
|
|
op = __sub__
|
|
for dtype in self.support_dtypes:
|
|
if 'complex' in dtype:
|
|
x = np.vectorize(complex)(
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
).astype(dtype)
|
|
y = np.vectorize(complex)(
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
).astype(dtype)
|
|
else:
|
|
x = np.random.randint(
|
|
-255, 255, size=self.coo_shape
|
|
).astype(dtype)
|
|
y = np.random.randint(
|
|
-255, 255, size=self.coo_shape
|
|
).astype(dtype)
|
|
|
|
self.dense_x = paddle.to_tensor(
|
|
x, dtype=dtype, stop_gradient=True
|
|
)
|
|
self.dense_y = paddle.to_tensor(
|
|
y, dtype=dtype, stop_gradient=True
|
|
)
|
|
|
|
self.expect_res = op(self.dense_x, self.dense_y)
|
|
|
|
self.x_indices_data, self.x_values_data = (
|
|
self.dense_x.detach().to_sparse_coo(sparse_dim).indices(),
|
|
self.dense_x.detach().to_sparse_coo(sparse_dim).values(),
|
|
)
|
|
|
|
self.y_indices_data, self.y_values_data = (
|
|
self.dense_y.detach().to_sparse_coo(sparse_dim).indices(),
|
|
self.dense_y.detach().to_sparse_coo(sparse_dim).values(),
|
|
)
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_indices = paddle.static.data(
|
|
name='x_indices',
|
|
shape=self.x_indices_data.shape,
|
|
dtype=self.x_indices_data.dtype,
|
|
)
|
|
x_values = paddle.static.data(
|
|
name='x_values',
|
|
shape=self.x_values_data.shape,
|
|
dtype=self.x_values_data.dtype,
|
|
)
|
|
sp_x = paddle.sparse.sparse_coo_tensor(
|
|
x_indices,
|
|
x_values,
|
|
shape=self.dense_x.shape,
|
|
dtype=self.dense_x.dtype,
|
|
)
|
|
|
|
y_indices = paddle.static.data(
|
|
name='y_indices',
|
|
shape=self.y_indices_data.shape,
|
|
dtype=self.y_indices_data.dtype,
|
|
)
|
|
y_values = paddle.static.data(
|
|
name='y_values',
|
|
shape=self.y_values_data.shape,
|
|
dtype=self.y_values_data.dtype,
|
|
)
|
|
sp_y = paddle.sparse.sparse_coo_tensor(
|
|
y_indices,
|
|
y_values,
|
|
shape=self.dense_y.shape,
|
|
dtype=self.dense_y.dtype,
|
|
)
|
|
|
|
sp_out = paddle.sparse.subtract(sp_x, sp_y)
|
|
|
|
sp_dense_out = sp_out.to_dense()
|
|
|
|
sparse_exe = paddle.static.Executor()
|
|
sparse_fetch = sparse_exe.run(
|
|
feed={
|
|
'x_indices': self.x_indices_data.numpy(),
|
|
"x_values": self.x_values_data.numpy(),
|
|
'y_indices': self.y_indices_data.numpy(),
|
|
"y_values": self.y_values_data.numpy(),
|
|
},
|
|
fetch_list=[sp_dense_out],
|
|
return_numpy=True,
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
self.expect_res.numpy(), sparse_fetch[0], rtol=1e-5
|
|
)
|
|
paddle.disable_static()
|
|
|
|
|
|
class TestSparseMulStaticAPI(unittest.TestCase):
|
|
"""
|
|
test paddle.sparse.multiply
|
|
"""
|
|
|
|
def setUp(self):
|
|
np.random.seed(2022)
|
|
self.op_list = op_list
|
|
self.coo_shape = [4, 8, 3, 5]
|
|
self.support_dtypes = [
|
|
'float32',
|
|
'float64',
|
|
'int32',
|
|
'int64',
|
|
'complex64',
|
|
'complex128',
|
|
]
|
|
|
|
def test_coo(self):
|
|
if in_pir_mode():
|
|
sparse_dim = len(self.coo_shape) - 1
|
|
op = __mul__
|
|
for dtype in self.support_dtypes:
|
|
if 'complex' in dtype:
|
|
x = np.vectorize(complex)(
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
).astype(dtype)
|
|
y = np.vectorize(complex)(
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
).astype(dtype)
|
|
else:
|
|
x = np.random.randint(
|
|
-255, 255, size=self.coo_shape
|
|
).astype(dtype)
|
|
y = np.random.randint(
|
|
-255, 255, size=self.coo_shape
|
|
).astype(dtype)
|
|
|
|
self.dense_x = paddle.to_tensor(
|
|
x, dtype=dtype, stop_gradient=True
|
|
)
|
|
self.dense_y = paddle.to_tensor(
|
|
y, dtype=dtype, stop_gradient=True
|
|
)
|
|
|
|
self.expect_res = op(self.dense_x, self.dense_y)
|
|
|
|
self.x_indices_data, self.x_values_data = (
|
|
self.dense_x.detach().to_sparse_coo(sparse_dim).indices(),
|
|
self.dense_x.detach().to_sparse_coo(sparse_dim).values(),
|
|
)
|
|
|
|
self.y_indices_data, self.y_values_data = (
|
|
self.dense_y.detach().to_sparse_coo(sparse_dim).indices(),
|
|
self.dense_y.detach().to_sparse_coo(sparse_dim).values(),
|
|
)
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_indices = paddle.static.data(
|
|
name='x_indices',
|
|
shape=self.x_indices_data.shape,
|
|
dtype=self.x_indices_data.dtype,
|
|
)
|
|
x_values = paddle.static.data(
|
|
name='x_values',
|
|
shape=self.x_values_data.shape,
|
|
dtype=self.x_values_data.dtype,
|
|
)
|
|
sp_x = paddle.sparse.sparse_coo_tensor(
|
|
x_indices,
|
|
x_values,
|
|
shape=self.dense_x.shape,
|
|
dtype=self.dense_x.dtype,
|
|
)
|
|
|
|
y_indices = paddle.static.data(
|
|
name='y_indices',
|
|
shape=self.y_indices_data.shape,
|
|
dtype=self.y_indices_data.dtype,
|
|
)
|
|
y_values = paddle.static.data(
|
|
name='y_values',
|
|
shape=self.y_values_data.shape,
|
|
dtype=self.y_values_data.dtype,
|
|
)
|
|
sp_y = paddle.sparse.sparse_coo_tensor(
|
|
y_indices,
|
|
y_values,
|
|
shape=self.dense_y.shape,
|
|
dtype=self.dense_y.dtype,
|
|
)
|
|
|
|
sp_out = paddle.sparse.multiply(sp_x, sp_y)
|
|
|
|
sp_dense_out = sp_out.to_dense()
|
|
|
|
sparse_exe = paddle.static.Executor()
|
|
sparse_fetch = sparse_exe.run(
|
|
feed={
|
|
'x_indices': self.x_indices_data.numpy(),
|
|
"x_values": self.x_values_data.numpy(),
|
|
'y_indices': self.y_indices_data.numpy(),
|
|
"y_values": self.y_values_data.numpy(),
|
|
},
|
|
fetch_list=[sp_dense_out],
|
|
return_numpy=True,
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
self.expect_res.numpy(), sparse_fetch[0], rtol=1e-5
|
|
)
|
|
paddle.disable_static()
|
|
|
|
|
|
class TestSparseDivStaticAPI(unittest.TestCase):
|
|
"""
|
|
test paddle.sparse.divide
|
|
"""
|
|
|
|
def setUp(self):
|
|
np.random.seed(2022)
|
|
self.op_list = op_list
|
|
self.coo_shape = [4, 8, 3, 5]
|
|
self.support_dtypes = [
|
|
'float32',
|
|
'float64',
|
|
'int32',
|
|
'int64',
|
|
'complex64',
|
|
'complex128',
|
|
]
|
|
|
|
def test_coo(self):
|
|
if in_pir_mode():
|
|
sparse_dim = len(self.coo_shape) - 1
|
|
op = __truediv__
|
|
for dtype in self.support_dtypes:
|
|
if 'complex' in dtype:
|
|
x = np.vectorize(complex)(
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
).astype(dtype)
|
|
y = np.vectorize(complex)(
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
np.random.randint(-255, 255, size=self.coo_shape),
|
|
).astype(dtype)
|
|
else:
|
|
x = np.random.randint(
|
|
-255, 255, size=self.coo_shape
|
|
).astype(dtype)
|
|
y = np.random.randint(
|
|
-255, 255, size=self.coo_shape
|
|
).astype(dtype)
|
|
|
|
self.dense_x = paddle.to_tensor(
|
|
x, dtype=dtype, stop_gradient=True
|
|
)
|
|
self.dense_y = paddle.to_tensor(
|
|
y, dtype=dtype, stop_gradient=True
|
|
)
|
|
|
|
self.expect_res = op(self.dense_x, self.dense_y)
|
|
|
|
self.x_indices_data, self.x_values_data = (
|
|
self.dense_x.detach().to_sparse_coo(sparse_dim).indices(),
|
|
self.dense_x.detach().to_sparse_coo(sparse_dim).values(),
|
|
)
|
|
|
|
self.y_indices_data, self.y_values_data = (
|
|
self.dense_y.detach().to_sparse_coo(sparse_dim).indices(),
|
|
self.dense_y.detach().to_sparse_coo(sparse_dim).values(),
|
|
)
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_indices = paddle.static.data(
|
|
name='x_indices',
|
|
shape=self.x_indices_data.shape,
|
|
dtype=self.x_indices_data.dtype,
|
|
)
|
|
x_values = paddle.static.data(
|
|
name='x_values',
|
|
shape=self.x_values_data.shape,
|
|
dtype=self.x_values_data.dtype,
|
|
)
|
|
sp_x = paddle.sparse.sparse_coo_tensor(
|
|
x_indices,
|
|
x_values,
|
|
shape=self.dense_x.shape,
|
|
dtype=self.dense_x.dtype,
|
|
)
|
|
|
|
y_indices = paddle.static.data(
|
|
name='y_indices',
|
|
shape=self.y_indices_data.shape,
|
|
dtype=self.y_indices_data.dtype,
|
|
)
|
|
y_values = paddle.static.data(
|
|
name='y_values',
|
|
shape=self.y_values_data.shape,
|
|
dtype=self.y_values_data.dtype,
|
|
)
|
|
sp_y = paddle.sparse.sparse_coo_tensor(
|
|
y_indices,
|
|
y_values,
|
|
shape=self.dense_y.shape,
|
|
dtype=self.dense_y.dtype,
|
|
)
|
|
|
|
sp_out = paddle.sparse.divide(sp_x, sp_y)
|
|
|
|
sp_dense_out = sp_out.to_dense()
|
|
|
|
sparse_exe = paddle.static.Executor()
|
|
sparse_fetch = sparse_exe.run(
|
|
feed={
|
|
'x_indices': self.x_indices_data.numpy(),
|
|
"x_values": self.x_values_data.numpy(),
|
|
'y_indices': self.y_indices_data.numpy(),
|
|
"y_values": self.y_values_data.numpy(),
|
|
},
|
|
fetch_list=[sp_dense_out],
|
|
return_numpy=True,
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
self.expect_res.numpy(), sparse_fetch[0], rtol=1e-5
|
|
)
|
|
paddle.disable_static()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
devices = []
|
|
if paddle.device.get_device() != "cpu":
|
|
devices.append(paddle.device.get_device())
|
|
else:
|
|
devices.append('cpu')
|
|
for device in devices:
|
|
paddle.device.set_device(device)
|
|
unittest.main()
|