wehub-resource-sync
295 lines
10 KiB
Python
295 lines
10 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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import numpy as np
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import scipy.sparse as sp
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import paddle
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import paddle.nn.functional as F
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np.random.seed(2022)
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devices = ['cpu']
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if paddle.device.get_device() != "cpu":
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devices.append(paddle.device.get_device())
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class TestCsrSoftmax(unittest.TestCase):
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def test_softmax2d(self):
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mask = np.random.rand(16, 128) < 0.5
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np_x = np.random.rand(16, 128) * mask
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np_csr = sp.csr_matrix(np_x)
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row_number = np_csr.shape[0]
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np_out = np.array([])
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for i in range(row_number):
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start = np_csr.indptr[i]
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end = np_csr.indptr[i + 1]
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if start == end:
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continue
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x = np_csr.data[start:end]
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x_max = np.max(x, keepdims=True)
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x_exp = np.exp(x - x_max)
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x_exp_sum = np.sum(x_exp, keepdims=True)
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np_out = np.concatenate([np_out, x_exp / x_exp_sum])
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csr = paddle.to_tensor(np_x, stop_gradient=False).to_sparse_csr()
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m = paddle.sparse.nn.Softmax()
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out = m(csr)
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np.testing.assert_allclose(
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out.crows().numpy(), np_csr.indptr, rtol=1e-05
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)
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np.testing.assert_allclose(
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out.cols().numpy(), np_csr.indices, rtol=1e-05
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)
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np.testing.assert_allclose(out.values().numpy(), np_out, rtol=1e-05)
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# dx = (dout - sum(dout * out)) * out, dout=rand_x
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out.backward(csr.detach())
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dx = np.array([])
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for i in range(row_number):
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start = np_csr.indptr[i]
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end = np_csr.indptr[i + 1]
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if start == end:
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continue
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out = np_out[start:end]
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dout = np_csr.data[start:end]
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sum = np.sum(dout * out, keepdims=True)
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dx = np.concatenate([dx, (dout - sum) * out])
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np.testing.assert_allclose(
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csr.grad.crows().numpy(), np_csr.indptr, rtol=1e-05
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)
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np.testing.assert_allclose(
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csr.grad.cols().numpy(), np_csr.indices, rtol=1e-05
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)
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np.testing.assert_allclose(csr.grad.values().numpy(), dx, rtol=1e-05)
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def test_softmax3d(self):
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batchNum = 16
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mask = np.random.rand(batchNum, 16, 128) < 0.5
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np_x = np.random.rand(batchNum, 16, 128) * mask
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np_out_list = []
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np_out = np.array([])
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for i in range(batchNum):
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np_csr = sp.csr_matrix(np_x[i, :, :])
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row_number = np_csr.shape[0]
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for j in range(
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row_number,
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):
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start = np_csr.indptr[j]
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end = np_csr.indptr[j + 1]
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if start == end:
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continue
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x = np_csr.data[start:end]
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x_max = np.max(x, keepdims=True)
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x_exp = np.exp(x - x_max)
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x_exp_sum = np.sum(x_exp, keepdims=True)
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np_out_list.append(x_exp / x_exp_sum)
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np_out = np.concatenate([np_out, x_exp / x_exp_sum])
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csr = paddle.to_tensor(np_x, stop_gradient=False).to_sparse_csr()
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m = paddle.sparse.nn.Softmax()
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out = m(csr)
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np.testing.assert_allclose(out.values().numpy(), np_out, rtol=1e-05)
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# dx = (dout - sum(dout * out)) * out, dout=rand_x
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out.backward(csr.detach())
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dx = np.array([])
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batch_offset = 0
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for i in range(batchNum):
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np_csr = sp.csr_matrix(np_x[i, :, :])
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row_number = np_csr.shape[0]
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for j in range(row_number):
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start = np_csr.indptr[j]
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end = np_csr.indptr[j + 1]
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if start == end:
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continue
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dout = np_csr.data[start:end]
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out = np_out[batch_offset + start : batch_offset + end]
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sum = np.sum(dout * out, keepdims=True)
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dx = np.concatenate([dx, (dout - sum) * out])
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batch_offset += np_csr.nnz
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np.testing.assert_allclose(csr.grad.values().numpy(), dx, rtol=1e-05)
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class TestCooSoftmax(unittest.TestCase):
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def sparse_softmax(self, sparse, dense_shape, sparse_dim, dim):
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"""
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sparse softmax algorithm in Python.
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"""
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inf = float('inf')
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indices = sparse.indices()
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values = sparse.values()
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size = sparse.shape
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dense_size = tuple(size[sparse_dim:])
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dense_dim = len(dense_size)
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if dim < sparse_dim:
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nnz = sparse.nnz()
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# compute pool indices
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strides = np.ones((sparse_dim, 1))
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for i in reversed(range(sparse_dim - 1)):
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strides[i, 0] = strides[i + 1, 0] * size[i + 1]
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strides[dim, 0] = 0
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strides = paddle.to_tensor(strides, dtype=indices.dtype)
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pool = paddle.sum((indices * strides), axis=0).numpy()
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i2p = {}
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for i in range(nnz):
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c = int(pool[i])
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if c not in i2p:
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i2p[c] = len(i2p)
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pool[i] = i2p[c]
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mx = paddle.empty((pool.max() + 1, *dense_size)).numpy()
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mx[:] = -inf
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np_values = values.numpy()
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for n in range(nnz):
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p = pool[n]
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mx[p] = np.where(mx[p] > np_values[n], mx[p], np_values[n])
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# apply exp to (v - mx) and sum the results
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exp_values = paddle.empty_like(values).numpy()
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exp_sums = np.zeros_like(mx)
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for n in range(nnz):
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p = pool[n]
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v = exp_values[n] = np.exp(np_values[n] - mx[p])
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exp_sums[p] = exp_sums[p] + v
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# normalize with the sum of exponents
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for n in range(nnz):
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p = pool[n]
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exp_values[n] = exp_values[n] / exp_sums[p]
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return paddle.sparse.sparse_coo_tensor(
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indices, exp_values, dense_shape
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)
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elif dim < sparse_dim + dense_dim:
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return paddle.sparse.sparse_coo_tensor(
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indices, F.softmax(values, dim - sparse_dim + 1), size
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)
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else:
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print(
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f"`dim(={dim})` must be smaller than `sparse_dim(={sparse_dim}) + dense_dim(={dense_dim})`"
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)
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def check_run(self, dense_shape):
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mask = np.random.rand(*dense_shape) < 0.5
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np_x = np.random.rand(*dense_shape) * mask
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for device in devices:
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paddle.device.set_device(device)
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for sparse_dim in range(1, len(dense_shape)):
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coo = (
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paddle.to_tensor(np_x, stop_gradient=False)
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.detach()
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.to_sparse_coo(sparse_dim)
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)
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size = coo.shape
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dense_size = tuple(size[sparse_dim:])
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dense_dim = len(dense_size)
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for axis in range(sparse_dim + dense_dim):
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coo = (
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paddle.to_tensor(np_x, stop_gradient=False)
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.detach()
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.to_sparse_coo(sparse_dim)
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)
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coo.stop_gradient = False
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py_out = self.sparse_softmax(
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coo, dense_shape, sparse_dim, axis
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)
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m = paddle.sparse.nn.Softmax(axis=axis)
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out = m(coo)
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np.testing.assert_allclose(
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py_out.indices().numpy(),
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out.indices().numpy(),
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rtol=1e-05,
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)
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np.testing.assert_allclose(
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py_out.values().numpy(),
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out.values().numpy(),
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rtol=1e-05,
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)
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out.backward(coo.detach())
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dense_tensor = paddle.to_tensor(np_x, stop_gradient=False)
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model_dense = paddle.nn.Softmax(axis=axis)
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dense_out = model_dense(dense_tensor)
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dense_out.backward(dense_tensor.detach())
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dg_npy = dense_tensor.grad.numpy()
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np.testing.assert_allclose(
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coo.grad.to_dense().numpy(), dg_npy, rtol=1e-05
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)
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def test_softmax2d(self):
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self.check_run((16, 128))
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def test_softmax3d(self):
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self.check_run((16, 16, 128))
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def test_softmax2d_static(self):
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for device in devices:
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paddle.device.set_device(device)
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np_x = np.array([[11, 0, 0, 14, 15], [0, 22, 0, 24, 0]]).astype(
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'float32'
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)
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coo = (
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paddle.to_tensor(np_x, stop_gradient=False)
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.detach()
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.to_sparse_coo(2)
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)
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m = paddle.sparse.nn.Softmax()
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dy_out = m(coo)
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dy_out_dense = dy_out.to_dense().numpy()
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paddle.enable_static()
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indices = paddle.static.data(
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name='indices', shape=[2, 5], dtype='int32'
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)
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values = paddle.static.data(
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name='values', shape=[5, 1], dtype='float32'
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)
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dense_shape = [2, 5]
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sp_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
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sparse_softmax = paddle.sparse.nn.Softmax()
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sp_y = sparse_softmax(sp_x)
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out = sp_y.to_dense()
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exe = paddle.static.Executor()
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indices_data = [[0, 0, 0, 1, 1], [0, 3, 4, 1, 3]]
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values_data = np.array([11, 14, 15, 22, 24]).astype('float32')
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fetch = exe.run(
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feed={'indices': indices_data, 'values': values_data},
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fetch_list=[out],
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return_numpy=True,
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)
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np.testing.assert_allclose(dy_out_dense, fetch[0], rtol=1e-5)
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paddle.disable_static()
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if __name__ == "__main__":
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unittest.main()
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