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test_operation.py
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test_operation.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
# =============================================================================
import unittest
from builtins import str
from singa import tensor
from singa import singa_wrap as singa
from singa import autograd
from singa import layer
from singa import singa_wrap
from cuda_helper import gpu_dev, cpu_dev
import numpy as np
autograd.training = True
CTensor = singa.Tensor
dy = CTensor([2, 1, 2, 2])
singa.Gaussian(0.0, 1.0, dy)
def _tuple_to_string(t):
lt = [str(x) for x in t]
return '(' + ', '.join(lt) + ')'
def axis_helper(y_shape, x_shape):
"""
check which axes the x has been broadcasted
Args:
y_shape: the shape of result
x_shape: the shape of x
Return:
a tuple refering the axes
"""
res = []
j = len(x_shape) - 1
for i in range(len(y_shape) - 1, -1, -1):
if j < 0 or x_shape[j] != y_shape[i]:
res.append(i)
j -= 1
return tuple(res[::-1])
def prepare_inputs_targets_for_rnn_test(dev):
x_0 = np.random.random((2, 3)).astype(np.float32)
x_1 = np.random.random((2, 3)).astype(np.float32)
x_2 = np.random.random((2, 3)).astype(np.float32)
h_0 = np.zeros((2, 2)).astype(np.float32)
t_0 = np.random.random((2, 2)).astype(np.float32)
t_1 = np.random.random((2, 2)).astype(np.float32)
t_2 = np.random.random((2, 2)).astype(np.float32)
x0 = tensor.Tensor(device=dev, data=x_0)
x1 = tensor.Tensor(device=dev, data=x_1)
x2 = tensor.Tensor(device=dev, data=x_2)
h0 = tensor.Tensor(device=dev, data=h_0)
t0 = tensor.Tensor(device=dev, data=t_0)
t1 = tensor.Tensor(device=dev, data=t_1)
t2 = tensor.Tensor(device=dev, data=t_2)
inputs = [x0, x1, x2]
targets = [t0, t1, t2]
return inputs, targets, h0
class TestPythonOperation(unittest.TestCase):
def check_shape(self, actual, expect):
self.assertEqual(
actual, expect, 'shape mismatch, actual shape is %s'
' exepcted is %s' %
(_tuple_to_string(actual), _tuple_to_string(expect)))
def _greater_helper(self, dev):
x0 = np.array([-0.9, -0.3, -0.1, 0.1, 0.5,
0.9]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3,
2).astype(np.float32)
y = np.greater(x0, x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(dev)
x1.to_device(dev)
result = autograd.greater(x0, x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
y,
decimal=5)
def test_Greater_cpu(self):
self._greater_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Greater_gpu(self):
self._greater_helper(gpu_dev)
def _conv2d_helper(self, dev):
# (out_channels, kernel_size)
conv_0 = layer.Conv2d(1, 2)
conv_without_bias_0 = layer.Conv2d(1, 2, bias=False)
cpu_input_tensor = tensor.Tensor(shape=(2, 3, 3, 3), device=dev)
cpu_input_tensor.gaussian(0.0, 1.0)
dy = tensor.Tensor(shape=(2, 1, 2, 2), device=dev)
dy.gaussian(0.0, 1.0)
y = conv_0(cpu_input_tensor) # PyTensor
dx, dW, db = y.creator.backward(dy.data) # CTensor
self.check_shape(y.shape, (2, 1, 2, 2))
self.check_shape(dx.shape(), (2, 3, 3, 3))
self.check_shape(dW.shape(), (1, 3, 2, 2))
self.check_shape(db.shape(), (1,))
# forward without bias
y_without_bias = conv_without_bias_0(cpu_input_tensor)
self.check_shape(y_without_bias.shape, (2, 1, 2, 2))
def test_conv2d_cpu(self):
self._conv2d_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_conv2d_gpu(self):
self._conv2d_helper(gpu_dev)
def _conv_same_pad(self, dev, pad_mode, is_2d):
if is_2d:
x_h, w_h, k_h, p_h = 32, 4, 4, 1
else:
x_h, w_h, k_h, p_h = 1, 1, 1, 0
x = tensor.Tensor(shape=(3, 3, x_h, 32), device=dev)
x.gaussian(0.0, 1.0)
# with the same padding, the padding should be 3
# for SAME_UPPER, is (1, 1) + (0, 1)
# for SAME_LOWER, is (1, 1) + (1, 0)
kernel = (k_h, 4)
padding = (p_h, 1)
stride = (1, 1)
group = 1
bias = False
out_channels = 3
conv_0 = layer.Conv2d(out_channels,
kernel,
stride=stride,
group=group,
bias=bias,
pad_mode=pad_mode)
y = conv_0(x)
dy = np.ones((3, 3, x_h, 32), dtype=np.float32)
dy = tensor.from_numpy(dy)
dy.to_device(dev)
dx, dW = y.creator.backward(dy.data)
self.check_shape(y.shape, (3, 3, x_h, 32))
self.check_shape(dx.shape(), (3, 3, x_h, 32))
self.check_shape(dW.shape(), (3, 3, w_h, 4))
def test_conv2d_same_pad_cpu(self):
self._conv_same_pad(cpu_dev, "SAME_LOWER", True)
self._conv_same_pad(cpu_dev, "SAME_UPPER", True)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_conv2d_same_pad_gpu(self):
self._conv_same_pad(gpu_dev, "SAME_LOWER", True)
self._conv_same_pad(gpu_dev, "SAME_UPPER", True)
def test_conv1d_same_pad_cpu(self):
self._conv_same_pad(cpu_dev, "SAME_LOWER", False)
self._conv_same_pad(cpu_dev, "SAME_UPPER", False)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_conv1d_same_pad_gpu(self):
self._conv_same_pad(gpu_dev, "SAME_LOWER", False)
self._conv_same_pad(gpu_dev, "SAME_UPPER", False)
def _pooling_same_pad(self, dev, pad_mode, is_2d):
if is_2d:
x_h, k_h, p_h = 32, 4, 1
else:
x_h, k_h, p_h = 1, 1, 0
x = tensor.Tensor(shape=(3, 3, x_h, 32), device=dev)
x.gaussian(0.0, 1.0)
# with the same padding, the padding should be 3
# for SAME_UPPER, is (1, 1) + (0, 1)
# for SAME_LOWER, is (1, 1) + (1, 0)
kernel = (k_h, 4)
# we add 4 padding here and hope the conv and trim one padding then
padding = (p_h, 1)
stride = (1, 1)
pooling = layer.Pooling2d(kernel, stride=stride, pad_mode=pad_mode)
y = pooling(x)
dy = np.ones((3, 3, x_h, 32), dtype=np.float32)
dy = tensor.from_numpy(dy)
dy.to_device(dev)
dx = y.creator.backward(dy.data)
self.check_shape(y.shape, (3, 3, x_h, 32))
self.check_shape(dx.shape(), (3, 3, x_h, 32))
def test_pooling2d_same_pad_cpu(self):
self._pooling_same_pad(cpu_dev, "SAME_LOWER", True)
self._pooling_same_pad(cpu_dev, "SAME_UPPER", True)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_pooling2d_same_pad_gpu(self):
self._pooling_same_pad(gpu_dev, "SAME_LOWER", True)
self._pooling_same_pad(gpu_dev, "SAME_UPPER", True)
def test_pooling1d_same_pad_cpu(self):
self._pooling_same_pad(cpu_dev, "SAME_LOWER", False)
self._pooling_same_pad(cpu_dev, "SAME_UPPER", False)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_pooling1d_same_pad_gpu(self):
self._pooling_same_pad(gpu_dev, "SAME_LOWER", False)
self._pooling_same_pad(gpu_dev, "SAME_UPPER", False)
def _sum_helper(self, dev):
x = np.array([0.1, -1.0, 0.4, 4.0, -0.9,
9.0]).reshape(3, 2).astype(np.float32)
x1 = np.array([0.1, 1.0, 0.4, 4.0, 0.9,
9.0]).reshape(3, 2).astype(np.float32)
y = x + x1
dy = np.ones((3, 2), dtype=np.float32)
grad0 = dy
grad1 = dy
x = tensor.from_numpy(x)
x1 = tensor.from_numpy(x1)
dy = tensor.from_numpy(dy)
x.to_device(dev)
x1.to_device(dev)
dy.to_device(dev)
result = autograd.sum(x, x1)
dx0, dx1 = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
y,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx0)),
grad0,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx1)),
grad1,
decimal=5)
def test_sum_cpu(self):
self._sum_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_sum_gpu(self):
self._sum_helper(gpu_dev)
def _SeparableConv2d_helper(self, dev):
# SeparableConv2d(in_channels, out_channels, kernel_size)
if dev == cpu_dev:
in_channels = 1
else:
in_channels = 8
separ_conv = layer.SeparableConv2d(16, 3, padding=1)
x = np.random.random((10, in_channels, 28, 28)).astype(np.float32)
x = tensor.Tensor(device=dev, data=x)
y = separ_conv(x)
self.check_shape(y.shape, (10, 16, 28, 28))
y1 = separ_conv.depthwise_conv(x)
y2 = separ_conv.point_conv(y1)
dy1, dW_depth = y2.creator.backward(y2.data)
dx, dW_spacial = y1.creator.backward(dy1)
self.check_shape(y2.shape, (10, 16, 28, 28))
self.check_shape(dy1.shape(), (10, in_channels, 28, 28))
self.check_shape(dW_depth.shape(), (16, in_channels, 1, 1))
self.check_shape(dx.shape(), (10, in_channels, 28, 28))
self.check_shape(dW_spacial.shape(), (in_channels, 1, 3, 3))
def test_SeparableConv2d_cpu(self):
self._SeparableConv2d_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_SeparableConv2d_gpu(self):
self._SeparableConv2d_helper(gpu_dev)
def _batchnorm2d_helper(self, dev):
batchnorm_0 = layer.BatchNorm2d(3)
cpu_input_tensor = tensor.Tensor(shape=(2, 3, 3, 3), device=dev)
cpu_input_tensor.gaussian(0.0, 1.0)
dy = cpu_input_tensor.clone().data
y = batchnorm_0(cpu_input_tensor)
dx, ds, db = y.creator.backward(dy)
self.check_shape(y.shape, (2, 3, 3, 3))
self.check_shape(dx.shape(), (2, 3, 3, 3))
self.check_shape(ds.shape(), (3,))
self.check_shape(db.shape(), (3,))
def test_batchnorm2d_cpu(self):
self._batchnorm2d_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_batchnorm2d_gpu(self):
self._batchnorm2d_helper(gpu_dev)
def gradients_check(self,
func,
param,
autograds,
h=0.0005,
df=1,
dev=cpu_dev):
# param: PyTensor
# autograds: numpy_tensor
p = tensor.to_numpy(param)
it = np.nditer(p, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
idx = it.multi_index
diff = np.zeros_like(p)
diff[idx] += h
diff = tensor.from_numpy(diff)
diff.to_device(dev)
param += diff
pos = func()
pos = tensor.to_numpy(pos)
param -= diff
param -= diff
neg = func()
neg = tensor.to_numpy(neg)
numerical_grad = np.sum((pos - neg) * df) / (2 * h)
#print((autograds[idx] - numerical_grad)/numerical_grad)
# threshold set as -5% to +5%
#self.assertAlmostEqual((autograds[idx] - numerical_grad)/(numerical_grad+0.0000001), 0., places=1)
self.assertAlmostEqual(autograds[idx] - numerical_grad,
0.,
places=2)
it.iternext()
def _vanillaRNN_gpu_tiny_ops_shape_check_helper(self, dev):
# gradients shape check.
inputs, target, h0 = prepare_inputs_targets_for_rnn_test(dev)
rnn = layer.RNN(3, 2)
hs, _ = rnn(inputs, h0)
loss = autograd.softmax_cross_entropy(hs[0], target[0])
for i in range(1, len(hs)):
l = autograd.softmax_cross_entropy(hs[i], target[i])
loss = autograd.add(loss, l)
# d=autograd.infer_dependency(loss.creator)
# print(d)
for t, dt in autograd.backward(loss):
self.check_shape(t.shape, dt.shape)
def test_vanillaRNN_gpu_tiny_ops_shape_check_cpu(self):
self._vanillaRNN_gpu_tiny_ops_shape_check_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_vanillaRNN_gpu_tiny_ops_shape_check_gpu(self):
self._vanillaRNN_gpu_tiny_ops_shape_check_helper(gpu_dev)
def _LSTM_gpu_tiny_ops_shape_check_helper(self, dev):
# gradients shape check.
inputs, target, h0 = prepare_inputs_targets_for_rnn_test(dev)
c_0 = np.random.random((2, 1)).astype(np.float32)
c0 = tensor.Tensor(device=dev, data=c_0)
rnn = layer.LSTM(3, 2)
hs, _, _ = rnn(inputs, (h0, c0))
loss = autograd.softmax_cross_entropy(hs[0], target[0])
for i in range(1, len(hs)):
l = autograd.softmax_cross_entropy(hs[i], target[i])
loss = autograd.add(loss, l)
# d=autograd.infer_dependency(loss.creator)
# print(d)
for t, dt in autograd.backward(loss):
self.check_shape(t.shape, dt.shape)
def test_LSTM_gpu_tiny_ops_shape_check_cpu(self):
self._LSTM_gpu_tiny_ops_shape_check_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_LSTM_gpu_tiny_ops_shape_check_gpu(self):
self._LSTM_gpu_tiny_ops_shape_check_helper(gpu_dev)
def _numerical_gradients_check_for_vallina_rnn_helper(self, dev):
inputs, target, h0 = prepare_inputs_targets_for_rnn_test(dev)
rnn = layer.RNN(3, 2)
def valinna_rnn_forward():
hs, _ = rnn(inputs, h0)
loss = autograd.softmax_cross_entropy(hs[0], target[0])
for i in range(1, len(hs)):
l = autograd.softmax_cross_entropy(hs[i], target[i])
loss = autograd.add(loss, l)
#grads = autograd.gradients(loss)
return loss
loss1 = valinna_rnn_forward()
auto_grads = autograd.gradients(loss1)
params = rnn.get_params()
for key, param in params.items():
auto_grad = tensor.to_numpy(auto_grads[id(param)])
self.gradients_check(valinna_rnn_forward, param, auto_grad, dev=dev)
def _gradient_check_cudnn_rnn(self, mode="vanilla", dev=gpu_dev):
seq = 10
bs = 2
fea = 10
hid = 10
x = np.random.random((seq, bs, fea)).astype(np.float32)
tx = tensor.Tensor(device=dev, data=x)
y = np.random.random((seq, bs, hid)).astype(np.float32)
y = np.reshape(y, (-1, hid))
ty = tensor.Tensor(device=dev, data=y)
rnn = layer.CudnnRNN(hid, rnn_mode=mode, return_sequences=True)
def vanilla_rnn_forward():
out = rnn(tx)
out = autograd.reshape(out, (-1, hid))
loss = autograd.softmax_cross_entropy(out, ty)
return loss
loss = vanilla_rnn_forward()
auto_grads = autograd.gradients(loss)
params = rnn.get_params()
for key, param in params.items():
auto_grad = tensor.to_numpy(auto_grads[id(param)])
self.gradients_check(vanilla_rnn_forward, param, auto_grad, dev=dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_gradient_check_cudnn_rnn_vanilla(self):
self._gradient_check_cudnn_rnn(mode="vanilla", dev=gpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_gradient_check_cudnn_rnn_lstm(self):
self._gradient_check_cudnn_rnn(mode="lstm", dev=gpu_dev)
# Cos Sim Gradient Check
def _gradient_check_cossim(self, dev=gpu_dev):
bs = 2
vec = 3
ta = tensor.random((bs, vec), dev)
tb = tensor.random((bs, vec), dev)
# treat ta, tb as params
ta.stores_grad = True
tb.stores_grad = True
ty = tensor.random((bs,), dev)
def _forward():
out = autograd.cossim(ta, tb)
loss = autograd.mse_loss(out, ty)
return loss
loss = _forward()
auto_grads = autograd.gradients(loss)
params = {id(ta): ta, id(tb): tb}
for key, param in params.items():
auto_grad = tensor.to_numpy(auto_grads[id(param)])
self.gradients_check(_forward, param, auto_grad, dev=dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_gradient_check_cossim_gpu(self):
self._gradient_check_cossim(dev=gpu_dev)
def test_gradient_check_cossim_cpu(self):
self._gradient_check_cossim(dev=cpu_dev)
def test_numerical_gradients_check_for_vallina_rnn_cpu(self):
self._numerical_gradients_check_for_vallina_rnn_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_numerical_gradients_check_for_vallina_rnn_gpu(self):
self._numerical_gradients_check_for_vallina_rnn_helper(gpu_dev)
def _numerical_gradients_check_for_lstm_helper(self, dev):
inputs, target, h0 = prepare_inputs_targets_for_rnn_test(dev)
c_0 = np.zeros((2, 2)).astype(np.float32)
c0 = tensor.Tensor(device=dev, data=c_0)
rnn = layer.LSTM(3, 2)
def lstm_forward():
hs, _, _ = rnn(inputs, (h0, c0))
loss = autograd.softmax_cross_entropy(hs[0], target[0])
for i in range(1, len(hs)):
l = autograd.softmax_cross_entropy(hs[i], target[i])
loss = autograd.add(loss, l)
return loss
loss1 = lstm_forward()
auto_grads = autograd.gradients(loss1)
params = rnn.get_params()
for key, param in params.items():
auto_grad = tensor.to_numpy(auto_grads[id(param)])
self.gradients_check(lstm_forward, param, auto_grad, dev=dev)
def test_numerical_gradients_check_for_lstm_cpu(self):
self._numerical_gradients_check_for_lstm_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_numerical_gradients_check_for_lstm_gpu(self):
self._numerical_gradients_check_for_lstm_helper(gpu_dev)
def _MeanSquareError_helper(self, dev):
X = np.array([4.3, 5.4, 3.3, 3.6, 5.7,
6.0]).reshape(3, 2).astype(np.float32)
T = np.array([4.4, 5.3, 3.2, 3.7, 5.4,
6.3]).reshape(3, 2).astype(np.float32)
x = tensor.from_numpy(X)
t = tensor.from_numpy(T)
x.to_device(dev)
t.to_device(dev)
loss = autograd.mse_loss(x, t)
dx = loss.creator.backward()
loss_np = tensor.to_numpy(loss)[0]
self.assertAlmostEqual(loss_np, 0.0366666, places=4)
self.check_shape(dx.shape(), (3, 2))
def test_MeanSquareError_cpu(self):
self._MeanSquareError_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_MeanSquareError_gpu(self):
self._MeanSquareError_helper(gpu_dev)
def _Abs_helper(self, dev):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5,
0.5]).reshape(3, 2).astype(np.float32)
XT = np.array([0.8, 1.2, 3.3, 3.6, 0.5,
0.5]).reshape(3, 2).astype(np.float32)
x = tensor.from_numpy(X)
x.to_device(dev)
result = autograd.abs(x)
dx = result.creator.backward(x.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT)
self.check_shape(dx.shape(), (3, 2))
def test_Abs_cpu(self):
self._Abs_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Abs_gpu(self):
self._Abs_helper(gpu_dev)
def _Mean_helper(self, dev):
x0 = np.array([-0.9, -0.3, -0.1, 0.1, 0.5,
0.9]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3,
2).astype(np.float32)
y = (x0 + x1) / 2
grad = np.ones(x0.shape) / 2
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(dev)
x1.to_device(dev)
result = autograd.mean(x0, x1)
dy = tensor.from_numpy(np.ones((3, 2)).astype(np.float32))
dy.to_device(dev)
dx0, dx1 = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
y,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx0)),
grad,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx1)),
grad,
decimal=5)
def test_Mean_cpu(self):
self._Mean_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Mean_gpu(self):
self._Mean_helper(gpu_dev)
def _Exp_helper(self, dev):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5,
0.5]).reshape(3, 2).astype(np.float32)
XT = np.exp(X)
x = tensor.from_numpy(X)
x.to_device(dev)
result = autograd.exp(x)
dx = result.creator.backward(x.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
XT,
decimal=5)
self.check_shape(dx.shape(), (3, 2))
def test_Exp_cpu(self):
self._Exp_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Exp_gpu(self):
self._Exp_helper(gpu_dev)
def _Identity_helper(self, dev):
x = np.array([-0.9, -0.3, -0.1, 0.1, 0.5,
0.9]).reshape(3, 2).astype(np.float32)
y = x.copy()
grad = np.ones(x.shape)
x = tensor.from_numpy(x)
x.to_device(dev)
result = autograd.identity(x)
dy = tensor.from_numpy(np.ones((3, 2)).astype(np.float32))
dy.to_device(dev)
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
y,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx)),
grad,
decimal=5)
self.check_shape(dx.shape(), (3, 2))
def test_Identity_cpu(self):
self._Identity_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Identity_gpu(self):
self._Identity_helper(gpu_dev)
def _LeakyRelu_helper(self, dev):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5,
0.5]).reshape(3, 2).astype(np.float32)
XT = np.array([0.8, -0.012, 3.3, -0.036, -0.005,
0.5]).reshape(3, 2).astype(np.float32)
x = tensor.from_numpy(X)
x.to_device(dev)
result = autograd.leakyrelu(x)
dx = result.creator.backward(x.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT)
self.check_shape(dx.shape(), (3, 2))
def test_LeakyRelu_cpu(self):
self._LeakyRelu_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_LeakyRelu_gpu(self):
self._LeakyRelu_helper(gpu_dev)
def _Relu_helper(self, dev):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5,
0.5]).reshape(3, 2).astype(np.float32)
XT = np.maximum(X, 0)
DY = np.ones((3, 2), dtype=np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(dev)
dy.to_device(dev)
result = autograd.relu(x)
dx = result.creator.backward(dy.data)
G = (X > 0).astype(np.float32)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
XT,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx)),
DX,
decimal=5)
def test_Relu_cpu(self):
self._Relu_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Relu_gpu(self):
self._Relu_helper(gpu_dev)
def _Cos_helper(self, dev):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5,
0.5]).reshape(3, 2).astype(np.float32)
XT = np.cos(X)
DY = np.ones((3, 2), dtype=np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(dev)
dy.to_device(dev)
result = autograd.cos(x)
dx = result.creator.backward(dy.data)
G = -np.sin(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
XT,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx)),
DX,
decimal=5)
def test_Cos_cpu(self):
self._Cos_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Cos_gpu(self):
self._Cos_helper(gpu_dev)
def _Cosh_helper(self, dev):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5,
0.5]).reshape(3, 2).astype(np.float32)
XT = np.cosh(X)
DY = np.ones((3, 2), dtype=np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(dev)
dy.to_device(dev)
result = autograd.cosh(x)
dx = result.creator.backward(dy.data)
G = np.sinh(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
XT,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx)),
DX,
decimal=5)
def test_Cosh_cpu(self):
self._Cosh_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Cosh_gpu(self):
self._Cosh_helper(gpu_dev)
def _Acos_helper(self, dev):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5,
0.9]).reshape(3, 2).astype(np.float32)
XT = np.arccos(X)
DY = np.ones((3, 2), dtype=np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(dev)
dy.to_device(dev)
result = autograd.acos(x)
dx = result.creator.backward(dy.data)
G = -1.0 / np.sqrt(1.0 - np.square(X))
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
XT,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx)),
DX,
decimal=5)
def test_Acos_cpu(self):
self._Acos_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Acos_gpu(self):
self._Acos_helper(gpu_dev)
def _Acosh_helper(self, dev):
X = np.array([1.1, 1.5, 1.9, 2.2, 2.5,
2.8]).reshape(3, 2).astype(np.float32)
XT = np.arccosh(X)
DY = np.ones((3, 2), dtype=np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(dev)
dy.to_device(dev)
result = autograd.acosh(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.multiply(np.sqrt(X - 1.0), np.sqrt(X + 1.0))
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
XT,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx)),
DX,
decimal=5)
def test_Acosh_cpu(self):
self._Acosh_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Acosh_gpu(self):
self._Acosh_helper(gpu_dev)
def _Sin_helper(self, dev):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5,
0.5]).reshape(3, 2).astype(np.float32)
XT = np.sin(X)
DY = np.ones((3, 2), dtype=np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(dev)
dy.to_device(dev)
result = autograd.sin(x)
dx = result.creator.backward(dy.data)
G = np.cos(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
XT,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx)),
DX,
decimal=5)
def test_Sin_cpu(self):
self._Sin_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Sin_gpu(self):
self._Sin_helper(gpu_dev)
def _Sinh_helper(self, dev):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5,
0.5]).reshape(3, 2).astype(np.float32)
XT = np.sinh(X)
DY = np.ones((3, 2), dtype=np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(dev)
dy.to_device(dev)
result = autograd.sinh(x)
dx = result.creator.backward(dy.data)
G = np.cosh(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
XT,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx)),
DX,
decimal=5)
def test_Sinh_cpu(self):
self._Sinh_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Sinh_gpu(self):
self._Sinh_helper(gpu_dev)
def _Asin_helper(self, dev):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5,
0.9]).reshape(3, 2).astype(np.float32)
XT = np.arcsin(X)
DY = np.ones((3, 2), dtype=np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(dev)
dy.to_device(dev)
result = autograd.asin(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.sqrt(1.0 - np.square(X))
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
XT,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx)),
DX,
decimal=5)
def test_Asin_cpu(self):
self._Asin_helper(cpu_dev)
@unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
def test_Asin_gpu(self):
self._Asin_helper(gpu_dev)
def _Asinh_helper(self, dev):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5,
0.9]).reshape(3, 2).astype(np.float32)
XT = np.arcsinh(X)
DY = np.ones((3, 2), dtype=np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(dev)
dy.to_device(dev)
result = autograd.asinh(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.sqrt(np.square(X) + 1.0)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result),
XT,
decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(
tensor.from_raw_tensor(dx)),
DX,
decimal=5)
def test_Asinh_cpu(self):
self._Asinh_helper(cpu_dev)