def test_vanillaRNN_gpu_tiny_ops_shape_check(self):
# gradients shape check.
inputs, target, h0 = prepare_inputs_targets_for_rnn_test()
rnn = autograd.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_numerical_gradients_check_for_vallina_rnn(self):
inputs, target, h0 = prepare_inputs_targets_for_rnn_test()
rnn = autograd.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)
for param in rnn.params:
auto_grad = tensor.to_numpy(auto_grads[param])
self.gradients_check(valinna_rnn_forward, param, auto_grad)
from singa import autograd
from singa import optimizer
from singa import *
from singa import sonnx
import onnx
from singa import device
import numpy as np
autograd.training = True
np.random.seed(0)
data = np.random.randn(4, 2).astype(np.float32)
h = np.random.randn(4, 2).astype(np.float32)
label = np.random.randn(4, 2).astype(np.float32)
print('train_data_shape:', data.shape)
print('train_label_shape:', label.shape)
rnn = autograd.RNN(2, 2)
sgd = optimizer.SGD(0.00)
dev = device.get_default_device()
# training process
for i in range(1):
inputs = tensor.Tensor(device=dev, data=data, stores_grad=False)
h0 = tensor.Tensor(device=dev, data=h, stores_grad=False)
targets = tensor.Tensor(device=dev,
data=label,
requires_grad=False,
stores_grad=False)
x = rnn(inputs, h0)
loss = autograd.mse_loss(x, targets)
gradient = autograd.backward(loss)
for p, gp in gradient: