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@ -39,15 +39,16 @@ In the backward pass
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This implementation allows to write mixed device program like this
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```python
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# get embedding feature on CPU
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feature = some_cpu_only_op(data)
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W1 = fluid.tensor(size=[100,20], parameter=true)
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W2 = fluid.tensor(size=[20,15], parameter=true)
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gpu_places = get_place(use_gpu=True)
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data = layers.data()
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gpu_places = layers.get_place(use_gpu=True)
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# parallel processing on multiple GPUs
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pd = ParallelDo(gpu_places)
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with pd.do():
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read_input(feature)
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prediction = my_net(feature)
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with pd.do(input=data):
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prediction = softmax(fc(fc(data, W1), W2))
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write_output(prediction)
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prediction = pd()
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loss = cross_entropy(prediction, label)
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@ -66,20 +67,20 @@ start_program
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main_program
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{
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block0 {
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vars: data, places, w1, w2
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vars: data, places, w1, w2, w1_grad, w2_grad,
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ops: data, get_place, parallel_do(block1),
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parallel_do_grad(block2),
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sgd(w2, w2_grad),
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sgd(w1, w1_grad)
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}
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block1 {
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block1 { # the forward pass
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parent_block: 0
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vars: data, h1, h2, loss
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ops: fc, fc, softmax
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}
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block2 {
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block2 { # the backward pass
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parent_block: 1
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vars: data_grad, h1_grad, h2_grad, loss_gard, w1_grad, w2_grad
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vars: data_grad, h1_grad, h2_grad, loss_gard, local_w1_grad, local_w2_grad
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ops: softmax_grad,
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fc_grad
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fc_grad
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dongzhihong
7 years ago