Chris Yeung created SINGA505:

Summary: Buffer Operators / Change the Autograd operators to be bufferable
Key: SINGA505
URL: https://issues.apache.org/jira/browse/SINGA505
Project: Singa
Issue Type: Improvement
Components: Core
Reporter: Chris Yeung
We can buffer the operators, so that we can extract all the operators in autograd to build
a graph after schedule, where the simplest scheduling can use the FIFO principle from the
buffered operators. A more complex scheduleing algorithm could be implemented which consider
the dependency of operators that could make it parallel. One more clear advantage is that
when we run the graph we only need to run the buffered operators called by the autograd function,
then there will be no need to run the autograd python code again throughout the training process.
So this ticket uses for two purpose:
1. Change the core components (e.g. tensor,device) to support buffering.
2. Change all the autograd operator to be bufferable, i.e. the input and output should be
inside the block. For example, the SoftMax backward cannot be buffered because it is not doing
the operating through the block, and it was using numpy:
def backward(self, dy):
# calculations are made on numpy array
if self.axis == 1:
dy = singa.DefaultTranspose(dy)
grad = ctensor2numpy(dy)
output = ctensor2numpy(self.output)
out_1 = np.einsum("ki,ki>ki", grad, output)
medium_out = np.einsum("ki,kj>kij", output, output)
out_2 = np.einsum("kij,kj>ki", medium_out, grad)
out = out_1  out_2
dx = CTensor(out_1.shape)
dx.CopyFloatDataFromHostPtr(out.flatten())
if self.axis == 0:
return dx
elif self.axis == 1:
return singa.DefaultTranspose(dx)

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