Comments (6)
A highlight is why the reshaped tensor have different shape?
see test_reshape_gpu
Then, other test failure should be due to very small numerical errors (order of 1e-5) that can be fixed by reducing the number of significant in comparison.
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should we make travis build fail when encountering errors raised from python unit test?
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should we make travis build fail when encountering errors raised from python unit test?
In my opinion, this is a very good feature. However, I am not sure if the machine that runs the test case by travis has GPU.
On the other hand, this test_operation.py is still important because it lets the developers to check whether the system has any problem after their commits.
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If I am correct, the reshape is due to the error in backward:
class Reshape(Operation):
def __init__(self,shape):
super(Reshape, self).__init__()
if isinstance(shape, tensor.Tensor):
self.shape = np.asarray(tensor.to_numpy(shape).astype(np.int32)).tolist()
else:
self.shape = list(shape)
def forward(self, x):
_shape = x.shape()
shape = self.shape
# handle the shape with 0
shape = [_shape[i] if i < len(_shape) and shape[i] == 0 else shape[i] for i in range(len(shape))]
# handle the shape with -1
hidden_shape = int(np.prod(_shape) // np.abs(np.prod(shape)))
self.cache=[s if s != -1 else hidden_shape for s in shape]
return singa.Reshape(x, self.cache)
def backward(self, dy):
return singa.Reshape(dy, self.cache)
I think the function should change to
class Reshape(Operation):
def __init__(self,shape):
super(Reshape, self).__init__()
if isinstance(shape, tensor.Tensor):
self.shape = np.asarray(tensor.to_numpy(shape).astype(np.int32)).tolist()
else:
self.shape = list(shape)
def forward(self, x):
self._shape = x.shape()
shape = self.shape
# handle the shape with 0
shape = [self._shape[i] if i < len(self._shape) and shape[i] == 0 else shape[i] for i in range(len(shape))]
# handle the shape with -1
hidden_shape = int(np.prod(self._shape) // np.abs(np.prod(shape)))
self.cache=[s if s != -1 else hidden_shape for s in shape]
return singa.Reshape(x, self.cache)
def backward(self, dy):
return singa.Reshape(dy, self._shape)
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To resolve the problem completely, I opened a hotfix at PR #579
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the problem is resolved completely
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Related Issues (20)
- Switch between CPU and GPU devices for cnn example HOT 4
- Save the downloaded datasets to local directory HOT 2
- Add running scripts for cnn and cifar_distributed_cnn examples HOT 4
- Intermediate information printing HOT 3
- Adding arguments for weight decay and momentum HOT 2
- Increase max epoch for cnn example for better convergence HOT 2
- Update CMakeLists.txt for release 4.0.0 HOT 1
- Check Apache license header for release 4.0.0
- OpenCL Compilation Fails
- Upload Release 4.0.0 Package to SVN HOT 1
- Update the NOTICE file for images HOT 1
- gitignore and gitmodules should be removed from the release tar file HOT 2
- Create a new branch dev-postgresql HOT 2
- Create the SumError New Loss Function HOT 1
- Dynamic Creation of Models HOT 2
- Need to return the gradients from optimizer HOT 4
- Maximum recursion depth exceeded in comparison for string HOT 1
- can sparse all-reduce keep efficiency with large number of gpu workers?
- Python 3.11, Model, ImportError HOT 9
- Update documentation for distributed training HOT 1
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