Summary: Softmax was not in the model helper, so added it there so we can set the CUDNN engine, as it is the preferred version.
Reviewed By: asaadaldien
Differential Revision: D4835624
fbshipit-source-id: 7f0c84b7a73653119901795782709a6a617345c5
Summary:
Uses the cudnnTransformTensor function. It works by shuffling the strides according to the transpose axis. Significant speedup over current GPU version .
+ moves the transpose test under utility_ops, because hypothesis_test is too big
Reviewed By: jamesr66a
Differential Revision: D4810993
fbshipit-source-id: 82577c4ced1389e70bd5992820ae4d8297a3817f
Summary:
Add ConvNd interface for Nd convluton and keep Conv for 2d convlution.
I added _BaseConv to share code between ConvNd and Conv.
Reviewed By: Yangqing
Differential Revision: D4660822
fbshipit-source-id: 8339421351ce9a36ce5a165f7fa455cfcc61733d
Summary: we don't use this one any more except a few tests
Reviewed By: urikz
Differential Revision: D4731401
fbshipit-source-id: c5c28b7594e3251f501fc28455dfc9bd2093a836
Summary: These python helpers are going to provide sufficient book keeping when adding quantization for conv layers
Reviewed By: Yangqing
Differential Revision: D4671478
fbshipit-source-id: 292e2f633dd30969c0afbe7a8075b340ce9a6d12
Summary:
If init_params is False, the parameters should not be initialized.
This is particularly important when testing a model that provides values for these BN parameters.
Closes https://github.com/caffe2/caffe2/pull/174
Differential Revision: D4621791
Pulled By: Yangqing
fbshipit-source-id: 518443925990a12c1d5729b0971ebe19ba5d8998
Summary: D4348953 added support for accuracy for top_k>1, which is only supported on CPU, requiring data to be copied to CUDA. But that diff did not take into account that we have top_k=1 version of AccuracyOp for CUDA. This diff ensures we use the CUDA version for top_k=1.
Differential Revision: D4607767
fbshipit-source-id: 8becda23890343043eb79ad04e4c6196e9010f0c
Summary:
This diff adds ability to train multiclass classifier on sampled subset of classes. This basically implements what described in https://arxiv.org/abs/1412.2007 without the sampling probability correction. Since this implement uniform sampling, sampling probabilities are cancelled out in softmax anyway.
The trick to make this work is to have 2 different nets for prediction and training, both shared parameters. The model is built normally until the last layer. If sampling is needed, then we do the following:
The class sampling works as following:
Reviewed By: xianjiec
Differential Revision: D4512859
fbshipit-source-id: ab537bcac81d5e5877a8795045e8682c8064da68
Summary:
Pass through the h-value recurrent output unchanged at each LSTM step beyond the valid part of a sequence (computed based on seqLengths, allowing batching of sequences of different length). This enables using the final-step output of each sequence as the output when one vector is desired for the entire sequence. Gradient also passed back unchanged.
Also made some cosmetic changes to recurrent_network_test.py (seq_lengths offset corrected, should be in [1, T] rather than [0, T-1]).
Reviewed By: urikz
Differential Revision: D4540307
fbshipit-source-id: 73a9f6326069d713dcb0cdc8d17869317c6dbe96
Summary:
(Caffe2) Modified RecurrentNetworkGradient operator so that training is possible with any of the output blob(s) receiving gradient during the backward pass. This is realized through a new argument for the RecurrentNetwork op, outputs_with_grads, which takes a list of the indices of the output blobs which will receive gradient. The default case (only receiving gradient from the first output blob) remains the default.
New unit test covers the case where outputs_with_grads = [1, 2] using Python LSTM wrapper.
Reviewed By: urikz
Differential Revision: D4518516
fbshipit-source-id: 5c531582b20f3cf727d1aa91239b4d5a2b8a7c1f
Summary:
I have forgotten to remove this one. The rest of indexing
instead of string names is comming after D4446813 lands as scratches
aren't inputs or outputs and thus can't be indexed.
Reviewed By: urikz
Differential Revision: D4465748
fbshipit-source-id: 2ccbedfb35541ef4a2231d1480eef59025bd5290
Summary: This diff use stack workspaces in RecurrentNetwork, which allows to simplify the implementation and get rid of scratches.
Reviewed By: salexspb
Differential Revision: D4446813
fbshipit-source-id: 514eec7e4300bdf492a9cb192b40cf4f89acf656
Summary: Remove usage of recurrent_sizes, so recurrent states' sizes can depend on input (in case of attention matrix for beam decoder). I removed recurrent_sizes from forward and backward steps.
Reviewed By: salexspb
Differential Revision: D4427688
fbshipit-source-id: 580420a294d309c86ec5cb4e677058623b7228e1
Summary:
In this diff I stop passing parameters by name and also remove hardcoded output ids which were there specifically for LSTM to work. It also allows to avoid using recurrent_sizes in the backward pass (for forward this is done in D4427688)
Using similar technic it should be simple enough to eliminate blob name passing at all. Then we can fix scoping. These can be done in a next diff.
Reviewed By: urikz
Differential Revision: D4444614
fbshipit-source-id: 3580a76365502b9f2f09e3d8b7e78084ca739f00
Summary:
Adds a thread pool for image decode, and optional GPU-based data conversion, mean subtraction and std division
Closes https://github.com/caffe2/caffe2/pull/56
Reviewed By: Yangqing
Differential Revision: D4341326
Pulled By: bwasti
fbshipit-source-id: 6485616ea7d212c7701274a40fae912db30dff4a
Summary:
I was testing perf difference between naive group conv and cudnn group conv. I am doing no_bias conv and added support for that in naive implementation
although its deprecated, i thought it would be nice to have working things in our code
Differential Revision: D4363168
fbshipit-source-id: 29719013d79b449fd359884709c7a1195be51ae3
Summary: As per discussion in D4355529
Reviewed By: prigoyal
Differential Revision: D4362162
fbshipit-source-id: 795fcf1507235a7dc3c7a10b0453037936d057aa
Summary:
It used to be that only the cudnn engine supports it, and now it should be
fully supported by any conv engine.
To ignore bias, simply use a convolution op that has two inputs instead of
3. The gradient operator will automatically figure out that it does not
compute the bias gradient.
Reviewed By: prigoyal
Differential Revision: D4354183
fbshipit-source-id: cf71b6289a254d15a6a663a85df63fbbaec3702b
Summary: As discussed, this improves performance a lot and is not a memory hog anymore. Anyway anyone can also turn it off.
Differential Revision: D4338798
fbshipit-source-id: bf0fdb594427ebe90e1e94b2effdc63196096b3f
Summary:
This is a first step in improving our RNN story. It provides a wrapper around current RecurrentNetworkOp implementation which infers most of the redundant parameters and makes API much simpler.
Also in order to support general step nets I added an extra argument to the RecurrentNetworkOp.
Future work:
1. Inferring step net output and internal blobs (scratches) sizes and type
2. Avoid accessing blobs by names in c++ part
3. Remove requirement for inputs / output 1:1 correspondence in the step net
4. Make python API support networks with operators like Sum being on the boarder of the Cell net (currently there is an issue with such networks where gradient blobs which are on the side are not explicitly created).
Differential Revision: D4268503
fbshipit-source-id: f8a66491c2b55daa730caeed7e9f2b3921541b49
Summary:
Yangqing This seems to work for me, not sure if it's implemented in the right way for you to accept :)
Allows user to specify "no_bias" as an option for convolution layers (only cuDNN at this point), so that the bias associated with that operator is not allocated or computed. This is useful in particular for conv + BatchNorm combinations (such as ResNets), as the bias term can be handled by both conv and Batch Norm, wasting memory and computation.
Closes https://github.com/caffe2/caffe2/pull/50
Reviewed By: Yangqing
Differential Revision: D4341288
Pulled By: bwasti
fbshipit-source-id: e6138d0024c83ed876dff2f83ffbebe7de502fd8
Summary:
prigoyal sharply noticed a bug in the Resnet models: we have not been checkpointing, nor synchronizing between gpus, the moving average and variance computed by the SpatialBN ops. Particularly the first problen is serious, since models starting from checkpoint would have started from a null-state for SpatialBN. Not synchronizing with the data parallel model is less tragic since each GPU should see very similar data.
Thus I propose keeping track of "computed params", i.e params that are computed from data but not optimized. I don't know if there are other examples, but SpatialBN's moving avg and var definitely are one.
- I modified the checkpointign for xray model to store those blobs + also ensure the synchronization of those blobs
- I modified data parallel model to broadcast those params from gpu0. I first tried averaging, but hit some NCCL deadlocks ... :(
Differential Revision: D4281265
fbshipit-source-id: 933311afeec4b7e9344a13cf2d38aa939c50ac31
Summary:
With parameter server, sparse features are updated on the parameter server.
Local update for sparse features are disabled. But that logic is removed in
D4144922. This diff is to add this logic back in a slightly different way.
Previously, in trainer_example, I did that in a hacky way just avoid adding
sparse weight to model.params. It will still generate grad, but will not add
optimization operators. At the same time, it is always registered directly in
the sparse_mapping, so the parameter server is aware of this parameter.
But with the new change for ParameterInfo. I can not do it in that way anymore.
Because the param registry and params are bind together in ParameterInfo.
For dper, there is a option in dper model helper to disable all of the sparse
parameter optimizer.
To combine these two together, I directly changed the ModelHelperBase in this
diff. It is not quite ideal. It is better to do it in Layer. But to fix the old
one, this seems to be more reasonable place to cover both cases.
With this diff, there is no spike anymore. So probably this is the root cause
for the convergence issue we have seen in D4144922. It explains that why the
model can recover, which is because adagrad decays local learning rate and
local updates cause less change.
Reviewed By: dzhulgakov
Differential Revision: D4229684
fbshipit-source-id: da1241d43d7c52cbf13560f9bb83e09897d8d56f
