Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34515
Once upon a time we thought this was necessary. In reality it is not, so
removing it.
For backcompat, our public interface (defined in `api/`) still has
typedefs to the old `script::` names.
There was only one collision: `Pass` as a `Stmt` and `Pass` as a graph
transform. I renamed one of them.
Test Plan: Imported from OSS
Differential Revision: D20353503
Pulled By: suo
fbshipit-source-id: 48bb911ce75120a8c9e0c6fb65262ef775dfba93
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30802
Change shape_hints from map<string, TensorShape> to ShapeInfoMap to catch dimType info from model file.
Reviewed By: ipiszy
Differential Revision: D18821486
fbshipit-source-id: c5d9ed72e158d3698aba38900aeda00f776745b4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23848
Problem:
In experiment running feed model 127607201 (/mnt/public/tracelog/feed_repro2/127607201_0.predictor), encountered blob dimensionality mismatch error when running onnxified net. This is due to the model initializing input blobs in current workspace with blob size 0, and onnxifi() falsely identified those input blobs as weight blobs and assigned wrong dimension.
Solution:
Add option to pass correct weight blob names to onnxifi() instead of using all blobs in current workspace.
Reviewed By: yinghai
Differential Revision: D16661396
fbshipit-source-id: cabe44db6b64e6538bef4b65e380312214b3ba9f
Summary:
As part of the Variable/Tensor merge, we want to be able to pass Variables into Caffe2 without doing extra shallow copy, to improve performance and also allow for in-place mutations in Caffe2 ops. There are a few approaches outlined in https://github.com/pytorch/pytorch/pull/22418, and this PR is the chosen approach.
Specifically, we can have the assumption that we won't be connecting autograd to C2 gradients at any point (as it's too tricky and not that useful). Therefore, we can pass Variable into Caffe2 ops by requiring that all Variables in Caffe2 don't require grad. For code paths in Caffe2 that might potentially track gradients (e.g. `ScriptModuleOp` and `call_caffe2_op_from_c10`), we use the `torch::NoGradGuard` to make sure gradients are not tracked.
This supersedes https://github.com/pytorch/pytorch/pull/22418.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22473
Differential Revision: D16099042
Pulled By: yf225
fbshipit-source-id: 57efc3c7cfb3048d9abe90e63759acc14ebd2972
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21718
adding a detection method on whether the package is built for AMD.
Reviewed By: bddppq
Differential Revision: D15795893
fbshipit-source-id: 91a21ee76b2273b1032507bdebe57e016717181d
Summary:
Resubmit #20698 which got messed up.
Idea is that when PyTorch is used in a custom build environment (e.g. Facebook), it's useful to track usage of various APIs centrally. This PR introduces a simple very lightweight mechanism to do so - only first invocation of a trigger point would be logged. This is significantly more lightweight than #18235 and thus we can allow to put logging in e.g. TensorImpl.
Also adds an initial list of trigger points. Trigger points are added in such a way that no static initialization triggers them, i.e. just linking with libtorch.so will not cause any logging. Further suggestions of what to log are welcomed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20745
Differential Revision: D15429196
Pulled By: dzhulgakov
fbshipit-source-id: a5e41a709a65b7ebccc6b95f93854e583cf20aca
Summary:
As part of the Variable/Tensor merge work: https://github.com/pytorch/pytorch/issues/13638, we make the following changes in this PR:
1. Remove the `Variable::Impl` class and the `DifferentiableViewImpl` class
2. Change all `Variable.data()` call sites to either use `Variable` directly, or use `Variable.tensor_data()`
3. Remove `Variable.data()` API
3. Add `Variable.variable_data()` that matches `tensor.data` in Python API, which creates a new `Variable` that shares the same storage and tensor metadata with the original `Variable`, but with a completely new autograd history.
After this PR, Variable doesn't wrap a Tensor internally anymore, and both Variable and Tensor use the same TensorImpl class as its `impl_`. The only difference is that Variable always has AutogradMeta in its TensorImpl, but Tensor doesn't.
**Note that this PR is BC-breaking in the following use cases:**
**Use Case 1:**
Previously, `x.data = y` works even if `x` and `y` are of different TensorImpl type (e.g. `x` is a CPU dense tensor whose impl is of type TensorImpl, while `y` is a CPU sparse tensor whose impl is of type SparseTensorImpl). However, after this PR, `x.data = y` doesn't work anymore if `x` and `y` are of different TensorImpl type, because the underlying implementation `variable.set_data(tensor)` no longer works if `variable` and `tensor` have different TensorImpl type.
**Use Case 2:**
If a tensor `x`'s `grad` is sparse, accumulating dense gradients to `x` will change the tensor that `x.grad` is pointing to. This is better illustrated with the following example:
```python
params = torch.tensor([1.5, 1.5]).requires_grad_()
with torch.no_grad():
# Change gradient to a sparse tensor
params.grad = torch.sparse_coo_tensor(torch.tensor([[1, 1]]).long(), torch.tensor([1., 1.]))
grad_saved = params.grad
params.backward(torch.tensor([1.5, 1.5]))
assert id(grad_saved) == id(params.grad) # This will fail after this PR
```
The assertion in the last line will fail after this PR, because adding dense gradients to sparse gradients will change the `params.grad` tensor reference.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17072
Differential Revision: D14075257
Pulled By: yf225
fbshipit-source-id: 0e681df641270dea586042dd26db59f2e76b5957
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20108
Add cpp runs for c2, hooked up via pybinds. Print output to terminal. This is not hooked up with the pep output yet because I'd like to verify the numbers first.
Note that this isn't quite the same mechanism as the pytorch cpp hookup, which uses cpp_python_extensions. If I can use the same mechanism to pull all the inputs for c2 through cpp and do FeedBlobs in cpp, then I'll switch to that.
Reviewed By: zheng-xq
Differential Revision: D15155976
fbshipit-source-id: 708079dacd3e19aacfe43d70c5e5bc54da2cf9e3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19442
For cases like CV, some of ops like transpose and tile will mangle the batch size so that we don't know how to adjust output batch size. In this case, the current solution is just fix the input batch statically and do not adjust output batch size.
Reviewed By: zrphercule
Differential Revision: D15007237
fbshipit-source-id: a21b943a52ee5462d9d7804dfae44360f579f8cf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18716
Might be useful as an intermediate stage for some systems that currently use Caffe2 nets as an execution mechanism.
Not sure it's a good idea all together, please comment.
Limitations:
- only Tensor types as inputs/outputs
- the entire module is serialized as a zip archive inside a proto in Caffe2 db, it'd be subject to 4Gb limit and is likely very slow. For small models it'd work though.
- no autograd, though it can be attached in principle
- no way to retrieve parameters inside the script module from C2 runtime perspective (though they potentially can be alias-fetched and stored as individual blobs)
- after deserialization, python wrappers returned don't have correct type (as we don't do module_lookup trick)
Build-wise, I had to add dependency from pybind_state to libtorch.so. I don't think we build Caffe2 python frontend independently anymore, so it should be fine.
Reviewed By: amirshim, houseroad
Differential Revision: D14339599
fbshipit-source-id: 88a37a8abd1f1c4703e5ef937031f222535d4080
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18740
Test utilities for writing Caffe2/PyTorch performance microbenchmarks. Brief description of the file structure
* benchmark_core.py : core utiltiites for running microbenchmark tests
* benchmark_caffe2.py : Caffe2 specific benchmark utilitites
* benchmark_pytorch.py: PyTorch specific benchmark utilities
* benchmark_runner.py : Main function. Currently it can run the microbenchmark tests in a stand-alone mode. The next step is to have this integrate with AI-PEP.
The utilities are located at https://github.com/pytorch/pytorch/tree/master/test to have access to both Caffe2/PyTorch Python's frontend.
Include two operator microbenchmarks; support both Caffe2/PyTorch:
* MatMul
* Add
Reference: PyTorch benchmarks : https://github.com/pytorch/benchmark/tree/master/timing/python. In this work, we start with two example binary operators MatMul and Add, but eventually we should to cover unary operators like in the PyTorch benchmark repo.
Reviewed By: zheng-xq
Differential Revision: D13887111
fbshipit-source-id: b7a56b95448c9ec3e674b0de0ffb96af4439bfce
Summary:
For MKL-DNN,the filter data will be reorderd to primitive format, it takes a lot of time.
So the patch provide a method to convert filter format before training.
And "OptimizeForIdeep" will be changed to "OptimizeForMkldnn" in this patch.
This patch depends on https://github.com/pytorch/pytorch/pull/12866
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15171
Differential Revision: D14590741
Pulled By: yinghai
fbshipit-source-id: 07971c9977edac3c8eec08ca2c39cda639683492
Summary:
Because of two separate python extensions with different pybind
instances I have to go through void* conversion. Since it's hidden from
user, it's fine.
New APIs added on C2 side:
- workspace.FetchTorch('blob')
- workspace.Workspace.current.blobs['blob'].to_torch()
- workspace.FeedBlob('blob', pytorch_tensor)
Works on CPU an GPU.
The only glitches are with resizing because of variable/tensor split.
But data sharing works properly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17190
Reviewed By: ezyang
Differential Revision: D14163882
Pulled By: dzhulgakov
fbshipit-source-id: d18e5b8fcae026f393c842a1149e972515732de2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17158
Because of Reshape op, batch size can be changed. This diff addresses first order issue raised from multiple batch size system. We need to export different real_batch_size for different max_batch_size input and attach it to the right output.
It also fixes a false exception.
Reviewed By: ipiszy
Differential Revision: D14099541
fbshipit-source-id: 0fa9e86826f417a11d2b5dd2ee60dff64a7ce8c4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17074
There are some common functionalities in backend lowering. This diff creates a base class which hosts these common stuff.
Reviewed By: ipiszy
Differential Revision: D14073192
fbshipit-source-id: 9617603d0e73db6f7fcc5572756b9dbab506dae5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17046
As we are moving to use bound shape inference, we can remove the awkward fake inference run path and make the code cleaner.
Reviewed By: ipiszy
Differential Revision: D14061501
fbshipit-source-id: b3ace98b3dabef3c3359086a0bb1410518cefa26
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16932
During onnxifi transformation net ssa is rewritten. At the last step the weight
names are changed back to what they were before. The diff keeps the weight
names unchanged thru the process.
Reviewed By: yinghai
Differential Revision: D13972597
fbshipit-source-id: 7c29857f788a674edf625c073b345f2b44267b33
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15453
Just move things around to facilitate further development. No logic change.
Reviewed By: rdzhabarov
Differential Revision: D13533959
fbshipit-source-id: eebab1306939e802aacffb24a711d372fd67916c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14196
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13641
FeedTensor function used to take a pointer to Tensor and feed the content using Resize
and mutable_data, but since Tensor is a pointer now, we can just return a Tensor instead.
Reviewed By: dzhulgakov
Differential Revision: D13091163
fbshipit-source-id: 9abf2fd320baca76e050530c500dd29f8e2d0211
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13641
FeedTensor function used to take a pointer to Tensor and feed the content using Resize
and mutable_data, but since Tensor is a pointer now, we can just return a Tensor instead.
Reviewed By: ezyang
Differential Revision: D12873145
fbshipit-source-id: 653735c20d611ff6ac9e380d8b3c721cb396a28f
Summary: Made the clangr rule more robust and it discovered more callsites.
Reviewed By: smessmer
Differential Revision: D12825017
fbshipit-source-id: 3be1eeb7ea697b36ef89e78ba64c0ee1259439c4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12848
Updated all non-test uses of protobuf::MessageLite::SerializeAsString to call
SerializeAsString_EnforceCheck so that the return value is checked and can
throw an exception if failing.
Most of the affected code was called from classes derived from BlobSerializeBase.
Didn't touch most tests and ENFORCE calls because they usually do checks
anyway.
Original commit changeset: c0760e73ecc7
Reviewed By: dzhulgakov
Differential Revision: D10453456
fbshipit-source-id: d2f2b7b4578e721924354149f08f627c7e3bf070
