Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43088
Create quantized module that the user can use to perform embedding bag quantization
The module uses the EmbeddingPackedParams to store the weights which can be serialized /deserialized
using TorchBind custom classes (C++ get/setstate code)
Following PR will add support for `from_float` to convert from float to quantized module
Test Plan:
python test/test_quantization.py TestDynamicQuantizedModule.test_embedding_bag_api
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23167519
fbshipit-source-id: 029d7bb44debf78c4ef08bfebf267580ed94d033
Summary:
Decouple DataParallel/DistributedDataParallel from CUDA to support more device types.
- Move torch/cuda/comm.py to torch/nn/parallel/comm.py with minor changes for common devices support. Torch.cuda.comm is kept as is for backward compatibility
- Provide common APIs to arbitrary device types without changing existing CUDA APIs in torch.cuda space.
- Replace the torch.cuda calls in DataParellel/DistributedDataParallel with the new APIs.
Related RFC: [https://github.com/pytorch/pytorch/issues/36160](https://github.com/pytorch/pytorch/issues/36160)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38454
Differential Revision: D22051557
Pulled By: mrshenli
fbshipit-source-id: 7842dad0e5d3ca0f6fb760bda49182dcf6653af8
Summary:
The quantizer use std::vector to save per_channel scales and zero_points, but when query scales(zero_points), it requires to return tensor. These lead to use std::vector to initialize tensors and it dose cost lots of time. So I change quantizer to save per_channel scales and zero_points by using tensor directly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31040
Differential Revision: D19701070
Pulled By: jerryzh168
fbshipit-source-id: 9043f16c44b74dd8289b8474e540171765a7f92a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26675
Based on offline poll, we're very unlikely to have multi-axis quantized tensors in the foreseeable future. Let's simplify API and just return int instead of list. It also matches the singular `axis` name.
Test Plan: Imported from OSS
Differential Revision: D17537052
Pulled By: dzhulgakov
fbshipit-source-id: 676abc3b251d288468aaed467b5e5ca4063b98b0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26243
This is an attempt to fix _empty_per_channel_affine_quantized to be more sane. It's a factory function that nevertheless receives a Tensor argument and it throws the codegen off course.
Before people did a hacky workaround of appending _like to the function name to trick codegen, it also required non-natural argument order.
This PR explicitly allows to override the 'category' of the function to make codegen do the right thing. Now name and the argument order (in C++) make more sense.
Test Plan: Imported from OSS
Differential Revision: D17443221
Pulled By: dzhulgakov
fbshipit-source-id: c98c1c74473d8cbf637f511d26ceb949d8ae2a1a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26339
Serializes per-channel tensor in both torch.serialization and jit. Since we didn't bind Quantizer properly yet, I chose to save a tuple representing quantizer settings. To avoid recursive tensor serialization calls, I'm using tuple instead of tensor to store scales and zero points.
driazati - please check the serialization logic. Is there a good test that compares that JIT serialization and python serialization are equivalent? (I haven't tested it yet)
Test Plan: Imported from OSS
Differential Revision: D17443222
Pulled By: dzhulgakov
fbshipit-source-id: a34758de1ffd2ec1cdc5355f5baf95284a4ccf4b
Summary:
When an exception occurs in one of the modules passed to `parallel_apply()`, it is caught and re-raised in the main thread. This preserves the original exception type and message, but has the traceback point at the position where it's re-raised, rather than the original point of failure.
This PR saves the exception information required to generate the traceback, and includes the original traceback in the message of the exception raised in the main thread.
Before:
```
...
File ".../torch/nn/parallel/data_parallel.py", line 153, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File ".../torch/nn/parallel/parallel_apply.py", line 84, in parallel_apply
raise output
RuntimeError: expected type torch.FloatTensor but got torch.cuda.FloatTensor
```
After:
```
...
File ".../torch/nn/parallel/data_parallel.py", line 153, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File ".../torch/nn/parallel/parallel_apply.py", line 88, in parallel_apply
''.join(traceback.format_exception(*exc_info)))
RuntimeError: Caught exception in replica 0. Original traceback and message:
Traceback (most recent call last):
...
File "../models/foo.py", line 319, in bar
baz = asdf / ghij[:, np.newaxis]
RuntimeError: expected type torch.FloatTensor but got torch.cuda.FloatTensor
```
I took care to raise an exception of the original type (in case the main code checks for that), but replaced the message. It helped me find a bug that did not occur outside `data_parallel()`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18055
Differential Revision: D16444972
Pulled By: zhangguanheng66
fbshipit-source-id: ec436c9d4677fad18106a8046cfa835a20a101ce
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18750
ghimport-source-id: f1475cfb67841c41d9867d4429ba9125d5c7dd07
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18751 Disallow changing the device of a tensor via set_.
* **#18750 Use non-legacy constructors for tensor deserialization.**
* #18749 Add device and dtype to storage.
Deserialization currently uses legacy constructors. This is bad because we need to maintain them, but there is a more immediate problem:
1) We are trying to implement device caching on TensorImpl to get rid of a virtual dispatch
2) This doesn't work if one is able to change the device of a Tensor underlying a Variable.
3) Deserialization does 2)
So the plan is to change deserialization, then enforce that we don't change the device out from underneath a Variable.
Differential Revision: D14729513
fbshipit-source-id: 090d6cdb375b94dc1bf4f554b2df243952b8cdc6
Summary:
We have:
- This is an initial stab at creating a type stub `torch/__init__.pyi` .
- This is only tested on Python 3, since that's the only Python version mypy
works on.
- So far, we only aim at doing this for torch functions and torch.Tensor.
- Quite a few methods and functions have to be typed manually. These are
done in `torch/__init__.pyi.in`
For me, PyCharm (the non-paid one) didn't seem to indicate errors in the .pyi when opening and seemed to be able to get the type hint for the few functions I tried, but I don't use PyCharm for my usual PyTorch activities, so I didn't extensively try this out.
An example of a generated PYI is at [this gist](https://gist.github.com/ezyang/bf9b6a5fa8827c52152858169bcb61b1).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12500
Differential Revision: D13695553
Pulled By: ezyang
fbshipit-source-id: 4566c71913ede4e4c23ebc4a72c17151f94e8e21
Summary:
This PR cleans up the `at::Tensor` class by removing all methods that start with an underscore in favor of functions in the `at::` namespace. This greatly cleans up the `Tensor` class and makes it clearer what is the public and non-public API.
For this I changed `native_functions.yaml` and `Declarations.cwrap` to make all underscore methods `variant: function` (or add such a statement to begin with), and then fixed all code locations using the underscore methods.
ezyang colesbury gchanan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11152
Differential Revision: D9683607
Pulled By: goldsborough
fbshipit-source-id: 97f869f788fa56639c05a439e2a33be49f10f543
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.
To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.
There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:
https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
The Tensor and Variable classes are being merged.
autograd.Function.forward is now called on Variables, but with "no-grad"
mode (torch.no_grad()) enabled.
One benefit is that we no longer have to explicitly track shared
storages.
* Replace async with non_blocking for Python 3.7 upgrade
* Remove trailing whitespace
* Give _cuda and _type kwargs and accept async for compatibility
* Rename async to non_blocking in all C++ code
* Add entries for async in python_variable_methods
* Friendlier backward compatibility for cuda and type
This moves the implementation of repeat to _utils so that the autograd
function can call it directly instead of relying on forward being called
on tensors.
This also removes _range, which was previously necessary because we
shadowed the built-in range() function.
As discussed in #1441.
I also added some docs giving clear guidance about how to coalescing
in sparse tensors.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
