Summary:
Changelog:
- Optimize btriunpack by using `torch.where` instead of indexing, inplace operations instead of out place operations and avoiding costly permutations by computing the final permutation over a list.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15286
Differential Revision: D13562038
Pulled By: soumith
fbshipit-source-id: e2c94cfab5322bf1d24bf56d7b056619f553acc6
Summary:
This PR removes the TH/THC binding for gesv.
Changelog:
- Remove TH/THC binding
- Port single matrix case to ATen
- Enable test_gesv for CUDA as well
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15510
Differential Revision: D13559990
Pulled By: soumith
fbshipit-source-id: 9da2825e94d3103627e719709e6b1f8b521a07fb
Summary:
Currently torch.isinf on integral tensor will raise RuntimeError: value cannot be converted to type int16_t without overflow: inf.
This pr will suppress the error and return false(0) for all integral tensors. The behavior will also be consistent with np.isinf
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15489
Reviewed By: zou3519
Differential Revision: D13540786
Pulled By: flashhack
fbshipit-source-id: e730dea849da6a59f3752d347bcfbadfd12c6483
Summary:
Followup PR of #14904, and the stretch goal of #12653.
Directly calculate coordinates in the original tensor using column index in the result tensor. Every GPU thread takes care of a column (two numbers) in the output tensor.
The implementation detects and handles precision loss during calculating the square root of a `int64_t` variable, and supports tensors with up to `row * column = 2 ^ 59` numbers.
Algorithm details are describe in [comments of TensorFactories.cu](23ddb6f58a/aten/src/ATen/native/cuda/TensorFactories.cu (L109-L255)).
zou3519
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15203
Reviewed By: zou3519
Differential Revision: D13517695
Pulled By: mrshenli
fbshipit-source-id: 86b305d22cac08c8962a3b0cf8e9e620b7ec33ea
Summary:
Changelog:
- Renames `potrs` to `cholesky_solve` to remain consistent with Tensorflow and Scipy (not really, they call their function chol_solve)
- Default argument for upper in cholesky_solve is False. This will allow a seamless interface between `cholesky` and `cholesky_solve`, since the `upper` argument in both function are the same.
- Rename all tests
- Create a tentative alias for `cholesky_solve` under the name `potrs`, and add deprecated warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15334
Differential Revision: D13507724
Pulled By: soumith
fbshipit-source-id: b826996541e49d2e2bcd061b72a38c39450c76d0
Summary:
…on](#12115)
mean is calculated in two step sum()/numel(). For half precision, data gets
casted back to half after sum().
We fused the division into the reduction kernel by adding pre_op/post_op.
This allows us to do torch.ones(65536).cuda().half().mean() to return correct
result.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14878
Differential Revision: D13491159
Pulled By: soumith
fbshipit-source-id: e83802e1628b6d2615c45e18d7acf991d143a09e
Summary:
tests work on ROCm 1.9.2 as present on CI (fp16 bringup, hipMemset and sparse improvements)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15232
Differential Revision: D13470991
Pulled By: bddppq
fbshipit-source-id: 45acc4f9ea5baaaf7672b86eb022948055779925
Summary:
This is an optimized implementation that does the following:
1. created an empty Tensor of correct size.
2. fill the Tensor with correct values.
The following three designs to fill in the Tensor result in roughly the same performance. Hence, the 2nd option is taken for simpler code, and to return contiguous tensors.
1. Sequential: fill row coordinates first, then columns. This results in two for-loop and more arithmetic operations.
2. Interleaved: fill in index coordinates one by one, which jumps between the two output Tensor rows in every iteration.
3. Transpose: create a n X 2 Tensor, fill the Tensor sequentially, and then transpose it.
<img width="352" alt="screen shot 2018-12-10 at 3 54 39 pm" src="https://user-images.githubusercontent.com/16999635/49769172-07bd3580-fc94-11e8-8164-41839185e9f9.png">
NOTE:
This implementation returns a 2D tensor, instead of a tuple of two tensors. It means that users will not be able to do the following:
```python
x = torch.ones(3, 3)
i = torch.tril_indices(3, 3)
x[i] # need to first convert the 2D tensor into a tuple of two 1D tensors.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14904
Reviewed By: zou3519
Differential Revision: D13433027
Pulled By: mrshenli
fbshipit-source-id: 41c876aafcf584832d7069f7c5929ffb59e0ae6a
Summary:
* Enable unit tests known to work on ROCm.
* Disable a few that are known to be flaky for the time being.
* Use std::abs for Half
* No more special casing for ROCm in TensorMathReduce
* Document an important detail for a hardcoded block size w.r.t. ROCm in TensorMathReduce
ezyang bddppq for awareness
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14011
Differential Revision: D13387679
Pulled By: bddppq
fbshipit-source-id: 4177f2a57b09d866ccbb82a24318f273e3292f71
Summary:
Removes cast of half to float in torch.sum, with float16 input tensor and
float32 output tensor, instead we cast data when loading input in kernel.
This supposingly would save a kernel launch as well as a full global memory load
on promoted data type (float).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14580
Differential Revision: D13356203
Pulled By: ezyang
fbshipit-source-id: 85e91225b880a65fe3ceb493371b9b36407fdf48
Summary:
Fixes https://github.com/pytorch/pytorch/issues/14673
As pointed out by vishwakftw , the root case of the `deepcopy` issue was that `storage.clone()` would create a new storage in the default device.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14751
Reviewed By: soumith
Differential Revision: D13323061
Pulled By: fmassa
fbshipit-source-id: bfe46ebd78f0b6cd9518c11d09de7849282ed2a2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13937
We can now replace s_copy_ with our new _copy_ function. Experimented with moving s_copy_ out of VariableManualType.cpp, but seemed like there was enough special casing to warrant it staying.
Reviewed By: ezyang
Differential Revision: D13053648
fbshipit-source-id: e9e04d460baf4ee49b500212cf91b95221acd769
Summary:
This speeds-up "advanced" indexing (indexing a tensor by a tensor)
on CPU and GPU. There's still a bunch of work to do, including
speeding up indexing by a byte (boolean) mask and speeding up the derivative
calculation for advanced indexing.
Here's some speed comparisons to indexing on master using a little [benchmark script](https://gist.github.com/colesbury/c369db72aad594e5e032c8fda557d909) with 16 OpenMP threads and on a P100. The test cases are listed as (input shape -> output shape).
| Test case | CPU (old vs. new) | CUDA (old vs. new) |
|-----------------------|---------------------|------------------------|
| 1024x1024 -> 512x1024 | 225 us vs. **57 us** | 297 us vs. **47 us** |
| 1024x1024 -> 1024x512 | 208 us vs. **153 us** | 335 us vs. **54 us** |
| 50x50 -> 20000x50 | 617 us vs. **77 us** | 239 us vs. **54 us** |
| 50x50 -> 50x20000 | 575 us vs. **236 us** | 262 us vs. **58 us** |
| 2x5x10 -> 10 | 65 us vs. **18 us** | 612 us vs. **93 us** |
See #11647
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13420
Reviewed By: soumith
Differential Revision: D13088936
Pulled By: colesbury
fbshipit-source-id: 0a5c2ee9aa54e15f96d06692d1694c3b24b924e2
Summary:
Implements batching for the Cholesky decomposition.
Performance could be improved with a dedicated batched `tril` and `triu` op, which is also impeding autograd operations.
Changes made:
- batching code
- tests in `test_torch.py`, `test_cuda.py` and `test_autograd.py`.
- doc string modification
- autograd modification
- removal of `_batch_potrf` in `MultivariateNormal`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14017
Differential Revision: D13087945
Pulled By: ezyang
fbshipit-source-id: 2386db887140295475ffc247742d5e9562a42f6e
Summary:
The size of the shared and global memory buffers were incorrect for float16.
They were sized based on float16 elements, but the buffers store intermediate
float32 values.
Fixes#13909
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13926
Differential Revision: D13048334
Pulled By: colesbury
fbshipit-source-id: 5a07df53f1152d5920258e91ed3f1e1de89b29e1
Summary:
torch.randn(big_number_here, dtype=torch.int8) is wrong because randn
isn't implemented for torch.int8. I've changed it to use torch.empty
instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13864
Differential Revision: D13032130
Pulled By: zou3519
fbshipit-source-id: d157b651b47b8bd736f3895cc242f07de4c1ea12
Summary:
This enables the distributions and utils test sets for ROCm.
Individual tests are enabled that now pass due to fixes in HIP/HCC/libraries versions in white rabbit.
For attention: bddppq ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13166
Differential Revision: D12814759
Pulled By: bddppq
fbshipit-source-id: ea70e775c707d7a8d2776fede6154a755adef43e
Summary:
- This is a straightforward PR, building up on the batch inverse PR, except for one change:
- The GENERATE_LINALG_HELPER_n_ARGS macro has been removed, since it is not very general and the resulting code is actually not very copy-pasty.
Billing of changes:
- Add batching for `potrs`
- Add relevant tests
- Modify doc string
Minor changes:
- Remove `_gesv_single`, `_getri_single` from `aten_interned_strings.h`.
- Add test for CUDA `potrs` (2D Tensor op)
- Move the batched shape checking to `LinearAlgebraUtils.h`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13453
Reviewed By: soumith
Differential Revision: D12942039
Pulled By: zou3519
fbshipit-source-id: 1b8007f00218e61593fc415865b51c1dac0b6a35
Summary:
```
The new error message now looks like (from Python):
RuntimeError: CUDA out of memory. Tried to allocate 16.00 GiB (GPU 0; 11.93 GiB total capacity; 4.00 GiB already allocated; 7.33 GiB free; 179.00 KiB cached)
Summary of terms:
"total capacity": total global memory on GPU
"already allocated": memory allocated by the program using the
caching allocator
"free": free memory as reported by the CUDA API
"cached": memory held by the allocator but not used by the program
The "allocated" amount does not include memory allocated outside
of the caching allocator, such as memory allocated by other programs
or memory held by the driver.
The sum of "allocated" + "free" + "cached" may be less than the
total capacity due to memory held by the driver and usage by other
programs.
Note that at this point cuda_malloc_retry has already returned all
possible "cached" memory to the driver. The only remaining "cached"
memory is split from a larger block that is partially in-use.
```
This also fixes an issue where on out-of-memory could cause an unrelated subsequent CUDA kernel launch to fail because `cudaGetLastError()` was not cleared.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13751
Differential Revision: D13007177
Pulled By: colesbury
fbshipit-source-id: ea7121461b3f2a34646102959b45bde19f2fabab
Summary:
In `broadcast_coalesced`, since multiple variables can be "views" of a big flattened tensor, they can share the same version counter. However, this base flat tensor is not exposed and they don't share any memory locations, so this is not necessary. Furthermore, it can cause problems, e.g., when two buffers are broadcast together in `DataParallel` and one of them is modified in-place during `forward` but the other is needed in backward, autograd engine will complain.
Fixing the bug discovered at https://github.com/pytorch/pytorch/pull/13350#issuecomment-436011370
edit: This is a very real problem. E.g., consider using Spectral Norm + Batch Norm together.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13594
Differential Revision: D12967311
Pulled By: SsnL
fbshipit-source-id: 52998dbabe149f575cf0fb79e7016f0b95e4b9e5
Summary:
Fixes#13326
Also now you can use `run_test.py` with `pytest`. E.g.,
```
python run_test.py -vci distributed -pt
```
Yes it works with `distributed` and `cpp_extension`.
cc zou3519 vishwakftw
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13416
Differential Revision: D12895622
Pulled By: SsnL
fbshipit-source-id: 2d18106f3a118d642a666bfb1318f41c859c3df7
Summary:
Since it fails due to insufficient precision for DoubleTensor .sum() on ROCm
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13341
Differential Revision: D12851335
Pulled By: bddppq
fbshipit-source-id: e211c3868b685aa705160ce98a2a18a915ad493f
Summary:
1. Refactors `TestTorch` into `TestTorchMixin` (subclass of `object`) and `TestTorch` (subclass of `TestCase`, MRO `(TestCase, TestTorchMixin)`, only defined if `__name__ == '__main__'`). So other scripts won't accidentally run it.
2. Adds an assertion in `load_tests` that each script only runs cases defined in itself.
cc yf225 ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13250
Differential Revision: D12823734
Pulled By: SsnL
fbshipit-source-id: 7a169f35fe0794ce76e310d8a137d9a3265c012b
Summary:
Reductions that used global memory, but didn't reduce
across threads in a warp did not have enough global memory
allocated for their intermediate results. These reductions
that were non-contiguous in their reduced dimension and
large enough to benefit from reducing across blocks in a
grid.
Fixes#13209
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13211
Differential Revision: D12815772
Pulled By: colesbury
fbshipit-source-id: f78be2cb302e7567a76097ca3ba1e7b801c0cdad
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12794
common.py is used in base_module for almost all tests in test/. The
name of this file is so common that can easily conflict with other dependencies
if they happen to have another common.py in the base module. Rename the file to
avoid conflict.
Reviewed By: orionr
Differential Revision: D10438204
fbshipit-source-id: 6a996c14980722330be0a9fd3a54c20af4b3d380
Summary:
Fixes: #12669
Thank you Changmao Cheng for reporting this on the forum with a small example!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12677
Differential Revision: D10391989
Pulled By: ezyang
fbshipit-source-id: 5aa7a705bdb8ce6511a8eb1b3a207f22741046bf
Summary:
- This was one of the few functions left out from the list of functions in
NumPy's `linalg` module
- `multi_mm` is particularly useful for DL research, for quick analysis of
deep linear networks
- Added tests and doc string
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12380
Differential Revision: D10357136
Pulled By: SsnL
fbshipit-source-id: 52b44fa18d6409bdeb76cbbb164fe4e88224458e
Summary:
Fixes#12260#2896
```
torch.multinomial(torch.FloatTensor([0, 1, 0, 0]), 3, replacement=False)
```
The old behavior is that we return `0` after we run out of postive categories. Now we raise an error based on discussion in the issue thread.
- Add testcase for cpu & cuda case, in cuda case `n_samples=1` is a simple special case, so we test against `n_sample=2` instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12490
Differential Revision: D10278794
Pulled By: ailzhang
fbshipit-source-id: d04de7a60f60d0c0d648b975db3f3961fcf42db1
Summary:
* Enable more tests that relied on CPU LAPACK at compile time.
* enabled min/max tests in test_cuda (ROCm 236)
bddppq ezyang
Tests ran as part of the ROCm CI here: https://github.com/ROCmSoftwarePlatform/pytorch/pull/255
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12486
Differential Revision: D10262534
Pulled By: ezyang
fbshipit-source-id: 167a06fc8232af006f4b33dcc625815fd4b06d6b
Summary:
The gpu_unary_kernel function was not handling arrays that
cannot use 32-bit indexing. This functions was only called directly
by CUDA division by a scalar. Other arithmetic operations go through
gpu_binary_kernel, which already properly handled large arrays.
This bug sometimes manifested as a crash and sometimes as an incorrect
answer.
Fixes#11788
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12023
Differential Revision: D10034017
Pulled By: colesbury
fbshipit-source-id: b17300f327de54035746bf02f576766007c9b144
Summary:
Changes the result type of half type and any integer type to return half
type (instead of float or double).
This is based on top of #11808. The first new commit is "Make promoteType(half, integer) -> half". I'll rebase on top of master once that PR lands.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11941
Differential Revision: D10014122
Pulled By: colesbury
fbshipit-source-id: 16a5eb3406a5712069201d872d8736d0599e9411
Summary:
Previously, we didn't cast any 0-dim tensors used in CUDA operations. We
can only avoid the casts for 0-dim CPU tensors used in CUDA operations.
Fixes#11795
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11808
Differential Revision: D9922406
Pulled By: colesbury
fbshipit-source-id: 940b8a8534770aa5cd70d5d09b96be0f0f8146ff
Summary:
We do this by being more NaN tolerant.
Fixes: #9062
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11933
Differential Revision: D9991129
Pulled By: soumith
fbshipit-source-id: c99b04462c1bee90d00eeabb0c111de12f855f4d
Summary:
+ https://github.com/pytorch/pytorch/issues/10236 : torch.bernoulli's out kwarg is broken
fixed in moving `bernoulli_out` to ATen
+ https://github.com/pytorch/pytorch/issues/9917 : BUG torch.bernoulli(p.expand(shape)) is broken
fixed in moving all `bernoulli` ops in ATen to use the modern apply utils methods
+ https://github.com/pytorch/pytorch/issues/10357 : torch.bernoulli inconsistent gpu/cpu results
fixed by adding CUDA asserts
In order to use `curand_uniform4`, I made some changes to `CUDAApplyUtils.cuh`. Specifically, I introduced an optional template parameter `int step` to the `CUDA_tensor_applyN` methods, representing that we want to process `step` values at each time for each of the `N` tensors.
The calling convention for `step = 1` (default) isn't changed. But if `step > 1`, the given lambda `op` must take in `int n` as its first argument, representing the number of valid values, because there may not be full `step` values at the boundary. E.g., here is what the `bernoulli(self, p_tensor)` call look like:
```cpp
// The template argument `4` below indicates that we want to operate on four
// element at each time. See NOTE [ CUDA_tensor_applyN helpers ] for details.
at::cuda::CUDA_tensor_apply2<scalar_t, prob_t, 4>(
ret, p,
[seeds] __device__(
int n, scalar_t& v1, scalar_t& v2, scalar_t& v3, scalar_t& v4,
const prob_t& p1, const prob_t& p2, const prob_t& p3, const prob_t& p4) {
curandStatePhilox4_32_10_t state;
curand_init(
seeds.first,
blockIdx.x * blockDim.x + threadIdx.x,
seeds.second,
&state);
float4 rand = curand_uniform4(&state);
switch (n) {
case 4: {
assert(0 <= p4 && p4 <= 1);
v4 = static_cast<scalar_t>(rand.w <= p4);
}
case 3: {
assert(0 <= p3 && p3 <= 1);
v3 = static_cast<scalar_t>(rand.z <= p3);
}
case 2: {
assert(0 <= p2 && p2 <= 1);
v2 = static_cast<scalar_t>(rand.y <= p2);
}
case 1: {
assert(0 <= p1 && p1 <= 1);
v1 = static_cast<scalar_t>(rand.x <= p1);
}
}
}
);
```
Benchmarking on `torch.rand(200, 300, 400)` 20 times, each time with 20 loops:
post patch
```
➜ ~ numactl --cpunodebind 1 --membind 1 -- taskset -c 12,13,14,15,16,17,18,19,20,21,22,23 env CUDA_LAUNCH_BLOCKING=1 python bern.py
torch.bernoulli(x)
6.841588497161865 +- 0.05413117632269859
torch.bernoulli(xc)
0.05963418632745743 +- 0.0008014909108169377
x.bernoulli_()
0.4024486541748047 +- 0.0021550932433456182
xc.bernoulli_()
0.02167394384741783 +- 2.3818030967959203e-05
```
pre-patch
```
➜ ~ numactl --cpunodebind 1 --membind 1 -- taskset -c 12,13,14,15,16,17,18,19,20,21,22,23 env CUDA_LAUNCH_BLOCKING=1 python bern.py
torch.bernoulli(x)
12.394511222839355 +- 0.0966421514749527
torch.bernoulli(xc)
0.08970972150564194 +- 0.0038722590543329716
x.bernoulli_()
1.654480218887329 +- 0.02364428900182247
xc.bernoulli_()
0.058352887630462646 +- 0.003094920190051198
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10273
Differential Revision: D9831294
Pulled By: SsnL
fbshipit-source-id: 65e0655a36b90d5278b675d35cb5327751604088
Summary:
- Incorporates MKL addition by mingfeima Thank you! (but all errors are my own)
- Native CPU implementation: defer to matrix multiplication for
small batches and parallelize over batch dimension for large
batches.
- Add bmm test for CUDA just to be sure.
This is a partial fix for #10661, getting down to a factor ~5.
Considerable overhead is incurred for the setup in einsum. It might
be more efficient to eventually define an optimized contraction
functions for arbitrary and several dimensions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11292
Differential Revision: D9784941
Pulled By: ezyang
fbshipit-source-id: f6dded2c6f5e8f0461fb38f31f9a824992a58358
Summary:
vishwakftw Your patch needed some updates because the default native function dispatches changed from `[function, method]` to `[function]`. The CI was run before that change happened so it still shows green, but the internal test caught it.
I did some changes when rebasing and updating so I didn't just force push to your branch. Let's see if this passes CI and internal test. If it does, let me know if you want me to force push to your branch or use this PR instead.
Note to reviewers: patch was already approved at #10068 .
cc yf225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11421
Differential Revision: D9733407
Pulled By: SsnL
fbshipit-source-id: cf2ed293bb9942dcc5158934ff4def2f63252599
Summary:
Disables two of the unit tests in test_cuda that got introduced after test_cuda was enabled that fail on ROCm.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11191
Differential Revision: D9628702
Pulled By: ezyang
fbshipit-source-id: 4c298c728f42bb43d39b57967aa3e44385980265
Summary:
* improve docker packages (install OpenBLAS to have at-compile-time LAPACK functionality w/ optimizations for both Intel and AMD CPUs)
* integrate rocFFT (i.e., enable Fourier functionality)
* fix bugs in ROCm caused by wrong warp size
* enable more test sets, skip the tests that don't work on ROCm yet
* don't disable asserts any longer in hipification
* small improvements
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10893
Differential Revision: D9615053
Pulled By: ezyang
fbshipit-source-id: 864b4d27bf089421f7dfd8065e5017f9ea2f7b3b
Summary:
Fix#10345, which only happens in CUDA case.
* Instead of returning some random buffer, we fill it with zeros.
* update torch.symeig doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10645
Reviewed By: soumith
Differential Revision: D9395762
Pulled By: ailzhang
fbshipit-source-id: 0f3ed9bb6a919a9c1a4b8eb45188f65a68bfa9ba
Summary:
In the shortcut for n_sample=1, when category 0 has 0 weight,
we should not map the (uniform) sample 0 to category 0.
The conversion uniform->multinomial was apparently written to work on
a (0,1] range (like curand uses), but PyTorch uses a [0,1) range.
Fixes: #4858. Thank you, Roy Fejgin for reporting.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9960
Reviewed By: soumith
Differential Revision: D9341793
Pulled By: ailzhang
fbshipit-source-id: 6b1a96419a7bc58cc594f761f34c6408ff6354cf
Summary:
```
This adds TensorIterator, a helper class for computing element-wise
operations that's intended to replace the CPU and CUDA apply utils
functions.
CPU kernels are implemented as functions that operate on strided 1-d
tensors compared to CPUApplyUtils which operated individual elements. This
allows the kernels to handle vectorization, while TensorIterator handles
parallelization and non-coalesced dimensions.
GPU kernels continue to operate on elements, but the number of
specializations is reduced. The contiguous case remains the same. The
non-contiguous case uses a single (reduced) shape for all operands and
the fast integer division from THCIntegerDivider. To avoid extra
specializations for indexing with 64-bits, large operations are split
into smaller operations that can be indexed with 32-bits.
Major semantic changes:
- No more s_add, s_mul, s_div, or s_sub. Broadcasting is handled by
TensorIterator. The autograd engine performs the reduction assuming
standard broadcasting if the gradient shape does not match the
expected shape. Functions that do not use standard broadcasting rules
should either continue to trace the expand calls or handle the
reduction in their derivative formula.
- Use ONNX v7, which supports broadcasting ops.
Performance impact:
- Small increased fixed overhead (~0.5 us)
- Larger overhead for wrapped numbers (~2.5 us)
- No significant change for ops on contiguous tensors
- Much faster worst-case performance for non-contiguous GPU tensors
- Faster CPU bias addition (~2x)
- Faster GPU bias addition (~30% faster)
Future work:
- Decrease overhead, especially for wrapping numbers in Tensors
- Handle general inter-type operations
- Extend to unary ops and reductions
- Use buffering for compute-bound operations on non-contiguous tensors
(pull in from CPUApplyUtils)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/8919
Differential Revision: D8677600
Pulled By: colesbury
fbshipit-source-id: 61bc9cc2a36931dfd00eb7153501003fe0584afd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9497Fixes#7883 by using `rfft`.
It's worth noting that this is BC breaking. And it's impossible to detect the change because the two signatures before and after this change supports a common subset of calling patterns, e.g., `stft(Tensor, int, int)`. (some other calling patterns will raise error).
soumith and I plan to change the current `stft` interface because it is a bit messy and non-standard. rafaelvalle suggested us that `librosa` is a good reference API to align with. After discussing with soumith and ezyang , and given that `stft` is only out for 1 release, I decide to go with directly changing the signature. Also, my understanding is that most researchers in this field will welcome this change as `librosa` seems to be the golden-standard here. (it doesn't yet support all `pad_mode` but those will become available if added to `F.pad`.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9308
Reviewed By: ezyang
Differential Revision: D8806148
Pulled By: SsnL
fbshipit-source-id: f6e8777d0c34d4a4d7024e638dc9c63242e8bb58
Summary:
test_cuda.py uses routine 'number' to prepare many testscases.
number should return a floating point value for float-type tensor
types, or integer otherwise. But number's test to classify the type
is incorrect, so it always returns the integer value.
(type(t).__name__ is always 'torch.tensortype' so never matches
'Double', 'Float', or 'Half'.)
Update number to use the existing is_floating() helper to make the
check.
The change to number causes a few tests to fail for HalfTensor. Relax
the tolerance for those in line with other HalfTensor testcases. The
failing tests--for addcdiv and fill--were not previously relaxed for
HalfTensor so are held to the over-strict 1e-5 default tolerance.
Finally, update a couple other tests for HalfTensor type to use the
existing is_half() helper.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9475
Reviewed By: yf225
Differential Revision: D8872112
Pulled By: ezyang
fbshipit-source-id: 016e3e15adb23f6606bd4c08218954c1396699db
Summary:
1. Let `ModuleTest` raise when they fail on non-contiguous inputs. Fix legacy modules.
2. Fix BN (both THNN and cuDNN) not working on non-contiguous inputs.
3. Fix CUDA EmbeddingBag not working on non-contiguous inputs. To prevent calling `.contiguous()` on in both `forward` and `backward`,
a. prefix all current `embedding_bag*` functions with `_`, indicating that they require input to be contiguous (there is a check in each function).
b. create `embedding_bag`, which makes input arguments `.contiguous()`, and calls `_embedding_bag`
3. Make many ATen `embedding*` functions to work on non-contiguous inputs so we don't need to call `input = input.contiguous()` in Python `nn.functional.embedding`.
4. Fix dense-sparse addition when the sparse input is not coalesced and indices or values tensor is not contiguous. This came up in the test cases of Embedding modules with `sparse=True`. Added tests.
5. Update `TensorUtils.cpp` to use `AT_*` macros.
Request:
review from cpuhrsch on the `Embedding*` changes.
review from ezyang on ATen sparse & BN changes.
Closes https://github.com/pytorch/pytorch/pull/9114
Differential Revision: D8717299
Pulled By: SsnL
fbshipit-source-id: 0acc6f1c9522b5b605361e75112c16bbe1e98527
Summary:
This will resolve some of the timeout issues in CPU and GPU tests internally.
Closes https://github.com/pytorch/pytorch/pull/9061
Reviewed By: ezyang
Differential Revision: D8707471
Pulled By: yf225
fbshipit-source-id: 9dc82a2c9da0c540ae015442f74b9b2b1a67a246
* cache cufft plans
* use an LRU cache
* suffix CuFFTParams members with _
* import print_function for py2
* lint
* fix potential race; add dummy impl for CPU only builds
* cpp formatting; remove nccl makefile change
* Use CUDA hooks instead
* comments and doc
* update the error message
* move LRU cachae to a separate file and native::detail namespace
* update comment
* specify NOTE location in CuFFTPlanCache.h
* update disabled_features.yaml to make amd ci work
* another fix for AMD CI in disabled_features.yaml
* Wrap cufft_plan_cache_* methods in __HIP_PLATFORM_HCC__
* improve the notes
* lint
* revert onnx change
* put back inlining for CUFFT_CHECK
* Some 0-sized dimension support, port catArray away from resizeLegacy.
The goal of this PR is to port catArray away from resizeLegacy (so we can delete the legacy resize calls), but since catArray has some weird behavior because
we don't have arbitrary 0-sized dimension support, I made some effort to fix these both in one pass.
The major changes here are:
1) catArray uses the new resize API, no longer the old resizeLegacy API.
2) As 1) is the last usage of resizeLegacy, it is deleted.
3) If compiled with USE_TH_SIZE_ZERO_DIM, catArray will work and properly check shapes for n-dimensional empty tensors.
4) However, we retain the old behavior of "ignoring" size [0] tensors in catArray. We previously allowed this because we didn't have n-dimensional empty tensors.
5) To get the above to work, we also add support for n-dimensional empty tensors for narrow and slice (ifdef USE_TH_SIZE_ZERO_DIM).
6) We change the stride formula for empty tensors to match NumPy; basically, we never multiply by 0 as the size, always at least 1, so the
strides are monotonically increasing in the empty tensor case.
7) We print the size of empty tensors if size != [0]; this matches NumPy behavior (even in cases where the size could be inferred from the brackets.
8) For test purposes, we add torch._C._use_zero_size_dim() to add tests for the above.
* Fix flake8.
* Address review comments.
* Created TensorOptions
Storing the type in TensorOptions to solve the Variable problem
Created convenience creation functions for TensorOptions and added tests
Converted zeros to TensorOptions
Converted rand to TensorOptions
Fix codegen for TensorOptions and multiple arguments
Put TensorOptions convenience functions into torch namespace too
All factory functions except *_like support TensorOptions
Integrated with recent JIT changes
Support *_like functions
Fix in place modification
Some cleanups and fixes
Support sparse_coo_tensor
Fix bug in Type.cpp
Fix .empty calls in C++ API
Fix bug in Type.cpp
Trying to fix device placement
Make AutoGPU CPU compatible
Remove some auto_gpu.h uses
Fixing some headers
Fix some remaining CUDA/AutoGPU issues
Fix some AutoGPU uses
Fixes to dispatch_tensor_conversion
Reset version of new variables to zero
Implemented parsing device strings
Random fixes to tests
Self review cleanups
flake8
Undo changes to variable.{h,cpp} because they fail on gcc7.2
Add [cuda] tag to tensor_options_cuda.cpp
Move AutoGPU::set_index_from into .cpp file because Windows is stupid and sucks
Fix linker error in AutoGPU.cpp
Fix bad merge conflict in native_functions.yaml
Fixed caffe2/contrib/aten
Fix new window functions added to TensorFactories.cpp
* Removed torch::TensorOptions
Added code to generate wrapper functions for factory methods
Add implicit constructor from Backend to TensorOptions
Remove Var() from C++ API and use torch:: functions
Use torch:: functions more subtly in C++ API
Make AutoGPU::set_device more exception safe
Check status directly in DynamicCUDAHooksInterface
Rename AutoGPU to DeviceGuard
Removed set_requires_grad from python_variables.h and warn appropriately in Variable::set_requires_grad
remove python_default_init: self.type()
Add back original factory functions, but with deprecation warnings
Disable DeviceGuard for a couple functions in ATen
Remove print statement
Fix DeviceGuard construction from undefined tensor
Fixing CUDA device compiler issues
Moved as many methods as possible into header files
Dont generate python functions for deprecated factories
Remove merge conflict artefact
Fix tensor_options_cuda.cpp
Fix set_requires_grad not being checked
Fix tensor_new.h
TEMPORARILY put some methods in .cpp files to see if it solves issues on windows and mac
Fix bug in DeviceGuard.h
Missing includes
TEMPORARILY moving a few more methods into .cpp to see if it fixes windows
Fixing linker errors
* Fix up SummaryOps to use new factories
Undo device agnostic behavior of DeviceGuard
Use -1 instead of optional for default device index
Also move DeviceGuard methods into header
Fixes around device index after optional -> int32_t switch
Fix use of DeviceGuard in new_with_tensor_copy
Fix tensor_options.cpp
* Fix Type::copy(
* Remove test_non_float_params from ONNX tests
* Set requires_grad=False in ONNX tests that use ints
* Put layout/dtype/device on Tensor
* Post merge fixes
* Change behavior of DeviceGuard to match AutoGPU
* Fix C++ API integration tests
* Fix flip functions
* Spelling fix in MultivariateNormal docstring (#7915)
* [c10d] MPI Process Group Implementation (#7783)
This provides a bare-minimum MPI Process Group implementation, the commit is on top of @pietern's Gloo Process Group PR.
* [c10d] MPI Process Group Implementation
ref: https://github.com/pytorch/pytorch/issues/7434
* Better exception, atexit func, and addressed comments
* Clang formatting changes
* Static initialization and addressed comments
* Added constness back
* Test will now launch mpi processes if found
* CMakeList Changed
* Fix Windows doc for import error (#7704)
* Fix Windows doc for import error
* Fix doc again
* Fix wrong format
* Moved condition for dilated grouped convolutions to CUDNN convolution implementation (#7465)
* Updates to caffe2 operator documentation (#7917)
* Significant updates to the operator docs in prep for merge
* [auto] Update onnx to 307995b - Update from upstream (onnx/onnx#1038)
307995b143
* Test if ASAN is actually working as part of ASAN tests. (#6050)
* Test if ASAN is actually working as part of ASAN tests.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Drop explicit use of libstdc++, we should not care.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Build with DEBUG=1
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Increase main thread stack size when using ASAN.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Split up detail.h (#7836)
* Fix THCUNN SpatialDepthwiseConvolution assuming contiguity (#7952)
* Fix fbcode compatibility (#7939)
* add test for correctness of transpose fusion (#7950)
* [JIT][script] Fix emitted gather and slice for dynamic indices (#7861)
* [JIT][script] Fix emitted gather for dynamic indices
* Also fix slice
* Address comments
* cache and use BLAS_SET_BY_USER so that it doesn't set itself to TRUE when run second time (#7942)
* Add unsafe flag to skip checking in prepare (#7832)
* Add unsafe flag to skip checking in prepare
* pop
* Rename cuda::type to cuda::into_type and provide cuda::from_type. (#7937)
These are used to convert Half -> half and half -> Half respectively.
from_type will be used for runtime type checking in THC.
* Try to fix TORCH_CUDA_ARCH_LIST for PyTorch again (#7936)
* try again
* use DEFINED
* use a loop
* Minor fixes
* remove sort requirement from pad-sequence (#7928)
* pad-sequence no longer requires sorting entries
pad-sequence can get the max_len from the list of sequences. entries only need to be sorted if output will be used for pack_padded_sequence, which can throw the error itself.
* remove sort requirement from pad-sequence
Picks up from #5974.
Removes the requirement that input sequences to pad_sequence have to be
sorted. Addressed the comments in the PR:
- Updated docstring for pad_sequence
- Remove sort requirement in pad_sequence test
- Test unsorted and sorted sequences in pad_sequence test
* Fix checkBackend error message (#7926)
* Fix checkBackend error message
Fixes#7849
* Switch order of printing args
* Split CI tests in half and run them in parallel (#7867)
* Split and run tests in parallel
* Refactor tests
* Handling of scalars in torch.Size (#5676)
* Handling of scalars in torch.Size
torch.Size() constructor uses python_arg_parser
IntList in python_arg_parser can take iter/range
Have IntList take python iterables and ranges.
Address comments: don't use python_arg_parser and instead call __index__ in THPSize_pynew
Address comments
Address comments
* Rebased
* Address nit
* [JIT] Fission and fusion passes for addmm (#7938)
* Addmm decomposition pass
* Addmm peephole pass
* Fix handling of output shape in fusion pass
* Add DCE to the peephole passes
* add comments
* maybe bugfix?
* Fix GPU tests
* fix py2/3 test issue
* Set smaller grain size for some cases (#7941)
* Fix returning scalar input in Python autograd function (#7934)
* fix _wrap_outputs not working with scalar inputs
* add a test
* Prevent git autocrlf for bash scripts (#7949)
* Delete unused file (#7919)
* Fix typo in autodiff formula for addmm (#7932)
* 1) use meshgrid for flip() CPU implementation, only need one copy of input tensor; 2) changed kernel of CUDA implementation, no need materialized indices tensor; 3) reusing error checking code
* [caffe2] YellowFin parameter update GPU code fix. (#6993)
* [Caffe2] Keep name of caffe2_pybind11_state and caffe2_pybind11_state_gpu in debug build (#7155)
* Allowing MatMul to create a gradient even with 3 inputs. useful if you are differentiating a graph twice (#6536)
* added const for local variables
* Fix the cpp libtorch CUDA build (#7975)
* Use mingfeima's mkldnn (#7977)
* Fix the import part of the windows doc (#7979)
* Change perf test folder after git checkout (#7980)
* Move the broadcast check in MKL Add/Sum to runtime (#7978)
* Use Glog's implementation of STL logging when possible. (#7206)
Inject custom workaround into namespace std so that it can be found by ADL.
* [Hotfix] Bring back warnings and -Werror to ATen (#7866)
* Bring back warnings and -Werror to ATen
* Unbreak...
* Fix tbb errors
* Enable ONNX backend Mean tests (#7985)
* Add third wayt to determine IS_CONDA (#7971)
* Fix EmbeddingBag max_norm option (#7959)
* fix EmbeddingBag max_norm option
* flake8
* add warning to the embedding bag arg change
* Raise error when torch.load a storage on a non-existing device (#7921)
* Raise error when torch.load a storage on a non-existing device
Before, doing torch.load(...) on a CUDA tensor on a CPU-only machine
would raise an unreadable error:
```
~/pytorch/pytorch/torch/cuda/__init__.py in __enter__(self)
223 if self.idx is -1:
224 return
--> 225 self.prev_idx = torch._C._cuda_getDevice()
226 if self.prev_idx != self.idx:
227 torch._C._cuda_setDevice(self.idx)
AttributeError: module 'torch._C' has no attribute '_cuda_getDevice'
```
This PR makes it so that torch.load raises a hard error if one tries to
load a storage onto a non-existing device and suggests the user to use
torch.load's map_location feature.
* Address comments
* missing dep
* Make THStorage / THCStorage have void* data ptr. (#7964)
* Make THStorage / THCStorage have void* data ptr.
This is the initial step in unifying the ATen and TH tensor representations, next is to only generate a single THStorage / THCStorage type.
The major changes here are:
1) data has been renamed to data_ptr and made void* in THStorage/THCStorage.
2) THStorage / THCStorage stores a at::ScalarType representing its data type (This will be useful when we generate a single THStorage/THCStorage).
3) APIs for Accessing the data as a real*:
a) storage->data<real>() -- this does runtime-type checking (checks that the at::ScalarType is correct).
b) storage->unsafeData<real>() -- as above, but no runtime-type checking (used in inner loops / fast code paths).
c) THStorage_(data)(storage) -- this already existed, just calls storage->data<real>().
* Add include.
* Attempt to fix clang build issues.
* Clarify comment and remove extra character.
* Rename unsafeData -> unsafe_data.
* Remove unnecessary 'to' function to get compile time rather than link time errors.
* Import/export observer symbols for DLL, which fixes the linking error in Visual Studio. (#6834)
* Import/export observer symbols for DLL, which fixes the linking error in Visual Studio.
* Add support of all default cmake build types for release to cuda.
* Remove python bindings for `torch.slice` (#7924)
* skip python bindings for slice
* remove tests
* convert slice test to indexing
* Build ONNX for PyTorch version of libcaffe2 (#7967)
* support loading gzip (#6490)
* support loading gzip
* address comments
* address comments
* fix lint
* fix test for python2
* Add memory leak check in CUDA tests (#7270)
* Add memory leak check in CUDA tests
* Tracking multi-GPU too
* fix run_test.py not running __name__ == '__main__' content; add test for make_cuda_memory_checked_test
* add a comment
* skip if cuda
* 1. Change the wrapper to a method in common.py:TestCase
2. Refactor common constants/method that initialize CUDA context into common_cuda.py
3. Update some test files to use TEST_CUDA and TEST_MULTIGPU
* Fix MaxUnpool3d forward memory leak
* Fix MultiLabelMarginCriterion forward memory leak
* Fix MultiMarginLoss backward memory leak
* default doCUDAMemoryCheck to False
* make the wrapper skip-able
* use TEST_MULTIGPU
* add align_corners=True/False tests for Upsample; fix TEST_CUDNN
* finalize interface
* VolumetricMaxUnpooling_updateOutput
* fix test_nccl
* rename THC caching allocator methods to be clearer
* make the wrapped function a method
* address comments; revert changes to aten/src/THC/THCCachingAllocator.cpp
* fix renamed var
* Revert "Set smaller grain size for some cases" (#7988)
* Entry for c10d in CODEOWNERS (#8001)
* Fix a couple of typos (#7998)
* Fix typo
* Fix typo
* Fix typo
* Fix typo
* Add on-stack observer cache for Observable (#7931)
observers_list_ stores all the observers for an observable. The list is allocated on heap, which
can cause LLC miss. Add an on-stack observer cache for fast access. In production, we have seen 20%
speed up for start and stop observer calls.
* Reduce grain size for Unary operations (#8003)
* [auto] Update onnx to 8ec0e5f - Add index check for Transpose's type inference function (onnx/onnx#1053)
8ec0e5fe9b
* Make AT_FORALL_SCALAR_TYPES usable outside of at::namespace. (#7935)
* Make AT_FORALL_SCALAR_TYPES usable outside of at::namespace.
This requires renaming the _cast functions which used the unqualified names.
* Separate onnx mapping of scalar type from cast name.
* Fix flake8.
* Properly cast onnx.
* Remove WITH_ROCM cmake flag/variable (use USE_ROCM solely) (#8013)
* Mention the pytorch-ci-hud on the README. (#8004)
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Re-enable build env check (#7969)
* Re-enable build env check
* Fix linux test error
* Try to fix macOS test error
* Update nn.rst (#8029)
* Example for Transformed Distribution (#8011)
* [auto] Update onnx to 33e9cd4 - Remove the usage of default value to fix invalid proto3 files. (onnx/onnx#1052)
33e9cd4182
* [auto] Update onnx to 1504a33 - Convert schema assert for duplicate type names to exception (onnx/onnx#1057)
1504a33abb
* Support CUDA tensors in ProcessGroupGloo (#7694)
This adds an unconditional dependency on CUDA, which is not desirable
for the long term. Ideally we have split like ATen where we have
different artifacts for different backends so you can decide at runtime
what to use.
* [auto] Update onnx to 3fb9656 - Fix for fbcode CI (onnx/onnx#1062)
3fb965666e
* propagate nan in some activations (#8033)
* propagate nan in some activations
* fix py2 not having math.nan
* flake8
* Fix profiler crash when no events register (#8034)
* Fix profiler crash when no events register
When trying to profile, attempting to print the event table throws a vague error because the event list is empty:
....
max_name_length = max(len(evt.key) for evt in events)
ValueError: max() arg is an empty sequence
This change fixes the error by returning an empty string.
* Update profiler.py
* Allow CI testing with different AVX configs (#8020)
* Allow CI testing with different AVX configs
* Unset ATEN_DISABLE_AVX and ATEN_DISABLE_AVX2 in default config
* Support for generating ATen during the fbcode build, rather than committing the generated files (#8002)
Paint the internal bikeshed a slightly different color to appease Buck tooling.
* Factor python dependency out of interpreter (#7970)
* Factor python dependency out of interpreter
* Remove NO_PYTHON for the autograd engine
If there is no python bindings, then a default Engine is constructed
the first time it is requested.
If the python libraries are loaded, then they override the default
accessor and the default engine becomes a python Engine.
Note: it is possible for two engines to be generated if a non-python
one gets created before the python bindings are loaded. This case
is rare, and just results in additional threads being spawned.
* Fixing AlexNet test which is skipped in CI
* [auto] Update onnx to 760c928 - add missing hasNInputShapes check for bidirectionalBroadcastShapeInference (onnx/onnx#1060)
760c9283d0
* Support modules that output scalar in Gather (and data parallel) (#7973)
* Support modules that output scalar in Gather (and data parallel)
* Improve warning msg
* [auto] Update onnx to 9e7855d - Remove PyTorch generated Upsample tests cases (onnx/onnx#1064)
9e7855dcd4
* [script] Add support for torch.zeros, torch.ones, etc. (#7799)
* [script] Add support for torch.zeros, torch.ones, etc.
* modifies gen_jit_dispatch to creating bindings for functions that do
not take tensor arguments, but do have an initial type argument
* adds tensor attributes to these functions for device, layout, and
dtype specification
* extends the list of valid compiler constants to include device, layout,
and dtype.
* allows functions with Generators, but only using the default generator
Known limitations:
* when using `torch.float`, we convert it to a scalar tensor and make
no checks that it is actually used only in a dtype specification.
This is similar to how we handle Python numbers, creating some situations
where the script is more permissive. Fixing this requires much more
significant changes to the IR, so is lower priority for now.
* devices specified using string literals e.g. 'cuda:1' do not work,
since we do not support string literals in general.
* Add profiling annotations to NeuralNet[Operator|Data] (#8005)
* Update from facebook 1ee4edd286a3 (#8040)
* Adding instance weight to batch distill loss
as title
* add bfloat 16-31
added bfloat 16-31 and their respective unit tests
* [CUDA9] Upgrade - fbcode
CUDA9 upgrade diff D5654023 has been out for a while thanks to Pieter. But with time growing it's becoming quite hard to rebase, because of the symlinks and auto-generated build/config files in tp2. Break D5654023 into two diffs, one touching tp2 config files, and another one touching fbcode TARGETS file (adding nvcc flag). These two should be a bit easier to rebase (for detailed procedure see "Test Plan").
This diff can only be committed if:
1. CUDA 9 rpm is rolled out fleet-wide (TBD)
2. NVidia driver 390.40 is rolled out fleet-wide (done)
3. Upgrade CUDA 9.1, cudnn 7.1, nccl 2.1 (done)
4. Make sure all dependents are built (done)
5. Test all C2 operators, PyTorch (see test plan)
* Share intermediate int32 buffer across Conv ops
Adding a known type
* [C2 fix] infer function for ensure_cpu_output_op
this is adding the missing device funtion for ensure_cpu_output_op
* [int8] Add blob serializer/deserializer for Int8TensorCPU
To export to logfiledb
* [nomnigraph] Add try catch block to optimization passes in predictor
This will catch failures that happen in the optimization pass.
* Caffe2: avoid static initialization order fiasco for CAFFE_ENFORCE
CAFFE_ENFORCE uses strack trace fetcher. Which is currently a
global static variable. If at static initialization time CAFFE_ENFORCE
is used, this is a SIOF. Recently CAFFE_ENFORCE was added into init
functions registration, so we started to see this.
Meyers singleton is going to provide safety here. If stacktrace
fetcher was not registered yet, it will just use a dummy one.
* NUMA support in SparseNN CPU benchmark
Adding support for NUMA in SparseNN CPU benchmark
* [mobile-roofline] Add logging needed for roofline model
This should be all that's needed
* Let the operators using the same input if the operators are not chained
or else, we have to change the input data dims
* fix null-pointer-use UBSAN errors in in reshape_op.h
* revert previous fix on input blob name
as title
* Adding flag to let MineHardNegative automatically extract single value from dict
Model exporter requires the output of the model to be a struct. This makes it convenient to use those models directly in MineHardNegative by allow automatic extraction of the single element of dict, which is a common use case.
* Reverting change that broke internal tests back to OSS compatible state
* Skip CUDA memory leak test on BN tests on windows (#8043)
* workaround for Sequential when one cannot retrieve python source (#8048)
* [auto] Update onnx to 0dbec2a - - Generate protoc type hints on Windows (onnx/onnx#1047)
0dbec2a047
* [auto] Update onnx to 4f8ef17 - Remove erroneous documentation around maps and sequences. (onnx/onnx#1069)
4f8ef17ad3
* [auto] Update onnx to e6a500e - Extract constant to initializer (onnx/onnx#1050)
e6a500e54c
* [auto] Update onnx to 033f956 - make gcc happy (onnx/onnx#1061)
033f956f41
* Remove NO_PYTHON macros from Exceptions.h/cpp (#8007)
Removes cases where NO_PYTHON was unnecessary in Exception.h/cpp
* [ready] Clean up torch.distributions (#8046)
* Have a single THStorage and THCStorage type. (#8030)
No longer generate data-type specific Storage types, since all Storage types are now identical anyway.
For (some) backwards compatibility and documentation purposes, the Real names, e.g. THLongStorage are now #defined as aliases to the single THStorage type
* Reduce usages of TensorUtils<T>::DataType in THC. (#8056)
TensorUtils<T> is basically ATen-dispatch-lite in that it allows one to do multi-type THC function dispatch with a single call.
However, it is templatized on the Tensor type, and since we are moving to a single Tensor type, this doesn't work.
Most of the functions in TensorUtils (e.g. getDims) can be pulled up a level, to just call THCTensor_nDimension (or directly accessing the member),
but the DataType specific functions are more problematic.
So, this PR does two things:
1) Replaces calls of 'TensorUtils<THCTensor>::DataType' with 'real' since these are identical
2) Templatizes the THC_pointwiseApplyX functions to take scalar types. To ensure this is done correctly, we static_assert that the scalar type template parameter matches the scalar type of
the corresponding template parameter. We will need to get rid of these static_asserts in the future, but this is useful for now.
* Support to run ONNX Upsample operator (mode=nearest) in Caffe2 (#8037)
* Added support to run ONNX Upsample operator (mode=nearest) in Caffe2
* adding error checks to upsample
* adding error checks to upsample
* adding error checks to upsample
* changing to np.isclose
* Revert onnx submodule update
* still fixing
* [auto] Update onnx to eb12f72 - Add conv transpose test cases (onnx/onnx#886)
eb12f72a86
* [auto] Update onnx to bd98abb - Add a hook for doing post-processing on protobuf generated header files (onnx/onnx#1068)
bd98abbba0
* Skip ConvTraspose ONNX backend tests (#8074)
* Post process onnx proto (#8064)
* Post processing onnx generated protobuf files to hide global symbols
* .
* .
* Add code for TensorBoard visualization of JIT GraphExecutors (#8050)
* [auto] Update onnx to cc26486 - bump version to 7 for prelu. (onnx/onnx#1063)
cc26486541
* [auto] Update onnx to 356208d - add input tensor dimension checks to shape inference (onnx/onnx#1070)
356208d756
* Move backtrace to its own header (#8096)
* Move backtrace to its own header
* Move cxxabi.h into Backtrace.cpp
* Fix and ignore some warnings (#8081)
* Do an additional sanity check that nvcc and CUDA include dir agree. (#8094)
If you set CUDA_HOME and CUDA_NVCC_EXECUTABLE together, you may
end up in a situation where the CUDA_VERSION of your includes
mismatches the CUDA version of your nvcc. See #8092 for a concrete
case where this can occur. Explicitly detect this situation and
give a good error message in this case!
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* use regex in kwarg parser (#8061)
* Removing remaining NO_PYTHON ifdefs (#8067)
* Remove NO_PYTHON in tracing
* Remove NO_PYTHON in ir.h
* Remove NO_PYTHON in test_jit.cpp
* Replace std::size_t with size_t (#8093)
* Remove out-of-date comment (#8114)
* [Caffe2] Enabling AMD GPU Backend for Caffe2 (#7955)
* Add hip support for caffe2 core
* Add MIOPEN header/wrapper to caffe2 core
* Add HIP device into caffe2 PB
* top level makefile change for rocm/hip
* makefile scaffolding for AMD/RocM/HIP
* Makefile scafodding for AMD/RocM/HIP; add makefile/utility for HIP files
* caffe2 PB update for AMD/ROCM HIP device
* Add AMD/RocM/Thrust dependency
* HIP threadpool update
* Fix makefile macro
* makefile fix: duplicate test/binary name
* makefile clean-up
* makefile clean-up
* add HIP operator registry
* add utilities for hip device
* Add USE_HIP to config summary
* makefile fix for BUILD_TEST
* merge latest
* Fix indentation
* code clean-up
* Guard builds without HIP and use the same cmake script as PyTorch to find HIP
* Setup rocm environment variables in build.sh (ideally should be done in the docker images)
* setup locale
* set HIP_PLATFORM
* Revert "set HIP_PLATFORM"
This reverts commit 8ec58db2b390c9259220c49fa34cd403568300ad.
* continue the build script environment variables mess
* HCC_AMDGPU_TARGET
* Cleanup the mess, has been fixed in the lastest docker images
* Assign protobuf field hip_gpu_id a new field number for backward compatibility
* change name to avoid conflict
* Fix duplicated thread pool flag
* Refactor cmake files to not add hip includes and libs globally
* Fix the wrong usage of environment variables detection in cmake
* Add MIOPEN CNN operators
* Revert "Add MIOPEN CNN operators"
This reverts commit 6e89ad4385b5b8967a7854c4adda52c012cee42a.
* Resolve merge conflicts
* .
* Update GetAsyncNetHIPThreadPool
* Enable BUILD_CAFFE2 in pytorch build
* Unifiy USE_HIP and USE_ROCM
* always check USE_ROCM
* .
* remove unrelated change
* move all core hip files to separate subdirectory
* .
* .
* recurse glob core directory
* .
* correct include
* .
* Detect CUDNN related environment variables in cmake (#8082)
* Implement adaptive softmax (#5287)
* Implement adaptive softmax
* fix test for python 2
* add return_logprob flag
* add a test for cross-entropy path
* address review comments
* Fix docs
* pytorch 0.4 fixes
* address review comments
* don't use no_grad when computing log-probs
* add predict method
* add test for predict
* change methods order
* get rid of hardcoded int values
* Add an optional bias term to the head of AdaptiveSoftmax
* Make libshm also test if rt requires pthread. (#8112)
In some configurations (e.g., our internal build of GCC 5 + GLIBC 2.23),
-lrt is not sufficient to use shm_open; you also need to declare
a dependency on pthread. This patch adds a surgical extra fix to
detect this situation, in the case that I noticed it failing in the
wild.
Fixes#8110
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* [auto] Update onnx to 2d5ce4a - Remove empty model (onnx/onnx#1058)
2d5ce4aeb6
* Add missing pragma once. (#8118)
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* [auto] Update onnx to 2a87616 - Tests for LRN operator (onnx/onnx#903)
2a876162ac
* Split SparseTensorImpl off from TensorImpl. (#7990)
* Split SparseTensorImpl off from TensorImpl.
At the moment they have the same data layout, but with the upcoming refactor
they will not, and we need a place to put all of the sparse tensor specific
fields.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Update SparseTensorImpl.h
* [Caffe2] Support non peer access in muji and fix bug when reduced_affix is empty (#6896)
* [Caffe2] Support non peer access in muji
* [Caffe2] Add test for 4 gpus and 2 groups
* [Caffe2] Add comments
* Fix bug when reduced_affix is empty
* Fix typo and add comments about cpu and amd gpu
* Skip OnnxBackendNodeModelTest::test_lrn_default_cuda that causes segfault (#8127)
* Replace most remaining usages of TensorUtils<T>::DataType. (#8124)
As in https://github.com/pytorch/pytorch/pull/8056, this doesn't work with a single TensorImpl type.
This replaces the usages of with a templatized parameter and static_asserts that the new and old are equal.
After this we can get rid of the old template parameter, but I want to ensure they are equivalent across all builds first.
* Add utf-8 header to Python file with Unicode. (#8131)
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Add back lrn test (#8134)
* Revert "Skip OnnxBackendNodeModelTest::test_lrn_default_cuda that causes segfault (#8127)"
This reverts commit 410191c417.
* Fix mismatched default values
* Add non_blocking to Tensor/Module.to (#7312)
* Add non_blocking to Tensor/Module.to
* flake8
* Add argparse tests
* cpp parse
* Use C++ parser
* use a commong parse function with Tensor.to
* fix test_jit
* use THPObjectPtr
* increase refcount for None, True, and False
* address comments
* address comments
* Fix job name checking for AVX tests (#8135)
* Fix a corner case for ReShapeOp (#8142)
In my use case, in the backward propogate pass, the reshape need to
change a [0] tensor into [0,0] shaped tensor. The original implementation would
cause out of index issue. This diff fix this problem.
* cpu/ideep context converter (#8139)
* fix type mismatch while call torch._C._cuda_setDevice (#8065)
* fix type mismatch while call torch._C._cuda_setDevice
* fix type mismatch in scatter
* fix type mismatch in scatter
* fix type mismatch while call torch._C._cuda_setDevice
* fix type mismatch while call torch._C._cuda_setDevice
* fix type mismatch while call torch._C._cuda_setDevice
* docs: Add warning to torch.repeat() (#8116)
* docs: Add warning to torch.repeat()
closes#7993
* docs: Add links for numpy functions
* docs: Break the too long line
* Accelerate bernoulli number generation on CPU (#7171)
* opt bernoulli rng with vsl and openmp
* detect cpu vendor for bernnoulli
* retrigger test platform
* check the vendor more severely
* use cpuinfo to check vendor
* docs: add canonical_url and fix redirect link (#8155)
* docs: enable redirect link to work for each specific page
* docs: add canonical_url for search engines
closes#7222
* docs: update redirect link to canonical_url
* docstring support for @script and @script_method (#7898)
* docstring support for @script and @script_method
* make it python2 compatible
* improve according to review
* improve build_stmts
* use filter instead of list comprehension
* improve the way wrap is handled for script_method
* stash the original method instead
* allow dynamic attr for ScriptMethod and GraphExecutor
* a bit comment on build_Expr
* remove _build_wrap
* a bit improve on comments
* rename to __original_methods
* should be _original_methods
* [auto] Update onnx to 968d28d - fix Node::isBefore (onnx/onnx#1075)
968d28d901
* remove some unnecessary cudaGetDevices (#8089)
* remove unnecessary cudaGetDevices
* make curDevice argument non-optional, add explicit checks to current_device
* Fix cuda.framework error on OSX. (#8136)
When compiling OSX with CUDA, Caffe2's build system uses
find_package(cuda) to get its grubby hands on the CUDA driver
library (for some strange reason, FindCUDA doesn't save this
information as a variable). Unfortunately, on OSX, sometimes
this picks up the cuda.framework folder, and then our build
system chokes to death because it doesn't try to link against
this as a framework. (Is the folder even a framework? I have
no idea).
This commit attempts to fix this in a two pronged fashion:
1. For some users, reducing the precedence of frameworks
using CMAKE_FIND_FRAMEWORK seems to help. So we set these
variables. However, this fix is not perfect; on my laptop
it doesn't actually solve the problem.
2. PyTorch doesn't actually need the CUDA driver API. So we
only add the dep when building Caffe2.
Fixes#8022
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* [C++ API] Improve and use OrderedDict for parameters / modules (#7823)
* Improve OrderedDict for C++ API
* Give OrderedDict a subject and fix review comments
* Fix OrderedDict use in torch/csrc/jit/script/init.cpp
* Fix __rshift__ bug (#8161)
* Fix __rshift__ bug
* Add small tests for __lshift__ and __rshift__ in test_cuda
* Add a more elaborate check for __lshift__ and __rshift__
* refactor the test to address @zou3519 's comments
* Move non-generic Storage code needed by TensorUtils to non-generic C++. (#8164)
For non-generic function call implementations in Storage used by TensorUtils, we do the following:
1) Move the declaration from generic/C to non-generic/C++; we don't need backwards compatibility on these functions and want to use e.g. at::ScalarType.
2) Move the implementation from generic/C++ to non-generic/C++.
3) Change the generic implementation to call the non-generic implementation.
This will allow us to get rid of the corresponding TensorUtils calls (once we move over the Tensor functions in the same manner).
* Pinning opencv to < 3.4 in conda builds (#7923)
* Pinning opencv to 3.1.0 in conda builds
* Also pinning numpy to 1.11
* Trying only specifying <3.4
* Adding -setup- path, and better code structure (#8122)
* Abstract parallelization to faciliate using threadpools (#8163)
* [Caffe2] Update elementwise ops to support numpy style boradcast (#8070)
* Update elementwise ops to support numpy style boradcast
Update elementwise ops to support numpy style boradcast
* Fix sqrt_op
* Fix compare ops
* Fix gradient test
* Fix optimizer legacy broadcast
* Fix legacy broadcast for elementwise ops
* Skip flaky test
* Fix eigen simple binary op
* Fix attention test
* Fix rnn test
* Fix LSTM test
* Fix tan grad
* Fix schema check
* Export getCudnnHandle (#7726)
* [JIT] Support a single TensorList argument anywhere in the argument list + index_put (#8173)
* [JIT] Support a single TensorList argument anywhere in the argument list
* [JIT] index_put
* use the correct datatype format (#8144)
* Add back onnx console scripts dropped during migration from onnx-caffe2 (#8143)
* Get rid of SOVERSION (again). (#8132)
We don't want SOVERSION because pip will lose the symlink and
double your distribution size, and also because our setup.py
accidentally links against both libcaffe2.dylib and libcaffe2.1.dylib
on OS X. This leads to a very puzzling error where you get
the error "cannot initialize CUDA without ATen_cuda", because
there are actually two copies of your registry in memory (because
there are two copies of the dynamic library). Dropping SOVERSION
makes it impossible to make this mistake.
In principle, if the shared library load is done with DYLD_GLOBAL,
that should also prevent two copies of the registry from popping up.
Worth checking at some later point, if you need to bring back
SOVERSION (because, e.g., pip finally fixed their software.)
Partially fixes#8022.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Fix a corner case for ReShapeOp (#8178)
In my use case, in the backward propogate pass, the reshape need to
change a [0] tensor into [0,0] shaped tensor. The original implementation would
cause out of index issue. This diff fix this problem.
* Better conv error message basing on weight shape (#8051)
* Add retry logic to sccache download for Windows build (#7697)
* Add retry logic to sccache download for Windows build
* fix script bug
* clean up
* fix caffe2 docker build (#7411)
* [ONNX] Fix type_as symbolic (#8183)
* [ONNX] Nuke type_as symbolic
* make it better
* Fix lookup + test
* Yangqing as an ONNX codeowner (#8185)
* Fix protobuf options (#8184)
* protobuf
* fix protobuf_MSVC_STATIC_RUNTIME
* Add a loop unrolling pass to PyTorch JIT (#7672)
* [auto] Update onnx to 4e65fd8 - fuse consecutive squeezes (onnx/onnx#1078)
4e65fd83ba
* [Caffe2] Merging setup.py with setup_caffe2.py (#8129)
* Mergine setup.pys, torch works, caffe2 works up to other KP
* Fix to super call for python 2
* Works on python2 on mac
* Consolidating Caffe2 flags
* Fix scalar check for sparse tensors. (#8197)
* Fix scalar check for sparse tensors.
As discovered in #8152
If `t` is a scalar sparse tensor, `t._indices` used to return a sparse
empty tensor because the scalar check was incorrect. This PR modifies
the scalar check to return a dense tensor instead of a sparse tensor.
i.e.
```
tensor = torch.sparse_coo_tensor([], [], torch.Size([]), device=device)
out = tensor._indices() # was a sparse tensor, now is dense.
```
* Fix typos
* fix lint
* Add more annotations for arguments in ATen schema (#8192)
* use THCThrustAllocator in BCECriterion (#8188)
* Allow parallel_apply to take in list[Tensor] (#8047)
* Docs for gradcheck and gradgradcheck; expose gradgradcheck (#8166)
* Docs for gradcheck and gradgradcheck; expose gradgradcheck
* address comments
* Implement randperm for CUDA (#7606)
* Implement randperm for CUDA
* Use Thrust to implement randperm
* clean up
* Fix test
* Offload small input scenario to CPU
* Fixed test
* Try to fix Windows error
* Fix Windows error and clean up
* Use fork_rng context manager
* Move test_randperm_cuda to test_cuda
* Add half tensor support
* Fix cuda::type error
* Fix CPU offloading
* Fix issues
* No need to check range for n == 0 case
* Update c10d build to link against Caffe2 (#8201)
This follows #7399.
* add wipe_cache option (#8204)
as title
* Replace (non-data) TensorUtils calls with non-generic THCTensor calls. (#8176)
* Replace (non-data) TensorUtils calls with non-generic THCTensor calls.
TensorUtils is templatized on the THTensor type, so to support a single tensor type (like ATen), we need to remove these.
This PR does the following:
1) Allows THCTensorTypeUtils.cuh to include THCTensor.hpp.
This involves moving includes of it outside of generic/, so we can use the new implementations.
2) Defines a single _THTensor struct and changes THCRealTensor to be a derived type of _THCTensor.
This allows us to implement a single non-generic function and avoid static_cast or void * tricks to call it from the generic functions.
3) For functions inside of TensorUtils that don't use data pointers:
a) Implement the functions in (non-generic) THTensor.cpp and declare them in (non-generic) THTensor.hpp.
b) Have the generic versions call the non-generic versions.
c) Replace the corresponding TensorUtils<THCTensor>::fn call with (non-generic) THTensor_fn.
* Add comment about THCTensor struct.
* Error if storage is null in setStorageNd or resizeNd.
* Fix c10d compiler warnings (#8206)
Copy compiler flags from the ones used in setup.py and fix warnings.
This makes the root build that includes c10d headers warning free.
* Bump gloo submodule (#8202)
This includes facebookincubator/gloo#125.
* rm -rf aten/contrib (#8165)
* Remove aten/contrib
* Remove from CMake
* Fix tanh_op on ios build (#8207)
* Fix tanh_op on ios build
* Fix tanh
* [auto] Update onnx to f28e2f1 - fix lrn spec (onnx/onnx#1090)
f28e2f1a60
* [cmake] deprecate caffe2_* specific cuda function in cmake. (#8200)
* deprecate caffe2_* specific cuda function in cmake.
* ENV{} -> $ENV{}
* CUDA_ARCH_NAME -> TORCH_CUDA_ARCH_LIST
* .
* .
* .
* skip CUDA memory leak check on Windows altogether (#8213)
* Record shape and type in autograd to validate gradients (#8168)
The check that the gradient is defined is currently disabled because
TestJit.test_ge_optimized will trigger the error.
* [auto] Update onnx to 18d70ff - Graph should only have one (input) kParam node (onnx/onnx#1088)
18d70ff529
* Set up a c10 source folder (#7822)
* Set up a c10 source folder
* Change the benchmark log format and also log flops (#8215)
as title
* Move helper functions to unnamed namespace. (#8224)
Currently, the helper functions in this file are in global
namespace. I am guessing the purpose of excluding them from was to
keep them local.
* [auto] Update onnx to e96d823 - Update Google benchmark to 1.4.1 (onnx/onnx#1083)
e96d823e5c
* Change new bernoulli implementation to be fully generic. (#8218)
The current implementation depends on THTensor types being unique, which is not guaranteed going forward.
* Structure THTensor like THCTensor is structured. (#8217)
In particular, define a base type, _THTensor, that can be used for all THRealTensor structs.
This is just to have less cognitive load when dealing with generic THTensor/THCTensor types (as in templates).
* move THCP-related utils to cuda/utils.cpp. (#8221)
These files don't follow the usual pattern: In general the files torch/csrc/X torch/csrc/cuda/X
both include the generic file torch/csrc/generic/X, where torch/csrc/X includes the cpu implementations and torch/csrc/cuda/X includes the cuda implementations.
(Aside: this is probably not the best structure, the torch/csrc/X fiels should probably be moved to torch/csrc/cpu/X).
utils.cpp combines these so that torch/csrc/utils.cpp has cuda specific code. This makes it impossible to declare a single THTensor and THCTensor template type (i.e. THPPointer<_THTensor>, THPointer<_THCTensor>).
* [READY TO MERGE] Use ccache in macOS build (#8009)
* Use ccache in macOS build
* Moving to sccache
* Don't use sccache in test job
* [NEEDS REVIEW] Add nan and inf probability check to multinomial (#7647)
* Add nan and inf probs check to multinomial
* fix bug
* Spawn CUDA test in subprocess
* Make sure invalid input won't pass the test case
* Try to fix error
* Test failure cases in Python 3 only
* Try to fix Windows error
* Move CUDA test to test_cuda.py
* fix issues
* fix module name error
* no need to check for CUDA existence in test_cuda
* Use PY3
* [READY TO MERGE] Enable tests that use DataLoader with multiple workers on Windows (#6745)
* Don't import TEST_CUDA for test_dataloader on Windows
* test_partial_workers is stuck on Windows
* Don't copy unneeded grads when using a function for several derivatives (Fixes#7722) (#7759)
Trying to copy all results fails when one of them is a tensor list which
has not been populated. This blew up for CuDNN RNNs when the weights
did not require grad.
Thanks to Sylvain Gugger for reporting!
* Fix win mkldnn (#7718)
* Sync build_pytorch_libs.bat with build_pytorch_libs.sh
* fix quoting
* add warnings
* fix warnings
* Add /EHa
* [Caffe2] Add ADD operator for IDEEP (#8220)
* Add ADD operator for IDEEP
* Add boradcast check
* Comments
* Allow optional build and installation of native test binaries (#8225)
* test finetuning
* install off by default
* Turn BUILD_TEST=ON for jenkins.
* Turn on install_test in jenkins as well
* Update MKL exporter to IDEEP ops (#8228)
IDEEP exporter support
* [ideep] Add IDEEP Squeeze op (#8227)
Similar to MKLSqueezeOp at caffe2/mkl/operators/squeeze_op.cc
* [auto] Update onnx to 62e63e9 - Fix build errors inside protobuf-bench (onnx/onnx#1084)
62e63e9de8
* Use .cc since some downstream libraries are configured for C++ only. (#8234)
* Rename SparseTensor to SparseTensorRef. (#8237)
I want to introduce using SparseTensor = Tensor (as a documentary
type alias for Tensor), but the name is already taken.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* [caffe2] Build Android tests and binaries in CI (#7593)
Update benchmark submodule to version with fixed Android/GNUSTL build
* Remove core and util warnings (#8239)
* Fix some signed/unsigned mismatches
* Skip unused result warning
* Explict fallthrough for murmur hash
* Enable aligned new support to eliminate warning
* Switch to int instead of unsigned in some cases
* Remove .gitmodules.aten since it is in .gitmodules now (#8232)
* Fix: gradcheck forced float32 (#8230)
* Print requires_grad and grad_fn in string repr of tensor (#8211)
For example:
>>> torch.ones(3).requires_grad_()
tensor([ 1., 1., 1.], requires_grad=True)
>>> torch.ones(3).requires_grad_() * 5
tensor([ 5., 5., 5.], grad_fn=<MulBackward0>)
The suffix (dtype, requires_grad, grad_fn) wraps to a new line if
it would cause the the line to exceed the linewidth.
>>> torch.ones(10).double().requires_grad_()
tensor([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
dtype=torch.float64, requires_grad=True)
* Fix TEST_CUDA import in test_cuda (#8246)
* Fix lifting cat into its constant version (#8174)
This fixes a bug where schema including varargs lists did not lift
properly blocking correct ONNX export.
* Don't override Tensor, Storage macros defined outside torch/csrc in t… (#8243)
* Don't override Tensor, Storage macros defined outside torch/csrc in torch/csrc.
This PR does the following:
1) Removes THSTensor macros in torch/csrc, which aren't used.
2) For macros defined outside of torch/csrc (THTensor, THTensor_, THStorage, THStorage_):
a) No longer override them, i.e. previously THTensor could actually be THCTensor if a generic file was included from a file including THCP.h.
b) Instead, introduce new macros THW* (e.g. THWTensor) to represent a (potentially empty) wildcard character.
In addition to making this code easier to read and codemod, this allows us to more freely change TH/THC; for example:
currently in the THC random code, the state is casted to THByteTensor*; this happens to work because the macros don't happen to override THByteTensor.
But if THByteTensor just becomes an alias of THTensor (which is the plan for a single tensor type), then this no longer works.
The whole thing is a bit of a mess previously because you really have to understand which macros and redefined and which aren't.
We could also rename the macros that live in torch/csrc (e.g. the THPTensor macros), but since that is more self contained, I punted for now.
* Don't change the plugin.
* [auto] Update onnx to 3a035f4 - Add retry logic to model downloading (onnx/onnx#1077)
3a035f4397
* Fully genericize THC/THCUNN (except for TensorUtils and DeviceTensorUtils). (#8251)
* [cmake] Use CAFFE2_USE_* for public/cuda.cmake (#8248)
* Fix app size check (#8256)
Fix app size check
* wip on CPU impl
* Stop BCELoss from returning negative results (#8147)
* Stop BCELoss from returning negative results
* check explicitly for 0 before taking log
* add tests
* fix lint
* address comments
* Relax CUDA_HOME detection logic, to build when libraries are found. (#8244)
Log when no cuda runtime is found, but CUDA is found
* Added backward function for kl_div target (#7839)
* added backward fn for target
* added module test for kl_div target, and assuming targets are probabilities
* Change the output format of caffe2 observers (#8261)
as title
* Remove TensorUtils<T>::getData, provide data<T>() in TH(C)Tensor. (#8247)
* Remove TensorUtils<T>::getData, provide data<T>() in TH(C)Tensor.
* Fix template parameter.
* [caffe2] Move submodule onnx-tensorrt forward (#7659)
Commit 82106f833dcb0070446a150e658e60ca9428f89b is essential.
* [ideep] Add IDEEP fallbacks for Faster-RCNN ops (#8260)
TSIA
* un-genericize THCDeviceTensorUtils. (#8258)
* provide data<T>() in TH(C)Tensor.
* un-genericize THCDeviceTensorUtils.
This is used outside of generic context, so we need to un-genericize it to have a single THCTensor type.
* [caffe2] Fix ATen dispatch for ops with TensorList arg (#8226)
* [cmake] Add and export Modules_CUDA_fix (#8271)
* Add and export Modules_CUDA_fix
* actually, need to include before finding cuda
* [auto] Update onnx to 2508156 - Make error message more verbose (onnx/onnx#1097)
2508156135
* [auto] Update onnx to 39e4668 - fix optimizer does not set ir_version bug (onnx/onnx#1098)
39e46687ea
* [cmake] Make cudnn optional (#8265)
* Make cudnn optional
* Remove cudnn file from cpu file
* Move signal window functions to ATen; add Blackman window (#8130)
* Move signal window functions to ATen; add Blackman window
* fix cuda test not checking scipy
* [ideep] Fuse Conv-Relu after IDEEP graph rewrite, skip group conv (#8233)
IDEEP supports fusion for non-group conv
* [c10d] NCCL Process Group implementation (#8182)
* [c10d] Process Group NCCL implementation
* Addressed comments
* Added one missing return and clang format again
* Use cmake/Modules for everything and fix gloo build
* Fixed compiler warnings
* Deleted duplicated FindNCCL
* Set up CI build for CUDA 9.2 + macOS (#8274)
* Add macOS CUDA build to CI
* Fix undefined symbols issue
* Use sccache for CUDA build
* Fix sccache issues
* clean up
* c10 build setup (#8264)
* Move c10/ to caffe2/dispatch/
* Set up caffe2/utils directory
* Remove remaining TensorTypeUtils functions. (#8286)
Mostly what's remaining is copy utilities -- these are now provided in THCTensorCopy.hpp and templatized on the ScalarType rather than the TensorType.
* Create initial Python bindings for c10d (#8119)
* Build and install c10d from tools/build_pytorch_libs.sh
* Create initial Python bindings for c10d
* clang-format
* Switch link order to include more symbols
* Add bindings and tests for ProcessGroupGloo
* Add broadcast test
* Separate build flag for c10d
* Explicit PIC property
* Skip c10d tests if not available
* Remove c10d from Windows blacklist
Let it skip by itself because it won't be available anyway.
* Make lint happy
* Comments
* Move c10d module into torch.distributed
* Close tempfile such that it is deleted
* Add option USE_NVRTC which defaults to off (#8289)
* [build] Remove /torch/lib/THD/cmake in favor of /cmake (#7159)
* Remove /torch/lib/THD/cmake in favor of /cmake
* path fix
* Explicitly marking gloo to use cuda
* Fix gloo path in THD
* Have a single THTensor / THCTensor type. (#8288)
* Remove remaining TensorTypeUtils functions.
Mostly what's remaining is copy utilities -- these are now provided in THCTensorCopy.hpp and templatized on the ScalarType rather than the TensorType.
* Have a single THTensor / THCTensor type.
As was previously done with Storages, have only a single (dtype-independent) THTensor / THCTensor.
For documentation and backwards compatibility purposes, the old names, e.g. TH(Cuda)LongTensor alias the new TH(C)Tensor type.
* undef GENERATE_SPARSE.
* [auto] Update onnx to 58efe0a - add float16 support back for math and reduction ops (onnx/onnx#1102)
58efe0a9ca
* Some utils for compile-time programming (#7778)
* Add some C++17 features, implemented with C++14
* Add some type traits
* Compile-time type list abstraction
* Some utils for compile-time programming
* Fix compatibility with a larger range of compilers
* Use guts::array instead of std::array because of std::array shortcomings
* code review comments
* Use quotes for includes
* Remove THC's FindMAGMA (#8299)
* Entries for torch.distributed in CODEOWNERS (#8293)
* Add depthwise convolution test for IDEEP (#8301)
* Fix dividing by zero segfault in Reshape (#8302)
when infer a dimension of zero size new shape
* Removes unused THCTensorConv (#8229)
* Replace Variables to Tensors (#8309)
* Clean up old sccache log before build (#8305)
* Remove unused grad ops on mobile to reduce app size (#8297)
Remove unused grad ops on mobile to reduce app size
* Small fixes (#8296)
* [auto] Update onnx to 5ed684e - Remove/replace /MX with /WX for MSVC build. Was typo in a previous ch… (onnx/onnx#1104)
5ed684ebe5
* Fix sample code for cuda stream (#8319)
* [auto] Update onnx to 4b4085c - Add missing warning ignoring flags to onnx_proto CMake target (onnx/onnx#1105)
4b4085c2e9
* [THD] fix broken THD build with NCCL (#8323)
* Add docstring for `torch.sparse_coo_tensor` (#8152)
* add sparse_coo_tensor docstring
* update empty tensor example
* whitespace
* whitespace again
* add error when backend is not supported by DDP (#8325)
* Fix collect_env.py for Windows (#8326)
* Fix collect_env.py for Windows
* Fix expect file for Win machine
* Fix the script doesn't stop eariler on error for MSVC and Ninja (#8277)
* Simplify the solution
* Remove the usage of set errorlevel
* Skip test_multinomial_invalid_probs_cuda on Windows (#8324)
* Support printing sparse tensors in ATen, fixes#8333. (#8334)
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* [C++ API] Cursors (#8190)
* Add cursors to C++ API
* Small self nits
* s/struct/class
* Use more STL like names for cursors
* Implement dim_arange operator (#8266)
* Implement arange_like operator
* add ONNX symbolic
* lint
* change name
* Comment the hack
* 1. fixed flip CPU impl for non-continuous flip dims; 2. added more tests; 3. using TensorInfo and collapseDims to speed up CUDA impl for cases where flip dim is the 1st or last dim
* nits
* 1. removed for loop in pointwise CUDA kernel; 2. using templated (int64_t) IndexType for indices in pointwise CUDA kernel
* added torch.flip.__doc__
* nits
* Implement CPU bincount feature support
* Incorporate feedback on renaming to SummaryOps file and other nits
* bincount gpu implementation
* refactor cuda code and incorporate nits
* doc fix
* cuda bincount - cast weights to double if integral type
* fix: signed unsigned comparison error
* fix: ssize_t error
* refactor
* make template typenames readable and other nist
* make compatible with v0.5
* incorporate comments
* update test cases to ensure CUDA code coverage
* Add nan and inf probs check to multinomial
* fix bug
* Spawn CUDA test in subprocess
* Make sure invalid input won't pass the test case
* Try to fix error
* Test failure cases in Python 3 only
* Try to fix Windows error
* Move CUDA test to test_cuda.py
* fix issues
* fix module name error
* no need to check for CUDA existence in test_cuda
* Use PY3
* Implement randperm for CUDA
* Use Thrust to implement randperm
* clean up
* Fix test
* Offload small input scenario to CPU
* Fixed test
* Try to fix Windows error
* Fix Windows error and clean up
* Use fork_rng context manager
* Move test_randperm_cuda to test_cuda
* Add half tensor support
* Fix cuda::type error
* Fix CPU offloading
* Fix issues
* No need to check range for n == 0 case
* Fix __rshift__ bug
* Add small tests for __lshift__ and __rshift__ in test_cuda
* Add a more elaborate check for __lshift__ and __rshift__
* refactor the test to address @zou3519 's comments
* Add memory leak check in CUDA tests
* Tracking multi-GPU too
* fix run_test.py not running __name__ == '__main__' content; add test for make_cuda_memory_checked_test
* add a comment
* skip if cuda
* 1. Change the wrapper to a method in common.py:TestCase
2. Refactor common constants/method that initialize CUDA context into common_cuda.py
3. Update some test files to use TEST_CUDA and TEST_MULTIGPU
* Fix MaxUnpool3d forward memory leak
* Fix MultiLabelMarginCriterion forward memory leak
* Fix MultiMarginLoss backward memory leak
* default doCUDAMemoryCheck to False
* make the wrapper skip-able
* use TEST_MULTIGPU
* add align_corners=True/False tests for Upsample; fix TEST_CUDNN
* finalize interface
* VolumetricMaxUnpooling_updateOutput
* fix test_nccl
* rename THC caching allocator methods to be clearer
* make the wrapped function a method
* address comments; revert changes to aten/src/THC/THCCachingAllocator.cpp
* fix renamed var
* Raise error when torch.load a storage on a non-existing device
Before, doing torch.load(...) on a CUDA tensor on a CPU-only machine
would raise an unreadable error:
```
~/pytorch/pytorch/torch/cuda/__init__.py in __enter__(self)
223 if self.idx is -1:
224 return
--> 225 self.prev_idx = torch._C._cuda_getDevice()
226 if self.prev_idx != self.idx:
227 torch._C._cuda_setDevice(self.idx)
AttributeError: module 'torch._C' has no attribute '_cuda_getDevice'
```
This PR makes it so that torch.load raises a hard error if one tries to
load a storage onto a non-existing device and suggests the user to use
torch.load's map_location feature.
* Address comments
* missing dep
This PR makes two improvements:
It fixes reduce kernels where accum type != type. Currently, for example, half tensors with small values may have norms that are (approximately) representable in fp16, but calling .norm() on them will result in underflow and a reported norm of zero. This PR fixes that behavior and adds a test in test_cuda.py to ensure underflow does not occur (test_tiny_half_norm).
It simplifies all reductions by removing excessive templating and the -2 contiguous special case from THC_reduceDim and THC_reduceAll. The latter was previously removed from pointwise apply. This has no performance impact as the -2 special case was already mapping to the 1D code path.
PyTorch currently attempts to handle accum type != type by either (1) writing kernels that immediately convert values to accum type after reading or (2) writing operations that take in type values and accumulate to the accum type. The latter path was not working properly (hence the current excessive half tensor underflow) and resulted in a lot of redundant code, with two reduce ops being passed to a kernel instead of one, and reduce ops frequently receiving the same template argument twice.
This PR makes the former approach THE approach. Kernels that accumulate to (potentially) different types should follow the pattern of converting their input to the accum type, performing all operations on that type, and then converting back to the appropriate type if writing their value back to the tensor. This pattern makes the second reduce op redundant and allows for simpler templating, which should improve readability, reduce build time, and reduce binary size. Also, this prevents ops from having to perform their own conversions, which could result in poor performance if the same value was operated on multiple times.
One exception to this simplification was that a new ThrustTensorDistOp was created to handle a call to thrust::inner_product(). This Op fuses the conversion and the TensorDistOp.
In addition to the expected simplification, there is also some cleanup of excessive template parameters. For example, kernelReduceAllPass2() had three template parameters: T, IndexType, and ReduceOp, but IndexType was never used.
* wip
* Adds tests
* Fixes Python linting
* mean and norm fusions, code cleanup
* fixes file permissions
* Add batched linear solver to torch.gesv()
Fixes#3164
Picks up from #4502
I moved `gesv` to ATen.
Adds bindings for MAGMA's `gesv_batched` function for CUDA.
For CPU, runs `THLapack(gesv)` in a for loop.
The new function supports arbitrary batch dimensions (and broadcasting
of those dimensions). For example, the 4-d tensor `A x B x M x M` should
be treated as having batch-size `(A x B)`.
The overhead of creating the magma_queue_t is: ~350000 microseconds
the first time it's called and ~6 microseconds every time after that.
* Tests and docs
* Address comments
* Address comments
* Rebase
* Address comments
* Fix rebase
* Addressed comments
* Address comments
* Address comments
* Addressed comments
* Changes incorrect "overlappingIndices" call to correct "maybeOverlappingIndices"
THE PROBLEM
The current overlappingIndices() is meant to detect if a tensor defines multiple valid indices for the same data element. There are two significant issues with this function:
(1) The algorithm it attempts to implement cannot do this.
(2) That algorithm is not implemented correctly.
This call is used by pointwiseApply() and scatter(). If a tensor is readable/writable and detected as overlapped these algorithms will create a non-overlapped copy of it to work on. When tensors are improperly identified as overlapped this causese extra work. If tensors are improperly identified as non-overlapped then this would cause the operations to exhibit unexpected behavior.
For example,
ref = torch.arange(0, 32 * 5).view(4, 8, 5).cuda().double()
p = ref[:,:,::2]
p += 1
Results in a call to pointwiseApply1, which detects p as an overlapped tensor (it is not), causing a call to pointwiseApply2 that copies it into a non-overlapped temporary, and then another call to pointwiseApply2 later that copies it back to the original tensor. If, however, the original tensor is given dimensions of (4, 8, 4), instead, it is correctly detected as non-overlapped and only a single pointwiseApply1 call is made.
DISCUSSION + FIX
The algorithm that overlappingIndices() attempts to implement tests for a sufficient but not necessary condition of a tensor to be non-overlapping. That is, if its algorithm were implemented properly then it would be a conservative check that would ensure all overlapped tensors were copied (as desired), but also that some non-overlapped tensors were copied too.
The algorithm can be thought of as trying to test whether the dimensions can be ordered like "nesting dolls," with each dimension fitting within the next one larger than it. If this is true then the tensor is non-overlapping, but if it's false the tensor may or may not be overlapped. For example, a tensor with dims (2, 3) and strides (4, 3) cannot be "nested," but is non-overlapping. (The tensor looks like [[0, 3, 6], [4, 7, 10]].)
The algorithm is currently implemented improperly, as can be seen in the example above. The tensor p has dimensions [4, 8, 3] and strides [40, 5, 2]. This confuses the current implementation, which thinks the innermost dimension needs a stride of 6, which is incorrect. The first row is [0, 2, 4] and the next row begins with 5. The current implementation also improperly implemented its sorting behavior. (qsort comparators require -1, 0, and 1, not true/false return values.)
Fixing the existing algorithm is straightforward (and what this PR does, see below), but it is important to note that the algorithm never performed as intended, so its name and the documentation around it has been updated, too. A natural question is if it's possible to write an efficient overlappingIndices(), and I believe the answer is "no." Disambiguating overlapping from non-overlapping tensors is equivalent to finding a nonzero solution to a linear diophantine equation with restricted coefficients, that is, an equation of the form x_0s_0 + x_1s_1 ... = 0 where s_X is the stride in dimension X and x_X is an integer from [-size_X + 1, size_X - 1].
Another note is that the CPU does not perform this check. For example, if we run:
a = torch.FloatTensor([[0,1], [10, 11]])
b = torch.FloatTensor([[0,0],[0,0]])
b = b.set_(a.storage(), storage_offset=0, size=a.size(), stride=(1,1))
b += 1
Then b is [[1, 3], [3, 11]] because the operation is applied twice to the second element of the original tensor. This causes no warning.
Since the CPU does not perform a similar check, another question is whether the GPU code should remove its check. While it may seem that writing to overlapping tensors is an error state, running test_cuda.py reveals 171 instances of possibly overlapped tensors being copied by pointwiseApply(). (The prior incorrect version has 176 copies.) Allowing writing to overlapped tensors on the GPU may violate assumptions about memory accesses, too. In fairness, these assumptions may be violated on the CPU already.
Leaving the CPU vs GPU behavior question for the future, this fix corrects the current intended GPU behavior. This means that there will be fewer unnecessary copies and no chance of an overlapped tensor sneaking through on the GPU. The CPU behavior remains unchanged. The fix also adds a test to test_cuda.py to ensure that overlapped tensors on the GPU are written to as expected.
* cleanup
* Fixes Python formatting
* Fix performance regression on simple cases of indexing
Dispatches to the old kernels
* Adapt JIT test
The test was expected to fail, but due to the change in the previous diff, it would now dispatch to index_select, which succeeds. I modified the function to go through the advanced indexing codepath
* Only do checks once, properly AutoNoGil, AutoGPU.
* Fix cross device indexing for more than 1 cuda device.
Cross device indexing is attempted from ATen, which doesn't work well because ATen doesn't have AutoGPU, etc.
Instead, before dispatching to ATen we do type conversion on the indices; it would probably be better if we
pushed all this down to ATen, but that will take some work.
* Small cleanup.
* Codemod to update our codebase to 0.4 standard
* Update some of the test scri[ts
* remove Variable in test_clip_grad_value
* fix _symbolic_override_wrapper_maker
* More precise digamma
Fixes#6190.
This is a rebase of #3955 with some tweaks for better performance around
poles. The code is ported over from cephes with permission.
By itself, the cephes code returns inf for the poles.
For better performance around the poles with float32, one intermediate
step is always computed with double precision, regardless of dtype.
This step does `PI / tan(PI * input)`. This is necessary because small (1e-6)
rounding errors for the inputs to tan have strong effects on the output
(ie, the derivative of tan is very large at some points).
* Replace usages of finite-differences digamma with newly implemented digamma
* Better behavior near and at poles
* ScalarConvert -> scalar_cast for readability
* Separate cuda-ness from dtype.
There are no longer torch.cuda.int64, etc; only torch.int64 that correspond to at::ScalarType.
At the python arg parser level, the corresponding ATen type is selected from the combination of (ScalarType, Layout, Device).
There is also currently unused code in here for support ScalarType in native_functions; this will be used for specifying aggregate types
on reduction functions.
* Fix test_autograd.
* Add defaults to randint_like.
* Track is_cuda in py tensor types.
* Fix test_sparse.
* Fix multiprocessing.
* Fix rnn.
* Fix test_nn.
* Fix flake8.
* Implemented log2 and log10
* Re-add incorrectly removed files
* Fix minor bugs
* Fix log1p docs
* Add a try-except for python2 math module in log2 test
* Revert changes made to aten/doc/*
* Fix docstring errors
* Fix windows build
This is in preparation for splitting out sparsity (layout) from dtypes; it's complex to maintain these
and tensor.new(...) is a legacy API in any case.
* Fix integer overflow in remainder
* Fix remainder operator in CUDA
* Add tests for remainder integer overflow
* Add has_different_sign static function
