Is it a hack?
No, the PR is legit and we should land it. We also could use
annotate_input_shape+shape_prop+dtype_prop when all that is ready. Both
workflows are legit.
[ghstack-poisoned]
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66279
This error appears when compiling with "-Wextra" and cannot be resolved by fixing the code since the return type of the instrinic being passed to `map` is fixed.
Fixes:
```
caffe2/aten/src/ATen/cpu/vec/vec256/vec256_bfloat16.h:204:28: error: 'const' type qualifier on return type has no effect [-Werror,-Wignored-qualifiers]
Vectorized<BFloat16> map(const __m256 (*const vop)(__m256)) const {
^~~~~~
caffe2/aten/src/ATen/cpu/vec/vec256/vec256_bfloat16.h:204:28: error: 'const' type qualifier on return type has no effect [-Werror,-Wignored-qualifiers]
Vectorized<BFloat16> map(const __m256 (*const vop)(__m256)) const {
^~~~~~
```
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D31480888
fbshipit-source-id: 919c0d48c8ce13ce1106a9df124a077945e36707
Summary:
Previously https://github.com/pytorch/pytorch/pull/64087 broke the test `binary_macos_wheel_3_7_cpu_build`, because wheel build is not happy with `model_tracer`. Considering it's prototype and there is no need to ship model_tracer via wheel at the moment, using the option `TRACING_BASED` for building tracer. When tracing-based is mature enough, we can ship the tracer binary via wheel eventually.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66267
Original commit changeset: 8ac3d75a52d0
ghstack-source-id: 140122106
Test Plan:
binary_macos_wheel_3_7_cpu_build passes
{F668643831}
Reviewed By: dhruvbird
Differential Revision: D31478593
fbshipit-source-id: 726cab1b31c4596f6268b7824eecb20e2e59d161
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66286
No need to take refcount bumps on each comparator call.
Test Plan: CI, review
Reviewed By: hlu1, JasonHanwen
Differential Revision: D31487058
fbshipit-source-id: 98d2447ac27a12695cb0ebe1e279a6b50744ff4f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66282
Now that a bunch of the `FooType::get()` functions return a const reference, we can forward that behavior through `getTypePtr()` using return type deduction.
Test Plan: Inspect assembly for List_test.cpp before/after the rest of the change; reference counting is no longer in the happy path.
Reviewed By: hlu1, JasonHanwen
Differential Revision: D31486117
fbshipit-source-id: 863b677bb6685452a5b325d327bdc2a0a09627bf
Summary:
- [x] Fix the Pyre type checking errors in `torch/quantization/fx/utils.py`
```
torch/quantization/fx/utils.py:490:4 Incompatible variable type [9]: target_module_type is declared to have type `Type[nn.modules.module.Module]` but is used as type `None`.
```
Fixes the issue: [MLH-Fellowship/pyre-check/issues/75](https://github.com/MLH-Fellowship/pyre-check/issues/75)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66311
Reviewed By: pradeep90
Differential Revision: D31506399
Pulled By: 0xedward
fbshipit-source-id: 3d866fba6005452378d4a2613b8689fa2d7a8b67
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66253
This was initially broken in #65829 and unbroken in #66003, this PR cleans
it up by removing the mypy ignore line.
Test Plan:
```
mypy torch/jit/_recursive.py --no-incremental
```
Imported from OSS
Reviewed By: supriyar
Differential Revision: D31475100
fbshipit-source-id: 46ab2ede72c08b926f4f9a6b03b1a1375b884c8a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65903
This changes the imports in the `caffe2/torch/nn/intrinsic` to include the new import locations.
```
codemod -d torch/nn/intrinsic --extensions py 'torch.quantization' 'torch.ao.quantization'
```
Test Plan: `python test/run_test.py`
Reviewed By: albanD
Differential Revision: D31301195
fbshipit-source-id: a5a9d84cb1ac33df6c90ee03cda3e2f1c5d5ff51
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65902
This changes the imports in the `caffe2/torch/nn/qat` to include the new import locations.
```
codemod -d torch/nn/qat --extensions py 'torch.quantization' 'torch.ao.quantization'
```
Test Plan: `python test/run_test.py`
Reviewed By: jerryzh168
Differential Revision: D31301196
fbshipit-source-id: ff237790d74cd3b3b5be642a997810f4f439a1d8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65901
This changes the imports in the `caffe2/torch/nn/quantizable` to include the new import locations.
```
codemod -d torch/nn/quantizable --extensions py 'torch.quantization' 'torch.ao.quantization'
```
Test Plan: `python test/run_test.py`
Reviewed By: jerryzh168
Differential Revision: D31301194
fbshipit-source-id: 8ce8a3015ea61da62d7658846d1ca64fbdabaf7a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65900
This changes the imports in the `caffe2/torch/nn/quantized` to include the new import locations.
```
codemod -d torch/nn/quantized --extensions py 'torch.quantization' 'torch.ao.quantization'
```
Test Plan: `python test/run_test.py`
Reviewed By: jerryzh168
Differential Revision: D31301193
fbshipit-source-id: 58efb1ad51a8b441e2a3bd5b91af11eab6b9331f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64397
This diff exposes a way to add events to kineto profiler from external
source.
This can be a backend that executes a subgraph and wants to record this
execution in kineto profiler.
This diff also adds "backend" metadata to identify the backend an event
would have executed on.
Test Plan:
test_lite_interpreter
Imported from OSS
Reviewed By: raziel
Differential Revision: D30710710
fbshipit-source-id: 51399f9b0b647bc2d0076074ad4ea9286d0ef3e2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63198
Linear layers using the same input tensor can be concatted together
as long as the weights and biases are compatible.
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D31240642
fbshipit-source-id: 1e78daa6b89822412ba2513d326ee0e072ceff1e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65177
There is no need to heap-allocate any vectors in this case.
ghstack-source-id: 140052520
Test Plan:
CI
Startup for static runtime on ctr_mobile_feed local net decreased from 7.8s to about 7.0s
Reviewed By: malfet
Differential Revision: D30984194
fbshipit-source-id: 85091e55445f653ec728b27da4b459a2f1873013
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65122
Failure to cache this seems to contribute to quadratic startup time for the static runtime.
Disclaimer: I am entirely un-versed in the performance considerations for the JIT and have no idea what the other impacts of this change may be. Let the reviewer beware.
ghstack-source-id: 140052522
Reviewed By: suo
Differential Revision: D30983268
fbshipit-source-id: 4329aee6b5781f5c2e2d2334c396fab8528d4b7b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65343
No reason not to save a bit on re-hashing.
ghstack-source-id: 140052518
Test Plan:
CI
Static runtime startup seems to go from 5.9-6.0s to 5.8s-6.0s, perf shows less time spent rehashing
Reviewed By: mikeiovine
Differential Revision: D31027362
fbshipit-source-id: 39dd53ecd462693b518535856ddd92df78a4977b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64964
log API usage for fsdp API in PyTorch
Test Plan: unit test
Reviewed By: rohan-varma
Differential Revision: D30915734
fbshipit-source-id: 5e3b335327f4a3ff59b025e8e17a0fa0b7f6597d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66167
Sometimes due to desync we see PG wrapper monitored barrier fail. In
this case it would be useful to print the info about the collective that was
trying to run along with the actual error.
ghstack-source-id: 140037653
Test Plan: CI
Reviewed By: cbalioglu
Differential Revision: D31353021
fbshipit-source-id: e2a515326c9314c98119978d5566eb5431cca96c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66166
These methods should be private.
ghstack-source-id: 139782587
Test Plan: CI
Reviewed By: cbalioglu
Differential Revision: D31353020
fbshipit-source-id: 583fb315cc2cacc37df3d29cd5793b42558930b3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65345
FooType::get() can return a const reference. Inconveniently, converting shared_ptr<FooType> to shared_ptr<Type> requires a copy & refcount bump, so to properly take advantage of this in unshapedType() we need to take a const Type& in isSubtypeOf(), which is good practice anyway -- don't require a shared_ptr if you don't need to take ownership.
ghstack-source-id: 140044165
Test Plan:
CI
perf says c10::unshapedType time decreased from 2.8% to 2.2% during static runtime startup, though I expect this to be generally beneficial.
Reviewed By: hlu1
Differential Revision: D31027361
fbshipit-source-id: 676feb81db9f74ad7b8651d8774f4ecb4cfa6ab8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65346
Tidying up the top sources of reference count decrements seen during static runtime startup.
ghstack-source-id: 140027349
Test Plan:
CI
perf now shows under 2% time spend in ~__shared_count instead of about 5%.
Reviewed By: suo
Differential Revision: D31057277
fbshipit-source-id: 9a16daf2e655fda80d4ec21290b30f02ba63d8da
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66277
Previously, it is grouped together with tests related to `MapDataPipe`, but it should be with `IterDataPipe`.
cc VitalyFedyunin ejguan NivekT
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D31485823
Pulled By: NivekT
fbshipit-source-id: d13d8c28cbfc305da0e3033d4109a0f971281a02
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66275
Once this is added to Core, TorchData's PR will not need a custom class and can use this wrapper instead.
cc VitalyFedyunin ejguan NivekT
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D31485822
Pulled By: NivekT
fbshipit-source-id: 790de27629c89c0ca7163a8ee5a09ee8b8233340
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66051
Make the error message clearer when quantized embedding is converted
with an unsupported dtype. This is helpful when debugging quantization
errors on new models.
Test Plan:
```
class M(nn.Module):
def __init__(self):
super().__init__()
self.embedding = nn.Embedding(1, 1)
m = M().eval()
m.qconfig = torch.quantization.QConfig(
activation=torch.quantization.MinMaxObserver.with_args(dtype=torch.qint8),
weight=torch.quantization.MinMaxObserver.with_args(dtype=torch.qint8))
m.embedding.qconfig = m.qconfig
mp = torch.quantization.prepare(m)
mq = torch.quantization.convert(m)
// error message now includes the incorrect dtype
```
Imported from OSS
Reviewed By: dagitses
Differential Revision: D31472848
fbshipit-source-id: 86f6d90bc0ad611aa9d1bdae24497bc6f3d2acaa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66050
Adds the dtype to an error message when trying to quantize something
other than a float. This is useful for debugging quantization tools on
new models.
Test Plan:
```
x = torch.randn(1, 1, 1, 1, dtype=torch.double)
xq = torch.quantize_per_tensor(x, 0.01, 0, torch.quint8)
// error message now includes Double
```
Imported from OSS
Reviewed By: dagitses
Differential Revision: D31472849
fbshipit-source-id: 2331ffacefcbc6f8eca79694757d740de74a0f1d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66049
Enables quantized add with broadcasting. As pointed out by jamesr66a,
this was disabled but TensorIterator already supports it. Added a test
case to verify.
Test Plan:
```
python test/test_quantization.py TestQuantizedOps.test_qadd_broadcast
```
Imported from OSS
Reviewed By: dagitses
Differential Revision: D31472850
fbshipit-source-id: a3b16d9000487918db743525d22db6864330762b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66108
BC-breaking change: intT is now longT (which aligns it more accurately with how
the types are referred to in C++). The benefit for this is we can idiomatically
express all C++ dtypes (with intT now mapping to int32_t). These types are needed
for ufunc codegen in a latter patch.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D31385761
Pulled By: ezyang
fbshipit-source-id: ec6f3a0953794313470dbe14911f23ac116be425
