Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35221
When weight is reused, we only need to insert one observer for the weight
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20602492
fbshipit-source-id: e003e6316f6615f3526f0d00fb7b722148b4749b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34411
To make sure dequantize and the node that uses the dequantized value reside in the
block, so that we can do quant fusion
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20519603
fbshipit-source-id: 3e4c68d0a73142716e19ea6a64ae3a5d6d51fa41
Summary:
This PR adds a preprocessing step in Conv2dBatchNorm folding.
It traverses the module to check if the bias of Conv2d module is set to
None. If it is, it assume that this a traced module and insert
Optional[Tensor] type bias.
Furthermore it insert getAttr for bias in the forward graph and fixes
_convolution op to take values from getAttr.
It also fixes parametere extraction from BN module which may not
have weight and bias attributes if affine was set to False. In scripted
mode such a BN module will get weight and bias attributes set to None.
For the case of eps it gets const propagated in tracing. This is also
fixed.
Few tests cases are added.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34932
Test Plan:
python test/test_jit.py TestJit.test_foldbn_trivial
python test/test_jit.py TestJit.test_foldbn_trivial_nobias
python test/test_jit.py TestJit.test_foldbn_in_submodule
python test/test_jit.py TestJit.test_foldbn_shared_classtype
python test/test_jit.py TestJit.test_foldbn_complex_cases
python test/test_jit.py TestJit.test_nofoldbn_complex_cases
Differential Revision: D20536478
Pulled By: kimishpatel
fbshipit-source-id: 4e842976a380d0575a71001bb4481390c08c259e
Summary:
add `id` function so to give uses a way of keeping a `seen` set of nn modules.
n practice, this is only used between values of `T` and `T` or `T` and `Optional[T]`, so in this implementation I made it so that None is the only value that can be zero. Python also only guarantees `id()` gives semantically meaningful results for pointer types.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34975
Differential Revision: D20549677
Pulled By: eellison
fbshipit-source-id: cca5ed4ef013f7540f93abf49f91f9830dfdca14
Summary:
https://github.com/pytorch/pytorch/issues/34563 accidentally introduced a lint error due to an unused import. This PR removes this import.
Jit tests run as expected after this change:
```
> python test/test_jit.py
.....
Ran 2435 tests in 100.077s
OK (skipped=140, expected failures=1)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34778
Differential Revision: D20459708
Pulled By: tugrulince
fbshipit-source-id: bb742085fafc849ff3d9507d1557556e01fbeb4b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34671
Like the python arg parser, this tries to convert to the schema in order.
It introduces schema_match_exception which gets thrown when the schema doesn't match,
allowing the overload handler to try the next option.
Behavior will not 100% match the schema argument parser but should work for
simple cases using custom binding.
Test Plan: Imported from OSS
Differential Revision: D20432206
Pulled By: zdevito
fbshipit-source-id: 280839a2205ea3497db3a9b5741fccc1e2bff9a8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34190
inplace modification of ClassType might affect other tests, so we want to do non-inplace modifications.
Actually the inplace argument will be removed soon.
Test Plan:
ci
Imported from OSS
Differential Revision: D20451765
fbshipit-source-id: e87ad528c4e7f84f5774b94a8e3e85568269682d
Summary:
With this PR, we can now support left and right shift operators in the JIT engine for <int, int> and <Tensor, int>.
Updated tests pass as expected:
```
> python test/test_jit.py
...
Ran 2427 tests in 84.861s
OK (skipped=139, expected failures=1)
```
Running the following code with Python results in the output below:
```
> cat ~/expressions.py
import torch
torch.jit.script
def fn(a, b):
# type: (int, int)
return (
a << b, # supported
b >> a, # supported
a & b,
a | b,
a ^ b
)
print(fn.graph)
```
```
> python ~/expressions.py
graph(%a.1 : int,
%b.1 : int):
%4 : int = aten::leftshift(%a.1, %b.1) # /home/ince/expressions.py:7:8
%7 : int = aten::rightshift(%b.1, %a.1) # /home/ince/expressions.py:8:8
%10 : int = aten::__and__(%a.1, %b.1) # /home/ince/expressions.py:9:8
%13 : int = aten::__or__(%a.1, %b.1) # /home/ince/expressions.py:10:8
%16 : int = aten::__xor__(%a.1, %b.1) # /home/ince/expressions.py:11:8
%17 : (int, int, int, int, int) = prim::TupleConstruct(%4, %7, %10, %13, %16)
return (%17)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34563
Differential Revision: D20434209
Pulled By: tugrulince
fbshipit-source-id: 886386c59755106e17b84778b8e495b80a6269cd
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33927
Test Plan:
test will be added in later PRs
Imported from OSS
Differential Revision: D20354879
fbshipit-source-id: 03976f4b86c46dbdc4e45764a1e72f1a3855a404
Summary:
`torch.nn.functional.interpolate` was written as a builtin op when we scripted the standard library, because it has four possible overloads. As a result, whenever we make a change to `interpolate`, we need to make changes in two places, and it also makes it impossible to optimize the interpolate op. The builtin is tech debt.
I talked with ailzhang, and the symbolic script changes are good to remove (i guess that makes a third place we needed to re-implement interpolate).
I'm trying to get rid of unneccessary builtin operators because we're standardizing mobile bytecode soon, so we should try to get this landed as soon as possible.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34514
Differential Revision: D20391089
Pulled By: eellison
fbshipit-source-id: abc84cdecfac67332bcba6b308fca4db44303121
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33853
Quant fusion relies on inline, but inline will break the CallFunction("linaer", ...) into a if block
it will be hard to recognize this block and swap it with quantized::linear, in order to
preserve the op, we will swap all quantized functional linear into aten::linear.
They might produce different backward graph, but this is called in the step before we get quantized
model, so it shouldn't affect anything.
We'll integrate this with convert_script later in the new "finalize_quant" API
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20343873
fbshipit-source-id: 423e03bf893b79267d2dc97bc997ee1bfe54ec0f
Summary:
Previously when emitting subscripts we only emitted actual values, but
now they may sometimes emit a `ModuleValue`, so it should stay as a
`SugaredValue`. This allows for the result of the subscript to be
treated as a real module (i.e. you can just do `self.modlist[1](inputs)`
instead of `self.modlist[1].forward(inputs)`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34320
Pulled By: driazati
Differential Revision: D20345642
fbshipit-source-id: 2bedf9a454af747b704422f6bbb8370cbdf4bf61
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33481
We have to propagate observed property of values through ops like max_pool2d, flatten and
avoid inserting duplicated observers.
For example:
```
x1 = self.conv(x)
x2 = maxpool(x1)
x3 = self.conv(x2)
```
If x1 is observed, we should propagate this information through maxpool and
we should consider x2 as observed as well.
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20261897
fbshipit-source-id: 7de354a3ccb2b6e1708f5c743d4d9f7272691a93
Summary:
`ConcreteModuleTypeBuilder` used to keep parameters together with all others attributes in an `unordered_map` often leading to reordering them while building up the type. Parameter order is semantically meaningful, so we need to preserve it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34131
Differential Revision: D20331542
Pulled By: suo
fbshipit-source-id: 5b860025f7902654d6099751d3fb14b12f6f5a67
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32814
We skip quantization for the intermediate values for patterns like `Conv - ReLU`,
but currently we didn't skip quantizing the input/output of the graphs of matched modules,
since we now changed the way we add observers, this also needs to be updated.
Test Plan:
python test/test_jit.py -- 'TestJit.test_insert_observers_skip_values'
Imported from OSS
Differential Revision: D20208785
fbshipit-source-id: ce30f2c4c8ce737500d0b41357c80ec8b33aecf9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33834
This changes how we report Tracebacks to make them more clear when
there are both serialized and non-serialized ranges. It now looks like:
```
Traceback (most recent call last):
File "foo.py", line 25, in <module>
s2(a, b)
File "/scratch/zdevito/pytorch/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
RuntimeError: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript, serialized code (most recent call last):
File "code/__torch__.py", line 7, in forward
x: Tensor,
y: Tensor) -> Tensor:
return (self).bar(x, y, )
~~~~~~~~~ <--- HERE
def bar(self: __torch__.Moo,
x: Tensor,
File "code/__torch__.py", line 11, in bar
x: Tensor,
y: Tensor) -> Tensor:
_0 = (self).baz(x, y, )
~~~~~~~~~ <--- HERE
_1 = torch.ones([3], dtype=None, layout=None, device=None, pin_memory=None)
return torch.add(_0, _1, alpha=1)
File "code/__torch__.py", line 17, in baz
x: Tensor,
y: Tensor) -> Tensor:
return torch.add(x, y, alpha=1)
~~~~~~~~~ <--- HERE
Traceback of TorchScript, original code (most recent call last):
File "foo.py", line 11, in forward
def forward(self, x, y):
return self.bar(x, y)
~~~~~~~~ <--- HERE
File "foo.py", line 9, in bar
def bar(self, x, y):
return self.baz(x, y) + torch.ones(3)
~~~~~~~~ <--- HERE
File "foo.py", line 7, in baz
def baz(self, x, y):
return x + y
~~~~~ <--- HERE
RuntimeError: The size of tensor a (4) must match the size of tensor b (5) at non-singleton dimension 1
```
It follows Python convension of having the most important information last
and reading from the bottom up.
Changes:
* Moved the error message to the end, to copy Python
* Report original traceback separate from serialized traceback
* Make sure root functions have names in the interpreter trace.
Test Plan: Imported from OSS
Differential Revision: D20126136
Pulled By: zdevito
fbshipit-source-id: fd01f9985e5d74e04c4d064c02e8bc320f4fac13
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33277
Currently we insert observer in the called graph, which is incorrect since graphs can be shared
and the decision of whether to insert observer or not might dependend on where the graph is called.
For example, for a call sequence `self.conv1(self.conv2(x))`, we can't inserting observer correctly
if `self.conv1` and `self.conv2` are sharing the same type in the current implementation, because we insert
observer in the graph of the forward method of Conv2d right now and this call sequence requires us to insert
only one observer for the output of self.conv1/input of self.conv2.
We'll need to insert observers for input/output values of the graph in call site instead.
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20208787
fbshipit-source-id: 739e1d877639c0d0ed24e573bbd36211defa6836
Summary:
This patch enables folding GetAttr nodes with their corresponding
values. _jit_pass_freeze_module API returns a new TorchScipt module
where all function calls and get attributes are inlined.
Usage:
frozen_model = torch._C._freeze_module(scrited_model._c)
frozen_model.forward(...)
This API currently optimizes the forward method. We will follow up to
to preserve and optimize methods and attributes that are annotated as
torch.jit.interface.
Several future improvements to JIT optimizations are required to maximize
clean up/de-sugar the graph and eliminate redundancies.
Ideally, we want to produce a graph that can easily be lowered to
GLOW and other low-level backends.
__
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32178
Differential Revision: D19419640
Pulled By: bzinodev
fbshipit-source-id: 52baffaba9bca2cd60a8e747baa68d57711ad42b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33705
The fact that there were two overloads appears to be a historical
artifact that dates back to when goldsborough originally added these
bindings in the first place. If TensorOptions is made optional,
then you only need one overload, not two, as they are exactly redundant
with each other. When MemoryFormat was added, it was made a little
harder to do this, as the C++ syntax at::empty_like(t, memory_format) would
not work if you collapsed the overload; but now it works because TensorOptions
supports MemoryFormat.
The upshot is, I can get rid of all the overloads and just have one overload.
Amazingly, this change is backwards compatible, as the test attests. While
I was at it, I also deleted the overload name from the functions entirely.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D20073355
Pulled By: bhosmer
fbshipit-source-id: c6a8908213b32ccf6737ea864d135e2cce34f56b
Summary:
This bug has been hit a couple times recently. We need to handle all bivariant types, not just optional, when asserting mutability/immutability of pointed-to elements in alias analysis.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33952
Differential Revision: D20166025
Pulled By: eellison
fbshipit-source-id: cf3df9897a639641ef8303a08ba2b13523d01ef1
Summary:
Fixes#30775
This adds TorchScript implementations (copied from `python_variable.cpp`) for the remainin `Tensor` properties that were missing from the jit, in addition to a test that ensures new properties will trigger a failure so we can decide whether we want to add them as well.
For `some_tensor`, adds:
* `some_tensor.T`
* `some_tensor.ndim`
* `some_tensor.is_leaf`
* `some_tensor.name`
](https://our.intern.facebook.com/intern/diff/20153288/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33906
Pulled By: driazati
Differential Revision: D20153288
fbshipit-source-id: 2ddc48a14267077bc176065267e5ce52181b3d6b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33173
How to deal with ops that’s defined for both floating point and quantized Tensor?
Category of ops: the ones that doesn’t require observers, which means the quantization parameters(scale/zero_point) of the output of this op can be inferred from the quantization parameters of inputs.
For example:
avg_pool, max_pool, flatten, transpose, upsample
Another related topic to previous one is how do we deal with things like adaptive_avg_pool2d that does not require to be observed and it works with quantized tensor as well? If we insert quant/dequant for them, even the quant fusion becomes a numerically changing operation because the scale/zero_point for input and output are different.
Proposal
We can swap the operator with dequantize whenever we see it. For example, for pattern
Let’s say aten::general_op is defined for both floating point and quantized
%r = aten::conv(...)
%q = quantize(%r)
%dq = dequantize(%q)
%f = aten::general_op(%dq)
...
We detect that all inputs of aten::general_op is produced by dequantize, we’ll first delete all the dequantize for the inputs and then insert dequantize for each use of the output of the aten::general_op, note that this should work generally for all the case we might encounter.
After transformation we’ll have:
%r = aten::conv(...)
%q = quantize(%r)
%x = aten::general_op(%q)
%f = dequantize(%x)
...
1. Multiple inputs
1. We need to make sure all inputs of the aten::general_op are produced by dequantize before we do this transformation
2. Input used by multiple operators
1. We already did this by inserting dequantize for each use of the value
3. Output used by multiple operators
1. We’ll reuse the code that inserts dequantize(might need some refactor)
Note that current concat does not belong to this category right now since it does not inherit quantization parameters from inputs.
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20123590
fbshipit-source-id: de2febe1f37e4079457a23acaeccbc6d9c9e1f8a
Summary:
Stacked PRs
* **#33578 - [jit] Unify augmented assign handling**
* #32993 - [jit] Fix aug assign for non-tensor attributes
We handle augmented assignments to `Select` and `Var` statements differently, but the actual in place update is the same for both, so this PR factors it out into a method so we don't have 2 code paths doing the same thing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33578
Pulled By: driazati
Differential Revision: D20127647
fbshipit-source-id: 94f37acbd2551498de9d2ca09a514508266f7d31
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33531
We already insert dequantize for each use of the value, but there might still be cases where we only
see the value is used multiple times after inline. This pass adds the support to replicate dequantize
after inline to ensure output of dequantize is only used by one node, which is necessary to preserve all
quantization patterns like `dequant - conv - quant`
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20123591
fbshipit-source-id: 6edb10a4566538bcf9379d332233f870372b7a63
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33779
This should eliminate random warnings and print spew from test_jit.
It also fixes a bug where we weren't properly comparing captured outputs
(!)
Test Plan: Imported from OSS
Differential Revision: D20124224
Pulled By: suo
fbshipit-source-id: 9241d21fdf9470531b0437427b28e325cdf08d3a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33504
Fix resolution fo functions that are bound onto torch in torch/functional.py. This does not fix compilation of all of those functions, those will be done in follow ups. Does torch.stft as a start.
Fixes#21478
Test Plan: Imported from OSS
Differential Revision: D20014591
Pulled By: eellison
fbshipit-source-id: bb362f1b5479adbb890e72a54111ef716679d127
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29495
This PR adds support for `_modules`, making it so we no longer need to special case support for `nn.Sequential`. I was getting internal errors around the previous approach using `self.define()`, so i am adding this PR as part of the stack.
Fix for https://github.com/pytorch/pytorch/issues/28998
Test Plan: Imported from OSS
Differential Revision: D18412561
Pulled By: eellison
fbshipit-source-id: a8b24ebee39638fccf63b2701f65f8bb0de84faa
Summary:
Stacked PRs
* **#33578 - [jit] Unify augmented assign handling**
* #32993 - [jit] Fix aug assign for non-tensor attributes
We handle augmented assignments to `Select` and `Var` statements differently, but the actual in place update is the same for both, so this PR factors it out into a method so we don't have 2 code paths doing the same thing.
](https://our.intern.facebook.com/intern/diff/20010383/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33578
Pulled By: driazati
Differential Revision: D20010383
fbshipit-source-id: 52e559ce907e95e5c169ab9d9690d0d235db36f3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32379
Folding Conv2d - BatchNorm2d modules means recalculate the weight and bias of Conv2d module by incorproating the parameters
of BatchNorm2d, and also change the method calls to calling only forward of Conv2d module, this involves change of both module
types and graph because the bias of Conv2d is a parameter when it has value and is an attribute when it is
None(since JIT code has assumption of prameter being Tensor in multiple places), therefore
we'll need to remove the bias attribute when it is None and add a bias attribute later. Since ClassType might be shared, we separate
remove and add in separate steps and also keep track of the processed graph to avoid modifying the graph and type multiple times.
However we'll have to record the slot index of bias as well so we can replay the slot removal on other instances of Conv2d module.
Test Plan:
tbd
Imported from OSS
Differential Revision: D20078719
fbshipit-source-id: cee5cf3764f3e0c0a4a2a167b78dbada2e3835cc
Summary:
this adds enough infrastructure to run bailout checks in `checkScript`. I'll need to figure out the best way to enable it for nightly builds now.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32802
Differential Revision: D19974718
Pulled By: Krovatkin
fbshipit-source-id: 40485503f6d3ae14edcce98e1eec1f0559f3ad08
Summary:
The `not inline_everything` check was causing the jitter check to be skipped whenever we emitted a function. thanks SplitInfinity for pointing this out.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33468
Differential Revision: D19975934
Pulled By: eellison
fbshipit-source-id: 03faf8d2fd93f148100d8cf49cb67b8e15cf1f04
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33508
Ever since we switched to not inlining by default, some users have
complained since they relied on inlining occuring to, e.g. process the
graph with some other tool. Add an inlined_graph for convenience in
those cases.
Test Plan: Imported from OSS
Differential Revision: D19977638
Pulled By: suo
fbshipit-source-id: fe1fa92ff888959203d5d1995930d488b5f9e24c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32804
Constants are interpreter primitives so the op was not actually used.
This cleans up some of the logic around it.
This also fixes constant prop such that failures to look up an op
do not silently stop constant propagation. Instead, only errors
inside the op implementation itself will do this.
Test Plan: Imported from OSS
Differential Revision: D19673156
Pulled By: zdevito
fbshipit-source-id: 7beee59a6a67a6c2f8261d86bd505280fefa999e
Summary:
If a value has the type None, we can always replace it with a None constant.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33264
Differential Revision: D19878695
Pulled By: eellison
fbshipit-source-id: 5d0e7ffb37c5747997df093fec3183039d8dff4d
Summary:
Stacked PRs
* #32955 - [jit] Fix flipped PackedSequence outputs in script
* **#32953 - [jit] Support properties on `Device`**
PyTorch devices have a `index` and `type` property. This PR adds support for both to TorchScript
](https://our.intern.facebook.com/intern/diff/19849320/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32953
Pulled By: driazati
Differential Revision: D19849320
fbshipit-source-id: ce845258c6110058dd9ea1f759ef74b7ed2e786e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32941
The Python grammar allows single-statement one-line functions. So we
should allow it in the string parser.
Test Plan: Imported from OSS
Differential Revision: D19704153
Pulled By: suo
fbshipit-source-id: 8c06cc9c600aa2a9567b484a1ecc0360aad443e3
Summary:
Add `torch.jit.is_scripting` to the list of CondValues, or values that if they are an input to a if statement we only compile one side of the if. I'm not sure if we actually want this PR.
Pros:
- Makes it easier to add features that are not yet supported in TorchScript (like has_torch_function)
- The current idiom of writing `torch.jit.is_scripting` and factoring out the block to a function annotated with `torch.jit.ignore` is functionally equivalent and much more cumbersome
Cons:
- Makes it easier to add features that are not yet supported in TorchScript
- Perhaps is confusing as a reader what is being compiled. Potentially could give all caps name or otherwise change name to make it more visually stand out.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32871
Differential Revision: D19670383
Pulled By: eellison
fbshipit-source-id: 5257b0bd23c66f199d59a7f2c911e948301e5588
Summary:
The Python document <https://www.python.org/dev/peps/pep-0263/> gives
all examples using lowercase letters. Although it doesn't say
straightly, the following paragraph seems to indicate that uppercase
letters aren't legitimate:
> If a source file uses both the UTF-8 BOM mark signature and a magic encoding comment, the only allowed encoding for the comment is 'utf-8'. Any other encoding will cause an error.
My Emacs also complains about the uppercase letters every time I save
the file.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32850
Differential Revision: D19663281
Pulled By: ezyang
fbshipit-source-id: 48127d3c2fd6e22dd732a2766913735136ec2ebc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32745
Some parameters (like `bias` in conv) are optional. To achieve this
previously, you had to add `bias` as a constant, which would invoke some
pretty weird behavior in the frontend, summarized as:
```
if bias is not None:
add it as a parameter normally
else: # bias is None
add it as a constant with the value None
```
There are several things bad about this:
1. Bias is not a constant. Marking it `__constants__` is confusing.
2. It basically relies on an implementation detail (the frontend
processes parameters before constants) to work.
Okay, whatever. I don't even know why we did this originally, but
getting rid of it doesn't break anything, so I assume improved NoneType
refinement has made this a non-issue.
Note on perf: this will make no difference; if bias was `None` it's still
folded out today, if bias is a Tensor it would be added as a parameter
both before and after this change
Test Plan: Imported from OSS
Differential Revision: D19628634
Pulled By: suo
fbshipit-source-id: d9128a09c5d096b938fcf567b8c23b09ac9ab37f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32642
Previously, if we defined `__setstate__` but not `__getstate__`, we
would segfault. This PR turns that into a comprehensible error message
(and improves another error message as well).
Fixes https://github.com/pytorch/pytorch/issues/25886
Test Plan: Imported from OSS
Differential Revision: D19596463
Pulled By: suo
fbshipit-source-id: dbe76bc36bc747d65fb0223184c009e0e9ba072c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32380
We'll clone the module first and then fold conv bn and return a new
module
Test Plan:
.
Imported from OSS
Differential Revision: D19508033
fbshipit-source-id: 328e91a2c9420761c904a7f2b62dab4cfaaa31ac
Summary:
This API seems to be quite useful to make sure all bailouts in a graph are triggered. I used it for testing torchvision models and I was wondering if this might be something we might actually want to have? zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32518
Differential Revision: D19553147
Pulled By: Krovatkin
fbshipit-source-id: 7542c99051588b622091aec6d041c70731ca5d26
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32260
This makes it so you can actually pass the custom class as an arg to ScriptFunctions
Test Plan: Imported from OSS
Differential Revision: D19424252
Pulled By: jamesr66a
fbshipit-source-id: c3530186619655781dedbea03c2ad321aaff1cb8
Summary:
There was a user who did this and it would seg fault.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32503
Differential Revision: D19538481
Pulled By: eellison
fbshipit-source-id: dc3752028b9eff6ac88c025e8a2b5f8fd44ce32f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30445
Create distributed and rpc directories under caffe/test for better management
of unit tests.
Differential Revision: D18702786
fbshipit-source-id: e9daeed0cfb846ef68806f6decfcb57c0e0e3606
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32491
This PR enables IValue to be able to hold a pure PyObject by adding a
new enum tag, a new jit_type to denote PyObject existance in IValue and
the JIT type system. We don't and not plan to expose this to user.
This is the basic piece that enable ivalue to be adopted broader like
making RRef always hold IValue, it might also simplify some compiler
logic
ghstack-source-id: 97039980
Test Plan: Imported from OSS
Differential Revision: D19502234
fbshipit-source-id: 90be001706d707d376cfbea25980fd82980df84a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31841
Add Tuple Constants to JIT. The constraint here is that all elements of a tuple must themself be insertable as a a constant. Previously tuples were special cased in constant propagation, but now that there are more passes that are inserted constants, such as freezing, we should just have tuples be representable as constants.
Test Plan: Imported from OSS
Differential Revision: D19439514
Pulled By: eellison
fbshipit-source-id: 3810ba08ee349fa5598f4b53ea64525996637b1a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31408
We'll error out when a graph is quantized with different QSchemes.
This only occurs when we have two modules that have same types (e.g. two Conv2d modules initialized with
same arguments) and quantized with two configs that would produce different quantized graphs, for example
per tensor affine and per channel affine. This is a rare case, so it should be OK to skip for now.
Actual support will come later.
Test Plan:
test_jit.py, test_quantization.py
Imported from OSS
Differential Revision: D19162366
fbshipit-source-id: 798f06d0ddef0c8458237ce88b62159cc77eec8b
Summary:
Unchecked cast just refines the type of a value, the value stays the same, so the output should alias the input.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32309
Differential Revision: D19439037
Pulled By: eellison
fbshipit-source-id: fe6902d0d9a5a9ef5e9c13e1dbd056576d8c327e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32232
Previously, we were using `operator<<` as the default way of printing
IValue constants during serialization. The semantics of `operator<<`
were ill-defined; and this bit us in particular with strings and lack of
quoting.
This PR defines the role of `operator<<`: much like Python `str()`, it
is intended to produce a human-readable-ish representation for
debugging purposes.
This PR also defines a new `repr()` function on IValue that is intended
to produce a valid Python expression that can be used to recreate an
object with the same value. `repr()` is not defined on all IValue kinds
(notably tensors!) for this reason.
Test Plan: Imported from OSS
Differential Revision: D19417036
Pulled By: suo
fbshipit-source-id: c102d509eaf95a28b6a62280bc99ca6f09603de5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32187Fixes#32058. Previously we would build documentation during the pytorch
linux cuda build. We don't actually need to do this because we have a
dedicated python_doc_build job that builds the docs. With this change,
the CUDA build should run ~10 minutes faster, giving devs faster signal.
Test Plan: - Check the CUDA (10.1) build on this PR, make sure it doesn't build the docs.
Differential Revision: D19400417
Pulled By: zou3519
fbshipit-source-id: e8fb2b818146f33330e06760377a9afbc18a71ed
Summary:
`test_init_ops` calls `orthogonal_` which fails without lapack (this test was just missing a skip condition)
The cpp tests would fail with a `undefined symbol` error if run with `BUILD_TESTS=0`, so this PR skips them if that flag is `0`
](https://our.intern.facebook.com/intern/diff/19320064/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31965
Pulled By: driazati
Differential Revision: D19320064
fbshipit-source-id: d1dcd36714107688ded25a414e8969abe026bd03
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31978
Currently we keep a `mangleIndex_` that's intenral to compilation unit and
just increment the index when we found the original name is mangled, this doesn't
guarantee the new name is not defined.
This PR fixes the problem by querying whether the new name is defined or not.
fixes: https://github.com/pytorch/pytorch/issues/31268
Test Plan:
fixes the issue
Imported from OSS
Differential Revision: D19350535
fbshipit-source-id: fe3262b2838d4208ab72e2cd4a5970b3a792ae86
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31800
If we know that two constants are the same object, we can ignore other constraints and pool them together. This fixes an issue introduced by the other PR where quantization relied on constant pooling happening for correctness.
Test Plan: Imported from OSS
Differential Revision: D19269499
Pulled By: eellison
fbshipit-source-id: 9d4396125aa6899cb081863d463d4f024135cbf4
Summary:
This hooks up `inspect` so that Python functions get their parameters
names attached instead of naming them `0, 1, 2, ...`. This also fixes
issue #28537 where `ignore` functions were improperly typing `self`.
](https://our.intern.facebook.com/intern/diff/19256434/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29300
Pulled By: driazati
Differential Revision: D19256434
fbshipit-source-id: 6a1fe7bd0afab708b8439517798955d0abfeb44c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31343
Fix an issue in TorchScript tracing for modules with `c10::List<at::Tensor>` as an output. TensorList was not supported properly.
Test Plan: unit tests
Reviewed By: wanchaol
Differential Revision: D18850722
fbshipit-source-id: 87a223104d1361fe754d55deceeb1e8bbcad629b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29220
Support for accessing constant is added in previous
PRs, this PR re-enables the foldbn tests
Test Plan:
test_jit.py
Imported from OSS
Differential Revision: D18846848
fbshipit-source-id: 90ceaf42539ffee80b984e0d8b2420da66c263c3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29219
We added class constant in previous PRs, this PR allows access to
class constant in the object API
Test Plan:
build/bin/test_jit
python test/test_jit.py
Imported from OSS
Differential Revision: D18846851
fbshipit-source-id: 888a6517d5f747d1f8ced283c0c2c30b2f6c72c6
Summary:
7zip and cmake are part of base image, no need to re-install. Remove the install step can make build/test more stable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30897
Differential Revision: D19232961
Pulled By: mingbowan
fbshipit-source-id: fa3bbd1325839a2a977bf13fdbd97fda43793b8d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31517
This is going to be used by upsample (which currently uses magic values to represent optionals).
For now, we just introduce a fake function for testing (torch._test_optional_float(x)).
Test Plan: Imported from OSS
Differential Revision: D19198721
Pulled By: gchanan
fbshipit-source-id: 0a1382fde0927c5d277d02d62bfb31fb574b8c74
Summary:
This is the first stab at running profile-insensitive optimizations on pre-profiled graphs. Running those optimizations has a potential to simplify graphs greatly before GuardElimination and GuardElimination should be able to remove more guards.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31392
Differential Revision: D19173639
Pulled By: Krovatkin
fbshipit-source-id: 2485a2a598c10f9b5445efb30b16439ad4551b3f
Summary:
Previously we would only catch `py::cast_error` which led to incomprehensible error messages like: `TypeError: 'NoneType' object is not iterable`. We are running arbitrary pybind code here, and not doing anything with the error message, so we should be less restrictive with the types of errors we catch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31398
Differential Revision: D19166655
Pulled By: eellison
fbshipit-source-id: 84db8b3714c718b475913f2f4bb6f19e62f2d9ec
Summary:
Fixes#27495
This adds builtins as another piece of a concrete type. They're separate from normal functions since they represent the `BuiltinFunction` sugared value (which is a direct call to a builtin op). It also moves the builtins related logic from `jit/__init__.py` to `jit/_builtins.py` so it can be used from `jit/_recursive.py` to look up functions in the builtins table.
](https://our.intern.facebook.com/intern/diff/19149779/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31269
Pulled By: driazati
Differential Revision: D19149779
fbshipit-source-id: d4e5e5d7d7d528b75a2f503e6004394251a4e82d
Summary:
Stacked PRs
* #29940 - [jit] Fix parsing of big float literals
* **#29935 - [jit] Fix hex literal parsing**
* #29931 - [jit] Throw a better error for int too big for int64_t
Previously these were all parsed as `0`
](https://our.intern.facebook.com/intern/diff/19124944/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29935
Pulled By: driazati
Differential Revision: D19124944
fbshipit-source-id: 1ee0c1dee589933363a5efba069a2cfaf94373c5
Summary:
Add a section for unsupported ops, and modules. Automatically generate the properties and attributes that aren't bound, and for ops that have semantic mismatches set up tests so the docs stay up to date.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31329
Differential Revision: D19164472
Pulled By: eellison
fbshipit-source-id: 46290bb8a64d9de928cfb1eda5ff4558c3799c88
Summary:
Remove most of the testing for `weak_script`, since we removed it. Refactor a few of the existing tests to use recursive scripting api.
Fix for https://github.com/pytorch/pytorch/issues/23965
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31193
Differential Revision: D18966291
Pulled By: eellison
fbshipit-source-id: 6b1e18c293f55017868a14610d87b69be42bde12
Summary:
Resubmit of https://github.com/pytorch/pytorch/pull/30356 and https://github.com/pytorch/pytorch/pull/31014 :'(
The last commit contains the fix. There was an internal FBcode error not able to compile the previous `impl_default->second.equal(default_val.second))` line. I tried various fixes in C++ internally but couldn't figure anything out. This is a good example of the programming costs of going from python -> c++ for different types of objects, because the conceptual overhead has expanded in scope from (python) -> (python, c++, pybind).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31123
Differential Revision: D18936128
Pulled By: eellison
fbshipit-source-id: 7d8fd66a6dd4a3e9838f3a0b68c219b6565a9462
Summary:
Previously list elements were only unified for tensor lists.
This improves error messages and expands the unification logic
to include all types.
](https://our.intern.facebook.com/intern/diff/18837726/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30777
Pulled By: driazati
Differential Revision: D18837726
fbshipit-source-id: c4d275562a8429700987569426d694faa8f6002e
Summary:
This makes `nn.Transformer` usable from TorchScript. It preserves backwards compatibility via `__setstate__` on the encoder/decoder.
Fixes https://github.com/pytorch/pytorch/issues/24173
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28561
Differential Revision: D18124753
Pulled By: driazati
fbshipit-source-id: 7314843e5aa9c9bf974c4672e4edb24ed8ef4a6f
Summary:
Peephole optimize out type refinements when they are no longer refining the type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31024
Differential Revision: D18920958
Pulled By: eellison
fbshipit-source-id: 6d05d9812b9f9dcf001de760a78a2042fb832773
Summary:
Adds `torch.floor_divide` following the numpy's `floor_divide` api. I only implemented the out-of-place version, I can add the inplace version if requested.
Also fixes https://github.com/pytorch/pytorch/issues/27512
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30493
Differential Revision: D18896211
Pulled By: eellison
fbshipit-source-id: ee401c96ab23a62fc114ed3bb9791b8ec150ecbd
