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:
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/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:
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30544
Run Constant Propagation upon compilation only on ops with non-aliasing inputs and outputs. This speeds up the first run of `torchvision.models.resnet18` by over 50% and speeds up compilation by about 25% (although the effects didn't seem additive with with https://github.com/pytorch/pytorch/pull/30503, so I'm going to land this PR first and then see if caching still has a sizable impact).
Running constant prop only with non-aliasing types does a lot of graph cleanup by removing constant ifs and a bunch of other smaller ops. It also avoids all the jitter problems we had when we tried running full constant prop previously. Bc it is idempotent it doesn't jitter, and it doesn't jitter graphs constructed from tracing because tracing doesn't emit any ops that only involve non-aliasing inputs.
Full constant prop isn't idempotent because what ops are run depends on the state of mutation in alias db, which will often change upon successive iterations of constant propagation, and bc it affects graphs constructed from tracing.
Edit: if we were okay with running constant propagation on graphs constructed from tracing (potentially making them hard to debug), an alternative would be to run constant propagation until the graph reaches a fixed point.
Test Plan: Imported from OSS
Differential Revision: D18833607
Pulled By: eellison
fbshipit-source-id: 92a0adb4882d67ed5a0db5c279f5e122aeeba54a
Summary:
For the same reason we don't allow iteration over heterogenous types (modulelists/tuples) with types that don't have a static length, we also can't break/continue within them - we need to statically know all types.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29474
Differential Revision: D18406097
Pulled By: eellison
fbshipit-source-id: 70ed3fc4947b6237cdd6703135a988a5c13ce786
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29332
Even though we're statically typed, this can be useful, e.g. as
shorthand when iterating through a module list.
Test Plan: Imported from OSS
Differential Revision: D18393097
Pulled By: suo
fbshipit-source-id: aa42e955f88d1b8a876d0727055eb596453b9839
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28255
Add support for treating Sequentials, ModuleLists, and ModuleDicts as iterables.
As previously, when emitting a for loop over a Module Container we unroll the for loop over all elements. We require that any Sugared Value in an iterable with a Module Container have a statically - determinable length.
Otherwise, if you zipped over a list of varying length and an nn.Sequential that alternated between returning a Tensor and a Dictionary, the output type would change based on the length of the list.
Fix for #17179
And https://github.com/pytorch/pytorch/issues/27401
and https://github.com/pytorch/pytorch/issues/27506
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision: D18278124
Pulled By: eellison
fbshipit-source-id: aca336a5b8da89c756b1f0884883649510cbde3c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28408
This enable interface to defined on a nn.Module, and the InterfaceType
now have a field of is_module_ to distinguish if it's a module interface
or a normal interface (This is similar to what ClassType distinguish on
module and torchscript classes).
The module interface can be assigned with any ScriptModule that has the
compatible signatures on schemas. A normal object that is not a
ScriptModule will not be able to assigned to an module interface and
will error out when user explicitly doing so. Assigning a ScriptModule
to class interface will make it only available in attribute_list, not
module_list. More details on subtyping relationship documented in the
jit_type.h
If you declare an module interface inside an nn.Module that is being
compiled to a ScriptModule, behavior to our internal compilation will
be:
1. ConcreteModuleType will record it as an module attribute and add to
the attributes_ list.
2. JitType that is created from the ConcreteModuleType will record it as
an attribute and pre-genenerate the slot. The slot will be marked as
EntityType::MODULE still to make sure JitType record it as a Module
slot
3. cpp_module will also register it as a Module as the Slot type is the
source of truth
Since JitType will record it as attribute as store its type, it will
behave normally as the class interface attribute behave now. This means
the submodule assigned to this module interface is not getting inlined
into the graph as the normal `Module::attr` behave, it will generate
interface callMethod and allow us to later swap this with another
ScriptModule that implicitly implements this module interface.
Test Plan: Imported from OSS
Differential Revision: D18284311
fbshipit-source-id: e0b8f6e8c34b2087fab337a969e5ea3fb37ec209
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28605
This was added because std::to_string isn't available in libstc++
on Android. Use it in more places to get the PyTorch Android
build working with libstdc++.
Test Plan: Internal android build.
Reviewed By: jerryzh168
Differential Revision: D18099520
fbshipit-source-id: 17a2b617c2d21deadd0fdac1db849823637981fc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27772
This replaces unchecked_unwrap_optional with unchecked_cast. This
enables the generalization of type refinement so that it works for
isinstance checks as well. This also removes unchecked_unwrap_optional from
code we generate, which is good because it is a hard op to serialize well
since it doesn't directly encode the Optional[T] being unwrapped. In contrast,
unchecked_cast always explicitly lists the type.
Test Plan: Imported from OSS
Differential Revision: D17885424
Pulled By: zdevito
fbshipit-source-id: ce81077d6fbeaf2a802a2e0b17349aca85670466
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27773
We've changed how these functions are used over time, so I cleaned up
the header file API to match. In particular:
* tryMatchSchemas was added since the overload logic got copy/pasted
into three separate locations.
* With this change, tryMatchSchema is no longer public, as it is not needed
outside of tryMatchSchemas
* emitBuiltinFunction no longer needs a requires argument (it was always true)
* Argument order for all the schema matching stuff now puts the 'self'
builtin override last. This is only rarely used and was inconsistent with
matchSchema
Test Plan: Imported from OSS
Differential Revision: D17885425
Pulled By: zdevito
fbshipit-source-id: 064bc9fa4bd57b2e5366fff9f3c6ab9b9945e08b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26499
We've changed how these functions are used over time, so I cleaned up
the header file API to match. In particular:
* tryMatchSchemas was added since the overload logic got copy/pasted
into three separate locations.
* With this change, tryMatchSchema is no longer public, as it is not needed
outside of tryMatchSchemas
* emitBuiltinFunction no longer needs a requires argument (it was always true)
* Argument order for all the schema matching stuff now puts the 'self'
builtin override last. This is only rarely used and was inconsistent with
matchSchema
Test Plan: Imported from OSS
Differential Revision: D17488297
Pulled By: zdevito
fbshipit-source-id: a32d838ce35544972fa8767557acc22149081b55
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26271
This replaces unchecked_unwrap_optional with unchecked_cast. This
enables the generalization of type refinement so that it works for
isinstance checks as well. This also removes unchecked_unwrap_optional from
code we generate, which is good because it is a hard op to serialize well
since it doesn't directly encode the Optional[T] being unwrapped. In contrast,
unchecked_cast always explicitly lists the type.
Test Plan: Imported from OSS
Differential Revision: D17412856
Pulled By: zdevito
fbshipit-source-id: ded47eb086c4610998ad92bb1174225af00220f7
Summary:
10 lines of error context (on both sides) is overkill, especially now
that we have line numbers. With a compilation stack of a couple
functions, it becomes a pain to scroll to the top of the stack to see
the real error every time.
This also fixes class names in the compilation stack to a format of
`ClassName.method_name` instead of the the full qualified name
Old output
```
clip_boxes_to_image(Tensor boxes, (int, int) size) -> (Tensor):
Expected a value of type 'Tuple[int, int]' for argument 'size' but instead found type 'Tuple[int, int, int]'.
:
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:365:20
top_n_idx = self._get_top_n_idx(objectness, num_anchors_per_level)
batch_idx = torch.arange(num_images, device=device)[:, None]
objectness = objectness[batch_idx, top_n_idx]
levels = levels[batch_idx, top_n_idx]
proposals = proposals[batch_idx, top_n_idx]
final_boxes = []
final_scores = []
for boxes, scores, lvl, img_shape in zip(proposals, objectness, levels, image_shapes):
boxes = box_ops.clip_boxes_to_image(boxes, img_shape)
~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
keep = box_ops.remove_small_boxes(boxes, self.min_size)
boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep]
# non-maximum suppression, independently done per level
keep = box_ops.batched_nms(boxes, scores, lvl, self.nms_thresh)
# keep only topk scoring predictions
keep = keep[:self.post_nms_top_n]
boxes, scores = boxes[keep], scores[keep]
final_boxes.append(boxes)
final_scores.append(scores)
'RegionProposalNetwork.filter_proposals' is being compiled since it was called from 'RegionProposalNetwork.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:446:8
num_images = len(anchors)
num_anchors_per_level = [o[0].numel() for o in objectness]
objectness, pred_bbox_deltas = \
concat_box_prediction_layers(objectness, pred_bbox_deltas)
# apply pred_bbox_deltas to anchors to obtain the decoded proposals
# note that we detach the deltas because Faster R-CNN do not backprop through
# the proposals
proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors)
proposals = proposals.view(num_images, -1, 4)
boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
losses = {}
if self.training:
assert targets is not None
labels, matched_gt_boxes = self.assign_targets_to_anchors(anchors, targets)
regression_targets = self.box_coder.encode(matched_gt_boxes, anchors)
loss_objectness, loss_rpn_box_reg = self.compute_loss(
objectness, pred_bbox_deltas, labels, regression_targets)
losses = {
'RegionProposalNetwork.forward' is being compiled since it was called from 'MaskRCNN.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/generalized_rcnn.py:53:8
"""
if self.training and targets is None:
raise ValueError("In training mode, targets should be passed")
original_image_sizes = [(img.shape[-2], img.shape[-3]) for img in images]
images, targets = self.transform(images, targets)
features = self.backbone(images.tensors)
if isinstance(features, torch.Tensor):
features = OrderedDict([(0, features)])
proposals, proposal_losses = self.rpn(images, features, targets)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes)
losses = {}
losses.update(detector_losses)
losses.update(proposal_losses)
# TODO: multiple return types??
# if self.training:
```
New output
```
RuntimeError:
clip_boxes_to_image(Tensor boxes, (int, int) size) -> (Tensor):
Expected a value of type 'Tuple[int, int]' for argument 'size' but instead found type 'Tuple[int, int, int]'.
:
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:365:20
final_scores = []
for boxes, scores, lvl, img_shape in zip(proposals, objectness, levels, image_shapes):
boxes = box_ops.clip_boxes_to_image(boxes, img_shape)
~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
keep = box_ops.remove_small_boxes(boxes, self.min_size)
boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep]
'RegionProposalNetwork.filter_proposals' is being compiled since it was called from 'RegionProposalNetwork.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:446:8
proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors)
proposals = proposals.view(num_images, -1, 4)
boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
losses = {}
'RegionProposalNetwork.forward' is being compiled since it was called from 'MaskRCNN.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/generalized_rcnn.py:53:8
if isinstance(features, torch.Tensor):
features = OrderedDict([(0, features)])
proposals, proposal_losses = self.rpn(images, features, targets)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
detections = self.transform.postprocess
```
](https://our.intern.facebook.com/intern/diff/17560963/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26765
Pulled By: driazati
Differential Revision: D17560963
fbshipit-source-id: e463548744b505ca17f0158079b80e08fda47d49
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26270
We've accumulated a lot of sugared values whose only purpose is
to be instanced-checked against in emitApplyExpr. I need to add
another one to insert an unchecked_cast, and do not want to continue
the pattern. This creates an abstraction for this concept (SpecialFormValue),
and removes all the unneeded sugared values. There is no functionality
change here just a bunch of code movement in compiler.cpp
Test Plan: Imported from OSS
Differential Revision: D17412854
Pulled By: zdevito
fbshipit-source-id: 15877c91decaea5a00d1fe737ed2d0f0f8a79a28
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26269
previously isinstance only worked when we could statically determine
if it were true/false. Now we actually can issue an isinstance check
in case where it is dependent on the runtime type, e.g. Optional[int]
being an instance of int. This is not very useful on its own yet,
but with type refinement and allowing Any as an argument type this will
allow for python-style "overloaded" functions such that we can
remove our __overload__ support.
Test Plan: Imported from OSS
Differential Revision: D17412853
Pulled By: zdevito
fbshipit-source-id: e2c37040f25f6b94ee1676854fceecd22de190ef
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26897
TORCH_INTERNAL_ASSERT("foo") doesn't do what you think it does :)
I'll try to do a fix to catch it in the compiler, but for now - let's fix usages
Found them using regex:
```
ag --cpp "TORCH_(CHECK|INTERNAL_ASSERT)\([ \n]*\"" --multiline
```
Test Plan: Imported from OSS
Differential Revision: D17624299
Pulled By: dzhulgakov
fbshipit-source-id: 74f05737ef598fd92b5e61541ee36de2405df23d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26758
This PR changes the order in which we import classes and functions so
that is is no longer necessary for them to defined in order in a file,
or for there to be proper import statements in the exported file.
Actually importing a function/class now is driven by the need to resolve
the entity during unpickling, type resolution, or value resolution.
While this should allow significant simplification to the code that
serializes classes, this work has not been done yet in order to avoid
inevitable forward compat issues in the transition period.
Notes:
* Individual functions have been replaced with a SourceImporter object
that exposes a resolveType method. This method loads the type if
it has not been loaded yet, potentially parsing (but not loading)
the file it exists in if that file hasn't been parsed yet.
* Some legacy functionality needed to be added as a method to this object
since the old format still used some of this logic for class resolution.
Test Plan: Imported from OSS
Differential Revision: D17558989
Pulled By: zdevito
fbshipit-source-id: 7eae3470bcbd388c4de463e3462d527776ed46c6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26145
This is step towards isinstance type refinement.
It primarily does yak shaving in compiler.cpp to unify the handling
of special case behavior that occurs in conditional expressions:
* Handling type refinement as part of emission.
* Handling `is None` static-if specialization.
It introduces a CondValue object that is a Value that also has
additional type refinements that are true when that Value is true,
and potentialy a static-true/false value that, if set, will cause if
statements to be handled statically, omitting typechecking of the other side.
This ends up expanding some behavior, for instance `is None` specialization
used to happen only for single expressions, but now works through
boolean logic.
Test Plan: Imported from OSS
Differential Revision: D17359500
Pulled By: zdevito
fbshipit-source-id: ce93804496c8b4c3197a5966bc28c608465fda64
Summary:
Follow up to https://github.com/pytorch/pytorch/pull/25664, add `class_type[ind] = val`. Like `__getitem__`, `__setitem__` has a custom compilation path so it wasn't added with the rest of the magic methods.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25750
Differential Revision: D17428725
Pulled By: eellison
fbshipit-source-id: ff3767ef41515baf04b0c0f5c896dbd3f1d20cd3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26061
This is in preparation for actually emitting a dynamic isinstance check instruction.
It re-arranges the logic so that all the types and properties to check
against are in a flat list. In the future this flat list will be encoded
into an actual instruction if we determine that we cannot perform
the check statically.
Test Plan: Imported from OSS
Differential Revision: D17332062
Pulled By: zdevito
fbshipit-source-id: 4c0b65436f8e030170d469fe747e79de24bb24eb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25361
Previously we had a different None object for each type T so that
unwrap optional could still recover the type T from it. After a few
months of having this conversion behavior, it has become clear that
only the unwrap optional operators cause this problem. Furthermore, it
is beneficial to have NoneType <: Optional[T] because this is how IValues
work (in particular the None IValue is not tagged). This patch makes the
necessary changes to do this. In particular it special cases unwrap optional
in export so that it annotates the None to make sure we can recover the type.
This also changes how matching and evaluating type values work so that we
can consider None matchable to type Optional[T], eventhough we cannot
derive T from that match.
Test Plan: Imported from OSS
Differential Revision: D17103072
Pulled By: zdevito
fbshipit-source-id: 37678ed3e5ce54f2eb3ee4dff2734a39f0bee028
Summary:
Doesn't really add much functionality, since inputs to `tuple()` which we can statically infer the output size is pretty much just tuples. Does improve the error message though.
Fix for https://github.com/pytorch/pytorch/issues/24000
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25474
Differential Revision: D17133800
Pulled By: eellison
fbshipit-source-id: 41a052895e6ed24a384ec6f8aef0a6769ac094e6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25228
This adds a facility to isSubtypeOf for it to explain why a type is
not a subtype of something else. It is used in situations where it
is not clear from the types python_str alone why the relationship
is now true. Because of subtle interaction between default arguments,
overloads, and virtual methods, it uses isSubtypeOfExt for the extended
version to avoid requiring readers to understand the interaction.
Test Plan: Imported from OSS
Differential Revision: D17066673
Pulled By: zdevito
fbshipit-source-id: 4de7c40fbf7f9eeae045d33a89a038538cf87155
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25227
Adds cases to NamedType serialization to so that interfaces are written.
Similar implementation to NamedTuples
Test Plan: Imported from OSS
Differential Revision: D17066674
Pulled By: zdevito
fbshipit-source-id: fda5419260fad29e8c4ddb92de1d3447d621d982
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25258
this is the first commit in a series to add interfaces to JIT.
Interfaces allow the specification through a blank python class of an
abstract interface that can be used in type annotations for Script functions.
If a TorchScript class implements all the methods in the interface with
the appropriate types, then it is implicitly considered to implement
that interface.
Follows required:
* implementation of serialization
* implementation in the parser frontend
* better error reporting for explaining why a class does not meet an
interface specification.
Test Plan: Imported from OSS
Differential Revision: D17079963
Pulled By: zdevito
fbshipit-source-id: a9986eeba2d4fdedd0064ce7d459c0251480a5a0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25059
This fixes the cases where a type annotated with optional cannot
be conditionally assigned to none:
```
x : Optional[int] = 4
if ...:
x = None
```
Test Plan: Imported from OSS
Differential Revision: D16975166
Pulled By: zdevito
fbshipit-source-id: 5a7a81224d08b9447e1f4d957fcd882091e02f32
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25065
Using global atomic variables is bad because sending the same AST through
the compiler twice will produce different graphs. This makes it a
member of the translation struct.
Test Plan: Imported from OSS
Differential Revision: D16975355
Pulled By: zdevito
fbshipit-source-id: 23e15ffd58937a207898a4c4bed82628237e3c2e
