Done via
```
git grep -l 'SymbolicIntNode' | xargs sed -i 's/SymbolicIntNode/SymIntNodeImpl/g'
```
Reasoning for the change:
* Sym is shorter than Symbolic, and consistent with SymInt
* You usually will deal in shared_ptr<...>, so we're going to
reserve the shorter name (SymIntNode) for the shared pointer.
But I don't want to update the Python name, so afterwards I ran
```
git grep -l _C.SymIntNodeImpl | xargs sed -i 's/_C.SymIntNodeImpl/_C.SymIntNode/'
```
and manually fixed up the binding code
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82350
Approved by: https://github.com/Krovatkin
Due to implicit conversion shenanigans, having both IntArrayRef
and SymIntArrayRef overloads makes {} ambiguous. While we could
fix this by making a single unified type that accepts all the overloads
we want, an easier fix was to just push the SymIntArrayRef overload
to its own name.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79281
Approved by: https://github.com/suo
Summary: Currently OpKind is stored as an object field called op_ for each IR
node, and one usage of op_ is to avoid dynamic_cast in NodeCast when we
need to downcast a base-node pointer into a concrete sub-node pointer.
As a result, we need to construct and pass in an op when downcasting
nodes, and this becomes quite anonnying when we start to implement the
trie-based IR node reusing. More importantly, the op for each subclass
should be unique for that subclass and thus making it a const static field
is a more logical design.
In this PR, we still keep the object-level op_ for easier XLA adoption. As
furture work, we can come back to remove op_, make the op() method
virtual, and get rid of OpKind in all the node constructors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76711
Approved by: https://github.com/wconstab, https://github.com/JackCaoG
Summary: TrieCache provides a way to look up an IR node before we
actually create it. If the lookup hits in TrieCache, we reuse the
existing node and move the current pointer in TrieCache to point to that
node; if the lookup misses, we create a new node and insert it into TrieCache.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76542
Approved by: https://github.com/wconstab, https://github.com/JackCaoG
Next stage of breaking up https://github.com/pytorch/pytorch/pull/74710
IR builder class introduced to decouple the explicit usage of `TsNode` in core lazy tensors.
Requires https://github.com/pytorch/pytorch/pull/75324 to be merged in first.
**Background**
- there are ~ 5 special ops used in lazy core but defined as :public {Backend}Node. (DeviceData, Expand, Scalar...)
- we currently require all nodes derive from {Backend}Node, so that backends can make this assumption safely
- it is hard to have shared 'IR classes' in core/ because they depend on 'Node'
**Motivation**
1. avoid copy-paste of "special" node classes for each backend
2. in general decouple and remove all dependencies that LTC has on the TS backend
**Summary of changes**
- new 'IRBuilder' interface that knows how to make 5 special ops
- move 'special' node classes to `ts_backend/`
- implement TSIRBuilder that makes the special TS Nodes
- new backend interface API to get the IRBuilder
- update core code to call the builder
CC: @wconstab @JackCaoG @henrytwo
Partially Fixes#74628
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75433
Approved by: https://github.com/wconstab
This **roughly** corresponds to Goal 3.2 in https://docs.google.com/document/d/1iiLNwR5ohAsw_ymfnOpDsyF6L9RTUaHMpD8YLw-jxEw/edit#
Namely:
It adds the following:
* SymbolicIntNode interface
* LazySymbolicIntNode implementation
* Lazy `narrow_copy` implementation
* Need add support for SymInt in codegen
* Test (below)
```cpp
TEST(LazyDynamicOpsTest, NarrowCopy) {
auto x = torch::rand({5, 10, 10}).to(kLazy);
const size_t Y_DIM = 3;
const size_t X_DIM_INDEX = 2;
auto y = torch::rand({Y_DIM}).to(kLazy);
auto ly = torch::lazy::TryGetLtcTensor(y);
auto dim_node = MakeNode<SizeNode>(ly->GetIrValue(), 0);
auto lmn = new torch::lazy::SymbolicIntNode(dim_node);
auto z = x.narrow_copy(X_DIM_INDEX, 0, lmn->toSymInt());
AllClose(z.cpu(), x.cpu().narrow_copy(X_DIM_INDEX, 0, Y_DIM));
}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75759
Approved by: https://github.com/wconstab
Summary:
Next stage of breaking up https://github.com/pytorch/pytorch/pull/74710
Move shape cache implementation to the backend interface. Also, clean up some of the hashing logic in the base node class.
CC: wconstab JackCaoG henrytwo
Partially Fixes https://github.com/pytorch/pytorch/issues/74628
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75324
Reviewed By: anjali411
Differential Revision: D35730823
Pulled By: wconstab
fbshipit-source-id: cf6fa326319b9324e5f422a78817b6fb5bf7e9b8
(cherry picked from commit faec5043df56639e2fd23de2d91ae796e4f3df70)
Summary:
Tensor.is_alias_of relies on Storage to perform. However, LTCTensorImpl was
not implemented with that in mind. This commit adds a fake storage to LazyTensor
as a marker to mark LazyTensors that point to the same storage. The reason
why it's not done at LTCTensorImpl is that LazyTensor maintains the view ops/alias
logic in LazyTensor class instead of relying on TensorImpl to do the check.
Test Plan:
./build/bin/test_lazy --gtest_filter=LazyOpsTest.IsAliasOf
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75246
Approved by: https://github.com/bdhirsh
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75230
Op diagonal is a view op which we can't code-gen yet. Therefore, support
it by making hand-written IR construction and lowering.
Test Plan: ./build/bin/test_lazy --gtest_filter=LazyOpsTest.TestDiagonal*
Reviewed By: wconstab
Differential Revision: D35378316
Pulled By: alanwaketan
fbshipit-source-id: 7958d00107aef20ac37aabcf2868346240977530
(cherry picked from commit 84155528fce484627c9688cfd92fd4aeb68219e5)
Summary:
Previously, the torchscript backend would be (partially) initialized at startup.
- the dispatcher registrations would be registered,
- but other backend components would not be initialized until explicitly calling
the backend init function
With this change, the torchscript backend is not initialized until its explicit
initialization function is called.
This enables external backends to register their own backend instead of the torchscript
backend to the same (Lazy) key.
Lands a change contributed by antoniojkim via lazy_tensor_staging branch (https://github.com/pytorch/pytorch/issues/73973)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74557
Reviewed By: bdhirsh
Differential Revision: D35051464
Pulled By: wconstab
fbshipit-source-id: 5a8b0851293e394f49427d1416ee571a8881fe9f
(cherry picked from commit ef745a4a2c8d1d7f9510541a20f1f40625ce29de)
Summary:
Also enables bazel build to run lazy codegen. Bazel (oss) build feeds off the same filelists as cmake/buck (build_variables.bzl), so enabling it is easier than keeping it disabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74111
Test Plan: Run CI and verify test_lazy_ops is running via OSS cmake builds
Reviewed By: bdhirsh
Differential Revision: D34772403
fbshipit-source-id: 8a63f58b9536e6ac1be530667932176ef2549496
(cherry picked from commit e807ffb1918853d10b924fdc24f85ee5b1a39021)
Summary:
This merges changes that have already been reviewed/landed onto lazy_tensor_staging branch. It combines changes from multiple PRs into one diff.
updated from lazy_tensor_staging on 3/16
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74311
Test Plan:
Run CI to ensure compilation on various platforms
Run unit tests on lazy_tensor_staging branch with source version of all these diffs
Reviewed By: desertfire
Differential Revision: D34929235
fbshipit-source-id: babbc3bbeabc5b8107ee9284ed7765887a148622
(cherry picked from commit d91577a6557343ec536f6859e4808ec1a8a9b685)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74309
Since the test file is large, it can be landed on its own and then switched on
in the diff that actually builds lazy tensor code.
Test Plan: verify CI passes
Reviewed By: desertfire
Differential Revision: D34928619
fbshipit-source-id: cd556155326f7fb55b3f29031f80bc36c936d565
(cherry picked from commit 60945adbefb6a8d19f89e330f8b344d076b13bfc)
Summary:
Hooks into existing autograd codegen script (generate_code.py) to take advantage of its integrations into buck/cmake/bazel.
Adds a new option (--gen_lazy_ts_backend) to. generate_code.py, calling this from CMake OSS build and fbcode build, but not from other internal xplat/ovrsource builds (these could be opted in later)
Bazel support is added in a later diff.
Includes one generated file (torch/csrc/lazy/generated/LazyIr.h) in a unit test (test/cpp/lazy/test_ir.cpp) to partially verify the generator is working, but does not compile the remaining output sources from the generator yet as they depend on other files not yet landed from lazy_tensor_staging branch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73996
Test Plan: OSS/internal CI - verify all builds are working and test_ir.cpp compiles LazyIr.h
Reviewed By: ezyang
Differential Revision: D34408536
fbshipit-source-id: 8af0aea3b95d81eccafc17d64390d70ddd176515
(cherry picked from commit f930612f2bad61c76eb02d85cfbec9f33a1459dc)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72875
This diff contains changes from several PRs landed to lazy_tensor_staging branch.
* generating 'fallback' overrides for each codegenned op, useful for debugging
* supports operators which are missing aten:: symbols for op names, instead using their string counterpart
* makes the IR class a base class instead of hardcoding the assumption of TS
It also resolves lint issues and in particular cleans up the following:
* {Type}s shouldn't be passed into isValueType, and using the catch-all base class of CType is nicer than specifying a list of types.
Fixes#72852
Test Plan: test manually on lazy_tensor_staging branch
Reviewed By: shunting314
Differential Revision: D34250357
fbshipit-source-id: aa7d589f605055d5d02bc77c77fa6f1182ff7497
(cherry picked from commit 2f8f5e4971)
