This PR introduces two new methods to the LazyGraphExecutor class:
- ClearComputationCache(): Allows clearing the entire computation cache.
- RemoveFromComputationCache(hash): Enables removal of specific cache entries based on their hash.
The main objective is to expose cache management functionality for debugging cache hits and misses across different computations. For instance:
- Reset the cache state in tests, allowing reuse of the same computation client to evaluate cache logic consistently.
- Selectively remove cache entries to analyze the impact on subsequent computations.
- Improve observability into the cache behavior, aiding in the investigation of cache-related issues or optimizations.
On the XLA lazy graph executor, we want to run a series of tests that modify some parts of the HLO module proto of the computation, and we need a means to ensure that the hash is agnostic to some elements (OpMetadata in the XLA proto data). Hence, it would be easy to parameterize the test, clear the cache and validate that the resulting hash is the same between runs. Otherwise, we'd need to hardcode the resulting serialized hash.
Simultaneously, **another motivation**, is that users could also clear some computation hashes for an added flexibility in their applications, by introducing their own custom strategies for maintaining the cache (without relying on the default LRU).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144489
Approved by: https://github.com/wconstab
This change fixes the RUNPATH of installed c++ tests so that the linker can find the shared libraries they depend on.
For example, currently:
```bash
venv/lib/python3.10/site-packages/torch $ ./bin/test_lazy
./bin/test_lazy: error while loading shared libraries: libtorch.so: cannot open shared object file: No such file or directory
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136627
Approved by: https://github.com/malfet
- rename `__HIP_PLATFORM_HCC__` to `__HIP_PLATFORM_AMD__`
- rename `HIP_HCC_FLAGS` to `HIP_CLANG_FLAGS`
- rename `PYTORCH_HIP_HCC_LIBRARIES` to `PYTORCH_HIP_LIBRARIES`
- workaround in tools/amd_build/build_amd.py until submodules are updated
These symbols have had a long deprecation cycle and will finally be removed in ROCm 6.0.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111975
Approved by: https://github.com/ezyang, https://github.com/hongxiayang
This function is an auxiliary function for `torch.norm`. This particular
overload was not even used or tested. I hope it's not used internally
either. If it is, we can simply drop this PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81762
Approved by: https://github.com/ngimel
Summary:
Since `c10::ArrayRef` now support `c10::ArrayRef<const T>`, let's restore `ComputePostOrder` to accept `const Node*` again, which is more suitable for the context of the given helpers.
Test Plan:
CI.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88773
Approved by: https://github.com/JackCaoG
Also Back out "Revert D39075159: [acc_tensor] Use SymIntArrayRef for overloaded empty.memory_format's signature"
Original commit changeset: dab4a9dba4fa
Original commit changeset: dcaf16c037a9
Original Phabricator Diff: D38984222
Original Phabricator Diff: D39075159
Also update Metal registrations for C++ registration changes.
Also update NNPI registration to account for tightened schema checking
Differential Revision: [D39084762](https://our.internmc.facebook.com/intern/diff/D39084762/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D39084762/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84173
Approved by: https://github.com/Krovatkin
Previously, we introduced new SymInt overloads for every function we wanted. This led to a lot of boilerplate, and also a lot of confusion about how the overloads needed to be implemented.
This PR takes a simpler but more risky approach: just take the original function and changes its ints to SymInts.
This is BC-breaking in the following ways:
* The C++ API for registering implementations for aten operators will change from int64_t to SymInt whenever you make this change. Code generated registrations in PyTorch do not change as codegen handles the translation automatically, but manual registrations will need to follow the change. Typically, if you now accept a SymInt where you previously only took int64_t, you have to convert it back manually. This will definitely break XLA, see companion PR https://github.com/pytorch/xla/pull/3914 Note that not all dispatch keys get the automatic translation; all the composite keys and Meta keys are modified to take SymInt directly (because they should handle them directly), and so there are adjustments for this.
This is not BC-breaking in the following ways:
* The user facing C++ API remains compatible. Even if a function changes from int to SymInt, the default C++ binding still takes only ints. (e.g., at::empty(IntArrayRef, ...). To call with SymInts, you must call at::empty_symint instead. This involved adding two more signatures to CppSignatureGroup; in many cases I refactored code to iterate over all signatures in the group instead of hard-coding the two that previously existed.
* This is TorchScript compatible; internally we treat SymInts as ints so there is no change to what happens at runtime in TorchScript. In particular, it's OK to reference an empty schema by its old type (using int types), as long as you're not doing string equality (which you shouldn't be), these parse to the same underyling type.
Structure of the PR:
* The general strategy of this PR is that, even when you write `SymInt` inside `native_functions.yaml`, sometimes, we will treat it *as if* it were an `int`. This idea pervades the codegen changes, where we have a translation from SymInt to c10::SymInt or int64_t, and this is controlled by a symint kwarg which I added and then audited all call sites to decide which I wanted. Here are some of the major places where we pick one or the other:
* The C++ FunctionSchema representation represents `SymInt` as `int`. There are a few places we do need to know that we actually have a SymInt and we consult `real_type()` to get the real type in this case. In particular:
* When we do schema validation of C++ operator registration, we must compare against true schema (as the C++ API will provide `c10::SymInt`, and this will only be accepted if the schema is `SymInt`. This is handled with cloneWithRealTypes before we check for schema differences.
* In `toIValue` argument parsing, we parse against the true schema value. For backwards compatibility reasons, I do still accept ints in many places where Layout/SymInt/etc were expected. (Well, accepting int where SymInt is expected is not BC, it's just the right logic!)
* In particular, because SymInt never shows up as type() in FunctionSchema, this means that we no longer need a dedicated Tag::SymInt. This is good, because SymInts never show up in mobile anyway.
* Changes to functorch/aten are mostly about tracking changes to the C++ API registration convention. Additionally, since SymInt overloads no longer exist, registrations for SymInt implementations are deleted. In many cases, the old implementations did not properly support SymInts; I did not add any new functionality with this PR, but I did try to annotate with TODOs where this is work to do. Finally, because the signature of `native::` API changed from int to SymInt, I need to find alternative APIs for people who were directly calling these functions to call. Typically, I insert a new dispatch call when perf doesn't matter, or use `at::compositeexplicitautograd` namespace to handle other caes.
* The change to `make_boxed_from_unboxed_functor.h` is so that we accept a plain IntList IValue anywhere a SymIntList is expected; these are read-only arguments so covariant typing is OK.
* I change how unboxing logic works slightly. Previously, we interpret the C++ type for Layout/etc directly as IntType JIT type, which works well because the incoming IValue is tagged as an integer. Now, we interpret the C++ type for Layout as its true type, e.g., LayoutType (change to `jit_type.h`), but then we accept an int IValue for it anyway. This makes it symmetric with SymInt, where we interpret the C++ type as SymIntType, and then accept SymInt and int IValues for it.
* I renamed the `empty.names` overload to `empty_names` to make it less confusing (I kept mixing it up with the real empty overload)
* I deleted the `empty.SymInt` overload, which ended up killing a pile of functions. (This was originally a separate PR but the profiler expect test was giving me grief so I folded it in.)
* I deleted the LazyDynamicOpsTest tests. These were failing after these changes, and I couldn't figure out why they used to be passing: they make use of `narrow_copy` which didn't actually support SymInts; they were immediately converted to ints.
* I bashed LTC into working. The patches made here are not the end of the story. The big problem is that SymInt translates into Value, but what if you have a list of SymInt? This cannot be conveniently represented in the IR today, since variadic Values are not supported. To work around this, I translate SymInt[] into plain int[] (this is fine for tests because LTC dynamic shapes never actually worked); but this will need to be fixed for proper LTC SymInt support. The LTC codegen also looked somewhat questionable; I added comments based on my code reading.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83628
Approved by: https://github.com/albanD, https://github.com/bdhirsh
Previously, we introduced new SymInt overloads for every function we wanted. This led to a lot of boilerplate, and also a lot of confusion about how the overloads needed to be implemented.
This PR takes a simpler but more risky approach: just take the original function and changes its ints to SymInts.
This is BC-breaking in the following ways:
* The C++ API for registering implementations for aten operators will change from int64_t to SymInt whenever you make this change. Code generated registrations in PyTorch do not change as codegen handles the translation automatically, but manual registrations will need to follow the change. Typically, if you now accept a SymInt where you previously only took int64_t, you have to convert it back manually. This will definitely break XLA, see companion PR https://github.com/pytorch/xla/pull/3914 Note that not all dispatch keys get the automatic translation; all the composite keys and Meta keys are modified to take SymInt directly (because they should handle them directly), and so there are adjustments for this.
This is not BC-breaking in the following ways:
* The user facing C++ API remains compatible. Even if a function changes from int to SymInt, the default C++ binding still takes only ints. (e.g., at::empty(IntArrayRef, ...). To call with SymInts, you must call at::empty_symint instead. This involved adding two more signatures to CppSignatureGroup; in many cases I refactored code to iterate over all signatures in the group instead of hard-coding the two that previously existed.
* This is TorchScript compatible; internally we treat SymInts as ints so there is no change to what happens at runtime in TorchScript. In particular, it's OK to reference an empty schema by its old type (using int types), as long as you're not doing string equality (which you shouldn't be), these parse to the same underyling type.
Structure of the PR:
* The general strategy of this PR is that, even when you write `SymInt` inside `native_functions.yaml`, sometimes, we will treat it *as if* it were an `int`. This idea pervades the codegen changes, where we have a translation from SymInt to c10::SymInt or int64_t, and this is controlled by a symint kwarg which I added and then audited all call sites to decide which I wanted. Here are some of the major places where we pick one or the other:
* The C++ FunctionSchema representation represents `SymInt` as `int`. There are a few places we do need to know that we actually have a SymInt and we consult `real_type()` to get the real type in this case. In particular:
* When we do schema validation of C++ operator registration, we must compare against true schema (as the C++ API will provide `c10::SymInt`, and this will only be accepted if the schema is `SymInt`. This is handled with cloneWithRealTypes before we check for schema differences.
* In `toIValue` argument parsing, we parse against the true schema value. For backwards compatibility reasons, I do still accept ints in many places where Layout/SymInt/etc were expected. (Well, accepting int where SymInt is expected is not BC, it's just the right logic!)
* In particular, because SymInt never shows up as type() in FunctionSchema, this means that we no longer need a dedicated Tag::SymInt. This is good, because SymInts never show up in mobile anyway.
* Changes to functorch/aten are mostly about tracking changes to the C++ API registration convention. Additionally, since SymInt overloads no longer exist, registrations for SymInt implementations are deleted. In many cases, the old implementations did not properly support SymInts; I did not add any new functionality with this PR, but I did try to annotate with TODOs where this is work to do. Finally, because the signature of `native::` API changed from int to SymInt, I need to find alternative APIs for people who were directly calling these functions to call. Typically, I insert a new dispatch call when perf doesn't matter, or use `at::compositeexplicitautograd` namespace to handle other caes.
* The change to `make_boxed_from_unboxed_functor.h` is so that we accept a plain IntList IValue anywhere a SymIntList is expected; these are read-only arguments so covariant typing is OK.
* I change how unboxing logic works slightly. Previously, we interpret the C++ type for Layout/etc directly as IntType JIT type, which works well because the incoming IValue is tagged as an integer. Now, we interpret the C++ type for Layout as its true type, e.g., LayoutType (change to `jit_type.h`), but then we accept an int IValue for it anyway. This makes it symmetric with SymInt, where we interpret the C++ type as SymIntType, and then accept SymInt and int IValues for it.
* I renamed the `empty.names` overload to `empty_names` to make it less confusing (I kept mixing it up with the real empty overload)
* I deleted the `empty.SymInt` overload, which ended up killing a pile of functions. (This was originally a separate PR but the profiler expect test was giving me grief so I folded it in.)
* I deleted the LazyDynamicOpsTest tests. These were failing after these changes, and I couldn't figure out why they used to be passing: they make use of `narrow_copy` which didn't actually support SymInts; they were immediately converted to ints.
* I bashed LTC into working. The patches made here are not the end of the story. The big problem is that SymInt translates into Value, but what if you have a list of SymInt? This cannot be conveniently represented in the IR today, since variadic Values are not supported. To work around this, I translate SymInt[] into plain int[] (this is fine for tests because LTC dynamic shapes never actually worked); but this will need to be fixed for proper LTC SymInt support. The LTC codegen also looked somewhat questionable; I added comments based on my code reading.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83628
Approved by: https://github.com/albanD, https://github.com/bdhirsh
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
