Summary:
Relands D69965761 / https://github.com/pytorch/pytorch/pull/147583
Before this PR, calling a triton kernel would look like:
```py
kernel.run(a, b, xnumel, grid=grid(xnumel), stream=stream0)
```
where the `grid=` was passed as a callable (function closure) arg. This PR removes the grid arg:
```py
kernel.run(a, b, xnumel, stream=stream0)
```
instead now the grid computation is included in the kernel launcher, with something like:
```py
def launcher(in_ptr0, out_ptr0, xnumel, stream):
grid_0 = ((xnumel + 1023) >> 10)
grid_1 = 1
grid_2 = 1
runner(grid_0, grid_1, grid_2, stream, function, metadata, None, launch_enter_hook, launch_exit_hook, in_ptr0, out_ptr0, xnumel)
```
This should be faster, since we remove multiple function/dict calls and are able to specialize the grid computation for each `triton.Config`.
It also allows us to unify the handling of grids between the Python and C++ wrapper code. Before this, C++ wrapper code didn't actually support dynamic grid sizes and instead burned in a static grid.
This unification allows this PR to be a net deletion of code.
Differential Revision: D70471332
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148305
Approved by: https://github.com/shunting314, https://github.com/eellison
Before this PR, calling a triton kernel would look like:
```py
kernel.run(a, b, xnumel, grid=grid(xnumel), stream=stream0)
```
where the `grid=` was passed as a callable (function closure) arg. This PR removes the grid arg:
```py
kernel.run(a, b, xnumel, stream=stream0)
```
instead now the grid computation is included in the kernel launcher, with something like:
```py
def launcher(in_ptr0, out_ptr0, xnumel, stream):
grid_0 = ((xnumel + 1023) >> 10)
grid_1 = 1
grid_2 = 1
runner(grid_0, grid_1, grid_2, stream, function, metadata, None, launch_enter_hook, launch_exit_hook, in_ptr0, out_ptr0, xnumel)
```
This should be faster, since we remove multiple function/dict calls and are able to specialize the grid computation for each `triton.Config`.
It also allows us to unify the handling of grids between the Python and C++ wrapper code. Before this, C++ wrapper code didn't actually support dynamic grid sizes and instead burned in a static grid.
This unification allows this PR to be a net deletion of code.
Note the attached diff contains some minor fbcode-only changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147583
Approved by: https://github.com/eellison, https://github.com/shunting314
Following triton # 4916, the generated cubin expects a global_scratch argument to support on-device TMA. We believe this is the source of many of the "invalid argument" failures on AOTI/cpp_wrapper tests. AFAIK, we don't use on-device TMA in Inductor as of now, so it should be safe to use a nullptr for the scratch space.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148051
Approved by: https://github.com/YUNQIUGUO
block ptr advancements should also be deferrered conditional on the associated buffer not being removed. For example, if `FusedSchedulerNode(op0-op1)` has a store in `SchedulerNode` `op0` that is read in `op1`, the store and associated block ptr that would be created for `op0` in isolation is no longer needed.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147193
Approved by: https://github.com/jansel
Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
block ptr advancements should also be deferrered conditional on the associated buffer not being removed. For example, if `FusedSchedulerNode(op0-op1)` has a store in `SchedulerNode` `op0` that is read in `op1`, the store and associated block ptr that would be created for `op0` in isolation is no longer needed.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147193
Approved by: https://github.com/jansel
Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
**Summary**
Fix issue: https://github.com/pytorch/pytorch/issues/146975, when create `InplacedBuffer` inner name, we only count the number of unique `InplacedBuffer` or `RemovedArg`. The name may have conflict, for example reported in this issue
```
---- make inplace create, input_name is: buf22; output_name is: buf27; buf.inner_name is: in_out_ptr2
dict_values([
InplacedBuffer(inner_name='in_out_ptr0', other_names=['buf6', 'buf11']),
InplacedBuffer(inner_name='in_out_ptr0', other_names=['buf6', 'buf11']),
InplacedBuffer(inner_name='in_out_ptr1', other_names=['buf24', 'buf26']),
InplacedBuffer(inner_name='in_out_ptr1', other_names=['buf24', 'buf26'])])
---- make inplace create, input_name is: buf0; output_name is: buf3; buf.inner_name is: in_out_ptr2
dict_values([
<torch._inductor.codegen.common.RemovedArg object at 0x7fbf75516350>,
<torch._inductor.codegen.common.RemovedArg object at 0x7fbf75516350>,
<torch._inductor.codegen.common.RemovedArg object at 0x7fbf75516350>,
<torch._inductor.codegen.common.RemovedArg object at 0x7fbf75516350>,
InplacedBuffer(inner_name='in_out_ptr2', other_names=['buf22', 'buf27', 'buf31', 'buf33']),
InplacedBuffer(inner_name='in_out_ptr2', other_names=['buf22', 'buf27', 'buf31', 'buf33'])
<torch._inductor.codegen.common.RemovedArg object at 0x7fbf75516350>,
InplacedBuffer(inner_name='in_out_ptr2', other_names=['buf22', 'buf27', 'buf31', 'buf33']),
InplacedBuffer(inner_name='in_out_ptr2', other_names=['buf22', 'buf27', 'buf31', 'buf33'])
])
```
- The first time create `in_out_ptr2`, there are 2 unique `InplacedBuffer`
- The second time create `in_out_ptr2`, there is 1 `RemovedArg` and 1 unique `InplacedBuffer`
They are 2 different `InplacedBuffer`, but with same name `in_out_ptr2`. In this PR, we fix this regression by counting the number of `RemovedArg`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147199
Approved by: https://github.com/jansel
This replaces the `__getattr__()` pattern used in remaining OpHandlers with a `DefaultHandler` class defined in part 2.
Some compile time wins from this as well:
```
2025-02-02T19:46:32.2033010Z
2025-02-02T19:46:32.2036607Z WIN: benchmark ('add_loop_inductor', 'compile_time_instruction_count') failed, actual result 29633182927 is -1.71% lower than expected 30150000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2037575Z
2025-02-02T19:46:32.2037907Z please update all results that changed significantly, and not only the failed ones
2025-02-02T19:46:32.2039291Z PASS: benchmark ('add_loop_inductor_dynamic_gpu', 'compile_time_instruction_count') pass, actual result 43986879172 -1.02% is within expected 44440000000 ±2.50%
2025-02-02T19:46:32.2040131Z
2025-02-02T19:46:32.2041180Z WIN: benchmark ('add_loop_inductor_gpu', 'compile_time_instruction_count') failed, actual result 26246225695 is -1.85% lower than expected 26740000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2042188Z
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146255
Approved by: https://github.com/shunting314
ghstack dependencies: #146252, #146254
We were codegening intermediary dtype asserts in some places but not all. expands assertions, fixes newly failing assertion in
`TORCHINDUCTOR_COMPILE_THREADS=1 TORCH_LOGS="output_code" PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=1 python test/inductor/test_torchinductor_opinfo.py TestInductorOpInfoCUDA.test_comprehensive_logcumsumexp_cuda_float16` for scan.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146067
Approved by: https://github.com/shunting314, https://github.com/jansel
This replaces the `__getattr__()` pattern used in remaining OpHandlers with a `DefaultHandler` class defined in part 2.
Some compile time wins from this as well:
```
2025-02-02T19:46:32.2033010Z
2025-02-02T19:46:32.2036607Z WIN: benchmark ('add_loop_inductor', 'compile_time_instruction_count') failed, actual result 29633182927 is -1.71% lower than expected 30150000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2037575Z
2025-02-02T19:46:32.2037907Z please update all results that changed significantly, and not only the failed ones
2025-02-02T19:46:32.2039291Z PASS: benchmark ('add_loop_inductor_dynamic_gpu', 'compile_time_instruction_count') pass, actual result 43986879172 -1.02% is within expected 44440000000 ±2.50%
2025-02-02T19:46:32.2040131Z
2025-02-02T19:46:32.2041180Z WIN: benchmark ('add_loop_inductor_gpu', 'compile_time_instruction_count') failed, actual result 26246225695 is -1.85% lower than expected 26740000000 ±1.50% please update the expected results.
2025-02-02T19:46:32.2042188Z
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146255
Approved by: https://github.com/shunting314
ghstack dependencies: #146225, #146226, #146235, #146252, #146254
This enforces the invariant that every backend implements the same set of ops and removes a layer of indirection for BasicMathOps.
Interestingly this is a small compile time win:
```
...
WIN: benchmark ('add_loop_inductor', 'compile_time_instruction_count') failed, actual result 30151159301 is -6.13% lower than expected 32120000000 ±1.50% please update the expected results.
please update all results that changed significantly, and not only the failed ones
PASS: benchmark ('add_loop_inductor_dynamic_gpu', 'compile_time_instruction_count') pass, actual result 44447549162 -1.69% is within expected 45210000000 ±2.50%
WIN: benchmark ('add_loop_inductor_gpu', 'compile_time_instruction_count') failed, actual result 26743557195 is -2.25% lower than expected 27360000000 ±1.50% please update the expected results.
please update all results that changed significantly, and not only the failed ones
PASS: benchmark ('basic_modules_ListOfLinears_eager', 'compile_time_instruction_count') pass, actual result 945129734 +0.93% is within expected 936400000 ±1.50%
WIN: benchmark ('basic_modules_ListOfLinears_inductor', 'compile_time_instruction_count') failed, actual result 18984384503 is -3.19% lower than expected 19610000000 ±1.50% please update the expected results.
please update all results that changed significantly, and not only the failed ones
WIN: benchmark ('basic_modules_ListOfLinears_inductor_gpu_force_shape_pad', 'compile_time_instruction_count') failed, actual result 17258025389 is -1.94% lower than expected 17600000000 ±1.50% please update the expected results.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146235
Approved by: https://github.com/shunting314
ghstack dependencies: #146225, #146226
This enforces the invariant that every backend implements the same set of ops and removes a layer of indirection for BasicMathOps.
Interestingly this is a small compile time win:
```
...
WIN: benchmark ('add_loop_inductor', 'compile_time_instruction_count') failed, actual result 30151159301 is -6.13% lower than expected 32120000000 ±1.50% please update the expected results.
please update all results that changed significantly, and not only the failed ones
PASS: benchmark ('add_loop_inductor_dynamic_gpu', 'compile_time_instruction_count') pass, actual result 44447549162 -1.69% is within expected 45210000000 ±2.50%
WIN: benchmark ('add_loop_inductor_gpu', 'compile_time_instruction_count') failed, actual result 26743557195 is -2.25% lower than expected 27360000000 ±1.50% please update the expected results.
please update all results that changed significantly, and not only the failed ones
PASS: benchmark ('basic_modules_ListOfLinears_eager', 'compile_time_instruction_count') pass, actual result 945129734 +0.93% is within expected 936400000 ±1.50%
WIN: benchmark ('basic_modules_ListOfLinears_inductor', 'compile_time_instruction_count') failed, actual result 18984384503 is -3.19% lower than expected 19610000000 ±1.50% please update the expected results.
please update all results that changed significantly, and not only the failed ones
WIN: benchmark ('basic_modules_ListOfLinears_inductor_gpu_force_shape_pad', 'compile_time_instruction_count') failed, actual result 17258025389 is -1.94% lower than expected 17600000000 ±1.50% please update the expected results.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146235
Approved by: https://github.com/shunting314
ghstack dependencies: #146225, #146226
Prior to this PR, constexprs were appearing in signatures as `{.. "XBLOCK : tl.constexpr": "constexpr"}` when they really should appear as `{.. "XBLOCK": "constexpr"}`.
This PR represents the argument names as ArgName objects, which can optionally be marked as constexpr.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145583
Approved by: https://github.com/jansel
Triton commit 5220 adds tuple support in Triton (changing the indexing format in AttrsDescriptor) and commit 5512 replaces AttrsDescriptor with raw tuples. This is an initial PR to add support for Triton versions after commit 5512 landed.
The main changes in 5220 and 5512 that need to be supported:
* AttrsDescriptor() gets replaced with a raw dict. The raw dict has the format `{(TUPLES): [["tt.divisibility", 16]]}`, where `(TUPLES)` is a tuple of indices, e.g. `((0,), (1,), (3,))` to indicate that args 0, 1, and 3 are divisible by 16. These indices are, themselves, represented as tuples to support nested inputs (e.g. an argument that's a tuple), but support for tuples is not implemented right now.
* "signature" changes: the signature now contains _all_ args, including constexpr and constant args.
* ASTSource now takes "constexprs" instead of "constants" - for example, equal-to-1 args are constants but not constexprs so we don't need to pass these args as "constants".
What this PR supports:
* Triton versions before Dec 9, 2024, and (partial support for) Triton versions after Jan 1, 2025
* (triton jan 1+) typical inductor-generated triton: updated AttrsDescriptor, signatures, constexpr/constant handling.
What this PR doesn't support (TODO in follow-up PRs):
* Triton versions between Dec 9, 2024 and before Jan 1, 2025
* (triton jan 1+) user-defined triton kernel support (this is implemented already in @anmyachev's patch)
* (triton jan 1+) triton_helper support (failing in triton codegen - needs investigation)
* (triton jan 1+) AOTI / cpp wrapper
thanks to @anmyachev for patches in https://github.com/intel/intel-xpu-backend-for-triton/blob/main/scripts/pytorch.patch, which contains most of these changes already
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145051
Approved by: https://github.com/jansel
