Merge OpenAI Triton commit f8b5301
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This should enable python 3.13 and 3.13t wheels and disable building python 3.8 wheels. Related to: pytorch/pytorch#143654
Running the example given in the [autotune docstring](https://triton-lang.org/main/python-api/generated/triton.autotune.html) gives the error ```python import triton import torch @triton.autotune(configs=[ triton.Config(kwargs={'BLOCK_SIZE': 128}, num_warps=4), triton.Config(kwargs={'BLOCK_SIZE': 1024}, num_warps=8), ], key=['x_size'] # the two above configs will be evaluated anytime # the value of x_size changes ) @triton.jit def kernel(x_ptr, x_size, **META): BLOCK_SIZE = META['BLOCK_SIZE'] if __name__ == '__main__': x = torch.ones(8, device="cuda") kernel[lambda _: (1,)](x, x.numel()) ``` ``` Traceback (most recent call last): File "...", line 18, in <module> kernel[lambda _: (1,)](x, x.size) File ".../triton/runtime/jit.py", line 345, in <lambda> return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs) File ".../triton/runtime/autotuner.py", line 156, in run timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs} File ".../triton/runtime/autotuner.py", line 156, in <dictcomp> timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs} File ".../triton/runtime/autotuner.py", line 133, in _bench return do_bench(kernel_call, warmup=self.num_warmups, rep=self.num_reps, quantiles=(0.5, 0.2, 0.8)) File ".../triton/testing.py", line 106, in do_bench fn() File ".../triton/runtime/autotuner.py", line 114, in kernel_call self.fn.run( File ".../triton/runtime/jit.py", line 618, in run bound_args, sig_and_spec, constexpr_vals, non_constexpr_vals, excess_kwargs = self.binder(*args, **kwargs) TypeError: dynamic_func() missing 1 required positional argument: 'META' ``` It seems it cannot parse the kwargs `**META`, so the keyword arguments must be manually specified. Also, `BLOCK_SIZE` should probably be marked as `tl.constexpr`. ```python @triton.autotune( configs=[ triton.Config(kwargs={"BLOCK_SIZE": 128}, num_warps=4), triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=8), ], key=["x_size"], # the two above configs will be evaluated anytime # the value of x_size changes ) @triton.jit def kernel(x_ptr, x_size, BLOCK_SIZE: tl.constexpr): ... ``` Similarly, for the [heuristics](https://triton-lang.org/main/python-api/generated/triton.heuristics.html) example, first, the same `**META` issue applies, second, `args` is no longer a list of positional argument values but a dictionary from argument name to value, and third, `2 ** int(math.ceil(math.log2(args[1])))` is awkward and `triton.next_power_of_2(args['x_size'])` should be preferred. ```python import torch import triton @triton.heuristics(values={'BLOCK_SIZE': lambda args: 2 ** int(math.ceil(math.log2(args[1])))}) @triton.jit def kernel(x_ptr, x_size, **META): BLOCK_SIZE = META['BLOCK_SIZE'] # smallest power-of-two >= x_size if __name__ == "__main__": x = torch.ones(8, device="cuda") kernel[lambda _: (1,)](x, x.numel()) ``` ``` Traceback (most recent call last): File "...", line 15, in <module> kernel[lambda _: (1,)](x, x.numel()) File ".../triton/runtime/jit.py", line 345, in <lambda> return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs) File ".../triton/runtime/autotuner.py", line 337, in run kwargs[v] = heur({**dict(zip(self.arg_names, args)), **kwargs}) File "...", line 7, in <lambda> @triton.heuristics(values={'BLOCK_SIZE': lambda args: 2 ** int(math.ceil(math.log2(args[1])))}) KeyError: 1 ``` Applying the suggested changes results in ```python # smallest power-of-two >= x_size @triton.heuristics(values={'BLOCK_SIZE': lambda args: triton.next_power_of_2(args['x_size'])}) @triton.jit def kernel(x_ptr, x_size, BLOCK_SIZE: tl.constexpr): ... ```
Fixes #5484. Since python objects can arbitrarily override `__contains__`, using `inspect.ismodule` seems to be the most general solution, beyond numpy arrays. Overriding a module's `__contains__` would be very strange. ```python >>> import triton.language as tl >>> import inspect >>> inspect.ismodule(tl) True >>> inspect.ismodule(tl.core) True ```
<!--- The core Triton is a small number of people, and we receive many PRs (thank you!). To help us review your code more quickly, **if you are a new contributor (less than 3 PRs merged) we ask that you complete the following tasks and include the filled-out checklist in your PR description.** Complete the following tasks before sending your PR, and replace `[ ]` with `[x]` to indicate you have done them. --> I accidentally deleted a newline in triton-lang/triton#5487 which causes the [website](https://triton-lang.org/main/python-api/generated/triton.heuristics.html) to not have the code block... oops. Generated locally and can confirm the syntax is now correct.  # New contributor declaration - [ ] I am not making a trivial change, such as fixing a typo in a comment. - [x] I have written a PR description following these [rules](https://cbea.ms/git-commit/#why-not-how). - [x] I have run `pre-commit run --from-ref origin/main --to-ref HEAD`. - Select one of the following. - [ ] I have added tests. - `/test` for `lit` tests - `/unittest` for C++ tests - `/python/test` for end-to-end tests - [x] This PR does not need a test because this is a documentation change. - Select one of the following. - [x] I have not added any `lit` tests. - [ ] The `lit` tests I have added follow these [best practices](https://mlir.llvm.org/getting_started/TestingGuide/#filecheck-best-practices), including the "tests should be minimal" section. (Usually running Python code and using the instructions it generates is not minimal.)
Also create the `getThreadsPerWarpForOperand` interface. This PR doesn't completely remove the decompose method because `getThreadsPerWarpForOperand` hasn't been implemented yet for some AMD specific encodings
Signed-off-by: Whitney Tsang <[email protected]>
whitneywhtsang
changed the title
755d416f8b5301
anmyachev
approved these changes
Dec 26, 2024
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This PR change the Triton base from 755d416 to f8b5301 (Dec 25).
Pass rate: 99.03%
Please do not squash and merge this PR.