7dc2a70edb
Reverts openai/triton#671 It seems like for some reason this caused out-of-memory errors on some of our internal workloads. I'm reverting this so that HEAD can be used in production at OpenAI, and I will work on digging into this issue asynchronously.
153 lines
3.5 KiB
Python
153 lines
3.5 KiB
Python
import os
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import re
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import shutil
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import pytest
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import torch
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import triton
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import triton.language as tl
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from triton.runtime.jit import JITFunction
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tmpdir = ".tmp"
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@triton.jit
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def function_1(i):
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i = i + 1
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i = function_2(i)
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return i
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@triton.jit
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def function_2(i):
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i = i + 1
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return i
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@triton.jit
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def kernel(X, i, BLOCK: tl.constexpr):
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i = i + 1
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i = function_1(i)
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tl.store(X, i)
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@triton.jit(do_not_specialize=["i"])
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def kernel_nospec(X, i, BLOCK: tl.constexpr):
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i = i + 1
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i = function_1(i)
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tl.store(X, i)
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def apply_src_change(target, old, new):
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kernel.hash = None
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function_1.hash = None
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function_2.hash = None
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function_1.src = function_1.src.replace(old, new)
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target.src = target.src.replace(old, new)
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ret = target.cache_key
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target.src = target.src.replace(new, old)
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return ret
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def test_nochange():
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baseline = kernel.cache_key
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updated = apply_src_change(kernel, 'i + 1', 'i + 1')
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assert baseline == updated
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def test_toplevel_change():
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baseline = kernel.cache_key
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updated = apply_src_change(kernel, 'i + 1', 'i + 2')
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assert baseline != updated
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def test_nested1_change():
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baseline = kernel.cache_key
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updated = apply_src_change(function_1, 'i + 1', 'i + 2')
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assert baseline != updated
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def reset_tmp_dir():
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os.environ["TRITON_CACHE_DIR"] = tmpdir
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if os.path.exists(tmpdir):
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shutil.rmtree(tmpdir)
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def test_reuse():
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counter = 0
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def inc_counter(*args, **kwargs):
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nonlocal counter
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counter += 1
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JITFunction.cache_hook = inc_counter
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reset_tmp_dir()
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x = torch.empty(1, dtype=torch.int32, device='cuda')
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for i in range(10):
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kernel[(1,)](x, 1, BLOCK=1024)
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assert counter == 1
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@pytest.mark.parametrize('mode', ['enable', 'disable'])
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def test_specialize(mode):
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counter = 0
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def inc_counter(*args, **kwargs):
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nonlocal counter
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counter += 1
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JITFunction.cache_hook = inc_counter
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reset_tmp_dir()
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x = torch.empty(1, dtype=torch.int32, device='cuda')
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function = {'enable': kernel, 'disable': kernel_nospec}[mode]
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target = {'enable': 3, 'disable': 1}[mode]
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for i in [1, 2, 4, 8, 16, 32]:
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function[(1,)](x, i, BLOCK=512)
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assert counter == target
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@pytest.mark.parametrize("value, value_type", [
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(-1, 'i32'), (0, 'i32'), (1, 'i32'), (-2**31, 'i32'), (2**31 - 1, 'i32'),
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(2**32, 'i64'), (2**63 - 1, 'i64'), (-2**63, 'i64'),
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(2**31, 'u32'), (2**32 - 1, 'u32'), (2**63, 'u64'), (2**64 - 1, 'u64')
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])
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def test_value_specialization(value: int, value_type: str, device='cuda') -> None:
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@triton.jit
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def kernel(VALUE, X):
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pass
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cache_str = None
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def get_cache_str(*args, **kwargs):
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nonlocal cache_str
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cache_str = kwargs["repr"]
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triton.JITFunction.cache_hook = get_cache_str
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reset_tmp_dir()
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x = torch.tensor([3.14159], device='cuda')
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kernel[(1, )](value, x)
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triton.JITFunction.cache_hook = None
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cache_str_match = re.match(r".*VALUE: (\w+).*", cache_str)
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spec_type = None if cache_str_match is None else cache_str_match.group(1)
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assert spec_type == value_type
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def test_constexpr_not_callable() -> None:
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@triton.jit
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def kernel(X, c: tl.constexpr):
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tl.store(X, 2)
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x = torch.empty(1, dtype=torch.int32, device='cuda')
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error = False
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try:
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kernel[(1, )](x, c="str")
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except BaseException:
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error = True
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assert error is False
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# try and catch
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try:
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kernel[(1, )](x, c=tl.abs)
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except BaseException:
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error = True
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assert error is True
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