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Rename inductor cache (#156128)

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Requested by Simon on a different PR

Pull Request resolved: https://github.com/pytorch/pytorch/pull/156128
Approved by: https://github.com/xmfan
This commit is contained in:
Oguz Ulgen
2025-06-16 15:28:16 -07:00
committed by PyTorch MergeBot
parent 45382b284d
commit a2a75be0f8
48 changed files with 232 additions and 232 deletions
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4
torchgen/_autoheuristic/mixed_mm/gen_data_mixed_mm.py
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@ -15,7 +15,7 @@ from benchmark_utils import ( # type: ignore[import-not-found]
)
import torch
from torch._inductor.utils import fresh_inductor_cache
from torch._inductor.utils import fresh_cache
class BenchmarkRunnerMixedMM(BenchmarkRunner): # type: ignore[misc, no-any-unimported]
@ -59,7 +59,7 @@ class BenchmarkRunnerMixedMM(BenchmarkRunner): # type: ignore[misc, no-any-unim
)
b = b.to(dtype=dtype_right)
with fresh_inductor_cache():
with fresh_cache():
def mixed_mm(A, B):
return torch.mm(A, B.to(A.dtype))

4
torchgen/_autoheuristic/mm/gen_data_mm.py
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@ -16,7 +16,7 @@ from benchmark_utils import ( # type: ignore[import-not-found]
)
import torch
from torch._inductor.utils import fresh_inductor_cache
from torch._inductor.utils import fresh_cache
class BenchmarkRunnerMM(BenchmarkRunner): # type: ignore[misc, no-any-unimported]
@ -57,7 +57,7 @@ class BenchmarkRunnerMM(BenchmarkRunner): # type: ignore[misc, no-any-unimporte
dtype_right=dtype,
)
with fresh_inductor_cache():
with fresh_cache():
def mixed_mm(A: Any, B: Any) -> Any:
return torch.mm(A, B)

4
torchgen/_autoheuristic/pad_mm/gen_data_pad_mm.py
View File

@ -18,7 +18,7 @@ import torch
from torch._inductor.fx_passes.pad_mm import ( # type: ignore[import-not-found]
get_alignment_size_dtype,
)
from torch._inductor.utils import fresh_inductor_cache
from torch._inductor.utils import fresh_cache
class BenchmarkRunnerPadMM(BenchmarkRunner): # type: ignore[misc, no-any-unimported]
@ -74,7 +74,7 @@ class BenchmarkRunnerPadMM(BenchmarkRunner): # type: ignore[misc, no-any-unimpo
print(f"transpose_left={transpose_left} transpose_right={transpose_right}")
print(f"prepadded_left={prepadded_left} prepadded_right={prepadded_right}")
with fresh_inductor_cache():
with fresh_cache():
def mm(a: Any, b: Any) -> Any:
return torch.mm(a, b)