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[inductor] Mark / restrict tests that only work if ATen is used for matmul (#166518)

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These tests only work if max_autotune=False (default), which for matmul means falling back to ATen. This PR just documents / makes that transparent.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/166518
Approved by: https://github.com/eellison
This commit is contained in:
Mwiza Kunda 2025-10-31 12:29:03 +00:00 committed by PyTorch MergeBot
parent c01636e1bc
commit b0831930ed
1 changed files with 12 additions and 2 deletions
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14
test/inductor/test_torchinductor.py
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@ -6474,7 +6474,11 @@ class CommonTemplate:
# Constant folding was explicitly turned off due to issue #108388
# Turn it back on for test
@unittest.skipIf(config.triton.native_matmul, "native matmul has better precision")
@torch._inductor.config.patch(joint_graph_constant_folding=True)
@torch._inductor.config.patch(
joint_graph_constant_folding=True,
# Numerical accuracy failure for triton fp16
max_autotune_gemm_backends="ATEN",
)
def test_remove_no_ops(self):
def matmul_with_op(x, y, fn):
return fn(x @ y)
@ -6902,7 +6906,11 @@ def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", ar
_, (code0, code1) = _run_and_get_stripped_kernels(b, x)
self.assertEqual(code0, code1)
@config.patch(force_disable_caches=True)
@config.patch(
force_disable_caches=True,
# Test expects a single (fused) kernel to be generated
max_autotune_gemm_backends="ATEN",
)
@skip_if_cpp_wrapper("run_and_get_kernels issue")
@unittest.skipIf(config.triton.native_matmul, "matmul is now generated")
def test_deterministic_codegen_with_suffix(self):
@ -14154,6 +14162,8 @@ def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", ar
code_disallowed = re.sub(r"AOT ID: .*", "AOT ID: ['test']", code_disallowed)
return code_allowed != code_disallowed
# If matmul is implemented by triton there is more reuse
@config.patch(max_autotune_gemm_backends="ATEN")
@unittest.skipIf(config.triton.native_matmul, "matmul is now generated")
def test_allow_reuse_disable_if_exceed_peak(self):
@torch.compile