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[Fix] Adding missing f prefixes to formatted strings [4/N] (#164068)

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As stated in the title.

* __->__ #164068
* #164067
* #164066
* #164065

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164068
Approved by: https://github.com/Skylion007
This commit is contained in:
can-gaa-hou
2025-09-29 04:07:03 +00:00
committed by PyTorch MergeBot
parent 047ae24e34
commit e64dd8c694
5 changed files with 6 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
test/distributed/test_store.py
View File

@ -1178,6 +1178,6 @@ class TestClientProtocol(TestCase):
if __name__ == "__main__":
if device_type != "cpu":
assert not torch.get_device_module()._initialized, (
"test_distributed must not have initialized {device_type} context on main process"
f"test_distributed must not have initialized {device_type} context on main process"
)
run_tests()

2
test/distributed/test_symmetric_memory.py
View File

@ -451,7 +451,7 @@ class AsyncTPTest(MultiProcContinuousTest):
elif gather_dim == 1:
leading_dims = (BATCH, M // self.world_size)
else:
raise AssertionError("Invalid scale_mode: {scale_mode}")
raise AssertionError(f"Invalid scale_mode: {scale_mode}")
torch.manual_seed(42 + rank)

2
test/quantization/core/test_workflow_module.py
View File

@ -97,7 +97,7 @@ class TestObserver(QuantizationTestCase):
reduce_range=reduce_range)]
def _get_ref_params(reduce_range, qscheme, dtype, input_scale, min_val, max_val):
assert dtype in _INT_DTYPES, "Not supported dtype: {dtype}, supported dtypes are {_INT_DTYPES}"
assert dtype in _INT_DTYPES, f"Not supported dtype: {dtype}, supported dtypes are {_INT_DTYPES}"
eps = torch.tensor([tolerance])
if dtype in [torch.qint8, torch.int8]:
if reduce_range:

2
test/quantization/pt2e/test_numeric_debugger.py
View File

@ -82,7 +82,7 @@ class TestNumericDebugger(TestCase):
prev_decomp_op_to_debug_handle_map[prev_decomp_op]
== debug_handle
), f"Node {node} has different debug handle {debug_handle}"
"than previous node sharing the same decomp op {prev_decomp_op}"
f"than previous node sharing the same decomp op {prev_decomp_op}"
bfs_trace_with_node_process(
model, _extract_debug_handles_with_prev_decomp_op_from_node

4
test/test_sparse_csr.py
View File

@ -2702,7 +2702,7 @@ class TestSparseCSR(TestCase):
# Sparse CSR only supports 2D tensors as inputs
# Fail early to prevent silent success with this test
if sample.input.ndim != 2:
raise ValueError("Expected 2D tensor but got tensor with dimension: {sample.input.ndim}.")
raise ValueError(f"Expected 2D tensor but got tensor with dimension: {sample.input.ndim}.")
sample.input = sample.input.to_sparse_csr()
expect = op(sample.input, *sample.args, **sample.kwargs)
@ -2726,7 +2726,7 @@ class TestSparseCSR(TestCase):
# Sparse CSR only supports 2D tensors as inputs
# Fail early to prevent silent success with this test
if sample.input.ndim != 2:
raise ValueError("Expected 2D tensor but got tensor with dimension: {sample.input.ndim}.")
raise ValueError(f"Expected 2D tensor but got tensor with dimension: {sample.input.ndim}.")
sample.input = sample.input.to_sparse_csr()
expect = op(sample.input, *sample.args, **sample.kwargs)