Convert logging f-strings to use % format, part five (#98765)

This does some annoying but simple cases by hand.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/98765
Approved by: https://github.com/wanchaol

benchmarks/dynamo/common.py

@@ -1393,7 +1393,7 @@ class BenchmarkRunner:
                     percentage = psutil.Process(os.getpid()).memory_percent()
                     peak_mem = percentage * total / 10**9
             except Exception:
-                log.exception(f"Backend {mode} failed in warmup()")
+                log.exception("Backend %s failed in warmup()", mode)
                 return sys.exit(-1)
             dynamo_stats = get_dynamo_stats()
             dynamo_stats.subtract(start_stats)

docs/source/conf.py

@@ -98,7 +98,6 @@ coverage_ignore_functions = [
     "cudart",
     "is_bf16_supported",
     # torch.cuda._sanitizer
-    "format_log_message",
     "zip_arguments",
     "zip_by_key",
     # torch.distributed.autograd

functorch/benchmarks/chrome_trace_parser.py

@@ -61,7 +61,7 @@ def main():
             utilization, mm_conv_utilization = compute_utilization(filenames, total_length)
             print(f"{modelname}, {utilization}, {mm_conv_utilization}")
         except BaseException:
-            logging.exception(f"{filename}, ERROR")
+            logging.exception("%s, ERROR", filename)
             print(f"{filename}, ERROR")
 
 if __name__ == "__main__":

tools/linter/adapters/s3_init.py

@@ -7,7 +7,6 @@ import platform
 import stat
 import subprocess
 import sys
-import textwrap
 import urllib.error
 import urllib.request
 from pathlib import Path
@@ -77,16 +76,17 @@ def check(binary_path: Path, reference_hash: str) -> bool:
         return True
 
     logging.warning(
-        textwrap.dedent(
-            f"""\
-            Found binary hash does not match reference!
+        """\
+Found binary hash does not match reference!
 
-            Found hash: {existing_binary_hash}
-            Reference hash: {reference_hash}
+Found hash: %s
+Reference hash: %s
 
-            Deleting {binary_path} just to be safe.
-            """
-        )
+Deleting %s just to be safe.
+""",
+        existing_binary_hash,
+        reference_hash,
+        binary_path,
     )
     if DRY_RUN:
         logging.critical(

torch/_dynamo/symbolic_convert.py

@@ -784,11 +784,11 @@ class InstructionTranslatorBase(Checkpointable[InstructionTranslatorGraphState])
         if package is not None:
             if spec is not None and package != spec.parent:
                 log.warning(
-                    "__package__ != __spec__.parent "
-                    f"({package!r} != {spec.parent!r})",
-                    ImportWarning,
+                    "__package__ != __spec__.parent (%r != %r)",
+                    package,
+                    spec.parent,
                     stacklevel=3,
-                )  # type: ignore[call-arg]
+                )
             return package
         elif spec is not None:
             return spec.parent

torch/_dynamo/utils.py

@@ -321,7 +321,7 @@ def write_record_to_file(filename, exec_record):
             with open(filename, "wb") as f:
                 exec_record.dump(f)
     except Exception:
-        log.error(f"Unable to write execution record {filename}", exc_info=1)
+        log.error("Unable to write execution record %s", filename, exc_info=1)
 
 
 def count_calls(g: fx.Graph):

torch/backends/_nnapi/serializer.py

@@ -491,7 +491,7 @@ class _NnapiSerializer:
                 raise Exception("Flexible size is not supported for this operand.")
             if s < 0:
                 # runtime flex
-                LOG.warning(f"Operand {oper} has runtime flex shape")
+                LOG.warning("Operand %s has runtime flex shape", oper)
         return op_id, oper
 
     def get_tensor_operand_or_constant(self, jitval, dim_order=DimOrder.PRESUMED_CONTIGUOUS):

torch/backends/xeon/run_cpu.py

@@ -332,10 +332,10 @@ or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib6
             if find_je:
                 logger.info("Use JeMalloc memory allocator")
                 return
-            logger.warning(f"""Neither TCMalloc nor JeMalloc is found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib
+            logger.warning("""Neither TCMalloc nor JeMalloc is found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib
                             or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or
-                           {expanduser("~")}/.local/lib/ so the LD_PRELOAD environment variable will not be set.
-                           This may drop the performance""")
+                           %s/.local/lib/ so the LD_PRELOAD environment variable will not be set.
+                           This may drop the performance""", expanduser("~"))
 
     def log_env_var(self, env_var_name=""):
         if env_var_name in os.environ:

torch/cuda/_sanitizer.py

@@ -142,10 +142,6 @@ class CUDASanitizerErrors(Exception):
         return f"detected {len(self.errors)} errors"
 
 
-def format_log_message(message: str) -> str:
-    return " ".join(line.strip() for line in message.strip().splitlines())
-
-
 @dataclass
 class TensorInfo:
     r"""Stores information about a single tensor and recent accesses to it.
@@ -169,27 +165,21 @@ class _TensorsAccessed:
     def ensure_tensor_exists(self, data_ptr: DataPtr) -> None:
         if data_ptr not in self.accesses:
             logger.info(
-                format_log_message(
-                    f"""
-                    Found tensor with pointer: {data_ptr}, but no matching tensor
-                    allocation in the trace. Backfilling the trace now.
-                    Perhaps the sanitizer was enabled after some torch operations?
-                    """
-                )
+                "Found tensor with pointer: %s, but no matching tensor "
+                "allocation in the trace. Backfilling the trace now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                data_ptr
             )
             self.create_tensor(data_ptr, None)
 
     def ensure_tensor_does_not_exist(self, data_ptr: DataPtr) -> None:
         if data_ptr in self.accesses:
             logger.info(
-                format_log_message(
-                    f"""
-                    Found duplicate tensor allocation in the trace for tensor with
-                    pointer: {data_ptr}. Assuming the trace for tensor deallocation
-                    wasn't caught and backfilling it now.
-                    Perhaps the sanitizer was enabled after some torch operations?
-                    """
-                )
+                "Found duplicate tensor allocation in the trace for tensor with "
+                "pointer: %s. Assuming the trace for tensor deallocation "
+                "wasn't caught and backfilling it now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                data_ptr
             )
             self.delete_tensor(data_ptr)
 
@@ -233,53 +223,41 @@ class StreamSynchronizations:
     def _ensure_stream_exists(self, stream: StreamId) -> None:
         if stream not in self.current_sync_states:
             logger.info(
-                format_log_message(
-                    f"""
-                    Found Stream with id: {stream}, but no matching stream
-                    creation in the trace. Backfilling the trace now.
-                    Perhaps the sanitizer was enabled after some torch operations?
-                    """
-                )
+                "Found Stream with id: %s, but no matching stream "
+                "creation in the trace. Backfilling the trace now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                stream
             )
             self.create_stream(stream)
 
     def _ensure_event_exists(self, event: EventId) -> None:
         if event not in self.recorded_sync_states:
             logger.info(
-                format_log_message(
-                    f"""
-                    Found Event with id: {event}, but no matching event
-                    creation in the trace. Backfilling the trace now.
-                    Perhaps the sanitizer was enabled after some torch operations?
-                    """
-                )
+                "Found Event with id: %s, but no matching event "
+                "creation in the trace. Backfilling the trace now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                event
             )
             self.create_event(event)
 
     def _ensure_event_does_not_exist(self, event: EventId) -> None:
         if event in self.recorded_sync_states:
             logger.info(
-                format_log_message(
-                    f"""
-                    Found duplicate event creation in the trace for event with
-                    id: {event}. Assuming the trace for event deletion wasn't caught
-                    and backfilling it now.
-                    Perhaps the sanitizer was enabled after some torch operations?
-                    """
-                )
+                "Found duplicate event creation in the trace for event with "
+                "id: %s. Assuming the trace for event deletion wasn't caught "
+                "and backfilling it now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                event
             )
             self.delete_event(event)
 
     def create_stream(self, stream: StreamId) -> None:
         if stream in self.current_sync_states:
             logger.info(
-                format_log_message(
-                    f"""
-                    Found duplicate Stream creation in the trace for Stream with
-                    id: {stream}. PyTorch Streams are only created once, so this
-                    trace entry is ignored.
-                    """
-                )
+                "Found duplicate Stream creation in the trace for Stream with "
+                "id: %s. PyTorch Streams are only created once, so this "
+                "trace entry is ignored.",
+                stream
             )
         else:
             self.host_sync_state[stream] = 0

torch/distributed/elastic/agent/server/api.py

@@ -415,7 +415,7 @@ class ElasticAgent(abc.ABC):
       if group_result.is_failed():
         # workers failed
         failure = group_result.failures[0]
-        log.exception(f"worker 0 failed with exit code : {failure.exit_code}")
+        log.exception("worker 0 failed with exit code : %s", failure.exit_code)
       else:
         return group_result.return_values[0] # return rank 0's results
 
@@ -949,5 +949,6 @@ class SimpleElasticAgent(ElasticAgent):
             raise
         except Exception:
             log.exception(
-                f"Error waiting on exit barrier. Elapsed: {time.time() - start} seconds"
+                "Error waiting on exit barrier. Elapsed: %s seconds",
+                time.time() - start
             )

torch/distributed/elastic/multiprocessing/api.py

@@ -491,9 +491,12 @@ class MultiprocessContext(PContext):
             error_filepath = self.error_files[failed_local_rank]
 
             log.error(
-                f"failed (exitcode: {failed_proc.exitcode})"
-                f" local_rank: {failed_local_rank} (pid: {e.pid})"
-                f" of fn: {fn_name} (start_method: {self.start_method})",
+                "failed (exitcode: %s)"
+                " local_rank: %s (pid: %s)"
+                " of fn: %s (start_method: %s)",
+                failed_proc.exitcode,
+                failed_local_rank, e.pid,
+                fn_name, self.start_method,
                 exc_info=True,
             )
 

torch/distributed/elastic/multiprocessing/errors/__init__.py

@@ -118,7 +118,7 @@ class ProcessFailure:
                         self.error_file_data
                     )
             except Exception:
-                log.exception(f"Failed to parse reply file: {self.error_file}")
+                log.exception("Failed to parse reply file: %s", self.error_file)
                 raise
         else:
             self._set_no_reply_file()
@@ -351,9 +351,10 @@ def record(
                 else:
                     log.info(
                         (
-                            f"local_rank {rank} FAILED with no error file."
-                            f" Decorate your entrypoint fn with @record for traceback info."
-                            f" See: https://pytorch.org/docs/stable/elastic/errors.html"
+                            "local_rank %s FAILED with no error file."
+                            " Decorate your entrypoint fn with @record for traceback info."
+                            " See: https://pytorch.org/docs/stable/elastic/errors.html",
+                            rank
                         )
                     )
                 raise

torch/distributed/elastic/multiprocessing/tail_log.py

@@ -132,8 +132,9 @@ class TailLog:
                 f.result()
             except Exception as e:
                 log.error(
-                    f"error in log tailor for {self._name}{local_rank}."
-                    f" {e.__class__.__qualname__}: {e}",
+                    "error in log tailor for %s%s. %s: %s",
+                    self._name, local_rank,
+                    e.__class__.__qualname__, e,
                 )
 
         if self._threadpool:

torch/distributed/elastic/timer/file_based_local_timer.py

@@ -329,5 +329,5 @@ class FileTimerServer:
             log.info("Process with pid=%s does not exist. Skipping", worker_pid)
             return True
         except Exception as e:
-            log.error(f"Error terminating pid={worker_pid}", exc_info=e)
+            log.error("Error terminating pid=%s", worker_pid, exc_info=e)
         return False

torch/distributed/elastic/timer/local_timer.py

@@ -121,5 +121,5 @@ class LocalTimerServer(TimerServer):
             log.info("Process with pid=%s does not exist. Skipping", worker_id)
             return True
         except Exception as e:
-            log.error(f"Error terminating pid={worker_id}", exc_info=e)
+            log.error("Error terminating pid=%s", worker_id, exc_info=e)
         return False

torch/distributed/rpc/__init__.py

@@ -148,10 +148,11 @@ if is_available():
             # Ignore type error because mypy doesn't handle dynamically generated type objects (#4865)
             if backend != BackendType.TENSORPIPE:  # type: ignore[attr-defined]
                 logger.warning(
-                    f"RPC was initialized with no explicit backend but with options "  # type: ignore[attr-defined]
-                    f"corresponding to {backend}, hence that backend will be used "
-                    f"instead of the default {BackendType.TENSORPIPE}. To silence this "
-                    f"warning pass `backend={backend}` explicitly."
+                    "RPC was initialized with no explicit backend but with options "  # type: ignore[attr-defined]
+                    "corresponding to %(backend)s, hence that backend will be used "
+                    "instead of the default BackendType.TENSORPIPE. To silence this "
+                    "warning pass `backend=%(backend)s` explicitly.",
+                    {'backend': backend}
                 )
 
         if backend is None:

torch/fx/passes/infra/partitioner.py

@@ -207,7 +207,7 @@ class CapabilityBasedPartitioner:
 
         logger.debug("Partitions proposed:")
         for id, partition in partitions_by_id.items():
-            logger.debug(f"partition #{id}", [node.name for node in partition.nodes])
+            logger.debug("partition #%s: %s", id, [node.name for node in partition.nodes])
 
         return list(partitions_by_id.values())
 

torch/fx/passes/infra/pass_manager.py

@@ -280,7 +280,7 @@ class PassManager:
                     modified = modified or res.modified
 
                     if isinstance(module, GraphModule):
-                        logger.debug(f"Graph after pass '{fn_name}':", module.graph)
+                        logger.debug("Graph after pass '%s': %s", fn_name, module.graph)
                         module.recompile()
 
                     # Check graph invariants

torch/fx/passes/net_min_base.py

@@ -450,7 +450,7 @@ class _MinimizerBase:
             report.append(f"Sequential traverse iteration {self.iteration}.")
             report.append(f"Visit node: {node.name}")
 
-            _LOGGER.info(f"Visit node: {node.name}")
+            _LOGGER.info("Visit node: %s", node.name)
             cur_nodes: NodeSet = {node}
 
             if node in self.fusions:

torch/package/_importlib.py

@@ -63,7 +63,7 @@ def _calc___package__(globals):
     if package is not None:
         if spec is not None and package != spec.parent:
             _warnings.warn(  # noqa: G010
-                "__package__ != __spec__.parent " f"({package!r} != {spec.parent!r})",
+                f"__package__ != __spec__.parent ({package!r} != {spec.parent!r})",  # noqa: G004
                 ImportWarning,
                 stacklevel=3,
             )

torch/testing/_internal/distributed/ddp_under_dist_autograd_test.py

@@ -244,8 +244,9 @@ class Trainer:
             if trainer_has_less_inputs:
                 input_batches = batches[: len(batches) // 2]
                 gLogger.info(
-                    f"""Trainer reduced input patches from {len(batches)}
-                    to {len(input_batches)} to simulate uneven inputs."""
+                    "Trainer reduced input patches from %s "
+                    "to %s to simulate uneven inputs.",
+                    len(batches), len(input_batches)
                 )
             else:
                 input_batches = batches