[RFC] Add pyrefly to lintrunner (#165179)

This will add pyrefly to lint runner as a warning only - and allow us to collect feedback about the tool before switching to pyrefly as the main type checker.

References the steps outlined here: : https://github.com/pytorch/pytorch/issues/163283:

test plan:
`lintrunner init`
`lintrunner`
confirm when pyrefly errors are present results look like: https://gist.github.com/maggiemoss/e6cb2d015dd1ded560ae1329098cf33f

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165179
Approved by: https://github.com/ezyang

.github/workflows/lint.yml

@@ -118,9 +118,9 @@ jobs:
         CHANGED_FILES="${{ needs.get-changed-files.outputs.changed-files }}"
         echo "Running all other linters"
         if [ "$CHANGED_FILES" = '*' ]; then
-          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT --all-files" .github/scripts/lintrunner.sh
+          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT,PYREFLY --all-files" .github/scripts/lintrunner.sh
         else
-          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT ${CHANGED_FILES}" .github/scripts/lintrunner.sh
+          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT,PYREFLY ${CHANGED_FILES}" .github/scripts/lintrunner.sh
         fi
 
   quick-checks:

.lintrunner.toml

@@ -209,6 +209,46 @@ command = [
     '@{{PATHSFILE}}'
 ]
 
+
+[[linter]]
+code = 'PYREFLY'
+include_patterns = [
+    'torch/**/*.py',
+    'torch/**/*.pyi',
+    'torchgen/**/*.py',
+    'torchgen/**/*.pyi',
+    'functorch/**/*.py',
+    'functorch/**/*.pyi',
+]
+exclude_patterns = []
+command = [
+    'python3',
+    'tools/linter/adapters/pyrefly_linter.py',
+    '--config=pyrefly.toml',
+]
+init_command = [
+    'python3',
+    'tools/linter/adapters/pip_init.py',
+    '--dry-run={{DRYRUN}}',
+    'numpy==2.1.0 ; python_version >= "3.12"',
+    'expecttest==0.3.0',
+    'pyrefly==0.36.2',
+    'sympy==1.13.3',
+    'types-requests==2.27.25',
+    'types-pyyaml==6.0.2',
+    'types-tabulate==0.8.8',
+    'types-protobuf==5.29.1.20250403',
+    'types-setuptools==79.0.0.20250422',
+    'types-jinja2==2.11.9',
+    'types-colorama==0.4.6',
+    'filelock==3.18.0',
+    'junitparser==2.1.1',
+    'rich==14.1.0',
+    'optree==0.17.0',
+    'types-openpyxl==3.1.5.20250919',
+    'types-python-dateutil==2.9.0.20251008'
+]
+
 [[linter]]
 code = 'CLANGTIDY'
 include_patterns = [

pyrefly.toml

@@ -1,5 +1,7 @@
 # A Pyrefly configuration for PyTorch
 # Based on https://github.com/pytorch/pytorch/blob/main/mypy.ini
+python-version = "3.12"
+
 project-includes = [
     "torch",
     "caffe2",
@@ -36,6 +38,7 @@ project-excludes = [
   "torch/nn/modules/rnn.py", # only remove when parsing errors are fixed
   "torch/_inductor/codecache.py",
   "torch/distributed/elastic/metrics/__init__.py",
+  "torch/_inductor/fx_passes/bucketing.py",
   # ====
   "benchmarks/instruction_counts/main.py",
   "benchmarks/instruction_counts/definitions/setup.py",

tools/linter/adapters/pyrefly_linter.py

@@ -0,0 +1,258 @@
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import os
+import re
+import subprocess
+import sys
+import time
+from enum import Enum
+from typing import NamedTuple
+
+
+class LintSeverity(str, Enum):
+    ERROR = "error"
+    WARNING = "warning"
+    ADVICE = "advice"
+    DISABLED = "disabled"
+
+
+class LintMessage(NamedTuple):
+    path: str | None
+    line: int | None
+    char: int | None
+    code: str
+    severity: LintSeverity
+    name: str
+    original: str | None
+    replacement: str | None
+    description: str | None
+
+
+# Note: This regex pattern is kept for reference but not used for pyrefly JSON parsing
+RESULTS_RE: re.Pattern[str] = re.compile(
+    r"""(?mx)
+    ^
+    (?P<file>.*?):
+    (?P<line>\d+):
+    (?:(?P<column>-?\d+):)?
+    \s(?P<severity>\S+?):?
+    \s(?P<message>.*)
+    \s(?P<code>\[.*\])
+    $
+    """
+)
+
+# torch/_dynamo/variables/tensor.py:363: error: INTERNAL ERROR
+INTERNAL_ERROR_RE: re.Pattern[str] = re.compile(
+    r"""(?mx)
+    ^
+    (?P<file>.*?):
+    (?P<line>\d+):
+    \s(?P<severity>\S+?):?
+    \s(?P<message>INTERNAL\sERROR.*)
+    $
+    """
+)
+
+
+def run_command(
+    args: list[str],
+    *,
+    extra_env: dict[str, str] | None,
+    retries: int,
+) -> subprocess.CompletedProcess[bytes]:
+    logging.debug("$ %s", " ".join(args))
+    start_time = time.monotonic()
+    try:
+        return subprocess.run(
+            args,
+            capture_output=True,
+        )
+    finally:
+        end_time = time.monotonic()
+        logging.debug("took %dms", (end_time - start_time) * 1000)
+
+
+# Severity mapping (currently only used for stderr internal errors)
+# Pyrefly JSON output doesn't include severity, so all errors default to ERROR
+severities = {
+    "error": LintSeverity.ERROR,
+    "note": LintSeverity.ADVICE,
+}
+
+
+def check_pyrefly_installed(code: str) -> list[LintMessage]:
+    cmd = ["pyrefly", "--version"]
+    try:
+        subprocess.run(cmd, check=True, capture_output=True)
+        return []
+    except subprocess.CalledProcessError as e:
+        msg = e.stderr.decode(errors="replace")
+        return [
+            LintMessage(
+                path=None,
+                line=None,
+                char=None,
+                code=code,
+                severity=LintSeverity.ERROR,
+                name="command-failed",
+                original=None,
+                replacement=None,
+                description=f"Could not run '{' '.join(cmd)}': {msg}",
+            )
+        ]
+
+
+def in_github_actions() -> bool:
+    return bool(os.getenv("GITHUB_ACTIONS"))
+
+
+def check_files(
+    code: str,
+    config: str,
+) -> list[LintMessage]:
+    try:
+        pyrefly_commands = [
+            "pyrefly",
+            "check",
+            "--config",
+            config,
+            "--output-format=json",
+        ]
+        proc = run_command(
+            [*pyrefly_commands],
+            extra_env={},
+            retries=0,
+        )
+    except OSError as err:
+        return [
+            LintMessage(
+                path=None,
+                line=None,
+                char=None,
+                code=code,
+                severity=LintSeverity.ERROR,
+                name="command-failed",
+                original=None,
+                replacement=None,
+                description=(f"Failed due to {err.__class__.__name__}:\n{err}"),
+            )
+        ]
+    stdout = str(proc.stdout, "utf-8").strip()
+    stderr = str(proc.stderr, "utf-8").strip()
+    if proc.returncode not in (0, 1):
+        return [
+            LintMessage(
+                path=None,
+                line=None,
+                char=None,
+                code=code,
+                severity=LintSeverity.ERROR,
+                name="command-failed",
+                original=None,
+                replacement=None,
+                description=stderr,
+            )
+        ]
+
+    # Parse JSON output from pyrefly
+    try:
+        if stdout:
+            result = json.loads(stdout)
+            errors = result.get("errors", [])
+        else:
+            errors = []
+        # For now filter out deprecated warnings and only report type errors as warnings
+        # until we remove mypy
+        errors = [error for error in errors if error["name"] != "deprecated"]
+        rc = [
+            LintMessage(
+                path=error["path"],
+                name=error["name"],
+                description=error.get(
+                    "description", error.get("concise_description", "")
+                ),
+                line=error["line"],
+                char=error["column"],
+                code=code,
+                severity=LintSeverity.ADVICE,
+                # uncomment and replace when we switch to pyrefly
+                # severity=LintSeverity.ADVICE if error["name"] == "deprecated" else LintSeverity.ERROR,
+                original=None,
+                replacement=None,
+            )
+            for error in errors
+        ]
+    except (json.JSONDecodeError, KeyError, TypeError) as e:
+        return [
+            LintMessage(
+                path=None,
+                line=None,
+                char=None,
+                code=code,
+                severity=LintSeverity.ERROR,
+                name="json-parse-error",
+                original=None,
+                replacement=None,
+                description=f"Failed to parse pyrefly JSON output: {e}",
+            )
+        ]
+
+    # Still check stderr for internal errors
+    rc += [
+        LintMessage(
+            path=match["file"],
+            name="INTERNAL ERROR",
+            description=match["message"],
+            line=int(match["line"]),
+            char=None,
+            code=code,
+            severity=severities.get(match["severity"], LintSeverity.ERROR),
+            original=None,
+            replacement=None,
+        )
+        for match in INTERNAL_ERROR_RE.finditer(stderr)
+    ]
+    return rc
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="pyrefly wrapper linter.",
+        fromfile_prefix_chars="@",
+    )
+    parser.add_argument(
+        "--code",
+        default="PYREFLY",
+        help="the code this lint should report as",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose logging",
+    )
+    parser.add_argument(
+        "--config",
+        required=True,
+        help="path to an mypy .ini config file",
+    )
+    args = parser.parse_args()
+
+    logging.basicConfig(
+        format="<%(threadName)s:%(levelname)s> %(message)s",
+        level=logging.INFO,
+        stream=sys.stderr,
+    )
+
+    lint_messages = check_pyrefly_installed(args.code) + check_files(
+        args.code, args.config
+    )
+    for lint_message in lint_messages:
+        print(json.dumps(lint_message._asdict()), flush=True)
+
+
+if __name__ == "__main__":
+    main()

torch/_dynamo/variables/streams.py

@@ -76,6 +76,7 @@ class StreamVariable(VariableTracker):
         super().__init__(**kwargs)
         self.proxy = proxy
         self.value = value
+        # pyrefly: ignore  # read-only
         self.device = device
 
     def python_type(self) -> type:

torch/_functorch/_aot_autograd/graph_compile.py

@@ -1492,6 +1492,7 @@ def _aot_stage2a_partition(
 
             # apply joint_gm callback here
             if callable(torch._functorch.config.joint_custom_pass):
+                # pyrefly: ignore  # bad-assignment
                 fx_g = torch._functorch.config.joint_custom_pass(fx_g, joint_inputs)
 
             static_lifetime_input_indices = fw_metadata.static_input_indices
@@ -1761,6 +1762,7 @@ def _aot_stage2b_bw_compile(
                     # tensor which is wrong.
 
                     ph_size = ph_arg.size()
+                    # pyrefly: ignore  # bad-argument-type
                     if len(ph_size) == 0 and len(real_stride) > 0:
                         # Fix for 0-dimensional tensors: When a tensor becomes 0-d
                         # (e.g., via squeeze), its stride should be () not (1,).

torch/_functorch/partitioners.py

@@ -628,6 +628,7 @@ def quantize_activation_fw(graph: torch.fx.Graph) -> None:
         position_to_quant.get(i, node) for i, node in enumerate(fwd_outputs)
     ]
     # add the scale nodes to the output find the first sym_node in the output
+    # pyrefly: ignore  # bad-argument-type
     idx = find_first_sym_node(output_updated_args)
     scale_nodes = tensor_scale_nodes + sym_scale_nodes
     if scale_nodes:

torch/_inductor/fx_passes/bucketing.py

@@ -86,7 +86,7 @@ def bucket_all_gather(
     mode: BucketMode = "default",
 ) -> None:
     if bucket_cap_mb_by_bucket_idx is None:
-        from torch._inductor.fx_passes.bucketing import (
+        from torch._inductor.fx_passes.bucketing import (  # pyrefly: ignore  # missing-module-attribute
             bucket_cap_mb_by_bucket_idx_default,
         )
 
@@ -103,7 +103,7 @@ def bucket_reduce_scatter(
     mode: BucketMode = "default",
 ) -> None:
     if bucket_cap_mb_by_bucket_idx is None:
-        from torch._inductor.fx_passes.bucketing import (
+        from torch._inductor.fx_passes.bucketing import (  # pyrefly: ignore  # missing-module-attribute
             bucket_cap_mb_by_bucket_idx_default,
         )
 

torch/_inductor/fx_passes/freezing_patterns.py

@@ -209,6 +209,7 @@ def addmm_patterns_init():
         # pyrefly: ignore  # bad-argument-type
         int8_woq_fusion_replacement,
         [val(), val(), val(), val(), scale(), scale(), scale()],
+        # pyrefly: ignore  # bad-argument-type
         fwd_only,
         # pyrefly: ignore  # bad-argument-type
         pass_patterns[0],
@@ -230,6 +231,7 @@ def addmm_patterns_init():
         # pyrefly: ignore  # bad-argument-type
         matmul_replacement,
         [val(), val(), val(), val()],
+        # pyrefly: ignore  # bad-argument-type
         fwd_only,
         # pyrefly: ignore  # bad-argument-type
         pass_patterns[0],
@@ -251,6 +253,7 @@ def addmm_patterns_init():
         # pyrefly: ignore  # bad-argument-type
         matmul_replacement_two,
         [val(), val(), val()],
+        # pyrefly: ignore  # bad-argument-type
         fwd_only,
         # pyrefly: ignore  # bad-argument-type
         pass_patterns[0],
@@ -276,6 +279,7 @@ def addmm_patterns_init():
         # pyrefly: ignore  # bad-argument-type
         addmm_fuse_replacement_second,
         [val() for _ in range(7)],
+        # pyrefly: ignore  # bad-argument-type
         fwd_only,
         # pyrefly: ignore  # bad-argument-type
         pass_patterns[0],

torch/_inductor/fx_passes/misc_patterns.py

@@ -49,6 +49,7 @@ def _misc_patterns_init():
         # pyrefly: ignore  # bad-argument-type
         randperm_index_add_replacement,
         [torch.empty(4, 8, device=device), torch.empty(2, 8, device=device)],
+        # pyrefly: ignore  # bad-argument-type
         fwd_only,
         # pyrefly: ignore  # bad-argument-type
         [post_grad_patterns, joint_graph_patterns],
@@ -68,6 +69,7 @@ def _misc_patterns_init():
         # pyrefly: ignore  # bad-argument-type
         randperm_index_replacement,
         [torch.empty(4, 8, device=device)],
+        # pyrefly: ignore  # bad-argument-type
         fwd_only,
         # pyrefly: ignore  # bad-argument-type
         [post_grad_patterns, joint_graph_patterns],

torch/_inductor/fx_passes/pad_mm.py

@@ -919,6 +919,7 @@ def _pad_mm_init() -> None:
             pattern,
             replacement,
             args,
+            # pyrefly: ignore  # bad-argument-type
             joint_fwd_bwd,
             # pyrefly: ignore  # bad-argument-type
             patterns,
@@ -931,6 +932,7 @@ def _pad_mm_init() -> None:
             pattern,
             replacement,
             args,
+            # pyrefly: ignore  # bad-argument-type
             fwd_only,
             # pyrefly: ignore  # bad-argument-type
             patterns,

torch/_inductor/fx_passes/post_grad.py

@@ -666,6 +666,7 @@ def lazy_init():
         prepare_softmax_replacement,
         [torch.empty(4, 8)],
         scalar_workaround=dict(dim=-1),
+        # pyrefly: ignore  # bad-argument-type
         trace_fn=fwd_only,
         # pyrefly: ignore  # bad-argument-type
         pass_dicts=pass_patterns[1],
@@ -730,6 +731,7 @@ def register_lowering_pattern(
     return pattern_matcher.register_lowering_pattern(
         pattern,
         extra_check,
+        # pyrefly: ignore  # bad-argument-type
         pass_dict=pass_patterns[pass_number],
     )
 
@@ -1573,6 +1575,7 @@ def register_partial_reduction_pattern():
 
         @register_graph_pattern(
             MultiOutputPattern([partial_reduc, full_reduc]),
+            # pyrefly: ignore  # bad-argument-type
             pass_dict=pass_patterns[2],
         )
         def reuse_partial(match, input, reduced_dims, keepdim):

torch/_inductor/lowering.py

@@ -27,7 +27,7 @@ from torch._dynamo.utils import counters
 from torch._higher_order_ops.associative_scan import associative_scan_op
 from torch._higher_order_ops.triton_kernel_wrap import triton_kernel_wrapper_mutation
 from torch._library.utils import get_layout_constraint_tag
-from torch._prims_common import (
+from torch._prims_common import (  # pyrefly: ignore  # deprecated
     canonicalize_dim,
     canonicalize_dims,
     check,

torch/_library/opaque_object.py

@@ -173,6 +173,7 @@ def register_opaque_type(cls: Any, name: Optional[str] = None) -> None:
             f"Unable to accept name, {name}, for this opaque type as it contains a '.'"
         )
     _OPAQUE_TYPES[cls] = name
+    # pyrefly: ignore  # missing-attribute
     torch._C._register_opaque_type(name)
 
 
@@ -182,4 +183,5 @@ def is_opaque_type(cls: Any) -> bool:
     """
     if cls not in _OPAQUE_TYPES:
         return False
+    # pyrefly: ignore  # missing-attribute
     return torch._C._is_opaque_type_registered(_OPAQUE_TYPES[cls])

torch/distributed/__init__.py

@@ -135,7 +135,7 @@ if is_available():
     # this.
     # pyrefly: ignore  # deprecated
     from .distributed_c10d import *  # noqa: F403
-    from .distributed_c10d import (
+    from .distributed_c10d import (  # pyrefly: ignore  # deprecated
         _all_gather_base,
         _coalescing_manager,
         _CoalescingManager,

torch/distributed/_functional_collectives.py

@@ -1009,8 +1009,8 @@ lib_impl.impl("broadcast", _broadcast_meta, "Meta")
 lib_impl.impl("broadcast_", _broadcast__meta, "Meta")
 
 # mark these ops has side effect so that they won't be removed by DCE
-torch.fx.node.has_side_effect(torch.ops._c10d_functional.wait_tensor.default)
+torch.fx.node.has_side_effect(torch.ops._c10d_functional.wait_tensor.default)  # type: ignore[has-type]
-torch.fx.node.has_side_effect(torch.ops._c10d_functional.wait_tensor)
+torch.fx.node.has_side_effect(torch.ops._c10d_functional.wait_tensor)  # type: ignore[has-type]
 
 # Register legacy ops for backward compatibility
 # TODO(yifu): remove these in functional collective beta release
@@ -1176,7 +1176,7 @@ def all_gather_inplace(
     return tensor_list
 
 
-from torch.distributed.distributed_c10d import (
+from torch.distributed.distributed_c10d import (  # pyrefly: ignore  # deprecated
     _all_gather_base as legacy_all_gather_base,
     _reduce_scatter_base as legacy_reduce_scatter_base,
     all_gather as legacy_all_gather,
@@ -1190,11 +1190,11 @@ from torch.distributed.distributed_c10d import (
 # This dict should contain sets of functions that dynamo is allowed to remap.
 # Functions in this set should accept the same args/kwargs 1:1 as their mapping.
 traceable_collective_remaps = {
-    legacy_allgather: all_gather_tensor_inplace,
+    legacy_allgather: all_gather_tensor_inplace,  # type: ignore[has-type]
-    legacy_reducescatter: reduce_scatter_tensor_inplace,
+    legacy_reducescatter: reduce_scatter_tensor_inplace,  # type: ignore[has-type]
-    legacy_allreduce: all_reduce_inplace,
+    legacy_allreduce: all_reduce_inplace,  # type: ignore[has-type]
-    legacy_all_to_all_single: all_to_all_inplace,
+    legacy_all_to_all_single: all_to_all_inplace,  # type: ignore[has-type]
-    legacy_all_gather: all_gather_inplace,
+    legacy_all_gather: all_gather_inplace,  # type: ignore[has-type]
-    legacy_reduce_scatter_base: reduce_scatter_tensor_inplace,
+    legacy_reduce_scatter_base: reduce_scatter_tensor_inplace,  # type: ignore[has-type]
-    legacy_all_gather_base: all_gather_tensor_inplace,
+    legacy_all_gather_base: all_gather_tensor_inplace,  # type: ignore[has-type]
 }

torch/distributed/_local_tensor/__init__.py

@@ -393,6 +393,7 @@ class LocalTensor(torch.Tensor):
     def __repr__(self) -> str:  # type: ignore[override]
         parts = []
         for k, v in self._local_tensors.items():
+            # pyrefly: ignore  # bad-argument-type
             parts.append(f"  {k}: {v}")
         tensors_str = ",\n".join(parts)
         return f"LocalTensor(\n{tensors_str}\n)"
@@ -680,6 +681,7 @@ class LocalTensorMode(TorchDispatchMode):
     def _unpatch_device_mesh(self) -> None:
         assert self._old_get_coordinate is not None
         DeviceMesh.get_coordinate = self._old_get_coordinate
+        # pyrefly: ignore  # bad-assignment
         self._old_get_coordinate = None
 
 

torch/distributed/_local_tensor/_c10d.py

@@ -316,6 +316,7 @@ def _local_all_gather_(
     assert len(input_tensors) == 1
 
     input_tensor = input_tensors[0]
+    # pyrefly: ignore  # bad-assignment
     output_tensors = output_tensors[0]
 
     ranks, group_offsets, _offset = _prepare_collective_groups(process_group_so)
@@ -336,10 +337,12 @@ def _local_all_gather_(
             source_tensor = input_tensor
             if isinstance(input_tensor, LocalTensor):
                 source_tensor = input_tensor._local_tensors[rank_i]
+            # pyrefly: ignore  # missing-attribute
             output_tensors[i].copy_(source_tensor)
 
     work = FakeWork()
     work_so = Work.boxed(work)
+    # pyrefly: ignore  # bad-return
     return ([output_tensors], work_so)
 
 
@@ -426,6 +429,7 @@ def _local_scatter_(
     assert len(output_tensors) == 1
     assert len(input_tensors) == 1
     output_tensor = output_tensors[0]
+    # pyrefly: ignore  # bad-assignment
     input_tensors = input_tensors[0]
 
     ranks, group_offsets, offset = _prepare_collective_groups(process_group_so)

torch/distributed/tensor/_dtensor_spec.py

@@ -90,6 +90,7 @@ class DTensorSpec:
         if not isinstance(self.placements, tuple):
             self.placements = tuple(self.placements)
         if self.shard_order is None:
+            # pyrefly: ignore  # bad-assignment
             self.shard_order = DTensorSpec.compute_default_shard_order(self.placements)
         self._hash: int | None = None
 

torch/export/_trace.py

@@ -701,6 +701,7 @@ def _restore_state_dict(
     for name, _ in list(
         chain(
             original_module.named_parameters(remove_duplicate=False),
+            # pyrefly: ignore  # bad-argument-type
             original_module.named_buffers(remove_duplicate=False),
         )
     ):

torch/nn/attention/flex_attention.py

@@ -218,6 +218,7 @@ class FlexKernelOptions(TypedDict, total=False):
     waves_per_eu: NotRequired[int]
     """ROCm-specific waves per execution unit."""
 
+    # pyrefly: ignore  # invalid-annotation
     force_flash: NotRequired[bool]
     """ If True, forces use of the cute-dsl flash attention kernel.
 

torch/nn/utils/__init__.py

@@ -1,5 +1,5 @@
 from . import parametrizations, parametrize, rnn, stateless
-from .clip_grad import (
+from .clip_grad import (  # pyrefly: ignore  # deprecated
     _clip_grads_with_norm_ as clip_grads_with_norm_,
     _get_total_norm as get_total_norm,
     clip_grad_norm,

torch/nn/utils/clip_grad.py

@@ -283,6 +283,7 @@ def clip_grad_value_(
     clip_value = float(clip_value)
 
     grads = [p.grad for p in parameters if p.grad is not None]
+    # pyrefly: ignore  # bad-argument-type
     grouped_grads = _group_tensors_by_device_and_dtype([grads])
 
     for (device, _), ([grads], _) in grouped_grads.items():

torch/nn/utils/parametrizations.py

@@ -111,8 +111,10 @@ class _Orthogonal(Module):
             Q = Q * X.diagonal(dim1=-2, dim2=-1).int().unsqueeze(-2)
 
         if hasattr(self, "base"):
+            # pyrefly: ignore  # unbound-name
             Q = self.base @ Q
         if transposed:
+            # pyrefly: ignore  # unbound-name
             Q = Q.mT
         return Q  # type: ignore[possibly-undefined]
 

torch/onnx/_internal/exporter/_core.py

@@ -170,6 +170,7 @@ class TorchTensor(ir.Tensor):
 
         if isinstance(tensor, torch._subclasses.fake_tensor.FakeTensor):
             raise TypeError(
+                # pyrefly: ignore  # missing-attribute
                 f"Cannot take content out from the FakeTensor ('{self.name}'). Please replace the tensor "
                 "with a tensor backed by real data using ONNXProgram.apply_weights() "
                 "or save the model without initializers by setting include_initializers=False."

torch/optim/swa_utils.py

@@ -297,6 +297,7 @@ class AveragedModel(Module):
                         avg_fn = get_swa_avg_fn()
                         n_averaged = self.n_averaged.to(device)
                         for p_averaged, p_model in zip(self_params, model_params):  # type: ignore[assignment]
+                            # pyrefly: ignore  # missing-attribute
                             p_averaged.copy_(avg_fn(p_averaged, p_model, n_averaged))
             else:
                 for p_averaged, p_model in zip(  # type: ignore[assignment]

torch/quantization/_quantized_conversions.py

@@ -71,6 +71,7 @@ def quantized_weight_reorder_for_mixed_dtypes_linear_cutlass(
                 nrows // 16, 16
             )
         ).view(-1)
+    # pyrefly: ignore  # unbound-name
     outp = outp.index_copy(1, cols_permuted, outp)
 
     # interleave_column_major_tensor

torch/sparse/_semi_structured_ops.py

@@ -67,6 +67,7 @@ def semi_sparse_t(func, types, args=(), kwargs=None) -> torch.Tensor:
     # Because we cannot go from the compressed representation back to the dense representation currently,
     # we just keep track of how many times we have been transposed. Depending on whether the sparse matrix
     # is the first or second argument, we expect an even / odd number of calls to transpose respectively.
+    # pyrefly: ignore  # no-matching-overload
     return self.__class__(
         torch.Size([self.shape[-1], self.shape[0]]),
         packed=self.packed_t,

torch/sparse/semi_structured.py

@@ -184,6 +184,7 @@ class SparseSemiStructuredTensor(torch.Tensor):
         outer_stride,
     ) -> torch.Tensor:
         shape, fuse_transpose_cusparselt, alg_id_cusparselt, requires_grad = tensor_meta
+        # pyrefly: ignore  # no-matching-overload
         return cls(
             shape=shape,
             packed=inner_tensors.get("packed", None),
@@ -413,6 +414,7 @@ class SparseSemiStructuredTensorCUTLASS(SparseSemiStructuredTensor):
             sparse_tensor_cutlass,
             meta_tensor_cutlass,
         ) = sparse_semi_structured_from_dense_cutlass(original_tensor)
+        # pyrefly: ignore  # no-matching-overload
         return cls(
             original_tensor.shape,
             packed=sparse_tensor_cutlass,
@@ -499,6 +501,7 @@ class SparseSemiStructuredTensorCUTLASS(SparseSemiStructuredTensor):
             original_tensor, algorithm=algorithm, use_cutlass=True
         )
 
+        # pyrefly: ignore  # no-matching-overload
         return cls(
             original_tensor.shape,
             packed=packed,
@@ -560,6 +563,7 @@ class SparseSemiStructuredTensorCUSPARSELT(SparseSemiStructuredTensor):
         cls, original_tensor: torch.Tensor
     ) -> "SparseSemiStructuredTensorCUSPARSELT":
         cls._validate_device_dim_dtype_shape(original_tensor)
+        # pyrefly: ignore  # no-matching-overload
         return cls(
             shape=original_tensor.shape,
             packed=torch._cslt_compress(original_tensor),
@@ -626,6 +630,7 @@ class SparseSemiStructuredTensorCUSPARSELT(SparseSemiStructuredTensor):
         packed = packed.view(original_tensor.shape[0], -1)
         packed_t = packed_t.view(original_tensor.shape[1], -1)
 
+        # pyrefly: ignore  # no-matching-overload
         return cls(
             original_tensor.shape,
             packed=packed,

torch/utils/_sympy/functions.py

@@ -1336,6 +1336,7 @@ class Identity(sympy.Function):
 
     def _sympystr(self, printer):
         """Controls how sympy's StrPrinter prints this"""
+        # pyrefly: ignore  # missing-attribute
         return f"({printer.doprint(self.args[0])})"
 
     def _eval_is_real(self):