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Add pyrefly suppressions to torch/distributed (7/n) (#165002)
Adds suppressions to pyrefly will typecheck clean: https://github.com/pytorch/pytorch/issues/163283 One more PR after this one. Test plan: dmypy restart && python3 scripts/lintrunner.py -a pyrefly check step 1: delete lines in the pyrefly.toml file from the project-excludes field step 2: run pyrefly check step 3: add suppressions, clean up unused suppressions before: https://gist.github.com/maggiemoss/4b3bf2037014e116bc00706a16aef199 after: INFO 0 errors (6,884 ignored) Pull Request resolved: https://github.com/pytorch/pytorch/pull/165002 Approved by: https://github.com/oulgen
This commit is contained in:
Maggie Moss
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PyTorch MergeBot
PyTorch MergeBot
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ab94a0d544
commit
7457d139c5
@ -372,6 +372,7 @@ class BackendConfig:
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def __init__(self, backend: Backend):
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"""Init."""
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self.device_backend_map: dict[str, Backend] = {}
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# pyrefly: ignore # bad-assignment
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backend = str(backend)
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if backend == Backend.UNDEFINED:
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@ -392,6 +393,7 @@ class BackendConfig:
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# e.g. "nccl", "gloo", "ucc", "mpi"
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supported_devices = Backend.backend_capability[backend.lower()]
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backend_val = Backend(backend)
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# pyrefly: ignore # bad-assignment
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self.device_backend_map = dict.fromkeys(supported_devices, backend_val)
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elif ":" in backend.lower():
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# Backend specified in "device:backend" format
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@ -410,6 +412,7 @@ class BackendConfig:
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f"Invalid device:backend pairing: \
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{device_backend_pair_str}. {backend_str_error_message}"
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)
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# pyrefly: ignore # bad-assignment
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device, backend = device_backend_pair
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if device in self.device_backend_map:
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raise ValueError(
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@ -1182,6 +1185,7 @@ def _as_iterable(obj) -> collections.abc.Iterable:
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def _ensure_all_tensors_same_dtype(*tensors) -> None:
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last_dtype = None
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# pyrefly: ignore # bad-assignment
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for tensor in itertools.chain.from_iterable(map(_as_iterable, tensors)):
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tensor_dtype = tensor.dtype
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# Mixing complex and its element type is allowed
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@ -1837,6 +1841,7 @@ def _get_split_source(pg):
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split_from = pg._get_backend(pg.bound_device_id)
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elif pg is _world.default_pg:
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try:
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# pyrefly: ignore # missing-attribute
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split_from = pg._get_backend(torch.device("cuda"))
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except RuntimeError:
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# no cuda device associated with this backend
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@ -1997,7 +2002,12 @@ def _new_process_group_helper(
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if not is_gloo_available():
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raise RuntimeError("Distributed package doesn't have Gloo built in")
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backend_class = ProcessGroupGloo(
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backend_prefix_store, group_rank, group_size, timeout=timeout
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# pyrefly: ignore # bad-argument-type
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backend_prefix_store,
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group_rank,
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group_size,
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# pyrefly: ignore # bad-argument-type
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timeout=timeout,
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)
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backend_class.options.global_ranks_in_group = global_ranks_in_group
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backend_class.options.group_name = group_name
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@ -2018,6 +2028,7 @@ def _new_process_group_helper(
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# default backend_options for NCCL
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backend_options = ProcessGroupNCCL.Options()
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backend_options.is_high_priority_stream = False
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# pyrefly: ignore # bad-argument-type
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backend_options._timeout = timeout
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if split_from:
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@ -2037,7 +2048,12 @@ def _new_process_group_helper(
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# RuntimeError if is_ucc_available() returns false.
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backend_class = ProcessGroupUCC(
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backend_prefix_store, group_rank, group_size, timeout=timeout
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# pyrefly: ignore # bad-argument-type
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backend_prefix_store,
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group_rank,
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group_size,
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# pyrefly: ignore # bad-argument-type
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timeout=timeout,
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)
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backend_type = ProcessGroup.BackendType.UCC
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elif backend_str == Backend.XCCL:
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@ -2046,6 +2062,7 @@ def _new_process_group_helper(
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backend_options = ProcessGroupXCCL.Options()
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backend_options.global_ranks_in_group = global_ranks_in_group
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backend_options.group_name = group_name
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# pyrefly: ignore # bad-argument-type
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backend_options._timeout = timeout
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backend_class = ProcessGroupXCCL(
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backend_prefix_store, group_rank, group_size, backend_options
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@ -2070,6 +2087,7 @@ def _new_process_group_helper(
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dist_backend_opts.store = backend_prefix_store
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dist_backend_opts.group_rank = group_rank
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dist_backend_opts.group_size = group_size
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# pyrefly: ignore # bad-argument-type
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dist_backend_opts.timeout = timeout
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dist_backend_opts.group_id = group_name
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dist_backend_opts.global_ranks_in_group = global_ranks_in_group
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@ -2113,6 +2131,7 @@ def _new_process_group_helper(
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store=backend_prefix_store,
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rank=group_rank,
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world_size=group_size,
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# pyrefly: ignore # bad-argument-type
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timeout=timeout,
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)
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@ -3322,6 +3341,7 @@ def gather_object(
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return
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assert object_gather_list is not None, "Must provide object_gather_list on dst rank"
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# pyrefly: ignore # unbound-name
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for i, tensor in enumerate(output_tensors):
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tensor = tensor.type(torch.uint8)
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tensor_size = object_size_list[i]
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@ -3698,8 +3718,10 @@ def broadcast_object_list(
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# has only one element, we can skip the copy.
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if my_group_rank == group_src:
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if len(tensor_list) == 1: # type: ignore[possibly-undefined]
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# pyrefly: ignore # unbound-name
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object_tensor = tensor_list[0]
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else:
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# pyrefly: ignore # unbound-name
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object_tensor = torch.cat(tensor_list)
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else:
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object_tensor = torch.empty( # type: ignore[call-overload]
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@ -3828,6 +3850,7 @@ def scatter_object_list(
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broadcast(max_tensor_size, group_src=group_src, group=group)
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# Scatter actual serialized objects
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# pyrefly: ignore # no-matching-overload
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output_tensor = torch.empty(
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max_tensor_size.item(), dtype=torch.uint8, device=pg_device
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)
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@ -4864,16 +4887,19 @@ def barrier(
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if isinstance(device_ids, list):
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opts.device_ids = device_ids
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# use only the first device id
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# pyrefly: ignore # read-only
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opts.device = torch.device(device.type, device_ids[0])
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elif getattr(group, "bound_device_id", None) is not None:
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# Use device id from `init_process_group(device_id=...)`
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opts.device = group.bound_device_id # type: ignore[assignment]
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elif device.type == "cpu" or _get_object_coll_device(group) == "cpu":
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# pyrefly: ignore # read-only
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opts.device = torch.device("cpu")
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else:
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# Use the current device set by the user. If user did not set any, this
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# may use default device 0, causing issues like hang or all processes
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# creating context on device 0.
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# pyrefly: ignore # read-only
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opts.device = device
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if group.rank() == 0:
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warnings.warn( # warn only once
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@ -5004,6 +5030,7 @@ def _hash_ranks_to_str(ranks: list[int]) -> str:
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# Takes a list of ranks and computes an integer color
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def _process_group_color(ranks: list[int]) -> int:
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# Convert list to tuple to make it hashable
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# pyrefly: ignore # bad-assignment
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ranks = tuple(ranks)
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hash_value = hash(ranks)
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# Split color must be:
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