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[BE][4/16] fix typos in torch/ (torch/_dynamo/) (#156314)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156314 Approved by: https://github.com/jingsh ghstack dependencies: #156313
This commit is contained in:
Xuehai Pan
PyTorch MergeBot
parent
3627270bdf
commit
ead741c5fb
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@ -1172,7 +1172,6 @@ exclude_patterns = [
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'test/distributed/**',
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'torch/**',
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'torch/_*/**',
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'torch/_dynamo/**',
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'torch/ao/**',
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'torch/fx/**',
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'torch/distributed/**',
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@ -921,7 +921,7 @@ Graph break: skip: from user code at:
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Data-dependent assertion failed (cannot compile partial graph)
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Explanation: Dynamo has determined when encountering a data-dependent assert failure that it should not compile the partial graph.
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Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.
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Hint: Use `torch._assert()` to raise a hard AssertionError when the check fails. This error will propagate back the user code that called the compiled function (i.e. Dynamo wil not trace any exception handling).
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Hint: Use `torch._assert()` to raise a hard AssertionError when the check fails. This error will propagate back the user code that called the compiled function (i.e. Dynamo will not trace any exception handling).
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Hint: Remove the assert statement.
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Hint: Move the assert statement outside of any context managers in order to graph break with partial graph compilation (if fullgraph=False).
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@ -50,7 +50,7 @@ __all__ = ["trace_wrapped"]
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if not torch._running_with_deploy():
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# torch.library.custom_op does not work with torch.deploy/multipy
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# torch.library.custom_op does not work with torch.deploy/multipy # codespell:ignore
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@torch.library.custom_op("flex_lib::zeros_and_scatter", mutates_args=()) # type: ignore[misc]
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def zeros_and_scatter(
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@ -56,7 +56,7 @@ def make_eager_backend_with_torch_function_mode(mode):
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def make_eager_backend_with_torch_function_modes(modes):
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"""Used to trace HOPs (cond and while) for eager exectution, the metadata
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"""Used to trace HOPs (cond and while) for eager execution, the metadata
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TF mode mutates vars outside of the scope of the HOP, and we can't have graph breaks
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in the HOP, so we need to externally run this mode and not trace it."""
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from contextlib import ExitStack
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@ -1649,7 +1649,7 @@ def bytecode_from_template(fn, varname_map=None, noreturn=True, noprefix=True):
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# replace returns with jumps
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for inst in returns:
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# don't replace inst with new instruction
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# due to targetting/exn table/etc.
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# due to targeting/exn table/etc.
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jump_inst = create_jump_absolute(insts[-1])
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inst.opname = jump_inst.opname
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inst.opcode = jump_inst.opcode
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@ -86,7 +86,7 @@ class CacheSizeRelevantForFrame:
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num_cache_entries_with_same_id_matched_objs: int = 0
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def will_compilation_exceed(self, limit: int) -> bool:
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# Checks if a compilation will exceed the given limit (thats why >=).
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# Checks if a compilation will exceed the given limit (that's why >=).
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return (
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self.will_compilation_exceed_accumulated_limit()
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or self.will_compilation_exceed_specific_limit(limit)
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@ -253,7 +253,7 @@ class PyCodegen:
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# above, export _wants to_ obtain an identity FX graph (despite it
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# appears unnecessarily expensive for `torch.compile`), so we have
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# the following option to override Dynamo's preference for codegen
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# from source. Morever, this option applies recursively, for cases
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# from source. Moreover, this option applies recursively, for cases
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# like input tensor being returned in a new dictionary.
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#
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# And why the `ValueMutationExisting` check? Not sure, so leaving it
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@ -591,7 +591,7 @@ class PyCodegen:
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def collect_temp_source(source):
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if source in seen_sources:
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# This source is used atleast twice, so it can be reused
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# This source is used at least twice, so it can be reused
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self.mark_source_temp(source)
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# Dont trace source further. This prevents us from marking too
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# many nodes as temp sources.
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@ -143,7 +143,7 @@ guard_nn_modules = True
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# guard_nn_modules_using_dict_tags, the guard_nn_modules is not really required
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# but kept around for debugging and discussing unspecializing nn module
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# variables.
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# TODO(janimesh, voz): Remove both of these flags (or atleast guard_nn_modules)
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# TODO(janimesh, voz): Remove both of these flags (or at least guard_nn_modules)
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# once we have reached stability for the guard_nn_modules_using_dict_tags.
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guard_nn_modules_using_dict_tags = True
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@ -402,7 +402,7 @@ use_numpy_random_stream = False
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# Use C++ guard manager (deprecated: always true)
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enable_cpp_guard_manager = True
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# Use C++ guard manger for symbolic shapes
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# Use C++ guard manager for symbolic shapes
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enable_cpp_symbolic_shape_guards = False
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# Enable tracing through contextlib.contextmanager
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@ -423,7 +423,7 @@ inline_inbuilt_nn_modules = Config( # type: ignore[var-annotated]
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# Install "free" tensor variables (globals, non-locals, nn module attributes)
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# as graph attributes. This is useful for export, as it
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# produces a consitent number of inputs to the graph.
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# produces a consistent number of inputs to the graph.
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install_free_tensors = False
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# Use C++ FrameLocalsMapping (raw array view of Python frame fastlocals) (deprecated: always True)
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@ -498,14 +498,14 @@ only_allow_pt2_compliant_ops = False
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# This flag is ignored and maintained for backwards compatibility.
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capture_autograd_function = True
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# This flag is ignored and maintained for backwards compatbility.
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# This flag is ignored and maintained for backwards compatibility.
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capture_func_transforms = True
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# If to log Dynamo compilation metrics into log files (for OSS) and Scuba tables (for fbcode).
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log_compilation_metrics = True
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# A set of logging functions which will be reordered to the end of graph breaks,
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# allowing dynamo to construct larget graph. Note that there are some
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# allowing dynamo to construct large graph. Note that there are some
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# limitations to this, such as how it does not correctly print objects that were
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# mutated after the print statement.
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reorderable_logging_functions: set[Callable[[Any], None]] = set()
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@ -707,7 +707,7 @@ def mark_static(t, index=None):
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if not isinstance(t, torch.Tensor):
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raise TypeError(
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f"mark_static expects a tensor/nn.Module class but recieved {type(t)}"
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f"mark_static expects a tensor/nn.Module class but received {type(t)}"
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)
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if isinstance(index, int):
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@ -733,7 +733,7 @@ def mark_static_address(t, guard=True):
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Tensors marked in this way will be kept alive until `torch._dynamo.reset()` is called.
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"""
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if not isinstance(t, torch.Tensor):
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raise TypeError(f"mark_static_address expects a tensor but recieved {type(t)}")
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raise TypeError(f"mark_static_address expects a tensor but received {type(t)}")
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if guard:
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t._dynamo_static_input_type = "guarded" # type: ignore[attr-defined]
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@ -855,7 +855,7 @@ def patch_dynamo_config(
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See _allowed_config_patches for the list of allowed config patches.
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Arguments are the same as with torch._dynamo.confing.patch.
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Arguments are the same as with torch._dynamo.config.patch.
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Can be used as a decorator or a context manager.
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@ -698,7 +698,7 @@ class _TorchDynamoContext:
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# something onto the DynamicLayerStack then we pop it off (the
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# constructed graph code isn't guarded with try/finally).
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#
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# This used to be a context but putting a `with` here is a noticible
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# This used to be a context but putting a `with` here is a noticeable
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# perf regression (#126293)
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saved_dynamic_layer_stack_depth = (
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torch._C._functorch.get_dynamic_layer_stack_depth()
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@ -1216,7 +1216,7 @@ class FlattenInputOutputSignature(torch.fx.Transformer):
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if i in matched_input_elements_to_fake:
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arg.node.meta["val"] = matched_input_elements_to_fake[i]
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else:
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# Fill node.mata["val"] with faketensor from the input,
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# Fill node.meta["val"] with faketensor from the input,
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# if it's not found in matched_input_elements_positions
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if fake_mode is not None and isinstance(flat_args[i], torch.Tensor):
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# TODO(zhxchen17) Also preserve all the user constraints here.
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@ -1859,7 +1859,7 @@ def export(
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"Failed to produce a graph during tracing as no tensor operations were found and same_signature is False."
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)
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# If the module does not contain any tensor computation, we would create a graph with inputs and outputs.
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# To be consitant with the graph traced by dynano, `graph` will have only tensor inputs as placeholders
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# To be consistent with the graph traced by dynano, `graph` will have only tensor inputs as placeholders
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# and tensor outputs as output nodes. non-tensor inputs and outputs will be added when rewriting signature.
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# We will also construct the `example_inputs`, `graph_captured_input`, and `graph_captured_result` corresponding
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# to `graph`.
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@ -432,7 +432,7 @@ def fully_expand_region_group(
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if add_to_all_regions:
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assert len(region_wrappers) == len(nodes_to_add), (
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"Numer of nodes to add must equal the number of regions"
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"Number of nodes to add must equal the number of regions"
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)
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for region_wrapper, node in zip(region_wrappers, nodes_to_add):
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region_wrapper.add(node)
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@ -555,7 +555,7 @@ class NNModuleAttrAccessorInfo:
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# Either the actual name or _parameters/_buffers/_modules
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l1_key: Optional[str] = None
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# Actual paramter/buffer/submodule name
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# Actual parameter/buffer/submodule name
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l2_key: Optional[str] = None
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@ -603,7 +603,7 @@ def getitem_on_dict_manager(
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def match_on_id_for_tensor(guard):
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source = guard.originating_source
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# For numpy tensors, always use TENSOR_MATCH because __from_numpy leads
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# to a new tensor everytime and therefore id differs.
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# to a new tensor every time and therefore id differs.
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if isinstance(source, NumpyTensorSource):
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return False
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@ -3588,7 +3588,7 @@ def make_dupe_guard(obj_source, dupe_source):
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dupe_source
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) or is_from_flatten_script_object_source(obj_source):
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raise exc.UnsafeScriptObjectError(
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f"{obj_source.name()} is alising {dupe_source.name()}. This is not supported."
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f"{obj_source.name()} is aliasing {dupe_source.name()}. This is not supported."
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f" Please do a clone for corresponding input."
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)
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@ -563,7 +563,7 @@ class OutputGraph(OutputGraphGuardsState):
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def install_builtins_dict_in_fglobals(self):
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# f_globals["__builtins__"] can be a dict or a module. This is an
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# implemenation detail -
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# implementation detail -
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# https://docs.python.org/3/library/builtins.html.
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# This makes guarding on any builtin messy because the guard check_fn
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@ -2736,7 +2736,7 @@ class SubgraphTracer(fx.Tracer):
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):
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return self.bound_symbols[example_value.node.expr]
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# Proxys are associated with VariableTracker.
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# Proxies are associated with VariableTracker.
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# It is possible that we've already lifted the Proxy to be an input.
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# If that is the case, just return the already lifted Proxy.
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if proxy in self.lifted_freevars:
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@ -2790,7 +2790,7 @@ class SubgraphTracer(fx.Tracer):
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self, example_value, e_proxy: Union[LazyProxy, torch.fx.Proxy]
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):
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# When binding the symbols in an exmaple_value, we bind the symbols
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# to the proxy's associatied Tracer instead of current tracer.
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# to the proxy's associated Tracer instead of current tracer.
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# This is because:
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# 1. We may be calling wrap_tensors during speculate_subgraph because
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# the variables are lazily realized. The proxy are top-level phs but
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@ -103,7 +103,7 @@ class _DynamoCodeCacheEntry:
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ingredients:
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1. The "original" code object, which serves as the entry point for eager
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execution, i.e. the code only executed when there's no cache entry hit.
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2. The python module name this code object belongs to, for idenfifying the
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2. The python module name this code object belongs to, for identifying the
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enclosing global scope to inject compiled and resume functions.
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3. A list of function names that pointing to this code object. There could be
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multiple function objects pointing to the same code such as recursive functions.
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@ -147,7 +147,7 @@ class CompilePackage:
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end users. It has the following interface:
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1. `CompilePackage.__init__()` which optionally takes previously serialized dynamo states.
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a. when `dynamo` argument is None, it will contruct a brand new CompilePackage object.
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a. when `dynamo` argument is None, it will construct a brand new CompilePackage object.
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b. when `dynamo` argument is not None, it will load a pre-compiled dynamo state.
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2. `package.save()` which dumps the dynamo and backend states to a DynamoCacheEntry object.
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3. `package.install(backends) which will handle all the side-effectful global scope
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@ -79,7 +79,7 @@ class PrecompileContext(CacheArtifactManager):
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# are transferred to _new_cache_artifacts before serialization.
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_new_cache_artifacts_by_key: dict[str, CacheArtifact] = {}
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_new_cache_artifacts: CacheArtifactsResult = defaultdict(list)
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# Keep a seperate seen artifacts list to make avoid unnecessary duplicates
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# Keep a separate seen artifacts list to make avoid unnecessary duplicates
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# This list will not be cleared between serialize() calls
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_seen_artifacts: OrderedSet[CacheArtifact] = OrderedSet()
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# When serialize() is called, artifacts are transferred from _cache_artifacts to
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@ -282,7 +282,7 @@ SpeculationLog diverged at index {self.index} (log had {len(self.entries)} entri
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- Expected: {entry.filename}:{entry.lineno} ({entry.inst.opname} at ip={entry.instruction_pointer})
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- Actual: {filename}:{lineno} ({inst.opname} at ip={instruction_pointer})
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{prev_entry_msg}
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There are two usual reasons why this may have occured:
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There are two usual reasons why this may have occurred:
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- When Dynamo analysis restarted, the second run took a different path than
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the first. If this occurred, the previous instruction is the critical instruction that
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behaved differently.
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@ -662,7 +662,7 @@ def generic_jump(truth_fn: typing.Callable[[object], bool], push: bool):
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*graph_break_hints.FUNDAMENTAL,
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"Use `torch._assert()` to raise a hard AssertionError when the check fails. "
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"This error will propagate back the user code "
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"that called the compiled function (i.e. Dynamo wil not trace any exception handling).",
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"that called the compiled function (i.e. Dynamo will not trace any exception handling).",
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"Remove the assert statement.",
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"Move the assert statement outside of any context managers in order to graph break with "
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"partial graph compilation (if fullgraph=False).",
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@ -1783,7 +1783,7 @@ class InstructionTranslatorBase(
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def _raise_exception_variable(self, val) -> NoReturn:
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# User can raise exception in 2 ways
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# 1) raise exception type - raise NotImplementedError
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# 2) raise execption instance - raise NotImplemetedError("foo")
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# 2) raise exception instance - raise NotImplemetedError("foo")
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# 1) when user raises exception type
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val = self._create_exception_type(val)
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@ -1940,7 +1940,7 @@ class InstructionTranslatorBase(
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self.jump(exn_tab_entry)
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else:
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# No handler found. Bubble the exception to the parent
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# instruction translater. We use special exception for this.
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# instruction translator. We use special exception for this.
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self.stack.clear()
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if type(self) is InstructionTranslator:
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unimplemented_v2(
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@ -1966,7 +1966,7 @@ class InstructionTranslatorBase(
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self.exn_vt_stack.pop()
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if len(self.block_stack) == 0:
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# No handler found in this frame. Bubble the exception to the parent
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# instruction translater.
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# instruction translator.
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self.stack.clear()
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if type(self) is InstructionTranslator:
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unimplemented_v2(
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@ -2020,7 +2020,7 @@ class InstructionTranslatorBase(
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self.jump(block_stack_entry)
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else:
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# No handler found. Bubble the exception to the parent
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# instruction translater. We use special exception for this.
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# instruction translator. We use special exception for this.
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self.stack.clear()
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if type(self) is InstructionTranslator:
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unimplemented_v2(
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@ -2127,7 +2127,7 @@ class InstructionTranslatorBase(
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unimplemented_v2(
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gb_type="Caught non-Exception value",
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context=str(exc_instance),
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explanation=f"Except expects to recieve an object of Exception type but received {exc_instance}.",
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explanation=f"Except expects to receive an object of Exception type but received {exc_instance}.",
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hints=[*graph_break_hints.USER_ERROR],
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)
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@ -4124,7 +4124,7 @@ class InliningInstructionTranslator(InstructionTranslatorBase):
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class InliningGeneratorInstructionTranslator(InliningInstructionTranslator):
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generated_items: list[VariableTracker]
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# Flag wether or not the InlineGenerator should consume the entire iterator
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# Flag whether or not the InlineGenerator should consume the entire iterator
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def __init__(self, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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@ -3723,9 +3723,9 @@ Let's illustrate the logic with an example:
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......
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There are mainly three call sites of check/check_verbose:
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* The compile region entrance (like function f1), the correspoinding code is located at eval_frame.py.
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* The compile region entrance (like function f1), the corresponding code is located at eval_frame.py.
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* When tracing the recursively called functions (like function f2 and f3).
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* Dynamo decides inline/skip everytime it encounters a new recursively function call, and the call site
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* Dynamo decides inline/skip every time it encounters a new recursively function call, and the call site
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is in InliningInstructionTranslator.check_inlineable of symbolic_convert.py.
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* If f2 is skipped by Dynamo, when evaluating the frame of f3, Dynamo need the inline/skip check again
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and the call site is in catch_errors_wrapper.catch_errors of convert_frame.py.
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@ -75,7 +75,7 @@ class ConvertFrameReturn:
|
|||
# default return is no compiled code (i.e. `return None`):
|
||||
# strategy is to skip non-recursively, for all future intercepted frames too
|
||||
|
||||
# eval fram execution strategy for this frame
|
||||
# eval frame execution strategy for this frame
|
||||
frame_exec_strategy: FrameExecStrategy = dataclasses.field(
|
||||
default_factory=lambda: FrameExecStrategy(FrameAction.SKIP, FrameAction.DEFAULT)
|
||||
)
|
||||
|
|
|
|||
|
|
@ -2714,7 +2714,7 @@ from torch._subclasses import UnsupportedFakeTensorException # noqa: F401
|
|||
def get_safe_global_name(tx, root, obj):
|
||||
# The global_mangled_class_name should be different for different
|
||||
# invocations of torch.compile. Otherwise, we can run into a situation
|
||||
# where multiple torch.compile invocations re-use the same global name,
|
||||
# where multiple torch.compile invocations reuse the same global name,
|
||||
# but the global's lifetime is tied to the first invocation (and
|
||||
# may be deleted when the first torch.compile invocation is deleted)
|
||||
# We mangle it based off of the output_graph's id.
|
||||
|
|
@ -2977,7 +2977,7 @@ def same(
|
|||
):
|
||||
# In the presence of noise, noise might dominate our error
|
||||
# metric for smaller tensors.
|
||||
# Similary, for 1x1 kernels, there seems to be high noise with amp.
|
||||
# Similarly, for 1x1 kernels, there seems to be high noise with amp.
|
||||
multiplier = 3.0
|
||||
return multiplier
|
||||
|
||||
|
|
|
|||
|
|
@ -713,7 +713,7 @@ class VariableBuilder:
|
|||
# 2) For non-constant objects, we also have to guard on the keys
|
||||
# (like TENSOR_MATCH on tensor). We might also have guards on
|
||||
# the attributes of the keys (like tensor.grad). To make this
|
||||
# work in tree strucutre is complicated.
|
||||
# work in tree structure is complicated.
|
||||
#
|
||||
# So, instead we guard on the key order. While guarding on key
|
||||
# order, we just save the indices and use it to access keys and
|
||||
|
|
@ -1050,7 +1050,7 @@ class VariableBuilder:
|
|||
return ItertoolsVariable(value, source=self.source)
|
||||
elif is_torch_sym(value):
|
||||
# Note: this doesn't handle nested symints.
|
||||
# For SymBool input, we re-use the infra for SymInt by simulating SymBool with a SymInt in dynamo.
|
||||
# For SymBool input, we reuse the infra for SymInt by simulating SymBool with a SymInt in dynamo.
|
||||
|
||||
# Concretely,
|
||||
# 1. We create a SymInt in dynamo's shape_env, whose source is constructed as ConvertIntSource(self.source).
|
||||
|
|
@ -1297,7 +1297,7 @@ class VariableBuilder:
|
|||
)
|
||||
|
||||
# setting is_unspecialized=False to not insert a as_tensor call in reconstruct by default
|
||||
# seting example to be real value because these example values will be used
|
||||
# setting example to be real value because these example values will be used
|
||||
# as example_inputs for user compiler.
|
||||
proxy.node.meta["grapharg"] = GraphArg(
|
||||
self.source, value, False, None, False, value
|
||||
|
|
@ -1342,7 +1342,7 @@ class VariableBuilder:
|
|||
)
|
||||
|
||||
# setting is_unspecialized=False to not insert a as_tensor call in reconstruct by default
|
||||
# seting example to be real value because these example values will be used
|
||||
# setting example to be real value because these example values will be used
|
||||
# as example_inputs for user compiler.
|
||||
proxy.node.meta["grapharg"] = GraphArg(
|
||||
self.source, value, False, None, False, fake_script_obj
|
||||
|
|
@ -1829,7 +1829,7 @@ class VariableBuilder:
|
|||
):
|
||||
# This means that it is an integer from a NN module.
|
||||
# Dynamo considers nn module int attributes to be static
|
||||
# (a good heursitic). But a user might want to mark the
|
||||
# (a good heuristic). But a user might want to mark the
|
||||
# int attribute to be a symint, so track this integer
|
||||
# for recompilation later.
|
||||
recompile_hint = (
|
||||
|
|
@ -1998,7 +1998,7 @@ class VariableBuilder:
|
|||
):
|
||||
# A hot fix for sparse tensors + torch.compile. Support for
|
||||
# export + sparsity is being added but we need to create
|
||||
# SPARSE_TENSOR_GUARDS for guards to work propertly.
|
||||
# SPARSE_TENSOR_GUARDS for guards to work properly.
|
||||
unimplemented_v2(
|
||||
gb_type="Attempted to wrap sparse Tensor",
|
||||
context="",
|
||||
|
|
|
|||
|
|
@ -824,7 +824,7 @@ class BuiltinVariable(VariableTracker):
|
|||
|
||||
if inspect.isclass(fn) and (
|
||||
issubclass(fn, Exception)
|
||||
# GeneratorExit doens't inherit from Exception
|
||||
# GeneratorExit doesn't inherit from Exception
|
||||
# >>> issubclass(GeneratorExit, Exception)
|
||||
# False
|
||||
or fn is GeneratorExit
|
||||
|
|
@ -2244,7 +2244,7 @@ class BuiltinVariable(VariableTracker):
|
|||
# get_fake_val will get the same fake tensor
|
||||
existing_fake_attr = get_fake_value(getattr_var.as_proxy().node, tx)
|
||||
|
||||
# same tensor identiy, setattr is a no-op
|
||||
# same tensor identity, setattr is a no-op
|
||||
mod_setattr = inspect.getattr_static(obj.module_type, "__setattr__")
|
||||
if (
|
||||
existing_fake_attr is assigning_fake_val
|
||||
|
|
|
|||
|
|
@ -155,7 +155,7 @@ class ContextWrappingVariable(VariableTracker):
|
|||
|
||||
class GenericContextWrappingVariable(UserDefinedObjectVariable):
|
||||
# Some methods in ContextWrappingVariable assumes the arguments are
|
||||
# python contants. Which might not always be the case here.
|
||||
# python constants. Which might not always be the case here.
|
||||
def __init__(self, cm_obj, **kwargs) -> None:
|
||||
assert cm_obj is not None
|
||||
super().__init__(
|
||||
|
|
@ -197,7 +197,7 @@ class GenericContextWrappingVariable(UserDefinedObjectVariable):
|
|||
|
||||
|
||||
class GradInplaceRequiresGradCtxManagerVariable(ContextWrappingVariable):
|
||||
"""represents torch grad requries grad"""
|
||||
"""represents torch grad requires grad"""
|
||||
|
||||
@staticmethod
|
||||
def create(tx: "InstructionTranslator", target_values, **kwargs):
|
||||
|
|
|
|||
|
|
@ -144,7 +144,7 @@ class ConstDictVariable(VariableTracker):
|
|||
def __init__(self, vt) -> None:
|
||||
# We specialize SymNodes
|
||||
vt = specialize_symnode(vt)
|
||||
# TODO Temorarily remove to figure out what keys are we breaking on
|
||||
# TODO Temporarily remove to figure out what keys are we breaking on
|
||||
# and add proper support for them
|
||||
if not is_hashable(vt):
|
||||
raise_unhashable(vt)
|
||||
|
|
@ -756,7 +756,7 @@ class DefaultDictVariable(ConstDictVariable):
|
|||
# footgun, because self method calls in dict will route back to the set
|
||||
# implementation, which is almost assuredly wrong
|
||||
class SetVariable(ConstDictVariable):
|
||||
"""We model a sets as dictonary with None values"""
|
||||
"""We model a sets as dictionary with None values"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -778,7 +778,7 @@ class SetVariable(ConstDictVariable):
|
|||
|
||||
@staticmethod
|
||||
def _default_value():
|
||||
# Variable to fill in he keys of the dictinary
|
||||
# Variable to fill in he keys of the dictionary
|
||||
return ConstantVariable.create(None)
|
||||
|
||||
def as_proxy(self):
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@ class DistributedVariable(VariableTracker):
|
|||
Concrete distributed objects could inherit this class and add object
|
||||
specific logic.
|
||||
|
||||
i.e. It provides the check on the distributed package existance
|
||||
i.e. It provides the check on the distributed package existence
|
||||
and hold the tracking value for the corresponding distributed object.
|
||||
"""
|
||||
|
||||
|
|
@ -59,7 +59,7 @@ class DistributedVariable(VariableTracker):
|
|||
unimplemented_v2(
|
||||
gb_type="torch.distributed package is not available!",
|
||||
context="",
|
||||
explanation="The PyTorch package doesn't include torch.distributed when builing from source.",
|
||||
explanation="The PyTorch package doesn't include torch.distributed when building from source.",
|
||||
hints=[
|
||||
"Set USE_DISTRIBUTED=1 to enable it when building PyTorch from source."
|
||||
],
|
||||
|
|
|
|||
|
|
@ -509,7 +509,7 @@ def _merge_graph_inputs(
|
|||
#
|
||||
# Note: ideally, dynamo should just create a single proxy for the same attribute of a nn module. But
|
||||
# true_branch and false_branch belong to two separate tracing contexts, they may register the same
|
||||
# attribute to top level seperately. This creates two get_attr proxies for the same attribute
|
||||
# attribute to top level separately. This creates two get_attr proxies for the same attribute
|
||||
# that have different meta data such as stack_trace (one stack trace for the true_branch,
|
||||
# and the other for false_branch). It seems better to discard the proxy explicitly in cond
|
||||
# than make dynamo create a single proxy for the same get_attr target.
|
||||
|
|
@ -580,7 +580,7 @@ def _merge_graph_inputs(
|
|||
if arg in lifted_freevars:
|
||||
old_ph = lifted_freevars[arg].node
|
||||
old_ph.replace_all_uses_with(new_ph)
|
||||
# replace_all_uses_with doesn't clean users. Clean it mannually so that we could erase it.
|
||||
# replace_all_uses_with doesn't clean users. Clean it manually so that we could erase it.
|
||||
old_ph.users = {}
|
||||
graph.erase_node(old_ph)
|
||||
|
||||
|
|
@ -752,8 +752,8 @@ def speculate_subgraph(
|
|||
|
||||
# NOTE: [HigherOrderOperator subgraph input ordering]
|
||||
# The input ordering of the higher order ops is determined by the order of
|
||||
# the creatation of the placehoder.
|
||||
# Mannually created inputs are created in validate_args_and_maybe_create_graph_inputs before
|
||||
# the creation of the placeholder.
|
||||
# Manually created inputs are created in validate_args_and_maybe_create_graph_inputs before
|
||||
# speculating subgraph.
|
||||
# During subgraph speculation, we may lift closured tensors and free symbols as inputs,
|
||||
# their ordering is determined by the time they are lifted: earlier lifted ones precede later
|
||||
|
|
@ -1403,7 +1403,7 @@ class WhileLoopHigherOrderVariable(TorchHigherOrderOperatorVariable):
|
|||
)
|
||||
|
||||
# Note: cond_shared and body_shared refer to the same proxy in parent graph
|
||||
# so using either of them is OK. Use cond_shared as it doesnt matter.
|
||||
# so using either of them is OK. Use cond_shared as it doesn't matter.
|
||||
additional_lifted_inputs = cond_shared + cond_unique + body_unique
|
||||
|
||||
body_nn_modules = dict(tx.output.nn_modules)
|
||||
|
|
@ -3229,7 +3229,7 @@ class AutogradFunctionApplyVariable(VariableTracker):
|
|||
# Store the invocation as a call
|
||||
from torch._functorch.autograd_function import autograd_function_apply
|
||||
|
||||
# We use speculate_subgraph to get the fwd graph, but it's alway under no grad mode like what eager mode does.
|
||||
# We use speculate_subgraph to get the fwd graph, but it's always under no grad mode like what eager mode does.
|
||||
# The fwd outputs (tensor's example_value) need to be inferred from fake tensor prop to get the correct attributes
|
||||
# (e.g, tensor.requires_grad), which would be used by downstream Dynamo tracing.
|
||||
# Since there can be other ops like Triton kernels, which depends on python dispatcher, we have to enable it.
|
||||
|
|
|
|||
|
|
@ -1441,7 +1441,9 @@ class NumpyVariable(VariableTracker):
|
|||
and config.use_numpy_random_stream
|
||||
):
|
||||
msg = f"delegate '{func.__qualname__}' to NumPy itself via "
|
||||
msg += f"confg.use_numpy_random_stream={config.use_numpy_random_stream}"
|
||||
msg += (
|
||||
f"config.use_numpy_random_stream={config.use_numpy_random_stream}"
|
||||
)
|
||||
unimplemented(msg)
|
||||
|
||||
args, kwargs = NumpyNdarrayVariable.patch_args(func.__name__, args, kwargs)
|
||||
|
|
|
|||
|
|
@ -868,7 +868,7 @@ class UnspecializedNNModuleVariable(UserDefinedObjectVariable):
|
|||
if type(value) is torch.jit._script.RecursiveScriptModule:
|
||||
raise Unsupported(
|
||||
"ScriptModules aren't supported in UnspecializedNNModuleVariable"
|
||||
" becuase their .forward function isn't a static member of their type"
|
||||
" because their .forward function isn't a static member of their type"
|
||||
)
|
||||
if "value_type" in kwargs:
|
||||
lazy_value_to_become = getattr(kwargs["value_type"], "cls_to_become", None)
|
||||
|
|
@ -1054,7 +1054,7 @@ class UnspecializedNNModuleVariable(UserDefinedObjectVariable):
|
|||
# Record if mutations happens on parameters/buffers/modules. The
|
||||
# mutations on these are not tracked by base class
|
||||
# UserDefinedObject vt. This will be used later to graph break
|
||||
# on seeing a paramters() and family calls.
|
||||
# on seeing a parameters() and family calls.
|
||||
# TODO(anijain2305) - This might not be needed if we let Dynamo
|
||||
# inline both getattr and setattr. In that case, it should see
|
||||
# the lowest level dicts - _parameters and family and
|
||||
|
|
@ -1130,7 +1130,7 @@ class UnspecializedNNModuleVariable(UserDefinedObjectVariable):
|
|||
|
||||
# For non-empty hook dicts, one way is to just fallback to VariableTracker.build() and create a ConstDictVariable.
|
||||
# However, ConstDictVariable guards on keys. This can cause recompiles when the same hook is installed for
|
||||
# differnt nn module instances, because the key keeps changing (look more into RemovableHandle to understand why
|
||||
# different nn module instances, because the key keeps changing (look more into RemovableHandle to understand why
|
||||
# key changes - also related https://github.com/pytorch/pytorch/issues/125836). Here, we carefully craft a
|
||||
# NNModuleHooksDictVariable (a subclass of ConstDictVariable) to avoid any guard on the keys.
|
||||
if (
|
||||
|
|
|
|||
|
|
@ -1264,7 +1264,7 @@ If the above doesn't work, please subtmit an issue to GitHub.
|
|||
# Guard against inplace view op on input tensor (not supported)
|
||||
if args and isinstance(args[0], variables.TensorVariable):
|
||||
tensor_var = args[0]
|
||||
# Check if input tensor and inplace_view op specifcally
|
||||
# Check if input tensor and inplace_view op specifically
|
||||
if tensor_var.source is not None and hasattr(torch.ops.aten, name):
|
||||
fn = getattr(torch.ops.aten, name)
|
||||
if (
|
||||
|
|
@ -1528,7 +1528,7 @@ Either create the tensor outside the compiled region, or do not set the tensor t
|
|||
# Alternate version if we have a .source
|
||||
varname = tx.output.new_var()
|
||||
|
||||
# construct the nn.Parmeter before the graph save it to varname
|
||||
# construct the nn.Parameter before the graph save it to varname
|
||||
cg = PyCodegen(tx)
|
||||
cg.add_push_null(lambda: cg.load_import_from("torch.nn", "Parameter"))
|
||||
cg(data.source)
|
||||
|
|
|
|||
|
|
@ -368,7 +368,7 @@ class TorchFunctionModeVariable(GenericContextWrappingVariable):
|
|||
# We are able to trace custom modes but if there are graph breaks under them
|
||||
# and they have a custom __enter__/__exit__ we don't handle this for the
|
||||
# same reason we don't handle generic context managers: there may be side effects
|
||||
# that are now affected by executing the funtion across two frames instead of one
|
||||
# that are now affected by executing the function across two frames instead of one
|
||||
# Today we support the enter/exit of the default TorchFunctionMode as well as
|
||||
# DeviceContext (which is used for set_default_device)
|
||||
return issubclass(ty, (NoEnterTorchFunctionMode, DeviceContext)) or (
|
||||
|
|
|
|||
|
|
@ -791,7 +791,7 @@ class UserDefinedObjectVariable(UserDefinedVariable):
|
|||
self.base_cls_vt = base_cls_vt
|
||||
self.init_args = init_args
|
||||
|
||||
# This records names of the attributes that were modifed via instance
|
||||
# This records names of the attributes that were modified via instance
|
||||
# `__dict__` directly, rather than the normal setattr path.
|
||||
#
|
||||
# TODO consider emulating `obj.__dict__` as a `ConstDictVariable` to get
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user