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[ez] fix a bunch of typos in dynamo (#152886)

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Pull Request resolved: https://github.com/pytorch/pytorch/pull/152886
Approved by: https://github.com/williamwen42
This commit is contained in:
bobrenjc93 2025-05-05 17:30:32 -07:00 committed by PyTorch MergeBot
parent 37c71820f3
commit e2eb845313
4 changed files with 7 additions and 7 deletions
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2
torch/_dynamo/config.py
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@ -286,7 +286,7 @@ allow_unspec_int_on_nn_module = False
# Specify how to optimize a compiled DDP module. The flag accepts a boolean
# value or a string. There are 3 modes.
# 1. "ddp_optimizer" (or True): with "ddp_ptimizer", Dynamo will automatically
# 1. "ddp_optimizer" (or True): with "ddp_optimizer", Dynamo will automatically
# split model graph into pieces to match DDP bucket sizes to allow DDP
# comm/compute overlap.
# 2. "python_reducer" (experimental): this optimization requires the usage

6
torch/_dynamo/guards.py
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@ -2228,7 +2228,7 @@ class GuardBuilder(GuardBuilderBase):
# But we deliberately take this soundness hit because this
# usecase is quite rare and there is substantial reduction in
# guard overhead.
# For numpy tensors, since those are ephemeral, we dont have to
# For numpy tensors, since those are ephemeral, we don't have to
# insert aliasing guards on them
if not (
config.skip_no_tensor_aliasing_guards_on_parameters
@ -3129,8 +3129,8 @@ def is_recompiles_verbose_enabled():
# this will only be used if cpp guards are disabled
def make_torch_function_mode_stack_guard(intial_stack):
types = [type(x) for x in intial_stack]
def make_torch_function_mode_stack_guard(initial_stack):
types = [type(x) for x in initial_stack]
def check_torch_function_mode_stack():
cur_stack = get_torch_function_mode_stack()

2
torch/_dynamo/output_graph.py
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@ -2634,7 +2634,7 @@ class SubgraphTracer(fx.Tracer):
self, example_value: Union[torch.SymInt, torch.Tensor], src: Optional[Source]
):
# The before arg is for inserting symints in the sizes/strides of a tensor
# before the tensor. This odering ensures that when we look at the tensor's
# before the tensor. This ordering ensures that when we look at the tensor's
# symbols, they're already lifted/tracked. E.g. this assumption is used
# in insert_deferred_runtime_asserts.
def _lift_symbols_in_symint(

4
torch/_dynamo/variables/builder.py
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@ -675,7 +675,7 @@ class VariableBuilder:
self.install_guards(GuardBuilder.TYPE_MATCH)
all_const = all(ConstantVariable.is_literal(k) for k in value.keys())
# For all_const, we dont have to guard on anything yet. We guard on
# For all_const, we don't have to guard on anything yet. We guard on
# keys lazily by adding a dict_getitem entry for each accessed key.
# For cases where we need to guard on all keys, we lazily put guards
# during the dict call_method (check dicts.py)
@ -1728,7 +1728,7 @@ class VariableBuilder:
def wrap_literal(self, value):
if type(value) is int:
# allowlist has higher precendence over specialization control.
# allowlist has higher precedence over specialization control.
if is_dynamic_source(self.source.name()):
return self.wrap_symint(value, True)