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This patch effectively ignores traceable_tensor_subclasses, allowing Dynamo to always try tracing into the `__torch_function__` of tensor subclass. This helps us with 2 things: 1. allowing users to directly benefit from better compilation of tensor subclass, by just upgrading pytorch, without having to change legacy library code (see earlier patches in the stack for examples). 2. potentially exposing more issues in compiling tensor subclass, so we can get signals and improve them. As a consequence, it exposed and fixes 2 subtle bugs: 1. In `build_torch_function_fn`, we could get `torch._C._disabled_torch_function_impl` because we have a `Parameter` subclass without `__torch_function__` override or if we have a tensor subclass with `__torch_dispatch__` override. We graph break on this for now, and plan to add support -- the logic for simulating `torch._C._disabled_torch_function_impl` is already in `SuperVariable`, we just need to reuse it. 2. Sometimes we create `SyntheticLocalSource` and need to remove all the guards installed on it, but we only removed the ones whose source _is_ the created synthetic source `s`, but forgot about chained source like `s.foo`, this showed up as `SYNTHETIC_LOCAL['tmp_0'].__torch_function__.__func__`. Differential Revision: [D71906141](https://our.internmc.facebook.com/intern/diff/D71906141) Pull Request resolved: https://github.com/pytorch/pytorch/pull/149792 Approved by: https://github.com/jansel, https://github.com/mlazos ghstack dependencies: #149482, #149483, #149484
900 lines
28 KiB
Python
900 lines
28 KiB
Python
# mypy: allow-untyped-defs
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"""
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This module provides Source classes that track the origins of values in PyTorch Dynamo.
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Sources represent where values come from (e.g. local variables, globals, attributes) and
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are used for guard generation and code reconstruction during compilation.
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The module includes specialized sources for:
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- Local variables and synthetic locals
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- Global variables and constants
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- Object attributes and method calls
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- NN module specialization (specialized vs unspecialized)
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- Random values and tensor properties
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- Default argument handling
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- FSDP (Fully Sharded Data Parallel) modules
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Sources play a key role in Dynamo's guard system by tracking value origins for
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guard generation, and in code reconstruction by providing methods to rebuild
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the code needed to recreate values.
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"""
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import dataclasses
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import enum
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from typing import Any, Optional, Union
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from torch._guards import ChainedSource, GuardSource, Source
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from . import utils
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from .bytecode_transformation import create_call_function, create_instruction
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# It shouldn't be supported to construct an NNModuleVariable inside an FSDP module,
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# so those cases are omitted intentionally
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# represents nn.Modules tracked with NNModuleVariable (specialized is implicit in the variable name)
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_GUARD_SOURCE_SPECIALIZED_NN_MODULE = {
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GuardSource.LOCAL: GuardSource.LOCAL_SPECIALIZED_NN_MODULE,
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GuardSource.GLOBAL: GuardSource.GLOBAL_SPECIALIZED_NN_MODULE,
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GuardSource.LOCAL_SPECIALIZED_NN_MODULE: GuardSource.LOCAL_SPECIALIZED_NN_MODULE,
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GuardSource.GLOBAL_SPECIALIZED_NN_MODULE: GuardSource.GLOBAL_SPECIALIZED_NN_MODULE,
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# Just to ensure that guard_source() works
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GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE,
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GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE,
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GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.LOCAL_FSDP_MODULE: GuardSource.LOCAL_FSDP_MODULE,
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GuardSource.GLOBAL_FSDP_MODULE: GuardSource.GLOBAL_FSDP_MODULE,
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}
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# represents nn.Modules tracked with UnspecializedNNModuleVariable
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_GUARD_SOURCE_UNSPECIALIZED_NN_MODULE = {
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GuardSource.LOCAL: GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE,
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GuardSource.GLOBAL: GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE,
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GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE,
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GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE,
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# this happens for an UnspecializedNNModule submodule on a NNModuleVariable
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GuardSource.LOCAL_SPECIALIZED_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE,
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GuardSource.GLOBAL_SPECIALIZED_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE,
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# Just to ensure that guard_source() works
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GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.LOCAL_FSDP_MODULE: GuardSource.LOCAL_FSDP_MODULE,
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GuardSource.GLOBAL_FSDP_MODULE: GuardSource.GLOBAL_FSDP_MODULE,
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}
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# represents nn.Modules tracked with UnspecializedBuiltinNNModuleVariable
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_GUARD_SOURCE_UNSPECIALIZED_BUILTIN_NN_MODULE = {
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GuardSource.LOCAL: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.GLOBAL: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.LOCAL_SPECIALIZED_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.GLOBAL_SPECIALIZED_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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# Just to ensure that guard_source() works
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GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE,
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GuardSource.LOCAL_FSDP_MODULE: GuardSource.LOCAL_FSDP_MODULE,
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GuardSource.GLOBAL_FSDP_MODULE: GuardSource.GLOBAL_FSDP_MODULE,
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}
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_GUARD_SOURCE_FSDP_MODULE = {
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GuardSource.LOCAL: GuardSource.LOCAL_FSDP_MODULE,
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GuardSource.GLOBAL: GuardSource.GLOBAL_FSDP_MODULE,
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GuardSource.LOCAL_SPECIALIZED_NN_MODULE: GuardSource.LOCAL_FSDP_MODULE,
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GuardSource.GLOBAL_SPECIALIZED_NN_MODULE: GuardSource.GLOBAL_FSDP_MODULE,
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GuardSource.LOCAL_FSDP_MODULE: GuardSource.LOCAL_FSDP_MODULE,
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GuardSource.GLOBAL_FSDP_MODULE: GuardSource.GLOBAL_FSDP_MODULE,
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GuardSource.LOCAL_UNSPECIALIZED_NN_MODULE: GuardSource.LOCAL_FSDP_MODULE,
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GuardSource.GLOBAL_UNSPECIALIZED_NN_MODULE: GuardSource.GLOBAL_FSDP_MODULE,
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GuardSource.LOCAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.LOCAL_FSDP_MODULE,
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GuardSource.GLOBAL_UNSPECIALIZED_BUILTIN_NN_MODULE: GuardSource.GLOBAL_FSDP_MODULE,
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}
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def is_constant_source(source):
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if isinstance(source, ConstantSource):
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return True
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try:
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if source.guard_source() == GuardSource.CONSTANT:
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return True
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except NotImplementedError:
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pass
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return False
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@dataclasses.dataclass(frozen=True)
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class LocalSource(Source):
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local_name: str
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# Whether this local is an input to the root frame.
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is_input: bool = False
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# Whether we know this input is dynamic (based on example_inputs)
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# For non tensors, we simply look at the first index of the tuple
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dynamism: Optional[frozenset[str]] = None
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# Whether the item at this source is the _content_ of a cell that is
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# dereferenced from the root frame, i.e., it's a part of the `co_cellvars`
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# or `co_freevars`.
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is_derefed_cell_contents: bool = False
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def reconstruct(self, codegen):
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if self.is_derefed_cell_contents:
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codegen.load_deref(self.local_name)
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else:
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codegen.append_output(codegen.create_load(self.local_name))
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def guard_source(self):
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return GuardSource.LOCAL
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def name(self):
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return f"L[{repr(self.local_name)}]"
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@dataclasses.dataclass(frozen=True)
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class SyntheticLocalSource(Source):
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local_name: str
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def reconstruct(self, codegen):
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codegen.append_output(codegen.create_load(self.local_name))
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def guard_source(self):
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return GuardSource.SYNTHETIC_LOCAL
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def name(self):
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return f"SYNTHETIC_LOCAL[{self.local_name!r}]"
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@dataclasses.dataclass(frozen=True)
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class RandomValueSource(Source):
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random_call_index: int
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def guard_source(self):
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return GuardSource.RANDOM_VALUE
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def reconstruct(self, codegen):
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codegen.append_output(codegen.create_load(codegen.tx.output.random_values_var))
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codegen.append_output(codegen.create_load_const(self.random_call_index))
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codegen.append_output(create_instruction("BINARY_SUBSCR"))
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def name(self):
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return f"random_value_{self.random_call_index}"
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@dataclasses.dataclass(frozen=True)
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class GlobalSource(Source):
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global_name: str
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def reconstruct(self, codegen):
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codegen.append_output(codegen.create_load_global(self.global_name, add=True))
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def guard_source(self):
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return GuardSource.GLOBAL
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def name(self):
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return f"G[{repr(self.global_name)}]"
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@dataclasses.dataclass(frozen=True)
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class GlobalWeakRefSource(Source):
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global_name: str
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def reconstruct(self, codegen):
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codegen.add_push_null(
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lambda: codegen.append_output(
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codegen.create_load_global(self.global_name, add=True)
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)
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)
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codegen.extend_output(create_call_function(0, False))
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def guard_source(self):
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return GuardSource.GLOBAL
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def name(self):
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return f"G[{repr(self.global_name)}]()"
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@dataclasses.dataclass(frozen=True)
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class WeakRefCallSource(ChainedSource):
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def reconstruct(self, codegen):
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codegen.add_push_null(lambda: codegen(self.base))
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codegen.extend_output(create_call_function(0, False))
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def guard_source(self):
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return self.base.guard_source()
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def name(self):
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return f"{self.base.name()}()"
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@dataclasses.dataclass(frozen=True)
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class CallFunctionNoArgsSource(WeakRefCallSource):
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pass
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@dataclasses.dataclass(frozen=True)
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class AttrSource(ChainedSource):
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member: str
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def __post_init__(self):
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assert self.base, "Can't construct an AttrSource without a valid base source"
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if "." in self.member:
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member_parts = self.member.split(".")
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object.__setattr__(
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self, "base", AttrSource(self.base, ".".join(member_parts[:-1]))
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)
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object.__setattr__(self, "member", member_parts[-1])
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def reconstruct(self, codegen):
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codegen(self.base)
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codegen.extend_output(codegen.create_load_attrs(self.member))
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def guard_source(self):
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return self.base.guard_source()
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def name(self):
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if not self.member.isidentifier():
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return f"getattr({self.base.name()}, {self.member!r})"
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return f"{self.base.name()}.{self.member}"
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@dataclasses.dataclass(frozen=True)
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class LocalCellSource(Source):
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"""
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Conceptually, this class is `LocalSource` for cell objects implicitly
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generated by Python (e.g., captured variables).
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"""
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local_name: str
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def reconstruct(self, codegen):
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# Although `LOAD_FAST` and `LOAD_CLOSURE` have the same semantics,
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# Dynamo's bytecode transformation differentiates them slightly, so we
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# always emit `LOAD_CLOSURE` here.
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codegen.append_output(codegen.create_load_closure(self.local_name))
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# All the other methods are intentionally unimplemented because e.g., a
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# local cell object should never be used for guards.
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# Represents tensor.grad source. It could be represented by AttrSource as well.
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# But, we could access grad field on tensor directly in C++ without going
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# through the Python bytecodes. Therefore, we use a separate source for grad
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# field.
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@dataclasses.dataclass(frozen=True)
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class GradSource(ChainedSource):
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member: str = "grad"
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def reconstruct(self, codegen):
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codegen(self.base)
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codegen.extend_output(codegen.create_load_attrs(self.member))
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def guard_source(self):
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return self.base.guard_source()
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def name(self):
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return f"{self.base.name()}.{self.member}"
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@dataclasses.dataclass(frozen=True)
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class ParamBufferSource(AttrSource):
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def guard_source(self):
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return _GUARD_SOURCE_SPECIALIZED_NN_MODULE[self.base.guard_source()]
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# Special AttrSource to differentiate module._buffers or module._parameters
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@dataclasses.dataclass(frozen=True)
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class UnspecializedParamBufferSource(AttrSource):
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pass
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# This source is intended to be used in places where a source is needed but it is expected
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# that the symbol will be simplified out later on. Symbols with ephemeral sources are
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# prioritized to be simplified out when e.g. compared against a symbol without an ephemeral
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# source. Guarding on this source is an error.
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#
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# Example: During subclass view fake-ification, any close-over ViewFunc state should be
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# symbolicized / fake-ified to avoid invalid specialization during view replay. This source
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# is useful for symbols utilized in the middle of the view chain that are not expected to be
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# present within the final view shape metadata.
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@dataclasses.dataclass(frozen=True)
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class EphemeralSource(Source):
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desc: Optional[str] = None
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def guard_source(self):
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return GuardSource.EPHEMERAL
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def name(self):
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return f"<ephemeral{': ' + self.desc if self.desc is not None else ''}>"
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def make_guard(self, fn):
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raise NotImplementedError
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def is_ephemeral(self):
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return True
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class TensorProperty(enum.Enum):
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SIZE = 0
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STRIDE = 1
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STORAGE_OFFSET = 2
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def method_name(self):
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if self is TensorProperty.SIZE:
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return "size"
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elif self is TensorProperty.STRIDE:
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return "stride"
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elif self is TensorProperty.STORAGE_OFFSET:
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return "storage_offset"
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@dataclasses.dataclass(frozen=True)
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class TensorPropertySource(ChainedSource):
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prop: TensorProperty
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idx: Optional[int] = None # None for STORAGE_OFFSET
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def __post_init__(self):
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assert self.base is not None
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if self.prop is TensorProperty.STORAGE_OFFSET:
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assert self.idx is None
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else:
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assert self.idx is not None
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def reconstruct(self, codegen):
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codegen.add_push_null(
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lambda: codegen.load_import_from(
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utils.__name__, f"call_{self.prop.method_name()}"
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)
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)
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codegen(self.base)
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if self.idx is not None:
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codegen.append_output(codegen.create_load_const(self.idx))
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codegen.extend_output(
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create_call_function(2 if self.idx is not None else 1, False)
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)
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def guard_source(self):
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return self.base.guard_source()
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def name(self):
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if self.prop is TensorProperty.SIZE:
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return f"{self.base.name()}.size()[{self.idx}]"
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elif self.prop is TensorProperty.STRIDE:
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return f"{self.base.name()}.stride()[{self.idx}]"
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elif self.prop is TensorProperty.STORAGE_OFFSET:
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assert self.idx is None
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return f"{self.base.name()}.storage_offset()"
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else:
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raise AssertionError(f"unhandled {self.prop}")
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@dataclasses.dataclass(frozen=True)
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class IndexedSource(ChainedSource):
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idx: int
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def __post_init__(self):
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assert self.base is not None
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def reconstruct(self, codegen):
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raise NotImplementedError
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def guard_source(self):
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return self.base.guard_source()
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def name(self):
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return f"({self.idx}, {self.base.name()})"
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@dataclasses.dataclass(frozen=True)
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class NegateSource(ChainedSource):
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def __post_init__(self):
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assert self.base is not None
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def reconstruct(self, codegen):
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raise NotImplementedError
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def guard_source(self):
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return self.base.guard_source()
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def name(self):
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# NB: use method call so that function stripping regexes work
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return f"{self.base.name()}.__neg__()"
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@dataclasses.dataclass(frozen=True)
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class ConvertIntSource(ChainedSource):
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def __post_init__(self):
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assert self.base is not None
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def reconstruct(self, codegen):
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codegen(self.base)
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def guard_source(self):
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return self.base.guard_source()
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def name(self):
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return f"cast_symbool_to_symint_guardless({self.base.name()})"
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@dataclasses.dataclass(frozen=True)
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class FlattenScriptObjectSource(ChainedSource):
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def __post_init__(self):
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assert self.base is not None
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def reconstruct(self, codegen):
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codegen(self.base)
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def guard_source(self):
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return self.base.guard_source()
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def name(self):
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return f"{self.base.name()}.__obj_flatten__()"
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@dataclasses.dataclass(frozen=True)
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class ScriptObjectQualifiedNameSource(ChainedSource):
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def __post_init__(self):
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assert self.base is not None
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def reconstruct(self, codegen):
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codegen(self.base)
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def guard_source(self):
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return self.base.guard_source()
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def name(self):
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return f"{self.base.name()}._type().qualified_name()"
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class AttrProxySource(ChainedSource):
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def reconstruct(self, codegen):
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codegen(self.base)
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def guard_source(self):
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return self.base.guard_source()
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def name(self):
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return f"{self.base.name()}.get_base()"
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@dataclasses.dataclass(frozen=True)
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|
class DefaultsSource(ChainedSource):
|
|
idx_key: Union[int, str]
|
|
is_kw: bool = False
|
|
field: str = dataclasses.field(init=False, repr=False, compare=False)
|
|
_name: str = dataclasses.field(init=False, repr=False, compare=False)
|
|
|
|
def __post_init__(self):
|
|
assert self.base, (
|
|
"Base must be a valid source in order to properly track and guard this Defaults to its origin."
|
|
)
|
|
if self.is_kw:
|
|
assert isinstance(self.idx_key, str)
|
|
object.__setattr__(self, "field", "__kwdefaults__")
|
|
object.__setattr__(
|
|
self, "_name", f"{self.base.name()}.{self.field}['{self.idx_key}']"
|
|
)
|
|
else:
|
|
assert isinstance(self.idx_key, int)
|
|
object.__setattr__(self, "field", "__defaults__")
|
|
object.__setattr__(
|
|
self, "_name", f"{self.base.name()}.{self.field}[{self.idx_key}]"
|
|
)
|
|
|
|
def reconstruct(self, codegen):
|
|
codegen(self.base)
|
|
codegen.extend_output(codegen.create_load_attrs(self.field))
|
|
codegen.append_output(codegen.create_load_const(self.idx_key))
|
|
codegen.append_output(create_instruction("BINARY_SUBSCR"))
|
|
|
|
def guard_source(self):
|
|
return self.base.guard_source()
|
|
|
|
def name(self):
|
|
return self._name
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class GetItemSource(ChainedSource):
|
|
index: Any
|
|
index_is_slice: bool = False
|
|
|
|
def __post_init__(self):
|
|
assert self.base is not None
|
|
if isinstance(self.index, slice):
|
|
# store the hashable version of the slice so the whole GetItemSource is hashable
|
|
super().__setattr__("index", self.index.__reduce__())
|
|
super().__setattr__("index_is_slice", True)
|
|
|
|
def reconstruct(self, codegen):
|
|
codegen(self.base)
|
|
if self.index_is_slice:
|
|
codegen.append_output(codegen.create_load_const(self.unpack_slice()))
|
|
else:
|
|
codegen.append_output(codegen.create_load_const(self.index))
|
|
codegen.append_output(create_instruction("BINARY_SUBSCR"))
|
|
|
|
def guard_source(self):
|
|
return self.base.guard_source()
|
|
|
|
def unpack_slice(self):
|
|
assert self.index_is_slice
|
|
slice_class, slice_args = self.index
|
|
return slice_class(*slice_args)
|
|
|
|
def name(self):
|
|
# Index can be of following types
|
|
# 1) index is a slice - example 1:4
|
|
# 2) index is a constant - example string, integer
|
|
assert not isinstance(self.index, Source)
|
|
if self.index_is_slice:
|
|
return f"{self.base.name()}[{self.unpack_slice()!r}]"
|
|
else:
|
|
return f"{self.base.name()}[{self.index!r}]"
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class ConstDictKeySource(ChainedSource):
|
|
index: Any
|
|
|
|
def guard_source(self):
|
|
return self.base.guard_source()
|
|
|
|
def reconstruct(self, codegen):
|
|
codegen.add_push_null(
|
|
lambda: codegen.load_import_from(utils.__name__, "dict_keys_getitem")
|
|
)
|
|
codegen(self.base)
|
|
codegen.append_output(codegen.create_load_const(self.index))
|
|
codegen.extend_output(create_call_function(2, False))
|
|
|
|
def name(self):
|
|
# The list creation will be CSE'd by PyExprCSEPass
|
|
return f"list(dict.keys({self.base.name()}))[{self.index!r}]"
|
|
|
|
def is_dict_key(self):
|
|
return True
|
|
|
|
|
|
# Used to access an item from the dictionary
|
|
@dataclasses.dataclass(frozen=True)
|
|
class DictGetItemSource(ChainedSource):
|
|
# Key to access in the dictionary. It can be one of the the following types
|
|
# 1) ConstDictKeySource
|
|
# 2) constant - like string, integer
|
|
index: Any
|
|
|
|
def __post_init__(self):
|
|
from .variables import ConstantVariable
|
|
|
|
assert isinstance(
|
|
self.index, ConstDictKeySource
|
|
) or ConstantVariable.is_literal(self.index)
|
|
|
|
def guard_source(self):
|
|
return self.base.guard_source()
|
|
|
|
def reconstruct(self, codegen):
|
|
# reconstruct dict.__getitem__(dct, key)
|
|
|
|
# Load dict.__getitem__
|
|
codegen.add_push_null(
|
|
lambda: codegen.load_import_from(utils.__name__, "dict_getitem")
|
|
)
|
|
|
|
# Load dict
|
|
codegen(self.base)
|
|
|
|
# Load key
|
|
if isinstance(self.index, Source):
|
|
codegen(self.index)
|
|
else:
|
|
codegen.append_output(codegen.create_load_const(self.index))
|
|
|
|
codegen.extend_output(create_call_function(2, False))
|
|
|
|
def name(self):
|
|
if isinstance(self.index, ConstDictKeySource):
|
|
return f"dict.__getitem__({self.base.name()}, {self.index.name()})"
|
|
else:
|
|
return f"{self.base.name()}[{self.index!r}]"
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class ListGetItemSource(GetItemSource):
|
|
"""
|
|
Same as GetItemSource with reconstruct and name overridden to be list specific.
|
|
"""
|
|
|
|
def reconstruct(self, codegen):
|
|
# Reconstruct list.__getitem__(lst, index) to avoid any side effects
|
|
# from possibly overridden __getitem__.
|
|
|
|
# Load list.__getitem__
|
|
codegen.add_push_null(
|
|
lambda: codegen.load_import_from(utils.__name__, "list_getitem")
|
|
)
|
|
|
|
# Load the list
|
|
codegen(self.base)
|
|
|
|
# Load the index
|
|
if self.index_is_slice:
|
|
raise RuntimeError(
|
|
"List[slice] is a temporary object and should not have a source"
|
|
)
|
|
else:
|
|
codegen.append_output(codegen.create_load_const(self.index))
|
|
|
|
codegen.extend_output(create_call_function(2, False))
|
|
|
|
def name(self):
|
|
# Index can be of following types
|
|
# 1) index is a slice - example 1:4
|
|
# 2) index is a constant - example string, integer
|
|
assert not isinstance(self.index, Source)
|
|
if self.index_is_slice:
|
|
raise RuntimeError(
|
|
"List[slice] is a temporary object and should not have a source"
|
|
)
|
|
else:
|
|
return f"list.__getitem__({self.base.name()}, {self.index!r})"
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class TupleIteratorGetItemSource(GetItemSource):
|
|
def reconstruct(self, codegen):
|
|
codegen.add_push_null(
|
|
lambda: codegen.load_import_from(utils.__name__, "tuple_iterator_getitem")
|
|
)
|
|
codegen(self.base)
|
|
codegen.append_output(codegen.create_load_const(self.index))
|
|
codegen.extend_output(create_call_function(2, False))
|
|
|
|
def name(self):
|
|
return f"___tuple_iterator_getitem({self.base.name()}, {self.index!r})"
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class TypeSource(ChainedSource):
|
|
def __post_init__(self):
|
|
assert self.base is not None
|
|
|
|
def reconstruct(self, codegen):
|
|
codegen.add_push_null(lambda: codegen.load_import_from("builtins", "type"))
|
|
codegen(self.base)
|
|
codegen.extend_output(create_call_function(1, False))
|
|
|
|
def guard_source(self):
|
|
return self.base.guard_source()
|
|
|
|
def name(self):
|
|
return f"type({self.base.name()})"
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class OptimizerSource(ChainedSource):
|
|
def reconstruct(self, codegen):
|
|
codegen(self.base)
|
|
|
|
def guard_source(self):
|
|
return self.base.guard_source()
|
|
|
|
def name(self):
|
|
return self.base.name()
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class NNModuleSource(ChainedSource):
|
|
def reconstruct(self, codegen):
|
|
codegen(self.base)
|
|
|
|
def guard_source(self):
|
|
return _GUARD_SOURCE_SPECIALIZED_NN_MODULE[self.base.guard_source()]
|
|
|
|
def name(self):
|
|
return self.base.name()
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class UnspecializedNNModuleSource(NNModuleSource):
|
|
def guard_source(self):
|
|
return _GUARD_SOURCE_UNSPECIALIZED_NN_MODULE[self.base.guard_source()]
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class UnspecializedBuiltinNNModuleSource(UnspecializedNNModuleSource):
|
|
def guard_source(self):
|
|
return _GUARD_SOURCE_UNSPECIALIZED_BUILTIN_NN_MODULE[self.base.guard_source()]
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class FSDPNNModuleSource(NNModuleSource):
|
|
def guard_source(self):
|
|
return _GUARD_SOURCE_FSDP_MODULE[self.base.guard_source()]
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class GlobalStateSource(Source):
|
|
def name(self):
|
|
return ""
|
|
|
|
def guard_source(self):
|
|
return GuardSource.GLOBAL
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class TorchFunctionModeStackSource(Source):
|
|
ind: int
|
|
|
|
def name(self):
|
|
return f"___get_torch_function_mode_stack_at({self._get_index()})"
|
|
|
|
def _get_index(self):
|
|
from .variables.torch_function import TorchFunctionModeStackVariable
|
|
|
|
return TorchFunctionModeStackVariable.get_mode_index(self.ind)
|
|
|
|
def reconstruct(self, codegen):
|
|
codegen.add_push_null(
|
|
lambda: codegen.load_import_from(
|
|
utils.__name__, "get_torch_function_mode_stack_at"
|
|
)
|
|
)
|
|
codegen.extend_output([codegen.create_load_const(self._get_index())])
|
|
codegen.extend_output(create_call_function(1, False))
|
|
|
|
def guard_source(self):
|
|
return GuardSource.GLOBAL
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class ConstantSource(Source):
|
|
source_name: str
|
|
|
|
def reconstruct(self, codegen):
|
|
codegen.append_output(codegen.create_load_global(self.source_name, add=False))
|
|
|
|
def guard_source(self):
|
|
return GuardSource.CONSTANT
|
|
|
|
def name(self):
|
|
return self.source_name
|
|
|
|
def make_guard(self, fn):
|
|
raise NotImplementedError
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class NumpyTensorSource(ChainedSource):
|
|
def name(self) -> str:
|
|
return f"___from_numpy({self.base.name()})"
|
|
|
|
def guard_source(self):
|
|
return self.base.guard_source()
|
|
|
|
def reconstruct(self, codegen):
|
|
codegen.add_push_null(lambda: codegen.load_import_from("torch", "as_tensor"))
|
|
codegen(self.base)
|
|
codegen.extend_output(create_call_function(1, False))
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class SubclassAttrListSource(ChainedSource):
|
|
def name(self) -> str:
|
|
return f"{self.base.name()}.__tensor_flatten__()[0]"
|
|
|
|
def guard_source(self):
|
|
return self.base.guard_source()
|
|
|
|
|
|
# NB: We don't expect you to actually ever generate guards against this
|
|
# source, it is ephemeral
|
|
@dataclasses.dataclass(frozen=True)
|
|
class FloatTensorSource(ChainedSource):
|
|
def name(self) -> str:
|
|
return f"___as_tensor({self.base.name()})"
|
|
|
|
def guard_source(self):
|
|
return self.base.guard_source()
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class CallMethodItemSource(ChainedSource):
|
|
def name(self) -> str:
|
|
return f"{self.base.name()}.item()"
|
|
|
|
def guard_source(self):
|
|
return self.base.guard_source()
|
|
|
|
|
|
# This is a synthetic source that is associated with the singleton
|
|
# shape env guard we always register for all frames. We get the actual
|
|
# guard contents from the ambient ShapeEnv
|
|
@dataclasses.dataclass(frozen=True)
|
|
class ShapeEnvSource(Source):
|
|
def name(self):
|
|
return ""
|
|
|
|
def guard_source(self):
|
|
return GuardSource.SHAPE_ENV
|
|
|
|
|
|
@dataclasses.dataclass(frozen=True)
|
|
class BackwardStateSource(Source):
|
|
def name(self):
|
|
return ""
|
|
|
|
def guard_source(self):
|
|
return GuardSource.BACKWARD_STATE
|
|
|
|
|
|
def is_from_local_source(source: Source, *, only_allow_input=False):
|
|
if isinstance(source, ChainedSource):
|
|
return is_from_local_source(source.base, only_allow_input=only_allow_input)
|
|
if not isinstance(source, LocalSource):
|
|
return False
|
|
if only_allow_input and not source.is_input:
|
|
return False
|
|
return True
|
|
|
|
|
|
def is_from_source(source: Source, target: Source):
|
|
if isinstance(source, ChainedSource):
|
|
return is_from_source(source.base, target)
|
|
return source == target
|
|
|
|
|
|
def is_from_unspecialized_param_buffer_source(source: Source):
|
|
if isinstance(source, UnspecializedParamBufferSource):
|
|
return True
|
|
if isinstance(source, ChainedSource):
|
|
return is_from_unspecialized_param_buffer_source(source.base)
|
|
return False
|
|
|
|
|
|
def is_from_flatten_script_object_source(source: Source):
|
|
if isinstance(source, FlattenScriptObjectSource):
|
|
return True
|
|
elif isinstance(source, ChainedSource):
|
|
return is_from_flatten_script_object_source(source.base)
|
|
return False
|
|
|
|
|
|
def is_from_optimizer_source(source: Source):
|
|
if isinstance(source, OptimizerSource):
|
|
return True
|
|
if isinstance(source, ChainedSource):
|
|
return is_from_optimizer_source(source.base)
|
|
return False
|
|
|
|
|
|
# TODO: can probably write a generic "test this on everything in the chain"
|
|
# helper
|
|
def is_from_defaults(source: Source):
|
|
if isinstance(source, DefaultsSource):
|
|
return True
|
|
|
|
# Accessed with func.__kwdefaults__["foo"]
|
|
if (
|
|
isinstance(source, DictGetItemSource)
|
|
and isinstance(source.base, AttrSource)
|
|
and source.base.member == "__kwdefaults__"
|
|
):
|
|
return True
|
|
|
|
# Accessed with func.__defaults__[0]
|
|
if (
|
|
isinstance(source, GetItemSource)
|
|
and isinstance(source.base, AttrSource)
|
|
and source.base.member == "__defaults__"
|
|
):
|
|
return True
|
|
|
|
if isinstance(source, ChainedSource):
|
|
return is_from_defaults(source.base)
|
|
return False
|