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https://github.com/pytorch/pytorch.git
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634659e262
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional) Plus few real fixes: - Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi` - Add missing return statement to `torch._export. deserialize_graph` - Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights` - TODO (in followup PR): - Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/91983 Approved by: https://github.com/kit1980, https://github.com/ZainRizvi, https://github.com/huydhn, https://github.com/thiagocrepaldi, https://github.com/aaronenyeshi
238 lines
10 KiB
Python
238 lines
10 KiB
Python
import functools
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from contextlib import contextmanager
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from dataclasses import dataclass
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from typing import Any, Callable, Dict, List, NamedTuple, Optional, Set, Tuple
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import torch
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import torch.nn as nn
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@dataclass
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class TracingConfig:
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"""
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This represents a symbolic tracing configuration.
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Args:
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tracer (torch.fx.Tracer): An instance of :class:`torch.fx.Tracer` to
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use for symbolic tracing. The default value is the native
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:class:`torch.fx.Tracer` constructed with default arguments.
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However, the user may want to pass a different value such as the
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``HFTracer`` for models in the HuggingFace Transformers_ library.
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.. _Transformers: https://huggingface.co/docs/transformers/index
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concrete_args (Optional[Dict[str, Any]]): Concrete arguments that
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should not be treated as ``torch.fx.Proxy`` when tracing the
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module ``forward()``. Passing ``concrete_args`` allows partially
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specializing the forward, e.g. to remove control flow or data
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structures. This ``concrete_args`` here is the same argument used
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in :meth:`~torch.fx.Tracer.trace`.
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"""
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tracer: torch.fx.Tracer = torch.fx.Tracer()
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concrete_args: Optional[Dict[str, Any]] = None
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class _ParamUsageInfo(NamedTuple):
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"""
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This is used for ``_ExecutionInfo.module_to_param_usage_infos`` to record
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execution information. The ``dict`` maps modules to a list of these
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``_ParamUsageInfo`` instances, where each instance represents a group of
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parameters used together.
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Specifically, for each module key in the ``dict``, each instance of this
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class represents either:
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(1) the module and some sublist of its ``named_parameters()`` used
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together in execution (see ``_patched_create_proxy()``), or
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(2) a submodule and all of ``submodule.named_parameters()`` (see
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``_patched_call_module()``).
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Type (1) corresponds to directly using parameters in ops without calling
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``forward()``, and type (2) corresponds to calling ``forward()``. The
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mapped-to lists in the ``dict`` follow the execution order.
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"""
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module: nn.Module
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named_params: List[Tuple[str, nn.Parameter]]
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class _ExecutionInfo:
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"""
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This represents the execution order information from the forward pass.
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Attributes:
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curr_module (nn.Module): Current module being traced.
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module_forward_order (List[nn.Module]): The modules in (pre-)forward
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order, i.e. the order in which their ``forward()`` methods are
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called. Each call to a module's ``forward()`` corresponds to one
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element in the list.
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module_to_param_usage_infos (Dict[nn.Module, List[_ParamUsageInfo]]):
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Maps a module to a list of module execution infos. See
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:class:`_ParamUsageInfo` for details.
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param_forward_order (List[nn.Parameter]): The parameters in forward
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execution order, where only a parameter's first participation is
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included.
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visited_params (Set[nn.Parameter]): The parameters visited so far
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during the trace. This is only used during tracing for fast
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membership check. Invariant: The parameters in
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``param_forward_order`` are exactly those in ``visited_params``.
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"""
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def __init__(self, root_module: nn.Module) -> None:
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self.curr_module: nn.Module = root_module
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self.module_forward_order: List[nn.Module] = [root_module]
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self.module_to_param_usage_infos: Dict[nn.Module, List[_ParamUsageInfo]] = {
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root_module: []
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}
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self.param_forward_order: List[nn.Parameter] = []
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self.visited_params: Set[nn.Parameter] = set()
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class _ExecOrderTracer:
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def __init__(self) -> None:
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self.exec_info: Optional[_ExecutionInfo] = None
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@contextmanager
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def patch_tracer(self, tracer: torch.fx.Tracer, root_module: nn.Module):
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self.exec_info = _ExecutionInfo(root_module)
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orig_call_module = tracer.call_module
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orig_create_proxy = tracer.create_proxy
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tracer.call_module = functools.partial(
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self._patched_call_module, orig_call_module, self.exec_info
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)
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fqn_to_param = dict(root_module.named_parameters())
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tracer.create_proxy = functools.partial(
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self._patched_create_proxy,
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orig_create_proxy,
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self.exec_info,
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fqn_to_param,
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)
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try:
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yield
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finally:
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tracer.call_module = orig_call_module
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tracer.create_proxy = orig_create_proxy
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def _patched_call_module(
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self,
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call_module: Callable,
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exec_info: _ExecutionInfo,
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# Below are the expected arguments to `call_module()`
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module: nn.Module,
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forward: Callable,
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args: Tuple[Any, ...],
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kwargs: Dict[str, Any],
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) -> Any:
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"""
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Overrides ``call_module`` to save execution information to
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``exec_info``. Note that ``call_module`` is called during symbolic
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tracing for each non-root module.
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Args:
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call_module (Callable): Original ``call_module`` to override.
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exec_info (_ExecutionInfo): Used to record execution information.
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module (nn.Module): Module corresponding to this ``call_module``.
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forward (Callable): ``forward()`` method of ``module`` to be called
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for this ``call_module``.
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args (Tuple[Any, ...]): Positional arguments for ``forward``.
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kwargs (Dict[str, Any]): Keyword arguments for ``forward``.
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Returns:
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Same return value as ``call_module``.
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"""
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exec_info.module_forward_order.append(module)
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named_params = list(module.named_parameters())
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curr_module = exec_info.curr_module
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if named_params:
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assert (
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curr_module in exec_info.module_to_param_usage_infos
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), "The current module should have already been processed by a patched `call_module`"
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exec_info.module_to_param_usage_infos[exec_info.curr_module].append(
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_ParamUsageInfo(module, named_params)
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)
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prev_curr_module = curr_module
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exec_info.curr_module = module
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exec_info.module_to_param_usage_infos[module] = []
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output = call_module(module, forward, args, kwargs)
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exec_info.curr_module = prev_curr_module
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return output
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def _patched_create_proxy(
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self,
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create_proxy: Callable,
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exec_info: _ExecutionInfo,
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fqn_to_param: Dict[str, nn.Parameter],
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# Below are the expected arguments to `create_proxy()`
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kind: str,
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target: torch.fx.node.Target,
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args: Tuple[Any, ...],
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kwargs: Dict[str, Any],
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name: Optional[str] = None,
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type_expr: Optional[Any] = None,
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proxy_factory_fn: Optional[Callable[[torch.fx.Node], torch.fx.Proxy]] = None,
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) -> torch.fx.Proxy:
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"""
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Overrides ``create_proxy`` to save execution information to
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``exec_info``. Note that ``create_proxy`` is called during symbolic
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tracing for each leaf function/method/module.
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Args:
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create_proxy (Callable): Original ``create_proxy`` to override.
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exec_info (_ExecutionInfo): Used to record execution information.
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fqn_to_param (Dict[str, nn.Parameter]): ``dict`` version of the
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root module's ``named_parameters()`` with FQN as key and
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parameter as value.
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kind (str): Kind of the target method ('call_function',
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'call_method', 'get_attr', 'call_module', 'placeholder', or
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'output'). See :class:`torch.fx.Graph` for details. This is
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passed to ``create_proxy``.
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target (torch.fx.node.Target): Contains the string name of the
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function/method/module. This is passed to ``create_proxy``.
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args (Tuple[Any, ...]): Positional arguments for the function/
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method/module. This is passed to ``create_proxy``.
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kwargs (Dict[str, Any]): Keyword arguments for the function/method/
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module. This is passed to ``create_proxy``
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name (Optional[str]): An optional string name for the ``Node``
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created in ``create_proxy``. This is passed to
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``create_proxy``.
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type_expr (Optional[Any]): An optional type annotation representing
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the Python type that the output of the node has. This is passed
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to ``create_proxy``.
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proxy_factory_fn (Callable[[torch.fx.Node], torch.fx.Proxy]):
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An alternative proxy constructor used in ``create_proxy``. This
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is passed to ``create_proxy``.
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Returns:
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torch.fx.Proxy: Created ``Node`` wrapped in a ``Proxy`` object.
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"""
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proxy = create_proxy(
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kind, target, args, kwargs, name, type_expr, proxy_factory_fn
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)
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curr_module = exec_info.curr_module
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if kind in ("call_function", "call_method"):
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if args is not None:
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named_params: List[Tuple[str, nn.Parameter]] = []
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for arg in args:
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if (
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isinstance(arg, torch.fx.Proxy)
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and arg.node.target in fqn_to_param
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):
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param = fqn_to_param[arg.node.target]
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named_params.append((arg.node.target, param))
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if param not in exec_info.visited_params:
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exec_info.visited_params.add(param)
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exec_info.param_forward_order.append(param)
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if named_params:
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exec_info.module_to_param_usage_infos[curr_module].append(
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_ParamUsageInfo(curr_module, named_params)
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)
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elif kind == "call_module":
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named_params = list(curr_module.named_parameters())
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if named_params:
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exec_info.module_to_param_usage_infos[curr_module].append(
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_ParamUsageInfo(curr_module, named_params)
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)
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for _, param in named_params:
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if param not in exec_info.visited_params:
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exec_info.visited_params.add(param)
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exec_info.param_forward_order.append(param)
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return proxy
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