dynamo configs to torch.compiler (#163517)

Moving some dynamo configs to torch.compiler

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163517
Approved by: https://github.com/williamwen42, https://github.com/anijain2305

Co-authored-by: Svetlana Karslioglu <svekars@meta.com>

docs/source/torch.compiler.config.md

@@ -1,14 +1,12 @@
 ```{eval-rst}
 .. currentmodule:: torch.compiler.config
-
 ```
 
 # torch.compiler.config
 
 ```{eval-rst}
 .. automodule:: torch.compiler.config
-```
-
-```{eval-rst}
-.. autodata:: torch.compiler.config.job_id
+   :members:
+   :undoc-members:
+   :show-inheritance:
 ```

docs/source/torch.rst

@@ -816,6 +816,10 @@ Operator Tags
 .. py:module:: torch.types
 .. py:module:: torch.version
 
+.. Compiler configuration module - documented in torch.compiler.config.md
+.. py:module:: torch.compiler.config
+   :noindex:
+
 .. Hidden aliases (e.g. torch.functional.broadcast_tensors()). We want `torch.broadcast_tensors()` to
    be visible only.
 .. toctree::

torch/_dynamo/config.py

@@ -3,6 +3,7 @@ Configuration module for TorchDynamo compiler and optimization settings.
 
 This module contains various configuration flags and settings that control TorchDynamo's
 behavior, including:
+
 - Runtime behavior flags (e.g., guard settings, specialization options)
 - Debugging and development options
 - Performance tuning parameters
@@ -187,8 +188,22 @@ disable = os.environ.get("TORCH_COMPILE_DISABLE", "0") == "1"
 # [@compile_ignored: runtime_behaviour] Get a cprofile trace of Dynamo
 cprofile = os.environ.get("TORCH_COMPILE_CPROFILE", False)
 
-# legacy config, does nothing now!
+# Legacy config, does nothing now!
 skipfiles_inline_module_allowlist: dict[Any, Any] = {}
+"""Allowlist of inline modules to skip during compilation.
+
+Legacy configuration that previously controlled which modules could be
+inlined during tracing. This configuration is deprecated and no longer used.
+
+:type: dict[Any, Any]
+:default: {}
+
+.. deprecated::
+   This configuration is deprecated and does nothing now.
+
+.. note::
+   DEPRECATED: This setting has no effect on current behavior.
+"""
 
 # If a string representing a PyTorch module is in this ignorelist,
 # the `allowed_functions.is_allowed` function will not consider it
@@ -620,6 +635,25 @@ graph_break_on_nn_param_ctor = True
 
 # Overrides torch.compile() kwargs for Compiled Autograd:
 compiled_autograd_kwargs_override: dict[str, Any] = {}
+"""Overrides torch.compile() kwargs for Compiled Autograd.
+
+This dictionary allows overriding specific torch.compile() keyword arguments
+when using Compiled Autograd. Only certain overrides are currently supported.
+
+:type: dict[str, Any]
+:default: {}
+
+Example::
+
+    torch._dynamo.config.compiled_autograd_kwargs_override = {
+        "fullgraph": True
+    }
+
+.. note::
+   Currently only the "fullgraph" kwarg override is supported. Other kwargs
+   may be added in future versions.
+"""
+
 
 # Enables use of collectives *during* compilation to synchronize behavior
 # across ranks.  Today, this is used solely to modify automatic_dynamic_shapes

torch/compiler/config.py

@@ -20,6 +20,21 @@ from torch.utils._config_module import Config, install_config_module
 
 __all__ = [
     "job_id",
+    "dynamic_shapes",
+    "assume_static_by_default",
+    "automatic_dynamic_shapes",
+    "recompile_limit",
+    "accumulated_recompile_limit",
+    "verbose",
+    "capture_scalar_outputs",
+    "capture_dynamic_output_shape_ops",
+    "log_file_name",
+    "fail_on_recompile_limit_hit",
+    "allow_unspec_int_on_nn_module",
+    "skip_tensor_guards_with_matching_dict_tags",
+    "enable_cpp_symbolic_shape_guards",
+    "wrap_top_frame",
+    "reorderable_logging_functions",
 ]
 
 
@@ -121,4 +136,145 @@ any cudagraph.
 """
 
 
+# Cross-cutting configuration options that affect the entire compilation pipeline
+
+dynamic_shapes: bool = Config(alias="torch._dynamo.config.dynamic_shapes")
+"""
+Controls whether the compilation pipeline supports dynamic tensor shapes.
+When enabled, the compiler can handle tensors with varying dimensions across
+different invocations. This is a cross-cutting setting that affects shape
+inference, guard generation, and code generation across the entire compilation
+stack.
+"""
+
+assume_static_by_default: bool = Config(
+    alias="torch._dynamo.config.assume_static_by_default"
+)
+"""
+When enabled, all tensor dimensions are assumed to be static unless explicitly
+marked as dynamic or detected as changing. This compilation-wide behavior affects
+how the entire stack handles shape specialization and can improve performance
+for static workloads.
+"""
+
+automatic_dynamic_shapes: bool = Config(
+    alias="torch._dynamo.config.automatic_dynamic_shapes"
+)
+"""
+Enables automatic detection and handling of dynamic shapes. When a tensor's
+shape changes between compilations, the system automatically marks those
+dimensions as dynamic rather than requiring manual specification. This
+cross-cutting optimization improves the user experience by reducing recompilations.
+"""
+
+recompile_limit: int = Config(alias="torch._dynamo.config.recompile_limit")
+"""
+Maximum number of recompilations allowed for a single function before falling
+back to eager execution. This compilation performance control prevents excessive
+recompilation overhead that can degrade overall performance.
+"""
+
+accumulated_recompile_limit: int = Config(
+    alias="torch._dynamo.config.accumulated_recompile_limit"
+)
+"""
+Global limit on total recompilations across all compiled functions to prevent
+runaway recompilation scenarios. This safeguard protects against compilation
+performance issues that could affect the entire program.
+"""
+
+verbose: bool = Config(alias="torch._dynamo.config.verbose")
+"""
+Enables verbose debugging output for Dynamo. When enabled, provides detailed
+information about Dynamo's compilation decisions, optimizations, and potential
+issues.
+"""
+
+
+# TorchDynamo-specific configuration options
+
+capture_scalar_outputs: bool = Config(
+    alias="torch._dynamo.config.capture_scalar_outputs"
+)
+"""
+Controls whether TorchDynamo captures operations that return scalar values (like .item())
+into the FX graph. When disabled, these operations cause graph breaks. This is a
+TorchDynamo-specific tracing behavior that affects how the tracer handles
+scalar-returning operations.
+"""
+
+capture_dynamic_output_shape_ops: bool = Config(
+    alias="torch._dynamo.config.capture_dynamic_output_shape_ops"
+)
+"""
+Controls whether TorchDynamo captures operations with dynamic output shapes (like
+nonzero, unique) into the FX graph. When disabled, these operations cause graph breaks.
+This is a TorchDynamo-specific setting for handling operations with unpredictable
+output shapes during tracing.
+"""
+
+log_file_name: Optional[str] = Config(alias="torch._dynamo.config.log_file_name")
+"""
+Specifies a file path for TorchDynamo-specific logging output. When set, internal
+TorchDynamo debug information is written to this file rather than stdout. This is
+useful for debugging TorchDynamo's internal tracing behavior.
+"""
+
+fail_on_recompile_limit_hit: bool = Config(
+    alias="torch._dynamo.config.fail_on_recompile_limit_hit"
+)
+"""
+Raises a hard error when recompile limits are exceeded instead of falling back
+to eager execution. This is useful for detecting excessive recompilation in
+performance-critical deployments where you want to ensure compilation overhead
+is kept under control.
+"""
+
+allow_unspec_int_on_nn_module: bool = Config(
+    alias="torch._dynamo.config.allow_unspec_int_on_nn_module"
+)
+"""
+Allows integer attributes of nn.Module instances to be unspecialized through
+the dynamic shape mechanism. By default, TorchDynamo specializes on all integer
+module attributes, but this can cause excessive recompilation when integers
+like step counters change frequently.
+"""
+
+skip_tensor_guards_with_matching_dict_tags: bool = Config(
+    alias="torch._dynamo.config.skip_tensor_guards_with_matching_dict_tags"
+)
+"""
+Optimizes guard generation by treating tensors as immutable when they are
+dictionary values with consistent dictionary tags across invocations. This
+reduces guard overhead for tensors stored in persistent data structures.
+"""
+
+enable_cpp_symbolic_shape_guards: bool = Config(
+    alias="torch._dynamo.config.enable_cpp_symbolic_shape_guards"
+)
+"""
+Uses C++ implementation for symbolic shape guard evaluation to improve performance.
+The C++ guard manager can significantly speed up guard checking for symbolic shapes
+in shape-polymorphic compilations.
+"""
+
+wrap_top_frame: bool = Config(alias="torch._dynamo.config.wrap_top_frame")
+"""
+Wraps the top-level decorated function/module in a frame wrapper to ensure
+nn.Module hooks are compiled within the same frame as the main function. This
+improves compilation coverage for models that rely on hooks.
+"""
+
+reorderable_logging_functions: set = Config(
+    alias="torch._dynamo.config.reorderable_logging_functions"
+)
+"""
+A set of logging functions that can be reordered to execute after the compiled
+portion of the graph, allowing larger graphs to be captured. Functions in this
+set will have their execution deferred to avoid graph breaks, though this may
+affect the timing of log output. In particular, mutated values will not be logged
+at the right time, leading to incorrect logging.
+"""
+
+
 install_config_module(sys.modules[__name__])