Document dynamo (#146736)

Many files in dynamo are currently lacking file/module-level documentation, which makes it hard to know what they do at a glance and without digging into the code. This fixes that.

Note: documentation was AI-generated and could be incorrect, please review carefully.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/146736
Approved by: https://github.com/jansel, https://github.com/StrongerXi, https://github.com/anijain2305, https://github.com/zou3519

torch/_dynamo/__init__.py

@@ -1,3 +1,13 @@
+"""
+TorchDynamo is a Python-level JIT compiler designed to make unmodified PyTorch programs faster.
+TorchDynamo hooks into the frame evaluation API in CPython (PEP 523) to dynamically modify Python
+bytecode right before it is executed. It rewrites Python bytecode in order to extract sequences of
+PyTorch operations into an FX Graph which is then just-in-time compiled with a customizable backend.
+It creates this FX Graph through bytecode analysis and is designed to mix Python execution with
+compiled backends to get the best of both worlds: usability and performance. This allows it to
+seamlessly optimize PyTorch programs, including those using modern Python features.
+"""
+
 import torch
 
 from . import convert_frame, eval_frame, resume_execution

torch/_dynamo/_trace_wrapped_higher_order_op.py

@@ -1,3 +1,33 @@
+"""trace_wrapped(*args, fn) is equivalent to fn(*args), but with a twist:
+if you make_fx trace through this call, we will not actually trace into fn; instead,
+we will directly insert it as a call_function to fn in the graph.
+(Unlike make_fx, Dynamo WILL inline into fn.)
+You can think of this as a one off allow_in_graph equivalent for proxy tensor tracing.
+
+Because proxy tensor tracing does not actually run the function, there are
+requirements on the behavior of fn. We are still figuring it out, but here is the current state:
+
+1) fn SHOULD only take a single argument, which must be a tensor
+2) fn MUST return a new tensor with the same metadata as the original tensor
+   (e.g., zeros_like(input) is a permissible implementation of fn).
+   This is verified via an extra assert that is inserted into the traced graph.
+3) fn MAY have side effects, but it MAY NOT perform metadata mutation on other tensors
+   participating in proxy tensor tracing (it MAY mutate other tensors, it MAY mutate Python state)
+These requirements stem from the requirement that we need to continue performing proxy tensor tracing,
+which assumes accurate fake tensor metadata, without actually running fn.
+In the future, we may allow for a "meta" function associated with fn to allow for more interesting input-output patterns.
+
+Note that tensors / Python state are allowed to be mutated.
+This is relaxed constraint is not always sound, but it is sound for backward tracing with fake
+tensors as it takes place in AOTAutograd, as the backward pass is guaranteed not to depend on concrete
+tensor values (via fake tensor) or Python state (because the autograd engine doesn't depend on Python).
+
+The intended use case for this function is to allow AOTAutograd to defer complex
+backward hooks to compiled autograd. AOTAutograd performs a make_fx trace which preserves
+the function call as is in the graph, and only when we Dynamo through the backward graph in
+compiled autograd do we inline into the function.
+"""
+
 from typing import Any, Optional
 
 import torch
@@ -19,36 +49,6 @@ Tensor = torch.Tensor
 __all__ = ["trace_wrapped"]
 
 
-# trace_wrapped(*args, fn) is equivalent to fn(*args), but with a twist:
-# if you make_fx trace through this call, we will not actually trace into fn; instead,
-# we will directly insert it as a call_function to fn in the graph.
-# (Unlike make_fx, Dynamo WILL inline into fn.)
-# You can think of this as a one off allow_in_graph equivalent for proxy tensor tracing.
-#
-# Because proxy tensor tracing does not actually run the function, there are
-# requirements on the behavior of fn. We are still figuring it out, but here is the current state:
-#
-# 1) fn SHOULD only take a single argument, which must be a tensor
-# 2) fn MUST return a new tensor with the same metadata as the original tensor
-#    (e.g., zeros_like(input) is a permissible implementation of fn).
-#    This is verified via an extra assert that is inserted into the traced graph.
-# 3) fn MAY have side effects, but it MAY NOT perform metadata mutation on other tensors
-#    participating in proxy tensor tracing (it MAY mutate other tensors, it MAY mutate Python state)
-# These requirements stem from the requirement that we need to continue performing proxy tensor tracing,
-# which assumes accurate fake tensor metadata, without actually running fn.
-# In the future, we may allow for a "meta" function associated with fn to allow for more interesting input-output patterns.
-#
-# Note that tensors / Python state are allowed to be mutated.
-# This is relaxed constraint is not always sound, but it is sound for backward tracing with fake
-# tensors as it takes place in AOTAutograd, as the backward pass is guaranteed not to depend on concrete
-# tensor values (via fake tensor) or Python state (because the autograd engine doesn't depend on Python).
-#
-# The intended use case for this function is to allow AOTAutograd to defer complex
-# backward hooks to compiled autograd. AOTAutograd performs a make_fx trace which preserves
-# the function call as is in the graph, and only when we Dynamo through the backward graph in
-# compiled autograd do we inline into the function.
-
-
 if not torch._running_with_deploy():
     # torch.library.custom_op does not work with torch.deploy/multipy
 

torch/_dynamo/backends/common.py

@@ -1,5 +1,23 @@
 # mypy: ignore-errors
 
+"""
+This module provides common utilities and base classes for TorchDynamo backends.
+
+Key components:
+- AotAutograd: Base class for implementing AOT (Ahead-of-Time) autograd backends
+- Backend utilities for handling:
+  - Fake tensor conversion
+  - Device/dtype detection from inputs
+  - Memory efficient fusion
+  - Graph flattening
+  - Common compiler configurations
+
+The utilities here are used by various backend implementations to handle
+common operations and provide consistent behavior across different backends.
+AOT autograd functionality is particularly important as it enables ahead-of-time
+optimization of both forward and backward passes.
+"""
+
 import contextlib
 import functools
 import logging

torch/_dynamo/backends/cudagraphs.py

@@ -1,5 +1,28 @@
 # mypy: ignore-errors
 
+"""
+This module implements CUDA graphs support for TorchDynamo backends.
+
+CUDA graphs allow for capturing and replaying GPU operations, which can significantly
+reduce CPU overhead in GPU-accelerated PyTorch models. This module provides:
+
+- CUDA graph creation and management for both forward and backward passes
+- Input mutation detection and handling
+- Device compatibility checking
+- Stack trace management for debugging
+- Integration with TorchInductor's cudagraph trees
+
+The backend supports two main modes:
+1. cudagraphs: Full CUDA graph support with both forward and backward pass optimization
+2. cudagraphs_inner: Lower-level CUDA graph implementation used for benchmarking
+
+Key components:
+- CudagraphsBackend: Main backend class for CUDA graph integration
+- Mutation detection utilities to ensure graph safety
+- Device mapping and compatibility checks
+- Stack trace collection for debugging
+"""
+
 import functools
 from collections import defaultdict
 from typing import Optional

torch/_dynamo/backends/debugging.py

@@ -1,5 +1,30 @@
 # mypy: ignore-errors
 
+"""
+This module provides debugging backends for TorchDynamo to help diagnose and troubleshoot
+compilation and execution issues. It includes:
+
+Key Debugging Backends:
+- eager: Simple pass-through backend that runs models in eager mode
+- eager_noexcept: Similar to eager but with additional exception handling
+- eager_debug: Adds schema validation checks for custom operators
+- aot_eager: Uses AOT Autograd with nop compiler for debugging
+- aot_eager_decomp_partition: Uses TorchInductor decompositions for debugging
+- torchscript: Compiles using TorchScript for debugging JIT-related issues
+
+Testing and Development Tools:
+- Backends for inducing specific errors (compile/runtime/accuracy)
+- ExplainOutput class for detailed graph compilation analysis
+- Utilities for cross-referencing and mode management
+- Tools for graph detail inspection and break reason analysis
+
+These backends are primarily used for:
+1. Debugging graph breaks and compilation failures
+2. Testing error handling and recovery mechanisms
+3. Analyzing performance bottlenecks
+4. Validating operator schemas and decompositions
+"""
+
 import dataclasses
 import functools
 import logging
@@ -19,11 +44,6 @@ from .registry import register_debug_backend as register_backend
 log = logging.getLogger(__name__)
 
 
-"""
-This file contains TorchDynamo backends intended for debugging uses.
-"""
-
-
 @register_backend
 def eager(gm, fake_tensor_inputs, **kwargs):
     if kwargs:

torch/_dynamo/backends/distributed.py

@@ -1,5 +1,23 @@
 # mypy: ignore-errors
 
+"""
+This module implements distributed training optimizations for TorchDynamo backends.
+
+It provides functionality to optimize models wrapped in DistributedDataParallel (DDP)
+by intelligently splitting compiled graphs to align with DDP's gradient synchronization
+boundaries. Key features include:
+
+- Graph partitioning based on parameter bucket sizes
+- Optimization of allreduce operations for distributed training
+- Support for parameter ignoring and buffer handling
+- Submodule compilation and management
+- Debugging utilities for distributed training
+
+The main component is the DDPOptimizer class, which handles graph splitting and
+recompilation to enable efficient distributed training while maintaining the benefits
+of compilation.
+"""
+
 import logging
 import traceback
 from dataclasses import dataclass, field

torch/_dynamo/backends/inductor.py

@@ -1,5 +1,16 @@
 # mypy: ignore-errors
 
+"""
+This module provides the TorchInductor backend integration for TorchDynamo.
+
+TorchInductor is a compiler backend that generates optimized code for both CPU and GPU.
+This module lazily imports and registers the TorchInductor compiler to avoid loading it
+into memory when it is not being used. This helps reduce memory overhead when using
+other backends.
+
+The inductor backend can be used with torch.compile():
+    model = torch.compile(model, backend="inductor")
+"""
 
 from torch._dynamo import register_backend
 

torch/_dynamo/backends/registry.py

@@ -1,5 +1,65 @@
 # mypy: ignore-errors
 
+"""
+This module implements TorchDynamo's backend registry system for managing compiler backends.
+
+The registry provides a centralized way to register, discover and manage different compiler
+backends that can be used with torch.compile(). It handles:
+
+- Backend registration and discovery through decorators and entry points
+- Lazy loading of backend implementations
+- Lookup and validation of backend names
+- Categorization of backends using tags (debug, experimental, etc.)
+
+Key components:
+- CompilerFn: Type for backend compiler functions that transform FX graphs
+- _BACKENDS: Registry mapping backend names to entry points
+- _COMPILER_FNS: Registry mapping backend names to loaded compiler functions
+
+Example usage:
+    @register_backend
+    def my_compiler(fx_graph, example_inputs):
+        # Transform FX graph into optimized implementation
+        return compiled_fn
+
+    # Use registered backend
+    torch.compile(model, backend="my_compiler")
+
+The registry also supports discovering backends through setuptools entry points
+in the "torch_dynamo_backends" group. Example:
+```
+setup.py
+---
+from setuptools import setup
+
+setup(
+    name='my_torch_backend',
+    version='0.1',
+    packages=['my_torch_backend'],
+    entry_points={
+        'torch_dynamo_backends': [
+            # name = path to entry point of backend implementation
+            'my_compiler = my_torch_backend.compiler:my_compiler_function',
+        ],
+    },
+)
+```
+```
+my_torch_backend/compiler.py
+---
+def my_compiler_function(fx_graph, example_inputs):
+    # Transform FX graph into optimized implementation
+    return compiled_fn
+```
+Using `my_compiler` backend:
+```
+import torch
+
+model = ...  # Your PyTorch model
+optimized_model = torch.compile(model, backend="my_compiler")
+```
+"""
+
 import functools
 import logging
 import sys

torch/_dynamo/backends/tvm.py

@@ -1,5 +1,27 @@
 # mypy: ignore-errors
 
+"""
+This module provides TVM backend integration for TorchDynamo.
+
+Apache TVM is a deep learning compiler framework that can optimize and execute
+models on various hardware backends. This module enables:
+
+- Compilation of PyTorch models to TVM's computation graphs
+- Multiple scheduling options:
+  - Default scheduler
+  - Auto-scheduler for automatic optimization
+  - Meta-schedule for evolutionary search-based tuning
+- Hardware-specific optimizations:
+  - CUDA GPU support
+  - CPU support with LLVM targeting and architecture-specific tuning
+  - Automatic detection of CPU capabilities (AVX2, AVX512)
+- Tensor conversion utilities between PyTorch and TVM formats
+- Configurable optimization levels and tuning trials
+
+The backend can be used with torch.compile():
+    model = torch.compile(model, backend="tvm")
+"""
+
 import functools
 import importlib
 import logging

torch/_dynamo/bytecode_analysis.py

@@ -1,4 +1,19 @@
 # mypy: allow-untyped-defs
+
+"""
+This module provides utilities for analyzing and optimizing Python bytecode.
+Key functionality includes:
+- Dead code elimination
+- Jump instruction optimization
+- Stack size analysis and verification
+- Live variable analysis
+- Line number propagation and cleanup
+- Exception table handling for Python 3.11+
+
+The utilities in this module are used to analyze and transform bytecode
+for better performance while maintaining correct semantics.
+"""
+
 import bisect
 import dataclasses
 import dis

torch/_dynamo/bytecode_transformation.py

@@ -1,4 +1,20 @@
 # mypy: allow-untyped-defs
+
+"""
+This module provides utilities for analyzing, transforming and manipulating Python bytecode.
+It includes functionality for:
+- Converting between different bytecode formats and versions
+- Virtualizing jumps and managing jump targets
+- Handling exception tables and their entries
+- Managing instruction offsets and extended arguments
+- Providing a clean API for bytecode modification and transformation
+- Supporting Python version-specific bytecode features
+- Generating bytecode from template functions
+
+The module is designed to work across different Python versions (3.7+) and handles
+version-specific bytecode differences transparently.
+"""
+
 import copy
 import dataclasses
 import dis

torch/_dynamo/callback.py

@@ -1,3 +1,30 @@
+"""
+This module provides callback management functionality for TorchDynamo's compilation process.
+
+It implements a thread-safe system for registering, managing and executing callbacks that run
+at the start and end of TorchDynamo compilations. Key features include:
+
+- Registration and deregistration of compilation callbacks
+- Thread-safe callback handling with proper locking mechanisms
+- Prevention of duplicate callback execution when configured
+- Decorator utilities for easy callback registration
+- Context manager for controlled callback lifecycle
+
+The module centers around the CompilationCallbackHandler class which maintains separate
+lists for start and end callbacks, manages their execution order, and ensures thread-safety.
+Utility decorators @on_compile_start and @on_compile_end provide a convenient way to
+register compilation hooks.
+
+Example usage:
+    @on_compile_start
+    def my_start_callback():
+        print("Starting compilation")
+
+    @on_compile_end
+    def my_end_callback():
+        print("Compilation complete")
+"""
+
 import threading
 from collections.abc import Generator
 from contextlib import contextmanager

torch/_dynamo/code_context.py

@@ -1,3 +1,33 @@
+"""
+This module provides thread-safe code context management for TorchDynamo using weak references.
+
+The CodeContextDict class maintains a mapping between Python code objects and their associated
+context data, using weak references to automatically clean up entries when code objects are
+garbage collected. This prevents memory leaks while allowing context data to be associated
+with code objects throughout their lifecycle.
+
+Key features:
+- Thread-safe context storage and retrieval
+- Automatic cleanup using weak references
+- Safe context management for Python code objects
+- Memory-leak prevention
+
+Example usage:
+    code_obj = compile('x = 1', '<string>', 'exec')
+
+    # Store context
+    context = code_context.get_context(code_obj)
+    context['metadata'] = {'optimized': True}
+
+    # Retrieve context
+    if code_context.has_context(code_obj):
+        ctx = code_context.get_context(code_obj)
+        # Use context data...
+
+    # Remove context
+    ctx = code_context.pop_context(code_obj)
+"""
+
 import types
 from typing import Any
 

torch/_dynamo/codegen.py

@@ -1,4 +1,17 @@
 # mypy: allow-untyped-defs
+
+"""
+This module provides utilities for generating Python bytecode in PyTorch's Dynamo system.
+It includes functionality for:
+- Constructing bytecode sequences for Python operations
+- Managing stack operations and variable tracking
+- Handling graph outputs and their conversions
+- Supporting different Python versions (3.11+, 3.12+, 3.13+)
+- Converting high-level operations to low-level bytecode instructions
+- Managing constant loading and attribute access
+- Supporting function creation and closure handling
+"""
+
 import collections
 import dataclasses
 import re

torch/_dynamo/compiled_autograd.py

@@ -1,4 +1,21 @@
 # mypy: allow-untyped-defs
+
+"""
+Provides functionality for compiling PyTorch's autograd (automatic differentiation) system.
+
+This module implements compiled autograd, which traces and optimizes backward pass
+computations at runtime. The key components are:
+
+- AutogradCompilerInstance: Traces and compiles autograd graphs using FX
+- Context managers (_enable/_disable): Control when compiled autograd is active
+- Utility functions: Support graph manipulation, tensor operations, and hooks
+
+Compiled autograd can significantly improve backward pass performance by removing
+Python overhead and enabling additional optimizations. It works by capturing
+backward computations into an FX graph that can be compiled and optimized,
+while maintaining the same semantics as eager mode autograd.
+"""
+
 import contextlib
 import functools
 import itertools

torch/_dynamo/comptime.py

@@ -1,10 +1,41 @@
 # mypy: allow-untyped-defs
-# This file establishes the public comptime interface to Dynamo.
-# This allows Dynamo users to execute arbitrary Python code while
-# Dynamo is symbolically evaluating their original programs.
-#
-# The goal of the public API is to give users rope, without actually
-# leaking private implementation details of Dynamo.
+
+"""
+This module provides the public comptime interface to TorchDynamo, enabling users to execute
+arbitrary Python code during symbolic evaluation of their programs.
+
+The comptime interface allows inspection and modification of TorchDynamo's compilation
+process while it is running. This can be useful for:
+
+- Debugging compilation issues
+- Inspecting intermediate state
+- Adding custom guards or graph breaks
+- Analyzing symbolic shapes and values
+
+Example usage:
+
+    import torch
+    from torch._dynamo.comptime import comptime
+
+    def my_model(x):
+        # Print the compile-time known information about x
+        comptime.print(x)
+
+        # Print the current FX graph being constructed
+        comptime.print_graph()
+
+        # Force a value to be treated as static
+        if comptime(lambda ctx: ctx.get_local("x").is_dynamic()):
+            comptime.force_static(x)
+
+        # Add a manual graph break
+        comptime.graph_break()
+
+Note: While this API provides significant flexibility, it intentionally avoids
+exposing internal implementation details of TorchDynamo to maintain compatibility
+across versions.
+"""
+
 
 import builtins
 import dis

torch/_dynamo/config.py

@@ -1,4 +1,16 @@
 # mypy: allow-untyped-defs
+
+"""
+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
+- Feature toggles for experimental features
+"""
+
 import getpass
 import os
 import sys

torch/_dynamo/convert_frame.py

@@ -1,4 +1,24 @@
 # mypy: allow-untyped-decorators
+
+"""
+This module implements TorchDynamo's core frame conversion functionality, transforming Python
+frames into FX graphs. It handles:
+
+- Frame analysis and bytecode transformation
+- Guard creation and management for dynamic behaviors
+- Cache management for recompilation
+- Error handling and fallback mechanisms
+
+Key classes:
+- ConvertFrame: Main entry point for frame conversion with error handling
+- ConvertFrameAssert: Implements core frame to graph conversion logic
+- Tracker: Tracks input/output code objects during conversion
+- CatchErrorsWrapper: Provides error handling and suppression logic
+
+The conversion process preserves program semantics while enabling optimizations
+through torch.compile() and related systems.
+"""
+
 from __future__ import annotations
 
 import collections

torch/_dynamo/current_scope_id.py

@@ -1,3 +1,20 @@
+"""
+Provides thread-local scope identification for SubgraphTracer instances.
+
+This module implements a thread-safe mechanism for tracking nested tracing contexts,
+which is essential when multiple SubgraphTracer instances are active. The scope ID
+helps identify which tracer context is currently active when direct access to the
+InstructionTranslator is difficult.
+
+Key components:
+- Thread-local scope ID storage (_current_scope_id)
+- Getter function (current_scope_id) to safely access the current scope
+- Context manager (enter_new_scope) for managing nested scope transitions
+
+The scope ID increments when entering a new context and decrements when exiting,
+allowing proper tracking of nested tracing operations across different threads.
+"""
+
 import contextlib
 import threading
 from collections.abc import Generator

torch/_dynamo/debug_utils.py

@@ -1,5 +1,24 @@
 # mypy: allow-untyped-defs
 # mypy: disable-error-code="method-assign"
+
+"""
+Debug utilities for TorchDynamo compilation and execution.
+
+This module provides various debugging tools and utilities for TorchDynamo, including:
+
+- Minification support for reducing test cases while preserving bugs
+- Input/output handling via InputReader and InputWriter for reproducible testing
+- Accuracy checking between original and compiled models
+- Neural network module string conversion via NNModuleToString
+- Profiling tools and system information collection
+- Buck build system integration for Meta-internal testing
+
+Key classes:
+- InputReader/InputWriter: Handle serialization of model inputs/outputs
+- NNModuleToString: Converts nn.Modules to string representations
+- BuckTargetWriter: Manages Buck build system integration
+"""
+
 import atexit
 import copy
 import cProfile

torch/_dynamo/decorators.py

@@ -1,5 +1,10 @@
 # mypy: allow-untyped-defs
 # ruff: noqa: TCH004
+
+"""
+This module provides decorators and utilities for controlling TorchDynamo's behavior during compilation.
+"""
+
 import functools
 import inspect
 import sys

torch/_dynamo/device_interface.py

@@ -1,4 +1,22 @@
 # mypy: allow-untyped-defs
+
+"""
+Device abstraction layer for TorchDynamo and Inductor backends.
+
+This module provides a unified interface for different hardware backends (CUDA, XPU,
+CPU, MPS) through a common device interface. Key components include:
+
+- DeviceInterface: Base class defining the common API for all device types
+- Device-specific implementations: CudaInterface, XpuInterface, CpuInterface, MpsInterface
+- Device registration system for managing available backends
+- Worker APIs for multi-processing scenarios
+- Stream and event management across different devices
+- Device property caching for worker processes
+
+The abstraction layer enables device-agnostic code in TorchDynamo while allowing
+specialized implementations for each hardware backend's unique features.
+"""
+
 import time
 from collections.abc import Iterable
 from dataclasses import dataclass

torch/_dynamo/distributed.py

@@ -1,3 +1,19 @@
+"""
+Manages process groups for distributed compilation in TorchDynamo.
+
+This module handles the initialization and management of process groups used for
+distributed compilation. Key features:
+
+- Lazy initialization of compilation process groups
+- Only creates groups when distributed mode is enabled and available
+- Integrates with compiler_collectives configuration setting
+- Provides a single global process group for compilation coordination
+
+The process group is created only when needed and if the distributed environment
+is properly initialized, making it safe to import and use this module even in
+non-distributed scenarios.
+"""
+
 from typing import Optional
 
 import torch.distributed as dist

torch/_dynamo/eval_frame.py

@@ -2,10 +2,25 @@
 # mypy: disable-error-code="method-assign"
 
 """
-Functions in this file are responsible for modifying the eval frame
-handler at RUNTIME.  Therefore, all functions in this file are hot.
-Functions that only execute at compile time should be placed
-in torch._dynamo.convert_frame.
+This module implements the core frame evaluation handler for TorchDynamo's compilation system.
+The eval frame handler intercepts Python bytecode execution at runtime to enable dynamic
+compilation and optimization of PyTorch code.
+
+Key components defined here:
+- Frame evaluation handlers that intercept and analyze Python execution frames
+- Guards management for tracking dependencies and invalidating compiled code
+- Optimization contexts and decorators (optimize, run_once, disable, etc.)
+- Export functionality for saving optimized graphs
+- Backend compiler integrations and callback management
+
+Functions in this file are responsible for modifying the eval frame handler at RUNTIME.
+Therefore, all functions in this file are hot and performance-critical. Functions that
+only execute at compile time should be placed in torch._dynamo.convert_frame.
+
+The eval frame handler is the core mechanism that enables TorchDynamo to dynamically
+intercept, analyze and optimize PyTorch code during execution. It works by registering
+a custom frame evaluation function that gets called for every Python frame, allowing
+us to detect PyTorch operations and trigger compilation as needed.
 """
 
 from __future__ import annotations

torch/_dynamo/exc.py

@@ -1,5 +1,31 @@
 from __future__ import annotations
 
+
+"""Exception handling and error reporting for TorchDynamo.
+
+This module provides a comprehensive set of exception classes and utilities for error
+handling in TorchDynamo. It includes:
+
+Base Exceptions:
+    - TorchDynamoException: Base class for all TorchDynamo-specific exceptions
+    - Various specialized subclasses for different error scenarios
+
+User Error Handling:
+    - UserError: Exceptions for user-facing errors in TorchDynamo usage
+    - UserErrorType: Enumeration of different categories of user errors
+    - Formatted error messages with debugging information
+
+Observed Exceptions:
+    - Classes for handling exceptions observed during tracing
+    - Special handling for StopIteration, LookupError, etc.
+    - Exception state management during compilation
+
+Error Formatting:
+    - Stack trace filtering and formatting
+    - Error message augmentation
+    - Debugging utilities for error reporting
+"""
+
 import logging
 import os
 import textwrap

torch/_dynamo/external_utils.py

@@ -1,5 +1,27 @@
 # This module contains functions that *will be allowed* by dynamo
 
+"""
+This module contains utility functions that are explicitly allowed to be called during
+TorchDynamo compilation. These functions are carefully vetted to ensure they work
+correctly within the TorchDynamo tracing and compilation process.
+
+Key functionality groups:
+
+- Compilation State:
+  Functions for checking compilation state (is_compiling)
+
+- Function Wrapping:
+  Utilities for wrapping functions (wrap_inline, wrap_numpy) to work with
+  TorchDynamo compilation
+
+- Autograd Hooks:
+  Functions and classes for handling autograd hooks and backward passes
+  (call_hook, FakeBackwardCFunction, etc.)
+
+- Tensor Operations:
+  Utility functions for tensor operations and transformations
+"""
+
 import functools
 import warnings
 from typing import Any, Callable, Optional, TYPE_CHECKING, TypeVar, Union

torch/_dynamo/funcname_cache.py

@@ -1,3 +1,21 @@
+"""
+This module provides functionality for caching and looking up fully qualified function
+and class names from Python source files by line number.
+
+It uses Python's tokenize module to parse source files and tracks function/class
+definitions along with their nesting to build fully qualified names (e.g. 'class.method'
+or 'module.function'). The results are cached in a two-level dictionary mapping:
+
+    filename -> (line_number -> fully_qualified_name)
+
+Example usage:
+    name = get_funcname("myfile.py", 42)  # Returns name of function/class at line 42
+    clearcache()  # Clear the cache if file contents have changed
+
+The parsing is done lazily when a file is first accessed. Invalid Python files or
+IO errors are handled gracefully by returning empty cache entries.
+"""
+
 import tokenize
 from typing import Optional
 

torch/_dynamo/graph_deduplication.py

@@ -1,3 +1,12 @@
+"""
+This module implements graph deduplication functionality for TorchDynamo's optimization pipeline.
+Graph deduplication identifies identical subgraphs in the computational graph and merges them
+to reduce redundancy and improve performance. The process involves analyzing regions of the graph,
+identifying structurally equivalent regions, and replacing them with a single shared implementation.
+This optimization is particularly effective for models with repeated patterns or similar computational
+structures across different parts of the network.
+"""
+
 import logging
 import operator
 from collections.abc import Iterable

torch/_dynamo/graph_region_tracker.py

@@ -1,3 +1,18 @@
+"""
+This module provides functionality for tracking and managing regions in computational graphs.
+It supports graph optimization by identifying and grouping similar regions based on their
+structure and behavior. The module implements algorithms for:
+
+1. Tracking nodes and their relationships in the computational graph
+2. Identifying identical or similar regions across the graph
+3. Managing graph regions for optimization purposes
+4. Supporting deduplication and other graph transformation passes
+
+The core functionality revolves around the GraphRegionTracker class which maintains
+mappings between nodes and their duplicates, enabling efficient graph analysis and
+optimization operations.
+"""
+
 import copyreg
 import io
 import logging

torch/_dynamo/guards.py

@@ -1,5 +1,22 @@
 # mypy: allow-untyped-defs
 
+"""
+Core guard system for Dynamo that detects when compiled code needs to be recompiled due to
+changes in program state. Guards are conditions that must remain true for previously-compiled
+code to be valid for reuse.
+
+This module provides the infrastructure for creating, managing and checking guards, including:
+- Guard creation and composition
+- Guard state management and invalidation
+- Guard checking and failure handling
+- Utilities for guard optimization and debugging
+- Integration with Dynamo's compilation caching
+
+The guard system is critical for Dynamo's ability to efficiently reuse compiled code while
+maintaining correctness by detecting when recompilation is necessary due to changes in
+program state, tensor properties, or control flow.
+"""
+
 from __future__ import annotations
 
 import ast

torch/_dynamo/hooks.py

@@ -1,3 +1,15 @@
+"""Hook system for Dynamo's guard functionality.
+
+This module provides a way to register callback functions that are triggered during
+guard-related operations.
+
+The Hooks class manages two types of hook functions:
+- guard_export_fn: Called when guards need to be exported, taking a GuardsSet as input
+- guard_fail_fn: Called when a guard check fails, taking a GuardFail object as input
+
+These hooks enable customization of guard export and failure handling behaviors.
+"""
+
 import dataclasses
 from typing import Callable, Optional
 

torch/_dynamo/logging.py

@@ -1,3 +1,14 @@
+"""Logging utilities for Dynamo and Inductor.
+
+This module provides specialized logging functionality including:
+- Step-based logging that prepends step numbers to log messages
+- Progress bar management for compilation phases
+- Centralized logger management for Dynamo and Inductor components
+
+The logging system helps track the progress of compilation phases and provides structured
+logging output for debugging and monitoring.
+"""
+
 import itertools
 import logging
 from typing import Any, Callable

torch/_dynamo/metrics_context.py

@@ -1,3 +1,18 @@
+"""Metrics collection and management system for Dynamo.
+
+This module provides context managers for gathering and reporting metrics during
+compilation and runtime.
+
+It includes two main components:
+- MetricsContext: A context manager for collecting metrics during compilation, supporting
+  nested contexts and various metric types (counters, sets, key-value pairs)
+- RuntimeMetricsContext: A specialized context for runtime metrics collection that doesn't
+  require explicit context management
+
+The metrics system enables comprehensive monitoring and analysis of both compilation and
+execution performance.
+"""
+
 import time
 from typing import Any, Callable, Optional
 from typing_extensions import TypeAlias

torch/_dynamo/mutation_guard.py

@@ -1,3 +1,17 @@
+"""Mutation tracking and dynamic module detection system for Dynamo.
+
+This module provides mechanisms to track and respond to mutations in PyTorch modules
+and detect dynamically created or modified modules.
+
+Key components:
+- MutationTracker: Tracks mutations to objects and invalidates associated cached code
+- GenerationTracker: Tracks module creation timing to identify dynamic instances
+- Patching system for nn.Module to detect mutations and dynamic creation
+
+The system ensures that Dynamo's optimizations remain valid by detecting and responding
+to runtime changes in module state and structure.
+"""
+
 import functools
 import weakref
 from collections.abc import MutableMapping

torch/_dynamo/output_graph.py

@@ -1,4 +1,26 @@
 # mypy: allow-untyped-defs
+
+"""
+Core graph building functionality for PyTorch's Dynamo system. This module contains
+the essential components for constructing and managing FX graphs during compilation:
+
+- OutputGraph: Manages the overall graph construction and compilation process. It owns
+  a SubgraphTracer and handles graph compilation, execution, and state management.
+  OutputGraph also manages features like graph deduplication, symbolic shape handling,
+  and tracking of side effects.
+
+- SubgraphTracer: Handles the actual FX graph construction by tracing Python code.
+  It supports advanced features like higher-order operators through nested tracers,
+  lifting of free variables, and handling of symbolic shapes.
+
+The module supports key Dynamo features including:
+- Higher-order operators through nested SubgraphTracers
+- Graph deduplication for optimization
+- Symbolic shape handling and propagation
+- Side effect tracking and management
+- Guard insertion and management
+"""
+
 import collections
 import contextlib
 import copy

torch/_dynamo/pgo.py

@@ -1,3 +1,14 @@
+"""
+Profile Guided Optimization (PGO) implementation for Dynamo.
+
+This module provides functionality for caching and managing code state profiles
+that guide optimization decisions in Dynamo. It implements both local and remote
+caching mechanisms for storing profile information across runs, handles profile
+merging across distributed ranks, and manages the lifecycle of profile data
+during compilation. The profiles track dynamic vs static properties of tensors
+and help Dynamo make better specialization decisions.
+"""
+
 from __future__ import annotations
 
 import base64

torch/_dynamo/profiler.py

@@ -1,3 +1,17 @@
+"""
+Dynamo profiling implementation.
+
+This module provides profiling functionality for Dynamo, including:
+- ProfileMetrics: Class for collecting and aggregating performance metrics like
+  execution time, operator counts, and fusion statistics
+- ProfileResult: Class for analyzing and reporting profiling results
+- Utilities for tracking missed/uncaptured operations
+- Functions for instrumenting FX graphs with profiling capabilities
+
+The profiler helps measure and optimize the performance of Dynamo-compiled code
+by tracking both captured and total operations, timing, and graph statistics.
+"""
+
 import dataclasses
 import os
 from typing import Any

torch/_dynamo/replay_record.py

@@ -1,3 +1,18 @@
+"""
+Python execution state recording and replay functionality.
+
+This module provides mechanisms for capturing and replaying Python execution state:
+
+- ModuleRecord: Tracks module access patterns and attribute usage
+- DummyModule: Lightweight module substitute for replay
+- ExecutionRecord: Manages execution context including globals, locals and builtins
+- ExecutionRecorder: Records variable states and module access during execution
+
+The module enables serialization and reproduction of Python execution environments,
+particularly useful for debugging and testing frameworks that need to capture
+and recreate specific program states.
+"""
+
 import dataclasses
 from dataclasses import field
 from types import CellType, CodeType, ModuleType

torch/_dynamo/repro/after_aot.py

@@ -1,5 +1,24 @@
 # mypy: allow-untyped-defs
 
+"""
+Utilities for reproducing and debugging issues in PyTorch's Dynamo AOT compilation.
+
+This module provides tools and infrastructure for:
+1. Generating minimal reproducible test cases ("repros") from failing compilations
+2. Analyzing accuracy issues between eager and compiled execution
+3. Minifying large models/inputs to isolate problematic patterns
+4. Debugging compiler errors and accuracy divergences
+
+The main components include:
+- Repro generation: Creates standalone Python files that reproduce compiler issues
+- Minification: Reduces large graphs to minimal failing examples
+- Accuracy analysis: Compares compiled vs eager execution, with fp64 reference
+- Debug tools: Dumps graph state, tracks intermediates, analyzes divergences
+
+This is primarily used by PyTorch developers and researchers to debug issues in
+the Dynamo AOT compilation pipeline, particularly for the Inductor backend.
+"""
+
 import argparse
 import copy
 import functools

torch/_dynamo/repro/after_dynamo.py

@@ -1,4 +1,23 @@
 # mypy: allow-untyped-defs
+
+"""
+Utilities for reproducing and debugging issues in Dynamo after graph capture.
+
+This file provides tools and infrastructure for debugging problems that occur
+after Dynamo has captured the graph but before/during backend compilation.
+Key components include:
+
+- Minification tools to reduce large graphs to minimal failing examples
+- Accuracy testing to validate compiled graph outputs match eager mode
+- Repro generation to create standalone reproduction scripts
+- Debug backends for capturing and analyzing failures
+- Utilities for saving/loading graph states and inputs
+
+The tools here focus specifically on the post-graph-capture stage, making them
+useful for debugging backend compilation issues, AOTAutograd problems, and
+accuracy discrepancies between compiled and eager execution.
+"""
+
 import argparse
 import copy
 import functools

torch/_dynamo/repro/aoti.py

@@ -1,4 +1,22 @@
 # mypy: allow-untyped-defs
+
+"""
+Utilities for debugging and reproducing issues in Ahead of Time with Inductor (AOTI) compilation.
+
+This file provides tools and utilities for:
+- Generating minimal reproducible test cases (minification)
+- Handling exported programs and graph modules
+- Creating debug repros for AOTI compilation issues
+- Supporting both accuracy testing and error reproduction
+- Managing configuration and environment for repro cases
+
+The main components include:
+- Minification tools to reduce test cases while preserving errors
+- Repro generation utilities for exported programs
+- Error handling specific to AOTI compilation
+- Command-line interface for running and managing repros
+"""
+
 import argparse
 import functools
 import io

torch/_dynamo/resume_execution.py

@@ -1,4 +1,20 @@
 # mypy: allow-untyped-defs
+
+"""
+This module provides functionality for resuming Python execution at specific points in code,
+primarily used by PyTorch Dynamo for control flow handling and optimization. It implements
+bytecode transformation and execution state management to enable:
+
+- Resuming execution at arbitrary points in Python bytecode
+- Managing context managers and their state across execution boundaries
+- Transforming and generating new code objects with preserved execution state
+- Supporting Python 3.11+ exception handling and block management
+- Restoring torch function mode stacks and other execution context
+
+The module is critical for PyTorch Dynamo's ability to optimize code while preserving
+Python semantics and execution state.
+"""
+
 import copy
 import dataclasses
 import sys

torch/_dynamo/side_effects.py

@@ -1,4 +1,5 @@
 # mypy: allow-untyped-defs
+
 import collections
 import contextlib
 import inspect
@@ -55,8 +56,22 @@ def _manual_list_update(list_from, list_to):
 
 class SideEffects:
     """
-    Track side effects (list mutation, setattr, etc) that need to be
+    Maintain records of mutations and provide methods to apply them during code generation.
+
+    Handles tracking and applying side effects during PyTorch Dynamo compilation,
+    maintaining Python semantics by managing mutations, attribute modifications,
+    and other side effects that occur during program execution.
+
+    Key responsibilities:
+    - Tracks mutations to Python objects, lists, and dictionaries that need to be
     applied after an FX graph is run.
+    - Manages attribute modifications and deletions
+    - Handles tensor hooks and backward pass state
+    - Tracks cell variable mutations and global variable changes
+    - Ensures correct ordering and application of side effects after graph execution
+
+    This ensures that optimized code behaves identically to the original Python code with
+    respect to object mutations and other side effects.
     """
 
     id_to_variable: dict[int, VariableTracker]

torch/_dynamo/source.py

@@ -1,4 +1,24 @@
 # mypy: allow-untyped-defs
+
+"""
+This module provides Source classes that track the origins of values in PyTorch Dynamo.
+Sources represent where values come from (e.g. local variables, globals, attributes) and
+are used for guard generation and code reconstruction during compilation.
+
+The module includes specialized sources for:
+- Local variables and synthetic locals
+- Global variables and constants
+- Object attributes and method calls
+- NN module specialization (specialized vs unspecialized)
+- Random values and tensor properties
+- Default argument handling
+- FSDP (Fully Sharded Data Parallel) modules
+
+Sources play a key role in Dynamo's guard system by tracking value origins for
+guard generation, and in code reconstruction by providing methods to rebuild
+the code needed to recreate values.
+"""
+
 import dataclasses
 import enum
 from typing import Any, Optional, Union

torch/_dynamo/symbolic_convert.py

@@ -1,4 +1,29 @@
 # mypy: allow-untyped-defs
+
+"""
+Core module responsible for converting Python bytecode into TorchDynamo's symbolic execution format.
+
+This module implements the bytecode-level tracing system that allows TorchDynamo to analyze
+and transform Python code. It converts Python bytecode instructions into a symbolic format
+that tracks the flow of tensors and other values through the program.
+
+Key components:
+- InstructionTranslatorBase: Base class for converting bytecode to symbolic execution
+- InstructionTranslator: Main translator for function bytecode
+- InliningInstructionTranslator: Handles inlining of called functions
+- SpeculationLog: Manages state for speculative execution and rollback
+
+The symbolic conversion process handles:
+- Control flow (loops, conditionals, etc.)
+- Function inlining and call stack management
+- Tracking of program values and side effects
+- Graph breaks and resumption points
+- Exception handling and stack frame management
+
+This is a core part of TorchDynamo's tracing system that enables ahead-of-time
+optimization of PyTorch programs.
+"""
+
 import collections
 import collections.abc
 import contextlib

torch/_dynamo/tensor_version_op.py

@@ -1,4 +1,23 @@
 # mypy: allow-untyped-defs
+
+"""This module implements tensor version operations for Dynamo tracing.
+
+It provides primitives for handling tensor versioning during tracing, particularly in the
+context of functionalization where version operations are handled eagerly on fake tensors.
+
+When we functionalize _tensor_version + _unsafe_set_version_counter, the ops disappear from
+the traced graph. We run them eagerly on the fake tensors used for tracing, in order to get
+past asserts that would fail in autograd.
+
+Why is this ok?
+1) Versions on functional tensors do not make any sense since you cannot mutate a functional
+   tensor.
+2) The whole point of version munging is to trick autograd into doing what we want, and after
+   AotAutograd there is no longer any need for these ops.
+
+Note this is similar to how no_grad is handled.
+"""
+
 import torch
 from torch._prims import _make_prim, RETURN_TYPE
 from torch._subclasses import FakeTensorMode
@@ -34,21 +53,6 @@ _unsafe_set_version_counter = _make_prim(
 torch.fx.node.has_side_effect(_unsafe_set_version_counter)
 
 
-"""
-When we functionalize _tensor_version + _unsafe_set_version_counter,
-the ops disappear from the traced graph.  We run them eagerly on the
-fake tensors used for tracing, in order to get past asserts that would
-fail in autograd.
-
-Why is this ok?
-1) Versions on functional tensors don't make any sense since you can't mutate a functional tensor.
-2) The whole point of version munging is to trick autograd into doing what we want, and after
-   AotAtuograd there is no longer any need for these ops.
-
-Note this is similar to how no_grad is handled.
-"""
-
-
 @_tensor_version.py_impl(FunctionalTensorMode)
 def _tensor_version_functional(mode, self):
     return self._version

torch/_dynamo/test_case.py

@@ -1,3 +1,13 @@
+"""Testing utilities for Dynamo, providing a specialized TestCase class and test running functionality.
+
+This module extends PyTorch's testing framework with Dynamo-specific testing capabilities.
+It includes:
+- A custom TestCase class that handles Dynamo-specific setup/teardown
+- Test running utilities with dependency checking
+- Automatic reset of Dynamo state between tests
+- Proper handling of gradient mode state
+"""
+
 import contextlib
 import importlib
 import logging

torch/_dynamo/test_minifier_common.py

@@ -1,4 +1,20 @@
 # mypy: allow-untyped-defs
+
+"""Common utilities for testing Dynamo's minifier functionality.
+
+This module provides the base infrastructure for running minification tests in Dynamo.
+It includes:
+- MinifierTestResult: A dataclass for storing and processing minifier test results
+- MinifierTestBase: A base test class with utilities for:
+  - Running tests in isolated environments
+  - Managing temporary directories and configurations
+  - Executing minifier launcher scripts
+  - Running and validating reproduction scripts
+  - Supporting both compile-time and runtime error testing
+
+The minifier helps reduce failing Dynamo compilations to minimal reproductions.
+"""
+
 import dataclasses
 import io
 import logging

torch/_dynamo/testing.py

@@ -1,3 +1,18 @@
+"""Testing utilities and infrastructure for Dynamo.
+
+This module provides a comprehensive set of testing utilities including:
+- Test result collection and validation
+- Graph manipulation and comparison tools
+- Test case management and execution helpers
+- Specialized test decorators for different Python versions and features
+- RNG state management
+- Compilation counting and monitoring
+- Debug utilities for bytecode transformation
+
+The utilities in this module are used across Dynamo's test suite to ensure
+consistent testing patterns and proper test isolation.
+"""
+
 import contextlib
 import dis
 import functools

torch/_dynamo/types.py

@@ -1,3 +1,16 @@
+"""This module contains the core type definitions and protocols used throughout Dynamo.
+
+The types defined here fall into several categories:
+- Guard related types (GuardFn, GuardFail, GuardedCode): Used for tracking and managing guards that protect compiled code
+- Frame and cache types (FrameState, CacheEntry): Used for managing interpreter frame state and caching
+- Callback protocols (DynamoCallbackFn): Define the interface for frame evaluation callbacks
+- Hook protocols (DynamoGuardHook, ProfilerStartHook, ProfilerEndHook, BytecodeHook): Define various hook points for
+  instrumentation and customization
+
+These types provide the foundational interfaces that enable Dynamo's dynamic compilation and optimization system,
+ensuring type safety and clear contracts between different components of the system.
+"""
+
 import dataclasses
 import types
 from typing import Any, Callable, NamedTuple, Optional, Protocol, Union

torch/_dynamo/utils.py

@@ -1,4 +1,19 @@
 # mypy: allow-untyped-defs
+
+"""
+Utility functions and classes used throughout the TorchDynamo system.
+
+This module contains a collection of helper utilities used by various parts of Dynamo for:
+- Performance metrics collection and reporting
+- Compilation timing and debugging
+- Graph manipulation and tensor operations
+- Runtime guards and checks
+- Common data structure operations
+- Testing and development tools
+
+This is an internal module that provides shared functionality used across the Dynamo codebase.
+"""
+
 from __future__ import annotations
 
 import atexit

torch/_dynamo/variables/__init__.py

@@ -1,3 +1,21 @@
+"""
+This package implements variable tracking and symbolic execution capabilities for Dynamo,
+which are essential for converting Python code into FX graphs. It provides a comprehensive
+set of variable types that handle different Python constructs during tracing.
+
+Each variable type (like BuiltinVariable, TensorVariable, NNModuleVariable, etc.) is responsible
+for tracking and symbolically executing operations on specific Python objects. This enables
+Dynamo to:
+- Track the flow of values through Python code
+- Maintain correct semantics during graph conversion
+- Handle complex Python features like context managers, iterators, and custom objects
+- Support both eager and symbolic execution modes
+
+The VariableTracker base class provides the foundation for all variable types, with each
+subclass implementing specific behavior for different Python constructs. This modular design
+allows Dynamo to accurately trace and optimize Python code while preserving its semantics.
+"""
+
 from .base import VariableTracker
 from .builtin import BuiltinVariable
 from .constant import ConstantVariable, EnumVariable

torch/_dynamo/variables/base.py

@@ -1,5 +1,20 @@
 # mypy: ignore-errors
 
+"""
+Core variable tracking functionality for Dynamo. This module defines the fundamental
+classes and systems used to track and manage variables during Dynamo's operation.
+
+The module provides:
+1. VariableTracker - The base class for tracking variables during compilation
+2. MutationType system - Classes for tracking and managing mutations to variables
+3. Source type management - Utilities for tracking variable origins and scope
+4. Variable state management - Tools for managing variable state and transformations
+
+These components form the foundation of Dynamo's variable handling system,
+enabling accurate tracking and transformation of Python code into optimized
+computations.
+"""
+
 import collections
 from enum import Enum
 from typing import Any, Callable, Optional, Sequence, TYPE_CHECKING

torch/_dynamo/variables/builder.py

@@ -1,5 +1,24 @@
 # mypy: ignore-errors
 
+"""
+This module contains classes and utilities for building variable trackers in Dynamo.
+Variable trackers are used to convert Python values into symbolic representations
+that can be traced and transformed during graph capture.
+
+The key classes are:
+
+- VariableBuilder: Handles source-tracked objects that need guards and proper
+  reconstruction in the output graph. Used for inputs, module attributes, etc.
+
+- SourcelessBuilder: Handles ephemeral objects created during tracing that don't
+  need source tracking or guards. Used for temporary lists, intermediate values, etc.
+
+Variable trackers enable Dynamo to track the flow of values through the program,
+maintain guards for dynamic properties, and reconstruct values in the output graph.
+The builders in this module handle converting Python values into appropriate
+VariableTracker instances based on their type and usage context.
+"""
+
 import abc
 import collections
 import contextlib

torch/_dynamo/variables/builtin.py

@@ -121,8 +121,13 @@ polyfill_fn_mapping = {
 
 class BuiltinVariable(VariableTracker):
     """
-    A VariableTracker that represents a built-in value. A lot of the code
-    here assumes it will be a function object.
+    A VariableTracker that represents a built-in value (functions and operators).
+    A lot of the code here assumes it will be a function object.
+
+    The BuiltinVariable class wraps Python built-in functions (like len, isinstance, etc.)
+    and operators (like +, -, *, etc.) to enable symbolic execution during tracing. This allows
+    Dynamo to properly handle these operations when converting Python code to FX graphs while
+    maintaining correct semantics and enabling optimizations.
     """
 
     _SENTINEL = object()

torch/_dynamo/variables/constant.py

@@ -1,5 +1,13 @@
 # mypy: ignore-errors
 
+"""
+Constant and enum variable tracking in Dynamo.
+
+This module is fundamental to Dynamo's ability to track and propagate constant
+values during compilation, ensuring proper handling of Python literals and
+maintaining type safety through the compilation process.
+"""
+
 import operator
 from typing import TYPE_CHECKING
 
@@ -17,6 +25,14 @@ if TYPE_CHECKING:
 
 
 class ConstantVariable(VariableTracker):
+    """
+    Variable tracker for Python literals and basic immutable types, with automatic
+    routing support for collection types (lists, tuples, sets, etc.).
+
+    The create() method intelligently constructs appropriate variable types for
+    nested collections.
+    """
+
     @staticmethod
     def create(value, **kwargs) -> VariableTracker:
         """
@@ -202,6 +218,12 @@ its type to `common_constant_types`.
 
 
 class EnumVariable(VariableTracker):
+    """VariableTracker for enum.Enum and enum.IntEnum instances
+
+    Provides specialized handling for Python enum types, supporting
+    both standard Enum and IntEnum with proper value tracking and comparison.
+    """
+
     def __init__(self, value, **kwargs) -> None:
         super().__init__(**kwargs)
         self.value = value

torch/_dynamo/variables/ctx_manager.py

@@ -1,4 +1,25 @@
 # mypy: ignore-errors
+
+"""
+This file contains a collection of context manager classes used by Dynamo for tracking
+and managing various PyTorch runtime states during graph compilation. These context
+managers handle different aspects of PyTorch's execution environment, including:
+
+- Autograd states (grad mode, inference mode)
+- CUDA streams and events
+- Profiling contexts
+- Deterministic algorithms
+- Forward/backward AD modes
+- SDPA (Scaled Dot Product Attention) kernels
+- FSDP (Fully Sharded Data Parallel) states
+- AMP (Automatic Mixed Precision) autocast states
+
+The context managers ensure proper state transitions during graph compilation by
+tracking enter/exit points and managing cleanup operations. They help maintain
+consistency between eager execution and compiled graph behavior by capturing and
+restoring state changes.
+"""
+
 import dataclasses
 import inspect
 import sys
@@ -34,7 +55,7 @@ if TYPE_CHECKING:
 
 
 @dataclasses.dataclass
-class ContextMangerState:
+class ContextManagerState:
     """
     Mutating `self` in VariableTracker is not allowed because we copy
     them.  This is a mutable container pointed to by context managers
@@ -69,7 +90,7 @@ class ContextWrappingVariable(VariableTracker):
         super().__init__(**kwargs)
         self.target_values = target_values
         self.initial_values = initial_values
-        self.state = ContextMangerState() if state is None else state
+        self.state = ContextManagerState() if state is None else state
 
     def enter(self, tx):
         self._call_func(tx, self.target_values)

torch/_dynamo/variables/dicts.py

@@ -1,5 +1,25 @@
 # mypy: ignore-errors
 
+"""
+Dictionary-related variable tracking classes for PyTorch Dynamo.
+
+This module implements variable tracking for different types of dictionary-like objects:
+- Regular Python dictionaries (dict)
+- Ordered dictionaries (collections.OrderedDict)
+- Default dictionaries (collections.defaultdict)
+- Dictionary views (keys and values)
+- Sets and frozensets (implemented internally using dictionaries)
+
+These classes are responsible for tracking dictionary operations during graph compilation,
+maintaining proper guards for dictionary mutations and key existence checks. They handle
+dictionary creation, modification, key/value access, and view operations while ensuring
+correct behavior in the compiled code through appropriate guard installation.
+
+The implementation uses a special _HashableTracker wrapper to handle dictionary keys
+while preserving proper aliasing semantics. Sets are implemented as dictionaries with
+None values for efficiency and code reuse.
+"""
+
 import collections
 import functools
 import types

torch/_dynamo/variables/distributed.py

@@ -1,4 +1,25 @@
 # mypy: ignore-errors
+
+"""
+Distributed computing variable tracking classes for PyTorch Dynamo.
+
+This module implements variable tracking for distributed computing components:
+- Process Groups (for collective communication)
+- Device Meshes (for distributed tensor sharding)
+- Placement Types (for specifying distribution strategies)
+- Distributed Tensors and their operations
+- Backward hooks for distributed module operations
+
+These classes are responsible for tracking distributed operations during graph
+compilation while maintaining proper guards and handling distributed-specific
+behaviors. They ensure correct handling of distributed components like process
+groups, device meshes, and placement strategies while preserving proper semantics
+for distributed tensor operations in the compiled code.
+
+The implementation provides special handling for distributed package availability
+checks and proper tracking of distributed state and operations across processes.
+"""
+
 import functools
 import inspect
 from typing import TYPE_CHECKING

torch/_dynamo/variables/functions.py

@@ -1,5 +1,28 @@
 # mypy: ignore-errors
 
+"""
+Function-related variable tracking classes for Dynamo's symbolic execution.
+
+This module contains classes that track different types of functions during graph
+compilation, including:
+- User-defined functions and methods
+- Built-in functions and methods
+- Wrapped functions (e.g. from decorators)
+- Special function types (e.g. functools.partial)
+- Triton kernels and related function types
+
+These classes are responsible for:
+- Tracking function calls and their arguments
+- Managing function closures and cell variables
+- Handling function attributes and special methods
+- Maintaining guards for function identity and closure contents
+- Supporting function inlining and specialization
+- Enabling proper symbolic execution of different function types
+
+The variable trackers here work together with the rest of Dynamo to enable
+accurate graph capture while handling Python's various function-related behaviors.
+"""
+
 import builtins
 import functools
 import inspect

torch/_dynamo/variables/higher_order_ops.py

@@ -1,5 +1,24 @@
 # mypy: ignore-errors
 
+"""
+This module contains classes and utilities for handling higher-order operators in Dynamo.
+It provides functionality for tracing and transforming control flow constructs like
+conditions (torch.cond), loops (torch.while_loop), maps (torch.ops.higher_order.map),
+and other higher-order operations.
+
+The module includes specialized VariableTracker classes for different types of
+higher-order operations, along with utilities for:
+- Speculating and capturing subgraphs
+- Managing control flow
+- Handling autograd function applications
+- Supporting function transformations
+- Processing activation checkpoints
+
+These classes work together to enable Dynamo to correctly trace and compile code
+containing complex control flow patterns and higher-order functions while preserving
+their semantic behavior.
+"""
+
 import contextlib
 import copy
 import functools

torch/_dynamo/variables/iter.py

@@ -1,5 +1,20 @@
 # mypy: ignore-errors
 
+"""
+This module provides iterator-related variable tracking functionality for Dynamo.
+It implements variable classes for handling Python iterators and itertools functions
+during symbolic execution and tracing.
+
+The module includes:
+- Base iterator variable classes for tracking iterator state
+- Implementations of built-in iterators (zip, map, filter)
+- Support for itertools functions (product, accumulate, combinations, etc.)
+- Mutation tracking and reconstruction capabilities for iterator operations
+
+These classes integrate with Dynamo's variable tracking system to enable proper
+handling of iterator operations during code transformation and optimization.
+"""
+
 import itertools
 import operator
 import sys

torch/_dynamo/variables/lists.py

@@ -1,5 +1,21 @@
 # mypy: ignore-errors
 
+"""
+Variable tracking implementations for list-like data structures in Dynamo.
+
+This module provides specialized variable tracking for various collection types:
+- Lists and list subclasses (including torch.nn.ModuleList, ParameterList)
+- Tuples and named tuples
+- Ranges and slices
+- Collections.deque
+- torch.Size with special proxy handling
+
+The implementations support both mutable and immutable collections, iteration,
+and common sequence operations. Each collection type has a dedicated Variable
+class that handles its unique behaviors while integrating with Dynamo's
+variable tracking system.
+"""
+
 import collections
 import inspect
 import operator

torch/_dynamo/variables/misc.py

@@ -1,4 +1,22 @@
 # mypy: ignore-errors
+
+"""
+This module contains miscellaneous variable tracker implementations for various Python types
+and features used in Dynamo's symbolic execution. These classes help track and propagate
+information about different kinds of variables during graph capture.
+
+Key classes include:
+- SuperVariable: Handles super() calls and method resolution
+- ExceptionVariable: Tracks exception objects
+- RandomVariable: Manages random number generators
+- GetAttrVariable: Tracks attribute access
+- MethodWrapperVariable: Handles method wrappers
+- PythonModuleVariable: Tracks Python modules
+- NumpyVariable: Handles numpy functions and types
+- StringFormatVariable: Manages string formatting
+- DebuggingVariable: Handles print and logging
+"""
+
 import dataclasses
 import functools
 import inspect

torch/_dynamo/variables/nn_module.py

@@ -1,5 +1,28 @@
 # mypy: ignore-errors
 
+"""
+This module implements variable tracking for PyTorch nn.Module instances during Dynamo tracing.
+
+It provides specialized handling for different types of nn.Module instances through several key classes:
+
+- NNModuleVariable: Handles instance-specific module tracing, specializing on module id() and placing
+  parameters directly on the torch.fx.GraphModule. This creates one graph per module instance.
+
+- UnspecializedNNModuleVariable: Provides class-level module tracing, treating nn.Modules like other
+  user-defined objects and passing parameters as inputs to the FX graph. This creates one graph per
+  module class.
+
+- UnspecializedBuiltinNNModuleVariable: Specifically handles built-in PyTorch modules (e.g. nn.Linear)
+  with appropriate optimizations.
+
+- FSDPManagedNNModuleVariable: Special handling for FSDP-wrapped modules with modified guarding behavior
+  and parameter handling.
+
+The module integrates with Dynamo's broader tracing functionality to handle module method calls,
+parameter access, hooks, and other nn.Module behaviors while maintaining proper scoping and guarding
+of module state.
+"""
+
 import functools
 import inspect
 import itertools

torch/_dynamo/variables/optimizer.py

@@ -1,5 +1,27 @@
 # mypy: ignore-errors
 
+"""
+This module implements variable tracking for PyTorch optimizers during Dynamo tracing.
+
+The OptimizerVariable class provides specialized handling for optimizer instances by:
+- Optimizing the tracing of expensive optimizer initialization
+- Managing optimizer state and parameter group tracking
+- Handling tensor sources and guards for optimizer state tensors
+- Supporting CUDA graph execution through static tensor address management
+- Providing special handling for parameter gradients and optimizer state tensors
+
+Key features include:
+- Efficient initialization tracing via _init_group optimization
+- Automatic marking of optimizer state tensors as static for CUDA graphs
+- Proper source tracking for parameter groups, gradients, and state tensors
+- Guard installation for optimizer state structure
+- Support for both CPU and GPU tensor handling
+- Cleanup of static tensor references via finalizers
+
+The module integrates with Dynamo's broader tracing system while providing
+optimizer-specific optimizations and safety guarantees.
+"""
+
 import logging
 import weakref
 from typing import TYPE_CHECKING

torch/_dynamo/variables/script_object.py

@@ -1,5 +1,26 @@
 # mypy: allow-untyped-decorators
 # mypy: allow-untyped-defs
+
+"""
+This module implements variable tracking for TorchScript objects during Dynamo tracing.
+
+The TorchScriptObjectVariable class provides specialized handling for TorchScript
+objects with strong safety guarantees by:
+- Enforcing method-call-only access to prevent unsafe attribute manipulation
+- Converting graph breaks into hard errors via _raise_hard_error_if_graph_break
+- Proper proxy and source tracking for TorchScript method calls
+- Integration with higher-order operators for method call handling
+
+Key safety features:
+- Strict validation that only method calls are allowed (no direct attribute access)
+- Immediate error reporting for potentially unsafe operations
+- Proper source tracking for debugging and guard installation
+- Safe handling of TorchScript object method calls through torchbind
+
+The module ensures that TorchScript objects are handled safely during tracing
+by limiting operations to known-safe patterns and failing fast for unsafe usage.
+"""
+
 import functools
 
 import torch

torch/_dynamo/variables/tensor.py

@@ -1,5 +1,22 @@
 # mypy: ignore-errors
 
+"""
+This module contains variable tracker classes for handling tensors and tensor-related operations in Dynamo.
+
+The main class is TensorVariable which represents torch.Tensor inputs and intermediate values in the FX graph.
+It handles tensor operations, method calls, and maintains metadata about tensor properties like dtype, device, etc.
+
+Other key classes include:
+- SymNodeVariable: Represents symbolic scalars (int/float/bool) used for size computation and unspecialized values
+- NumpyNdarrayVariable: Handles numpy array interop through torch._numpy
+- UnspecializedPythonVariable: Represents unspecialized Python numeric values as 1-element tensors
+- TensorSubclassVariable: Handles tensor subclasses with __torch_function__ overrides
+- UntypedStorageVariable: Represents tensor storage objects
+- DataPtrVariable: Handles tensor data pointer operations
+
+These classes work together to track tensor operations and properties during Dynamo's tracing process.
+"""
+
 import functools
 import inspect
 import logging

torch/_dynamo/variables/torch.py

@@ -1,5 +1,33 @@
 # mypy: allow-untyped-decorators
 # mypy: allow-untyped-defs
+
+"""
+This module implements variable tracking for torch functions and operations during Dynamo tracing.
+
+It provides classes to handle different types of torch operations:
+
+TorchInGraphFunctionVariable: Handles torch.* functions that should be captured in the FX graph.
+Provides special handling for constant folding, tensor methods, and torch function overrides.
+Manages complex cases like out= variants and parameter construction.
+
+TorchCtxManagerClassVariable: Handles torch context managers like torch.no_grad(), autocast, etc.
+Provides implementations for entering/exiting these contexts during tracing.
+
+DispatchKeySetVariable: Represents torch.DispatchKeySet for managing dispatch keys and
+device-specific operations during tracing.
+
+The module includes special handling for:
+- Constant folding of pure functions
+- Tensor method calls
+- torch.nn.Parameter construction
+- __torch_function__ overrides
+- Context manager state tracking
+- Device and dtype management
+
+This is a core part of Dynamo's tracing system, translating torch operations into
+traceable graph nodes while preserving correct semantics and handling edge cases.
+"""
+
 import functools
 import inspect
 import logging

torch/_dynamo/variables/torch_function.py

@@ -1,5 +1,34 @@
 # mypy: ignore-errors
 
+"""TorchDynamo support for __torch_function__ tensor subclasses.
+
+This module implements support for tensor subclasses with __torch_function__ overrides.
+A tensor subclass instance is represented as a TensorWithTFOverrideVariable, which handles
+dispatching __torch_function__ on attribute accesses, method calls, and torch API calls.
+
+Unsupported features:
+- Triggering __torch_function__ on tensor subclass non-tensor custom attributes
+- Graph breaking on mutating guardable tensor properties within a __torch_function__ context
+  (can cause excessive recompiles in certain cases)
+- Matching exact eager behavior of ignoring __torch_function__ objects in non-tensor
+  argument positions of Torch API calls
+
+Supported features:
+- Static method implementations of __torch_function__ on custom objects (triggers on torch
+  API calls with the object as any argument)
+- Triggering __torch_function__ on torch API calls with tensor subclass arguments
+- __torch_function__ calls on base tensor attribute access and method calls for tensor
+  subclass instances
+- Matches dispatch ordering behavior of eager __torch_function__ with subclass/object
+  arguments in any position
+
+See https://docs.google.com/document/d/1WBxBSvW3NXhRp9ncmtokJloMLCtF4AYNhJaffvHe8Kw/edit#heading=h.vacn73lozd9w
+for more information on the design.
+
+To enable subclass behavior, add your tensor subclass type to traceable_tensor_subclasses
+in torch/_dynamo/config.py
+"""
+
 import collections
 import contextlib
 import functools
@@ -43,27 +72,6 @@ if TYPE_CHECKING:
     from torch._dynamo.symbolic_convert import InstructionTranslator
 
 
-# [Note: __torch_function__] This feature is a prototype and has some rough edges (contact mlazos with issues):
-# At a high level, a torch function tensor subclass is represented as a TensorWithTFOverrideVariable, which dispatches
-# __torch_function__ on attribute accesses, method calls, and torch API calls.
-# The following is not supported:
-# - triggering __torch_function__ on tensor subclass non-tensor custom attributes
-# - graph breaking on mutating guardable tensor properties within a __torch_function__ context, this can cause
-# excessive recompiles in certain degenerate cases
-# - Matching the exact eager behavior of *ignoring* __torch_function__ objects in non-tensor argument positions of Torch API calls
-
-# The following is supported:
-# - static method impls of __torch_function__ on custom objects; this will trigger on torch API calls with the object as
-# any argument
-# - triggering __torch_function__ on torch API calls with tensor subclass arguments
-# - __torch_function__ calls on base tensor attribute access and method calls for tensor subclass instances
-# - matches the dispatch ordering behavior of eager __torch_function__ with subclass/object argumnents in any argument position
-
-# See https://docs.google.com/document/d/1WBxBSvW3NXhRp9ncmtokJloMLCtF4AYNhJaffvHe8Kw/edit#heading=h.vacn73lozd9w
-# for more information on the design.
-
-# To enable subclass behavior, add your tensor subclass type to traceable_tensor_subclasses in dynamo/config.py
-
 bin_ops = [
     operator.pow,
     operator.mul,

torch/_dynamo/variables/user_defined.py

@@ -1,5 +1,26 @@
 # mypy: ignore-errors
 
+"""
+This module contains variable classes for handling user-defined objects in Dynamo's tracing system.
+
+The key classes are:
+- UserDefinedVariable: Base class for representing custom Python objects
+- UserDefinedClassVariable: Handles Python class objects/types
+- UserDefinedObjectVariable: Fallback class for instance objects, with support for method calls,
+  attribute access, and other Python object behaviors.
+- Specialized subclasses for common patterns:
+  - UserDefinedDictVariable: For dict subclasses
+  - UserDefinedTupleVariable: For tuple subclasses
+  - FrozenDataClassVariable: Special handling of frozen dataclasses
+  - MutableMappingVariable: For collections.abc.MutableMapping subclasses
+
+Dynamo specializes to VariableTracker subclasses like FrozenDataClassVariable if available; if no
+subclass qualifies, it falls back to UserDefinedObjectVariable.
+
+These classes help Dynamo track and handle arbitrary Python objects during tracing,
+maintaining proper semantics while enabling optimizations where possible.
+"""
+
 import collections
 import contextlib
 import dataclasses