adding documentation

docs/source/conf.py

@@ -382,20 +382,6 @@ coverage_ignore_functions = [
     # torch.ao.quantization.backend_config.tensorrt
     "get_tensorrt_backend_config",
     "get_tensorrt_backend_config_dict",
-    # torch.ao.quantization.backend_config.utils
-    "entry_to_pretty_str",
-    "get_fused_module_classes",
-    "get_fuser_method_mapping",
-    "get_fusion_pattern_to_extra_inputs_getter",
-    "get_fusion_pattern_to_root_node_getter",
-    "get_module_to_qat_module",
-    "get_pattern_to_dtype_configs",
-    "get_pattern_to_input_type_to_index",
-    "get_qat_module_classes",
-    "get_root_module_to_quantized_reference_module",
-    "pattern_to_human_readable",
-    "remove_boolean_dispatch_from_name",
-    # torch.ao.quantization.backend_config.x86
     "get_x86_backend_config",
     # torch.ao.quantization.fuse_modules
     "fuse_known_modules",
@@ -426,25 +412,6 @@ coverage_ignore_functions = [
     "insert_observers_for_model",
     "prepare",
     "propagate_dtypes_for_known_nodes",
-    # torch.ao.quantization.fx.utils
-    "all_node_args_except_first",
-    "all_node_args_have_no_tensors",
-    "assert_and_get_unique_device",
-    "collect_producer_nodes",
-    "create_getattr_from_value",
-    "create_node_from_old_node_preserve_meta",
-    "get_custom_module_class_keys",
-    "get_linear_prepack_op_for_dtype",
-    "get_new_attr_name_with_prefix",
-    "get_non_observable_arg_indexes_and_types",
-    "get_qconv_prepack_op",
-    "get_skipped_module_name_and_classes",
-    "graph_module_from_producer_nodes",
-    "maybe_get_next_module",
-    "node_arg_is_bias",
-    "node_arg_is_weight",
-    "return_arg_list",
-    # torch.ao.quantization.pt2e.graph_utils
     "bfs_trace_with_node_process",
     "find_sequential_partitions",
     "get_equivalent_types",
@@ -860,80 +827,10 @@ coverage_ignore_functions = [
     "get_latency_of_one_partition",
     "get_latency_of_partitioned_graph",
     "get_partition_to_latency_mapping",
-    # torch.fx.experimental.proxy_tensor
-    "decompose",
-    "disable_autocast_cache",
-    "disable_proxy_modes_tracing",
-    "dispatch_trace",
-    "extract_val",
-    "fake_signature",
-    "fetch_sym_proxy",
-    "fetch_object_proxy",
-    "get_innermost_proxy_mode",
-    "get_isolated_graphmodule",
-    "get_proxy_slot",
-    "get_torch_dispatch_modes",
-    "has_proxy_slot",
-    "is_sym_node",
-    "maybe_handle_decomp",
-    "proxy_call",
-    "set_meta",
-    "set_original_aten_op",
-    "set_proxy_slot",
-    "snapshot_fake",
-    "thunkify",
-    "track_tensor",
-    "track_tensor_tree",
-    "wrap_key",
-    "wrapper_and_args_for_make_fx",
-    # torch.fx.experimental.recording
     "record_shapeenv_event",
     "replay_shape_env_events",
     "shape_env_check_state_equal",
-    # torch.fx.experimental.sym_node
-    "ceil_impl",
-    "floor_ceil_helper",
-    "floor_impl",
-    "method_to_operator",
-    "sympy_is_channels_last_contiguous_2d",
-    "sympy_is_channels_last_contiguous_3d",
-    "sympy_is_channels_last_strides_2d",
-    "sympy_is_channels_last_strides_3d",
-    "sympy_is_channels_last_strides_generic",
-    "sympy_is_contiguous",
-    "sympy_is_contiguous_generic",
-    "to_node",
-    "wrap_node",
     "sym_sqrt",
-    # torch.fx.experimental.symbolic_shapes
-    "bind_symbols",
-    "cast_symbool_to_symint_guardless",
-    "create_contiguous",
-    "error",
-    "eval_guards",
-    "eval_is_non_overlapping_and_dense",
-    "expect_true",
-    "find_symbol_binding_fx_nodes",
-    "free_symbols",
-    "free_unbacked_symbols",
-    "fx_placeholder_targets",
-    "fx_placeholder_vals",
-    "guard_bool",
-    "guard_float",
-    "guard_int",
-    "guard_scalar",
-    "has_hint",
-    "has_symbolic_sizes_strides",
-    "is_channels_last_contiguous_2d",
-    "is_channels_last_contiguous_3d",
-    "is_channels_last_strides_2d",
-    "is_channels_last_strides_3d",
-    "is_contiguous",
-    "is_non_overlapping_and_dense_indicator",
-    "is_nested_int",
-    "is_symbol_binding_fx_node",
-    "is_symbolic",
-    # torch.fx.experimental.unification.core
     "reify",
     # torch.fx.experimental.unification.match
     "edge",
@@ -971,24 +868,6 @@ coverage_ignore_functions = [
     "reverse_dict",
     # torch.fx.experimental.unification.multipledispatch.variadic
     "isvariadic",
-    # torch.fx.experimental.unification.unification_tools
-    "assoc",
-    "assoc_in",
-    "dissoc",
-    "first",
-    "get_in",
-    "getter",
-    "groupby",
-    "itemfilter",
-    "itemmap",
-    "keyfilter",
-    "keymap",
-    "merge",
-    "merge_with",
-    "update_in",
-    "valfilter",
-    "valmap",
-    # torch.fx.experimental.unification.utils
     "freeze",
     "hashable",
     "raises",

docs/source/fx.experimental.md

@@ -12,6 +12,37 @@ These APIs are experimental and subject to change without notice.
 .. autoclass:: torch.fx.experimental.sym_node.DynamicInt
 ```
 
+## torch.fx.experimental.sym_node
+
+```{eval-rst}
+.. currentmodule:: torch.fx.experimental.sym_node
+```
+
+```{eval-rst}
+.. automodule:: torch.fx.experimental.sym_node
+```
+
+```{eval-rst}
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    is_channels_last_contiguous_2d
+    is_channels_last_contiguous_3d
+    is_channels_last_strides_2d
+    is_channels_last_strides_3d
+    is_contiguous
+    is_non_overlapping_and_dense_indicator
+    method_to_operator
+    sympy_is_channels_last_contiguous_2d
+    sympy_is_channels_last_contiguous_3d
+    sympy_is_channels_last_strides_2d
+    sympy_is_channels_last_strides_3d
+    sympy_is_channels_last_strides_generic
+    sympy_is_contiguous
+    sympy_is_contiguous_generic
+```
+
 ## torch.fx.experimental.symbolic_shapes
 
 ```{eval-rst}
@@ -69,6 +100,25 @@ These APIs are experimental and subject to change without notice.
     rebind_unbacked
     resolve_unbacked_bindings
     is_accessor_node
+    cast_symbool_to_symint_guardless
+    create_contiguous
+    error
+    eval_guards
+    eval_is_non_overlapping_and_dense
+    find_symbol_binding_fx_nodes
+    free_symbols
+    free_unbacked_symbols
+    fx_placeholder_targets
+    fx_placeholder_vals
+    guard_bool
+    guard_float
+    guard_int
+    guard_scalar
+    has_hint
+    has_symbolic_sizes_strides
+    is_nested_int
+    is_symbol_binding_fx_node
+    is_symbolic
 ```
 
 ## torch.fx.experimental.proxy_tensor
@@ -91,4 +141,46 @@ These APIs are experimental and subject to change without notice.
     get_proxy_mode
     maybe_enable_thunkify
     maybe_disable_thunkify
+    decompose
+    disable_autocast_cache
+    disable_proxy_modes_tracing
+    extract_val
+    fake_signature
+    fetch_object_proxy
+    fetch_sym_proxy
+    has_proxy_slot
+    is_sym_node
+    maybe_handle_decomp
+    proxy_call
+    set_meta
+    set_original_aten_op
+    set_proxy_slot
+    snapshot_fake
 ```
+
+## torch.fx.experimental.unification.unification_tools
+
+```{eval-rst}
+.. currentmodule:: torch.fx.experimental.unification.unification_tools
+```
+
+```{eval-rst}
+.. automodule:: torch.fx.experimental.unification.unification_tools
+```
+
+```{eval-rst}
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    assoc
+    assoc_in
+    dissoc
+    first
+    keyfilter
+    keymap
+    merge
+    merge_with
+    update_in
+    valfilter
+    valmap

docs/source/fx.md

@@ -1134,7 +1134,6 @@ The set of leaf modules can be customized by overriding
 .. py:module:: torch.fx.experimental.refinement_types
 .. py:module:: torch.fx.experimental.rewriter
 .. py:module:: torch.fx.experimental.schema_type_annotation
-.. py:module:: torch.fx.experimental.sym_node
 .. py:module:: torch.fx.experimental.unification.core
 .. py:module:: torch.fx.experimental.unification.dispatch
 .. py:module:: torch.fx.experimental.unification.match
@@ -1144,7 +1143,6 @@ The set of leaf modules can be customized by overriding
 .. py:module:: torch.fx.experimental.unification.multipledispatch.dispatcher
 .. py:module:: torch.fx.experimental.unification.multipledispatch.utils
 .. py:module:: torch.fx.experimental.unification.multipledispatch.variadic
-.. py:module:: torch.fx.experimental.unification.unification_tools
 .. py:module:: torch.fx.experimental.unification.utils
 .. py:module:: torch.fx.experimental.unification.variable
 .. py:module:: torch.fx.experimental.unify_refinements

docs/source/quantization-support.md

@@ -134,6 +134,23 @@ Quantization to work with this as well.
     ObservationType
 ```
 
+## torch.ao.quantization.backend_config.utils
+```{eval-rst}
+.. currentmodule:: torch.ao.quantization.backend_config.utils
+```
+
+```{eval-rst}
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    entry_to_pretty_str
+    pattern_to_human_readable
+    remove_boolean_dispatch_from_name
+
+```
+
 ## torch.ao.quantization.fx.custom_config
 
 This module contains a few CustomConfig classes that's used in both eager mode and FX graph mode quantization
@@ -154,6 +171,30 @@ This module contains a few CustomConfig classes that's used in both eager mode a
     StandaloneModuleConfigEntry
 ```
 
+## torch.ao.quantization.fx.utils
+
+```{eval-rst}
+.. currentmodule:: torch.ao.quantization.fx.utils
+```
+
+```{eval-rst}
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    all_node_args_except_first
+    all_node_args_have_no_tensors
+    collect_producer_nodes
+    create_getattr_from_value
+    create_node_from_old_node_preserve_meta
+    graph_module_from_producer_nodes
+    maybe_get_next_module
+    node_arg_is_bias
+    node_arg_is_weight
+    return_arg_list
+```
+
 ## torch.ao.quantization.quantizer
 
 ```{eval-rst}

torch/ao/quantization/fx/utils.py

@@ -195,10 +195,12 @@ def get_new_attr_name_with_prefix(prefix: str) -> Callable:
 def collect_producer_nodes(node: Node) -> Optional[list[Node]]:
     r"""Starting from a target node, trace back until we hit input or
     getattr node. This is used to extract the chain of operators
-    starting from getattr to the target node, for example
-    def forward(self, x):
-      observed = self.observer(self.weight)
-      return F.linear(x, observed)
+    starting from getattr to the target node, for example::
+
+        def forward(self, x):
+            observed = self.observer(self.weight)
+            return F.linear(x, observed)
+
     collect_producer_nodes(observed) will either return a list of nodes that
     produces the observed node or None if we can't extract a self contained
     graph without free variables(inputs of the forward function).