Format source with new rules (google-ai-edge#118)

* update scripts and configs

* fmt

Author: chunnienc

.isort.cfg

@@ -0,0 +1,9 @@
+[settings]
+profile=google
+multi_line_output=7
+line_length=200
+skip=.downloads,venv,bazel
+known_third_party=ai_edge_torch
+known_internal=tensorflow.python.platform,tensorflow.compiler.tf2xla.python
+default_section=THIRDPARTY
+sections=FUTURE,STDLIB,LOCALFOLDER,THIRDPARTY,INTERNAL

ai_edge_torch/convert/conversion.py

@@ -18,10 +18,6 @@
 import os
 from typing import Optional
 
-import torch
-from torch.export import ExportedProgram
-from torch_xla import stablehlo
-
 from ai_edge_torch import model
 from ai_edge_torch.convert import conversion_utils as cutils
 from ai_edge_torch.convert.fx_passes import BuildAtenCompositePass
@@ -32,6 +28,9 @@
 from ai_edge_torch.convert.fx_passes import run_passes
 from ai_edge_torch.generative.fx_passes import run_generative_passes
 from ai_edge_torch.quantize import quant_config as qcfg
+import torch
+from torch.export import ExportedProgram
+from torch_xla import stablehlo
 
 os.environ["EXPERIMENTAL_XLA_UNBOUNDED_DYNAMISM"] = "1"
 
@@ -61,8 +60,9 @@ def _warn_training_modules(signatures: list[cutils.Signature]):
       continue
 
     message = (
-        "Your model {sig_name}is converted in training mode. "
-        "Please set the module in evaluation mode with `module.eval()` for better on-device performance and compatibility."
+        "Your model {sig_name}is converted in training mode. Please set the"
+        " module in evaluation mode with `module.eval()` for better on-device"
+        " performance and compatibility."
     )
     if len(signatures) == 1 and sig.name == cutils.DEFAULT_SIGNATURE_NAME:
       # User does not specify any signature names explicitly.
@@ -88,7 +88,9 @@ def convert_signatures(
   _warn_training_modules(signatures)
 
   exported_programs: torch.export.ExportedProgram = [
-      torch.export.export(sig.module, sig.flat_args, dynamic_shapes=sig.dynamic_shapes)
+      torch.export.export(
+          sig.module, sig.flat_args, dynamic_shapes=sig.dynamic_shapes
+      )
       for sig in signatures
   ]
 
@@ -100,7 +102,9 @@ def convert_signatures(
   ]
 
   merged_shlo_graph_module: stablehlo.StableHLOGraphModule = (
-      cutils.merge_stablehlo_bundles(shlo_bundles, signatures, exported_programs)
+      cutils.merge_stablehlo_bundles(
+          shlo_bundles, signatures, exported_programs
+      )
   )
   del exported_programs
   del shlo_bundles

ai_edge_torch/convert/conversion_utils.py

@@ -22,15 +22,15 @@
 import tempfile
 from typing import Any, Dict, List, Optional, Tuple, Union
 
+from ai_edge_torch.generative.quantize.ai_edge_quantizer_glue import translate_recipe  # NOQA
+from ai_edge_torch.quantize import quant_config as qcfg
 import torch
 import torch.utils._pytree as pytree
 from torch_xla import stablehlo
 
-from ai_edge_torch.generative.quantize.ai_edge_quantizer_glue import translate_recipe  # NOQA
-from ai_edge_torch.quantize import quant_config as qcfg
-
 try:
   import tensorflow as tf
+
   from tensorflow.compiler.tf2xla.python import xla as tfxla
 
   from tensorflow.lite.python import conversion_metadata_schema_py_generated as conversion_metadata_fb  # isort:skip
@@ -90,18 +90,20 @@ def _flat_kwarg_names(self, specs, context) -> List[str]:
     if context is None:
       for i, spec in enumerate(specs):
         if spec.children_specs:
-          flat_names.extend(
-              [
-                  f"{i}_{name}"
-                  for name in self._flat_kwarg_names(spec.children_specs, spec.context)
-              ]
-          )
+          flat_names.extend([
+              f"{i}_{name}"
+              for name in self._flat_kwarg_names(
+                  spec.children_specs, spec.context
+              )
+          ])
         else:
           flat_names.append(f"{i}")
     else:
       flat_ctx = self._flatten_list(context)
       for prefix, spec in zip(flat_ctx, specs):
-        leaf_flat_names = self._flat_kwarg_names(spec.children_specs, spec.context)
+        leaf_flat_names = self._flat_kwarg_names(
+            spec.children_specs, spec.context
+        )
         if leaf_flat_names:
           flat_names.extend([f"{prefix}_{name}" for name in leaf_flat_names])
         else:
@@ -125,7 +127,8 @@ def flat_args(self) -> tuple[Any]:
 
 
 def exported_program_to_stablehlo_bundle(
-    exported_program: torch.export.ExportedProgram, sample_args: tuple[torch.Tensor]
+    exported_program: torch.export.ExportedProgram,
+    sample_args: tuple[torch.Tensor],
 ) -> stablehlo.StableHLOModelBundle:
   # Setting export_weights to False here so that pytorch/xla avoids copying the weights
   # to a numpy array which would lead to memory bloat. This means that the state_dict
@@ -146,15 +149,18 @@ def _torch_to_tf_tensor(torch_tensor: torch.Tensor):
     dlpack_capsule = torch.utils.dlpack.to_dlpack(torch_tensor)
     tf_tensor = tf.experimental.dlpack.from_dlpack(dlpack_capsule)
   except Exception:
-    logging.info("Can not use dlpack to convert torch tensors. Falling back to numpy.")
+    logging.info(
+        "Can not use dlpack to convert torch tensors. Falling back to numpy."
+    )
     nparray = torch_tensor.cpu().detach().numpy()
     tf_tensor = tf.convert_to_tensor(nparray)
 
   return tf_tensor
 
 
 def _get_states(
-    exported_programs: list[torch.export.ExportedProgram], signatures: list[Signature]
+    exported_programs: list[torch.export.ExportedProgram],
+    signatures: list[Signature],
 ):
   for exported_program, signature in zip(exported_programs, signatures):
     args, _ = exported_program.example_inputs
@@ -166,7 +172,8 @@ def _get_states(
       # Only interested in Tensors that are part of the state (and not user input).
       if (
           not isinstance(tensor, torch.Tensor)
-          or input_spec.kind == torch.export.graph_signature.InputKind.USER_INPUT
+          or input_spec.kind
+          == torch.export.graph_signature.InputKind.USER_INPUT
       ):
         continue
       yield signature, tensor, input_spec
@@ -192,9 +199,13 @@ def _gather_state_dict(
     deduped_tensor_map[unique_id] = _torch_to_tf_tensor(tensor)
 
   state_dict = {}
-  for signature, tensor, input_spec in _get_states(exported_programs, signatures):
+  for signature, tensor, input_spec in _get_states(
+      exported_programs, signatures
+  ):
     unique_id = _tensor_unique_id(tensor)
-    state_dict[signature.name + "_" + input_spec.target] = deduped_tensor_map[unique_id]
+    state_dict[signature.name + "_" + input_spec.target] = deduped_tensor_map[
+        unique_id
+    ]
 
   return state_dict
 
@@ -236,7 +247,9 @@ def _wrap_as_tf_func(
 ):
   def inner(*args):
     type_info = [sig.dtype for sig in func.meta.output_signature]
-    shape_info = [_get_shape_with_dynamic(sig) for sig in func.meta.output_signature]
+    shape_info = [
+        _get_shape_with_dynamic(sig) for sig in func.meta.output_signature
+    ]
     call_args = stablehlo._extract_call_parameters(args, func.meta, bundle)
     return tfxla.call_module(
         tuple(call_args),
@@ -369,7 +382,9 @@ def convert_stablehlo_to_tflite(
         )
     )
 
-  tf_module._variables = list(bundle.state_dict.values()) + bundle.additional_constants
+  tf_module._variables = (
+      list(bundle.state_dict.values()) + bundle.additional_constants
+  )
   del bundle
   gc.collect()
 
@@ -385,7 +400,8 @@ def convert_stablehlo_to_tflite(
         tf_module,
         temp_dir_path,
         signatures={
-            sig.name: tf_concrete_funcs[idx] for idx, sig in enumerate(signatures)
+            sig.name: tf_concrete_funcs[idx]
+            for idx, sig in enumerate(signatures)
         },
     )
     # Clean up intermediate memory early.
@@ -416,6 +432,8 @@ def convert_stablehlo_to_tflite(
         and quant_config._quantizer_mode
         == quant_config._QuantizerMode.AI_EDGE_QUANTIZER
     ):
-      tflite_model = translate_recipe.quantize_model(tflite_model, translated_recipe)
+      tflite_model = translate_recipe.quantize_model(
+          tflite_model, translated_recipe
+      )
 
   return tflite_model

ai_edge_torch/convert/converter.py

@@ -17,12 +17,11 @@
 
 from typing import Any, Dict, Optional, Tuple, Union
 
-import torch
-
 from ai_edge_torch import model
 from ai_edge_torch.convert import conversion
 from ai_edge_torch.convert import conversion_utils as cutils
 from ai_edge_torch.quantize import quant_config as qcfg
+import torch
 
 
 class Converter:
@@ -68,14 +67,20 @@ def add_signature(
     """
 
     if name in [sig.name for sig in self._signatures]:
-      raise ValueError(f"A signature with the provided name ({name}) is already added.")
+      raise ValueError(
+          f"A signature with the provided name ({name}) is already added."
+      )
 
     if sample_args is None and sample_kwargs is None:
       raise ValueError("sample_args or sample_kwargs must be provided.")
 
     self._signatures.append(
         cutils.Signature(
-            name, module, sample_args, sample_kwargs, dynamic_shapes=dynamic_shapes
+            name,
+            module,
+            sample_args,
+            sample_kwargs,
+            dynamic_shapes=dynamic_shapes,
         )
     )
     return self
@@ -128,7 +133,8 @@ def convert(
         )
       else:  # module is provided but not args
         raise ValueError(
-            "sample_args or sample_kwargs must be provided if a module is specified."
+            "sample_args or sample_kwargs must be provided if a module is"
+            " specified."
         )
 
     return conversion.convert_signatures(

ai_edge_torch/convert/fx_passes/__init__.py

@@ -15,10 +15,6 @@
 
 from typing import Sequence, Union
 
-from torch.export import ExportedProgram
-from torch.fx.passes.infra.pass_manager import pass_result_wrapper
-import torch.utils._pytree as pytree
-
 from ai_edge_torch.convert.fx_passes._pass_base import ExportedProgramPassBase
 from ai_edge_torch.convert.fx_passes._pass_base import ExportedProgramPassResult  # NOQA
 from ai_edge_torch.convert.fx_passes._pass_base import FxPassBase
@@ -28,6 +24,9 @@
 from ai_edge_torch.convert.fx_passes.canonicalize_pass import CanonicalizePass
 from ai_edge_torch.convert.fx_passes.inject_mlir_debuginfo_pass import InjectMlirDebuginfoPass  # NOQA
 from ai_edge_torch.convert.fx_passes.optimize_layout_transposes_pass import OptimizeLayoutTransposesPass  # NOQA
+from torch.export import ExportedProgram
+from torch.fx.passes.infra.pass_manager import pass_result_wrapper
+import torch.utils._pytree as pytree
 
 
 # TODO(cnchan): make a PassManager class.

ai_edge_torch/convert/fx_passes/_pass_base.py

@@ -32,14 +32,18 @@ def __new__(cls, exported_program, modified):
 
 class ExportedProgramPassBase(abc.ABC):
 
-  def __call__(self, exported_program: ExportedProgram) -> ExportedProgramPassResult:
+  def __call__(
+      self, exported_program: ExportedProgram
+  ) -> ExportedProgramPassResult:
     self.requires(exported_program)
     res = self.call(exported_program)
     self.ensures(exported_program)
     return res
 
   @abc.abstractmethod
-  def call(self, exported_program: ExportedProgram) -> ExportedProgramPassResult:
+  def call(
+      self, exported_program: ExportedProgram
+  ) -> ExportedProgramPassResult:
     pass
 
   def requires(self, exported_program: ExportedProgram) -> None: