Switch ANE llama model to use to_edge_transform_and_lower + torchao quantization APIs (#12665)

This switches the ANE model to use to_edge_transform_and_lower and
torchao quantization APIs.

To use to_edge_transform_and_lower, we first need to land:
#12629

To use torchao quant APIs, we first need to land
#12648 and
#12664.

This PR contains all of the changes from those PRs because it is rebased
on them. I will rebase on main once those PRs land to make this easier
to review.

Author: metascroy

.ci/scripts/test_ane_static_llama.sh

@@ -28,6 +28,6 @@ pushd $EXECUTORCH_ROOT/examples/apple/coreml/llama
 # Download stories llama110m artifacts
 download_stories_model_artifacts
 
-python export.py -n model.pte -p params.json -c stories110M.pt --seq_length 32 --max_seq_length 64 --dtype fp16 --coreml-quantize c4w
+python export.py -n model.pte -p params.json -c stories110M.pt --seq_length 32 --max_seq_length 64 --dtype fp16 --coreml-quantize c4w --embedding-quantize 4,32
 
 popd

examples/apple/coreml/llama/export.py

@@ -18,18 +18,19 @@
 from executorch.examples.apple.coreml.llama.utils import (
     replace_linear_with_split_linear,
 )
-from executorch.examples.models.llama.source_transformation.quantize import (
-    EmbeddingQuantHandler,
-)
 
+from executorch.exir import to_edge_transform_and_lower
 from executorch.exir.backend.utils import format_delegated_graph
 from executorch.exir.capture._config import EdgeCompileConfig, ExecutorchBackendConfig
 from executorch.exir.passes import MemoryPlanningPass
 from executorch.exir.passes.quant_fusion_pass import QuantFusionPass
 from executorch.exir.passes.sym_shape_eval_pass import ConstraintBasedSymShapeEvalPass
-from executorch.exir.program._program import to_edge
 from executorch.extension.export_util.utils import save_pte_program
 
+from torchao.quantization.granularity import PerAxis, PerGroup
+from torchao.quantization.quant_api import IntxWeightOnlyConfig, quantize_
+from torchao.utils import unwrap_tensor_subclass
+
 
 def main() -> None:
     parser = argparse.ArgumentParser()
@@ -115,19 +116,8 @@ def main() -> None:
         export_args.dtype
     ]  # dtype for model/inputs
 
-    if export_args.embedding_quantize:
-        bitwidth, group_size = export_args.embedding_quantize.split(",")
-        if group_size == "none" or group_size == "None" or group_size == "0":
-            group_size = None
-        else:
-            group_size = int(group_size)
-        bitwidth = int(bitwidth)
-        model = EmbeddingQuantHandler(
-            model,
-            bitwidth=bitwidth,
-            group_size=group_size,
-            packed=(bitwidth in [2, 4]),
-        ).quantized_model()
+    model.eval()
+    model.to(float_dtype)
 
     if export_args.target_split_size is not None:
         replace_linear_with_split_linear(
@@ -140,24 +130,40 @@ def main() -> None:
             in_max_splits=1,
         )
 
-    model.eval()
-    model.to(float_dtype)
+    # Quantization
+    if export_args.embedding_quantize:
+        bitwidth, group_size = export_args.embedding_quantize.split(",")
+        bitwidth = int(bitwidth)
+        assert bitwidth in [4, 8], "CoreML only supports 4-bit and 8-bit quantization"
+        group_size = int(group_size)
+        if group_size == 0:
+            granularity = PerAxis(0)
+        else:
+            granularity = PerGroup(group_size)
+        weight_dtype = getattr(torch, f"int{bitwidth}")
+
+        quantize_(
+            model,
+            IntxWeightOnlyConfig(weight_dtype=weight_dtype, granularity=granularity),
+            lambda m, fqn: isinstance(m, torch.nn.Embedding),
+        )
 
-    op_linear_quantizer_config = None
     if export_args.coreml_quantize == "b4w":
-        op_linear_quantizer_config = {
-            "mode": "linear_symmetric",
-            "dtype": "int4",
-            "granularity": "per_block",
-            "block_size": 32,
-            "weight_threshold": 512,
-        }
+        quantize_(
+            model,
+            IntxWeightOnlyConfig(
+                weight_dtype=torch.int4,
+                granularity=PerGroup(32),
+            ),
+        )
     elif export_args.coreml_quantize == "c4w":
-        op_linear_quantizer_config = {
-            "mode": "linear_symmetric",
-            "dtype": "int4",
-            "granularity": "per_channel",
-        }
+        quantize_(
+            model,
+            IntxWeightOnlyConfig(
+                weight_dtype=torch.int4,
+                granularity=PerAxis(0),
+            ),
+        )
 
     compile_specs = CoreMLBackend.generate_compile_specs(  # pyre-fixme[16]
         minimum_deployment_target=ct.target.iOS18,
@@ -167,15 +173,11 @@ def main() -> None:
         }[float_dtype],
         compute_unit=ct.ComputeUnit.CPU_AND_NE,
         model_type=CoreMLBackend.MODEL_TYPE.MODEL,  # pyre-fixme[16]
-        op_linear_quantizer_config=op_linear_quantizer_config,
     )
     partitioner = CoreMLPartitioner(  # pyre-fixme[16]
         compile_specs=compile_specs,
         take_over_mutable_buffer=False,
-        skip_ops_for_coreml_delegation=[
-            "quantized_decomposed.embedding_4bit.dtype",
-            "aten.embedding.default",
-        ],
+        skip_ops_for_coreml_delegation=[],
     )
 
     input_manager = InputManager(
@@ -192,33 +194,22 @@ def main() -> None:
     )
     example_inputs = input_manager.get_inputs(tokens=[0])
 
+    model = unwrap_tensor_subclass(model)
+
     ep = torch.export.export(model, example_inputs, strict=True)
     print("Exported program")
     print(ep)
 
-    edge_manager = to_edge(
+    edge_manager = to_edge_transform_and_lower(
         ep,
+        partitioner=[partitioner],
         compile_config=EdgeCompileConfig(
-            _check_ir_validity=False,
+            # TODO: fix lowering when dim_order is enabled
             _skip_dim_order=True,
-            preserve_ops=[
-                torch.ops.aten.scaled_dot_product_attention.default,
-                # preserve norm op for numerical stability
-                torch.ops.aten.linalg_vector_norm.default,
-                torch.ops.aten.reciprocal.default,
-            ],
         ),
     )
-    print("Edge program")
-    print(edge_manager.exported_program())
-
-    for node in edge_manager.exported_program().graph_module.graph.nodes:
-        print(node.name, node.target, node.args, node.kwargs)
-
-    edge_manager = edge_manager.to_backend(partitioner)
 
     print("Delegated program")
-
     print(format_delegated_graph(edge_manager.exported_program().graph_module))
 
     executorch_program = edge_manager.to_executorch(