example (#64)

Summary:
Pull Request resolved: #64

Adding export example for XNNPACK delegated models, also adding to executor runner to run

Differential Revision: D48371417

fbshipit-source-id: b5a4c3b341e0e3e1607a6ac16bf82d0e1ccaac5c

Author: mcr229

examples/backend/TARGETS

@@ -0,0 +1,13 @@
+load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime")
+
+runtime.python_binary(
+    name = "xnnpack_lowering_examples",
+    main_src = "xnnpack_lowering_examples.py",
+    deps = [
+        "//caffe2:torch",
+        "//executorch/backends/xnnpack:xnnpack_preprocess",
+        "//executorch/backends/xnnpack/partition:xnnpack_partitioner",
+        "//executorch/examples/models:models",
+        "//executorch/exir/backend:backend_api",
+    ],
+)

examples/backend/xnnpack_lowering_examples.py

@@ -0,0 +1,100 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Example script for exporting simple models to flatbuffer
+
+import argparse
+import copy
+
+import executorch.exir as exir
+import torch._export as export
+from executorch.backends.xnnpack.partition.xnnpack_partitioner import (
+    XnnpackFloatingPointPartitioner,
+    XnnpackQuantizedPartitioner2,
+)
+from executorch.exir.backend.backend_api import to_backend, validation_disabled
+
+from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
+from torch.ao.quantization.quantizer.xnnpack_quantizer import (
+    get_symmetric_quantization_config,
+    XNNPACKQuantizer,
+)
+
+from ..models import MODEL_NAME_TO_MODEL
+
+# Note: for mv3, the mul op is not supported in XNNPACKQuantizer, that could be supported soon
+XNNPACK_MODEL_NAME_TO_MODEL = {
+    name: MODEL_NAME_TO_MODEL[name] for name in ["linear", "add", "add_mul", "mv2"]
+}
+
+
+def quantize(model, example_inputs):
+    """This is the official recommended flow for quantization in pytorch 2.0 export"""
+    m = model.eval()
+    m = export.capture_pre_autograd_graph(m, copy.deepcopy(example_inputs))
+    quantizer = XNNPACKQuantizer()
+    # if we set is_per_channel to True, we also need to add out_variant of quantize_per_channel/dequantize_per_channel
+    operator_config = get_symmetric_quantization_config(is_per_channel=False)
+    quantizer.set_global(operator_config)
+    m = prepare_pt2e(m, quantizer)
+    # calibration
+    m(*example_inputs)
+    m = convert_pt2e(m)
+    return m
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-m",
+        "--model_name",
+        required=True,
+        help=f"Provide model name. Valid ones: {list(XNNPACK_MODEL_NAME_TO_MODEL.keys())}",
+    )
+    parser.add_argument(
+        "-q",
+        "--quantize",
+        action="store_true",
+        required=False,
+        default=False,
+        help="Flag for producing quantized or floating-point model",
+    )
+    args = parser.parse_args()
+
+    if args.model_name not in XNNPACK_MODEL_NAME_TO_MODEL:
+        raise RuntimeError(
+            f"Model {args.model_name} is not a valid name. or not quantizable right now, "
+            "please contact executorch team if you want to learn why or how to support "
+            "quantization for the requested model"
+            f"Available models are {list(XNNPACK_MODEL_NAME_TO_MODEL.keys())}."
+        )
+
+    model, example_inputs = MODEL_NAME_TO_MODEL[args.model_name]()
+    model = model.eval()
+
+    partitioner = XnnpackFloatingPointPartitioner
+    if args.quantize:
+        print("Quantizing Model...")
+        model = quantize(model, example_inputs)
+        # Partitioner will eventually be a single partitioner for both fp32 and quantized models
+        partitioner = XnnpackQuantizedPartitioner2
+
+    edge = exir.capture(
+        model, example_inputs, exir.CaptureConfig(enable_aot=True, _unlift=True)
+    ).to_edge(exir.EdgeCompileConfig(_check_ir_validity=False))
+    print("Exported graph:\n", edge.exported_program.graph)
+
+    with validation_disabled():
+        edge.exported_program = to_backend(edge.exported_program, partitioner)
+    print("Lowered graph:\n", edge.exported_program.graph)
+
+    exec_prog = edge.to_executorch()
+    buffer = exec_prog.buffer
+    quant_tag = "_quantize" if args.quantize else ""
+    filename = f"xnnpack_{args.model_name}{quant_tag}.pte"
+    print(f"Saving exported program to {filename}.")
+    with open(filename, "wb") as f:
+        f.write(buffer)

examples/executor_runner/targets.bzl

@@ -50,3 +50,16 @@ def define_common_targets():
         define_static_target = True,
         **get_oss_build_kwargs()
     )
+
+    # executor runner for XNNPACK Backend and portable kernels.
+    runtime.cxx_binary(
+        name = "xnn_executor_runner",
+        srcs = [],
+        deps = [
+            ":executor_runner_lib",
+            "//executorch/backends/xnnpack:xnnpack_backend",
+            "//executorch/kernels/portable:generated_lib_all_ops",
+        ] + custom_ops_lib,
+        define_static_target = True,
+        **get_oss_build_kwargs()
+    )