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This PR introduces 3 BC changes: First, this PR propagates `BUILD_CAFFE2` flag to `libtorch` and `libtorch_python`, which is necessary for non-caffe2 ONNX runtimes when using `ONNX_ATEN_FALLBACK` operator export type. Second, as a complement of https://github.com/pytorch/pytorch/pull/68490, this PR refactors Caffe2's Aten ops symbolics to consider not only the `operator_export_type` (aka `ONNX_ATEN_FALLBACK`) to emit Caffe2 Aten ops, but also whether `BUILD_CAFFE2` (which is called `torch.onnx._CAFFE2_ATEN_FALLBACK` in python binding) is set. Lastly, it renames `onnx::ATen` to `aten::ATen` for ONNX spec consistency in a BC fashion. ONNX doesn't have `ATen` op on its spec, but PyTorch ONNX converter emits them. Non-Caffe2 backend engines would be mislead by such operator's name/domain. A non-ideal workaround would be to have Aten ops handled based on its name and ignore the (non-complaint) domain. Moreover, users could incorrectly file bugs to either ONNX or ONNX Runtime when they inspect the model and notice the presence of an unspecified ONNX operator. Pull Request resolved: https://github.com/pytorch/pytorch/pull/73954 Approved by: https://github.com/BowenBao, https://github.com/malfet, https://github.com/garymm, https://github.com/jiafatom
91 lines
3.3 KiB
Python
Executable File
91 lines
3.3 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import ast
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from caffe2.python.model_helper import ModelHelper
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from caffe2.python.predictor import mobile_exporter
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from caffe2.python import workspace, brew
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def parse_kwarg(kwarg_str):
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key, value = kwarg_str.split('=')
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try:
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value = ast.literal_eval(value)
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except ValueError:
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pass
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return key, value
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def main(args):
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# User defined keyword arguments
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kwargs = {"order": "NCHW", "use_cudnn": False}
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kwargs.update(dict(args.kwargs))
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model = ModelHelper(name=args.benchmark_name)
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op_type = args.operator # assumes a brew type op name
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input_name = args.input_name
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output_name = args.output_name
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iters = int(args.instances)
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for i in range(iters):
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input_blob_name = input_name + (str(i) if i > 0 and args.chain else '')
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output_blob_name = output_name + str(i + 1)
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add_op = getattr(brew, op_type)
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add_op(model, input_blob_name, output_blob_name, **kwargs)
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if args.chain:
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input_name, output_name = output_name, input_name
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workspace.RunNetOnce(model.param_init_net)
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init_net, predict_net = mobile_exporter.Export(
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workspace, model.net, model.params
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)
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if args.debug:
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print("init_net:")
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for op in init_net.op:
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print(" ", op.type, op.input, "-->", op.output)
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print("predict_net:")
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for op in predict_net.op:
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print(" ", op.type, op.input, "-->", op.output)
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with open(args.predict_net, 'wb') as f:
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f.write(predict_net.SerializeToString())
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with open(args.init_net, 'wb') as f:
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f.write(init_net.SerializeToString())
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Utility to generate Caffe2 benchmark models.")
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parser.add_argument("operator", help="Caffe2 operator to benchmark.")
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parser.add_argument("-b", "--blob",
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help="Instantiate a blob --blob name=dim1,dim2,dim3",
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action='append')
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parser.add_argument("--context", help="Context to run on.", default="CPU")
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parser.add_argument("--kwargs", help="kwargs to pass to operator.",
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nargs="*", type=parse_kwarg, default=[])
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parser.add_argument("--init_net", help="Output initialization net.",
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default="init_net.pb")
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parser.add_argument("--predict_net", help="Output prediction net.",
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default="predict_net.pb")
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parser.add_argument("--benchmark_name",
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help="Name of the benchmark network",
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default="benchmark")
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parser.add_argument("--input_name", help="Name of the input blob.",
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default="data")
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parser.add_argument("--output_name", help="Name of the output blob.",
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default="output")
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parser.add_argument("--instances",
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help="Number of instances to run the operator.",
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default="1")
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parser.add_argument("-d", "--debug", help="Print debug information.",
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action='store_true')
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parser.add_argument("-c", "--chain",
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help="Chain ops together (create data dependencies)",
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action='store_true')
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args = parser.parse_args()
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main(args)
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