113 lines
4.2 KiB
Python
113 lines
4.2 KiB
Python
import os
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import sys
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import argparse
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import onnx
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import subprocess
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import numpy as np
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import tempfile
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from collections import OrderedDict
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# Reference backend, use onnxruntime by default
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import onnxruntime
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prepare = onnxruntime.InferenceSession
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if (not os.environ.get('ONNX_MLIR_HOME', None)):
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raise RuntimeError(
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"Environment variable ONNX_MLIR_HOME is not set, please set it to the path to "
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"the HOME directory for onnx-mlir. The HOME directory for onnx-mlir refers to "
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"the parent folder containing the bin, lib, etc sub-folders in which ONNX-MLIR "
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"executables and libraries can be found.")
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VERBOSE = os.environ.get('VERBOSE', False)
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ONNX_MLIR = os.path.join(os.environ['ONNX_MLIR_HOME'], "bin/onnx-mlir")
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# Include runtime directory in python paths, so pyruntime can be imported.
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RUNTIME_DIR = os.path.join(os.environ['ONNX_MLIR_HOME'], "lib")
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sys.path.append(RUNTIME_DIR)
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try:
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from pyruntime import ExecutionSession
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except ImportError:
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raise ImportError(
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"Looks like you did not build the pyruntime target, build it by running `make pyruntime`."
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)
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def execute_commands(cmds):
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if (VERBOSE):
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print(" ".join(cmds))
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subprocess.run(cmds, stdout=subprocess.PIPE, check=True)
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def extend_model_output(model, intermediate_outputs):
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# onnx-mlir doesn't care about manually specified output types & shapes.
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DUMMY_TENSOR_TYPE = onnx.TensorProto.FLOAT
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while (len(model.graph.output)):
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model.graph.output.pop()
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for output_name in intermediate_outputs:
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output_value_info = onnx.helper.make_tensor_value_info(
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output_name, DUMMY_TENSOR_TYPE, None)
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model.graph.output.extend([output_value_info])
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return model
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def main(model_path):
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model = onnx.load(model_path)
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intermediate_outputs = sum(
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[list(node.output) for node in model.graph.node], [])
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intermediate_outputs = list(OrderedDict.fromkeys(intermediate_outputs))
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model = extend_model_output(model, intermediate_outputs)
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with tempfile.TemporaryDirectory() as temp_dir:
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print("Temporary directory has been created at {}".format(temp_dir))
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# Save modified model & invoke onnx-mlir to compile it.
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temp_model_path = os.path.join(temp_dir, "model.onnx")
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onnx.save(model, temp_model_path)
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execute_commands([ONNX_MLIR, temp_model_path])
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# Use the generated shared library to create an execution session.
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temp_shared_lib_path = os.path.join(temp_dir, "model.so")
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sess = ExecutionSession(temp_shared_lib_path,
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"_dyn_entry_point_main_graph")
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# Generate random data as input.
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inputs = []
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input_names = []
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initializers = list(map(lambda x: x.name, model.graph.initializer))
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np.random.seed(42)
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for input_proto in model.graph.input:
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if input_proto.name not in initializers:
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input_names.append(input_proto.name)
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shape_proto = input_proto.type.tensor_type.shape
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explicit_shape = []
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for dim in shape_proto.dim:
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assert dim.dim_value, "Can only debug models with inputs that have explicit shapes."
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explicit_shape.append(dim.dim_value)
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inputs.append(
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np.random.uniform(-1.0, 1.0, explicit_shape).astype(np.float32))
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# Run the compiled inference function on the randomly generated data.
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outs = sess.run(inputs)
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# Run the model with reference backend and get results.
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ref_session = prepare(temp_model_path)
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output_names = list(map(lambda x: x.name, model.graph.output))
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input_feed = dict(zip(input_names, inputs))
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ref_outs = ref_session.run(output_names, input_feed)
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# For each intermediate output tensor, compare results.
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for i, name in enumerate(intermediate_outputs):
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print("Verifying value of {}".format(name))
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np.testing.assert_array_almost_equal(ref_outs[i], outs[i], decimal=5)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('model_path', type=str, help="Path to the model to debug.")
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args = parser.parse_args()
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main(**vars(args))
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