36 lines
1.2 KiB
Python
36 lines
1.2 KiB
Python
import sys, time
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from tinygrad import TinyJit, GlobalCounters, fetch, getenv
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from tinygrad.frontend.onnx import OnnxRunner
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from extra.onnx_helpers import get_example_inputs, validate
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def load_onnx_model(onnx_file):
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run_onnx = OnnxRunner(onnx_file)
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run_onnx_jit = TinyJit(lambda **kwargs: next(iter(run_onnx({k:v.to(None) for k,v in kwargs.items()}).values())), prune=True, optimize=True)
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return run_onnx_jit, run_onnx.graph_inputs
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if __name__ == "__main__":
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onnx_file = fetch(sys.argv[1])
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run_onnx_jit, input_specs = load_onnx_model(onnx_file)
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print("loaded model")
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for i in range(3):
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new_inputs = get_example_inputs(input_specs)
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GlobalCounters.reset()
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print(f"run {i}")
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run_onnx_jit(**new_inputs)
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# run 20 times
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for _ in range(20):
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new_inputs = get_example_inputs(input_specs)
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GlobalCounters.reset()
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st = time.perf_counter()
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out = run_onnx_jit(**new_inputs)
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mt = time.perf_counter()
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val = out.numpy()
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et = time.perf_counter()
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print(f"enqueue {(mt-st)*1e3:6.2f} ms -- total run {(et-st)*1e3:6.2f} ms")
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if getenv("ORT"):
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validate(onnx_file, new_inputs, rtol=1e-3, atol=1e-3)
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print("model validated")
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