Automatic m.half() profile on x.half()
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@ -88,7 +88,8 @@ def profile(x, ops, n=100, device=None):
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print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '')
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print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
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for m in ops if isinstance(ops, list) else [ops]:
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m = m.to(device) if hasattr(m, 'to') else m
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m = m.to(device) if hasattr(m, 'to') else m # device
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m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m # type
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dtf, dtb, t = 0., 0., [0., 0., 0.] # dt forward, backward
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try:
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flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPS
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