def check_params_fp32()

in apex/apex/amp/_initialize.py [0:0]

• 36 lines of code
• 13 McCabe index (conditional complexity)

def check_params_fp32(models):
    for model in models:
        for name, param in model.named_parameters():
            if param.is_floating_point():
                if 'Half' in param.type():
                    warn_or_err("Found param {} with type {}, expected torch.cuda.FloatTensor.\n"
                        "When using amp.initialize, you do not need to call .half() on your model\n"
                        "before passing it, no matter what optimization level you choose.".format(
                        name, param.type()))
                elif not param.is_cuda:
                    warn_or_err("Found param {} with type {}, expected torch.cuda.FloatTensor.\n"
                        "When using amp.initialize, you need to provide a model with parameters\n"
                        "located on a CUDA device before passing it no matter what optimization level\n"
                        "you chose. Use model.to('cuda') to use the default device.".format(
                        name, param.type()))

        # Backward compatibility for PyTorch 0.4
        if hasattr(model, 'named_buffers'):
            buf_iter = model.named_buffers()
        else:
            buf_iter = model._buffers
        for obj in buf_iter:
            if type(obj)==tuple:
                name, buf = obj
            else:
                name, buf = obj, buf_iter[obj]
            if buf.is_floating_point():
                if 'Half' in buf.type():
                    warn_or_err("Found buffer {} with type {}, expected torch.cuda.FloatTensor.\n"
                        "When using amp.initialize, you do not need to call .half() on your model\n"
                        "before passing it, no matter what optimization level you choose.".format(
                        name, buf.type()))
                elif not buf.is_cuda:
                    warn_or_err("Found buffer {} with type {}, expected torch.cuda.FloatTensor.\n"
                        "When using amp.initialize, you need to provide a model with buffers\n"
                        "located on a CUDA device before passing it no matter what optimization level\n"
                        "you chose. Use model.to('cuda') to use the default device.".format(
                        name, buf.type()))