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import torch as t |
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def checkpoint(func, inputs, params, flag): |
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if flag: |
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args = inputs + tuple(params) |
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return CheckpointFunction.apply(func, len(inputs), *args) |
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else: |
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return func(*inputs) |
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class CheckpointFunction(t.autograd.Function): |
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@staticmethod |
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def forward(ctx, run_function, length, *args): |
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ctx.run_function = run_function |
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ctx.input_tensors = list(args[:length]) |
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ctx.input_params = list(args[length:]) |
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with t.no_grad(): |
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output_tensors = ctx.run_function(*ctx.input_tensors) |
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return output_tensors |
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@staticmethod |
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def backward(ctx, *output_grads): |
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for i in range(len(ctx.input_tensors)): |
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temp = ctx.input_tensors[i] |
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ctx.input_tensors[i] = temp.detach() |
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ctx.input_tensors[i].requires_grad = temp.requires_grad |
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with t.enable_grad(): |
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output_tensors = ctx.run_function(*ctx.input_tensors) |
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input_grads = t.autograd.grad(output_tensors, ctx.input_tensors + ctx.input_params, output_grads, allow_unused=True) |
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del ctx.input_tensors |
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del output_tensors |
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return (None, None) + input_grads |
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