forked from mrq/DL-Art-School
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
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import torch
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import warnings
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def detach_variable(inputs):
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if isinstance(inputs, tuple):
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out = []
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for inp in inputs:
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x = inp.detach()
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x.requires_grad = inp.requires_grad
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out.append(x)
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return tuple(out)
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else:
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raise RuntimeError(
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"Only tuple of tensors is supported. Got Unsupported input type: ", type(inputs).__name__)
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def check_backward_validity(inputs):
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if not any(inp.requires_grad for inp in inputs):
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warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
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class CheckpointFunction(torch.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 torch.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 torch.enable_grad():
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output_tensors = ctx.run_function(*ctx.input_tensors)
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input_grads = torch.autograd.grad(output_tensors, ctx.input_tensors + ctx.input_params, output_grads, allow_unused=True)
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return (None, None) + input_grads
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def checkpoint(module, *params):
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differentiable_params = tuple(filter(lambda p: p.requires_grad, module.parameters()))
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if len(differentiable_params) > 0:
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args = params + differentiable_params
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return CheckpointFunction.apply(module, len(params), *args)
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else:
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return module(*params)
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