164 lines
4.3 KiB
Python
164 lines
4.3 KiB
Python
"""
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PyTorch DNC implementation from
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-->
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https://github.com/ixaxaar/pytorch-dnc
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<--
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"""
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import torch.nn as nn
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import torch as T
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import torch.nn.functional as F
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import numpy as np
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import torch
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from torch.autograd import Variable
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import re
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import string
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def recursiveTrace(obj):
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print(type(obj))
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if hasattr(obj, 'grad_fn'):
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print(obj.grad_fn)
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recursiveTrace(obj.grad_fn)
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elif hasattr(obj, 'saved_variables'):
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print(obj.requires_grad, len(obj.saved_tensors), len(obj.saved_variables))
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[print(v) for v in obj.saved_variables]
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[recursiveTrace(v.grad_fn) for v in obj.saved_variables]
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def cuda(x, grad=False, gpu_id=-1):
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x = x.float() if T.is_tensor(x) else x
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if gpu_id == -1:
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t = T.FloatTensor(x)
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t.requires_grad=grad
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return t
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else:
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t = T.FloatTensor(x.pin_memory()).cuda(gpu_id)
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t.requires_grad=grad
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return t
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def cudavec(x, grad=False, gpu_id=-1):
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if gpu_id == -1:
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t = T.Tensor(T.from_numpy(x))
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t.requires_grad = grad
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return t
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else:
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t = T.Tensor(T.from_numpy(x).pin_memory()).cuda(gpu_id)
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t.requires_grad = grad
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return t
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def cudalong(x, grad=False, gpu_id=-1):
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if gpu_id == -1:
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t = T.LongTensor(T.from_numpy(x.astype(np.long)))
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t.requires_grad = grad
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return t
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else:
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t = T.LongTensor(T.from_numpy(x.astype(np.long)).pin_memory()).cuda(gpu_id)
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t.requires_grad = grad
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return t
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def θ(a, b, normBy=2):
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"""Batchwise Cosine similarity
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Cosine similarity
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Arguments:
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a {Tensor} -- A 3D Tensor (b * m * w)
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b {Tensor} -- A 3D Tensor (b * r * w)
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Returns:
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Tensor -- Batchwise cosine similarity (b * r * m)
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"""
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dot = T.bmm(a, b.transpose(1,2))
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a_norm = T.norm(a, normBy, dim=2).unsqueeze(2)
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b_norm = T.norm(b, normBy, dim=2).unsqueeze(1)
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cos = dot / (a_norm * b_norm + δ)
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return cos.transpose(1,2).contiguous()
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def σ(input, axis=1):
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"""Softmax on an axis
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Softmax on an axis
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Arguments:
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input {Tensor} -- input Tensor
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Keyword Arguments:
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axis {number} -- axis on which to take softmax on (default: {1})
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Returns:
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Tensor -- Softmax output Tensor
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"""
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input_size = input.size()
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trans_input = input.transpose(axis, len(input_size) - 1)
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trans_size = trans_input.size()
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input_2d = trans_input.contiguous().view(-1, trans_size[-1])
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soft_max_2d = F.softmax(input_2d, -1)
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soft_max_nd = soft_max_2d.view(*trans_size)
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return soft_max_nd.transpose(axis, len(input_size) - 1)
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δ = 1e-6
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def register_nan_checks(model):
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def check_grad(module, grad_input, grad_output):
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# print(module) you can add this to see that the hook is called
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# print('hook called for ' + str(type(module)))
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if any(np.all(np.isnan(gi.data.cpu().numpy())) for gi in grad_input if gi is not None):
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print('NaN gradient in grad_input ' + type(module).__name__)
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model.apply(lambda module: module.register_backward_hook(check_grad))
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def apply_dict(dic):
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for k, v in dic.items():
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apply_var(v, k)
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if isinstance(v, nn.Module):
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key_list = [a for a in dir(v) if not a.startswith('__')]
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for key in key_list:
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apply_var(getattr(v, key), key)
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for pk, pv in v._parameters.items():
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apply_var(pv, pk)
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def apply_var(v, k):
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if isinstance(v, Variable) and v.requires_grad:
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v.register_hook(check_nan_gradient(k))
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def check_nan_gradient(name=''):
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def f(tensor):
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if np.isnan(T.mean(tensor).data.cpu().numpy()):
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print('\nnan gradient of {} :'.format(name))
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# print(tensor)
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# assert 0, 'nan gradient'
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return tensor
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return f
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def ptr(tensor):
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if T.is_tensor(tensor):
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return tensor.storage().data_ptr()
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elif hasattr(tensor, 'data'):
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return tensor.clone().data.storage().data_ptr()
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else:
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return tensor
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# TODO: EWW change this shit
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def ensure_gpu(tensor, gpu_id):
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if "cuda" in str(type(tensor)) and gpu_id != -1:
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return tensor.cuda(gpu_id)
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elif "cuda" in str(type(tensor)):
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return tensor.cpu()
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elif "Tensor" in str(type(tensor)) and gpu_id != -1:
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return tensor.cuda(gpu_id)
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elif "Tensor" in str(type(tensor)):
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return tensor
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elif type(tensor) is np.ndarray:
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return cudavec(tensor, gpu_id=gpu_id).data
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else:
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return tensor
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def print_gradient(x, name):
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s = "Gradient of " + name + " ----------------------------------"
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x.register_hook(lambda y: print(s, y.squeeze()))
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