262 lines
10 KiB
Python
262 lines
10 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import torch.nn as nn
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import torch as T
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from torch.autograd import Variable as var
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import torch.nn.functional as F
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import numpy as np
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from .util import *
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class Memory(nn.Module):
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def __init__(self, input_size, mem_size=512, cell_size=32, read_heads=4, gpu_id=-1, independent_linears=True):
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super(Memory, self).__init__()
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self.mem_size = mem_size
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self.cell_size = cell_size
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self.read_heads = read_heads
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self.gpu_id = gpu_id
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self.input_size = input_size
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self.independent_linears = independent_linears
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m = self.mem_size
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w = self.cell_size
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r = self.read_heads
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if self.independent_linears:
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self.read_keys_transform = nn.Linear(self.input_size, w * r)
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self.read_strengths_transform = nn.Linear(self.input_size, r)
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self.write_key_transform = nn.Linear(self.input_size, w)
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self.write_strength_transform = nn.Linear(self.input_size, 1)
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self.erase_vector_transform = nn.Linear(self.input_size, w)
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self.write_vector_transform = nn.Linear(self.input_size, w)
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self.free_gates_transform = nn.Linear(self.input_size, r)
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self.allocation_gate_transform = nn.Linear(self.input_size, 1)
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self.write_gate_transform = nn.Linear(self.input_size, 1)
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self.read_modes_transform = nn.Linear(self.input_size, 3 * r)
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else:
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self.interface_size = (w * r) + (3 * w) + (5 * r) + 3
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self.interface_weights = nn.Linear(self.input_size, self.interface_size)
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self.I = cuda(1 - T.eye(m).unsqueeze(0), gpu_id=self.gpu_id) # (1 * n * n)
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def reset(self, batch_size=1, hidden=None, erase=True):
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m = self.mem_size
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w = self.cell_size
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r = self.read_heads
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b = batch_size
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if hidden is None:
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return {
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'memory': cuda(T.zeros(b, m, w).fill_(0), gpu_id=self.gpu_id),
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'link_matrix': cuda(T.zeros(b, 1, m, m), gpu_id=self.gpu_id),
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'precedence': cuda(T.zeros(b, 1, m), gpu_id=self.gpu_id),
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'read_weights': cuda(T.zeros(b, r, m).fill_(0), gpu_id=self.gpu_id),
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'write_weights': cuda(T.zeros(b, 1, m).fill_(0), gpu_id=self.gpu_id),
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'usage_vector': cuda(T.zeros(b, m), gpu_id=self.gpu_id)
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}
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else:
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hidden['memory'] = hidden['memory'].clone()
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hidden['link_matrix'] = hidden['link_matrix'].clone()
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hidden['precedence'] = hidden['precedence'].clone()
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hidden['read_weights'] = hidden['read_weights'].clone()
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hidden['write_weights'] = hidden['write_weights'].clone()
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hidden['usage_vector'] = hidden['usage_vector'].clone()
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if erase:
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hidden['memory'].data.fill_(0)
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hidden['link_matrix'].data.zero_()
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hidden['precedence'].data.zero_()
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hidden['read_weights'].data.fill_(0)
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hidden['write_weights'].data.fill_(0)
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hidden['usage_vector'].data.zero_()
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return hidden
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def get_usage_vector(self, usage, free_gates, read_weights, write_weights):
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# write_weights = write_weights.detach() # detach from the computation graph
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usage = usage + (1 - usage) * (1 - T.prod(1 - write_weights, 1))
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ψ = T.prod(1 - free_gates.unsqueeze(2) * read_weights, 1)
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return usage * ψ
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def allocate(self, usage, write_gate):
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# ensure values are not too small prior to cumprod.
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usage = δ + (1 - δ) * usage
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batch_size = usage.size(0)
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# free list
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sorted_usage, φ = T.topk(usage, self.mem_size, dim=1, largest=False)
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# cumprod with exclusive=True
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# https://discuss.pytorch.org/t/cumprod-exclusive-true-equivalences/2614/8
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v = var(sorted_usage.data.new(batch_size, 1).fill_(1))
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cat_sorted_usage = T.cat((v, sorted_usage), 1)
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prod_sorted_usage = T.cumprod(cat_sorted_usage, 1)[:, :-1]
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sorted_allocation_weights = (1 - sorted_usage) * prod_sorted_usage.squeeze()
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# construct the reverse sorting index https://stackoverflow.com/questions/2483696/undo-or-reverse-argsort-python
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_, φ_rev = T.topk(φ, k=self.mem_size, dim=1, largest=False)
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allocation_weights = sorted_allocation_weights.gather(1, φ_rev.long())
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return allocation_weights.unsqueeze(1), usage
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def write_weighting(self, memory, write_content_weights, allocation_weights, write_gate, allocation_gate):
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ag = allocation_gate.unsqueeze(-1)
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wg = write_gate.unsqueeze(-1)
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return wg * (ag * allocation_weights + (1 - ag) * write_content_weights)
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def get_link_matrix(self, link_matrix, write_weights, precedence):
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precedence = precedence.unsqueeze(2)
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write_weights_i = write_weights.unsqueeze(3)
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write_weights_j = write_weights.unsqueeze(2)
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prev_scale = 1 - write_weights_i - write_weights_j
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new_link_matrix = write_weights_i * precedence
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link_matrix = prev_scale * link_matrix + new_link_matrix
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# trick to delete diag elems
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return self.I.expand_as(link_matrix) * link_matrix
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def update_precedence(self, precedence, write_weights):
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return (1 - T.sum(write_weights, 2, keepdim=True)) * precedence + write_weights
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def write(self, write_key, write_vector, erase_vector, free_gates, read_strengths, write_strength, write_gate, allocation_gate, hidden):
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# get current usage
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hidden['usage_vector'] = self.get_usage_vector(
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hidden['usage_vector'],
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free_gates,
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hidden['read_weights'],
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hidden['write_weights']
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)
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# lookup memory with write_key and write_strength
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write_content_weights = self.content_weightings(hidden['memory'], write_key, write_strength)
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# get memory allocation
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alloc, _ = self.allocate(
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hidden['usage_vector'],
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allocation_gate * write_gate
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)
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# get write weightings
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hidden['write_weights'] = self.write_weighting(
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hidden['memory'],
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write_content_weights,
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alloc,
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write_gate,
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allocation_gate
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)
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weighted_resets = hidden['write_weights'].unsqueeze(3) * erase_vector.unsqueeze(2)
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reset_gate = T.prod(1 - weighted_resets, 1)
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# Update memory
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hidden['memory'] = hidden['memory'] * reset_gate
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hidden['memory'] = hidden['memory'] + \
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T.bmm(hidden['write_weights'].transpose(1, 2), write_vector)
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# update link_matrix
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hidden['link_matrix'] = self.get_link_matrix(
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hidden['link_matrix'],
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hidden['write_weights'],
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hidden['precedence']
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)
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hidden['precedence'] = self.update_precedence(hidden['precedence'], hidden['write_weights'])
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return hidden
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def content_weightings(self, memory, keys, strengths):
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d = θ(memory, keys)
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return σ(d * strengths.unsqueeze(2), 2)
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def directional_weightings(self, link_matrix, read_weights):
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rw = read_weights.unsqueeze(1)
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f = T.matmul(link_matrix, rw.transpose(2, 3)).transpose(2, 3)
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b = T.matmul(rw, link_matrix)
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return f.transpose(1, 2), b.transpose(1, 2)
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def read_weightings(self, memory, content_weights, link_matrix, read_modes, read_weights):
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forward_weight, backward_weight = self.directional_weightings(link_matrix, read_weights)
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content_mode = read_modes[:, :, 2].contiguous().unsqueeze(2) * content_weights
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backward_mode = T.sum(read_modes[:, :, 0:1].contiguous().unsqueeze(3) * backward_weight, 2)
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forward_mode = T.sum(read_modes[:, :, 1:2].contiguous().unsqueeze(3) * forward_weight, 2)
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return backward_mode + content_mode + forward_mode
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def read_vectors(self, memory, read_weights):
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return T.bmm(read_weights, memory)
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def read(self, read_keys, read_strengths, read_modes, hidden):
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content_weights = self.content_weightings(hidden['memory'], read_keys, read_strengths)
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hidden['read_weights'] = self.read_weightings(
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hidden['memory'],
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content_weights,
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hidden['link_matrix'],
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read_modes,
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hidden['read_weights']
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)
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read_vectors = self.read_vectors(hidden['memory'], hidden['read_weights'])
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return read_vectors, hidden
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def forward(self, ξ, hidden):
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# ξ = ξ.detach()
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m = self.mem_size
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w = self.cell_size
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r = self.read_heads
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b = ξ.size()[0]
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if self.independent_linears:
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# r read keys (b * r * w)
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read_keys = T.tanh(self.read_keys_transform(ξ).view(b, r, w))
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# r read strengths (b * r)
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read_strengths = F.softplus(self.read_strengths_transform(ξ).view(b, r))
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# write key (b * 1 * w)
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write_key = T.tanh(self.write_key_transform(ξ).view(b, 1, w))
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# write strength (b * 1)
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write_strength = F.softplus(self.write_strength_transform(ξ).view(b, 1))
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# erase vector (b * 1 * w)
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erase_vector = T.sigmoid(self.erase_vector_transform(ξ).view(b, 1, w))
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# write vector (b * 1 * w)
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write_vector = T.tanh(self.write_vector_transform(ξ).view(b, 1, w))
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# r free gates (b * r)
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free_gates = T.sigmoid(self.free_gates_transform(ξ).view(b, r))
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# allocation gate (b * 1)
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allocation_gate = T.sigmoid(self.allocation_gate_transform(ξ).view(b, 1))
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# write gate (b * 1)
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write_gate = T.sigmoid(self.write_gate_transform(ξ).view(b, 1))
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# read modes (b * r * 3)
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read_modes = σ(self.read_modes_transform(ξ).view(b, r, 3), -1)
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else:
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ξ = self.interface_weights(ξ)
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# r read keys (b * w * r)
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read_keys = T.tanh(ξ[:, :r * w].contiguous().view(b, r, w))
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# r read strengths (b * r)
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read_strengths = F.softplus(ξ[:, r * w:r * w + r].contiguous().view(b, r))
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# write key (b * w * 1)
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write_key = T.tanh(ξ[:, r * w + r:r * w + r + w].contiguous().view(b, 1, w))
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# write strength (b * 1)
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write_strength = F.softplus(ξ[:, r * w + r + w].contiguous().view(b, 1))
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# erase vector (b * w)
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erase_vector = T.sigmoid(ξ[:, r * w + r + w + 1: r * w + r + 2 * w + 1].contiguous().view(b, 1, w))
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# write vector (b * w)
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write_vector = T.tanh(ξ[:, r * w + r + 2 * w + 1: r * w + r + 3 * w + 1].contiguous().view(b, 1, w))
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# r free gates (b * r)
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free_gates = T.sigmoid(ξ[:, r * w + r + 3 * w + 1: r * w + 2 * r + 3 * w + 1].contiguous().view(b, r))
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# allocation gate (b * 1)
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allocation_gate = T.sigmoid(ξ[:, r * w + 2 * r + 3 * w + 1].contiguous().unsqueeze(1).view(b, 1))
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# write gate (b * 1)
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write_gate = T.sigmoid(ξ[:, r * w + 2 * r + 3 * w + 2].contiguous()).unsqueeze(1).view(b, 1)
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# read modes (b * 3*r)
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read_modes = σ(ξ[:, r * w + 2 * r + 3 * w + 3: r * w + 5 * r + 3 * w + 3].contiguous().view(b, r, 3), -1)
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hidden = self.write(write_key, write_vector, erase_vector, free_gates,
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read_strengths, write_strength, write_gate, allocation_gate, hidden)
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return self.read(read_keys, read_strengths, read_modes, hidden)
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