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|
- import torch
- from torch import nn
- from torch.utils.checkpoint import checkpoint
- import math
- from typing import NamedTuple
- from utils.tensor_functions import compute_in_batches
- from nets.graph_encoder import GraphAttentionEncoder
- from torch.nn import DataParallel
- from utils.beam_search import CachedLookup
- from utils.functions import sample_many
- def set_decode_type(model, decode_type):
- if isinstance(model, DataParallel):
- model = model.module
- model.set_decode_type(decode_type)
- class AttentionModelFixed(NamedTuple):
- """
- Context for AttentionModel decoder that is fixed during decoding so can be precomputed/cached
- This class allows for efficient indexing of multiple Tensors at once
- """
- node_embeddings: torch.Tensor
- context_node_projected: torch.Tensor
- glimpse_key: torch.Tensor
- glimpse_val: torch.Tensor
- logit_key: torch.Tensor
- def __getitem__(self, key):
- if torch.is_tensor(key) or isinstance(key, slice):
- return AttentionModelFixed(
- node_embeddings=self.node_embeddings[key],
- context_node_projected=self.context_node_projected[key],
- glimpse_key=self.glimpse_key[:, key], # dim 0 are the heads
- glimpse_val=self.glimpse_val[:, key], # dim 0 are the heads
- logit_key=self.logit_key[key]
- )
- return super(AttentionModelFixed, self).__getitem__(key)
- class AttentionModel(nn.Module):
- def __init__(self,
- embedding_dim,
- hidden_dim,
- problem,
- n_encode_layers=2,
- tanh_clipping=10.,
- mask_inner=True,
- mask_logits=True,
- normalization='batch',
- n_heads=8,
- checkpoint_encoder=False,
- shrink_size=None):
- super(AttentionModel, self).__init__()
- self.embedding_dim = embedding_dim
- self.hidden_dim = hidden_dim
- self.n_encode_layers = n_encode_layers
- self.decode_type = None
- self.temp = 1.0
- self.allow_partial = problem.NAME == 'sdvrp'
- self.is_vrp = problem.NAME == 'cvrp' or problem.NAME == 'sdvrp'
- self.is_orienteering = problem.NAME == 'op'
- self.is_pctsp = problem.NAME == 'pctsp'
- self.tanh_clipping = tanh_clipping
- self.mask_inner = mask_inner
- self.mask_logits = mask_logits
- self.problem = problem
- self.n_heads = n_heads
- self.checkpoint_encoder = checkpoint_encoder
- self.shrink_size = shrink_size
- # Problem specific context parameters (placeholder and step context dimension)
- if self.is_vrp or self.is_orienteering or self.is_pctsp:
- # Embedding of last node + remaining_capacity / remaining length / remaining prize to collect
- step_context_dim = embedding_dim + 1
- if self.is_pctsp:
- node_dim = 4 # x, y, expected_prize, penalty
- else:
- node_dim = 3 # x, y, demand / prize
- # Special embedding projection for depot node
- self.init_embed_depot = nn.Linear(2, embedding_dim)
-
- if self.is_vrp and self.allow_partial: # Need to include the demand if split delivery allowed
- self.project_node_step = nn.Linear(1, 3 * embedding_dim, bias=False)
- else: # TSP
- assert problem.NAME == "tsp", "Unsupported problem: {}".format(problem.NAME)
- step_context_dim = 2 * embedding_dim # Embedding of first and last node
- node_dim = 2 # x, y
-
- # Learned input symbols for first action
- self.W_placeholder = nn.Parameter(torch.Tensor(2 * embedding_dim))
- self.W_placeholder.data.uniform_(-1, 1) # Placeholder should be in range of activations
- self.init_embed = nn.Linear(node_dim, embedding_dim)
- self.embedder = GraphAttentionEncoder(
- n_heads=n_heads,
- embed_dim=embedding_dim,
- n_layers=self.n_encode_layers,
- normalization=normalization
- )
- # For each node we compute (glimpse key, glimpse value, logit key) so 3 * embedding_dim
- self.project_node_embeddings = nn.Linear(embedding_dim, 3 * embedding_dim, bias=False)
- self.project_fixed_context = nn.Linear(embedding_dim, embedding_dim, bias=False)
- self.project_step_context = nn.Linear(step_context_dim, embedding_dim, bias=False)
- assert embedding_dim % n_heads == 0
- # Note n_heads * val_dim == embedding_dim so input to project_out is embedding_dim
- self.project_out = nn.Linear(embedding_dim, embedding_dim, bias=False)
- def set_decode_type(self, decode_type, temp=None):
- self.decode_type = decode_type
- if temp is not None: # Do not change temperature if not provided
- self.temp = temp
- def forward(self, input, return_pi=False):
- """
- :param input: (batch_size, graph_size, node_dim) input node features or dictionary with multiple tensors
- :param return_pi: whether to return the output sequences, this is optional as it is not compatible with
- using DataParallel as the results may be of different lengths on different GPUs
- :return:
- """
- if self.checkpoint_encoder and self.training: # Only checkpoint if we need gradients
- embeddings, _ = checkpoint(self.embedder, self._init_embed(input))
- else:
- embeddings, _ = self.embedder(self._init_embed(input))
- _log_p, pi = self._inner(input, embeddings)
- cost, mask = self.problem.get_costs(input, pi)
- # Log likelyhood is calculated within the model since returning it per action does not work well with
- # DataParallel since sequences can be of different lengths
- ll = self._calc_log_likelihood(_log_p, pi, mask)
- if return_pi:
- return cost, ll, pi
- return cost, ll
- def beam_search(self, *args, **kwargs):
- return self.problem.beam_search(*args, **kwargs, model=self)
- def precompute_fixed(self, input):
- embeddings, _ = self.embedder(self._init_embed(input))
- # Use a CachedLookup such that if we repeatedly index this object with the same index we only need to do
- # the lookup once... this is the case if all elements in the batch have maximum batch size
- return CachedLookup(self._precompute(embeddings))
- def propose_expansions(self, beam, fixed, expand_size=None, normalize=False, max_calc_batch_size=4096):
- # First dim = batch_size * cur_beam_size
- log_p_topk, ind_topk = compute_in_batches(
- lambda b: self._get_log_p_topk(fixed[b.ids], b.state, k=expand_size, normalize=normalize),
- max_calc_batch_size, beam, n=beam.size()
- )
- assert log_p_topk.size(1) == 1, "Can only have single step"
- # This will broadcast, calculate log_p (score) of expansions
- score_expand = beam.score[:, None] + log_p_topk[:, 0, :]
- # We flatten the action as we need to filter and this cannot be done in 2d
- flat_action = ind_topk.view(-1)
- flat_score = score_expand.view(-1)
- flat_feas = flat_score > -1e10 # != -math.inf triggers
- # Parent is row idx of ind_topk, can be found by enumerating elements and dividing by number of columns
- flat_parent = torch.arange(flat_action.size(-1), out=flat_action.new()) / ind_topk.size(-1)
- # Filter infeasible
- feas_ind_2d = torch.nonzero(flat_feas)
- if len(feas_ind_2d) == 0:
- # Too bad, no feasible expansions at all :(
- return None, None, None
- feas_ind = feas_ind_2d[:, 0]
- return flat_parent[feas_ind], flat_action[feas_ind], flat_score[feas_ind]
- def _calc_log_likelihood(self, _log_p, a, mask):
- # Get log_p corresponding to selected actions
- log_p = _log_p.gather(2, a.unsqueeze(-1)).squeeze(-1)
- # Optional: mask out actions irrelevant to objective so they do not get reinforced
- if mask is not None:
- log_p[mask] = 0
- assert (log_p > -1000).data.all(), "Logprobs should not be -inf, check sampling procedure!"
- # Calculate log_likelihood
- return log_p.sum(1)
- def _init_embed(self, input):
- if self.is_vrp or self.is_orienteering or self.is_pctsp:
- if self.is_vrp:
- features = ('demand', )
- elif self.is_orienteering:
- features = ('prize', )
- else:
- assert self.is_pctsp
- features = ('deterministic_prize', 'penalty')
- return torch.cat(
- (
- self.init_embed_depot(input['depot'])[:, None, :],
- self.init_embed(torch.cat((
- input['loc'],
- *(input[feat][:, :, None] for feat in features)
- ), -1))
- ),
- 1
- )
- # TSP
- return self.init_embed(input)
- def _inner(self, input, embeddings):
- outputs = []
- sequences = []
- state = self.problem.make_state(input)
- # Compute keys, values for the glimpse and keys for the logits once as they can be reused in every step
- fixed = self._precompute(embeddings)
- batch_size = state.ids.size(0)
- # Perform decoding steps
- i = 0
- while not (self.shrink_size is None and state.all_finished()):
- if self.shrink_size is not None:
- unfinished = torch.nonzero(state.get_finished() == 0)
- if len(unfinished) == 0:
- break
- unfinished = unfinished[:, 0]
- # Check if we can shrink by at least shrink_size and if this leaves at least 16
- # (otherwise batch norm will not work well and it is inefficient anyway)
- if 16 <= len(unfinished) <= state.ids.size(0) - self.shrink_size:
- # Filter states
- state = state[unfinished]
- fixed = fixed[unfinished]
- log_p, mask = self._get_log_p(fixed, state)
- # Select the indices of the next nodes in the sequences, result (batch_size) long
- selected = self._select_node(log_p.exp()[:, 0, :], mask[:, 0, :]) # Squeeze out steps dimension
- state = state.update(selected)
- # Now make log_p, selected desired output size by 'unshrinking'
- if self.shrink_size is not None and state.ids.size(0) < batch_size:
- log_p_, selected_ = log_p, selected
- log_p = log_p_.new_zeros(batch_size, *log_p_.size()[1:])
- selected = selected_.new_zeros(batch_size)
- log_p[state.ids[:, 0]] = log_p_
- selected[state.ids[:, 0]] = selected_
- # Collect output of step
- outputs.append(log_p[:, 0, :])
- sequences.append(selected)
- i += 1
- # Collected lists, return Tensor
- return torch.stack(outputs, 1), torch.stack(sequences, 1)
- def sample_many(self, input, batch_rep=1, iter_rep=1):
- """
- :param input: (batch_size, graph_size, node_dim) input node features
- :return:
- """
- # Bit ugly but we need to pass the embeddings as well.
- # Making a tuple will not work with the problem.get_cost function
- return sample_many(
- lambda input: self._inner(*input), # Need to unpack tuple into arguments
- lambda input, pi: self.problem.get_costs(input[0], pi), # Don't need embeddings as input to get_costs
- (input, self.embedder(self._init_embed(input))[0]), # Pack input with embeddings (additional input)
- batch_rep, iter_rep
- )
- def _select_node(self, probs, mask):
- assert (probs == probs).all(), "Probs should not contain any nans"
- if self.decode_type == "greedy":
- _, selected = probs.max(1)
- assert not mask.gather(1, selected.unsqueeze(
- -1)).data.any(), "Decode greedy: infeasible action has maximum probability"
- elif self.decode_type == "sampling":
- selected = probs.multinomial(1).squeeze(1)
- # Check if sampling went OK, can go wrong due to bug on GPU
- # See https://discuss.pytorch.org/t/bad-behavior-of-multinomial-function/10232
- while mask.gather(1, selected.unsqueeze(-1)).data.any():
- print('Sampled bad values, resampling!')
- selected = probs.multinomial(1).squeeze(1)
- else:
- assert False, "Unknown decode type"
- return selected
- def _precompute(self, embeddings, num_steps=1):
- # The fixed context projection of the graph embedding is calculated only once for efficiency
- graph_embed = embeddings.mean(1)
- # fixed context = (batch_size, 1, embed_dim) to make broadcastable with parallel timesteps
- fixed_context = self.project_fixed_context(graph_embed)[:, None, :]
- # The projection of the node embeddings for the attention is calculated once up front
- glimpse_key_fixed, glimpse_val_fixed, logit_key_fixed = \
- self.project_node_embeddings(embeddings[:, None, :, :]).chunk(3, dim=-1)
- # No need to rearrange key for logit as there is a single head
- fixed_attention_node_data = (
- self._make_heads(glimpse_key_fixed, num_steps),
- self._make_heads(glimpse_val_fixed, num_steps),
- logit_key_fixed.contiguous()
- )
- return AttentionModelFixed(embeddings, fixed_context, *fixed_attention_node_data)
- def _get_log_p_topk(self, fixed, state, k=None, normalize=True):
- log_p, _ = self._get_log_p(fixed, state, normalize=normalize)
- # Return topk
- if k is not None and k < log_p.size(-1):
- return log_p.topk(k, -1)
- # Return all, note different from torch.topk this does not give error if less than k elements along dim
- return (
- log_p,
- torch.arange(log_p.size(-1), device=log_p.device, dtype=torch.int64).repeat(log_p.size(0), 1)[:, None, :]
- )
- def _get_log_p(self, fixed, state, normalize=True):
- # Compute query = context node embedding
- query = fixed.context_node_projected + \
- self.project_step_context(self._get_parallel_step_context(fixed.node_embeddings, state))
- # Compute keys and values for the nodes
- glimpse_K, glimpse_V, logit_K = self._get_attention_node_data(fixed, state)
- # Compute the mask
- mask = state.get_mask()
- # Compute logits (unnormalized log_p)
- log_p, glimpse = self._one_to_many_logits(query, glimpse_K, glimpse_V, logit_K, mask)
- if normalize:
- log_p = torch.log_softmax(log_p / self.temp, dim=-1)
- assert not torch.isnan(log_p).any()
- return log_p, mask
- def _get_parallel_step_context(self, embeddings, state, from_depot=False):
- """
- Returns the context per step, optionally for multiple steps at once (for efficient evaluation of the model)
-
- :param embeddings: (batch_size, graph_size, embed_dim)
- :param prev_a: (batch_size, num_steps)
- :param first_a: Only used when num_steps = 1, action of first step or None if first step
- :return: (batch_size, num_steps, context_dim)
- """
- current_node = state.get_current_node()
- batch_size, num_steps = current_node.size()
- if self.is_vrp:
- # Embedding of previous node + remaining capacity
- if from_depot:
- # 1st dimension is node idx, but we do not squeeze it since we want to insert step dimension
- # i.e. we actually want embeddings[:, 0, :][:, None, :] which is equivalent
- return torch.cat(
- (
- embeddings[:, 0:1, :].expand(batch_size, num_steps, embeddings.size(-1)),
- # used capacity is 0 after visiting depot
- self.problem.VEHICLE_CAPACITY - torch.zeros_like(state.used_capacity[:, :, None])
- ),
- -1
- )
- else:
- return torch.cat(
- (
- torch.gather(
- embeddings,
- 1,
- current_node.contiguous()
- .view(batch_size, num_steps, 1)
- .expand(batch_size, num_steps, embeddings.size(-1))
- ).view(batch_size, num_steps, embeddings.size(-1)),
- self.problem.VEHICLE_CAPACITY - state.used_capacity[:, :, None]
- ),
- -1
- )
- elif self.is_orienteering or self.is_pctsp:
- return torch.cat(
- (
- torch.gather(
- embeddings,
- 1,
- current_node.contiguous()
- .view(batch_size, num_steps, 1)
- .expand(batch_size, num_steps, embeddings.size(-1))
- ).view(batch_size, num_steps, embeddings.size(-1)),
- (
- state.get_remaining_length()[:, :, None]
- if self.is_orienteering
- else state.get_remaining_prize_to_collect()[:, :, None]
- )
- ),
- -1
- )
- else: # TSP
-
- if num_steps == 1: # We need to special case if we have only 1 step, may be the first or not
- if state.i.item() == 0:
- # First and only step, ignore prev_a (this is a placeholder)
- return self.W_placeholder[None, None, :].expand(batch_size, 1, self.W_placeholder.size(-1))
- else:
- return embeddings.gather(
- 1,
- torch.cat((state.first_a, current_node), 1)[:, :, None].expand(batch_size, 2, embeddings.size(-1))
- ).view(batch_size, 1, -1)
- # More than one step, assume always starting with first
- embeddings_per_step = embeddings.gather(
- 1,
- current_node[:, 1:, None].expand(batch_size, num_steps - 1, embeddings.size(-1))
- )
- return torch.cat((
- # First step placeholder, cat in dim 1 (time steps)
- self.W_placeholder[None, None, :].expand(batch_size, 1, self.W_placeholder.size(-1)),
- # Second step, concatenate embedding of first with embedding of current/previous (in dim 2, context dim)
- torch.cat((
- embeddings_per_step[:, 0:1, :].expand(batch_size, num_steps - 1, embeddings.size(-1)),
- embeddings_per_step
- ), 2)
- ), 1)
- def _one_to_many_logits(self, query, glimpse_K, glimpse_V, logit_K, mask):
- batch_size, num_steps, embed_dim = query.size()
- key_size = val_size = embed_dim // self.n_heads
- # Compute the glimpse, rearrange dimensions so the dimensions are (n_heads, batch_size, num_steps, 1, key_size)
- glimpse_Q = query.view(batch_size, num_steps, self.n_heads, 1, key_size).permute(2, 0, 1, 3, 4)
- # Batch matrix multiplication to compute compatibilities (n_heads, batch_size, num_steps, graph_size)
- compatibility = torch.matmul(glimpse_Q, glimpse_K.transpose(-2, -1)) / math.sqrt(glimpse_Q.size(-1))
- if self.mask_inner:
- assert self.mask_logits, "Cannot mask inner without masking logits"
- compatibility[mask[None, :, :, None, :].expand_as(compatibility)] = -math.inf
- # Batch matrix multiplication to compute heads (n_heads, batch_size, num_steps, val_size)
- heads = torch.matmul(torch.softmax(compatibility, dim=-1), glimpse_V)
- # Project to get glimpse/updated context node embedding (batch_size, num_steps, embedding_dim)
- glimpse = self.project_out(
- heads.permute(1, 2, 3, 0, 4).contiguous().view(-1, num_steps, 1, self.n_heads * val_size))
- # Now projecting the glimpse is not needed since this can be absorbed into project_out
- # final_Q = self.project_glimpse(glimpse)
- final_Q = glimpse
- # Batch matrix multiplication to compute logits (batch_size, num_steps, graph_size)
- # logits = 'compatibility'
- logits = torch.matmul(final_Q, logit_K.transpose(-2, -1)).squeeze(-2) / math.sqrt(final_Q.size(-1))
- # From the logits compute the probabilities by clipping, masking and softmax
- if self.tanh_clipping > 0:
- logits = torch.tanh(logits) * self.tanh_clipping
- if self.mask_logits:
- logits[mask] = -math.inf
- return logits, glimpse.squeeze(-2)
- def _get_attention_node_data(self, fixed, state):
- if self.is_vrp and self.allow_partial:
- # Need to provide information of how much each node has already been served
- # Clone demands as they are needed by the backprop whereas they are updated later
- glimpse_key_step, glimpse_val_step, logit_key_step = \
- self.project_node_step(state.demands_with_depot[:, :, :, None].clone()).chunk(3, dim=-1)
- # Projection of concatenation is equivalent to addition of projections but this is more efficient
- return (
- fixed.glimpse_key + self._make_heads(glimpse_key_step),
- fixed.glimpse_val + self._make_heads(glimpse_val_step),
- fixed.logit_key + logit_key_step,
- )
- # TSP or VRP without split delivery
- return fixed.glimpse_key, fixed.glimpse_val, fixed.logit_key
- def _make_heads(self, v, num_steps=None):
- assert num_steps is None or v.size(1) == 1 or v.size(1) == num_steps
- return (
- v.contiguous().view(v.size(0), v.size(1), v.size(2), self.n_heads, -1)
- .expand(v.size(0), v.size(1) if num_steps is None else num_steps, v.size(2), self.n_heads, -1)
- .permute(3, 0, 1, 2, 4) # (n_heads, batch_size, num_steps, graph_size, head_dim)
- )
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