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baselines.py refactoring (6/N) #52

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Feb 18, 2022
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baselines.py refactoring (6/N) #52

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115 changes: 27 additions & 88 deletions clrs/_src/baselines.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,8 +72,6 @@ def __init__(
decode_hints: bool,
decode_diffs: bool,
kind: str,
inf_bias: bool,
inf_bias_edge: bool,
use_lstm: bool,
dropout_prob: float,
nb_heads: int,
Expand All @@ -85,8 +83,6 @@ def __init__(

self._dropout_prob = dropout_prob
self.spec = spec
self.inf_bias = inf_bias
self.inf_bias_edge = inf_bias_edge
self.hidden_dim = hidden_dim
self.encode_hints = encode_hints
self.decode_hints = decode_hints
Expand Down Expand Up @@ -191,12 +187,16 @@ def __call__(self, features: _Features, repred: bool):
nb_nodes = inp.data.shape[1]
break

self._construct_encoders_decoders()
self._construct_processor()
# Construct encoders and decoders.
(self.encoders, self.decoders,
self.diff_decoders) = self._construct_encoders_decoders()
self.processor = processors.construct_processor(
kind=self.kind, hidden_dim=self.hidden_dim, nb_heads=self.nb_heads)

nb_mp_steps = max(1, hints[0].data.shape[0] - 1)
hiddens = jnp.zeros((self.batch_size, nb_nodes, self.hidden_dim))

# Optionally construct LSTM.
if self.use_lstm:
self.lstm = hk.LSTM(
hidden_size=self.hidden_dim,
Expand Down Expand Up @@ -258,58 +258,25 @@ def invert(d):

def _construct_encoders_decoders(self):
"""Constructs encoders and decoders."""
self.encoders = {}
self.decoders = {}
encoders_ = {}
decoders_ = {}

for name, (stage, loc, t) in self.spec.items():
if stage == _Stage.INPUT or (stage == _Stage.HINT and self.encode_hints):
# Build input encoders.
self.encoders[name] = encoders.construct_encoders(
encoders_[name] = encoders.construct_encoders(
loc, t, hidden_dim=self.hidden_dim)

if stage == _Stage.OUTPUT or (stage == _Stage.HINT and self.decode_hints):
# Build output decoders.
self.decoders[name] = decoders.construct_decoders(
decoders_[name] = decoders.construct_decoders(
loc, t, hidden_dim=self.hidden_dim, nb_dims=self.nb_dims[name])

if self.decode_diffs:
# Optionally build diff decoders.
self.diff_decoders = decoders.construct_diff_decoders()

def _construct_processor(self):
"""Constructs processor."""

if self.kind in ['deepsets', 'mpnn', 'pgn']:
self.mpnn = processors.MPNN(
out_size=self.hidden_dim,
mid_act=jax.nn.relu,
activation=jax.nn.relu,
reduction=jnp.max,
msgs_mlp_sizes=[
self.hidden_dim,
self.hidden_dim,
])
elif self.kind in ['gat', 'gat_full']:
self.mpnn = processors.GAT(
out_size=self.hidden_dim,
nb_heads=self.nb_heads,
activation=jax.nn.relu,
residual=True)
elif self.kind in ['gatv2', 'gatv2_full']:
self.mpnn = processors.GATv2(
out_size=self.hidden_dim,
nb_heads=self.nb_heads,
activation=jax.nn.relu,
residual=True)
elif self.kind == 'memnet_full' or self.kind == 'memnet_masked':
self.memnet = processors.MemNet(
vocab_size=self.hidden_dim,
embedding_size=16,
sentence_size=self.hidden_dim,
linear_output_size=self.hidden_dim,
memory_size=128,
num_hops=1,
apply_embeddings=True)
diff_decoders = decoders.construct_diff_decoders()

return encoders_, decoders_, diff_decoders

def _one_step_pred(
self,
Expand All @@ -329,7 +296,6 @@ def _one_step_pred(
jnp.expand_dims(jnp.eye(nb_nodes), 0), self.batch_size, axis=0)

# ENCODE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Encode node/edge/graph features from inputs and (optionally) hints.
trajectories = [inputs]
if self.encode_hints:
Expand All @@ -348,33 +314,15 @@ def _one_step_pred(
raise Exception(f'Failed to process {dp}') from e

# PROCESS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

if self.kind == 'deepsets':
adj_mat = jnp.repeat(
jnp.expand_dims(jnp.eye(nb_nodes), 0), self.batch_size, axis=0)
elif (self.kind == 'mpnn' or self.kind == 'gat_full' or
self.kind == 'gatv2_full' or self.kind == 'memnet_full'):
adj_mat = jnp.ones_like(adj_mat)
elif (self.kind == 'pgn' or self.kind == 'gat' or self.kind == 'gatv2' or
self.kind == 'memnet_masked'):
adj_mat = (adj_mat > 0.0) * 1.0
else:
raise ValueError('Unsupported kind of model')

z = jnp.concatenate([node_fts, hidden], axis=-1)
if self.kind == 'memnet_full' or self.kind == 'memnet_masked':
node_and_graph_fts = jnp.concatenate(
[node_fts, graph_fts[:, None]], axis=1)
edge_fts_padded = jnp.pad(edge_fts * adj_mat[..., None],
((0, 0), (0, 1), (0, 1), (0, 0)))
nxt_hidden = jax.vmap(self.memnet, (1), 1)(node_and_graph_fts,
edge_fts_padded)
# Broadcast hidden state corresponding to graph features across the nodes.
nxt_hidden = nxt_hidden[:, :-1] + nxt_hidden[:, -1:]
else:
nxt_hidden = self.mpnn(z, edge_fts, graph_fts,
(adj_mat > 0.0).astype('float32'))

nxt_hidden = self.processor(
node_fts,
edge_fts,
graph_fts,
adj_mat,
hidden,
batch_size=self.batch_size,
nb_nodes=nb_nodes,
)
nxt_hidden = hk.dropout(hk.next_rng_key(), self._dropout_prob, nxt_hidden)

if self.use_lstm:
Expand All @@ -384,10 +332,9 @@ def _one_step_pred(
else:
nxt_lstm_state = None

h_t = jnp.concatenate([z, nxt_hidden], axis=-1)
h_t = jnp.concatenate([node_fts, hidden, nxt_hidden], axis=-1)

# DECODE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Decode features and (optionally) hints.
hint_preds, output_preds = decoders.decode_fts(
decoders=self.decoders,
Expand All @@ -396,8 +343,8 @@ def _one_step_pred(
adj_mat=adj_mat,
edge_fts=edge_fts,
graph_fts=graph_fts,
inf_bias=self.inf_bias,
inf_bias_edge=self.inf_bias_edge,
inf_bias=self.processor.inf_bias,
inf_bias_edge=self.processor.inf_bias_edge,
)

# Optionally decode diffs.
Expand Down Expand Up @@ -447,14 +394,6 @@ def __init__(
self._freeze_processor = freeze_processor
self.opt = optax.adam(learning_rate)

if kind == 'pgn_mask':
inf_bias = True
inf_bias_edge = True
kind = 'pgn'
else:
inf_bias = False
inf_bias_edge = False

self.nb_dims = {}
for inp in dummy_trajectory.features.inputs:
self.nb_dims[inp.name] = inp.data.shape[-1]
Expand All @@ -465,8 +404,8 @@ def __init__(

def _use_net(*args, **kwargs):
return Net(spec, hidden_dim, encode_hints, decode_hints, decode_diffs,
kind, inf_bias, inf_bias_edge, use_lstm, dropout_prob,
nb_heads, self.nb_dims)(*args, **kwargs)
kind, use_lstm, dropout_prob, nb_heads, self.nb_dims)(*args,
**kwargs)

self.net_fn = hk.transform(_use_net)
self.net_fn_apply = jax.jit(self.net_fn.apply, static_argnums=3)
Expand Down
6 changes: 3 additions & 3 deletions clrs/_src/encoders.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,11 +50,11 @@ def preprocess(dp: _DataPoint, nb_nodes: int) -> _Array:
def accum_adj_mat(dp: _DataPoint, data: _Array, adj_mat: _Array) -> _Array:
"""Accumulates adjacency matrix."""
if dp.location == _Location.NODE and dp.type_ == _Type.POINTER:
adj_mat += ((data + jnp.transpose(data, (0, 2, 1))) > 0.0).astype('float32')
adj_mat += ((data + jnp.transpose(data, (0, 2, 1))) > 0.0)
elif dp.location == _Location.EDGE and dp.type_ == _Type.MASK:
adj_mat += (data > 0.0).astype('float32')
adj_mat += (data > 0.0)

return adj_mat
return (adj_mat > 0.).astype('float32')


def accum_edge_fts(encoders, dp: _DataPoint, data: _Array,
Expand Down
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