def _encode_board(self, board_state, name, reuse=None):
""" Encodes a board state or prev orders state
:param board_state: The board state / prev orders state to encode - (batch, NB_NODES, initial_features)
:param name: The name to use for the encoding
:param reuse: Whether to reuse or not the weights from another encoding operation
:return: The encoded board state / prev_orders state
"""
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.graph_convolution import GraphConvolution, preprocess_adjacency
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
relu = tf.nn.relu
# Computing norm adjacency
norm_adjacency = preprocess_adjacency(get_adjacency_matrix())
norm_adjacency = tf.tile(tf.expand_dims(norm_adjacency, axis=0), [tf.shape(board_state)[0], 1, 1])
# Building scope
scope = tf.VariableScope(name='policy/%s' % name, reuse=reuse)
with tf.variable_scope(scope):
batch_size = tf.shape(board_state)[0]
# Adding noise to break symmetry
board_state = board_state + tf.random_normal(tf.shape(board_state), stddev=0.01)
# Projecting (if needed) to 'gcn_size'
if board_state.shape[-1].value == NB_FEATURES:
with tf.variable_scope('proj', reuse=tf.AUTO_REUSE):
proj_w = tf.get_variable('W', shape=[1, NB_FEATURES, hps('gcn_size')], dtype=tf.float32)
graph_conv = relu(tf.matmul(board_state, tf.tile(proj_w, [batch_size, 1, 1])))
else:
graph_conv = board_state
# First and intermediate layers
for _ in range(hps('nb_graph_conv') - 1):
graph_conv = GraphConvolution(input_dim=hps('gcn_size'), # (b, NB_NODES, gcn_size)
output_dim=hps('gcn_size'),
norm_adjacency=norm_adjacency,
activation_fn=relu,
residual=True,
bias=True)(graph_conv)
# Last Layer
graph_conv = GraphConvolution(input_dim=hps('gcn_size'), # (b, NB_NODES, final_size)
output_dim=hps('attn_size') // 2,
norm_adjacency=norm_adjacency,
activation_fn=relu,
residual=False,
bias=True)(graph_conv)
# Returning
return graph_conv
def _build_value_initial(self):
""" Builds the value model (initial step) """
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.utils.tensorflow import to_float
if not self.placeholders:
self.placeholders = self.get_placeholders()
else:
self.placeholders.update(self.get_placeholders())
# Quick function to retrieve hparams and placeholders and function shorthands
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
# Training loop
with tf.variable_scope('value', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.
cluster_config else None):
# Features
board_state = to_float(
self.features['board_state']
) # tf.float32 - (b, NB_NODES, NB_FEATURES)
current_power = self.features[
'current_power'] # tf.int32 - (b,)
value_target = self.features[
'value_target'] # tf.float32 - (b,)
# Placeholders
stop_gradient_all = pholder('stop_gradient_all')
# Computing value for the current power
state_value = self.get_board_value(board_state, current_power)
# Computing value loss
with tf.variable_scope('value_loss'):
value_loss = tf.reduce_mean(
tf.square(value_target - state_value))
value_loss = tf.cond(
stop_gradient_all,
lambda: tf.stop_gradient(value_loss), # pylint: disable=cell-var-from-loop
lambda: value_loss) # pylint: disable=cell-var-from-loop
# Building output tags
outputs = {
'tag/value/v001_val_relu_7': True,
'state_value': state_value,
'value_loss': value_loss
}
# Adding features, placeholders and outputs to graph
self.add_meta_information(outputs)
def __init__(self,
input_dim,
output_dim,
norm_adjacency,
activation_fn=tf.nn.relu,
residual=False,
bias=False,
scope=None,
reuse=None):
""" Initializes the graph convolutional network
:param input_dim: The number of features per node in the input
:param output_dim: The number of features per node desired in the output
:param norm_adjacency: [PLACEHOLDER] The sparse normalized adjacency matrix (NxN matrix)
:param activation_fn: The activation function to use after the graph convolution
:param residual: Use residual connection or not.
:param bias: Boolean flag that indicates we also want to include a bias term
:param scope: Optional. The scope to use for this layer
:param reuse: Optional. Boolean. Whether or not the layer and its variables should be reused.
"""
self.activation_fn = activation_fn if activation_fn is not None else lambda x: x
self.norm_adjacency = norm_adjacency
self.bias = bias
self.var_w, self.var_b = None, None
self.residual = residual
# Initializing variables
with tf.variable_scope(scope, 'GraphConv', reuse=reuse):
self.var_w = he('W', [NB_NODES, input_dim, output_dim])
if self.bias:
self.var_b = zeros('b', [output_dim])
def _encode_board(self, board_state, name, reuse=None):
""" Encodes a board state or prev orders state
:param board_state: The board state / prev orders state to encode - (batch, NB_NODES, initial_features)
:param name: The name to use for the encoding
:param reuse: Whether to reuse or not the weights from another encoding operation
:return: The encoded board state / prev_orders state
"""
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.graph_convolution import film_gcn_res_block, preprocess_adjacency
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
relu = tf.nn.relu
# Getting film gammas and betas
film_gammas = self.outputs['_%s_film_gammas' % name]
film_betas = self.outputs['_%s_film_betas' % name]
# Computing norm adjacency
norm_adjacency = preprocess_adjacency(get_adjacency_matrix())
norm_adjacency = tf.tile(tf.expand_dims(norm_adjacency, axis=0),
[tf.shape(board_state)[0], 1, 1])
# Building scope
scope = tf.VariableScope(name='policy/%s' % name, reuse=reuse)
with tf.variable_scope(scope):
# Adding noise to break symmetry
board_state = board_state + tf.random_normal(tf.shape(board_state),
stddev=0.01)
graph_conv = tf.layers.Dense(units=hps('gcn_size'),
activation=relu)(board_state)
# First and intermediate layers
for layer_idx in range(hps('nb_graph_conv') - 1):
graph_conv = film_gcn_res_block(
inputs=graph_conv, # (b, NB_NODES, gcn_size)
gamma=film_gammas[layer_idx],
beta=film_betas[layer_idx],
gcn_out_dim=hps('gcn_size'),
norm_adjacency=norm_adjacency,
is_training=pholder('is_training'),
residual=True)
# Last layer
graph_conv = film_gcn_res_block(
inputs=graph_conv, # (b, NB_NODES, final_size)
gamma=film_gammas[-1],
beta=film_betas[-1],
gcn_out_dim=hps('attn_size') // 2,
norm_adjacency=norm_adjacency,
is_training=pholder('is_training'),
residual=False)
# Returning
return graph_conv
def convert_to_noisy_variables(variables, activation=None):
""" Converts a list of variables to noisy variables
:param variables: A list of variables to make noisy
:param activation: Optional. The activation function to use on the linear noisy transformation
:return: Nothing, but modifies the graph in-place
Reference: 1706.10295 - Noisy Networks for exploration
"""
if tf.get_collection(tf.GraphKeys.TRAIN_OP):
raise RuntimeError(
'You must call convert_to_noisy_variables before applying an optimizer on the graph.'
)
graph = tf.get_default_graph()
if not isinstance(variables, list):
variables = list(variables)
# Replacing each variable
for variable in variables:
variable_read_op = _get_variable_read_op(variable, graph)
variable_outputs = _get_variable_outputs(variable_read_op, graph)
variable_scope = variable.name.split(':')[0]
variable_shape = variable.shape.as_list()
fan_in = variable_shape[0]
# Creating noisy variables
with tf.variable_scope(variable_scope + '_noisy'):
with tf.device(variable.device):
s_init = tf.constant_initializer(0.5 / sqrt(fan_in))
noisy_u = tf.identity(variable, name='mu')
noisy_s = tf.get_variable(
name='sigma',
shape=variable.shape,
dtype=tf.float32,
initializer=s_init,
caching_device=variable._caching_device) # pylint: disable=protected-access
noise = tf.random.normal(shape=variable_shape)
replaced_var = noisy_u + noisy_s * noise
replaced_var = activation(
replaced_var) if activation else replaced_var
# Replacing in-place
inputs_index = [
var_index for var_index, var_input in enumerate(
graph_editor.sgv(*variable_outputs).inputs) if
var_input.name.split(':')[0] == variable_read_op.name.split(':')[0]
]
graph_editor.connect(
graph_editor.sgv(replaced_var.op),
graph_editor.sgv(*variable_outputs).remap_inputs(inputs_index),
disconnect_first=True)
def _encode_board(self, board_state, name, reuse=None):
""" Encodes a board state or prev orders state
:param board_state: The board state / prev orders state to encode - (batch, NB_NODES, initial_features)
:param name: The name to use for the encoding
:param reuse: Whether to reuse or not the weights from another encoding operation
:return: The encoded board state / prev_orders state
"""
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.graph_convolution import GraphConvolution, preprocess_adjacency
from diplomacy_research.utils.tensorflow import batch_norm
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
relu = tf.nn.relu
# Computing norm adjacency
norm_adjacency = preprocess_adjacency(get_adjacency_matrix())
norm_adjacency = tf.tile(tf.expand_dims(norm_adjacency, axis=0), [tf.shape(board_state)[0], 1, 1])
# Building scope
scope = tf.VariableScope(name='policy/%s' % name, reuse=reuse)
with tf.variable_scope(scope):
# Adding noise to break symmetry
board_state = board_state + tf.random_normal(tf.shape(board_state), stddev=0.01)
graph_conv = board_state
# First Layer
graph_conv = GraphConvolution(input_dim=graph_conv.shape[-1].value, # (b, NB_NODES, gcn_size)
output_dim=hps('gcn_size'),
norm_adjacency=norm_adjacency,
activation_fn=relu,
bias=True)(graph_conv)
# Intermediate Layers
for _ in range(1, hps('nb_graph_conv') - 1):
graph_conv = GraphConvolution(input_dim=hps('gcn_size'), # (b, NB_NODES, gcn_size)
output_dim=hps('gcn_size'),
norm_adjacency=norm_adjacency,
activation_fn=relu,
bias=True)(graph_conv)
graph_conv = batch_norm(graph_conv, is_training=pholder('is_training'), fused=True)
# Final Layer
graph_conv = GraphConvolution(input_dim=hps('gcn_size'), # (b, NB_NODES, attn_size)
output_dim=hps('attn_size'),
norm_adjacency=norm_adjacency,
activation_fn=None,
bias=True)(graph_conv)
# Returning
return graph_conv
def _build_policy_final(self):
""" Builds the policy model (final step) """
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.attention import StaticAttentionWrapper
from diplomacy_research.models.layers.beam_decoder import DiverseBeamSearchDecoder
from diplomacy_research.models.layers.decoder import MaskedBasicDecoder
from diplomacy_research.models.layers.dropout import SeededDropoutWrapper
from diplomacy_research.models.layers.dynamic_decode import dynamic_decode
from diplomacy_research.models.policy.token_based.helper import CustomHelper, CustomBeamHelper
from diplomacy_research.utils.tensorflow import cross_entropy, sequence_loss, to_int32, to_float, get_tile_beam
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
# Training loop
with tf.variable_scope('policy', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.cluster_config else None):
# Features
player_seeds = self.features['player_seed'] # tf.int32 - (b,)
temperature = self.features['temperature'] # tf,flt32 - (b,)
dropout_rates = self.features['dropout_rate'] # tf.flt32 - (b,)
# Placeholders
stop_gradient_all = pholder('stop_gradient_all')
# Outputs (from initial steps)
batch_size = self.outputs['batch_size']
board_alignments = self.outputs['board_alignments']
decoder_inputs = self.outputs['decoder_inputs']
decoder_mask = self.outputs['decoder_mask']
decoder_type = self.outputs['decoder_type']
raw_decoder_lengths = self.outputs['raw_decoder_lengths']
decoder_lengths = self.outputs['decoder_lengths']
board_state_conv = self.outputs['board_state_conv']
word_embedding = self.outputs['word_embedding']
# --- Decoding ---
with tf.variable_scope('decoder_scope', reuse=tf.AUTO_REUSE):
lstm_cell = tf.contrib.rnn.LSTMBlockCell(hps('lstm_size'))
# decoder output to token
decoder_output_layer = tf.layers.Dense(units=VOCABULARY_SIZE,
activation=None,
kernel_initializer=tf.random_normal_initializer,
use_bias=True)
# ======== Regular Decoding ========
# Applying dropout to input + attention and to output layer
decoder_cell = SeededDropoutWrapper(cell=lstm_cell,
seeds=player_seeds,
input_keep_probs=1. - dropout_rates,
output_keep_probs=1. - dropout_rates,
variational_recurrent=hps('use_v_dropout'),
input_size=hps('word_emb_size') + hps('attn_size'),
dtype=tf.float32)
# Apply attention over orderable location at each position
decoder_cell = StaticAttentionWrapper(cell=decoder_cell,
memory=board_state_conv,
alignments=board_alignments,
sequence_length=raw_decoder_lengths,
output_attention=False)
# Setting initial state
decoder_init_state = decoder_cell.zero_state(batch_size, tf.float32)
# ---- Helper ----
helper = CustomHelper(decoder_type=decoder_type,
inputs=decoder_inputs[:, :-1],
embedding=word_embedding,
sequence_length=decoder_lengths,
mask=decoder_mask,
time_major=False,
softmax_temperature=temperature)
# ---- Decoder ----
sequence_mask = tf.sequence_mask(raw_decoder_lengths,
maxlen=tf.reduce_max(decoder_lengths),
dtype=tf.float32)
maximum_iterations = TOKENS_PER_ORDER * NB_SUPPLY_CENTERS
model_decoder = MaskedBasicDecoder(cell=decoder_cell,
helper=helper,
initial_state=decoder_init_state,
output_layer=decoder_output_layer,
extract_state=True)
training_results, _, _ = dynamic_decode(decoder=model_decoder,
output_time_major=False,
maximum_iterations=maximum_iterations,
swap_memory=hps('swap_memory'))
global_vars_after_decoder = set(tf.global_variables())
# ======== Beam Search Decoding ========
tile_beam = get_tile_beam(hps('beam_width'))
# Applying dropout to input + attention and to output layer
decoder_cell = SeededDropoutWrapper(cell=lstm_cell,
seeds=tile_beam(player_seeds),
input_keep_probs=tile_beam(1. - dropout_rates),
output_keep_probs=tile_beam(1. - dropout_rates),
variational_recurrent=hps('use_v_dropout'),
input_size=hps('word_emb_size') + hps('attn_size'),
dtype=tf.float32)
# Apply attention over orderable location at each position
decoder_cell = StaticAttentionWrapper(cell=decoder_cell,
memory=tile_beam(board_state_conv),
alignments=tile_beam(board_alignments),
sequence_length=tile_beam(raw_decoder_lengths),
output_attention=False)
# Setting initial state
decoder_init_state = decoder_cell.zero_state(batch_size * hps('beam_width'), tf.float32)
# ---- Beam Helper and Decoder ----
beam_helper = CustomBeamHelper(cell=decoder_cell,
embedding=word_embedding,
mask=decoder_mask,
sequence_length=decoder_lengths,
output_layer=decoder_output_layer,
initial_state=decoder_init_state,
beam_width=hps('beam_width'))
beam_decoder = DiverseBeamSearchDecoder(beam_helper=beam_helper,
sequence_length=decoder_lengths,
nb_groups=hps('beam_groups'))
beam_results, beam_state, _ = dynamic_decode(decoder=beam_decoder,
output_time_major=False,
maximum_iterations=maximum_iterations,
swap_memory=hps('swap_memory'))
# Making sure we haven't created new global variables
assert not set(tf.global_variables()) - global_vars_after_decoder, 'New global vars were created'
# Processing results
logits = training_results.rnn_output # (b, dec_len, VOCAB_SIZE)
logits_length = tf.shape(logits)[1] # dec_len
decoder_target = decoder_inputs[:, 1:1 + logits_length]
# Selected tokens are the token that was actually fed at the next position
sample_mask = to_float(tf.math.equal(training_results.sample_id, -1))
selected_tokens = to_int32(
sequence_mask * (sample_mask * to_float(decoder_target)
+ (1. - sample_mask) * to_float(training_results.sample_id)))
# Argmax tokens are the most likely token outputted at each position
argmax_tokens = to_int32(to_float(tf.argmax(logits, axis=-1)) * sequence_mask)
log_probs = -1. * cross_entropy(logits=logits, labels=selected_tokens) * sequence_mask
# Computing policy loss
with tf.variable_scope('policy_loss'):
policy_loss = sequence_loss(logits=logits,
targets=decoder_target,
weights=sequence_mask,
average_across_batch=True,
average_across_timesteps=True)
policy_loss = tf.cond(stop_gradient_all,
lambda: tf.stop_gradient(policy_loss), # pylint: disable=cell-var-from-loop
lambda: policy_loss) # pylint: disable=cell-var-from-loop
# Building output tags
outputs = {'tag/policy/token_based/v005_markovian_film_board_align': True,
'targets': decoder_inputs[:, 1:],
'selected_tokens': selected_tokens,
'argmax_tokens': argmax_tokens,
'logits': logits,
'log_probs': log_probs,
'beam_tokens': tf.transpose(beam_results.predicted_ids, perm=[0, 2, 1]), # [batch, beam, steps]
'beam_log_probs': beam_state.log_probs,
'rnn_states': training_results.rnn_state,
'policy_loss': policy_loss,
'draw_prob': self.outputs.get('draw_prob', tf.zeros_like(self.features['draw_target'])),
'learning_rate': self.learning_rate}
# Adding features, placeholders and outputs to graph
self.add_meta_information(outputs)
def _build_policy_initial(self):
""" Builds the policy model (initial step) """
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.initializers import uniform
from diplomacy_research.utils.tensorflow import build_sparse_batched_tensor, pad_axis, to_float, to_bool
if not self.placeholders:
self.placeholders = self.get_placeholders()
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
# Training loop
with tf.variable_scope('policy', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.cluster_config else None):
# Features
board_state = to_float(self.features['board_state']) # tf.flt32 - (b, NB_NODES, NB_FEATURES)
board_alignments = to_float(self.features['board_alignments']) # (b, NB_NODES * len)
decoder_inputs = self.features['decoder_inputs'] # tf.int32 - (b, <= 1 + TOK/ORD * NB_SCS)
decoder_lengths = self.features['decoder_lengths'] # tf.int32 - (b,)
current_power = self.features['current_power'] # tf.int32 - (b,)
current_season = self.features['current_season'] # tf.int32 - (b,)
dropout_rates = self.features['dropout_rate'] # tf.flt32 - (b,)
# Batch size
batch_size = tf.shape(board_state)[0]
# Reshaping board alignments
board_alignments = tf.reshape(board_alignments, [batch_size, -1, NB_NODES])
board_alignments /= tf.math.maximum(1., tf.reduce_sum(board_alignments, axis=-1, keepdims=True))
# Building decoder mask
decoder_mask_indices = self.features['decoder_mask_indices'] # tf.int64 - (b, 3 * len)
decoder_mask_shape = self.proto_fields['decoder_mask'].shape
# Overriding dropout_rates if pholder('dropout_rate') > 0
dropout_rates = tf.cond(tf.greater(pholder('dropout_rate'), 0.),
true_fn=lambda: tf.zeros_like(dropout_rates) + pholder('dropout_rate'),
false_fn=lambda: dropout_rates)
# Padding inputs
board_alignments = pad_axis(board_alignments, axis=1, min_size=tf.reduce_max(decoder_lengths))
decoder_inputs = pad_axis(decoder_inputs, axis=-1, min_size=2)
decoder_mask_indices = pad_axis(decoder_mask_indices, axis=-1, min_size=len(decoder_mask_shape))
# Reshaping to (b, len, 3)
# decoder_mask is -- tf.bool (batch, TOK/ORD * NB_SC, VOCAB_SIZE, VOCAB_SIZE)
decoder_mask_indices = tf.reshape(decoder_mask_indices, [batch_size, -1, len(decoder_mask_shape)])
decoder_mask = build_sparse_batched_tensor(decoder_mask_indices,
value=True,
dtype=tf.bool,
dense_shape=decoder_mask_shape)
# Making sure all RNN lengths are at least 1
# No need to trim, because the fields are variable length
raw_decoder_lengths = decoder_lengths
decoder_lengths = tf.math.maximum(1, decoder_lengths)
# Placeholders
decoder_type = tf.reduce_max(pholder('decoder_type'))
is_training = pholder('is_training')
# Computing FiLM Gammas and Betas
with tf.variable_scope('film_scope'):
power_embedding = uniform(name='power_embedding',
shape=[NB_POWERS, hps('power_emb_size')],
scale=1.)
current_power_mask = tf.one_hot(current_power, NB_POWERS, dtype=tf.float32)
current_power_embedding = tf.reduce_sum(power_embedding[None]
* current_power_mask[:, :, None], axis=1) # (b, power_emb)
film_embedding_input = current_power_embedding
# Also conditioning on current_season
season_embedding = uniform(name='season_embedding',
shape=[NB_SEASONS, hps('season_emb_size')],
scale=1.)
current_season_mask = tf.one_hot(current_season, NB_SEASONS, dtype=tf.float32)
current_season_embedding = tf.reduce_sum(season_embedding[None] # (b,season_emb)
* current_season_mask[:, :, None], axis=1)
film_embedding_input = tf.concat([film_embedding_input, current_season_embedding], axis=1)
film_output_dims = [hps('gcn_size')] * (hps('nb_graph_conv') - 1) + [hps('attn_size')]
film_weights = tf.layers.Dense(units=2 * sum(film_output_dims), # (b, 1, 750)
use_bias=True,
activation=None)(film_embedding_input)[:, None, :]
film_gammas, film_betas = tf.split(film_weights, 2, axis=2) # (b, 1, 750)
film_gammas = tf.split(film_gammas, film_output_dims, axis=2)
film_betas = tf.split(film_betas, film_output_dims, axis=2)
# Storing as temporary output
self.add_output('_board_state_conv_film_gammas', film_gammas)
self.add_output('_board_state_conv_film_betas', film_betas)
# Creating graph convolution
with tf.variable_scope('graph_conv_scope'):
assert hps('nb_graph_conv') >= 2
# Encoding board state
board_state_0yr_conv = self.encode_board(board_state, name='board_state_conv')
board_state_conv = self.get_board_state_conv(board_state_0yr_conv, is_training)
# Creating word embedding vector (to embed word_ix)
# Embeddings needs to be cached locally on the worker, otherwise TF can't compute their gradients
with tf.variable_scope('word_embedding_scope'):
# embedding: (voc_size, 256)
caching_device = self.cluster_config.caching_device if self.cluster_config else None
word_embedding = uniform(name='word_embedding',
shape=[VOCABULARY_SIZE, hps('word_emb_size')],
scale=1.,
caching_device=caching_device)
# Building output tags
outputs = {'batch_size': batch_size,
'board_alignments': board_alignments,
'decoder_inputs': decoder_inputs,
'decoder_mask': decoder_mask,
'decoder_type': decoder_type,
'raw_decoder_lengths': raw_decoder_lengths,
'decoder_lengths': decoder_lengths,
'board_state_conv': board_state_conv,
'board_state_0yr_conv': board_state_0yr_conv,
'word_embedding': word_embedding,
'in_retreat_phase': tf.math.logical_and( # 1) board not empty, 2) disl. units present
tf.reduce_sum(board_state[:], axis=[1, 2]) > 0,
tf.math.logical_not(to_bool(tf.reduce_min(board_state[:, :, 23], -1))))}
# Adding to graph
self.add_meta_information(outputs)
def _build_draw_initial(self):
""" Builds the draw model (initial step) """
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.graph_convolution import GraphConvolution, preprocess_adjacency
from diplomacy_research.utils.tensorflow import to_float
if not self.placeholders:
self.placeholders = self.get_placeholders()
else:
self.placeholders.update(self.get_placeholders())
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
relu = tf.nn.relu
sigmoid = tf.nn.sigmoid
# Training loop
with tf.variable_scope('draw', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.
cluster_config else None):
# Features
board_state = to_float(
self.features['board_state']
) # tf.float32 - (b, NB_NODES, NB_FEATURES)
current_power = self.features[
'current_power'] # tf.int32 - (b,)
draw_target = self.features['draw_target'] # tf.float32 - (b,)
# Placeholders
stop_gradient_all = pholder('stop_gradient_all')
# Norm Adjacency
batch_size = tf.shape(board_state)[0]
norm_adjacency = preprocess_adjacency(get_adjacency_matrix())
norm_adjacency = tf.tile(
tf.expand_dims(norm_adjacency, axis=0), [batch_size, 1, 1])
# Graph embeddings
with tf.variable_scope('graph_conv_scope'):
board_state_h0 = board_state # (b, 81, 35)
board_state_h1 = GraphConvolution(
input_dim=NB_FEATURES,
output_dim=hps('draw_gcn_1_output_size'),
norm_adjacency=norm_adjacency,
activation_fn=relu,
bias=True)(board_state_h0) # (b, 81, 25)
# board_state_h2: (b, 2025)
# board_state_h3: (b, 128)
board_state_h2 = tf.reshape(
board_state_h1,
shape=[-1, NB_NODES * hps('draw_gcn_1_output_size')])
board_state_graph_conv = tf.layers.Dense(
units=hps('draw_embedding_size'),
activation=relu,
use_bias=True)(board_state_h2)
# Calculating draw for all powers
with tf.variable_scope('draw_scope'):
current_power_mask = tf.one_hot(current_power,
NB_POWERS,
dtype=tf.float32)
draw_h0 = board_state_graph_conv # (b, 128)
draw_h1 = tf.layers.Dense(
units=hps('draw_h1_size'), # (b, 64)
activation=relu,
use_bias=True)(draw_h0)
draw_h2 = tf.layers.Dense(
units=hps('draw_h2_size'), # (b, 64)
activation=relu,
use_bias=True)(draw_h1)
draw_probs = tf.layers.Dense(
units=NB_POWERS, # (b, 7)
activation=sigmoid,
use_bias=True)(draw_h2)
draw_prob = tf.reduce_sum(draw_probs * current_power_mask,
axis=1) # (b,)
# Computing draw loss
with tf.variable_scope('draw_loss'):
draw_loss = tf.reduce_mean(
tf.square(draw_target - draw_prob))
draw_loss = tf.cond(
stop_gradient_all,
lambda: tf.stop_gradient(draw_loss), # pylint: disable=cell-var-from-loop
lambda: draw_loss) # pylint: disable=cell-var-from-loop
# Building output tags
outputs = {
'tag/draw/v001_draw_relu': True,
'draw_prob': draw_prob,
'draw_loss': draw_loss
}
# Adding features, placeholders and outputs to graph
self.add_meta_information(outputs)
def build(self):
""" Builds the RL model using the correct optimizer """
from diplomacy_research.utils.tensorflow import tf, tfp, normalize, to_float
from diplomacy_research.models.layers.avg_grad_optimizer import AvgGradOptimizer
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.model.hparams[hparam_name]
# Training loop
with tf.variable_scope('policy', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.
cluster_config else None):
# Placeholders
stop_gradient_all = self.model.placeholders[
'stop_gradient_all']
# Features
decoder_lengths = self.model.features[
'decoder_lengths'] # tf.int32 - (b,)
draw_action = self.model.features[
'draw_action'] # tf.bool - (b,)
reward_target = self.model.features[
'reward_target'] # tf.float32 - (b,)
value_target = self.model.features[
'value_target'] # tf.float32 - (b,)
old_log_probs = self.model.features[
'old_log_probs'] # tf.float32 - (b, dec_len)
# current_power = self.model.features['current_power'] # tf.int32 - (b,)
# Making sure all RNN lengths are at least 1
# Trimming to the maximum decoder length in the batch
raw_decoder_lengths = decoder_lengths
decoder_lengths = tf.math.maximum(1, decoder_lengths)
# Retrieving model outputs
baseline = values = self.model.outputs['state_value'] # (b,)
logits = self.model.outputs['logits'] # (b, dec, VOCAB)
sequence_mask = tf.sequence_mask(
raw_decoder_lengths, # (b, dec)
maxlen=tf.reduce_max(decoder_lengths),
dtype=tf.float32)
# Computing Baseline Mean Square Error Loss
with tf.variable_scope('baseline_scope'):
baseline_mse_loss = tf.minimum(
tf.square(value_target - values),
hps('clip_value_threshold'))
baseline_mse_loss = tf.reduce_sum(baseline_mse_loss) # ()
# Calculating surrogate loss
with tf.variable_scope('policy_gradient_scope'):
new_policy_log_probs = self.model.outputs[
'log_probs'] * sequence_mask # (b, dec_len)
old_policy_log_probs = old_log_probs * sequence_mask # (b, dec_len)
new_sum_log_probs = tf.reduce_sum(new_policy_log_probs,
axis=-1) # (b,)
old_sum_log_probs = tf.reduce_sum(old_policy_log_probs,
axis=-1) # (b,)
ratio = tf.math.exp(new_sum_log_probs -
old_sum_log_probs) # (b,)
clipped_ratio = tf.clip_by_value(ratio,
1. - hps('epsilon'), 1. +
hps('epsilon')) # (b,)
advantages = tf.stop_gradient(
normalize(reward_target - baseline)) # (b,)
surrogate_loss_1 = ratio * advantages # (b,)
surrogate_loss_2 = clipped_ratio * advantages # (b,)
surrogate_loss = -tf.reduce_mean(
tf.math.minimum(surrogate_loss_1,
surrogate_loss_2)) # ()
# Calculating policy gradient for draw action
with tf.variable_scope('draw_gradient_scope'):
draw_action = to_float(draw_action) # (b,)
draw_prob = self.model.outputs['draw_prob'] # (b,)
log_prob_of_draw = draw_action * tf.log(draw_prob) + (
1. - draw_action) * tf.log(1. - draw_prob)
draw_gradient_loss = -1. * log_prob_of_draw * advantages # (b,)
draw_gradient_loss = tf.reduce_mean(
draw_gradient_loss) # ()
# Calculating entropy loss
with tf.variable_scope('entropy_scope'):
entropy = tfp.distributions.Categorical(
logits=logits).entropy()
entropy_loss = -tf.reduce_mean(entropy) # ()
# Scopes
scope = ['policy', 'value', 'draw']
global_ignored_scope = None if not hps(
'ignored_scope') else hps('ignored_scope').split(',')
# Creating PPO loss
ppo_loss = surrogate_loss \
+ hps('value_coeff') * baseline_mse_loss \
+ hps('draw_coeff') * draw_gradient_loss \
+ hps('entropy_coeff') * entropy_loss
ppo_loss = tf.cond(
stop_gradient_all,
lambda: tf.stop_gradient(ppo_loss), # pylint: disable=cell-var-from-loop
lambda: ppo_loss) # pylint: disable=cell-var-from-loop
cost_and_scope = [(ppo_loss, scope, None)]
# Creating optimizer op
ppo_op = self.model.create_optimizer_op(
cost_and_scope=cost_and_scope,
ignored_scope=global_ignored_scope,
max_gradient_norm=hps('max_gradient_norm'))
# Making sure we are not using the AvgGradOptimizer, but directly the AdamOptimizer
assert not isinstance(
self.model.optimizer,
AvgGradOptimizer), 'PPO does not use AvgGradOptimizer'
# Storing outputs
self._add_output('rl_policy_loss', surrogate_loss)
self._add_output('rl_value_loss', baseline_mse_loss)
self._add_output('rl_draw_loss', draw_gradient_loss)
self._add_output('rl_entropy_loss', entropy_loss)
self._add_output('rl_total_loss', ppo_loss)
self._add_output('optimizer_op', ppo_op)
# --------------------------------------
# Hooks
# --------------------------------------
def hook_baseline_pre_condition(dataset):
""" Pre-Condition: First queue to run """
if not hasattr(dataset, 'last_queue') or dataset.last_queue == '':
return True
return False
def hook_baseline_post_queue(dataset):
""" Post-Queue: Marks the baseline queue as processed """
dataset.last_queue = 'ppo_policy_baseline'
# --------------------------------------
# Queues
# --------------------------------------
self.queue_dataset.create_queue(
'ppo_policy_baseline',
placeholders={
self.model.placeholders['decoder_type']: [TRAINING_DECODER]
},
outputs=[
self.model.outputs[output_name]
for output_name in ['optimizer_op'] +
self.get_evaluation_tags()
],
pre_condition=hook_baseline_pre_condition,
post_queue=hook_baseline_post_queue)
self.queue_dataset.create_queue(
'ppo_increase_version',
placeholders={
self.model.placeholders['decoder_type']: [GREEDY_DECODER]
},
outputs=[tf.assign_add(self.version_step, 1)],
with_status=True)
def _build_policy_initial(self):
""" Builds the policy model (initial step) """
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.initializers import uniform
from diplomacy_research.utils.tensorflow import build_sparse_batched_tensor, pad_axis, to_float, to_bool
if not self.placeholders:
self.placeholders = self.get_placeholders()
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
# Training loop
with tf.variable_scope('policy', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.cluster_config else None):
# Features
board_state = to_float(self.features['board_state']) # tf.flt32 - (b, NB_NODES, NB_FEATURES)
decoder_inputs = self.features['decoder_inputs'] # tf.int32 - (b, <= 1 + TOK/ORD * NB_SCS)
decoder_lengths = self.features['decoder_lengths'] # tf.int32 - (b,)
dropout_rates = self.features['dropout_rate'] # tf.flt32 - (b,)
# Batch size
batch_size = tf.shape(board_state)[0]
# Building decoder mask
decoder_mask_indices = self.features['decoder_mask_indices'] # tf.int64 - (b, 3 * len)
decoder_mask_shape = self.proto_fields['decoder_mask'].shape
# Overriding dropout_rates if pholder('dropout_rate') > 0
dropout_rates = tf.cond(tf.greater(pholder('dropout_rate'), 0.),
true_fn=lambda: tf.zeros_like(dropout_rates) + pholder('dropout_rate'),
false_fn=lambda: dropout_rates)
# Padding inputs
decoder_inputs = pad_axis(decoder_inputs, axis=-1, min_size=2)
decoder_mask_indices = pad_axis(decoder_mask_indices, axis=-1, min_size=len(decoder_mask_shape))
# Reshaping to (b, len, 3)
# decoder_mask is -- tf.bool (batch, TOK/ORD * NB_SC, VOCAB_SIZE, VOCAB_SIZE)
decoder_mask_indices = tf.reshape(decoder_mask_indices, [batch_size, -1, len(decoder_mask_shape)])
decoder_mask = build_sparse_batched_tensor(decoder_mask_indices,
value=True,
dtype=tf.bool,
dense_shape=decoder_mask_shape)
# Making sure all RNN lengths are at least 1
# No need to trim, because the fields are variable length
raw_decoder_lengths = decoder_lengths
decoder_lengths = tf.math.maximum(1, decoder_lengths)
# Placeholders
decoder_type = tf.reduce_max(pholder('decoder_type'))
# Creating word embedding vector (to embed word_ix)
# Embeddings needs to be cached locally on the worker, otherwise TF can't compute their gradients
with tf.variable_scope('word_embedding_scope'):
# embedding: (voc_size, 256)
caching_device = self.cluster_config.caching_device if self.cluster_config else None
word_embedding = uniform(name='word_embedding',
shape=[VOCABULARY_SIZE, hps('word_emb_size')],
scale=1.,
caching_device=caching_device)
# Building output tags
outputs = {'batch_size': batch_size,
'decoder_inputs': decoder_inputs,
'decoder_mask': decoder_mask,
'decoder_type': decoder_type,
'raw_decoder_lengths': raw_decoder_lengths,
'decoder_lengths': decoder_lengths,
'board_state_conv': tf.zeros([batch_size, NB_NODES, 0], dtype=tf.float32),
'board_state_0yr_conv': tf.zeros([batch_size, NB_NODES, 0], dtype=tf.float32),
'word_embedding': word_embedding,
'in_retreat_phase': tf.math.logical_and( # 1) board not empty, 2) disl. units present
tf.reduce_sum(board_state[:], axis=[1, 2]) > 0,
tf.math.logical_not(to_bool(tf.reduce_min(board_state[:, :, 23], -1))))}
# Adding to graph
self.add_meta_information(outputs)
def _build_policy_final(self):
""" Builds the policy model (final step) """
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.attention import AttentionWrapper, BahdanauAttention
from diplomacy_research.models.layers.beam_decoder import DiverseBeamSearchDecoder
from diplomacy_research.models.layers.decoder import CandidateBasicDecoder
from diplomacy_research.models.layers.dropout import SeededDropoutWrapper
from diplomacy_research.models.layers.dynamic_decode import dynamic_decode
from diplomacy_research.models.policy.order_based.helper import CustomHelper, CustomBeamHelper
from diplomacy_research.utils.tensorflow import cross_entropy, sequence_loss, to_int32, to_float, get_tile_beam
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
# Training loop
with tf.variable_scope('policy', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.cluster_config else None):
# Features
player_seeds = self.features['player_seed'] # tf.int32 - (b,)
temperature = self.features['temperature'] # tf,flt32 - (b,)
dropout_rates = self.features['dropout_rate'] # tf.flt32 - (b,)
# Placeholders
stop_gradient_all = pholder('stop_gradient_all')
# Outputs (from initial steps)
batch_size = self.outputs['batch_size']
decoder_inputs = self.outputs['decoder_inputs']
decoder_type = self.outputs['decoder_type']
raw_decoder_lengths = self.outputs['raw_decoder_lengths']
decoder_lengths = self.outputs['decoder_lengths']
board_state_conv = self.outputs['board_state_conv']
order_embedding = self.outputs['order_embedding']
candidate_embedding = self.outputs['candidate_embedding']
candidates = self.outputs['candidates']
max_candidate_length = self.outputs['max_candidate_length']
# --- Decoding ---
with tf.variable_scope('decoder_scope', reuse=tf.AUTO_REUSE):
lstm_cell = tf.contrib.rnn.LSTMBlockCell(hps('lstm_size'))
# ======== Regular Decoding ========
# Applying dropout to input + attention and to output layer
decoder_cell = SeededDropoutWrapper(cell=lstm_cell,
seeds=player_seeds,
input_keep_probs=1. - dropout_rates,
output_keep_probs=1. - dropout_rates,
variational_recurrent=hps('use_v_dropout'),
input_size=hps('order_emb_size') + hps('attn_size'),
dtype=tf.float32)
# apply attention over location
# curr_state [batch, NB_NODES, attn_size]
attention_scope = tf.VariableScope(name='policy/decoder_scope/Attention', reuse=tf.AUTO_REUSE)
attention_mechanism = BahdanauAttention(num_units=hps('attn_size'),
memory=board_state_conv,
normalize=True,
name_or_scope=attention_scope)
decoder_cell = AttentionWrapper(cell=decoder_cell,
attention_mechanism=attention_mechanism,
output_attention=False,
name_or_scope=attention_scope)
# Setting initial state
decoder_init_state = decoder_cell.zero_state(batch_size, tf.float32)
decoder_init_state = decoder_init_state.clone(attention=tf.reduce_mean(board_state_conv, axis=1))
# ---- Helper ----
helper = CustomHelper(decoder_type=decoder_type,
inputs=decoder_inputs[:, :-1],
order_embedding=order_embedding,
candidate_embedding=candidate_embedding,
sequence_length=decoder_lengths,
candidates=candidates,
time_major=False,
softmax_temperature=temperature)
# ---- Decoder ----
sequence_mask = tf.sequence_mask(raw_decoder_lengths,
maxlen=tf.reduce_max(decoder_lengths),
dtype=tf.float32)
maximum_iterations = NB_SUPPLY_CENTERS
model_decoder = CandidateBasicDecoder(cell=decoder_cell,
helper=helper,
initial_state=decoder_init_state,
max_candidate_length=max_candidate_length,
extract_state=True)
training_results, _, _ = dynamic_decode(decoder=model_decoder,
output_time_major=False,
maximum_iterations=maximum_iterations,
swap_memory=hps('swap_memory'))
global_vars_after_decoder = set(tf.global_variables())
# ======== Beam Search Decoding ========
tile_beam = get_tile_beam(hps('beam_width'))
# Applying dropout to input + attention and to output layer
decoder_cell = SeededDropoutWrapper(cell=lstm_cell,
seeds=tile_beam(player_seeds),
input_keep_probs=tile_beam(1. - dropout_rates),
output_keep_probs=tile_beam(1. - dropout_rates),
variational_recurrent=hps('use_v_dropout'),
input_size=hps('order_emb_size') + hps('attn_size'),
dtype=tf.float32)
# apply attention over location
# curr_state [batch, NB_NODES, attn_size]
attention_mechanism = BahdanauAttention(num_units=hps('attn_size'),
memory=tile_beam(board_state_conv),
normalize=True,
name_or_scope=attention_scope)
decoder_cell = AttentionWrapper(cell=decoder_cell,
attention_mechanism=attention_mechanism,
output_attention=False,
name_or_scope=attention_scope)
# Setting initial state
decoder_init_state = decoder_cell.zero_state(batch_size * hps('beam_width'), tf.float32)
decoder_init_state = decoder_init_state.clone(attention=tf.reduce_mean(tile_beam(board_state_conv),
axis=1))
# ---- Beam Helper and Decoder ----
beam_helper = CustomBeamHelper(cell=decoder_cell,
order_embedding=order_embedding,
candidate_embedding=candidate_embedding,
candidates=candidates,
sequence_length=decoder_lengths,
initial_state=decoder_init_state,
beam_width=hps('beam_width'))
beam_decoder = DiverseBeamSearchDecoder(beam_helper=beam_helper,
sequence_length=decoder_lengths,
nb_groups=hps('beam_groups'))
beam_results, beam_state, _ = dynamic_decode(decoder=beam_decoder,
output_time_major=False,
maximum_iterations=maximum_iterations,
swap_memory=hps('swap_memory'))
# Making sure we haven't created new global variables
assert not set(tf.global_variables()) - global_vars_after_decoder, 'New global vars were created'
# Processing results
candidate_logits = training_results.rnn_output # (b, dec_len, max_cand_len)
logits_length = tf.shape(candidate_logits)[1] # dec_len
decoder_target = decoder_inputs[:, 1:1 + logits_length]
# Selected tokens are the token that was actually fed at the next position
sample_mask = to_float(tf.math.equal(training_results.sample_id, -1))
selected_tokens = to_int32(
sequence_mask * (sample_mask * to_float(decoder_target)
+ (1. - sample_mask) * to_float(training_results.sample_id)))
# Computing ArgMax tokens
argmax_id = to_int32(tf.argmax(candidate_logits, axis=-1))
max_nb_candidate = tf.shape(candidate_logits)[2]
candidate_ids = \
tf.reduce_sum(tf.one_hot(argmax_id, max_nb_candidate, dtype=tf.int32) * candidates, axis=-1)
argmax_tokens = to_int32(to_float(candidate_ids) * sequence_mask)
# Extracting the position of the target candidate
tokens_labels = tf.argmax(to_int32(tf.math.equal(selected_tokens[:, :, None], candidates)), -1)
target_labels = tf.argmax(to_int32(tf.math.equal(decoder_target[:, :, None], candidates)), -1)
# Log Probs
log_probs = -1. * cross_entropy(logits=candidate_logits, labels=tokens_labels) * sequence_mask
# Computing policy loss
with tf.variable_scope('policy_loss'):
policy_loss = sequence_loss(logits=candidate_logits,
targets=target_labels,
weights=sequence_mask,
average_across_batch=True,
average_across_timesteps=True)
policy_loss = tf.cond(stop_gradient_all,
lambda: tf.stop_gradient(policy_loss), # pylint: disable=cell-var-from-loop
lambda: policy_loss) # pylint: disable=cell-var-from-loop
# Building output tags
outputs = {'tag/policy/order_based/v001_markovian_no_film': True,
'targets': decoder_inputs[:, 1:],
'selected_tokens': selected_tokens,
'argmax_tokens': argmax_tokens,
'logits': candidate_logits,
'log_probs': log_probs,
'beam_tokens': tf.transpose(beam_results.predicted_ids, perm=[0, 2, 1]), # [batch, beam, steps]
'beam_log_probs': beam_state.log_probs,
'rnn_states': training_results.rnn_state,
'policy_loss': policy_loss,
'draw_prob': self.outputs.get('draw_prob', tf.zeros_like(self.features['draw_target'])),
'learning_rate': self.learning_rate}
# Adding features, placeholders and outputs to graph
self.add_meta_information(outputs)
def _build_policy_initial(self):
""" Builds the policy model (initial step) """
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.initializers import uniform
from diplomacy_research.utils.tensorflow import pad_axis, to_int32, to_float, to_bool
if not self.placeholders:
self.placeholders = self.get_placeholders()
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
# Training loop
with tf.variable_scope('policy', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.cluster_config else None):
# Features
board_state = to_float(self.features['board_state']) # tf.flt32 - (b, NB_NODES, NB_FEATURES)
decoder_inputs = self.features['decoder_inputs'] # tf.int32 - (b, <= 1 + NB_SCS)
decoder_lengths = self.features['decoder_lengths'] # tf.int32 - (b,)
candidates = self.features['candidates'] # tf.int32 - (b, nb_locs * MAX_CANDIDATES)
dropout_rates = self.features['dropout_rate'] # tf.flt32 - (b,)
# Batch size
batch_size = tf.shape(board_state)[0]
# Overriding dropout_rates if pholder('dropout_rate') > 0
dropout_rates = tf.cond(tf.greater(pholder('dropout_rate'), 0.),
true_fn=lambda: tf.zeros_like(dropout_rates) + pholder('dropout_rate'),
false_fn=lambda: dropout_rates)
# Padding decoder_inputs and candidates
decoder_inputs = pad_axis(decoder_inputs, axis=-1, min_size=2)
candidates = pad_axis(candidates, axis=-1, min_size=MAX_CANDIDATES)
# Making sure all RNN lengths are at least 1
# No need to trim, because the fields are variable length
raw_decoder_lengths = decoder_lengths
decoder_lengths = tf.math.maximum(1, decoder_lengths)
# Placeholders
decoder_type = tf.reduce_max(pholder('decoder_type'))
is_training = pholder('is_training')
# Reshaping candidates
candidates = tf.reshape(candidates, [batch_size, -1, MAX_CANDIDATES])
candidates = candidates[:, :tf.reduce_max(decoder_lengths), :] # tf.int32 - (b, nb_locs, MAX_CAN)
# Creating graph convolution
with tf.variable_scope('graph_conv_scope'):
assert hps('nb_graph_conv') >= 2
# Encoding board state
board_state_0yr_conv = self.encode_board(board_state, name='board_state_conv')
board_state_conv = self.get_board_state_conv(board_state_0yr_conv, is_training)
# Creating order embedding vector (to embed order_ix)
# Embeddings needs to be cached locally on the worker, otherwise TF can't compute their gradients
with tf.variable_scope('order_embedding_scope'):
# embedding: (order_vocab_size, 64)
caching_device = self.cluster_config.caching_device if self.cluster_config else None
partitioner = tf.fixed_size_partitioner(NB_PARTITIONS) if hps('use_partitioner') else None
order_embedding = uniform(name='order_embedding',
shape=[ORDER_VOCABULARY_SIZE, hps('order_emb_size')],
scale=1.,
partitioner=partitioner,
caching_device=caching_device)
# Creating candidate embedding
with tf.variable_scope('candidate_embedding_scope'):
# embedding: (order_vocab_size, 64)
caching_device = self.cluster_config.caching_device if self.cluster_config else None
partitioner = tf.fixed_size_partitioner(NB_PARTITIONS) if hps('use_partitioner') else None
candidate_embedding = uniform(name='candidate_embedding',
shape=[ORDER_VOCABULARY_SIZE, hps('lstm_size') + 1],
scale=1.,
partitioner=partitioner,
caching_device=caching_device)
# Trimming to the maximum number of candidates
candidate_lengths = tf.reduce_sum(to_int32(tf.math.greater(candidates, PAD_ID)), -1) # int32 - (b,)
max_candidate_length = tf.math.maximum(1, tf.reduce_max(candidate_lengths))
candidates = candidates[:, :, :max_candidate_length]
# Building output tags
outputs = {'batch_size': batch_size,
'decoder_inputs': decoder_inputs,
'decoder_type': decoder_type,
'raw_decoder_lengths': raw_decoder_lengths,
'decoder_lengths': decoder_lengths,
'board_state_conv': board_state_conv,
'board_state_0yr_conv': board_state_0yr_conv,
'order_embedding': order_embedding,
'candidate_embedding': candidate_embedding,
'candidates': candidates,
'max_candidate_length': max_candidate_length,
'in_retreat_phase': tf.math.logical_and( # 1) board not empty, 2) disl. units present
tf.reduce_sum(board_state[:], axis=[1, 2]) > 0,
tf.math.logical_not(to_bool(tf.reduce_min(board_state[:, :, 23], -1))))}
# Adding to graph
self.add_meta_information(outputs)
def build(self):
""" Builds the RL model using the correct optimizer """
from diplomacy_research.utils.tensorflow import tf, tfp, normalize, to_float
from diplomacy_research.models.layers.avg_grad_optimizer import AvgGradOptimizer
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.model.hparams[hparam_name]
# Training loop
with tf.variable_scope('policy', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.
cluster_config else None):
# Placeholders
stop_gradient_all = self.model.placeholders[
'stop_gradient_all']
# Features
decoder_lengths = self.model.features[
'decoder_lengths'] # tf.int32 - (b,)
draw_action = self.model.features[
'draw_action'] # tf.bool - (b,)
reward_target = self.model.features[
'reward_target'] # tf.float32 - (b,)
value_target = self.model.features[
'value_target'] # tf.float32 - (b,)
# current_power = self.model.features['current_power'] # tf.int32 - (b,)
# Making sure all RNN lengths are at least 1
# Trimming to the maximum decoder length in the batch
raw_decoder_lengths = decoder_lengths
decoder_lengths = tf.math.maximum(1, decoder_lengths)
# Retrieving model outputs
# Using a fixed baseline (e.g. moving average) rather than a parameterized value function
baseline = value_target # (b,)
logits = self.model.outputs[
'logits'] # (b, dec_len, VOCAB_SIZE)
sequence_mask = tf.sequence_mask(
raw_decoder_lengths, # (b, dec)
maxlen=tf.reduce_max(decoder_lengths),
dtype=tf.float32)
# Calculating policy gradient loss
with tf.variable_scope('policy_gradient_scope'):
log_prob_of_tokens = self.model.outputs[
'log_probs'] * sequence_mask # (b, dec_len)
# Calculating loss and optimizer op
advantages = tf.stop_gradient(
normalize(reward_target - baseline)) # (b,)
policy_gradient_loss = -tf.reduce_sum(
log_prob_of_tokens, axis=-1) * advantages # (b,)
policy_gradient_loss = tf.reduce_mean(
policy_gradient_loss) # ()
# Calculating policy gradient for draw action
with tf.variable_scope('draw_gradient_scope'):
draw_action = to_float(draw_action) # (b,)
draw_prob = self.model.outputs['draw_prob'] # (b,)
log_prob_of_draw = draw_action * tf.log(draw_prob) + (
1. - draw_action) * tf.log(1. - draw_prob)
draw_gradient_loss = -1. * log_prob_of_draw * advantages # (b,)
draw_gradient_loss = tf.reduce_mean(
draw_gradient_loss) # ()
# Calculating entropy loss
with tf.variable_scope('entropy_scope'):
categorial_dist = tfp.distributions.Categorical(
logits=logits)
entropy = categorial_dist.entropy()
entropy_loss = -tf.reduce_mean(entropy) # ()
# Scopes
scope = ['policy', 'draw']
global_ignored_scope = [] if not hps('ignored_scope') else hps(
'ignored_scope').split(',')
global_ignored_scope += ['value']
# Creating REINFORCE loss with baseline
reinforce_loss = policy_gradient_loss \
+ hps('draw_coeff') * draw_gradient_loss \
+ hps('entropy_coeff') * entropy_loss
reinforce_loss = tf.cond(
stop_gradient_all,
lambda: tf.stop_gradient(reinforce_loss), # pylint: disable=cell-var-from-loop
lambda: reinforce_loss) # pylint: disable=cell-var-from-loop
cost_and_scope = [(reinforce_loss, scope, None)]
# Creating optimizer op
reinforce_op = self.model.create_optimizer_op(
cost_and_scope=cost_and_scope,
ignored_scope=global_ignored_scope,
max_gradient_norm=None) # AvgGradOptimizer will clip
# Getting AvgGradOptimizer.update(version_step)
assert isinstance(
self.model.optimizer,
AvgGradOptimizer), 'REINFORCE requires gradient averaging'
update_op = self.model.optimizer.update(self.version_step)
init_op = self.model.optimizer.init()
# Storing outputs
self._add_output('rl_policy_loss', policy_gradient_loss)
self._add_output('rl_draw_loss', draw_gradient_loss)
self._add_output('rl_entropy_loss', entropy_loss)
self._add_output('rl_total_loss', reinforce_loss)
self._add_output('optimizer_op', reinforce_op)
self._add_output('update_op', update_op)
self._add_output('init_op', init_op)
# --------------------------------------
# Hooks
# --------------------------------------
def hook_baseline_pre_condition(dataset):
""" Pre-Condition: First queue to run """
if not hasattr(dataset, 'last_queue') or dataset.last_queue == '':
return True
return False
def hook_baseline_post_queue(dataset):
""" Post-Queue: Marks the baseline queue as processed """
dataset.last_queue = 'reinforce_policy'
def hook_update_pre_condition(dataset):
""" Pre-Condition: last_queue must be baseline """
if hasattr(
dataset,
'last_queue') and dataset.last_queue == 'reinforce_policy':
return True
return False
def hook_update_pre_queue(dataset):
""" Pre-Queue: Restricts the queue to 1 dequeue maximum """
dataset.nb_items_to_pull_from_queue = min(
dataset.nb_items_to_pull_from_queue, 1)
def hook_update_post_queue(dataset):
""" Post-Queue: Marks the update as processed """
dataset.last_queue = 'reinforce_update'
# --------------------------------------
# Queues
# --------------------------------------
self.queue_dataset.create_queue(
'reinforce_policy',
placeholders={
self.model.placeholders['decoder_type']: [TRAINING_DECODER]
},
outputs=[
self.model.outputs[output_name]
for output_name in ['optimizer_op'] +
self.get_evaluation_tags()
],
with_status=True,
pre_condition=hook_baseline_pre_condition,
post_queue=hook_baseline_post_queue)
self.queue_dataset.create_queue(
'reinforce_update',
placeholders={
self.model.placeholders['decoder_type']: [GREEDY_DECODER]
},
outputs=[self.model.outputs['update_op']],
with_status=True,
pre_condition=hook_update_pre_condition,
pre_queue=hook_update_pre_queue,
post_queue=hook_update_post_queue)
self.queue_dataset.create_queue(
'optimizer_init',
placeholders={
self.model.placeholders['decoder_type']: [GREEDY_DECODER]
},
outputs=[self.model.outputs['init_op']],
with_status=True)
def _build_policy_initial(self):
""" Builds the policy model (initial step) """
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.initializers import uniform
from diplomacy_research.utils.tensorflow import pad_axis, to_int32, to_float, to_bool
if not self.placeholders:
self.placeholders = self.get_placeholders()
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
# Training loop
with tf.variable_scope('policy', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.
cluster_config else None):
# Features
board_state = to_float(
self.features['board_state']
) # tf.flt32 - (b, NB_NODES, NB_FEATURES)
board_alignments = to_float(
self.features['board_alignments']) # (b, NB_NODES * len)
prev_orders_state = to_float(
self.features['prev_orders_state']
) # (b, NB_PRV_OD, NB_ND, NB_OD_FT)
decoder_inputs = self.features[
'decoder_inputs'] # tf.int32 - (b, <= 1 + NB_SCS)
decoder_lengths = self.features[
'decoder_lengths'] # tf.int32 - (b,)
candidates = self.features[
'candidates'] # tf.int32 - (b, nb_locs * MAX_CANDIDATES)
current_power = self.features[
'current_power'] # tf.int32 - (b,)
current_season = self.features[
'current_season'] # tf.int32 - (b,)
dropout_rates = self.features[
'dropout_rate'] # tf.flt32 - (b,)
# Batch size
batch_size = tf.shape(board_state)[0]
# Reshaping board alignments
board_alignments = tf.reshape(board_alignments,
[batch_size, -1, NB_NODES])
board_alignments /= tf.math.maximum(
1., tf.reduce_sum(board_alignments, axis=-1,
keepdims=True))
# Overriding dropout_rates if pholder('dropout_rate') > 0
dropout_rates = tf.cond(
tf.greater(pholder('dropout_rate'), 0.),
true_fn=lambda: tf.zeros_like(dropout_rates) + pholder(
'dropout_rate'),
false_fn=lambda: dropout_rates)
# Padding decoder_inputs and candidates
board_alignments = pad_axis(
board_alignments,
axis=1,
min_size=tf.reduce_max(decoder_lengths))
decoder_inputs = pad_axis(decoder_inputs, axis=-1, min_size=2)
candidates = pad_axis(candidates,
axis=-1,
min_size=MAX_CANDIDATES)
# Making sure all RNN lengths are at least 1
# No need to trim, because the fields are variable length
raw_decoder_lengths = decoder_lengths
decoder_lengths = tf.math.maximum(1, decoder_lengths)
# Placeholders
decoder_type = tf.reduce_max(pholder('decoder_type'))
is_training = pholder('is_training')
# Reshaping candidates
candidates = tf.reshape(candidates,
[batch_size, -1, MAX_CANDIDATES])
candidates = candidates[:, :tf.reduce_max(
decoder_lengths), :] # tf.int32 - (b, nb_locs, MAX_CAN)
# Computing FiLM Gammas and Betas
with tf.variable_scope('film_scope'):
power_embedding = uniform(
name='power_embedding',
shape=[NB_POWERS, hps('power_emb_size')],
scale=1.)
current_power_mask = tf.one_hot(current_power,
NB_POWERS,
dtype=tf.float32)
current_power_embedding = tf.reduce_sum(
power_embedding[None] * current_power_mask[:, :, None],
axis=1) # (b, power_emb)
film_embedding_input = current_power_embedding
# Also conditioning on current_season
season_embedding = uniform(
name='season_embedding',
shape=[NB_SEASONS, hps('season_emb_size')],
scale=1.)
current_season_mask = tf.one_hot(current_season,
NB_SEASONS,
dtype=tf.float32)
current_season_embedding = tf.reduce_sum(
season_embedding[None] # (b,season_emb)
* current_season_mask[:, :, None],
axis=1)
film_embedding_input = tf.concat(
[film_embedding_input, current_season_embedding],
axis=1)
film_output_dims = [hps('gcn_size')] * (
hps('nb_graph_conv') - 1) + [hps('attn_size') // 2]
# For board_state
board_film_weights = tf.layers.Dense(
units=2 * sum(film_output_dims), # (b, 1, 750)
use_bias=True,
activation=None)(film_embedding_input)[:, None, :]
board_film_gammas, board_film_betas = tf.split(
board_film_weights, 2, axis=2) # (b, 1, 750)
board_film_gammas = tf.split(board_film_gammas,
film_output_dims,
axis=2)
board_film_betas = tf.split(board_film_betas,
film_output_dims,
axis=2)
# For prev_orders
prev_ord_film_weights = tf.layers.Dense(
units=2 * sum(film_output_dims), # (b, 1, 750)
use_bias=True,
activation=None)(film_embedding_input)[:, None, :]
prev_ord_film_weights = tf.tile(
prev_ord_film_weights,
[NB_PREV_ORDERS, 1, 1]) # (n_pr, 1, 750)
prev_ord_film_gammas, prev_ord_film_betas = tf.split(
prev_ord_film_weights, 2, axis=2)
prev_ord_film_gammas = tf.split(prev_ord_film_gammas,
film_output_dims,
axis=2)
prev_ord_film_betas = tf.split(prev_ord_film_betas,
film_output_dims,
axis=2)
# Storing as temporary output
self.add_output('_board_state_conv_film_gammas',
board_film_gammas)
self.add_output('_board_state_conv_film_betas',
board_film_betas)
self.add_output('_prev_orders_conv_film_gammas',
prev_ord_film_gammas)
self.add_output('_prev_orders_conv_film_betas',
prev_ord_film_betas)
# Creating graph convolution
with tf.variable_scope('graph_conv_scope'):
assert hps('nb_graph_conv') >= 2
assert hps('attn_size') % 2 == 0
# Encoding board state
board_state_0yr_conv = self.encode_board(
board_state, name='board_state_conv')
# Encoding prev_orders
prev_orders_state = tf.reshape(prev_orders_state, [
batch_size * NB_PREV_ORDERS, NB_NODES,
NB_ORDERS_FEATURES
])
prev_ord_conv = self.encode_board(prev_orders_state,
name='prev_orders_conv')
# Splitting back into (b, nb_prev, NB_NODES, attn_size // 2)
# Reducing the prev ord conv using avg
prev_ord_conv = tf.reshape(prev_ord_conv, [
batch_size, NB_PREV_ORDERS, NB_NODES,
hps('attn_size') // 2
])
prev_ord_conv = tf.reduce_mean(prev_ord_conv, axis=1)
# Concatenating the current board conv with the prev ord conv
# The final board_state_conv should be of dimension (b, NB_NODE, attn_size)
board_state_conv = self.get_board_state_conv(
board_state_0yr_conv, is_training, prev_ord_conv)
# Creating order embedding vector (to embed order_ix)
# Embeddings needs to be cached locally on the worker, otherwise TF can't compute their gradients
with tf.variable_scope('order_embedding_scope'):
# embedding: (order_vocab_size, 64)
caching_device = self.cluster_config.caching_device if self.cluster_config else None
partitioner = tf.fixed_size_partitioner(
NB_PARTITIONS) if hps('use_partitioner') else None
order_embedding = uniform(
name='order_embedding',
shape=[ORDER_VOCABULARY_SIZE,
hps('order_emb_size')],
scale=1.,
partitioner=partitioner,
caching_device=caching_device)
# Creating candidate embedding
with tf.variable_scope('candidate_embedding_scope'):
# embedding: (order_vocab_size, 64)
caching_device = self.cluster_config.caching_device if self.cluster_config else None
partitioner = tf.fixed_size_partitioner(
NB_PARTITIONS) if hps('use_partitioner') else None
candidate_embedding = uniform(
name='candidate_embedding',
shape=[ORDER_VOCABULARY_SIZE,
hps('lstm_size') + 1],
scale=1.,
partitioner=partitioner,
caching_device=caching_device)
# Trimming to the maximum number of candidates
candidate_lengths = tf.reduce_sum(
to_int32(tf.math.greater(candidates, PAD_ID)),
-1) # int32 - (b,)
max_candidate_length = tf.math.maximum(
1, tf.reduce_max(candidate_lengths))
candidates = candidates[:, :, :max_candidate_length]
# Building output tags
outputs = {
'batch_size':
batch_size,
'board_alignments':
board_alignments,
'decoder_inputs':
decoder_inputs,
'decoder_type':
decoder_type,
'raw_decoder_lengths':
raw_decoder_lengths,
'decoder_lengths':
decoder_lengths,
'board_state_conv':
board_state_conv,
'board_state_0yr_conv':
board_state_0yr_conv,
'prev_ord_conv':
prev_ord_conv,
'order_embedding':
order_embedding,
'candidate_embedding':
candidate_embedding,
'candidates':
candidates,
'max_candidate_length':
max_candidate_length,
'in_retreat_phase':
tf.math.logical_and( # 1) board not empty, 2) disl. units present
tf.reduce_sum(board_state[:], axis=[1, 2]) > 0,
tf.math.logical_not(
to_bool(tf.reduce_min(board_state[:, :, 23], -1))))
}
# Adding to graph
self.add_meta_information(outputs)
def _build_value_final(self):
""" Builds the value model (final step) """
from diplomacy_research.utils.tensorflow import tf
if not self.placeholders:
self.placeholders = self.get_placeholders()
else:
self.placeholders.update(self.get_placeholders())
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
relu = tf.nn.relu
# Training loop
with tf.variable_scope('value', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.
cluster_config else None):
# Outputs from the policy model
assert 'rnn_states' in self.outputs
# Inputs and Features
rnn_states = self.outputs['rnn_states']
current_power = self.features[
'current_power'] # tf.int32 - (b,)
value_target = self.features[
'value_target'] # tf.float32 - (b,)
# Placeholders
stop_gradient_all = pholder('stop_gradient_all')
# Computing the value
value_h0 = tf.stop_gradient(rnn_states) if hps(
'stop_gradient_value') else rnn_states
value_h0_pos_0 = value_h0[:, 0, :] # (b, lstm_size)
# Linear with relu
# Then linear without relu
value_h1_pos_0 = tf.layers.Dense(
units=hps('value_h1_size'), # (b, 256)
use_bias=True,
activation=relu)(value_h0_pos_0)
value_h2_pos_0 = tf.layers.Dense(
units=NB_POWERS, # (b, 7)
use_bias=True,
activation=None)(value_h1_pos_0)
# Computing for the current power
current_power_mask = tf.one_hot(current_power,
NB_POWERS,
dtype=tf.float32)
state_value = tf.reduce_sum(current_power_mask *
value_h2_pos_0,
axis=-1) # (b,)
# Computing value loss
with tf.variable_scope('value_loss'):
value_loss = tf.reduce_mean(
tf.square(value_target - state_value))
value_loss = tf.cond(
stop_gradient_all,
lambda: tf.stop_gradient(value_loss), # pylint: disable=cell-var-from-loop
lambda: value_loss) # pylint: disable=cell-var-from-loop
# Building output tags
outputs = {
'tag/value/v003_rnn_step_0': True,
'state_value': state_value,
'value_loss': value_loss
}
# Adding features, placeholders and outputs to graph
self.add_meta_information(outputs)
def _build_policy_final(self):
""" Builds the policy model (final step) """
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.attention import StaticAttentionWrapper
from diplomacy_research.models.layers.beam_decoder import DiverseBeamSearchDecoder
from diplomacy_research.models.layers.decoder import CandidateBasicDecoder
from diplomacy_research.models.layers.dropout import SeededDropoutWrapper
from diplomacy_research.models.layers.dynamic_decode import dynamic_decode
from diplomacy_research.models.layers.initializers import uniform
from diplomacy_research.models.layers.transformer import TransformerCell
from diplomacy_research.models.layers.wrappers import IdentityCell
from diplomacy_research.models.policy.order_based.helper import CustomHelper, CustomBeamHelper
from diplomacy_research.utils.tensorflow import cross_entropy, sequence_loss, to_int32, to_float, get_tile_beam
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
pholder = lambda placeholder_name: self.placeholders[placeholder_name]
# Training loop
with tf.variable_scope('policy', reuse=tf.AUTO_REUSE):
with tf.device(self.cluster_config.worker_device if self.
cluster_config else None):
# Features
player_seeds = self.features['player_seed'] # tf.int32 - (b,)
temperature = self.features['temperature'] # tf,flt32 - (b,)
dropout_rates = self.features[
'dropout_rate'] # tf.flt32 - (b,)
# Placeholders
stop_gradient_all = pholder('stop_gradient_all')
# Outputs (from initial steps)
batch_size = self.outputs['batch_size']
board_alignments = self.outputs['board_alignments']
decoder_inputs = self.outputs['decoder_inputs']
decoder_type = self.outputs['decoder_type']
raw_decoder_lengths = self.outputs['raw_decoder_lengths']
decoder_lengths = self.outputs['decoder_lengths']
board_state_conv = self.outputs['board_state_conv']
order_embedding = self.outputs['order_embedding']
candidate_embedding = self.outputs['candidate_embedding']
candidates = self.outputs['candidates']
max_candidate_length = self.outputs['max_candidate_length']
# Creating a smaller position embedding if it's not present in the outputs
# Embeddings needs to be cached locally on the worker, otherwise TF can't compute their gradients
with tf.variable_scope('position_embedding_scope'):
caching_device = self.cluster_config.caching_device if self.cluster_config else None
position_embedding = uniform(
name='position_embedding',
shape=[NB_SUPPLY_CENTERS,
hps('trsf_emb_size')],
scale=1.,
caching_device=caching_device)
# Past Attentions
past_attentions, message_lengths = None, None
# --- Decoding ---
with tf.variable_scope('decoder_scope', reuse=tf.AUTO_REUSE):
feeder_cell = IdentityCell(
output_size=hps('trsf_emb_size') + hps('attn_size'))
# ======== Regular Decoding ========
# Applying Dropout to input, attention and output
feeder_cell = SeededDropoutWrapper(
cell=feeder_cell,
seeds=player_seeds,
input_keep_probs=1. - dropout_rates,
variational_recurrent=hps('use_v_dropout'),
input_size=hps('trsf_emb_size') + hps('attn_size'),
dtype=tf.float32)
# Apply attention over orderable location at each position
feeder_cell = StaticAttentionWrapper(
cell=feeder_cell,
memory=board_state_conv,
alignments=board_alignments,
sequence_length=raw_decoder_lengths,
output_attention=False)
# Setting initial state
feeder_cell_init_state = feeder_cell.zero_state(
batch_size, tf.float32)
# ---- Helper ----
helper = CustomHelper(
decoder_type=decoder_type,
inputs=decoder_inputs[:, :-1],
order_embedding=order_embedding,
candidate_embedding=candidate_embedding,
sequence_length=decoder_lengths,
candidates=candidates,
time_major=False,
softmax_temperature=temperature)
# ---- Transformer Cell ----
trsf_scope = tf.VariableScope(
name='policy/training_scope/transformer', reuse=False)
transformer_cell = TransformerCell(
nb_layers=hps('trsf_nb_layers'),
nb_heads=hps('trsf_nb_heads'),
word_embedding=order_embedding,
position_embedding=position_embedding,
batch_size=batch_size,
feeder_cell=feeder_cell,
feeder_init_state=feeder_cell_init_state,
past_attentions=past_attentions,
past_seq_lengths=message_lengths,
scope=trsf_scope,
name='transformer')
transformer_cell_init_state = transformer_cell.zero_state(
batch_size, tf.float32)
# ---- Invariants ----
invariants_map = {
'past_attentions':
tf.TensorShape([
None, # batch size
hps('trsf_nb_layers'), # nb_layers
2, # key, value
hps('trsf_nb_heads'), # nb heads
None, # Seq len
hps('trsf_emb_size') // hps('trsf_nb_heads')
])
} # Head size
# ---- Decoder ----
sequence_mask = tf.sequence_mask(
raw_decoder_lengths,
maxlen=tf.reduce_max(decoder_lengths),
dtype=tf.float32)
maximum_iterations = NB_SUPPLY_CENTERS
model_decoder = CandidateBasicDecoder(
cell=transformer_cell,
helper=helper,
initial_state=transformer_cell_init_state,
max_candidate_length=max_candidate_length,
extract_state=True)
training_results, _, _ = dynamic_decode(
decoder=model_decoder,
output_time_major=False,
maximum_iterations=maximum_iterations,
invariants_map=invariants_map,
swap_memory=hps('swap_memory'))
global_vars_after_decoder = set(tf.global_variables())
# ======== Beam Search Decoding ========
tile_beam = get_tile_beam(hps('beam_width'))
beam_feeder_cell = IdentityCell(
output_size=hps('trsf_emb_size') + hps('attn_size'))
# Applying Dropout to input, attention and output
beam_feeder_cell = SeededDropoutWrapper(
cell=beam_feeder_cell,
seeds=tile_beam(player_seeds),
input_keep_probs=tile_beam(1. - dropout_rates),
variational_recurrent=hps('use_v_dropout'),
input_size=hps('trsf_emb_size') + hps('attn_size'),
dtype=tf.float32)
# Apply attention over orderable location at each position
beam_feeder_cell = StaticAttentionWrapper(
cell=beam_feeder_cell,
memory=tile_beam(board_state_conv),
alignments=tile_beam(board_alignments),
sequence_length=tile_beam(raw_decoder_lengths),
output_attention=False)
# Setting initial state
beam_feeder_init_state = beam_feeder_cell.zero_state(
batch_size * hps('beam_width'), tf.float32)
# ---- Transformer Cell ----
trsf_scope = tf.VariableScope(
name='policy/training_scope/transformer', reuse=True)
beam_trsf_cell = TransformerCell(
nb_layers=hps('trsf_nb_layers'),
nb_heads=hps('trsf_nb_heads'),
word_embedding=order_embedding,
position_embedding=position_embedding,
batch_size=batch_size * hps('beam_width'),
feeder_cell=beam_feeder_cell,
feeder_init_state=beam_feeder_init_state,
past_attentions=tile_beam(past_attentions),
past_seq_lengths=tile_beam(message_lengths),
scope=trsf_scope,
name='transformer')
beam_trsf_cell_init_state = beam_trsf_cell.zero_state(
batch_size * hps('beam_width'), tf.float32)
# ---- Beam Helper and Decoder ----
beam_helper = CustomBeamHelper(
cell=beam_trsf_cell,
order_embedding=order_embedding,
candidate_embedding=candidate_embedding,
candidates=candidates,
sequence_length=decoder_lengths,
initial_state=beam_trsf_cell_init_state,
beam_width=hps('beam_width'))
beam_decoder = DiverseBeamSearchDecoder(
beam_helper=beam_helper,
sequence_length=decoder_lengths,
nb_groups=hps('beam_groups'))
beam_results, beam_state, _ = dynamic_decode(
decoder=beam_decoder,
output_time_major=False,
maximum_iterations=maximum_iterations,
invariants_map=invariants_map,
swap_memory=hps('swap_memory'))
# Making sure we haven't created new global variables
assert not set(
tf.global_variables()
) - global_vars_after_decoder, 'New global vars were created'
# Processing results
candidate_logits = training_results.rnn_output # (b, dec_len, max_cand_len)
logits_length = tf.shape(candidate_logits)[1] # dec_len
decoder_target = decoder_inputs[:, 1:1 + logits_length]
# Selected tokens are the token that was actually fed at the next position
sample_mask = to_float(
tf.math.equal(training_results.sample_id, -1))
selected_tokens = to_int32(
sequence_mask *
(sample_mask * to_float(decoder_target) +
(1. - sample_mask) *
to_float(training_results.sample_id)))
# Computing ArgMax tokens
argmax_id = to_int32(tf.argmax(candidate_logits, axis=-1))
max_nb_candidate = tf.shape(candidate_logits)[2]
candidate_ids = \
tf.reduce_sum(tf.one_hot(argmax_id, max_nb_candidate, dtype=tf.int32) * candidates, axis=-1)
argmax_tokens = to_int32(
to_float(candidate_ids) * sequence_mask)
# Extracting the position of the target candidate
tokens_labels = tf.argmax(
to_int32(
tf.math.equal(selected_tokens[:, :, None],
candidates)), -1)
target_labels = tf.argmax(
to_int32(
tf.math.equal(decoder_target[:, :, None],
candidates)), -1)
# Log Probs
log_probs = -1. * cross_entropy(
logits=candidate_logits,
labels=tokens_labels) * sequence_mask
# Computing policy loss
with tf.variable_scope('policy_loss'):
policy_loss = sequence_loss(logits=candidate_logits,
targets=target_labels,
weights=sequence_mask,
average_across_batch=True,
average_across_timesteps=True)
policy_loss = tf.cond(
stop_gradient_all,
lambda: tf.stop_gradient(policy_loss), # pylint: disable=cell-var-from-loop
lambda: policy_loss) # pylint: disable=cell-var-from-loop
# Building output tags
outputs = {
'tag/policy/order_based/v015_film_transformer_gpt':
True,
'targets':
decoder_inputs[:, 1:],
'selected_tokens':
selected_tokens,
'argmax_tokens':
argmax_tokens,
'logits':
candidate_logits,
'log_probs':
log_probs,
'beam_tokens':
tf.transpose(beam_results.predicted_ids,
perm=[0, 2, 1]), # [batch, beam, steps]
'beam_log_probs':
beam_state.log_probs,
'rnn_states':
training_results.rnn_state,
'policy_loss':
policy_loss,
'draw_prob':
self.outputs.get('draw_prob',
tf.zeros_like(self.features['draw_target'])),
'learning_rate':
self.learning_rate
}
# Adding features, placeholders and outputs to graph
self.add_meta_information(outputs)
def _get_board_value(self,
board_state,
current_power,
name='board_state_value',
reuse=None):
""" Computes the estimated value of a board state
:param board_state: The board state - (batch, NB_NODES, NB_FEATURES)
:param current_power: The power for which we want the board value - (batch,)
:param name: The name to use for the operaton
:param reuse: Whether to reuse or not the weights from another operation
:return: The value of the board state for the specified power - (batch,)
"""
from diplomacy_research.utils.tensorflow import tf
from diplomacy_research.models.layers.graph_convolution import GraphConvolution, preprocess_adjacency
# Quick function to retrieve hparams and placeholders and function shorthands
hps = lambda hparam_name: self.hparams[hparam_name]
relu = tf.nn.relu
# Computing norm adjacency
norm_adjacency = preprocess_adjacency(get_adjacency_matrix())
norm_adjacency = tf.tile(tf.expand_dims(norm_adjacency, axis=0),
[tf.shape(board_state)[0], 1, 1])
# Building scope
# No need to use 'stop_gradient_value' - Because this model does not share parameters.
scope = tf.VariableScope(name='value/%s' % name, reuse=reuse)
with tf.variable_scope(scope):
with tf.variable_scope('graph_conv_scope'):
graph_conv = board_state # (b, NB_NODES, NB_FEAT)
graph_conv = GraphConvolution(
input_dim=graph_conv.shape[-1].
value, # (b, NB_NODES, gcn_1)
output_dim=hps('value_gcn_1_output_size'),
norm_adjacency=norm_adjacency,
activation_fn=relu,
bias=True)(graph_conv)
flat_graph_conv = tf.reshape(
graph_conv,
shape=[-1, NB_NODES * hps('value_gcn_1_output_size')])
flat_graph_conv = tf.layers.Dense(
units=hps('value_embedding_size'),
activation=relu,
use_bias=True)(flat_graph_conv) # (b, value_emb_size)
with tf.variable_scope('value_scope'):
current_power_mask = tf.one_hot(current_power,
NB_POWERS,
dtype=tf.float32)
state_value = flat_graph_conv # (b, value_emb_size)
state_value = tf.layers.Dense(
units=hps('value_h1_size'), # (b, value_h1_size)
activation=relu,
use_bias=True)(state_value)
state_value = tf.layers.Dense(
units=hps('value_h2_size'), # (b, value_h2_size)
activation=relu,
use_bias=True)(state_value)
state_value = tf.layers.Dense(
units=NB_POWERS, # (b, NB_POWERS)
activation=None,
use_bias=True)(state_value)
state_value = tf.reduce_sum(state_value * current_power_mask,
axis=1) # (b,)
# Returning
return state_value
