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 _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_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_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_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 gradients(ys, xs, grad_ys=None, checkpoints='collection', aggregation_method=None, **kwargs):
""" Authors: Tim Salimans and Yaroslav Bulatov
Memory efficient gradient implementation inspired by "Training Deep Nets with Sublinear Memory Cost"
by Chen et al. 2016 (https://arxiv.org/abs/1604.06174)
:param ys: Tensor or list of tensors.
:param xs: Tensor or list of tensors
:param grad_ys: List of tensors holding the gradients received by the ys. Same length as ys.
:param checkpoints: One of
1) a list consisting of tensors from the forward pass of the neural net that we should re-use when
calculating the gradients in the backward pass all other tensors that do not appear in this list will
be re-computed
2) The string 'speed': checkpoint all outputs of convolutions and matmuls. these ops are usually the most
expensive, so checkpointing them maximizes the running speed (this is a good option
if nonlinearities, concats, batchnorms, etc are taking up a lot of memory)
3) The string 'memory': try to minimize the memory usage (currently using a very simple strategy that
identifies a number of bottleneck tensors in the graph to checkpoint)
4) The string 'collection': look for a tensorflow collection named 'checkpoints', which holds the tensors
to checkpoint
:param aggregation_method:
:param kwargs: Optional kwargs to pass to tf.gradients
:return: The gradients
"""
# pylint: disable=invalid-name
# Computes forwards and backwards ops
# Forward ops are all ops that are candidates for recomputation
ys = [ys] if not isinstance(ys, list) else ys
xs = [xs] if not isinstance(xs, list) else xs
bwd_ops = graph_editor.get_backward_walk_ops([y.op for y in ys], inclusive=True)
fwd_ops = graph_editor.get_forward_walk_ops([x.op for x in xs], inclusive=True, within_ops=bwd_ops)
debug_print("bwd_ops: %s", bwd_ops)
debug_print("fwd_ops: %s", fwd_ops)
# Exclude ops with no inputs, or ops linked to xs or variables
xs_ops = _to_ops(xs)
fwd_ops = [op for op in fwd_ops if (op.inputs
and not op in xs_ops
and not '/assign' in op.name
and not '/Assign' in op.name
and not '/read' in op.name)]
# Computes the list of tensors that can be recomputed from fw_ops
ts_all = graph_editor.filter_ts(fwd_ops, True)
ts_all = [t for t in ts_all if '/read' not in t.name]
ts_all = set(ts_all) - set(xs) - set(ys)
# Construct list of tensors to checkpoint during forward pass, if not given as input
# At this point automatic selection happened and checkpoints is list of nodes
if not isinstance(checkpoints, list):
checkpoints = {'collection': _get_collection_checkpoints,
'speed': _get_speed_checkpoints,
'memory': _get_memory_checkpoints}[checkpoints](fwd_ops, ts_all,
ys=ys, xs=xs, grad_ys=grad_ys,
aggregation_method=aggregation_method,
**kwargs)
checkpoints = list(set(checkpoints).intersection(ts_all))
assert isinstance(checkpoints, list)
debug_print("Checkpoint nodes used: %s", checkpoints)
# Better error handling of special cases
# xs are already handled as checkpoint nodes, so no need to include them
xs_intersect_checkpoints = set(xs).intersection(set(checkpoints))
if xs_intersect_checkpoints:
debug_print('Warning, some input nodes are also checkpoint nodes: %s', xs_intersect_checkpoints)
ys_intersect_checkpoints = set(ys).intersection(set(checkpoints))
debug_print('ys: %s, checkpoints: %s, intersect: %s', ys, checkpoints, ys_intersect_checkpoints)
# Saving an output node (ys) gives no benefit in memory while creating new edge cases, exclude them
if ys_intersect_checkpoints:
debug_print('Warning, some output nodes are also checkpoints nodes: %s', ys_intersect_checkpoints)
# Remove initial and terminal nodes from checkpoints list if present
# Only keeping checkpoints not in a control flow context
checkpoints = list(set(checkpoints) - set(ys) - set(xs))
checkpoints = [ckpt for ckpt in checkpoints if ckpt._op._control_flow_context is None] # pylint: disable=protected-access
# Check that we have some nodes to checkpoint
if not checkpoints:
raise RuntimeError('No checkpoints nodes found or given as input!')
# Disconnect dependencies between checkpointed tensors
checkpoints_disconnected = {}
for ckpt in checkpoints:
if ckpt.op and ckpt.op.name is not None:
grad_node = tf.stop_gradient(ckpt, name=ckpt.op.name + '_stop_grad')
else:
grad_node = tf.stop_gradient(ckpt)
checkpoints_disconnected[ckpt] = grad_node
# Partial derivatives to the checkpointed tensors and xs
ops_to_copy = fast_backward_ops(seed_ops=[y.op for y in ys], stop_at_ts=checkpoints, within_ops=fwd_ops)
debug_print('Found %s ops to copy within fwd_ops %s, seed %s, stop_at %s',
len(ops_to_copy), fwd_ops, [r.op for r in ys], checkpoints)
debug_print('ops_to_copy = %s', ops_to_copy)
debug_print('Processing list %s', ys)
_, info = graph_editor.copy_with_input_replacements(graph_editor.sgv(ops_to_copy), {})
for origin_op, op in info._transformed_ops.items(): # pylint: disable=protected-access
op._set_device(origin_op.node_def.device) # pylint: disable=protected-access
copied_ops = info._transformed_ops.values() # pylint: disable=protected-access
debug_print('Copied %s to %s', ops_to_copy, copied_ops)
graph_editor.reroute_ts(checkpoints_disconnected.values(), checkpoints_disconnected.keys(), can_modify=copied_ops)
debug_print('Rewired %s in place of %s restricted to %s',
checkpoints_disconnected.values(), checkpoints_disconnected.keys(), copied_ops)
# Get gradients with respect to current boundary + original x's
copied_ys = [info._transformed_ops[y.op]._outputs[0] for y in ys] # pylint: disable=protected-access
boundary = list(checkpoints_disconnected.values())
dv = TF_GRADIENTS(ys=copied_ys,
xs=boundary + xs,
grad_ys=grad_ys,
aggregation_method=aggregation_method,
**kwargs)
debug_print('Got gradients %s', dv)
debug_print('for %s', copied_ys)
debug_print('with respect to %s', boundary + xs)
# Adding control inputs to the graph
inputs_to_do_before = [y.op for y in ys]
if grad_ys is not None:
inputs_to_do_before += grad_ys
wait_to_do_ops = list(copied_ops) + [g.op for g in dv if g is not None]
my_add_control_inputs(wait_to_do_ops, inputs_to_do_before)
# Partial derivatives to the checkpointed nodes
# Dictionary of "node: backprop" for nodes in the boundary
# Partial derivatives to xs (usually the params of the neural net)
d_checkpoints = {r: dr for r, dr in zip(checkpoints_disconnected.keys(), dv[:len(checkpoints_disconnected)])}
d_xs = dv[len(checkpoints_disconnected):]
# Incorporate derivatives flowing through the checkpointed nodes
checkpoints_sorted_lists = tf_toposort(checkpoints, within_ops=fwd_ops)
for ts in checkpoints_sorted_lists[::-1]:
debug_print('Processing list %s', ts)
checkpoints_other = [r for r in checkpoints if r not in ts]
checkpoints_disconnected_other = [checkpoints_disconnected[r] for r in checkpoints_other]
# Copy part of the graph below current checkpoint node, stopping at other checkpoints nodes
ops_to_copy = fast_backward_ops(within_ops=fwd_ops, seed_ops=[r.op for r in ts], stop_at_ts=checkpoints_other)
debug_print('Found %s ops to copy within %s, seed %s, stop_at %s',
len(ops_to_copy), fwd_ops, [r.op for r in ts], checkpoints_other)
debug_print('ops_to_copy = %s', ops_to_copy)
# We are done!
if not ops_to_copy:
break
_, info = graph_editor.copy_with_input_replacements(graph_editor.sgv(ops_to_copy), {})
for origin_op, op in info._transformed_ops.items(): # pylint: disable=protected-access
op._set_device(origin_op.node_def.device) # pylint: disable=protected-access
copied_ops = info._transformed_ops.values() # pylint: disable=protected-access
debug_print('Copied %s to %s', ops_to_copy, copied_ops)
graph_editor.reroute_ts(checkpoints_disconnected_other, checkpoints_other, can_modify=copied_ops)
debug_print('Rewired %s in place of %s restricted to %s',
checkpoints_disconnected_other, checkpoints_other, copied_ops)
# Gradient flowing through the checkpointed node
boundary = [info._transformed_ops[r.op]._outputs[0] for r in ts] # pylint: disable=protected-access
substitute_backprops = [d_checkpoints[r] for r in ts]
dv = TF_GRADIENTS(ys=boundary,
xs=checkpoints_disconnected_other + xs,
grad_ys=substitute_backprops,
aggregation_method=aggregation_method,
**kwargs)
debug_print("Got gradients %s", dv)
debug_print("for %s", boundary)
debug_print("with respect to %s", checkpoints_disconnected_other + xs)
debug_print("with boundary backprop substitutions %s", substitute_backprops)
# Adding control inputs
inputs_to_do_before = [d_checkpoints[r].op for r in ts]
wait_to_do_ops = list(copied_ops) + [g.op for g in dv if g is not None]
my_add_control_inputs(wait_to_do_ops, inputs_to_do_before)
# Partial derivatives to the checkpointed nodes
for r, dr in zip(checkpoints_other, dv[:len(checkpoints_other)]):
if dr is not None:
if d_checkpoints[r] is None:
d_checkpoints[r] = dr
else:
d_checkpoints[r] += dr
# Partial derivatives to xs (usually the params of the neural net)
d_xs_new = dv[len(checkpoints_other):]
for j in range(len(xs)):
if d_xs_new[j] is not None:
if d_xs[j] is None:
d_xs[j] = _unsparsify(d_xs_new[j])
else:
d_xs[j] += _unsparsify(d_xs_new[j])
# Returning the new gradients
return d_xs
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)