예제 #1
0
    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)
예제 #2
0
    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)
예제 #3
0
    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)
예제 #4
0
    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)
예제 #5
0
    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)