def export(cls, trainer, actions, normalization_parameters): """ Creates DiscreteActionPredictor from a list of action trainers :param trainer DiscreteActionTrainer :param features list of state feature names :param actions list of action names """ model = model_helper.ModelHelper(name="predictor") net = model.net workspace.FeedBlob('input/float_features.lengths', np.zeros(1, dtype=np.int32)) workspace.FeedBlob('input/float_features.keys', np.zeros(1, dtype=np.int32)) workspace.FeedBlob('input/float_features.values', np.zeros(1, dtype=np.float32)) preprocessor = PreprocessorNet(net, True) parameters = [] parameters.extend(preprocessor.parameters) normalized_dense_matrix, new_parameters = preprocessor.normalize_sparse_matrix( 'input/float_features.lengths', 'input/float_features.keys', 'input/float_features.values', normalization_parameters, 'state_norm', ) parameters.extend(new_parameters) new_parameters = RLPredictor._forward_pass( model, trainer, normalized_dense_matrix, actions, ) parameters.extend(new_parameters) workspace.RunNetOnce(model.param_init_net) workspace.CreateNet(net) return DiscreteActionPredictor(net, parameters)
def export(cls, trainer, actions, state_normalization_parameters, int_features=False): """ Creates a DiscreteActionPredictor from a DiscreteActionTrainer. :param trainer DiscreteActionTrainer :param actions list of action names :param state_normalization_parameters state NormalizationParameters :param int_features boolean indicating if int features blob will be present """ model = model_helper.ModelHelper(name="predictor") net = model.net C2.set_model(model) workspace.FeedBlob('input/image', np.zeros([1, 1, 1, 1], dtype=np.int32)) workspace.FeedBlob('input/float_features.lengths', np.zeros(1, dtype=np.int32)) workspace.FeedBlob('input/float_features.keys', np.zeros(1, dtype=np.int64)) workspace.FeedBlob('input/float_features.values', np.zeros(1, dtype=np.float32)) input_feature_lengths = 'input_feature_lengths' input_feature_keys = 'input_feature_keys' input_feature_values = 'input_feature_values' if int_features: workspace.FeedBlob('input/int_features.lengths', np.zeros(1, dtype=np.int32)) workspace.FeedBlob('input/int_features.keys', np.zeros(1, dtype=np.int64)) workspace.FeedBlob('input/int_features.values', np.zeros(1, dtype=np.int32)) C2.net().Cast(['input/int_features.values'], ['input/int_features.values_float'], dtype=caffe2_pb2.TensorProto.FLOAT) C2.net().MergeMultiScalarFeatureTensors([ 'input/float_features.lengths', 'input/float_features.keys', 'input/float_features.values', 'input/int_features.lengths', 'input/int_features.keys', 'input/int_features.values_float' ], [ input_feature_lengths, input_feature_keys, input_feature_values ]) else: C2.net().Copy(['input/float_features.lengths'], [input_feature_lengths]) C2.net().Copy(['input/float_features.keys'], [input_feature_keys]) C2.net().Copy(['input/float_features.values'], [input_feature_values]) parameters = [] if state_normalization_parameters is not None: preprocessor = PreprocessorNet(net, True) parameters.extend(preprocessor.parameters) normalized_dense_matrix, new_parameters = \ preprocessor.normalize_sparse_matrix( input_feature_lengths, input_feature_keys, input_feature_values, state_normalization_parameters, 'state_norm', ) parameters.extend(new_parameters) else: # Image input. Note: Currently this does the wrong thing if # more than one image is passed at a time. normalized_dense_matrix = 'input/image' new_parameters, q_values = RLPredictor._forward_pass( model, trainer, normalized_dense_matrix, actions, ) parameters.extend(new_parameters) # Get 1 x n action index tensor under the max_q policy max_q_act_idxs = 'max_q_policy_actions' C2.net().Flatten([C2.ArgMax(q_values)], [max_q_act_idxs], axis=0) shape_of_num_of_states = 'num_states_shape' C2.net().FlattenToVec([max_q_act_idxs], [shape_of_num_of_states]) num_states, _ = C2.Reshape(C2.Size(shape_of_num_of_states), shape=[1]) # Get 1 x n action index tensor under the softmax policy temperature = C2.NextBlob("temperature") parameters.append(temperature) workspace.FeedBlob( temperature, np.array([trainer.rl_temperature], dtype=np.float32)) tempered_q_values = C2.Div(q_values, "temperature", broadcast=1) softmax_values = C2.Softmax(tempered_q_values) softmax_act_idxs_nested = 'softmax_act_idxs_nested' C2.net().WeightedSample([softmax_values], [softmax_act_idxs_nested]) softmax_act_idxs = 'softmax_policy_actions' C2.net().Flatten([softmax_act_idxs_nested], [softmax_act_idxs], axis=0) # Concat action index tensors to get 2 x n tensor - [[max_q], [softmax]] # transpose & flatten to get [a1_maxq, a1_softmax, a2_maxq, a2_softmax, ...] max_q_act_blob = C2.Cast(max_q_act_idxs, to=caffe2_pb2.TensorProto.INT32) softmax_act_blob = C2.Cast(softmax_act_idxs, to=caffe2_pb2.TensorProto.INT32) C2.net().Append([max_q_act_blob, softmax_act_blob], [max_q_act_blob]) transposed_action_idxs = C2.Transpose(max_q_act_blob) flat_transposed_action_idxs = C2.FlattenToVec(transposed_action_idxs) output_values = 'output/string_single_categorical_features.values' workspace.FeedBlob(output_values, np.zeros(1, dtype=np.int64)) C2.net().Gather(["action_names", flat_transposed_action_idxs], [output_values]) output_lengths = 'output/string_single_categorical_features.lengths' workspace.FeedBlob(output_lengths, np.zeros(1, dtype=np.int32)) C2.net().ConstantFill([shape_of_num_of_states], [output_lengths], value=2, dtype=caffe2_pb2.TensorProto.INT32) output_keys = 'output/string_single_categorical_features.keys' workspace.FeedBlob(output_keys, np.zeros(1, dtype=np.int64)) output_keys_tensor, _ = C2.Concat( C2.ConstantFill(shape=[1, 1], value=0, dtype=caffe2_pb2.TensorProto.INT64), C2.ConstantFill(shape=[1, 1], value=1, dtype=caffe2_pb2.TensorProto.INT64), axis=0, ) output_key_tile = C2.Tile(output_keys_tensor, num_states, axis=0) C2.net().FlattenToVec([output_key_tile], [output_keys]) workspace.RunNetOnce(model.param_init_net) workspace.CreateNet(net) return DiscreteActionPredictor(net, parameters, int_features)
def preprocess_samples_discrete( self, samples: Samples, minibatch_size: int) -> List[TrainingDataPage]: samples.shuffle() net = core.Net("gridworld_preprocessing") C2.set_net(net) preprocessor = PreprocessorNet(True) saa = StackedAssociativeArray.from_dict_list(samples.states, "states") state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, "state_norm", False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.next_states, "next_states") next_state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, "next_state_norm", False, False, ) workspace.RunNetOnce(net) actions_one_hot = np.zeros( [len(samples.actions), len(self.ACTIONS)], dtype=np.float32) for i, action in enumerate(samples.actions): actions_one_hot[i, self.action_to_index(action)] = 1 rewards = np.array(samples.rewards, dtype=np.float32).reshape(-1, 1) propensities = np.array(samples.propensities, dtype=np.float32).reshape(-1, 1) next_actions_one_hot = np.zeros( [len(samples.next_actions), len(self.ACTIONS)], dtype=np.float32) for i, action in enumerate(samples.next_actions): if action == "": continue next_actions_one_hot[i, self.action_to_index(action)] = 1 possible_next_actions_mask = [] for pna in samples.possible_next_actions: pna_mask = [0] * self.num_actions for action in pna: pna_mask[self.action_to_index(action)] = 1 possible_next_actions_mask.append(pna_mask) possible_next_actions_mask = np.array(possible_next_actions_mask, dtype=np.float32) is_terminals = np.array(samples.is_terminal, dtype=np.bool).reshape(-1, 1) not_terminals = np.logical_not(is_terminals) if samples.reward_timelines is not None: reward_timelines = np.array(samples.reward_timelines, dtype=np.object) states_ndarray = workspace.FetchBlob(state_matrix) next_states_ndarray = workspace.FetchBlob(next_state_matrix) tdps = [] for start in range(0, states_ndarray.shape[0], minibatch_size): end = start + minibatch_size if end > states_ndarray.shape[0]: break tdps.append( TrainingDataPage( states=states_ndarray[start:end], actions=actions_one_hot[start:end], propensities=propensities[start:end], rewards=rewards[start:end], next_states=next_states_ndarray[start:end], not_terminals=not_terminals[start:end], next_actions=next_actions_one_hot[start:end], possible_next_actions=possible_next_actions_mask[ start:end], reward_timelines=reward_timelines[start:end] if reward_timelines is not None else None, )) return tdps
def preprocess_samples(self, samples: Samples, minibatch_size: int) -> List[TrainingDataPage]: samples.shuffle() net = core.Net("gridworld_preprocessing") C2.set_net(net) preprocessor = PreprocessorNet(True) saa = StackedAssociativeArray.from_dict_list(samples.states, "states") state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, "state_norm", False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.next_states, "next_states") next_state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, "next_state_norm", False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.actions, "action") action_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, "action_norm", False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.next_actions, "next_action") next_action_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, "next_action_norm", False, False, ) propensities = np.array(samples.propensities, dtype=np.float32).reshape(-1, 1) rewards = np.array(samples.rewards, dtype=np.float32).reshape(-1, 1) pnas_lengths_list = [] pnas_flat: List[List[str]] = [] for pnas in samples.possible_next_actions: pnas_lengths_list.append(len(pnas)) pnas_flat.extend(pnas) saa = StackedAssociativeArray.from_dict_list(pnas_flat, "possible_next_actions") pnas_lengths = np.array(pnas_lengths_list, dtype=np.int32) possible_next_actions_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, "possible_next_action_norm", False, False, ) workspace.RunNetOnce(net) states_ndarray = workspace.FetchBlob(state_matrix) actions_ndarray = workspace.FetchBlob(action_matrix) next_states_ndarray = workspace.FetchBlob(next_state_matrix) next_actions_ndarray = workspace.FetchBlob(next_action_matrix) possible_next_actions_ndarray = workspace.FetchBlob( possible_next_actions_matrix) tdps = [] pnas_start = 0 for start in range(0, states_ndarray.shape[0], minibatch_size): end = start + minibatch_size if end > states_ndarray.shape[0]: break pnas_end = pnas_start + np.sum(pnas_lengths[start:end]) pnas = possible_next_actions_ndarray[pnas_start:pnas_end] pnas_start = pnas_end tdps.append( TrainingDataPage( states=states_ndarray[start:end], actions=actions_ndarray[start:end], propensities=propensities[start:end], rewards=rewards[start:end], next_states=next_states_ndarray[start:end], next_actions=next_actions_ndarray[start:end], possible_next_actions=StackedArray(pnas_lengths[start:end], pnas), not_terminals=(pnas_lengths[start:end] > 0).reshape(-1, 1), reward_timelines=samples.reward_timelines[start:end] if samples.reward_timelines else None, )) return tdps
def preprocess_samples(self, samples: Samples, minibatch_size: int) -> List[TrainingDataPage]: samples.shuffle() net = core.Net("gridworld_preprocessing") C2.set_net(net) preprocessor = PreprocessorNet(True) saa = StackedAssociativeArray.from_dict_list(samples.states, "states") state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, "state_norm", False, False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.next_states, "next_states") next_state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, "next_state_norm", False, False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.actions, "action") action_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, "action_norm", False, False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.next_actions, "next_action") next_action_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, "next_action_norm", False, False, False, ) propensities = np.array(samples.propensities, dtype=np.float32).reshape(-1, 1) rewards = np.array(samples.rewards, dtype=np.float32).reshape(-1, 1) pnas_lengths_list = [] pnas_flat: List[List[str]] = [] for pnas in samples.possible_next_actions: pnas_lengths_list.append(len(pnas)) pnas_flat.extend(pnas) saa = StackedAssociativeArray.from_dict_list(pnas_flat, "possible_next_actions") pnas_lengths = np.array(pnas_lengths_list, dtype=np.int32) pna_lens_blob = "pna_lens_blob" workspace.FeedBlob(pna_lens_blob, pnas_lengths) possible_next_actions_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, "possible_next_action_norm", False, False, False, ) state_pnas_tile_blob = C2.LengthsTile(next_state_matrix, pna_lens_blob) workspace.RunNetOnce(net) state_preprocessor = Preprocessor(self.normalization, False) action_preprocessor = Preprocessor(self.normalization_action, False) states_ndarray = workspace.FetchBlob(state_matrix) states_ndarray = state_preprocessor.forward(states_ndarray).numpy() actions_ndarray = workspace.FetchBlob(action_matrix) actions_ndarray = action_preprocessor.forward(actions_ndarray).numpy() next_states_ndarray = workspace.FetchBlob(next_state_matrix) next_states_ndarray = state_preprocessor.forward( next_states_ndarray).numpy() next_actions_ndarray = workspace.FetchBlob(next_action_matrix) next_actions_ndarray = action_preprocessor.forward( next_actions_ndarray).numpy() logged_possible_next_actions = action_preprocessor.forward( workspace.FetchBlob(possible_next_actions_matrix)) state_pnas_tile = state_preprocessor.forward( workspace.FetchBlob(state_pnas_tile_blob)) logged_possible_next_state_actions = torch.cat( (state_pnas_tile, logged_possible_next_actions), dim=1) possible_next_actions_ndarray = logged_possible_next_actions.cpu( ).numpy() next_state_pnas_concat = logged_possible_next_state_actions.cpu( ).numpy() time_diffs = np.ones(len(states_ndarray)) episode_values = None if samples.reward_timelines is not None: episode_values = np.zeros(rewards.shape, dtype=np.float32) for i, reward_timeline in enumerate(samples.reward_timelines): for time_diff, reward in reward_timeline.items(): episode_values[i, 0] += reward * (DISCOUNT**time_diff) tdps = [] pnas_start = 0 for start in range(0, states_ndarray.shape[0], minibatch_size): end = start + minibatch_size if end > states_ndarray.shape[0]: break pnas_end = pnas_start + np.sum(pnas_lengths[start:end]) pnas = possible_next_actions_ndarray[pnas_start:pnas_end] pnas_concat = next_state_pnas_concat[pnas_start:pnas_end] pnas_start = pnas_end tdps.append( TrainingDataPage( states=states_ndarray[start:end], actions=actions_ndarray[start:end], propensities=propensities[start:end], rewards=rewards[start:end], next_states=next_states_ndarray[start:end], next_actions=next_actions_ndarray[start:end], possible_next_actions=StackedArray(pnas_lengths[start:end], pnas), not_terminals=(pnas_lengths[start:end] > 0).reshape(-1, 1), episode_values=episode_values[start:end] if episode_values is not None else None, time_diffs=time_diffs[start:end], possible_next_actions_lengths=pnas_lengths[start:end], next_state_pnas_concat=pnas_concat, )) return tdps
def export( cls, trainer, state_normalization_parameters, action_normalization_parameters, ): """ Creates ContinuousActionDQNPredictor from a list of action trainers :param trainer ContinuousActionDQNPredictor :param state_features list of state feature names :param action_features list of action feature names """ # ensure state and action IDs have no intersection assert (len( set(state_normalization_parameters.keys()) & set(action_normalization_parameters.keys())) == 0) model = model_helper.ModelHelper(name="predictor") net = model.net C2.set_model(model) workspace.FeedBlob('input/float_features.lengths', np.zeros(1, dtype=np.int32)) workspace.FeedBlob('input/float_features.keys', np.zeros(1, dtype=np.int32)) workspace.FeedBlob('input/float_features.values', np.zeros(1, dtype=np.float32)) preprocessor = PreprocessorNet(net, True) parameters = [] parameters.extend(preprocessor.parameters) state_normalized_dense_matrix, new_parameters = \ preprocessor.normalize_sparse_matrix( 'input/float_features.lengths', 'input/float_features.keys', 'input/float_features.values', state_normalization_parameters, 'state_norm', ) parameters.extend(new_parameters) action_normalized_dense_matrix, new_parameters = \ preprocessor.normalize_sparse_matrix( 'input/float_features.lengths', 'input/float_features.keys', 'input/float_features.values', action_normalization_parameters, 'action_norm', ) parameters.extend(new_parameters) state_action_normalized = 'state_action_normalized' state_action_normalized_dim = 'state_action_normalized_dim' net.Concat( [state_normalized_dense_matrix, action_normalized_dense_matrix], [state_action_normalized, state_action_normalized_dim], axis=1) new_parameters, q_values = RLPredictor._forward_pass( model, trainer, state_action_normalized, ['Q'], ) parameters.extend(new_parameters) flat_q_values_key = \ 'output/string_weighted_multi_categorical_features.values.values' num_examples, _ = C2.Reshape(C2.Size(flat_q_values_key), shape=[1]) q_value_blob, _ = C2.Reshape(flat_q_values_key, shape=[1, -1]) # Get 1 x n (number of examples) action index tensor under the max_q policy max_q_act_idxs = 'max_q_policy_actions' C2.net().FlattenToVec([C2.ArgMax(q_value_blob)], [max_q_act_idxs]) max_q_act_blob = C2.Tile(max_q_act_idxs, num_examples, axis=0) # Get 1 x n (number of examples) action index tensor under the softmax policy temperature = C2.NextBlob("temperature") parameters.append(temperature) workspace.FeedBlob( temperature, np.array([trainer.rl_temperature], dtype=np.float32)) tempered_q_values = C2.Div(q_value_blob, "temperature", broadcast=1) softmax_values = C2.Softmax(tempered_q_values) softmax_act_idxs_nested = 'softmax_act_idxs_nested' C2.net().WeightedSample([softmax_values], [softmax_act_idxs_nested]) softmax_act_blob = C2.Tile(C2.FlattenToVec(softmax_act_idxs_nested), num_examples, axis=0) # Concat action idx vecs to get 2 x n tensor [[a_maxq, ..], [a_softmax, ..]] # transpose & flatten to get [a_maxq, a_softmax, a_maxq, a_softmax, ...] max_q_act_blob = C2.Cast(max_q_act_blob, to=caffe2_pb2.TensorProto.INT64) softmax_act_blob = C2.Cast(softmax_act_blob, to=caffe2_pb2.TensorProto.INT64) max_q_act_blob_nested, _ = C2.Reshape(max_q_act_blob, shape=[1, -1]) softmax_act_blob_nested, _ = C2.Reshape(softmax_act_blob, shape=[1, -1]) C2.net().Append([max_q_act_blob_nested, softmax_act_blob_nested], [max_q_act_blob_nested]) transposed_action_idxs = C2.Transpose(max_q_act_blob_nested) flat_transposed_action_idxs = C2.FlattenToVec(transposed_action_idxs) output_values = 'output/int_single_categorical_features.values' workspace.FeedBlob(output_values, np.zeros(1, dtype=np.int64)) C2.net().Copy([flat_transposed_action_idxs], [output_values]) output_lengths = 'output/int_single_categorical_features.lengths' workspace.FeedBlob(output_lengths, np.zeros(1, dtype=np.int32)) C2.net().ConstantFill([flat_q_values_key], [output_lengths], value=2, dtype=caffe2_pb2.TensorProto.INT32) output_keys = 'output/int_single_categorical_features.keys' workspace.FeedBlob(output_keys, np.zeros(1, dtype=np.int64)) output_keys_tensor, _ = C2.Concat( C2.ConstantFill(shape=[1, 1], value=0, dtype=caffe2_pb2.TensorProto.INT64), C2.ConstantFill(shape=[1, 1], value=1, dtype=caffe2_pb2.TensorProto.INT64), axis=0, ) output_key_tile = C2.Tile(output_keys_tensor, num_examples, axis=0) C2.net().FlattenToVec([output_key_tile], [output_keys]) workspace.RunNetOnce(model.param_init_net) workspace.CreateNet(net) return ContinuousActionDQNPredictor(net, parameters)
def preprocess_samples(self, samples: Samples, minibatch_size: int) -> List[TrainingDataPage]: logger.info("Shuffling...") samples.shuffle() logger.info("Sparse2Dense...") net = core.Net("gridworld_preprocessing") C2.set_net(net) preprocessor = PreprocessorNet(True) saa = StackedAssociativeArray.from_dict_list(samples.states, "states") state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, "state_norm", False, False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.next_states, "next_states") next_state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, "next_state_norm", False, False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.actions, "action") action_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, "action_norm", False, False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.next_actions, "next_action") next_action_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, "next_action_norm", False, False, False, ) action_probabilities = torch.tensor(samples.action_probabilities, dtype=torch.float32).reshape( -1, 1) rewards = torch.tensor(samples.rewards, dtype=torch.float32).reshape(-1, 1) pnas_lengths_list = [] pnas_flat: List[List[str]] = [] for pnas in samples.possible_next_actions: pnas_lengths_list.append(len(pnas)) pnas_flat.extend(pnas) saa = StackedAssociativeArray.from_dict_list(pnas_flat, "possible_next_actions") pnas_lengths = torch.tensor(pnas_lengths_list, dtype=torch.int32) pna_lens_blob = "pna_lens_blob" workspace.FeedBlob(pna_lens_blob, pnas_lengths.numpy()) possible_next_actions_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, "possible_next_action_norm", False, False, False, ) state_pnas_tile_blob = C2.LengthsTile(next_state_matrix, pna_lens_blob) workspace.RunNetOnce(net) logger.info("Preprocessing...") state_preprocessor = Preprocessor(self.normalization, False) action_preprocessor = Preprocessor(self.normalization_action, False) states_ndarray = workspace.FetchBlob(state_matrix) states_ndarray = state_preprocessor.forward(states_ndarray) actions_ndarray = workspace.FetchBlob(action_matrix) actions_ndarray = action_preprocessor.forward(actions_ndarray) next_states_ndarray = workspace.FetchBlob(next_state_matrix) next_states_ndarray = state_preprocessor.forward(next_states_ndarray) next_actions_ndarray = workspace.FetchBlob(next_action_matrix) next_actions_ndarray = action_preprocessor.forward( next_actions_ndarray) logged_possible_next_actions = action_preprocessor.forward( workspace.FetchBlob(possible_next_actions_matrix)) state_pnas_tile = state_preprocessor.forward( workspace.FetchBlob(state_pnas_tile_blob)) logged_possible_next_state_actions = torch.cat( (state_pnas_tile, logged_possible_next_actions), dim=1) logger.info("Reward Timeline to Torch...") possible_next_actions_ndarray = logged_possible_next_actions possible_next_actions_state_concat = logged_possible_next_state_actions time_diffs = torch.ones([len(samples.states), 1]) episode_values = torch.tensor(samples.episode_values, dtype=torch.float32).reshape(-1, 1) tdps = [] pnas_start = 0 logger.info("Batching...") for start in range(0, states_ndarray.shape[0], minibatch_size): end = start + minibatch_size if end > states_ndarray.shape[0]: break pnas_end = pnas_start + torch.sum(pnas_lengths[start:end]) pnas = possible_next_actions_ndarray[pnas_start:pnas_end] pnas_concat = possible_next_actions_state_concat[ pnas_start:pnas_end] pnas_start = pnas_end tdp = TrainingDataPage( states=states_ndarray[start:end], actions=actions_ndarray[start:end], propensities=action_probabilities[start:end], rewards=rewards[start:end], next_states=next_states_ndarray[start:end], next_actions=next_actions_ndarray[start:end], possible_next_actions=None, not_terminals=(pnas_lengths[start:end] > 0).reshape(-1, 1), episode_values=episode_values[start:end] if episode_values is not None else None, time_diffs=time_diffs[start:end], possible_next_actions_lengths=pnas_lengths[start:end], possible_next_actions_state_concat=pnas_concat, ) tdp.set_type(torch.FloatTensor) tdps.append(tdp) return tdps
def export( cls, trainer, actions, state_normalization_parameters, int_features=False, model_on_gpu=False, ): """Export caffe2 preprocessor net and pytorch DQN forward pass as one caffe2 net. :param trainer DQNTrainer :param state_normalization_parameters state NormalizationParameters :param int_features boolean indicating if int features blob will be present :param model_on_gpu boolean indicating if the model is a GPU model or CPU model """ input_dim = trainer.num_features if isinstance(trainer.q_network, DataParallel): trainer.q_network = trainer.q_network.module buffer = PytorchCaffe2Converter.pytorch_net_to_buffer( PreprocesserAndForwardPassContainer( Preprocessor(state_normalization_parameters, model_on_gpu), trainer.q_network, ), input_dim, model_on_gpu, ) qnet_input_blob, qnet_output_blob, caffe2_netdef = PytorchCaffe2Converter.buffer_to_caffe2_netdef( buffer) torch_workspace = caffe2_netdef.workspace parameters = torch_workspace.Blobs() for blob_str in parameters: workspace.FeedBlob(blob_str, torch_workspace.FetchBlob(blob_str)) torch_init_net = core.Net(caffe2_netdef.init_net) torch_predict_net = core.Net(caffe2_netdef.predict_net) # While converting to metanetdef, the external_input of predict_net # will be recomputed. Add the real output of init_net to parameters # to make sure they will be counted. parameters.extend( set(caffe2_netdef.init_net.external_output) - set(caffe2_netdef.init_net.external_input)) model = model_helper.ModelHelper(name="predictor") net = model.net C2.set_model(model) workspace.FeedBlob("input/image", np.zeros([1, 1, 1, 1], dtype=np.int32)) workspace.FeedBlob("input/float_features.lengths", np.zeros(1, dtype=np.int32)) workspace.FeedBlob("input/float_features.keys", np.zeros(1, dtype=np.int64)) workspace.FeedBlob("input/float_features.values", np.zeros(1, dtype=np.float32)) input_feature_lengths = "input_feature_lengths" input_feature_keys = "input_feature_keys" input_feature_values = "input_feature_values" if int_features: workspace.FeedBlob("input/int_features.lengths", np.zeros(1, dtype=np.int32)) workspace.FeedBlob("input/int_features.keys", np.zeros(1, dtype=np.int64)) workspace.FeedBlob("input/int_features.values", np.zeros(1, dtype=np.int32)) C2.net().Cast( ["input/int_features.values"], ["input/int_features.values_float"], dtype=caffe2_pb2.TensorProto.FLOAT, ) C2.net().MergeMultiScalarFeatureTensors( [ "input/float_features.lengths", "input/float_features.keys", "input/float_features.values", "input/int_features.lengths", "input/int_features.keys", "input/int_features.values_float", ], [ input_feature_lengths, input_feature_keys, input_feature_values ], ) else: C2.net().Copy(["input/float_features.lengths"], [input_feature_lengths]) C2.net().Copy(["input/float_features.keys"], [input_feature_keys]) C2.net().Copy(["input/float_features.values"], [input_feature_values]) if state_normalization_parameters is not None: preprocessor = PreprocessorNet(clip_anomalies=True) state_normalized_dense_matrix, new_parameters = preprocessor.normalize_sparse_matrix( input_feature_lengths, input_feature_keys, input_feature_values, state_normalization_parameters, blobname_prefix="state_norm", split_sparse_to_dense=False, split_expensive_feature_groups=False, normalize=False, ) parameters.extend(new_parameters) else: # Image input. Note: Currently this does the wrong thing if # more than one image is passed at a time. state_normalized_dense_matrix = "input/image" net.Copy([state_normalized_dense_matrix], [qnet_input_blob]) workspace.RunNetOnce(model.param_init_net) workspace.RunNetOnce(torch_init_net) net.AppendNet(torch_predict_net) new_parameters, q_values = RLPredictor._forward_pass( model, trainer, state_normalized_dense_matrix, actions, qnet_output_blob) parameters.extend(new_parameters) # Get 1 x n action index tensor under the max_q policy max_q_act_idxs = "max_q_policy_actions" C2.net().Flatten([C2.ArgMax(q_values)], [max_q_act_idxs], axis=0) shape_of_num_of_states = "num_states_shape" C2.net().FlattenToVec([max_q_act_idxs], [shape_of_num_of_states]) num_states, _ = C2.Reshape(C2.Size(shape_of_num_of_states), shape=[1]) # Get 1 x n action index tensor under the softmax policy temperature = C2.NextBlob("temperature") parameters.append(temperature) workspace.FeedBlob( temperature, np.array([trainer.rl_temperature], dtype=np.float32)) tempered_q_values = C2.Div(q_values, temperature, broadcast=1) softmax_values = C2.Softmax(tempered_q_values) softmax_act_idxs_nested = "softmax_act_idxs_nested" C2.net().WeightedSample([softmax_values], [softmax_act_idxs_nested]) softmax_act_idxs = "softmax_policy_actions" C2.net().Flatten([softmax_act_idxs_nested], [softmax_act_idxs], axis=0) action_names = C2.NextBlob("action_names") parameters.append(action_names) workspace.FeedBlob(action_names, np.array(actions)) # Concat action index tensors to get 2 x n tensor - [[max_q], [softmax]] # transpose & flatten to get [a1_maxq, a1_softmax, a2_maxq, a2_softmax, ...] max_q_act_blob = C2.Cast(max_q_act_idxs, to=caffe2_pb2.TensorProto.INT32) softmax_act_blob = C2.Cast(softmax_act_idxs, to=caffe2_pb2.TensorProto.INT32) C2.net().Append([max_q_act_blob, softmax_act_blob], [max_q_act_blob]) transposed_action_idxs = C2.Transpose(max_q_act_blob) flat_transposed_action_idxs = C2.FlattenToVec(transposed_action_idxs) workspace.FeedBlob(OUTPUT_SINGLE_CAT_VALS_NAME, np.zeros(1, dtype=np.int64)) C2.net().Gather([action_names, flat_transposed_action_idxs], [OUTPUT_SINGLE_CAT_VALS_NAME]) workspace.FeedBlob(OUTPUT_SINGLE_CAT_LENGTHS_NAME, np.zeros(1, dtype=np.int32)) C2.net().ConstantFill( [shape_of_num_of_states], [OUTPUT_SINGLE_CAT_LENGTHS_NAME], value=2, dtype=caffe2_pb2.TensorProto.INT32, ) workspace.FeedBlob(OUTPUT_SINGLE_CAT_KEYS_NAME, np.zeros(1, dtype=np.int64)) output_keys_tensor, _ = C2.Concat( C2.ConstantFill(shape=[1, 1], value=0, dtype=caffe2_pb2.TensorProto.INT64), C2.ConstantFill(shape=[1, 1], value=1, dtype=caffe2_pb2.TensorProto.INT64), axis=0, ) output_key_tile = C2.Tile(output_keys_tensor, num_states, axis=0) C2.net().FlattenToVec([output_key_tile], [OUTPUT_SINGLE_CAT_KEYS_NAME]) workspace.CreateNet(net) return DQNPredictor(net, torch_init_net, parameters, int_features)
def export_actor( cls, trainer, state_normalization_parameters, min_action_range_tensor_serving, max_action_range_tensor_serving, int_features=False, model_on_gpu=False, ): """Export caffe2 preprocessor net and pytorch actor forward pass as one caffe2 net. :param trainer DDPGTrainer :param state_normalization_parameters state NormalizationParameters :param min_action_range_tensor_serving pytorch tensor that specifies min action value for each dimension :param max_action_range_tensor_serving pytorch tensor that specifies min action value for each dimension :param state_normalization_parameters state NormalizationParameters :param int_features boolean indicating if int features blob will be present :param model_on_gpu boolean indicating if the model is a GPU model or CPU model """ input_dim = trainer.state_dim buffer = PytorchCaffe2Converter.pytorch_net_to_buffer( trainer.actor, input_dim, model_on_gpu ) actor_input_blob, actor_output_blob, caffe2_netdef = PytorchCaffe2Converter.buffer_to_caffe2_netdef( buffer ) torch_workspace = caffe2_netdef.workspace parameters = torch_workspace.Blobs() for blob_str in parameters: workspace.FeedBlob(blob_str, torch_workspace.FetchBlob(blob_str)) torch_init_net = core.Net(caffe2_netdef.init_net) torch_predict_net = core.Net(caffe2_netdef.predict_net) model = model_helper.ModelHelper(name="predictor") net = model.net C2.set_model(model) # Feed action scaling tensors for serving min_action_serving_blob = C2.NextBlob("min_action_range_tensor_serving") workspace.FeedBlob( min_action_serving_blob, min_action_range_tensor_serving.cpu().data.numpy() ) parameters.append(str(min_action_serving_blob)) max_action_serving_blob = C2.NextBlob("max_action_range_tensor_serving") workspace.FeedBlob( max_action_serving_blob, max_action_range_tensor_serving.cpu().data.numpy() ) parameters.append(str(max_action_serving_blob)) # Feed action scaling tensors for training [-1, 1] due to tanh actor min_vals_training = trainer.min_action_range_tensor_training.cpu().data.numpy() min_action_training_blob = C2.NextBlob("min_action_range_tensor_training") workspace.FeedBlob(min_action_training_blob, min_vals_training) parameters.append(str(min_action_training_blob)) max_vals_training = trainer.max_action_range_tensor_training.cpu().data.numpy() max_action_training_blob = C2.NextBlob("max_action_range_tensor_training") workspace.FeedBlob(max_action_training_blob, max_vals_training) parameters.append(str(max_action_training_blob)) workspace.FeedBlob("input/float_features.lengths", np.zeros(1, dtype=np.int32)) workspace.FeedBlob("input/float_features.keys", np.zeros(1, dtype=np.int64)) workspace.FeedBlob("input/float_features.values", np.zeros(1, dtype=np.float32)) input_feature_lengths = "input_feature_lengths" input_feature_keys = "input_feature_keys" input_feature_values = "input_feature_values" if int_features: workspace.FeedBlob( "input/int_features.lengths", np.zeros(1, dtype=np.int32) ) workspace.FeedBlob("input/int_features.keys", np.zeros(1, dtype=np.int64)) workspace.FeedBlob("input/int_features.values", np.zeros(1, dtype=np.int32)) C2.net().Cast( ["input/int_features.values"], ["input/int_features.values_float"], dtype=caffe2_pb2.TensorProto.FLOAT, ) C2.net().MergeMultiScalarFeatureTensors( [ "input/float_features.lengths", "input/float_features.keys", "input/float_features.values", "input/int_features.lengths", "input/int_features.keys", "input/int_features.values_float", ], [input_feature_lengths, input_feature_keys, input_feature_values], ) else: C2.net().Copy(["input/float_features.lengths"], [input_feature_lengths]) C2.net().Copy(["input/float_features.keys"], [input_feature_keys]) C2.net().Copy(["input/float_features.values"], [input_feature_values]) preprocessor = PreprocessorNet(True) state_normalized_dense_matrix, new_parameters = preprocessor.normalize_sparse_matrix( input_feature_lengths, input_feature_keys, input_feature_values, state_normalization_parameters, "state_norm", False, False, ) parameters.extend(new_parameters) net.Copy([state_normalized_dense_matrix], [actor_input_blob]) workspace.RunNetOnce(model.param_init_net) workspace.RunNetOnce(torch_init_net) net.AppendNet(torch_predict_net) C2.FlattenToVec(C2.ArgMax(actor_output_blob)) output_lengths = "output/float_features.lengths" workspace.FeedBlob(output_lengths, np.zeros(1, dtype=np.int32)) C2.net().ConstantFill( [C2.FlattenToVec(C2.ArgMax(actor_output_blob))], [output_lengths], value=trainer.actor.layers[-1].out_features, dtype=caffe2_pb2.TensorProto.INT32, ) output_keys = "output/float_features.keys" workspace.FeedBlob(output_keys, np.zeros(1, dtype=np.int32)) C2.net().LengthsRangeFill([output_lengths], [output_keys]) output_values = "output/float_features.values" workspace.FeedBlob(output_values, np.zeros(1, dtype=np.float32)) # Scale actors actions from [-1, 1] to serving range prev_range = C2.Sub(max_action_training_blob, min_action_training_blob) new_range = C2.Sub(max_action_serving_blob, min_action_serving_blob) subtract_prev_min = C2.Sub(actor_output_blob, min_action_training_blob) div_by_prev_range = C2.Div(subtract_prev_min, prev_range) scaled_for_serving_actions = C2.Add( C2.Mul(div_by_prev_range, new_range), min_action_serving_blob ) C2.net().FlattenToVec([scaled_for_serving_actions], [output_values]) workspace.CreateNet(net) return DDPGPredictor(net, parameters, int_features)
def export_critic( cls, trainer, state_normalization_parameters, action_normalization_parameters, int_features=False, model_on_gpu=False, ): """Export caffe2 preprocessor net and pytorch critic forward pass as one caffe2 net. :param trainer DDPGTrainer :param state_normalization_parameters state NormalizationParameters :param action_normalization_parameters action NormalizationParameters :param int_features boolean indicating if int features blob will be present """ input_dim = trainer.state_dim + trainer.action_dim buffer = PytorchCaffe2Converter.pytorch_net_to_buffer( trainer.critic, input_dim, model_on_gpu ) critic_input_blob, critic_output_blob, caffe2_netdef = PytorchCaffe2Converter.buffer_to_caffe2_netdef( buffer ) torch_workspace = caffe2_netdef.workspace parameters = [] for blob_str in torch_workspace.Blobs(): workspace.FeedBlob(blob_str, torch_workspace.FetchBlob(blob_str)) parameters.append(blob_str) torch_init_net = core.Net(caffe2_netdef.init_net) torch_predict_net = core.Net(caffe2_netdef.predict_net) model = model_helper.ModelHelper(name="predictor") net = model.net C2.set_model(model) workspace.FeedBlob("input/float_features.lengths", np.zeros(1, dtype=np.int32)) workspace.FeedBlob("input/float_features.keys", np.zeros(1, dtype=np.int64)) workspace.FeedBlob("input/float_features.values", np.zeros(1, dtype=np.float32)) input_feature_lengths = "input_feature_lengths" input_feature_keys = "input_feature_keys" input_feature_values = "input_feature_values" if int_features: workspace.FeedBlob( "input/int_features.lengths", np.zeros(1, dtype=np.int32) ) workspace.FeedBlob("input/int_features.keys", np.zeros(1, dtype=np.int64)) workspace.FeedBlob("input/int_features.values", np.zeros(1, dtype=np.int32)) C2.net().Cast( ["input/int_features.values"], ["input/int_features.values_float"], dtype=caffe2_pb2.TensorProto.FLOAT, ) C2.net().MergeMultiScalarFeatureTensors( [ "input/float_features.lengths", "input/float_features.keys", "input/float_features.values", "input/int_features.lengths", "input/int_features.keys", "input/int_features.values_float", ], [input_feature_lengths, input_feature_keys, input_feature_values], ) else: C2.net().Copy(["input/float_features.lengths"], [input_feature_lengths]) C2.net().Copy(["input/float_features.keys"], [input_feature_keys]) C2.net().Copy(["input/float_features.values"], [input_feature_values]) preprocessor = PreprocessorNet(True) state_normalized_dense_matrix, new_parameters = preprocessor.normalize_sparse_matrix( input_feature_lengths, input_feature_keys, input_feature_values, state_normalization_parameters, "state_norm", False, False, ) parameters.extend(new_parameters) # Don't normalize actions, just go from sparse -> dense action_dense_matrix, new_parameters = preprocessor.normalize_sparse_matrix( input_feature_lengths, input_feature_keys, input_feature_values, action_normalization_parameters, "action_norm", False, False, normalize=False, ) parameters.extend(new_parameters) state_action_normalized = "state_action_normalized" state_action_normalized_dim = "state_action_normalized_dim" net.Concat( [state_normalized_dense_matrix, action_dense_matrix], [state_action_normalized, state_action_normalized_dim], axis=1, ) net.Copy([state_action_normalized], [critic_input_blob]) workspace.RunNetOnce(model.param_init_net) workspace.RunNetOnce(torch_init_net) net.AppendNet(torch_init_net) net.AppendNet(torch_predict_net) C2.FlattenToVec(C2.ArgMax(critic_output_blob)) output_lengths = "output/float_features.lengths" workspace.FeedBlob(output_lengths, np.zeros(1, dtype=np.int32)) C2.net().ConstantFill( [C2.FlattenToVec(C2.ArgMax(critic_output_blob))], [output_lengths], value=trainer.critic.layers[-1].out_features, dtype=caffe2_pb2.TensorProto.INT32, ) output_keys = "output/float_features.keys" workspace.FeedBlob(output_keys, np.zeros(1, dtype=np.int32)) C2.net().LengthsRangeFill([output_lengths], [output_keys]) output_values = "output/float_features.values" workspace.FeedBlob(output_values, np.zeros(1, dtype=np.float32)) C2.net().FlattenToVec([critic_output_blob], [output_values]) workspace.CreateNet(net) return DDPGPredictor(net, parameters, int_features)
def export( cls, trainer, state_normalization_parameters, action_normalization_parameters, int_features=False, ): """ Creates a ContinuousActionDQNPredictor from a ContinuousActionDQNTrainer. :param trainer ContinuousActionDQNTrainer :param state_normalization_parameters state NormalizationParameters :param action_normalization_parameters action NormalizationParameters :param int_features boolean indicating if int features blob will be present """ # ensure state and action IDs have no intersection assert (len( set(state_normalization_parameters.keys()) & set(action_normalization_parameters.keys())) == 0) model = model_helper.ModelHelper(name="predictor") net = model.net C2.set_model(model) workspace.FeedBlob("input/float_features.lengths", np.zeros(1, dtype=np.int32)) workspace.FeedBlob("input/float_features.keys", np.zeros(1, dtype=np.int64)) workspace.FeedBlob("input/float_features.values", np.zeros(1, dtype=np.float32)) input_feature_lengths = "input_feature_lengths" input_feature_keys = "input_feature_keys" input_feature_values = "input_feature_values" if int_features: workspace.FeedBlob("input/int_features.lengths", np.zeros(1, dtype=np.int32)) workspace.FeedBlob("input/int_features.keys", np.zeros(1, dtype=np.int64)) workspace.FeedBlob("input/int_features.values", np.zeros(1, dtype=np.int32)) C2.net().Cast( ["input/int_features.values"], ["input/int_features.values_float"], dtype=caffe2_pb2.TensorProto.FLOAT, ) C2.net().MergeMultiScalarFeatureTensors( [ "input/float_features.lengths", "input/float_features.keys", "input/float_features.values", "input/int_features.lengths", "input/int_features.keys", "input/int_features.values_float", ], [ input_feature_lengths, input_feature_keys, input_feature_values ], ) else: C2.net().Copy(["input/float_features.lengths"], [input_feature_lengths]) C2.net().Copy(["input/float_features.keys"], [input_feature_keys]) C2.net().Copy(["input/float_features.values"], [input_feature_values]) preprocessor = PreprocessorNet(True) parameters = [] state_normalized_dense_matrix, new_parameters = preprocessor.normalize_sparse_matrix( input_feature_lengths, input_feature_keys, input_feature_values, state_normalization_parameters, "state_norm", False, False, ) parameters.extend(new_parameters) action_normalized_dense_matrix, new_parameters = preprocessor.normalize_sparse_matrix( input_feature_lengths, input_feature_keys, input_feature_values, action_normalization_parameters, "action_norm", False, False, ) parameters.extend(new_parameters) state_action_normalized = "state_action_normalized" state_action_normalized_dim = "state_action_normalized_dim" net.Concat( [state_normalized_dense_matrix, action_normalized_dense_matrix], [state_action_normalized, state_action_normalized_dim], axis=1, ) new_parameters, q_values = RLPredictor._forward_pass( model, trainer, state_action_normalized, ["Q"]) parameters.extend(new_parameters) flat_q_values_key = ( "output/string_weighted_multi_categorical_features.values.values") num_examples, _ = C2.Reshape(C2.Size(flat_q_values_key), shape=[1]) q_value_blob, _ = C2.Reshape(flat_q_values_key, shape=[1, -1]) # Get 1 x n (number of examples) action index tensor under the max_q policy max_q_act_idxs = "max_q_policy_actions" C2.net().FlattenToVec([C2.ArgMax(q_value_blob)], [max_q_act_idxs]) max_q_act_blob = C2.Tile(max_q_act_idxs, num_examples, axis=0) # Get 1 x n (number of examples) action index tensor under the softmax policy temperature = C2.NextBlob("temperature") parameters.append(temperature) workspace.FeedBlob( temperature, np.array([trainer.rl_temperature], dtype=np.float32)) tempered_q_values = C2.Div(q_value_blob, temperature, broadcast=1) softmax_values = C2.Softmax(tempered_q_values) softmax_act_idxs_nested = "softmax_act_idxs_nested" C2.net().WeightedSample([softmax_values], [softmax_act_idxs_nested]) softmax_act_blob = C2.Tile(C2.FlattenToVec(softmax_act_idxs_nested), num_examples, axis=0) # Concat action idx vecs to get 2 x n tensor [[a_maxq, ..], [a_softmax, ..]] # transpose & flatten to get [a_maxq, a_softmax, a_maxq, a_softmax, ...] max_q_act_blob = C2.Cast(max_q_act_blob, to=caffe2_pb2.TensorProto.INT64) softmax_act_blob = C2.Cast(softmax_act_blob, to=caffe2_pb2.TensorProto.INT64) max_q_act_blob_nested, _ = C2.Reshape(max_q_act_blob, shape=[1, -1]) softmax_act_blob_nested, _ = C2.Reshape(softmax_act_blob, shape=[1, -1]) C2.net().Append([max_q_act_blob_nested, softmax_act_blob_nested], [max_q_act_blob_nested]) transposed_action_idxs = C2.Transpose(max_q_act_blob_nested) flat_transposed_action_idxs = C2.FlattenToVec(transposed_action_idxs) output_values = "output/int_single_categorical_features.values" workspace.FeedBlob(output_values, np.zeros(1, dtype=np.int64)) C2.net().Copy([flat_transposed_action_idxs], [output_values]) output_lengths = "output/int_single_categorical_features.lengths" workspace.FeedBlob(output_lengths, np.zeros(1, dtype=np.int32)) C2.net().ConstantFill( [flat_q_values_key], [output_lengths], value=2, dtype=caffe2_pb2.TensorProto.INT32, ) output_keys = "output/int_single_categorical_features.keys" workspace.FeedBlob(output_keys, np.zeros(1, dtype=np.int64)) output_keys_tensor, _ = C2.Concat( C2.ConstantFill(shape=[1, 1], value=0, dtype=caffe2_pb2.TensorProto.INT64), C2.ConstantFill(shape=[1, 1], value=1, dtype=caffe2_pb2.TensorProto.INT64), axis=0, ) output_key_tile = C2.Tile(output_keys_tensor, num_examples, axis=0) C2.net().FlattenToVec([output_key_tile], [output_keys]) workspace.RunNetOnce(model.param_init_net) workspace.CreateNet(net) return ContinuousActionDQNPredictor(net, parameters, int_features)
def preprocess_samples( self, states: List[Dict[int, float]], actions: List[Dict[int, float]], rewards: List[float], next_states: List[Dict[int, float]], next_actions: List[Dict[int, float]], is_terminals: List[bool], possible_next_actions: List[List[Dict[int, float]]], reward_timelines: List[Dict[int, float]], minibatch_size: int, ) -> List[TrainingDataPage]: # Shuffle merged = list( zip(states, actions, rewards, next_states, next_actions, is_terminals, possible_next_actions, reward_timelines)) random.shuffle(merged) states, actions, rewards, next_states, next_actions, is_terminals, \ possible_next_actions, reward_timelines = zip(*merged) net = core.Net('gridworld_preprocessing') C2.set_net(net) preprocessor = PreprocessorNet(net, True) saa = StackedAssociativeArray.from_dict_list(states, 'states') state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, 'state_norm', ) saa = StackedAssociativeArray.from_dict_list(next_states, 'next_states') next_state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, 'next_state_norm', ) saa = StackedAssociativeArray.from_dict_list(actions, 'action') action_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, 'action_norm', ) saa = StackedAssociativeArray.from_dict_list(next_actions, 'next_action') next_action_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, 'next_action_norm', ) rewards = np.array(rewards, dtype=np.float32).reshape(-1, 1) pnas_lengths_list = [] pnas_flat = [] for pnas in possible_next_actions: pnas_lengths_list.append(len(pnas)) pnas_flat.extend(pnas) saa = StackedAssociativeArray.from_dict_list(pnas_flat, 'possible_next_actions') pnas_lengths = np.array(pnas_lengths_list, dtype=np.int32) possible_next_actions_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization_action, 'possible_next_action_norm', ) workspace.RunNetOnce(net) states_ndarray = workspace.FetchBlob(state_matrix) actions_ndarray = workspace.FetchBlob(action_matrix) next_states_ndarray = workspace.FetchBlob(next_state_matrix) next_actions_ndarray = workspace.FetchBlob(next_action_matrix) possible_next_actions_ndarray = workspace.FetchBlob( possible_next_actions_matrix) tdps = [] pnas_start = 0 for start in range(0, states_ndarray.shape[0], minibatch_size): end = start + minibatch_size if end > states_ndarray.shape[0]: break pnas_end = pnas_start + np.sum(pnas_lengths[start:end]) pnas = possible_next_actions_ndarray[pnas_start:pnas_end] pnas_start = pnas_end tdps.append( TrainingDataPage( states=states_ndarray[start:end], actions=actions_ndarray[start:end], rewards=rewards[start:end], next_states=next_states_ndarray[start:end], next_actions=next_actions_ndarray[start:end], possible_next_actions=StackedArray(pnas_lengths[start:end], pnas), not_terminals=(pnas_lengths[start:end] > 0).reshape(-1, 1), reward_timelines=reward_timelines[start:end] if reward_timelines else None, )) return tdps
def export( cls, trainer, state_normalization_parameters, action_normalization_parameters, ): """ Creates ContinuousActionDQNPredictor from a list of action trainers :param trainer ContinuousActionDQNPredictor :param state_features list of state feature names :param action_features list of action feature names """ # ensure state and action IDs have no intersection assert ( len( set(state_normalization_parameters.keys()) & set(action_normalization_parameters.keys()) ) == 0 ) model = model_helper.ModelHelper(name="predictor") net = model.net workspace.FeedBlob( 'input/float_features.lengths', np.zeros(1, dtype=np.int32) ) workspace.FeedBlob( 'input/float_features.keys', np.zeros(1, dtype=np.int32) ) workspace.FeedBlob( 'input/float_features.values', np.zeros(1, dtype=np.float32) ) preprocessor = PreprocessorNet(net, True) parameters = [] parameters.extend(preprocessor.parameters) state_normalized_dense_matrix, new_parameters = \ preprocessor.normalize_sparse_matrix( 'input/float_features.lengths', 'input/float_features.keys', 'input/float_features.values', state_normalization_parameters, 'state_norm', ) parameters.extend(new_parameters) action_normalized_dense_matrix, new_parameters = \ preprocessor.normalize_sparse_matrix( 'input/float_features.lengths', 'input/float_features.keys', 'input/float_features.values', action_normalization_parameters, 'action_norm', ) parameters.extend(new_parameters) state_action_normalized = 'state_action_normalized' state_action_normalized_dim = 'state_action_normalized_dim' net.Concat( [state_normalized_dense_matrix, action_normalized_dense_matrix], [state_action_normalized, state_action_normalized_dim], axis=1 ) new_parameters = RLPredictor._forward_pass( model, trainer, state_action_normalized, ['Q'], ) parameters.extend(new_parameters) workspace.RunNetOnce(model.param_init_net) workspace.CreateNet(net) return ContinuousActionDQNPredictor(net, parameters)
def preprocess_samples_discrete( self, states: List[Dict[int, float]], actions: List[str], rewards: List[float], next_states: List[Dict[int, float]], next_actions: List[str], is_terminals: List[bool], possible_next_actions: List[List[str]], reward_timelines: Optional[List[Dict[int, float]]], minibatch_size: int, ) -> List[TrainingDataPage]: # Shuffle if reward_timelines is None: merged = list( zip(states, actions, rewards, next_states, next_actions, is_terminals, possible_next_actions)) random.shuffle(merged) states, actions, rewards, next_states, next_actions, \ is_terminals, possible_next_actions = zip(*merged) else: merged = list( zip(states, actions, rewards, next_states, next_actions, is_terminals, possible_next_actions, reward_timelines)) random.shuffle(merged) states, actions, rewards, next_states, next_actions, \ is_terminals, possible_next_actions, reward_timelines = zip(*merged) net = core.Net('gridworld_preprocessing') C2.set_net(net) preprocessor = PreprocessorNet(net, True) saa = StackedAssociativeArray.from_dict_list(states, 'states') state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, 'state_norm', ) saa = StackedAssociativeArray.from_dict_list(next_states, 'next_states') next_state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, 'next_state_norm', ) workspace.RunNetOnce(net) actions_one_hot = np.zeros( [len(actions), len(self.ACTIONS)], dtype=np.float32) for i, action in enumerate(actions): actions_one_hot[i, self.ACTIONS.index(action)] = 1 rewards = np.array(rewards, dtype=np.float32).reshape(-1, 1) next_actions_one_hot = np.zeros( [len(next_actions), len(self.ACTIONS)], dtype=np.float32) for i, action in enumerate(next_actions): if action == '': continue next_actions_one_hot[i, self.ACTIONS.index(action)] = 1 possible_next_actions_mask = [] for pna in possible_next_actions: pna_mask = [0] * self.num_actions for action in pna: pna_mask[self.ACTIONS.index(action)] = 1 possible_next_actions_mask.append(pna_mask) possible_next_actions_mask = np.array(possible_next_actions_mask, dtype=np.float32) is_terminals = np.array(is_terminals, dtype=np.bool).reshape(-1, 1) not_terminals = np.logical_not(is_terminals) if reward_timelines is not None: reward_timelines = np.array(reward_timelines, dtype=np.object) states_ndarray = workspace.FetchBlob(state_matrix) next_states_ndarray = workspace.FetchBlob(next_state_matrix) tdps = [] for start in range(0, states_ndarray.shape[0], minibatch_size): end = start + minibatch_size if end > states_ndarray.shape[0]: break tdps.append( TrainingDataPage( states=states_ndarray[start:end], actions=actions_one_hot[start:end], rewards=rewards[start:end], next_states=next_states_ndarray[start:end], not_terminals=not_terminals[start:end], next_actions=next_actions_one_hot[start:end], possible_next_actions=possible_next_actions_mask[ start:end], reward_timelines=reward_timelines[start:end] if reward_timelines is not None else None, )) return tdps
def export( cls, trainer, state_normalization_parameters, action_normalization_parameters, int_features=False, model_on_gpu=False, ): """Export caffe2 preprocessor net and pytorch DQN forward pass as one caffe2 net. :param trainer ParametricDQNTrainer :param state_normalization_parameters state NormalizationParameters :param action_normalization_parameters action NormalizationParameters :param int_features boolean indicating if int features blob will be present :param model_on_gpu boolean indicating if the model is a GPU model or CPU model """ input_dim = trainer.num_features if isinstance(trainer.q_network, DataParallel): trainer.q_network = trainer.q_network.module buffer = PytorchCaffe2Converter.pytorch_net_to_buffer( trainer.q_network, input_dim, model_on_gpu) qnet_input_blob, qnet_output_blob, caffe2_netdef = PytorchCaffe2Converter.buffer_to_caffe2_netdef( buffer) torch_workspace = caffe2_netdef.workspace parameters = torch_workspace.Blobs() for blob_str in parameters: workspace.FeedBlob(blob_str, torch_workspace.FetchBlob(blob_str)) torch_init_net = core.Net(caffe2_netdef.init_net) torch_predict_net = core.Net(caffe2_netdef.predict_net) # While converting to metanetdef, the external_input of predict_net # will be recomputed. Add the real output of init_net to parameters # to make sure they will be counted. parameters.extend( set(caffe2_netdef.init_net.external_output) - set(caffe2_netdef.init_net.external_input)) # ensure state and action IDs have no intersection assert (len( set(state_normalization_parameters.keys()) & set(action_normalization_parameters.keys())) == 0) model = model_helper.ModelHelper(name="predictor") net = model.net C2.set_model(model) workspace.FeedBlob("input/float_features.lengths", np.zeros(1, dtype=np.int32)) workspace.FeedBlob("input/float_features.keys", np.zeros(1, dtype=np.int64)) workspace.FeedBlob("input/float_features.values", np.zeros(1, dtype=np.float32)) input_feature_lengths = "input_feature_lengths" input_feature_keys = "input_feature_keys" input_feature_values = "input_feature_values" if int_features: workspace.FeedBlob("input/int_features.lengths", np.zeros(1, dtype=np.int32)) workspace.FeedBlob("input/int_features.keys", np.zeros(1, dtype=np.int64)) workspace.FeedBlob("input/int_features.values", np.zeros(1, dtype=np.int32)) C2.net().Cast( ["input/int_features.values"], ["input/int_features.values_float"], dtype=caffe2_pb2.TensorProto.FLOAT, ) C2.net().MergeMultiScalarFeatureTensors( [ "input/float_features.lengths", "input/float_features.keys", "input/float_features.values", "input/int_features.lengths", "input/int_features.keys", "input/int_features.values_float", ], [ input_feature_lengths, input_feature_keys, input_feature_values ], ) else: C2.net().Copy(["input/float_features.lengths"], [input_feature_lengths]) C2.net().Copy(["input/float_features.keys"], [input_feature_keys]) C2.net().Copy(["input/float_features.values"], [input_feature_values]) preprocessor = PreprocessorNet(clip_anomalies=True) state_normalized_dense_matrix, new_parameters = preprocessor.normalize_sparse_matrix( input_feature_lengths, input_feature_keys, input_feature_values, state_normalization_parameters, "state_norm", False, False, ) parameters.extend(new_parameters) action_normalized_dense_matrix, new_parameters = preprocessor.normalize_sparse_matrix( input_feature_lengths, input_feature_keys, input_feature_values, action_normalization_parameters, "action_norm", False, False, ) parameters.extend(new_parameters) state_action_normalized = "state_action_normalized" state_action_normalized_dim = "state_action_normalized_dim" net.Concat( [state_normalized_dense_matrix, action_normalized_dense_matrix], [state_action_normalized, state_action_normalized_dim], axis=1, ) net.Copy([state_action_normalized], [qnet_input_blob]) workspace.RunNetOnce(model.param_init_net) workspace.RunNetOnce(torch_init_net) net.AppendNet(torch_predict_net) new_parameters, q_values = RLPredictor._forward_pass( model, trainer, state_action_normalized, ["Q"], qnet_output_blob) parameters.extend(new_parameters) flat_q_values_key = ( "output/string_weighted_multi_categorical_features.values.values") num_examples, _ = C2.Reshape(C2.Size(flat_q_values_key), shape=[1]) q_value_blob, _ = C2.Reshape(flat_q_values_key, shape=[1, -1]) # Get 1 x n (number of examples) action index tensor under the max_q policy max_q_act_idxs = "max_q_policy_actions" C2.net().FlattenToVec([C2.ArgMax(q_value_blob)], [max_q_act_idxs]) max_q_act_blob = C2.Tile(max_q_act_idxs, num_examples, axis=0) # Get 1 x n (number of examples) action index tensor under the softmax policy temperature = C2.NextBlob("temperature") parameters.append(temperature) workspace.FeedBlob( temperature, np.array([trainer.rl_temperature], dtype=np.float32)) tempered_q_values = C2.Div(q_value_blob, temperature, broadcast=1) softmax_values = C2.Softmax(tempered_q_values) softmax_act_idxs_nested = "softmax_act_idxs_nested" C2.net().WeightedSample([softmax_values], [softmax_act_idxs_nested]) softmax_act_blob = C2.Tile(C2.FlattenToVec(softmax_act_idxs_nested), num_examples, axis=0) # Concat action idx vecs to get 2 x n tensor [[a_maxq, ..], [a_softmax, ..]] # transpose & flatten to get [a_maxq, a_softmax, a_maxq, a_softmax, ...] max_q_act_blob = C2.Cast(max_q_act_blob, to=caffe2_pb2.TensorProto.INT64) softmax_act_blob = C2.Cast(softmax_act_blob, to=caffe2_pb2.TensorProto.INT64) max_q_act_blob_nested, _ = C2.Reshape(max_q_act_blob, shape=[1, -1]) softmax_act_blob_nested, _ = C2.Reshape(softmax_act_blob, shape=[1, -1]) C2.net().Append([max_q_act_blob_nested, softmax_act_blob_nested], [max_q_act_blob_nested]) transposed_action_idxs = C2.Transpose(max_q_act_blob_nested) flat_transposed_action_idxs = C2.FlattenToVec(transposed_action_idxs) output_values = "output/int_single_categorical_features.values" workspace.FeedBlob(output_values, np.zeros(1, dtype=np.int64)) C2.net().Copy([flat_transposed_action_idxs], [output_values]) output_lengths = "output/int_single_categorical_features.lengths" workspace.FeedBlob(output_lengths, np.zeros(1, dtype=np.int32)) C2.net().ConstantFill( [flat_q_values_key], [output_lengths], value=2, dtype=caffe2_pb2.TensorProto.INT32, ) output_keys = "output/int_single_categorical_features.keys" workspace.FeedBlob(output_keys, np.zeros(1, dtype=np.int64)) output_keys_tensor, _ = C2.Concat( C2.ConstantFill(shape=[1, 1], value=0, dtype=caffe2_pb2.TensorProto.INT64), C2.ConstantFill(shape=[1, 1], value=1, dtype=caffe2_pb2.TensorProto.INT64), axis=0, ) output_key_tile = C2.Tile(output_keys_tensor, num_examples, axis=0) C2.net().FlattenToVec([output_key_tile], [output_keys]) workspace.CreateNet(net) return ParametricDQNPredictor(net, torch_init_net, parameters, int_features)
def preprocess_samples_discrete( self, samples: Samples, minibatch_size: int, one_hot_action: bool = True) -> List[TrainingDataPage]: logger.info("Shuffling...") samples.shuffle() logger.info("Preprocessing...") net = core.Net("gridworld_preprocessing") C2.set_net(net) preprocessor = PreprocessorNet(True) saa = StackedAssociativeArray.from_dict_list(samples.states, "states") state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, "state_norm", False, False, False, ) saa = StackedAssociativeArray.from_dict_list(samples.next_states, "next_states") next_state_matrix, _ = preprocessor.normalize_sparse_matrix( saa.lengths, saa.keys, saa.values, self.normalization, "next_state_norm", False, False, False, ) workspace.RunNetOnce(net) logger.info("Converting to Torch...") actions_one_hot = torch.tensor((np.array(samples.actions).reshape( -1, 1) == np.array(self.ACTIONS)).astype(np.int64)) actions = actions_one_hot.argmax(dim=1, keepdim=True) rewards = torch.tensor(samples.rewards, dtype=torch.float32).reshape(-1, 1) action_probabilities = torch.tensor(samples.action_probabilities, dtype=torch.float32).reshape( -1, 1) next_actions_one_hot = torch.tensor( (np.array(samples.next_actions).reshape(-1, 1) == np.array( self.ACTIONS)).astype(np.int64)) logger.info("Converting PNA to Torch...") possible_next_action_strings = np.array( list( itertools.zip_longest(*samples.possible_next_actions, fillvalue=""))).T possible_next_actions_mask = torch.zeros( [len(samples.next_actions), len(self.ACTIONS)]) for i, action in enumerate(self.ACTIONS): possible_next_actions_mask[:, i] = torch.tensor( np.max(possible_next_action_strings == action, axis=1).astype(np.int64)) terminals = torch.tensor(samples.terminals, dtype=torch.int32).reshape(-1, 1) not_terminals = 1 - terminals episode_values = None logger.info("Converting RT to Torch...") episode_values = torch.tensor(samples.episode_values, dtype=torch.float32).reshape(-1, 1) time_diffs = torch.ones([len(samples.states), 1]) logger.info("Preprocessing...") preprocessor = Preprocessor(self.normalization, False) states_ndarray = workspace.FetchBlob(state_matrix) states_ndarray = preprocessor.forward(states_ndarray) next_states_ndarray = workspace.FetchBlob(next_state_matrix) next_states_ndarray = preprocessor.forward(next_states_ndarray) logger.info("Batching...") tdps = [] for start in range(0, states_ndarray.shape[0], minibatch_size): end = start + minibatch_size if end > states_ndarray.shape[0]: break tdp = TrainingDataPage( states=states_ndarray[start:end], actions=actions_one_hot[start:end] if one_hot_action else actions[start:end], propensities=action_probabilities[start:end], rewards=rewards[start:end], next_states=next_states_ndarray[start:end], not_terminals=not_terminals[start:end], next_actions=next_actions_one_hot[start:end], possible_next_actions=possible_next_actions_mask[start:end], episode_values=episode_values[start:end] if episode_values is not None else None, time_diffs=time_diffs[start:end], ) tdp.set_type(torch.FloatTensor) tdps.append(tdp) return tdps
def export_actor(cls, trainer, state_normalization_parameters, int_features=False): """Export caffe2 preprocessor net and pytorch actor forward pass as one caffe2 net. :param trainer DDPGTrainer :param state_normalization_parameters state NormalizationParameters :param int_features boolean indicating if int features blob will be present """ input_dim = len(state_normalization_parameters) buffer = PytorchCaffe2Converter.pytorch_net_to_buffer( trainer.actor, input_dim) actor_input_blob, actor_output_blob, caffe2_netdef =\ PytorchCaffe2Converter.buffer_to_caffe2_netdef(buffer) torch_workspace = caffe2_netdef.workspace parameters = [] for blob_str in torch_workspace.Blobs(): workspace.FeedBlob(blob_str, torch_workspace.FetchBlob(blob_str)) parameters.append(blob_str) torch_init_net = core.Net(caffe2_netdef.init_net) torch_predict_net = core.Net(caffe2_netdef.predict_net) model = model_helper.ModelHelper(name="predictor") net = model.net C2.set_model(model) workspace.FeedBlob('input/float_features.lengths', np.zeros(1, dtype=np.int32)) workspace.FeedBlob('input/float_features.keys', np.zeros(1, dtype=np.int64)) workspace.FeedBlob('input/float_features.values', np.zeros(1, dtype=np.float32)) input_feature_lengths = 'input_feature_lengths' input_feature_keys = 'input_feature_keys' input_feature_values = 'input_feature_values' if int_features: workspace.FeedBlob('input/int_features.lengths', np.zeros(1, dtype=np.int32)) workspace.FeedBlob('input/int_features.keys', np.zeros(1, dtype=np.int64)) workspace.FeedBlob('input/int_features.values', np.zeros(1, dtype=np.int32)) C2.net().Cast(['input/int_features.values'], ['input/int_features.values_float'], dtype=caffe2_pb2.TensorProto.FLOAT) C2.net().MergeMultiScalarFeatureTensors([ 'input/float_features.lengths', 'input/float_features.keys', 'input/float_features.values', 'input/int_features.lengths', 'input/int_features.keys', 'input/int_features.values_float' ], [ input_feature_lengths, input_feature_keys, input_feature_values ]) else: C2.net().Copy(['input/float_features.lengths'], [input_feature_lengths]) C2.net().Copy(['input/float_features.keys'], [input_feature_keys]) C2.net().Copy(['input/float_features.values'], [input_feature_values]) preprocessor = PreprocessorNet(net, True) parameters.extend(preprocessor.parameters) state_normalized_dense_matrix, new_parameters = \ preprocessor.normalize_sparse_matrix( input_feature_lengths, input_feature_keys, input_feature_values, state_normalization_parameters, 'state_norm', ) parameters.extend(new_parameters) net.Copy([state_normalized_dense_matrix], [actor_input_blob]) workspace.RunNetOnce(model.param_init_net) workspace.RunNetOnce(torch_init_net) net.AppendNet(torch_init_net) net.AppendNet(torch_predict_net) C2.FlattenToVec(C2.ArgMax(actor_output_blob)) output_lengths = 'output/float_features.lengths' workspace.FeedBlob(output_lengths, np.zeros(1, dtype=np.int32)) C2.net().ConstantFill([C2.FlattenToVec(C2.ArgMax(actor_output_blob))], [output_lengths], value=trainer.actor.layers[-1].out_features, dtype=caffe2_pb2.TensorProto.INT32) output_keys = 'output/float_features.keys' workspace.FeedBlob(output_keys, np.zeros(1, dtype=np.int32)) C2.net().LengthsRangeFill([output_lengths], [output_keys]) output_values = 'output/float_features.values' workspace.FeedBlob(output_values, np.zeros(1, dtype=np.float32)) C2.net().FlattenToVec([actor_output_blob], [output_values]) workspace.CreateNet(net) return DDPGPredictor(net, parameters, int_features)