def __init__(self, net, init_net, parameters, int_features): RLPredictor.__init__(self, net, init_net, parameters, int_features) self._output_blobs.extend([ OUTPUT_SINGLE_CAT_KEYS_NAME, OUTPUT_SINGLE_CAT_LENGTHS_NAME, OUTPUT_SINGLE_CAT_VALS_NAME, ])
def __init__(self, net, init_net, parameters, int_features) -> None: RLPredictor.__init__(self, net, init_net, parameters, int_features) self._output_blobs = [ "output/float_features.lengths", "output/float_features.keys", "output/float_features.values", ]
def __init__(self, net, init_net, parameters, int_features): RLPredictor.__init__(self, net, init_net, parameters, int_features) self._output_blobs.extend( [ OUTPUT_SINGLE_CAT_KEYS_NAME, OUTPUT_SINGLE_CAT_LENGTHS_NAME, OUTPUT_SINGLE_CAT_VALS_NAME, ] )
def predict(self, float_state_features, int_state_features, actions): """ Returns values for each state/action pair. :param float_state_features states as list of feature -> float value dict :param int_state_features states as list of feature -> int value dict :param actions actions as list of feature -> value dict """ float_examples = [] for i in range(len(float_state_features)): float_examples.append({**float_state_features[i], **actions[i]}) if int_state_features is None: return RLPredictor.predict(self, float_examples) return RLPredictor.predict(self, float_examples, int_state_features)
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(True) sorted_state_features, _ = sort_features_by_normalization( state_normalization_parameters ) state_dense_matrix, new_parameters = sparse_to_dense( input_feature_lengths, input_feature_keys, input_feature_values, sorted_state_features, ) parameters.extend(new_parameters) state_normalized_dense_matrix, new_parameters = preprocessor.normalize_dense_matrix( state_dense_matrix, sorted_state_features, state_normalization_parameters, "state_norm", False, ) parameters.extend(new_parameters) sorted_action_features, _ = sort_features_by_normalization( action_normalization_parameters ) action_dense_matrix, new_parameters = sparse_to_dense( input_feature_lengths, input_feature_keys, input_feature_values, sorted_action_features, ) parameters.extend(new_parameters) action_normalized_dense_matrix, new_parameters = preprocessor.normalize_dense_matrix( action_dense_matrix, sorted_action_features, action_normalization_parameters, "action_norm", 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 export( cls, trainer, actions, state_normalization_parameters, int_features=False, model_on_gpu=False, set_missing_value_to_zero=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 q_network = (trainer.q_network.module if isinstance( trainer.q_network, DataParallel) else trainer.q_network) buffer = PytorchCaffe2Converter.pytorch_net_to_buffer( 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) logger.info("Generated ONNX predict net:") logger.info(str(torch_predict_net.Proto())) # 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: sorted_feature_ids = sort_features_by_normalization( state_normalization_parameters)[0] dense_matrix, new_parameters = sparse_to_dense( input_feature_lengths, input_feature_keys, input_feature_values, sorted_feature_ids, set_missing_value_to_zero=set_missing_value_to_zero, ) parameters.extend(new_parameters) preprocessor_net = PreprocessorNet() state_normalized_dense_matrix, new_parameters = preprocessor_net.normalize_dense_matrix( dense_matrix, sorted_feature_ids, state_normalization_parameters, "state_norm_", True, ) 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( cls, trainer, actions, state_normalization_parameters, int_features=False, model_on_gpu=False, set_missing_value_to_zero=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 q_network = ( trainer.q_network.module if isinstance(trainer.q_network, DataParallel) else trainer.q_network ) buffer = PytorchCaffe2Converter.pytorch_net_to_buffer( 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) logger.info("Generated ONNX predict net:") logger.info(str(torch_predict_net.Proto())) # 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: sorted_feature_ids = sort_features_by_normalization( state_normalization_parameters )[0] dense_matrix, new_parameters = sparse_to_dense( input_feature_lengths, input_feature_keys, input_feature_values, sorted_feature_ids, set_missing_value_to_zero=set_missing_value_to_zero, ) parameters.extend(new_parameters) preprocessor_net = PreprocessorNet() state_normalized_dense_matrix, new_parameters = preprocessor_net.normalize_dense_matrix( dense_matrix, sorted_feature_ids, state_normalization_parameters, "state_norm_", True, ) 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)