def _create_rl_train_net(self) -> None:
self.rl_train_model = ModelHelper(name="rl_train_" + self.model_id)
C2.set_model(self.rl_train_model)
if self.maxq_learning:
next_q_values = self.get_max_q_values(
'next_states',
self.get_possible_next_actions(),
True,
)
else:
next_q_values = self.get_q_values('next_states', 'next_actions',
True)
q_vals_target = C2.Add(
'rewards',
C2.Mul(
C2.Mul(
C2.Cast('not_terminals',
to=caffe2_pb2.TensorProto.FLOAT), # type: ignore
self.rl_discount_rate,
broadcast=1,
),
next_q_values))
self.update_model('states', 'actions', q_vals_target)
workspace.RunNetOnce(self.rl_train_model.param_init_net)
workspace.CreateNet(self.rl_train_model.net)
C2.set_model(None)
def _create_rl_train_net(self) -> None:
self.rl_train_model = ModelHelper(name="rl_train_" + self.model_id)
C2.set_model(self.rl_train_model)
if self.reward_shape is not None:
for action_index, boost in self.reward_shape.items():
action_boost = C2.Mul(
C2.Slice(
"actions", starts=[0, action_index], ends=[-1, action_index + 1]
),
boost,
broadcast=1,
)
C2.net().Sum(["rewards", action_boost], ["rewards"])
if self.maxq_learning:
next_q_values = self.get_max_q_values(
"next_states", self.get_possible_next_actions(), True
)
else:
next_q_values = self.get_q_values("next_states", "next_actions", True)
discount_blob = C2.ConstantFill("time_diff", value=self.rl_discount_rate)
if self.use_seq_num_diff_as_time_diff:
time_diff_adjusted_discount_blob = C2.Pow(
discount_blob, C2.Cast("time_diff", to=caffe2_pb2.TensorProto.FLOAT)
)
else:
time_diff_adjusted_discount_blob = discount_blob
q_vals_target = C2.Add(
"rewards",
C2.Mul(
C2.Mul(
C2.Cast(
"not_terminals", to=caffe2_pb2.TensorProto.FLOAT
), # type: ignore
time_diff_adjusted_discount_blob,
broadcast=1,
),
next_q_values,
),
)
self.update_model("states", "actions", q_vals_target)
workspace.RunNetOnce(self.rl_train_model.param_init_net)
self.rl_train_model.net.Proto().num_workers = (
RLTrainer.DEFAULT_TRAINING_NUM_WORKERS
)
self.rl_train_model.net.Proto().type = "async_scheduling"
workspace.CreateNet(self.rl_train_model.net)
C2.set_model(None)
def get_max_q_values(self, states: str, possible_actions: str,
use_target_network: bool) -> str:
"""
Takes in an array of states and outputs an array of the same shape
whose ith entry = max_{pna} Q(state_i, pna).
:param states: Numpy array with shape (batch_size, state_dim). Each
row contains a representation of a state.
:param possible_next_actions: Numpy array with shape (batch_size, action_dim).
possible_next_actions[i][j] = 1 iff the agent can take action j from
state i.
:use_target_network: Boolean that indicates whether or not to use this
trainer's TargetNetwork to compute Q values.
"""
q_values = self.get_q_values_all_actions(states, use_target_network)
# Set the q values of impossible actions to a very large negative
# number.
inverse_pna = C2.ConstantFill(possible_actions, value=1.0)
possible_actions_float = C2.Cast(possible_actions,
to=core.DataType.FLOAT)
inverse_pna = C2.Sub(inverse_pna, possible_actions_float)
inverse_pna = C2.Mul(inverse_pna,
self.ACTION_NOT_POSSIBLE_VAL,
broadcast=1)
q_values = C2.Add(q_values, inverse_pna)
q_values_max = C2.ReduceBackMax(q_values, num_reduce_dims=1)
return C2.ExpandDims(q_values_max, dims=[1])
def update_model(self, states: str, actions: str, q_vals_target: str) -> None:
"""
Takes in states, actions, and target q values. Updates the model:
Runs the forward pass, computing Q(states, actions).
Q(states, actions)[i][j] is an approximation of Q*(states[i], action_j).
Comptutes Loss of Q(states, actions) with respect to q_vals_targets
Updates Q Network's weights according to loss and optimizer
:param states: Numpy array with shape (batch_size, state_dim). The ith
row is a representation of the ith transition's state.
:param actions: Numpy array with shape (batch_size, action_dim). The ith
row contains the one-hotted representation of the ith action.
:param q_vals_targets: Numpy array with shape (batch_size, 1). The ith
row is the label to train against for the data from the ith transition.
"""
model = C2.model()
q_vals_target = C2.StopGradient(q_vals_target)
output_blob = C2.NextBlob("train_output")
if self.conv_ml_trainer is not None:
conv_output_blob = C2.NextBlob("conv_output")
self.conv_ml_trainer.make_conv_pass_ops(model, states, conv_output_blob)
states = conv_output_blob
self.ml_trainer.make_forward_pass_ops(model, states, output_blob, False)
q_val_select = C2.ReduceBackSum(C2.Mul(output_blob, actions))
q_values = C2.ExpandDims(q_val_select, dims=[1])
self.loss_blob = self.ml_trainer.generateLossOps(model, q_values, q_vals_target)
model.AddGradientOperators([self.loss_blob])
for param in model.params:
if param in model.param_to_grad:
param_grad = model.param_to_grad[param]
param_grad = C2.NanCheck(param_grad)
self.ml_trainer.addParameterUpdateOps(model)
def update_model(
self,
states: str,
actions: str,
q_vals_target: str,
) -> None:
"""
Takes in states, actions, and target q values. Updates the model:
Runs the forward pass, computing Q(states, actions).
Q(states, actions)[i][j] is an approximation of Q*(states[i], action_j).
Comptutes Loss of Q(states, actions) with respect to q_vals_targets
Updates Q Network's weights according to loss and optimizer
:param states: Numpy array with shape (batch_size, state_dim). The ith
row is a representation of the ith transition's state.
:param actions: Numpy array with shape (batch_size, action_dim). The ith
row contains the one-hotted representation of the ith action.
:param q_vals_targets: Numpy array with shape (batch_size, 1). The ith
row is the label to train against for the data from the ith transition.
"""
model = C2.model()
q_vals_target = C2.StopGradient(q_vals_target)
output_blob = C2.NextBlob("train_output")
MakeForwardPassOps(
model,
self.model_id,
states,
output_blob,
self.weights,
self.biases,
self.activations,
self.layers,
self.dropout_ratio,
False,
)
q_val_select = C2.ReduceBackSum(C2.Mul(output_blob, actions))
q_values = C2.ExpandDims(q_val_select, dims=[1])
self.loss_blob = GenerateLossOps(
model,
q_values,
q_vals_target,
)
model.AddGradientOperators([self.loss_blob])
for param in model.params:
if param in model.param_to_grad:
param_grad = model.param_to_grad[param]
param_grad = C2.NanCheck(param_grad)
AddParameterUpdateOps(
model,
optimizer_input=self.optimizer,
base_learning_rate=self.learning_rate,
gamma=self.gamma,
policy=self.lr_policy,
)
def _create_rl_train_net(self) -> None:
self.rl_train_model = ModelHelper(name="rl_train_" + self.model_id)
C2.set_model(self.rl_train_model)
if self.reward_shape is not None:
for action_index, boost in self.reward_shape.items():
action_boost = C2.Mul(
C2.Slice(
'actions',
starts=[0, action_index],
ends=[-1, action_index + 1],
),
boost,
broadcast=1,
)
C2.net().Sum(['rewards', action_boost], ['rewards'])
if self.maxq_learning:
next_q_values = self.get_max_q_values(
'next_states',
self.get_possible_next_actions(),
True,
)
else:
next_q_values = self.get_q_values('next_states', 'next_actions',
True)
q_vals_target = C2.Add(
'rewards',
C2.Mul(
C2.Mul(
C2.Cast('not_terminals',
to=caffe2_pb2.TensorProto.FLOAT), # type: ignore
self.rl_discount_rate,
broadcast=1,
),
next_q_values))
self.update_model('states', 'actions', q_vals_target)
workspace.RunNetOnce(self.rl_train_model.param_init_net)
workspace.CreateNet(self.rl_train_model.net)
C2.set_model(None)
def _create_rl_train_net(self) -> None:
self.rl_train_model = ModelHelper(name="rl_train_" + self.model_id)
C2.set_model(self.rl_train_model)
if self.maxq_learning:
next_q_values = self.get_max_q_values(
'next_states',
self.get_possible_next_actions(),
True,
)
else:
next_q_values = self.get_q_values('next_states', 'next_actions',
True)
discount_blob = C2.ConstantFill("time_diff",
value=self.rl_discount_rate)
time_diff_adjusted_discount_blob = C2.Pow(
discount_blob, C2.Cast("time_diff",
to=caffe2_pb2.TensorProto.FLOAT))
q_vals_target = C2.Add(
"rewards",
C2.Mul(
C2.Mul(
C2.Cast("not_terminals",
to=caffe2_pb2.TensorProto.FLOAT), # type: ignore
time_diff_adjusted_discount_blob,
broadcast=1,
),
next_q_values,
),
)
self.update_model('states', 'actions', q_vals_target)
workspace.RunNetOnce(self.rl_train_model.param_init_net)
self.rl_train_model.net.Proto().num_workers = \
RLTrainer.DEFAULT_TRAINING_NUM_WORKERS
workspace.CreateNet(self.rl_train_model.net)
C2.set_model(None)
def _create_reward_train_net(self) -> None:
self.reward_train_model = ModelHelper(name="reward_train_" +
self.model_id)
C2.set_model(self.reward_train_model)
if self.reward_shape is not None:
for action_index, boost in self.reward_shape.items():
action_boost = C2.Mul(
C2.Slice("actions",
starts=[0, action_index],
ends=[-1, action_index + 1]),
boost,
broadcast=1,
)
C2.net().Sum(["rewards", action_boost], ["rewards"])
self.update_model("states", "actions", "rewards")
workspace.RunNetOnce(self.reward_train_model.param_init_net)
workspace.CreateNet(self.reward_train_model.net)
C2.set_model(None)
def _create_reward_train_net(self) -> None:
self.reward_train_model = ModelHelper(name="reward_train_" +
self.model_id)
C2.set_model(self.reward_train_model)
if self.reward_shape is not None:
for action_index, boost in self.reward_shape.items():
action_boost = C2.Mul(
C2.Slice("actions",
starts=[0, action_index],
ends=[-1, action_index + 1]),
boost,
broadcast=1,
)
C2.net().Sum(["rewards", action_boost], ["rewards"])
self.update_model("states", "actions", "rewards")
workspace.RunNetOnce(self.reward_train_model.param_init_net)
self.reward_train_model.net.Proto().num_workers = (
RLTrainer.DEFAULT_TRAINING_NUM_WORKERS)
self.reward_train_model.net.Proto().type = "async_scheduling"
workspace.CreateNet(self.reward_train_model.net)
C2.set_model(None)
def export_actor(
cls,
trainer,
state_normalization_parameters,
action_feature_ids,
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
"""
model = model_helper.ModelHelper(name="predictor")
net = model.net
C2.set_model(model)
parameters: List[str] = []
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()
sorted_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_features,
)
parameters.extend(new_parameters)
state_normalized_dense_matrix, new_parameters = preprocessor.normalize_dense_matrix(
state_dense_matrix,
sorted_features,
state_normalization_parameters,
"state_norm",
False,
)
parameters.extend(new_parameters)
torch_init_net, torch_predict_net, new_parameters, actor_input_blob, actor_output_blob, min_action_training_blob, max_action_training_blob, min_action_serving_blob, max_action_serving_blob = DDPGPredictor.generate_train_net(
trainer,
model,
min_action_range_tensor_serving,
max_action_range_tensor_serving,
model_on_gpu,
)
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)
# 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
)
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,
)
action_feature_ids_blob = C2.NextBlob("action_feature_ids")
workspace.FeedBlob(
action_feature_ids_blob, np.array(action_feature_ids, dtype=np.int64)
)
parameters.append(action_feature_ids_blob)
output_keys = "output/float_features.keys"
workspace.FeedBlob(output_keys, np.zeros(1, dtype=np.int64))
num_examples, _ = C2.Reshape(C2.Size("input/float_features.lengths"), shape=[1])
C2.net().Tile([action_feature_ids_blob, num_examples], [output_keys], axis=1)
output_values = "output/float_features.values"
workspace.FeedBlob(output_values, np.zeros(1, dtype=np.float32))
C2.net().FlattenToVec([scaled_for_serving_actions], [output_values])
workspace.CreateNet(net)
return DDPGPredictor(net, torch_init_net, parameters, int_features)
def preprocess_blob(self, blob, normalization_parameters):
"""
Takes in a blob and its normalization parameters. Outputs a tuple
whose first element is a blob containing the normalized input blob
and whose second element contains all the parameter blobs used to
create it.
Call this from a CPU context and ensure the input blob exists in it.
"""
parameters: List[str] = []
ZERO = self._store_parameter(parameters, "ZERO",
np.array([0], dtype=np.float32))
ONE = self._store_parameter(parameters, "ONE",
np.array([1], dtype=np.float32))
NEGATIVE_ONE = self._store_parameter(parameters, "NEGATIVE_ONE",
np.array([-1], dtype=np.float32))
MISSING_U = self._store_parameter(
parameters, "MISSING_U",
np.array([MISSING_VALUE + 1e-4], dtype=np.float32))
MISSING_L = self._store_parameter(
parameters, "MISSING_L",
np.array([MISSING_VALUE - 1e-4], dtype=np.float32))
is_empty_l = C2.GT(blob, MISSING_L, broadcast=1)
is_empty_u = C2.LT(blob, MISSING_U, broadcast=1)
is_empty = C2.And(is_empty_l, is_empty_u)
for i in range(len(normalization_parameters) - 1):
if (normalization_parameters[i].feature_type !=
normalization_parameters[i + 1].feature_type):
raise Exception(
"Only one feature type is allowed per call to preprocess_blob!"
)
feature_type = normalization_parameters[0].feature_type
if feature_type == identify_types.BINARY:
TOLERANCE = self._store_parameter(parameters, "TOLERANCE",
np.array(1e-3, dtype=np.float32))
is_gt_zero = C2.GT(blob,
C2.Add(ZERO, TOLERANCE, broadcast=1),
broadcast=1)
is_lt_zero = C2.LT(blob,
C2.Sub(ZERO, TOLERANCE, broadcast=1),
broadcast=1)
bool_blob = C2.Or(is_gt_zero, is_lt_zero)
blob = C2.Cast(bool_blob, to=caffe2_pb2.TensorProto.FLOAT)
elif feature_type == identify_types.PROBABILITY:
clipped = C2.Clip(blob, min=0.01, max=0.99)
blob = C2.Mul(
C2.Log(C2.Sub(C2.Pow(clipped, exponent=-1.0), ONE,
broadcast=1)),
NEGATIVE_ONE,
broadcast=1,
)
elif feature_type == identify_types.ENUM:
for parameter in normalization_parameters:
possible_values = parameter.possible_values
for x in possible_values:
if x < 0:
logger.fatal(
"Invalid enum possible value for feature: " +
str(x) + " " + str(parameter.possible_values))
raise Exception(
"Invalid enum possible value for feature " + blob +
": " + str(x) + " " +
str(parameter.possible_values))
int_blob = C2.Cast(blob, to=core.DataType.INT32)
# Batch one hot transform with MISSING_VALUE as a possible value
feature_lengths = [
len(p.possible_values) + 1 for p in normalization_parameters
]
feature_lengths_blob = self._store_parameter(
parameters,
"feature_lengths_blob",
np.array(feature_lengths, dtype=np.int32),
)
feature_values = [
x for p in normalization_parameters
for x in p.possible_values + [int(MISSING_VALUE)]
]
feature_values_blob = self._store_parameter(
parameters,
"feature_values_blob",
np.array(feature_values, dtype=np.int32),
)
one_hot_output = C2.BatchOneHot(int_blob, feature_lengths_blob,
feature_values_blob)
flattened_one_hot = C2.FlattenToVec(one_hot_output)
# Remove missing values with a mask
cols_to_include = [[1] * len(p.possible_values) + [0]
for p in normalization_parameters]
cols_to_include = [x for col in cols_to_include for x in col]
mask = self._store_parameter(
parameters, "mask", np.array(cols_to_include, dtype=np.int32))
zero_vec = C2.ConstantFill(one_hot_output,
value=0,
dtype=caffe2_pb2.TensorProto.INT32)
repeated_mask_bool = C2.Cast(C2.Add(zero_vec, mask, broadcast=1),
to=core.DataType.BOOL)
flattened_repeated_mask = C2.FlattenToVec(repeated_mask_bool)
flattened_one_hot_proc = C2.NextBlob("flattened_one_hot_proc")
flattened_one_hot_proc_indices = C2.NextBlob(
"flattened_one_hot_proc_indices")
C2.net().BooleanMask(
[flattened_one_hot, flattened_repeated_mask],
[flattened_one_hot_proc, flattened_one_hot_proc_indices],
)
one_hot_shape = C2.Shape(one_hot_output)
shape_delta = self._store_parameter(
parameters,
"shape_delta",
np.array([0, len(normalization_parameters)], dtype=np.int64),
)
target_shape = C2.Sub(one_hot_shape, shape_delta, broadcast=1)
output_int_blob = C2.NextBlob("output_int_blob")
output_int_blob_old_shape = C2.NextBlob(
"output_int_blob_old_shape")
C2.net().Reshape(
[flattened_one_hot_proc, target_shape],
[output_int_blob, output_int_blob_old_shape],
)
output_blob = C2.Cast(output_int_blob, to=core.DataType.FLOAT)
return output_blob, parameters
elif feature_type == identify_types.QUANTILE:
# This transformation replaces a set of values with their quantile.
# The quantile boundaries are provided in the normalization params.
quantile_sizes = [
len(norm.quantiles) for norm in normalization_parameters
]
num_boundaries_blob = self._store_parameter(
parameters,
"num_boundaries_blob",
np.array(quantile_sizes, dtype=np.int32),
)
quantile_values = np.array([], dtype=np.float32)
quantile_labels = np.array([], dtype=np.float32)
for norm in normalization_parameters:
quantile_values = np.append(
quantile_values, np.array(norm.quantiles,
dtype=np.float32))
quantile_labels = np.append(
quantile_labels,
np.arange(len(norm.quantiles), dtype=np.float32) /
float(len(norm.quantiles) - 1.0),
)
quantiles = np.vstack([quantile_values, quantile_labels]).T
quantiles_blob = self._store_parameter(parameters,
"quantiles_blob", quantiles)
quantile_blob = C2.Percentile(blob, quantiles_blob,
num_boundaries_blob)
blob = quantile_blob
elif (feature_type == identify_types.CONTINUOUS
or feature_type == identify_types.BOXCOX):
boxcox_shifts = []
boxcox_lambdas = []
means = []
stddevs = []
for norm in normalization_parameters:
if feature_type == identify_types.BOXCOX:
assert (norm.boxcox_shift is not None
and norm.boxcox_lambda is not None)
boxcox_shifts.append(norm.boxcox_shift)
boxcox_lambdas.append(norm.boxcox_lambda)
means.append(norm.mean)
stddevs.append(norm.stddev)
if feature_type == identify_types.BOXCOX:
boxcox_shift_blob = self._store_parameter(
parameters,
"boxcox_shift",
np.array(boxcox_shifts, dtype=np.float32),
)
boxcox_lambda_blob = self._store_parameter(
parameters,
"boxcox_shift",
np.array(boxcox_lambdas, dtype=np.float32),
)
blob = C2.BatchBoxCox(blob, boxcox_lambda_blob,
boxcox_shift_blob)
means_blob = self._store_parameter(
parameters, "means_blob", np.array([means], dtype=np.float32))
stddevs_blob = self._store_parameter(
parameters, "stddevs_blob",
np.array([stddevs], dtype=np.float32))
blob = C2.Sub(blob, means_blob, broadcast=1, axis=0)
blob = C2.Div(blob, stddevs_blob, broadcast=1, axis=0)
if self.clip_anomalies:
blob = C2.Clip(blob, min=-5.0, max=5.0)
else:
raise NotImplementedError(
"Invalid feature type: {}".format(feature_type))
zeros = C2.ConstantFill(blob, value=0.0)
output_blob = C2.Where(is_empty, zeros, blob)
return output_blob, parameters
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)