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 is a representation of the ith transition's 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)
q_values = C2.NextBlob("train_output")
state_action_pairs, _ = C2.Concat(states, actions, axis=1)
self.ml_trainer.make_forward_pass_ops(model, state_action_pairs,
q_values, False)
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")
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 normalize_dense_matrix(
self,
input_matrix: str,
features: List[str],
normalization_parameters: Dict[str, NormalizationParameters],
blobname_prefix: str,
) -> Tuple[str, List[str]]:
"""
Normalizes inputs according to parameters. Expects a dense matrix whose ith
column corresponds to feature i.
Note that the Caffe2 BatchBoxCox operator isn't implemented on CUDA GPU so
we need to use a CPU context.
:param input_matrix: Input matrix to normalize.
:param features: Array that maps feature ids to column indices.
:param normalization_parameters: Mapping from feature names to
NormalizationParameters.
:param blobname_prefix: Prefix for input blobs to norm_net.
:param num_output_features: The number of features in an output processed
datapoint. If set to None, this function will compute it.
"""
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
feature_starts = self._get_type_boundaries(
features, normalization_parameters)
normalized_input_blobs = []
parameters: List[str] = []
for i, feature_type in enumerate(FEATURE_TYPES):
start_index = feature_starts[i]
if (i + 1) == len(FEATURE_TYPES):
end_index = len(normalization_parameters)
else:
end_index = feature_starts[i + 1]
if start_index == end_index:
continue # No features of this type
sliced_input_features = self._get_input_blob(
blobname_prefix, feature_type)
C2.net().Slice(
[input_matrix],
[sliced_input_features],
starts=[0, start_index],
ends=[-1, end_index],
)
normalized_input_blob, blob_parameters = self.preprocess_blob(
sliced_input_features,
[
normalization_parameters[x]
for x in features[start_index:end_index]
],
)
parameters.extend(blob_parameters)
normalized_input_blobs.append(normalized_input_blob)
for i, inp in enumerate(normalized_input_blobs):
logger.info("input# {}: {}".format(i, inp))
concatenated_input_blob, concatenated_input_blob_dim = C2.Concat(
*normalized_input_blobs, axis=1)
concatenated_input_blob = C2.NanCheck(concatenated_input_blob)
return concatenated_input_blob, parameters
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 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] = []
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)
)
ZERO = self._store_parameter(
parameters, "ZERO", np.array([0], 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:
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)
)
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)
blob = C2.Clip(blob, min=MIN_FEATURE_VALUE, max=MAX_FEATURE_VALUE)
elif feature_type == identify_types.CONTINUOUS_ACTION:
serving_min_value = np.array(
[norm.min_value for norm in normalization_parameters], dtype=np.float32
)
serving_max_value = np.array(
[norm.max_value for norm in normalization_parameters], dtype=np.float32
)
training_min_value = (
np.ones(len(normalization_parameters), dtype=np.float32) * -1 + EPS
)
scaling_factor = (
(np.ones(len(normalization_parameters), dtype=np.float32) - EPS)
* 2
/ (serving_max_value - serving_min_value)
)
serving_min_blob = self._store_parameter(
parameters, "serving_min_blob", serving_min_value
)
training_min_blob = self._store_parameter(
parameters, "training_min_blob", training_min_value
)
scaling_factor_blob = self._store_parameter(
parameters, "scaling_factor_blob", scaling_factor
)
blob = C2.Sub(blob, serving_min_blob, broadcast=1, axis=1)
blob = C2.Mul(blob, scaling_factor_blob, broadcast=1, axis=1)
blob = C2.Add(blob, training_min_blob, broadcast=1, axis=1)
blob = C2.Clip(blob, min=-1 + EPS, max=1 - EPS)
else:
raise NotImplementedError("Invalid feature type: {}".format(feature_type))
zeros = C2.ConstantFill(blob, value=0.0)
output_blob = C2.Where(is_empty, zeros, blob)
output_blob = C2.NanCheck(output_blob)
return output_blob, parameters