def test_normalize_dense_matrix_enum(self):
normalization_parameters = {
1: NormalizationParameters(
identify_types.ENUM,
None,
None,
None,
None,
[12, 4, 2],
None,
None,
None,
),
2: NormalizationParameters(
identify_types.CONTINUOUS, None, 0, 0, 1, None, None, None, None
),
3: NormalizationParameters(
identify_types.ENUM, None, None, None, None, [15, 3], None, None, None
),
}
norm_net = core.Net("net")
C2.set_net(norm_net)
preprocessor = PreprocessorNet()
inputs = np.zeros([4, 3], dtype=np.float32)
feature_ids = [2, 1, 3] # Sorted according to feature type
inputs[:, feature_ids.index(1)] = [12, 4, 2, 2]
inputs[:, feature_ids.index(2)] = [1.0, 2.0, 3.0, 3.0]
inputs[:, feature_ids.index(3)] = [15, 3, 15, normalization.MISSING_VALUE]
input_blob = C2.NextBlob("input_blob")
workspace.FeedBlob(input_blob, np.array([0], dtype=np.float32))
normalized_output_blob, _ = preprocessor.normalize_dense_matrix(
input_blob, feature_ids, normalization_parameters, "", False
)
workspace.FeedBlob(input_blob, inputs)
workspace.RunNetOnce(norm_net)
normalized_feature_matrix = workspace.FetchBlob(normalized_output_blob)
np.testing.assert_allclose(
np.array(
[
[1.0, 1, 0, 0, 1, 0],
[2.0, 0, 1, 0, 0, 1],
[3.0, 0, 0, 1, 1, 0],
[3.0, 0, 0, 1, 0, 0], # Missing values should go to all 0
]
),
normalized_feature_matrix,
)
def test_normalize_dense_matrix_enum(self):
normalization_parameters = {
1:
NormalizationParameters(
identify_types.ENUM,
None,
None,
None,
None,
[12, 4, 2],
None,
None,
None,
),
2:
NormalizationParameters(identify_types.CONTINUOUS, None, 0, 0, 1,
None, None, None, None),
3:
NormalizationParameters(identify_types.ENUM, None, None, None,
None, [15, 3], None, None, None),
}
norm_net = core.Net("net")
C2.set_net(norm_net)
preprocessor = PreprocessorNet()
inputs = np.zeros([4, 3], dtype=np.float32)
feature_ids = [2, 1, 3] # Sorted according to feature type
inputs[:, feature_ids.index(1)] = [12, 4, 2, 2]
inputs[:, feature_ids.index(2)] = [1.0, 2.0, 3.0, 3.0]
inputs[:, feature_ids.index(3)] = [
15, 3, 15, normalization.MISSING_VALUE
]
input_blob = C2.NextBlob("input_blob")
workspace.FeedBlob(input_blob, np.array([0], dtype=np.float32))
normalized_output_blob, _ = preprocessor.normalize_dense_matrix(
input_blob, feature_ids, normalization_parameters, "", False)
workspace.FeedBlob(input_blob, inputs)
workspace.RunNetOnce(norm_net)
normalized_feature_matrix = workspace.FetchBlob(normalized_output_blob)
np.testing.assert_allclose(
np.array([
[1.0, 1, 0, 0, 1, 0],
[2.0, 0, 1, 0, 0, 1],
[3.0, 0, 0, 1, 1, 0],
[3.0, 0, 0, 1, 0, 0], # Missing values should go to all 0
]),
normalized_feature_matrix,
)
def test_prepare_normalization_and_normalize(self):
feature_value_map = read_data()
normalization_parameters = {}
for name, values in feature_value_map.items():
normalization_parameters[name] = normalization.identify_parameter(
name,
values,
10,
feature_type=self._feature_type_override(name))
for k, v in normalization_parameters.items():
if id_to_type(k) == CONTINUOUS:
self.assertEqual(v.feature_type, CONTINUOUS)
self.assertIs(v.boxcox_lambda, None)
self.assertIs(v.boxcox_shift, None)
elif id_to_type(k) == BOXCOX:
self.assertEqual(v.feature_type, BOXCOX)
self.assertIsNot(v.boxcox_lambda, None)
self.assertIsNot(v.boxcox_shift, None)
else:
assert v.feature_type == id_to_type(k)
sorted_features, _ = sort_features_by_normalization(
normalization_parameters)
norm_net = core.Net("net")
C2.set_net(norm_net)
preprocessor = PreprocessorNet()
input_matrix = np.zeros([10000, len(sorted_features)],
dtype=np.float32)
for i, feature in enumerate(sorted_features):
input_matrix[:, i] = feature_value_map[feature]
input_matrix_blob = "input_matrix_blob"
workspace.FeedBlob(input_matrix_blob, np.array([], dtype=np.float32))
output_blob, _ = preprocessor.normalize_dense_matrix(
input_matrix_blob, sorted_features, normalization_parameters, "",
False)
workspace.FeedBlob(input_matrix_blob, input_matrix)
workspace.RunNetOnce(norm_net)
normalized_feature_matrix = workspace.FetchBlob(output_blob)
normalized_features = {}
on_column = 0
for feature in sorted_features:
norm = normalization_parameters[feature]
if norm.feature_type == ENUM:
column_size = len(norm.possible_values)
else:
column_size = 1
normalized_features[
feature] = normalized_feature_matrix[:,
on_column:(on_column +
column_size)]
on_column += column_size
self.assertTrue(
all([
np.isfinite(parameter.stddev) and np.isfinite(parameter.mean)
for parameter in normalization_parameters.values()
]))
for k, v in six.iteritems(normalized_features):
self.assertTrue(np.all(np.isfinite(v)))
feature_type = normalization_parameters[k].feature_type
if feature_type == identify_types.PROBABILITY:
sigmoidv = special.expit(v)
self.assertTrue(
np.all(
np.logical_and(np.greater(sigmoidv, 0),
np.less(sigmoidv, 1))))
elif feature_type == identify_types.ENUM:
possible_values = normalization_parameters[k].possible_values
self.assertEqual(v.shape[0], len(feature_value_map[k]))
self.assertEqual(v.shape[1], len(possible_values))
possible_value_map = {}
for i, possible_value in enumerate(possible_values):
possible_value_map[possible_value] = i
for i, row in enumerate(v):
original_feature = feature_value_map[k][i]
self.assertEqual(possible_value_map[original_feature],
np.where(row == 1)[0][0])
elif feature_type == identify_types.QUANTILE:
for i, feature in enumerate(v[0]):
original_feature = feature_value_map[k][i]
expected = NumpyFeatureProcessor.value_to_quantile(
original_feature,
normalization_parameters[k].quantiles)
self.assertAlmostEqual(feature, expected, 2)
elif feature_type == identify_types.BINARY:
pass
elif (feature_type == identify_types.CONTINUOUS
or feature_type == identify_types.BOXCOX):
one_stddev = np.isclose(np.std(v, ddof=1), 1, atol=0.01)
zero_stddev = np.isclose(np.std(v, ddof=1), 0, atol=0.01)
zero_mean = np.isclose(np.mean(v), 0, atol=0.01)
self.assertTrue(
np.all(zero_mean),
"mean of feature {} is {}, not 0".format(k, np.mean(v)),
)
self.assertTrue(np.all(np.logical_or(one_stddev, zero_stddev)))
elif feature_type == identify_types.CONTINUOUS_ACTION:
less_than_max = v < 1
more_than_min = v > -1
self.assertTrue(
np.all(less_than_max),
"values are not less than 1: {}".format(
v[less_than_max == False]),
)
self.assertTrue(
np.all(more_than_min),
"values are not more than -1: {}".format(
v[more_than_min == False]),
)
else:
raise NotImplementedError()
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 benchmark(num_forward_passes):
"""
Benchmark preprocessor speeds:
1 - PyTorch
2 - PyTorch -> ONNX -> C2
3 - C2
"""
feature_value_map = gen_data(
num_binary_features=10,
num_boxcox_features=10,
num_continuous_features=10,
num_enum_features=10,
num_prob_features=10,
num_quantile_features=10,
)
normalization_parameters = {}
for name, values in feature_value_map.items():
normalization_parameters[name] = normalization.identify_parameter(
name, values, 10
)
sorted_features, _ = sort_features_by_normalization(normalization_parameters)
# Dummy input
input_matrix = np.zeros([10000, len(sorted_features)], dtype=np.float32)
# PyTorch Preprocessor
pytorch_preprocessor = Preprocessor(normalization_parameters, False)
for i, feature in enumerate(sorted_features):
input_matrix[:, i] = feature_value_map[feature]
#################### time pytorch ############################
start = time.time()
for _ in range(NUM_FORWARD_PASSES):
_ = pytorch_preprocessor.forward(input_matrix)
end = time.time()
logger.info(
"PyTorch: {} forward passes done in {} seconds".format(
NUM_FORWARD_PASSES, end - start
)
)
################ time pytorch -> ONNX -> caffe2 ####################
buffer = PytorchCaffe2Converter.pytorch_net_to_buffer(
pytorch_preprocessor, len(sorted_features), False
)
input_blob, 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)
input_matrix_blob = "input_matrix_blob"
workspace.FeedBlob(input_blob, input_matrix)
workspace.RunNetOnce(torch_init_net)
start = time.time()
for _ in range(NUM_FORWARD_PASSES):
workspace.RunNetOnce(torch_predict_net)
_ = workspace.FetchBlob(output_blob)
end = time.time()
logger.info(
"PyTorch -> ONNX -> Caffe2: {} forward passes done in {} seconds".format(
NUM_FORWARD_PASSES, end - start
)
)
#################### time caffe2 ############################
norm_net = core.Net("net")
C2.set_net(norm_net)
preprocessor = PreprocessorNet()
input_matrix_blob = "input_matrix_blob"
workspace.FeedBlob(input_matrix_blob, np.array([], dtype=np.float32))
output_blob, _ = preprocessor.normalize_dense_matrix(
input_matrix_blob, sorted_features, normalization_parameters, "", False
)
workspace.FeedBlob(input_matrix_blob, input_matrix)
start = time.time()
for _ in range(NUM_FORWARD_PASSES):
workspace.RunNetOnce(norm_net)
workspace.FetchBlob(output_blob)
end = time.time()
logger.info(
"Caffe2: {} forward passes done in {} seconds".format(
NUM_FORWARD_PASSES, end - start
)
)
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_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 test_prepare_normalization_and_normalize(self):
feature_value_map = read_data()
normalization_parameters = {}
for name, values in feature_value_map.items():
normalization_parameters[name] = normalization.identify_parameter(
name, values, 10, feature_type=self._feature_type_override(name)
)
for k, v in normalization_parameters.items():
if id_to_type(k) == CONTINUOUS:
self.assertEqual(v.feature_type, CONTINUOUS)
self.assertIs(v.boxcox_lambda, None)
self.assertIs(v.boxcox_shift, None)
elif id_to_type(k) == BOXCOX:
self.assertEqual(v.feature_type, BOXCOX)
self.assertIsNot(v.boxcox_lambda, None)
self.assertIsNot(v.boxcox_shift, None)
else:
assert v.feature_type == id_to_type(k)
sorted_features, _ = sort_features_by_normalization(normalization_parameters)
norm_net = core.Net("net")
C2.set_net(norm_net)
preprocessor = PreprocessorNet()
input_matrix = np.zeros([10000, len(sorted_features)], dtype=np.float32)
for i, feature in enumerate(sorted_features):
input_matrix[:, i] = feature_value_map[feature]
input_matrix_blob = "input_matrix_blob"
workspace.FeedBlob(input_matrix_blob, np.array([], dtype=np.float32))
output_blob, _ = preprocessor.normalize_dense_matrix(
input_matrix_blob, sorted_features, normalization_parameters, "", False
)
workspace.FeedBlob(input_matrix_blob, input_matrix)
workspace.RunNetOnce(norm_net)
normalized_feature_matrix = workspace.FetchBlob(output_blob)
normalized_features = {}
on_column = 0
for feature in sorted_features:
norm = normalization_parameters[feature]
if norm.feature_type == ENUM:
column_size = len(norm.possible_values)
else:
column_size = 1
normalized_features[feature] = normalized_feature_matrix[
:, on_column : (on_column + column_size)
]
on_column += column_size
self.assertTrue(
all(
[
np.isfinite(parameter.stddev) and np.isfinite(parameter.mean)
for parameter in normalization_parameters.values()
]
)
)
for k, v in six.iteritems(normalized_features):
self.assertTrue(np.all(np.isfinite(v)))
feature_type = normalization_parameters[k].feature_type
if feature_type == identify_types.PROBABILITY:
sigmoidv = special.expit(v)
self.assertTrue(
np.all(
np.logical_and(np.greater(sigmoidv, 0), np.less(sigmoidv, 1))
)
)
elif feature_type == identify_types.ENUM:
possible_values = normalization_parameters[k].possible_values
self.assertEqual(v.shape[0], len(feature_value_map[k]))
self.assertEqual(v.shape[1], len(possible_values))
possible_value_map = {}
for i, possible_value in enumerate(possible_values):
possible_value_map[possible_value] = i
for i, row in enumerate(v):
original_feature = feature_value_map[k][i]
self.assertEqual(
possible_value_map[original_feature], np.where(row == 1)[0][0]
)
elif feature_type == identify_types.QUANTILE:
for i, feature in enumerate(v[0]):
original_feature = feature_value_map[k][i]
expected = NumpyFeatureProcessor.value_to_quantile(
original_feature, normalization_parameters[k].quantiles
)
self.assertAlmostEqual(feature, expected, 2)
elif feature_type == identify_types.BINARY:
pass
elif (
feature_type == identify_types.CONTINUOUS
or feature_type == identify_types.BOXCOX
):
one_stddev = np.isclose(np.std(v, ddof=1), 1, atol=0.01)
zero_stddev = np.isclose(np.std(v, ddof=1), 0, atol=0.01)
zero_mean = np.isclose(np.mean(v), 0, atol=0.01)
self.assertTrue(
np.all(zero_mean),
"mean of feature {} is {}, not 0".format(k, np.mean(v)),
)
self.assertTrue(np.all(np.logical_or(one_stddev, zero_stddev)))
elif feature_type == identify_types.CONTINUOUS_ACTION:
less_than_max = v < 1
more_than_min = v > -1
self.assertTrue(
np.all(less_than_max),
"values are not less than 1: {}".format(v[less_than_max == False]),
)
self.assertTrue(
np.all(more_than_min),
"values are not more than -1: {}".format(v[more_than_min == False]),
)
else:
raise NotImplementedError()
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 = C2.NextBlob("input_feature_lengths")
input_feature_keys = C2.NextBlob("input_feature_keys")
input_feature_values = C2.NextBlob("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(True)
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)
normalized_dense_matrix, new_parameters = preprocessor.normalize_dense_matrix(
state_dense_matrix,
sorted_features,
state_normalization_parameters,
"state_norm",
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.
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)
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)
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 test_prepare_normalization_and_normalize(self):
features, feature_value_map = preprocessing_util.read_data()
normalization_parameters = {}
for name, values in feature_value_map.items():
normalization_parameters[name] = normalization.identify_parameter(
values, 10)
for k, v in normalization_parameters.items():
if k == CONTINUOUS:
self.assertEqual(v.feature_type, CONTINUOUS)
self.assertIs(v.boxcox_lambda, None)
self.assertIs(v.boxcox_shift, None)
elif k == BOXCOX:
self.assertEqual(v.feature_type, BOXCOX)
self.assertIsNot(v.boxcox_lambda, None)
self.assertIsNot(v.boxcox_shift, None)
else:
assert v.feature_type == k or v.feature_type + "_2" + k
norm_net = core.Net("net")
preprocessor = PreprocessorNet(norm_net, False)
input_matrix = np.zeros([10000, len(features)], dtype=np.float32)
for i, feature in enumerate(features):
input_matrix[:, i] = feature_value_map[feature]
input_matrix_blob = 'input_matrix_blob'
workspace.FeedBlob(input_matrix_blob, np.array([], dtype=np.float32))
output_blob, _ = preprocessor.normalize_dense_matrix(
input_matrix_blob, features, normalization_parameters, '')
workspace.FeedBlob(input_matrix_blob, input_matrix)
workspace.RunNetOnce(norm_net)
normalized_feature_matrix = workspace.FetchBlob(output_blob)
normalized_features = {}
on_column = 0
for feature in features:
norm = normalization_parameters[feature]
if norm.feature_type == ENUM:
column_size = len(norm.possible_values)
else:
column_size = 1
normalized_features[feature] = \
normalized_feature_matrix[:, on_column:(
on_column + column_size
)]
on_column += column_size
self.assertTrue(
all([
np.isfinite(parameter.stddev) and np.isfinite(parameter.mean)
for parameter in normalization_parameters.values()
]))
for k, v in six.iteritems(normalized_features):
self.assertTrue(np.all(np.isfinite(v)))
feature_type = normalization_parameters[k].feature_type
if feature_type == identify_types.PROBABILITY:
sigmoidv = special.expit(v)
self.assertTrue(
np.all(
np.logical_and(np.greater(sigmoidv, 0),
np.less(sigmoidv, 1))))
elif feature_type == identify_types.ENUM:
possible_values = normalization_parameters[k].possible_values
self.assertEqual(v.shape[0], len(feature_value_map[k]))
self.assertEqual(v.shape[1], len(possible_values))
possible_value_map = {}
for i, possible_value in enumerate(possible_values):
possible_value_map[possible_value] = i
for i, row in enumerate(v):
original_feature = feature_value_map[k][i]
self.assertEqual(possible_value_map[original_feature],
np.where(row == 1)[0][0])
elif feature_type == identify_types.QUANTILE:
for i, feature in enumerate(v[0]):
original_feature = feature_value_map[k][i]
expected = self._value_to_quantile(
original_feature,
normalization_parameters[k].quantiles)
self.assertAlmostEqual(feature, expected, 2)
elif feature_type == identify_types.BINARY:
pass
elif feature_type == identify_types.CONTINUOUS or \
feature_type == identify_types.BOXCOX:
one_stddev = np.isclose(np.std(v, ddof=1), 1, atol=0.01)
zero_stddev = np.isclose(np.std(v, ddof=1), 0, atol=0.01)
zero_mean = np.isclose(np.mean(v), 0, atol=0.01)
self.assertTrue(
np.all(zero_mean),
'mean of feature {} is {}, not 0'.format(k, np.mean(v)))
self.assertTrue(np.all(np.logical_or(one_stddev, zero_stddev)))
else:
raise NotImplementedError()
def benchmark(num_forward_passes):
"""
Benchmark preprocessor speeds:
1 - PyTorch
2 - PyTorch -> ONNX -> C2
3 - C2
"""
feature_value_map = gen_data(
num_binary_features=10,
num_boxcox_features=10,
num_continuous_features=10,
num_enum_features=10,
num_prob_features=10,
num_quantile_features=10,
)
normalization_parameters = {}
for name, values in feature_value_map.items():
normalization_parameters[name] = normalization.identify_parameter(
name, values, 10
)
sorted_features, _ = sort_features_by_normalization(normalization_parameters)
# Dummy input
input_matrix = np.zeros([10000, len(sorted_features)], dtype=np.float32)
# PyTorch Preprocessor
pytorch_preprocessor = Preprocessor(normalization_parameters, False)
for i, feature in enumerate(sorted_features):
input_matrix[:, i] = feature_value_map[feature]
#################### time pytorch ############################
start = time.time()
for _ in range(NUM_FORWARD_PASSES):
_ = pytorch_preprocessor.forward(input_matrix)
end = time.time()
logger.info(
"PyTorch: {} forward passes done in {} seconds".format(
NUM_FORWARD_PASSES, end - start
)
)
################ time pytorch -> ONNX -> caffe2 ####################
buffer = PytorchCaffe2Converter.pytorch_net_to_buffer(
pytorch_preprocessor, len(sorted_features), False
)
input_blob, 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)
input_matrix_blob = "input_matrix_blob"
workspace.FeedBlob(input_blob, input_matrix)
workspace.RunNetOnce(torch_init_net)
start = time.time()
for _ in range(NUM_FORWARD_PASSES):
workspace.RunNetOnce(torch_predict_net)
_ = workspace.FetchBlob(output_blob)
end = time.time()
logger.info(
"PyTorch -> ONNX -> Caffe2: {} forward passes done in {} seconds".format(
NUM_FORWARD_PASSES, end - start
)
)
#################### time caffe2 ############################
norm_net = core.Net("net")
C2.set_net(norm_net)
preprocessor = PreprocessorNet()
input_matrix_blob = "input_matrix_blob"
workspace.FeedBlob(input_matrix_blob, np.array([], dtype=np.float32))
output_blob, _ = preprocessor.normalize_dense_matrix(
input_matrix_blob, sorted_features, normalization_parameters, "", False
)
workspace.FeedBlob(input_matrix_blob, input_matrix)
start = time.time()
for _ in range(NUM_FORWARD_PASSES):
workspace.RunNetOnce(norm_net)
_ = workspace.FetchBlob(output_blob)
end = time.time()
logger.info(
"Caffe2: {} forward passes done in {} seconds".format(
NUM_FORWARD_PASSES, end - start
)
)
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
if isinstance(trainer.critic, DataParallel):
trainer.critic = trainer.critic.module
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)
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)
# Don't normalize actions, just go from sparse -> dense
action_dense_matrix, new_parameters = sparse_to_dense(
input_feature_lengths,
input_feature_keys,
input_feature_values,
list(action_normalization_parameters.keys()
), # TODO: Clean up in D10161240
)
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_int32 = "output_keys_int32"
output_keys = "output/float_features.keys"
workspace.FeedBlob(output_keys, np.zeros(1, dtype=np.int64))
C2.net().LengthsRangeFill([output_lengths], [output_keys_int32])
C2.net().Cast([output_keys_int32], [output_keys],
to=caffe2_pb2.TensorProto.INT64)
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, 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
if isinstance(trainer.actor, DataParallel):
trainer.actor = trainer.actor.module
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)
# 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)
# 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)
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)
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_int32 = "output_keys_int32"
output_keys = "output/float_features.keys"
workspace.FeedBlob(output_keys, np.zeros(1, dtype=np.int64))
C2.net().LengthsRangeFill([output_lengths], [output_keys_int32])
C2.net().Cast([output_keys_int32], [output_keys],
to=caffe2_pb2.TensorProto.INT64)
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, 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)
