def test_collect_tensor_ops(self):
init_net = core.Net('init_net')
blobs = ['blob_1', 'blob_2', 'blob_3']
bvec_map = {}
ONE = init_net.ConstantFill([], 'ONE', shape=[1, 2], value=1)
for b in blobs:
init_net.ConstantFill([], [b], shape=[1, 2], value=0)
bvec_map[b] = b + '_vec'
init_net.CreateTensorVector([], [bvec_map[b]])
reader_net = core.Net('reader_net')
for b in blobs:
reader_net.Add([b, ONE], [b])
collect_net = core.Net('collect_net')
num_to_collect = 1000
max_example_to_cover = 100000
bvec = [bvec_map[b] for b in blobs]
collect_net.CollectTensor(
bvec + blobs,
bvec,
num_to_collect=num_to_collect,
)
print('Collect Net Proto: {}'.format(collect_net.Proto()))
plan = core.Plan('collect_data')
plan.AddStep(core.execution_step('collect_init', init_net))
plan.AddStep(
core.execution_step('collect_data', [reader_net, collect_net],
num_iter=max_example_to_cover))
workspace.RunPlan(plan)
# concat the collected tensors
concat_net = core.Net('concat_net')
bconcated_map = {}
bsize_map = {}
for b in blobs:
bconcated_map[b] = b + '_concated'
bsize_map[b] = b + '_size'
concat_net.ConcatTensorVector([bvec_map[b]], [bconcated_map[b]])
concat_net.TensorVectorSize([bvec_map[b]], [bsize_map[b]])
workspace.RunNetOnce(concat_net)
# check data
reference_result = workspace.FetchBlob(bconcated_map[blobs[0]])
self.assertEqual(reference_result.shape,
(min(num_to_collect, max_example_to_cover), 2))
size = workspace.FetchBlob(bsize_map[blobs[0]])
self.assertEqual(tuple(), size.shape)
self.assertEqual(min(num_to_collect, max_example_to_cover),
size.item())
hist, _ = np.histogram(reference_result[:, 0],
bins=10,
range=(1, max_example_to_cover))
print('Sample histogram: {}'.format(hist))
self.assertTrue(all(hist > 0.6 * (num_to_collect / 10)))
for i in range(1, len(blobs)):
result = workspace.FetchBlob(bconcated_map[blobs[i]])
self.assertEqual(reference_result.tolist(), result.tolist())
def test_groupwise_dnnlowp_conv_int(
self,
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
prepack_weight,
preserve_activation_sparsity,
preserve_weight_sparsity,
gc,
dc,
):
assume(group == 1 or dilation == 1)
assume((not prepack_weight) or order == "NHWC")
X, W, b = generate_conv_inputs(
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
groupwise_quantization=True,
preserve_activation_sparsity=preserve_activation_sparsity,
preserve_weight_sparsity=preserve_weight_sparsity,
)
Output = collections.namedtuple("Output",
["Y", "op_type", "engine", "order"])
outputs = []
op_engine_list = [
("Conv", ""),
("Conv", "DNNLOWP"),
("Conv", "DNNLOWP_16"),
("Int8Conv", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
init_net = core.Net("test_init_net")
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine
do_dequantize = "DNNLOWP" in engine
do_prepack_weight = engine == "DNNLOWP" and prepack_weight
if do_quantize:
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
preserve_activation_sparsity=preserve_activation_sparsity,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
if do_prepack_weight:
X_min = 0 if X.size == 0 else X.min()
X_max = 0 if X.size == 0 else X.max()
x_q_param = hardcode_scale_zp.choose_quantization_params(
X_min, X_max)
inputs = ["W"]
if do_dequantize:
inputs += ["b"]
pack = core.CreateOperator(
"Int8ConvPackWeight",
inputs,
["W_packed"],
group=group,
quantize_groupwise=1,
preserve_weight_sparsity=preserve_weight_sparsity,
in_scale=x_q_param.scale,
engine=engine,
)
init_net.Proto().op.extend([pack])
conv = core.CreateOperator(
op_type,
[
"X_q" if do_quantize else "X",
"W_packed" if do_prepack_weight else "W",
"b",
],
["Y_q" if do_dequantize else "Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
preserve_activation_sparsity=preserve_activation_sparsity,
preserve_weight_sparsity=preserve_weight_sparsity,
engine=engine,
group=group,
quantize_groupwise=1,
device_option=gc,
)
if do_dequantize or do_prepack_weight:
# groupwise quantization only works with static quantization
# so we need to set quantization parameters
dnnlowp_utils.add_quantization_param_args(
conv, outputs[0][0], preserve_activation_sparsity)
net.Proto().op.extend([conv])
if do_dequantize:
dequantize = core.CreateOperator(
"Dequantize",
["Y_q"],
["Y"],
preserve_activation_sparsity=preserve_activation_sparsity,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([dequantize])
run_conv_or_fc(self, init_net, net, X, W, b, op_type, engine,
order, gc, outputs)
check_quantized_results_close(outputs,
symmetric=preserve_activation_sparsity)
def test_dnnlowp_elementwise_mul_int(
self, N, is_empty, in_quantized, out_quantized, in_place, gc, dc
):
if is_empty:
N = 0
# FIXME: DNNLOWP Mul doesn't support inplace operation and
# dequantize_output=1 at the same time
if in_place[0] or in_place[1]:
in_quantized = True
out_quantized = True
# All inputs have scale 1, so exactly represented after quantization
min_ = -100
max_ = min_ + 255
A = np.round(np.random.rand(N) * (max_ - min_) + min_)
A = A.astype(np.float32)
if N != 0:
A[0] = min_
A[1] = max_
B = np.round(np.random.rand(N) * 255 - 128).astype(np.float32)
if N != 0:
B[0] = -128
B[1] = 127
Output = collections.namedtuple("Output", ["Y", "engine"])
outputs = []
engine_list = ["", "DNNLOWP"]
for engine in engine_list:
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine and in_quantized
do_dequantize = "DNNLOWP" in engine and out_quantized
if do_quantize:
quantize_A = core.CreateOperator(
"Quantize", ["A"], ["A_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize_A])
quantize_B = core.CreateOperator(
"Quantize", ["B"], ["B_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize_B])
out = "Y"
if in_place[0]:
out = "A"
elif in_place[1]:
out = "B"
mul = core.CreateOperator(
"Mul",
["A_q", "B_q"] if do_quantize else ["A", "B"],
[(out + "_q") if do_dequantize else out],
dequantize_output=not do_dequantize,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([mul])
if do_dequantize:
dequantize = core.CreateOperator(
"Dequantize", [out + "_q"], [out], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
self.ws.create_blob("A").feed(A, device_option=gc)
self.ws.create_blob("B").feed(B, device_option=gc)
self.ws.run(net)
outputs.append(Output(Y=self.ws.blobs[out].fetch(), engine=engine))
check_quantized_results_close(outputs)
def CreateModel(self):
log.debug("Start training")
model = cnn.CNNModelHelper(name="char_rnn")
input_blob, seq_lengths, hidden_init, cell_init, target = \
model.net.AddExternalInputs(
'input_blob',
'seq_lengths',
'hidden_init',
'cell_init',
'target',
)
hidden_output_all, self.hidden_output, _, self.cell_state = LSTM(
model,
input_blob,
seq_lengths, (hidden_init, cell_init),
self.D,
self.hidden_size,
scope="LSTM")
output = model.FC(hidden_output_all,
None,
dim_in=self.hidden_size,
dim_out=self.D,
axis=2)
# axis is 2 as first two are T (time) and N (batch size).
# We treat them as one big batch of size T * N
softmax = model.Softmax(output, 'softmax', axis=2)
softmax_reshaped, _ = model.Reshape(softmax, ['softmax_reshaped', '_'],
shape=[-1, self.D])
# Create a copy of the current net. We will use it on the forward
# pass where we don't need loss and backward operators
self.forward_net = core.Net(model.net.Proto())
xent = model.LabelCrossEntropy([softmax_reshaped, target], 'xent')
# Loss is average both across batch and through time
# Thats why the learning rate below is multiplied by self.seq_length
loss = model.AveragedLoss(xent, 'loss')
model.AddGradientOperators([loss])
# Hand made SGD update. Normally one can use helper functions
# to build an optimizer
ITER = model.Iter("iter")
LR = model.LearningRate(ITER,
"LR",
base_lr=-0.1 * self.seq_length,
policy="step",
stepsize=1,
gamma=0.9999)
ONE = model.param_init_net.ConstantFill([],
"ONE",
shape=[1],
value=1.0)
# Update weights for each of the model parameters
for param in model.params:
param_grad = model.param_to_grad[param]
model.WeightedSum([param, ONE, param_grad, LR], param)
self.model = model
self.predictions = softmax
self.loss = loss
self.prepare_state = core.Net("prepare_state")
self.prepare_state.Copy(self.hidden_output, hidden_init)
self.prepare_state.Copy(self.cell_state, cell_init)
def test_dnnlowp_average_pool(
self,
ndim,
stride,
pad,
kernel,
size,
input_channels,
batch_size,
order,
in_quantized,
gc,
dc,
):
kernel = 2 # Only kernel size 2 is supported
assume(kernel <= size)
assume(pad < kernel)
C = input_channels
N = batch_size
strides = (stride, ) * ndim
pads = (pad, ) * (ndim * 2)
kernels = (kernel, ) * ndim
sizes = (size, ) * ndim
# X has scale 1, so no input quantization error
min_ = -100
max_ = min_ + 255
if order == "NCHW":
X = np.round(
np.random.rand(*((N, C) + sizes)) * (max_ - min_) + min_)
X = X.astype(np.float32)
X[(0, ) * (ndim + 2)] = min_
X[(0, ) * (ndim + 1) + (1, )] = max_
elif order == "NHWC":
X = np.round(
np.random.rand(*((N, ) + sizes + (C, ))) * (max_ - min_) +
min_)
X = X.astype(np.float32)
X[(0, ) * (ndim + 2)] = min_
X[(0, 1) + (0, ) * ndim] = max_
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [
("AveragePool", ""),
("AveragePool", "DNNLOWP"),
("Int8AveragePool", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine and in_quantized
if do_quantize:
quantize = core.CreateOperator("Quantize", ["X"], ["X_q"],
engine=engine,
device_option=gc)
net.Proto().op.extend([quantize])
max_pool = core.CreateOperator(
op_type,
["X_q" if do_quantize else "X"],
["Y_q" if engine == "DNNLOWP" else "Y"],
strides=strides,
kernels=kernels,
pads=pads,
order=order,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([max_pool])
if engine == "DNNLOWP":
dequantize = core.CreateOperator("Dequantize", ["Y_q"], ["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(),
op_type=op_type,
engine=engine))
check_quantized_results_close(outputs)
def write_ex(self, fields, local_init_net, local_finish_net, status):
self._wrapper._new_writer(self.schema(), local_init_net)
enqueue_net = core.Net('enqueue')
enqueue(enqueue_net, self._wrapper.queue(), fields, status)
return [enqueue_net]
def test_dnnlowp_conv_acc16_outlier(
self,
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
in_quantized,
out_quantized,
weight_quantized,
prepack_weight,
nbits_in_non_outlier,
share_col_buffer,
preserve_activation_sparsity,
preserve_weight_sparsity,
gc,
dc,
):
assume(group == 1 or dilation == 1)
assume(size >= dilation * (kernel - 1) + 1)
input_channels = input_channels_per_group * group
output_channels = output_channels_per_group * group
if nbits_in_non_outlier == 0:
X, W, b = generate_conv_inputs(
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
preserve_activation_sparsity=preserve_activation_sparsity,
preserve_weight_sparsity=preserve_weight_sparsity,
)
else:
X_min = 0 if preserve_activation_sparsity else -77
X_max = X_min + 255
X = np.random.rand(batch_size, size, size,
input_channels) * 4 + X_min
X = np.round(X).astype(np.float32)
X[..., 0] = X_min
X[0, 0, 0, 1] = X_max
if preserve_weight_sparsity:
W_min = -128
W_max = 100
else:
W_min = -100
W_max = W_min + 255
W = (np.random.rand(output_channels, kernel, kernel,
input_channels_per_group) * 4 - 2 + W_min +
128)
W = np.round(W).astype(np.float32)
W[0, 0, 0, 0] = W_min
W[1, 0, 0, 0] = W_max
W[..., 1] = W_min + 128
# No input quantization error in bias
b = np.round(np.random.randn(output_channels)).astype(np.float32)
Output = collections.namedtuple("Output",
["Y", "op_type", "engine", "order"])
outputs = []
op_engine_list = [
("Conv", ""),
("Conv", "DNNLOWP_ACC16"),
("Int8Conv", "DNNLOWP_ACC16"),
]
for op_type, engine in op_engine_list:
init_net = core.Net("test_init_net")
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine and in_quantized
do_dequantize = "DNNLOWP" in engine and out_quantized
do_quantize_weight = "DNNLOWP" in engine and weight_quantized
do_prepack_weight = "DNNLOWP" in engine and prepack_weight
if do_quantize:
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
preserve_activation_sparsity=preserve_activation_sparsity,
engine="DNNLOWP",
device_option=gc,
)
net.Proto().op.extend([quantize])
x_q_param = dnnlowp_utils.choose_quantization_params(
X.min(), X.max(), preserve_activation_sparsity)
if do_quantize_weight:
int8_given_tensor_fill, w_q_param = dnnlowp_utils.create_int8_given_tensor_fill(
W, "W_q", preserve_weight_sparsity)
init_net.Proto().op.extend([int8_given_tensor_fill])
# Bias
int8_bias_tensor_fill = dnnlowp_utils.create_int8_bias_tensor_fill(
b, "b_q", x_q_param, w_q_param)
init_net.Proto().op.extend([int8_bias_tensor_fill])
if do_prepack_weight:
inputs = ["W_q" if do_quantize_weight else "W"]
if do_dequantize:
inputs += ["b_q" if do_quantize_weight else "b"]
pack = core.CreateOperator(
"Int8ConvPackWeight",
inputs,
["W_packed"],
group=group,
nbits_in_non_outlier=nbits_in_non_outlier,
preserve_weight_sparsity=preserve_weight_sparsity,
in_scale=x_q_param.scale,
engine=engine,
)
init_net.Proto().op.extend([pack])
conv = core.CreateOperator(
op_type,
[
"X_q" if do_quantize else "X",
"W_packed" if do_prepack_weight else
("W_q" if do_quantize_weight else "W"),
"b_q" if do_quantize_weight else "b",
],
["Y_q" if do_dequantize else "Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
dequantize_output=not do_dequantize,
nbits_in_non_outlier=nbits_in_non_outlier,
shared_buffer=(1 if share_col_buffer else 0),
preserve_activation_sparsity=preserve_activation_sparsity,
preserve_weight_sparsity=preserve_weight_sparsity,
engine=engine,
group=group,
device_option=gc,
)
if do_dequantize or do_quantize_weight or do_prepack_weight:
# When quantized weight is provided, we can't rescale the
# output dynamically by looking at the range of output of each
# batch, so here we provide the range of output observed from
# fp32 reference implementation
dnnlowp_utils.add_quantization_param_args(
conv, outputs[0][0], preserve_activation_sparsity)
net.Proto().op.extend([conv])
if do_dequantize:
dequantize = core.CreateOperator("Dequantize", ["Y_q"], ["Y"],
engine="DNNLOWP",
device_option=gc)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.create_blob("W").feed(W, device_option=gc)
self.ws.create_blob("b").feed(b, device_option=gc)
self.ws.run(init_net)
self.ws.run(net)
Y = self.ws.blobs["Y"].fetch()
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
check_quantized_results_close(outputs,
symmetric=preserve_activation_sparsity)
def test_imageinput(self, size_tuple, means, stds, gc, dc):
# TODO: Does not test on GPU and does not test use_gpu_transform
# WARNING: Using ModelHelper automatically does NHWC to NCHW
# transformation if needed.
width, height, minsize, crop = size_tuple
means = [float(m) for m in means]
stds = [float(s) for s in stds]
out_dir = tempfile.mkdtemp()
count_images = 2 # One with bounding box and one without
expected_images = create_test(out_dir,
width=width,
height=height,
default_bound=(3, 5, height - 3,
width - 5),
minsize=minsize,
crop=crop,
means=means,
stds=stds,
count=count_images)
for device_option in dc:
with hu.temp_workspace():
reader_net = core.Net('reader')
reader_net.CreateDB([], 'DB', db=out_dir, db_type="lmdb")
workspace.RunNetOnce(reader_net)
imageop = core.CreateOperator(
'ImageInput', ['DB'], ["data", "label"],
batch_size=count_images,
color=3,
minsize=minsize,
crop=crop,
is_test=True,
bounding_ymin=3,
bounding_xmin=5,
bounding_height=height - 3,
bounding_width=width - 5,
mean_per_channel=means,
std_per_channel=stds,
use_gpu_transform=(device_option.device_type == 1))
imageop.device_option.CopyFrom(device_option)
main_net = core.Net('main')
main_net.Proto().op.extend([imageop])
workspace.RunNetOnce(main_net)
l = workspace.FetchBlob('label')
result = workspace.FetchBlob('data').astype(np.int32)
# If we don't use_gpu_transform, the output is in NHWC
# Our reference output is CHW so we swap
if device_option.device_type != 1:
expected = [
img.swapaxes(0, 1).swapaxes(1, 2)
for img in expected_images
]
else:
expected = expected_images
for i in range(count_images):
self.assertEqual(l[i], i)
self.assertEqual((expected[i] - result[i] > 1).sum(), 0)
# End for
# End with
# End for
shutil.rmtree(out_dir)
def test_dnnlowp_conv_relu_int(
self,
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
share_col_buffer,
gc,
dc,
):
assume(group == 1 or dilation == 1)
X, W, b = generate_conv_inputs(
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
)
Output = collections.namedtuple("Output",
["Y", "op_type", "engine", "order"])
outputs = []
op_engine_list = [
("Conv", ""),
("ConvRelu", "DNNLOWP"),
("ConvRelu", "DNNLOWP_16"),
("Int8ConvRelu", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
if "DNNLOWP" in engine:
quantize = core.CreateOperator("Quantize", ["X"], ["X_q"],
engine=engine,
device_option=gc)
net.Proto().op.extend([quantize])
conv = core.CreateOperator(
op_type,
["X_q", "W", "b"],
["Y_q"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
shared_buffer=(1 if share_col_buffer else 0),
group=group,
device_option=gc,
)
net.Proto().op.extend([conv])
dequantize = core.CreateOperator("Dequantize", ["Y_q"], ["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([dequantize])
else:
conv = core.CreateOperator(
op_type,
["X", "W", "b"],
["Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
shared_buffer=(1 if share_col_buffer else 0),
engine=engine,
group=group,
device_option=gc,
)
net.Proto().op.extend([conv])
relu = core.CreateOperator("Relu", ["Y"], ["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([relu])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.create_blob("W").feed(W, device_option=gc)
self.ws.create_blob("b").feed(b, device_option=gc)
self.ws.run(net)
Y = self.ws.blobs["Y"].fetch()
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
check_quantized_results_close(outputs)
def create_init_net(self, name):
init_net = core.Net(name)
self._add_global_constants(init_net)
return init_net
def test_meta_net_def_net_runs(self):
for param, value in viewitems(self.params):
workspace.FeedBlob(param, value)
extra_init_net = core.Net('extra_init')
extra_init_net.ConstantFill('data', 'data', value=1.0)
pem = pe.PredictorExportMeta(
predict_net=self.predictor_export_meta.predict_net,
parameters=self.predictor_export_meta.parameters,
inputs=self.predictor_export_meta.inputs,
outputs=self.predictor_export_meta.outputs,
shapes=self.predictor_export_meta.shapes,
extra_init_net=extra_init_net,
net_type='dag',
)
db_type = 'minidb'
db_file = tempfile.NamedTemporaryFile(delete=False,
suffix=".{}".format(db_type))
pe.save_to_db(db_type=db_type,
db_destination=db_file.name,
predictor_export_meta=pem)
workspace.ResetWorkspace()
meta_net_def = pe.load_from_db(
db_type=db_type,
filename=db_file.name,
)
self.assertTrue("data" not in workspace.Blobs())
self.assertTrue("y" not in workspace.Blobs())
init_net = pred_utils.GetNet(meta_net_def, pc.PREDICT_INIT_NET_TYPE)
# 0-fills externalblobs blobs and runs extra_init_net
workspace.RunNetOnce(init_net)
self.assertTrue("data" in workspace.Blobs())
self.assertTrue("y" in workspace.Blobs())
print(workspace.FetchBlob("data"))
np.testing.assert_array_equal(workspace.FetchBlob("data"),
np.ones(shape=(1, 5)))
np.testing.assert_array_equal(workspace.FetchBlob("y"),
np.zeros(shape=(1, 10)))
# Load parameters from DB
global_init_net = pred_utils.GetNet(meta_net_def,
pc.GLOBAL_INIT_NET_TYPE)
workspace.RunNetOnce(global_init_net)
# Run the net with a reshaped input and verify we are
# producing good numbers (with our custom implementation)
workspace.FeedBlob("data", np.random.randn(2, 5).astype(np.float32))
predict_net = pred_utils.GetNet(meta_net_def, pc.PREDICT_NET_TYPE)
self.assertEqual(predict_net.type, 'dag')
workspace.RunNetOnce(predict_net)
np.testing.assert_array_almost_equal(
workspace.FetchBlob("y"),
workspace.FetchBlob("data").dot(self.params["y_w"].T) +
self.params["y_b"])
def test_inject_copy_multi_use(self):
net = core.Net("test")
device_option = caffe2_pb2.DeviceOption()
device_option.device_type = caffe2_pb2.CUDA
device_option.cuda_gpu_id = 1
with core.DeviceScope(device_option):
net.Relu("data", "relu1")
net.Relu("data", "relu2")
with core.DeviceScope(device_option):
net.Relu("data", "relu3")
net.Relu("data", "relu4")
device_option.cuda_gpu_id = 0
with core.DeviceScope(device_option):
net.Relu("data", "relu5")
device_option.cuda_gpu_id = 1
with core.DeviceScope(device_option):
net.Relu("data", "relu6")
new_net, _ = core.InjectCrossDeviceCopies(net)
op = new_net._net.op[0]
self.assertEqual(op.type, "CopyCPUToGPU")
self.assertEqual(op.device_option.device_type, 1)
self.assertEqual(op.device_option.cuda_gpu_id, 1)
self.assertEqual(op.output[0], "data_cuda_1")
op = new_net._net.op[1]
self.assertEqual(op.type, "Relu")
self.assertEqual(op.device_option.device_type, 1)
self.assertEqual(op.device_option.cuda_gpu_id, 1)
self.assertEqual(op.output[0], "relu1")
op = new_net._net.op[2]
self.assertEqual(op.type, "Relu")
self.assertEqual(op.device_option.device_type, 0)
self.assertEqual(op.output[0], "relu2")
op = new_net._net.op[3]
self.assertEqual(op.type, "Relu")
self.assertEqual(op.device_option.device_type, 1)
self.assertEqual(op.device_option.cuda_gpu_id, 1)
self.assertEqual(op.input[0], "data_cuda_1")
self.assertEqual(op.output[0], "relu3")
op = new_net._net.op[4]
self.assertEqual(op.type, "Relu")
self.assertEqual(op.device_option.device_type, 0)
self.assertEqual(op.output[0], "relu4")
op = new_net._net.op[5]
self.assertEqual(op.type, "CopyCPUToGPU")
self.assertEqual(op.device_option.device_type, 1)
self.assertEqual(op.device_option.cuda_gpu_id, 0)
self.assertEqual(op.output[0], "data_cuda_0")
op = new_net._net.op[6]
self.assertEqual(op.type, "Relu")
self.assertEqual(op.device_option.device_type, 1)
self.assertEqual(op.device_option.cuda_gpu_id, 0)
self.assertEqual(op.input[0], "data_cuda_0")
self.assertEqual(op.output[0], "relu5")
op = new_net._net.op[7]
self.assertEqual(op.type, "Relu")
self.assertEqual(op.device_option.device_type, 1)
self.assertEqual(op.device_option.cuda_gpu_id, 1)
self.assertEqual(op.input[0], "data_cuda_1")
self.assertEqual(op.output[0], "relu6")
"""
def testPartialClone(self):
params = core.Net('params')
p1 = params.ConstantFill([], ['p1'])
workspace.CreateNet(params)
workspace.RunNetOnce(params)
n = core.Net('original')
a1 = n.AddExternalInput('a1')
a2 = n.AddExternalInput('a2')
b1, b2 = n.Concat([a1, a2], ['b1', 'b2'], axis=0)
c1 = n.Sum([b1, p1], ['c1'])
c2 = n.Sum([b2], ['c2'])
d = n.Sum([c1, c2], ['d'])
# test that gradient ops are ignored when partial-cloning
n.AddGradientOperators([d])
# test some in-place ops
k = n.Sum([p1], ['k'])
e = n.Sum([d], ['e'])
e = n.Sum([e, k], [e])
e = n.Sum([e], [e])
f = n.Sum(e, ['f'])
def net_assert(net, num_ops, inputs, outputs, internals):
self.assertEqual(len(net.Proto().op), num_ops)
self.assertEqual(set(net.Proto().external_input), inputs)
self.assertEqual(set(net.Proto().external_output), outputs)
all_blobs = set(net.Proto().external_input)
all_blobs |= set(net.Proto().external_output)
for op in net.Proto().op:
all_blobs |= set(op.input) | set(op.output)
self.assertEqual(all_blobs, inputs | outputs | internals)
# create net to make sure its valid
for input in inputs:
workspace.FeedBlob(input, np.array([]))
workspace.CreateNet(net)
n2, (d22, ) = n.ClonePartial('f1', {a1: 'a11', a2: 'a22'}, [d])
net_assert(n2, 4, {'p1', 'a11', 'a22'}, {'f1/d'},
{'f1/b1', 'f1/b2', 'f1/c1', 'f1/c2', 'p1'})
self.assertTrue(isinstance(d22, core.BlobReference))
self.assertEqual(d22.Net(), n2)
self.assertEqual(str(d22), 'f1/d')
n3, (d22, ) = n.ClonePartial('f2', [b1, b2], [d])
net_assert(n3, 3, {'p1', 'b1', 'b2'}, {'f2/d'},
{'f2/c1', 'f2/c2', 'p1'})
self.assertEqual(str(d22), 'f2/d')
n4, (c22, ) = n.ClonePartial('f3', [b1], [c1])
net_assert(n4, 1, {'p1', 'b1'}, {'f3/c1'}, {'p1'})
self.assertEqual(str(c22), 'f3/c1')
n5, (c11, c22) = n.ClonePartial('f4', [b1, b2], [c1, c2])
net_assert(n5, 2, {'p1', 'b1', 'b2'}, {'f4/c1', 'f4/c2'}, {'p1'})
self.assertEqual(str(c11), 'f4/c1')
self.assertEqual(str(c22), 'f4/c2')
with self.assertRaises(AssertionError):
n.ClonePartial('f4', [a1, a2, c2], [d])
n6, (e22, ) = n.ClonePartial('f5', [d], [e])
net_assert(n6, 4, {'p1', 'd'}, {'f5/e'}, {'f5/k', 'p1'})
self.assertEqual(str(e22), 'f5/e')
n8, (e22, f22) = n.ClonePartial('f7', [d], [e, f])
net_assert(n8, 5, {'p1', 'd'}, {'f7/e', 'f7/f'}, {'p1', 'f7/k'})
self.assertEqual(str(e22), 'f7/e')
self.assertEqual(str(f22), 'f7/f')
params._CheckLookupTables()
n._CheckLookupTables()
def _create_rnn_variant(cls, init_model, pred_model, n, opset_version):
assert init_model is not None, "cannot convert RNNs without access to the full model"
assert pred_model is not None, "cannot convert RNNs without access to the full model"
attrs = dict(n.attrs) # make a copy, which is safe to mutate
hidden_size = attrs.pop('hidden_size')
direction = force_unicode(attrs.pop('direction', 'forward'))
if n.op_type == 'RNN':
activation = force_unicode(attrs.pop('activations', ('tanh',))[0])
elif n.op_type == 'GRU':
linear_before_reset = attrs.pop('linear_before_reset', 0)
assert not attrs, "unsupported RNN attributes: " + str(attrs.keys())
assert direction in ['forward', 'bidirectional'], "unsupported backwards RNN/GRU/LSTM"
if n.op_type in ['RNN', 'GRU']:
input_blob, W, R, B, sequence_lens, initial_h = n.inputs
elif n.op_type == 'LSTM':
input_blob, W, R, B, sequence_lens, initial_h, initial_c = n.inputs
if sequence_lens == "":
sequence_lens = None
for x in itertools.chain(init_model.graph.input,
init_model.graph.value_info,
pred_model.graph.input,
pred_model.graph.value_info):
if x.name == W:
input_size = x.type.tensor_type.shape.dim[2].dim_value
break
else:
raise RuntimeError("best-effort shape inference for RNN/GRU/LSTM failed")
init_net = core.Net("init-net")
pred_mh = ModelHelper()
init_net.Reshape(W, [W, cls.dummy_name()], shape=[1,-1,0])
init_net.Squeeze(W, W, dims=[0])
init_net.Reshape(R, [R, cls.dummy_name()], shape=[1,-1,0])
init_net.Squeeze(R, R, dims=[0])
init_net.Reshape(B, [B, cls.dummy_name()], shape=[1,-1])
init_net.Squeeze(B, B, dims=[0])
if n.op_type == 'RNN':
def reform(*args):
pass
def make_cell(*args, **kwargs):
return rnn_cell.BasicRNN(*args, activation=activation, **kwargs)
def make_rnn(direction_offset):
return cls._make_rnn_direction(
input_blob, B, W, R, [(initial_h, '/initial_h')], sequence_lens,
pred_mh, init_net, input_size, hidden_size, 1, direction_offset,
"/i2h_b", "/gates_t_b", "/i2h_w", "/gates_t_w",
reform, make_cell, lambda x: x)
elif n.op_type == 'GRU':
def reform(Bi, Br, W_, R_, name, hidden_size, init_net):
# caffe2 has a different order from onnx. We need to rearrange
# z r h -> r z h
reforms = ((W_, 'i2h_w', True, [(0,-1)]),
(R_, 'gate_t_w', False, [(0,-1)]),
(Bi, 'i2h_b', True, []),
(Br, 'gate_t_b', False, []))
cls._rnn_reform_weights(reforms, name, hidden_size, init_net,
['update', 'reset', 'output'], [1, 0, 2])
def make_cell(*args, **kwargs):
return gru_cell.GRU(*args, linear_before_reset=linear_before_reset, **kwargs)
def make_rnn(direction_offset):
return cls._make_rnn_direction(
input_blob, B, W, R, [(initial_h, '/initial_h')], sequence_lens,
pred_mh, init_net, input_size, hidden_size, 3, direction_offset,
"_bias_i2h", "_bias_gates", "/i2h_w_pre", "/gates_t_w_pre",
reform, make_cell, lambda x: x)
elif n.op_type == 'LSTM':
def reform(Bi, Br, W_, R_, name, hidden_size, init_net):
# caffe2 has a different order from onnx. We need to rearrange
# i o f c -> i f o c
reforms = ((W_, 'i2h_w', True, [(0, -1)]),
(R_, 'gates_t_w', True, [(0, -1)]),
(Bi, 'i2h_b' , True, []),
(Br, 'gates_t_b', True, []))
cls._rnn_reform_weights(reforms, name, hidden_size, init_net,
['input', 'output', 'forget', 'cell'], [0, 2, 1, 3])
def make_cell(*args, **kwargs):
return rnn_cell.LSTM(*args, **kwargs)
def make_rnn(direction_offset):
return cls._make_rnn_direction(
input_blob, B, W, R, [(initial_h, '/initial_h'), (initial_c, '/initial_c')], sequence_lens,
pred_mh, init_net, input_size, hidden_size, 4, direction_offset,
"/i2h_b", "/gates_t_b", "/i2h_w", "/gates_t_w",
reform, make_cell, lambda x: [x[0], x[1], x[3]])
if direction == 'forward':
outputs = make_rnn(0)
# in the forward case, storage is shared between the
# last outputs. We need to decouple them so that the
# VariableLengthSequencePadding only mutates
# n.outputs[0]
for i in range(1, len(outputs)):
pred_mh.net.Copy(outputs[i], n.outputs[i])
if sequence_lens is not None:
pred_mh.net.VariableLengthSequencePadding(
[outputs[0], sequence_lens], [outputs[0]])
pred_mh.net.ExpandDims([outputs[0]], [n.outputs[0]], dims=[1])
elif direction == 'bidirectional':
outputs_f = make_rnn(0)
outputs_b = make_rnn(1)
concatted_output, _ = pred_mh.net.Concat(
[outputs_f[0], outputs_b[0]], [cls.dummy_name(), cls.dummy_name()], axis=2)
if sequence_lens is not None:
pred_mh.net.VariableLengthSequencePadding(
[concatted_output, sequence_lens], [concatted_output])
unsqueezed_output = pred_mh.net.ExpandDims([concatted_output], [cls.dummy_name()], dims=[1])
pred_mh.net.Reshape(unsqueezed_output, [n.outputs[0], cls.dummy_name()], shape=[0,2,0,-1])
for i in range(1, len(n.outputs)):
pred_mh.net.Concat([outputs_f[i], outputs_b[i]],
[n.outputs[i], cls.dummy_name()], axis=0)
return Caffe2Ops(list(pred_mh.Proto().op),
list(init_net.Proto().op),
list(pred_mh.Proto().external_input))
def create_net(self):
net = core.Net("feature_extractor")
init_net = core.Net("feature_extractor_init")
missing_scalar = self.create_const(init_net, "MISSING_SCALAR",
MISSING_VALUE)
action_schema = map_schema(
) if self.sorted_action_features else schema.Scalar()
if self.max_q_learning:
next_action_field = InputColumn.POSSIBLE_NEXT_ACTIONS
next_action_schema = schema.List(action_schema)
else:
next_action_field = InputColumn.NEXT_ACTION
next_action_schema = action_schema
input_schema = schema.Struct(
(InputColumn.STATE_FEATURES, map_schema()),
(InputColumn.NEXT_STATE_FEATURES, map_schema()),
(InputColumn.ACTION, action_schema),
(next_action_field, next_action_schema),
)
input_record = net.set_input_record(input_schema)
state = self.extract_float_features(
net,
"state",
input_record[InputColumn.STATE_FEATURES],
self.sorted_state_features,
missing_scalar,
)
next_state = self.extract_float_features(
net,
"next_state",
input_record[InputColumn.NEXT_STATE_FEATURES],
self.sorted_state_features,
missing_scalar,
)
if self.max_q_learning and self.sorted_action_features is not None:
next_state_field = "tiled_next_state"
# TODO: this will need to be more complicated to support sparse features
next_state = net.LengthsTile(
[next_state,
input_record.possible_next_actions.lengths()],
["tiled_next_state"],
)
else:
next_state_field = "next_state"
action = input_record.action
next_action = input_record[next_action_field]
if self.max_q_learning:
next_action = next_action["values"]
if self.sorted_action_features:
action = self.extract_float_features(net, "action", action,
self.sorted_action_features,
missing_scalar)
next_action = self.extract_float_features(
net,
next_action_field,
next_action,
self.sorted_action_features,
missing_scalar,
)
next_action_output = (schema.List(
next_action,
lengths_blob=input_record.possible_next_actions.lengths)
if self.max_q_learning else next_action)
net.set_output_record(
schema.Struct(
("state", state),
("action", action),
(next_state_field, next_state),
(next_action_field, next_action_output),
))
return FeatureExtractorNet(net, init_net)
def _test_dnnlowp_nd_int(
self,
stride,
pad,
kernels,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
prepack_weight,
gc,
dc,
):
assume(group == 1 or dilation == 1)
assume((not prepack_weight) or order == "NHWC")
ndim = len(kernels)
X, W, b = generate_convnd_inputs(
(stride, ) * ndim,
(pad, ) * ndim,
kernels,
(dilation, ) * ndim,
(size, ) * ndim,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
)
Output = collections.namedtuple("Output",
["Y", "op_type", "engine", "order"])
outputs = []
op_engine_list = [("Conv", ""), ("Conv", "DNNLOWP_16"),
("Int8Conv", "DNNLOWP")]
for op_type, engine in op_engine_list:
init_net = core.Net("test_init_net")
net = core.Net("test_net")
fall_back_to_NCHW = "DNNLOWP" not in engine and order == "NHWC"
if fall_back_to_NCHW:
X_nchw = utils.NHWC2NCHW(X)
W_nchw = utils.NHWC2NCHW(W)
do_quantize = "DNNLOWP" in engine
do_dequantize = "DNNLOWP" in engine
# If output scale/zp aren't set, it gets computed from ref fp32 op
# in DNNLOWP, which isn't possible when we quantize input weights.
# Make sure atleast one output is collected to compute output
# scale/zp.
do_quantize_weight = engine == "DNNLOWP" and len(outputs) > 0
do_prepack_weight = engine == "DNNLOWP" and prepack_weight
if do_quantize:
quantize = core.CreateOperator("Quantize", ["X"], ["X_q"],
engine=engine,
device_option=gc)
net.Proto().op.extend([quantize])
x_q_param = dnnlowp_utils.choose_quantization_params(
X.min(), X.max())
if do_quantize_weight:
int8_given_tensor_fill, w_q_param = dnnlowp_utils.create_int8_given_tensor_fill(
W, "W_q")
init_net.Proto().op.extend([int8_given_tensor_fill])
# Bias
int8_bias_tensor_fill = dnnlowp_utils.create_int8_bias_tensor_fill(
b, "b_q", x_q_param, w_q_param)
init_net.Proto().op.extend([int8_bias_tensor_fill])
if do_prepack_weight:
inputs = ["W_q" if do_quantize_weight else "W"]
if do_dequantize:
inputs += ["b_q" if do_quantize_weight else "b"]
pack = core.CreateOperator(
"Int8ConvPackWeight",
inputs,
["W_packed"],
group=group,
in_scale=x_q_param.scale,
engine=engine,
)
init_net.Proto().op.extend([pack])
conv = core.CreateOperator(
op_type,
[
"X_q" if do_quantize else "X",
"W_packed" if do_prepack_weight else
("W_q" if do_quantize_weight else "W"),
"b_q" if do_quantize_weight else "b",
],
["Y_q" if do_dequantize else "Y"],
strides=[stride] * ndim,
kernels=kernels,
dilations=[dilation] * ndim,
pads=[pad] * (ndim * 2),
order="NCHW" if fall_back_to_NCHW else order,
dequantize_output=not do_dequantize,
engine=engine,
group=group,
device_option=gc,
)
if do_quantize_weight or do_prepack_weight:
# When quantized weight is provided, we can't rescale the
# output dynamically by looking at the range of output of each
# batch, so here we provide the range of output observed from
# fp32 reference implementation
dnnlowp_utils.add_quantization_param_args(conv, outputs[0][0])
net.Proto().op.extend([conv])
if do_dequantize:
dequantize = core.CreateOperator("Dequantize", ["Y_q"], ["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X_nchw if fall_back_to_NCHW else X,
device_option=gc)
self.ws.create_blob("W").feed(W_nchw if fall_back_to_NCHW else W,
device_option=gc)
self.ws.create_blob("b").feed(b, device_option=gc)
self.ws.run(init_net)
self.ws.run(net)
Y = self.ws.blobs["Y"].fetch()
if fall_back_to_NCHW:
Y = utils.NCHW2NHWC(Y)
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
check_quantized_results_close(outputs)
def read_ex(self, local_init_net, local_finish_net):
self._wrapper._new_reader(local_init_net)
dequeue_net = core.Net('dequeue')
fields, status_blob = dequeue(dequeue_net, self._wrapper.queue(),
len(self.schema().field_names()))
return [dequeue_net], status_blob, fields
def test_dnnlowp_conv_int(
self,
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
weight_quantized,
prepack_weight,
share_col_buffer,
preserve_activation_sparsity,
preserve_weight_sparsity,
gc,
dc,
):
assume(group == 1 or dilation == 1)
assume((not prepack_weight) or order == "NHWC")
X, W, b = generate_conv_inputs(
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
preserve_activation_sparsity=preserve_activation_sparsity,
preserve_weight_sparsity=preserve_weight_sparsity,
)
Output = collections.namedtuple("Output",
["Y", "op_type", "engine", "order"])
outputs = []
op_engine_list = [
("Conv", ""),
("Conv", "DNNLOWP"),
("Conv", "DNNLOWP_16"),
("Int8Conv", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
init_net = core.Net("test_init_net")
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine
do_dequantize = "DNNLOWP" in engine
# If output scale/zp aren't set, it gets computed from ref fp32 op
# in DNNLOWP, which isn't possible when we quantize input weights.
# Make sure atleast one output is collected to compute output
# scale/zp.
do_quantize_weight = (engine == "DNNLOWP" and weight_quantized
and len(outputs) > 0)
do_prepack_weight = engine == "DNNLOWP" and prepack_weight
if do_quantize:
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
preserve_activation_sparsity=preserve_activation_sparsity,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
x_q_param = dnnlowp_utils.choose_quantization_params(
X.min(), X.max(), preserve_activation_sparsity) # noqa
if do_quantize_weight:
int8_given_tensor_fill, w_q_param = dnnlowp_utils.create_int8_given_tensor_fill(
W, "W_q", preserve_weight_sparsity)
init_net.Proto().op.extend([int8_given_tensor_fill])
# Bias
int8_bias_tensor_fill = dnnlowp_utils.create_int8_bias_tensor_fill(
b, "b_q", x_q_param, w_q_param)
init_net.Proto().op.extend([int8_bias_tensor_fill])
if do_prepack_weight:
inputs = ["W_q" if do_quantize_weight else "W"]
if do_dequantize:
inputs += ["b_q" if do_quantize_weight else "b"]
pack = core.CreateOperator(
"Int8ConvPackWeight",
inputs,
["W_packed"],
group=group,
preserve_weight_sparsity=preserve_weight_sparsity,
in_scale=x_q_param.scale,
engine=engine,
)
init_net.Proto().op.extend([pack])
conv = core.CreateOperator(
op_type,
[
"X_q" if do_quantize else "X",
"W_packed" if do_prepack_weight else
("W_q" if do_quantize_weight else "W"),
"b_q" if do_quantize_weight else "b",
],
["Y_q" if do_dequantize else "Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
shared_buffer=(1 if share_col_buffer else 0),
preserve_activation_sparsity=preserve_activation_sparsity,
preserve_weight_sparsity=preserve_weight_sparsity,
engine=engine,
group=group,
device_option=gc,
)
if do_quantize_weight or do_prepack_weight:
# When quantized weight is provided, we can't rescale the
# output dynamically by looking at the range of output of each
# batch, so here we provide the range of output observed from
# fp32 reference implementation
dnnlowp_utils.add_quantization_param_args(
conv, outputs[0][0], preserve_activation_sparsity)
net.Proto().op.extend([conv])
if do_dequantize:
dequantize = core.CreateOperator("Dequantize", ["Y_q"], ["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.create_blob("W").feed(W, device_option=gc)
self.ws.create_blob("b").feed(b, device_option=gc)
self.ws.run(init_net)
self.ws.run(net)
Y = self.ws.blobs["Y"].fetch()
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
check_quantized_results_close(outputs,
symmetric=preserve_activation_sparsity)
def test_rowwise_dnnlowp_fully_connected_int(
self,
input_channels,
output_channels,
batch_size,
in_quantized,
out_quantized,
prepack_weight,
gc,
dc,
):
print("@given M ", batch_size, " K ", input_channels, " N ",
output_channels)
print("@given in_quantized ", in_quantized, " out_quantized ",
out_quantized)
# X has scale 1, so exactly represented after quantization
X_min = -77
X_max = X_min + 255
X = np.round(
np.random.rand(batch_size, input_channels) * (X_max - X_min) +
X_min)
X = X.astype(np.float32)
# input channels 0 and 1 are all X_min to avoid overflow from vpmaddubsw
# when multiplied with W_min and W_max
X[:, 0:2] = X_min
X[0, 2] = X_max
# Each row of W has scale 1 but with different offset, so row-wise
# quantization shouldn't have any input quantization error.
W = np.zeros((output_channels, input_channels))
W = W.astype(np.float32)
for i in range(output_channels):
W_min = -100 + i
W_max = W_min + 255
W[i, :] = np.round(
np.random.rand(input_channels) * (W_max - W_min) + W_min)
W[i, 0] = W_min
W[i, 1] = W_max
# Make sure we won't have overflows from vpmaddubsw instruction used in
# fbgemm
avoid_vpmaddubsw_overflow_fc(
batch_size,
input_channels,
1,
X,
X_min,
X_max,
W[i:i + 1, ],
W_min,
W_max,
)
b = np.random.randn(output_channels).astype(np.float32)
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [
("FC", ""),
("FC", "DNNLOWP_ROWWISE"),
("FC", "DNNLOWP_ROWWISE_16"),
("Int8FC", "DNNLOWP_ROWWISE"),
("Int8FCRowWise", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
init_net = core.Net("test_init_net")
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine and in_quantized
do_dequantize = "DNNLOWP" in engine and out_quantized
do_prepack_weight = engine == "DNNLOWP_ROWWISE" and prepack_weight
if do_quantize:
quantize = core.CreateOperator("Quantize", ["X"], ["X_q"],
engine=engine,
device_option=gc)
net.Proto().op.extend([quantize])
x_q_param = dnnlowp_utils.choose_quantization_params(
X.min(), X.max())
if do_prepack_weight:
inputs = ["W"]
if do_dequantize:
inputs += ["b"]
pack = core.CreateOperator(
"Int8FCPackWeight",
inputs,
["W_packed"],
in_scale=x_q_param.scale,
engine=engine,
)
init_net.Proto().op.extend([pack])
fc = core.CreateOperator(
op_type,
[
"X_q" if do_quantize else "X",
"W_packed" if do_prepack_weight else "W",
"b",
],
["Y_q" if do_dequantize else "Y"],
dequantize_output=not do_dequantize,
engine=engine,
device_option=gc,
)
if do_prepack_weight:
# When pre-packed quantized weight is provided, we can't rescale
# the output dynamically by looking at the range of output of
# each batch, so here we provide the range of output observed
# from fp32 reference implementation
dnnlowp_utils.add_quantization_param_args(fc, outputs[0][0])
net.Proto().op.extend([fc])
if do_dequantize:
dequantize = core.CreateOperator("Dequantize", ["Y_q"], ["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.create_blob("W").feed(W, device_option=gc)
self.ws.create_blob("b").feed(b, device_option=gc)
self.ws.run(init_net)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(),
op_type=op_type,
engine=engine))
check_quantized_results_close(outputs)
def _create_rnn(cls, init_model, pred_model, n, opset_version):
assert init_model is not None, "cannot convert RNNs without access to the full model"
assert pred_model is not None, "cannot convert RNNs without access to the full model"
attrs = dict(n.attrs) # make a copy, which is safe to mutate
hidden_size = attrs.pop('hidden_size')
activation = force_unicode(attrs.pop('activations', ('tanh', ))[0])
direction = force_unicode(attrs.pop('direction', 'forward'))
assert not attrs, "unsupported RNN attributes: " + str(attrs.keys())
assert direction in ['forward',
'bidirectional'], "unsupported backwards RNN"
input_blob, W, R, B, sequence_lens, initial_h = n.inputs
if sequence_lens == "":
sequence_lens = None
input_size = cls._rnn_shape_inference(init_model, pred_model, n,
input_blob, W)
if input_size is None:
raise RuntimeError(
"best-effort shape inference for RNN input failed")
init_net = core.Net("init-net")
pred_mh = ModelHelper()
def make_rnn(direction_offset):
name = dummy_name()
# input and recurrence biases are squashed together in
# onnx but not in caffe2
bias_offset = 2 * direction_offset * hidden_size
init_net.Slice(B,
name + "/i2h_b",
starts=[bias_offset + 0 * hidden_size],
ends=[bias_offset + 1 * hidden_size])
init_net.Slice(B,
name + "/gates_t_b",
starts=[bias_offset + 1 * hidden_size],
ends=[bias_offset + 2 * hidden_size])
weight_offset = direction_offset * hidden_size
init_net.Slice(W,
name + '/i2h_w',
starts=[weight_offset + 0 * hidden_size, 0],
ends=[weight_offset + 1 * hidden_size, -1])
init_net.Slice(R,
name + '/gates_t_w',
starts=[weight_offset + 0 * hidden_size, 0],
ends=[weight_offset + 1 * hidden_size, -1])
initial_h_sliced = name + '/initial_h'
init_net.Slice(initial_h,
initial_h_sliced,
starts=[direction_offset + 0, 0, 0],
ends=[direction_offset + 1, -1, -1])
if direction_offset == 1:
input = pred_mh.net.ReversePackedSegs(
[input_blob, sequence_lens], name + "/input-reversed")
else:
input = input_blob
hidden_t_all, hidden_t_last = rnn_cell.BasicRNN(
pred_mh,
input,
sequence_lens, [initial_h_sliced],
input_size,
hidden_size,
name,
drop_states=False,
forward_only=True,
activation=activation)
if direction_offset == 1:
hidden_t_all = pred_mh.net.ReversePackedSegs(
[hidden_t_all, sequence_lens], name + "/output-reversed")
return hidden_t_all, hidden_t_last
if direction == 'forward':
hidden_t_all, hidden_t_last = make_rnn(0)
# in the forward case, storage is shared between the two
# outputs. We need to decouple them so that the
# VariableLengthSequencePadding only mutates n.outputs[0]
pred_mh.net.Copy(hidden_t_last, n.outputs[1])
pred_mh.net = pred_mh.net.Clone(
"dummy-clone-net", blob_remap={hidden_t_all: n.outputs[0]})
elif direction == 'bidirectional':
hidden_t_all_f, hidden_t_last_f = make_rnn(0)
hidden_t_all_b, hidden_t_last_b = make_rnn(1)
pred_mh.net.Concat([hidden_t_all_f, hidden_t_all_b],
[n.outputs[0], dummy_name()],
axis=2)
pred_mh.net.Concat([hidden_t_last_f, hidden_t_last_b],
[n.outputs[1], dummy_name()],
axis=0)
if sequence_lens is not None:
pred_mh.net.VariableLengthSequencePadding(
[n.outputs[0], sequence_lens], [n.outputs[0]])
return Caffe2Ops(list(pred_mh.Proto().op), list(init_net.Proto().op),
list(pred_mh.Proto().external_input))
def test_dnnlowp_conv_acc16_int(
self,
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
in_quantized,
out_quantized,
weight_quantized,
share_col_buffer,
preserve_activation_sparsity,
preserve_weight_sparsity,
gc,
dc,
):
assume(group == 1 or dilation == 1)
assume(size >= dilation * (kernel - 1) + 1)
input_channels = input_channels_per_group * group
output_channels = output_channels_per_group * group
# X and W have scale 1, so exactly represented after quantization
# This was made sure by having at least one 0 and one 255 for unsigned
# 8-bit tensors, and at least one -128 and one 127 for signed 8-bit
# tensors.
# Since fbgemm_acc16 accumulates to 16-bit, To avoid overflow, we use
# small numbers except for those 0, 255, -128, and 127, for this test
# We also make sure 255, -128, or 127 are not multiplied together by
# putting them in different input channels and the corresponding input
# channel in other matrix is 0.
# For example, we put 255 in input channel 1 in X, so we make the
# corresponding input channel in W all zeros.
X_min = 0 if preserve_activation_sparsity else -77
X_max = X_min + 255
X = np.random.rand(batch_size, size, size, input_channels) * 4 + X_min
X = np.round(X).astype(np.float32)
X[..., 0] = X_min
X[0, 0, 0, 1] = X_max
if preserve_weight_sparsity:
W_min = -128
W_max = 100
else:
W_min = -100
W_max = W_min + 255
W = (np.random.rand(output_channels, kernel, kernel,
input_channels_per_group) * 4 - 2 + W_min + 128)
W = np.round(W).astype(np.float32)
W[0, 0, 0, 0] = W_min
W[1, 0, 0, 0] = W_max
W[..., 1] = W_min + 128 # "zeros"
if order == "NCHW":
X = nhwc2nchw(X)
W = nhwc2nchw(W)
# No input quantization error in bias
b = np.round(np.random.randn(output_channels)).astype(np.float32)
Output = collections.namedtuple("Output",
["Y", "op_type", "engine", "order"])
outputs = []
op_engine_list = [
("Conv", ""),
("Conv", "DNNLOWP_ACC16"),
("Int8Conv", "DNNLOWP_ACC16"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine and in_quantized
do_dequantize = "DNNLOWP" in engine and out_quantized
do_quantize_weight = ("DNNLOWP" in engine and weight_quantized
and len(outputs) > 0)
if do_quantize:
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
preserve_activation_sparsity=preserve_activation_sparsity,
engine="DNNLOWP",
device_option=gc,
)
net.Proto().op.extend([quantize])
if do_quantize_weight:
int8_given_tensor_fill, w_q_param = dnnlowp_utils.create_int8_given_tensor_fill(
W, "W_q", preserve_weight_sparsity)
net.Proto().op.extend([int8_given_tensor_fill])
# Bias
x_q_param = dnnlowp_utils.choose_quantization_params(
X.min(), X.max(), preserve_activation_sparsity)
int8_bias_tensor_fill = dnnlowp_utils.create_int8_bias_tensor_fill(
b, "b_q", x_q_param, w_q_param)
net.Proto().op.extend([int8_bias_tensor_fill])
conv = core.CreateOperator(
op_type,
[
"X_q" if do_quantize else "X",
"W_q" if do_quantize_weight else "W",
"b_q" if do_quantize_weight else "b",
],
["Y_q" if do_dequantize else "Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
dequantize_output=not do_dequantize,
shared_buffer=(1 if share_col_buffer else 0),
preserve_activation_sparsity=preserve_activation_sparsity,
preserve_weight_sparsity=preserve_weight_sparsity,
engine=engine,
group=group,
device_option=gc,
)
if do_dequantize or do_quantize_weight:
# When quantized weight is provided, we can't rescale the
# output dynamically by looking at the range of output of each
# batch, so here we provide the range of output observed from
# fp32 reference implementation
dnnlowp_utils.add_quantization_param_args(
conv, outputs[0][0], preserve_activation_sparsity)
net.Proto().op.extend([conv])
if do_dequantize:
dequantize = core.CreateOperator("Dequantize", ["Y_q"], ["Y"],
engine="DNNLOWP",
device_option=gc)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.create_blob("W").feed(W, device_option=gc)
self.ws.create_blob("b").feed(b, device_option=gc)
self.ws.run(net)
Y = self.ws.blobs["Y"].fetch()
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
check_quantized_results_close(outputs,
symmetric=preserve_activation_sparsity)
def _create_gru(cls, init_model, pred_model, n, opset_version):
assert init_model is not None, "cannot convert GRUs without access to the full model"
assert pred_model is not None, "cannot convert GRUs without access to the full model"
attrs = dict(n.attrs) # make a copy, which is safe to mutate
hidden_size = attrs.pop('hidden_size')
linear_before_reset = attrs.pop('linear_before_reset', 0)
direction = force_unicode(attrs.pop('direction', 'forward'))
assert not attrs, "unsupported GRU attributes: " + str(attrs.keys())
assert direction in ['forward',
'bidirectional'], "unsupported backwards GRU"
input_blob, W, R, B, sequence_lens, initial_h = n.inputs
if sequence_lens == "":
sequence_lens = None
input_size = cls._rnn_shape_inference(init_model, pred_model, n,
input_blob, W)
if input_size is None:
raise RuntimeError(
"best-effort shape inference for GRU input failed")
init_net = core.Net("init-net")
pred_mh = ModelHelper()
def make_gru(direction_offset):
name = dummy_name()
# input and recurrence biases are squashed together in
# onnx but not in caffe2
bias_offset = 6 * direction_offset * hidden_size
Bi = init_net.Slice(B,
name + "_bias_i2h",
starts=[bias_offset + 0 * hidden_size],
ends=[bias_offset + 3 * hidden_size])
Br = init_net.Slice(B,
name + "_bias_gates",
starts=[bias_offset + 3 * hidden_size],
ends=[bias_offset + 6 * hidden_size])
weight_offset = 3 * direction_offset * hidden_size
W_ = init_net.Slice(W,
name + '/i2h_w_pre',
starts=[weight_offset + 0 * hidden_size, 0],
ends=[weight_offset + 3 * hidden_size, -1])
R_ = init_net.Slice(R,
name + '/gates_t_w_pre',
starts=[weight_offset + 0 * hidden_size, 0],
ends=[weight_offset + 3 * hidden_size, -1])
# caffe2 has a different order from onnx. We need to rearrange
# z r h -> r z h
reforms = ((W_, 'i2h_w', True, [(0, -1)]), (R_, 'gate_t_w', False,
[(0, -1)]),
(Bi, 'i2h_b', True, []), (Br, 'gate_t_b', False, []))
for name_from, name_to, do_concat, extra_dims in reforms:
xz, xr, xh = [
'%s/%s_%s' % (name, prefix, name_to)
for prefix in ('update', 'reset', 'output')
]
for i, x in enumerate([xz, xr, xh]):
dim0 = i * hidden_size, (i + 1) * hidden_size
starts, ends = zip(dim0, *extra_dims)
init_net.Slice(name_from, x, starts=starts, ends=ends)
if do_concat:
init_net.Concat([xr, xz, xh],
['%s/%s' % (name, name_to),
dummy_name()],
axis=0)
initial_h_sliced = name + '/initial_h'
init_net.Slice(initial_h,
initial_h_sliced,
starts=[direction_offset + 0, 0, 0],
ends=[direction_offset + 1, -1, -1])
if direction_offset == 1:
input = pred_mh.net.ReversePackedSegs(
[input_blob, sequence_lens], name + "/input-reversed")
else:
input = input_blob
hidden_t_all, hidden_t_last = gru_cell.GRU(
pred_mh,
input,
sequence_lens, [initial_h_sliced],
input_size,
hidden_size,
name,
drop_states=False,
forward_only=True,
linear_before_reset=linear_before_reset)
if direction_offset == 1:
hidden_t_all = pred_mh.net.ReversePackedSegs(
[hidden_t_all, sequence_lens], name + "/output-reversed")
return hidden_t_all, hidden_t_last
if direction == 'forward':
hidden_t_all, hidden_t_last = make_gru(0)
# in the forward case, storage is shared between the two
# outputs. We need to decouple them so that the
# VariableLengthSequencePadding only mutates n.outputs[0]
pred_mh.net.Copy(hidden_t_last, n.outputs[1])
pred_mh.net = pred_mh.net.Clone(
"dummy-clone-net", blob_remap={hidden_t_all: n.outputs[0]})
elif direction == 'bidirectional':
hidden_t_all_f, hidden_t_last_f = make_gru(0)
hidden_t_all_b, hidden_t_last_b = make_gru(1)
pred_mh.net.Concat([hidden_t_all_f, hidden_t_all_b],
[n.outputs[0], dummy_name()],
axis=2)
pred_mh.net.Concat([hidden_t_last_f, hidden_t_last_b],
[n.outputs[1], dummy_name()],
axis=0)
if sequence_lens is not None:
pred_mh.net.VariableLengthSequencePadding(
[n.outputs[0], sequence_lens], [n.outputs[0]])
return Caffe2Ops(list(pred_mh.Proto().op), list(init_net.Proto().op),
list(pred_mh.Proto().external_input))
[], # GaussianFill does not need any parameters.
["Z"],
shape=[100, 100], # shape argument as a list of ints.
mean=1.0, # mean as a single float
std=1.0, # std as a single float
)
print("Content of op:\n")
print(str(op))
workspace.RunOperatorOnce(op)
temp = workspace.FetchBlob("Z")
pyplot.hist(temp.flatten(), bins=50)
pyplot.title("Distribution of Z")
pyplot.show()
net = core.Net("my_first_net")
X = net.GaussianFill([], ["X"], mean=0.0, std=1.0, shape=[2, 3], run_once=0)
print("New network proto:\n\n{}".format(net.Proto()))
print("Type of X is: {}".format(type(X)))
print("The blob name is: {}".format(str(X)))
W = net.GaussianFill([], ["W"], mean=0.0, std=1.0, shape=[5, 3], run_once=0)
b = net.ConstantFill([], ["b"], shape=[
5,
], value=1.0, run_once=0)
Y = X.FC([W, b], ["Y"])
from caffe2.python import net_drawer
from IPython import display
graph = net_drawer.GetPydotGraph(net, rankdir="LR")
def preprocess_samples(
self,
states: List[Dict[int, float]],
actions: List[Dict[int, float]],
rewards: List[float],
next_states: List[Dict[int, float]],
next_actions: List[Dict[int, float]],
is_terminals: List[bool],
possible_next_actions: List[List[Dict[int, float]]],
reward_timelines: List[Dict[int, float]],
minibatch_size: int,
) -> List[TrainingDataPage]:
# Shuffle
merged = list(
zip(states, actions, rewards, next_states, next_actions,
is_terminals, possible_next_actions, reward_timelines))
random.shuffle(merged)
states, actions, rewards, next_states, next_actions, is_terminals, \
possible_next_actions, reward_timelines = zip(*merged)
net = core.Net('gridworld_preprocessing')
C2.set_net(net)
preprocessor = PreprocessorNet(net, True)
saa = StackedAssociativeArray.from_dict_list(states, 'states')
state_matrix, _ = preprocessor.normalize_sparse_matrix(
saa.lengths,
saa.keys,
saa.values,
self.normalization,
'state_norm',
)
saa = StackedAssociativeArray.from_dict_list(next_states,
'next_states')
next_state_matrix, _ = preprocessor.normalize_sparse_matrix(
saa.lengths,
saa.keys,
saa.values,
self.normalization,
'next_state_norm',
)
saa = StackedAssociativeArray.from_dict_list(actions, 'action')
action_matrix, _ = preprocessor.normalize_sparse_matrix(
saa.lengths,
saa.keys,
saa.values,
self.normalization_action,
'action_norm',
)
saa = StackedAssociativeArray.from_dict_list(next_actions,
'next_action')
next_action_matrix, _ = preprocessor.normalize_sparse_matrix(
saa.lengths,
saa.keys,
saa.values,
self.normalization_action,
'next_action_norm',
)
rewards = np.array(rewards, dtype=np.float32).reshape(-1, 1)
pnas_lengths_list = []
pnas_flat = []
for pnas in possible_next_actions:
pnas_lengths_list.append(len(pnas))
pnas_flat.extend(pnas)
saa = StackedAssociativeArray.from_dict_list(pnas_flat,
'possible_next_actions')
pnas_lengths = np.array(pnas_lengths_list, dtype=np.int32)
possible_next_actions_matrix, _ = preprocessor.normalize_sparse_matrix(
saa.lengths,
saa.keys,
saa.values,
self.normalization_action,
'possible_next_action_norm',
)
workspace.RunNetOnce(net)
states_ndarray = workspace.FetchBlob(state_matrix)
actions_ndarray = workspace.FetchBlob(action_matrix)
next_states_ndarray = workspace.FetchBlob(next_state_matrix)
next_actions_ndarray = workspace.FetchBlob(next_action_matrix)
possible_next_actions_ndarray = workspace.FetchBlob(
possible_next_actions_matrix)
tdps = []
pnas_start = 0
for start in range(0, states_ndarray.shape[0], minibatch_size):
end = start + minibatch_size
if end > states_ndarray.shape[0]:
break
pnas_end = pnas_start + np.sum(pnas_lengths[start:end])
pnas = possible_next_actions_ndarray[pnas_start:pnas_end]
pnas_start = pnas_end
tdps.append(
TrainingDataPage(
states=states_ndarray[start:end],
actions=actions_ndarray[start:end],
rewards=rewards[start:end],
next_states=next_states_ndarray[start:end],
next_actions=next_actions_ndarray[start:end],
possible_next_actions=StackedArray(pnas_lengths[start:end],
pnas),
not_terminals=(pnas_lengths[start:end] > 0).reshape(-1, 1),
reward_timelines=reward_timelines[start:end]
if reward_timelines else None,
))
return tdps
def test_dnnlowp_max_pool(
self,
stride,
pad,
kernel,
size,
input_channels,
batch_size,
order,
in_quantized,
gc,
dc,
):
assume(kernel <= size)
assume(pad < kernel)
C = input_channels
N = batch_size
H = W = size
min_ = -10
max_ = 20
if order == "NCHW":
X = np.round(np.random.rand(N, C, H, W) * (max_ - min_) + min_)
elif order == "NHWC":
X = np.round(np.random.rand(N, H, W, C) * (max_ - min_) + min_)
X = X.astype(np.float32)
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [
("MaxPool", ""),
("MaxPool", "DNNLOWP"),
("Int8MaxPool", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine and in_quantized
if do_quantize:
quantize = core.CreateOperator("Quantize", ["X"], ["X_q"],
engine=engine,
device_option=gc)
net.Proto().op.extend([quantize])
max_pool = core.CreateOperator(
op_type,
["X_q" if do_quantize else "X"],
["Y_q" if engine == "DNNLOWP" else "Y"],
stride=stride,
kernel=kernel,
pad=pad,
order=order,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([max_pool])
if engine == "DNNLOWP":
dequantize = core.CreateOperator("Dequantize", ["Y_q"], ["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(),
op_type=op_type,
engine=engine))
# Y_i = max(X_j) so the only error is in quantization of inputs
check_quantized_results_close(outputs, ref=X)
def recurrent_net(net,
cell_net,
inputs,
initial_cell_inputs,
links,
timestep=None,
scope=None,
outputs_with_grads=(0, )):
'''
net: the main net operator should be added to
cell_net: cell_net which is executed in a recurrent fasion
inputs: sequences to be fed into the recurrent net. Currently only one input
is supported. It has to be in a format T x N x (D1...Dk) where T is lengths
of the sequence. N is a batch size and (D1...Dk) are the rest of dimentions
initial_cell_inputs: inputs of the cell_net for the 0 timestamp.
Format for each input is:
(cell_net_input_name, external_blob_with_data)
links: a dictionary from cell_net input names in moment t+1 and
output names of moment t. Currently we assume that each output becomes
an input for the next timestep.
timestep: name of the timestep blob to be used. If not provided "timestep"
is used.
scope: Internal blobs are going to be scoped in a format
<scope_name>/<blob_name>
If not provided we generate a scope name automatically
outputs_with_grads : position indices of output blobs which will receive
error gradient (from outside recurrent network) during backpropagation
'''
assert len(inputs) == 1, "Only one input blob is supported so far"
input_blobs = [str(i[0]) for i in inputs]
initial_input_blobs = [str(x[1]) for x in initial_cell_inputs]
op_name = net.NextName('recurrent')
def s(name):
# We have to manually scope due to our internal/external blob
# relationships.
scope_name = op_name if scope is None else scope
return "{}/{}".format(str(scope_name), str(name))
# determine inputs that are considered to be references
# it is those that are not referred to in inputs or initial_cell_inputs
known_inputs = map(str, input_blobs + initial_input_blobs)
known_inputs += [str(x[0]) for x in initial_cell_inputs]
if timestep is not None:
known_inputs.append(str(timestep))
references = [
b for b in cell_net.Proto().external_input if b not in known_inputs
]
inner_outputs = list(cell_net.Proto().external_output)
# These gradients are expected to be available during the backward pass
inner_outputs_map = {o: o + '_grad' for o in inner_outputs}
# compute the backward pass of the cell net
backward_ops, backward_mapping = core.GradientRegistry.GetBackwardPass(
cell_net.Proto().op, inner_outputs_map)
backward_mapping = {str(k): str(v) for k, v in backward_mapping.items()}
backward_cell_net = core.Net("RecurrentBackwardStep")
del backward_cell_net.Proto().op[:]
backward_cell_net.Proto().op.extend(backward_ops)
# compute blobs used but not defined in the backward pass
ssa, _ = core.get_ssa(backward_cell_net.Proto())
undefined = core.get_undefined_blobs(ssa)
# also add to the output list the intermediate outputs of fwd_step that
# are used by backward.
ssa, blob_versions = core.get_ssa(cell_net.Proto())
scratches = [
blob for (blob, ver) in blob_versions.items() if ver > 0
and blob in undefined and blob not in cell_net.Proto().external_output
]
backward_cell_net.Proto().external_input.extend(scratches)
all_inputs = [i[1] for i in inputs] + [x[1] for x in initial_cell_inputs
] + references
all_outputs = []
cell_net.Proto().type = 'simple'
backward_cell_net.Proto().type = 'simple'
# Internal arguments used by RecurrentNetwork operator
# Links are in the format blob_name, recurrent_states, offset.
# In the moment t we know that corresponding data block is at
# t + offset position in the recurrent_states tensor
forward_links = []
backward_links = []
# Aliases are used to expose outputs to external world
# Format (internal_blob, external_blob, offset)
# Negative offset stands for going from the end,
# positive - from the beginning
aliases = []
# States held inputs to the cell net
recurrent_states = []
for cell_input, _ in initial_cell_inputs:
cell_input = str(cell_input)
# Recurrent_states is going to be (T + 1) x ...
# It stores all inputs and outputs of the cell net over time.
# Or their gradients in the case of the backward pass.
state = s(cell_input + "_states")
states_grad = state + "_grad"
cell_output = links[str(cell_input)]
forward_links.append((cell_input, state, 0))
forward_links.append((cell_output, state, 1))
backward_links.append((cell_input + "_grad", states_grad, 0))
backward_links.append((cell_output + "_grad", states_grad, 1))
backward_cell_net.Proto().external_input.append(
str(cell_output) + "_grad")
aliases.append((state, cell_output + "_all", 1))
aliases.append((state, cell_output + "_last", -1))
all_outputs.extend([cell_output + "_all", cell_output + "_last"])
recurrent_states.append(state)
for input_t, input_blob in inputs:
forward_links.append((str(input_t), str(input_blob), 0))
backward_links.append(
(backward_mapping[str(input_t)], str(input_blob) + "_grad", 0))
backward_cell_net.Proto().external_input.extend(
cell_net.Proto().external_input)
backward_cell_net.Proto().external_input.extend(
cell_net.Proto().external_output)
def unpack_triple(x):
if x:
a, b, c = zip(*x)
return a, b, c
return [], [], []
# Splitting to separate lists so we can pass them to c++
# where we ensemle them back
link_internal, link_external, link_offset = unpack_triple(forward_links)
backward_link_internal, backward_link_external, backward_link_offset = \
unpack_triple(backward_links)
alias_src, alias_dst, alias_offset = unpack_triple(aliases)
params = [x for x in references if x in backward_mapping.keys()]
recurrent_inputs = [str(x[1]) for x in initial_cell_inputs]
results = net.RecurrentNetwork(
all_inputs,
all_outputs + [s("step_workspaces")],
param=map(all_inputs.index, params),
alias_src=alias_src,
alias_dst=map(str, alias_dst),
alias_offset=alias_offset,
recurrent_states=recurrent_states,
initial_recurrent_state_ids=map(all_inputs.index, recurrent_inputs),
link_internal=map(str, link_internal),
link_external=map(str, link_external),
link_offset=link_offset,
backward_link_internal=map(str, backward_link_internal),
backward_link_external=map(str, backward_link_external),
backward_link_offset=backward_link_offset,
step_net=str(cell_net.Proto()),
backward_step_net=str(backward_cell_net.Proto()),
timestep="timestep" if timestep is None else str(timestep),
outputs_with_grads=outputs_with_grads,
)
# The last output is a list of step workspaces,
# which is only needed internally for gradient propogation
return results[:-1]
def __init__(self, model, input_record, output_names_or_num, function,
name='functional', output_dtypes=None, tags=None, **kwargs):
# allow coercion
input_record = schema.as_record(input_record)
super(Functional, self).__init__(model, name, input_record, tags=tags, **kwargs)
self._function = function
self._kwargs = kwargs
return_struct = (
isinstance(output_names_or_num, list) or
(isinstance(output_names_or_num, six.integer_types) and
output_names_or_num != 1)
)
with scope.NameScope(self.name, reset=True):
if isinstance(output_names_or_num, int):
struct_output_schema = schema.NewRecord(
model.net, schema.RawTuple(output_names_or_num))
elif isinstance(output_names_or_num, schema.Field):
self.output_schema = output_names_or_num.clone(keep_blobs=True)
return
else:
if not isinstance(output_names_or_num, list):
output_names_or_num = [output_names_or_num]
out_tuple = [(out, np.void) for out in output_names_or_num]
struct_output_schema = schema.NewRecord(
model.net, schema.Struct(*out_tuple))
num_outputs = len(struct_output_schema.field_blobs())
# functional layer returns Struct if more than one outputs or output is
# a list, otherwise Scalar
if return_struct:
self.output_schema = struct_output_schema
else:
self.output_schema = struct_output_schema[0]
# If output_dtypes is provided, use it for output schema. Otherwise
# the shape and type will be inferred.
if output_dtypes is not None:
if not isinstance(output_dtypes, list):
output_dtypes = [output_dtypes] * num_outputs
assert len(output_dtypes) == num_outputs
for dtype, scalar in zip(output_dtypes,
self.output_schema.all_scalars()):
scalar.set_type(dtype)
return
# Fake execution of the function to infer shapes and types automatically
had_issues = False
try:
type_net = core.Net('_temp_type_and_shape_inference_net')
schema.InitEmptyRecord(type_net, input_record, enforce_types=True)
function(type_net, self.input_record, self.output_schema, **kwargs)
(shapes, types) = workspace.InferShapesAndTypes([type_net], {})
for i in range(num_outputs):
scalar_schema = (self.output_schema[i] if return_struct
else self.output_schema)
blob = scalar_schema()
if blob not in types or blob not in shapes:
had_issues = True
continue
if shapes[blob] == []:
# Scalar type
shape = tuple()
elif shapes[blob][0] == 0:
shape = tuple(shapes[blob][1:])
else:
logger.warning("unexpected shape: {}".format(shapes[blob]))
# If batch dimension is not first - give up on shape
# inference for that blob
had_issues = True
continue
# TODO(amalevich): Move it to some shared library
dtype = None
if types[blob] == caffe2_pb2.TensorProto.DOUBLE:
dtype = (np.float64, shape)
elif types[blob] == caffe2_pb2.TensorProto.FLOAT:
dtype = (np.float32, shape)
elif types[blob] == caffe2_pb2.TensorProto.INT32:
dtype = (np.int32, shape)
elif types[blob] == caffe2_pb2.TensorProto.INT64:
dtype = (np.int64, shape)
elif types[blob] == caffe2_pb2.TensorProto.FLOAT16:
dtype = (np.float16, shape)
if dtype is not None:
scalar_schema.set_type(dtype)
except TypeError as ex:
had_issues = True
logger.warning(str(ex))
if had_issues:
logger.warning(
"Type inference had problems for layer: {}".format(self.name))
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(
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(False)
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 = 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)))
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 test_groupwise_dnnlowp_conv_relu_int(
self,
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
gc,
dc,
):
if group > 1:
dilation = 1
X, W, b = generate_conv_inputs(
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
True, # group-wise
)
Output = collections.namedtuple("Output",
["Y", "op_type", "engine", "order"])
outputs = []
op_engine_list = [
("Conv", ""),
("ConvRelu", "DNNLOWP"),
("ConvRelu", "DNNLOWP_16"),
("Int8ConvRelu", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
if "DNNLOWP" in engine:
quantize = core.CreateOperator("Quantize", ["X"], ["X_q"],
engine=engine,
device_option=gc)
net.Proto().op.extend([quantize])
conv = core.CreateOperator(
op_type,
["X_q", "W", "b"],
["Y_q"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
group=group,
quantize_groupwise=1,
device_option=gc,
)
# groupwise quantization only works with static quantization
# so we need to set quantization parameters
dnnlowp_utils.add_quantization_param_args(conv, outputs[0][0])
net.Proto().op.extend([conv])
dequantize = core.CreateOperator("Dequantize", ["Y_q"], ["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([dequantize])
else:
conv = core.CreateOperator(
op_type,
["X", "W", "b"],
["Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
group=group,
device_option=gc,
)
net.Proto().op.extend([conv])
relu = core.CreateOperator("Relu", ["Y"], ["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([relu])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.create_blob("W").feed(W, device_option=gc)
self.ws.create_blob("b").feed(b, device_option=gc)
self.ws.run(net)
Y = self.ws.blobs["Y"].fetch()
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
check_quantized_results_close(outputs)
def test_dataset_ops(self):
"""
1. Defining the schema of our dataset.
This example schema could represent, for example, a search query log.
"""
schema = Struct(
# fixed size vector, which will be stored as a matrix when batched
('dense', Scalar((np.float32, 3))),
# could represent a feature map from feature ID to float value
('floats', Map(Scalar(np.int32), Scalar(np.float32))),
# could represent a multi-valued categorical feature map
('int_lists', Map(
Scalar(np.int32),
List(Scalar(np.int64)),
)),
# could represent a multi-valued, weighted categorical feature map
('id_score_pairs',
Map(
Scalar(np.int32),
Map(Scalar(np.int64),
Scalar(np.float32),
keys_name='ids',
values_name='scores'),
)),
# additional scalar information
('metadata',
Struct(
('user_id', Scalar(np.int64)),
('user_embed', Scalar((np.float32, 2))),
('query', Scalar(str)),
)),
)
"""
This is what the flattened fields for this schema look like, along
with its type. Each one of these fields will be stored, read and
written as a tensor.
"""
expected_fields = [
('dense', (np.float32, 3)),
('floats:lengths', np.int32),
('floats:values:keys', np.int32),
('floats:values:values', np.float32),
('int_lists:lengths', np.int32),
('int_lists:values:keys', np.int32),
('int_lists:values:values:lengths', np.int32),
('int_lists:values:values:values', np.int64),
('id_score_pairs:lengths', np.int32),
('id_score_pairs:values:keys', np.int32),
('id_score_pairs:values:values:lengths', np.int32),
('id_score_pairs:values:values:values:ids', np.int64),
('id_score_pairs:values:values:values:scores', np.float32),
('metadata:user_id', np.int64),
('metadata:user_embed', (np.float32, 2)),
('metadata:query', str),
]
zipped = zip(expected_fields, schema.field_names(),
schema.field_types())
for (ref_name, ref_type), name, dtype in zipped:
self.assertEquals(ref_name, name)
self.assertEquals(np.dtype(ref_type), dtype)
"""
2. The contents of our dataset.
Contents as defined below could represent, for example, a log of
search queries along with dense, sparse features and metadata.
The dataset below has 3 top-level entries.
"""
contents_raw = [
# dense
[[1.1, 1.2, 1.3], [2.1, 2.2, 2.3], [3.1, 3.2, 3.3]],
# floats
[1, 2, 3], # len
[11, 21, 22, 31, 32, 33], # key
[1.1, 2.1, 2.2, 3.1, 3.2, 3.3], # value
# int lists
[2, 0, 1], # len
[11, 12, 31], # key
[2, 4, 3], # value:len
[111, 112, 121, 122, 123, 124, 311, 312, 313], # value:value
# id score pairs
[1, 2, 2], # len
[11, 21, 22, 31, 32], # key
[1, 1, 2, 2, 3], # value:len
[111, 211, 221, 222, 311, 312, 321, 322, 323], # value:ids
[11.1, 21.1, 22.1, 22.2, 31.1, 31.2, 32.1, 32.2,
32.3], # val:score
# metadata
[123, 234, 456], # user_id
[[0.2, 0.8], [0.5, 0.5], [0.7, 0.3]], # user_embed
['dog posts', 'friends who like to', 'posts about ca'], # query
]
# convert the above content to ndarrays, checking against the schema
contents = from_blob_list(schema, contents_raw)
"""
3. Creating and appending to the dataset.
We first create an empty dataset with the given schema.
Then, a Writer is used to append these entries to the dataset.
"""
ds = dataset.Dataset(schema)
net = core.Net('init')
with core.NameScope('init'):
ds.init_empty(net)
content_blobs = NewRecord(net, contents)
FeedRecord(content_blobs, contents)
writer = ds.writer(init_net=net)
writer.write_record(net, content_blobs)
workspace.RunNetOnce(net)
"""
4. Iterating through the dataset contents.
If we were to iterate through the top level entries of our dataset,
this is what we should expect to see:
"""
entries_raw = [
(
[[1.1, 1.2, 1.3]], # dense
[1],
[11],
[1.1], # floats
[2],
[11, 12],
[2, 4],
[111, 112, 121, 122, 123, 124], # intlst
[1],
[11],
[1],
[111],
[11.1], # id score pairs
[123],
[[0.2, 0.8]],
['dog posts'], # metadata
),
(
[[2.1, 2.2, 2.3]], # dense
[2],
[21, 22],
[2.1, 2.2], # floats
[0],
[],
[],
[], # int list
[2],
[21, 22],
[1, 2],
[211, 221, 222],
[21.1, 22.1, 22.2],
[234],
[[0.5, 0.5]],
['friends who like to'], # metadata
),
(
[[3.1, 3.2, 3.3]], # dense
[3],
[31, 32, 33],
[3.1, 3.2, 3.3], # floats
[1],
[31],
[3],
[311, 312, 313], # int lst
[2],
[31, 32],
[2, 3],
[311, 312, 321, 322, 323],
[31.1, 31.2, 32.1, 32.2, 32.3], # id score list
[456],
[[0.7, 0.3]],
['posts about ca'], # metadata
),
# after the end of the dataset, we will keep getting empty vectors
(
[], ) * 16,
([], ) * 16,
]
entries = [from_blob_list(schema, e) for e in entries_raw]
"""
Let's go ahead and create the reading nets.
We will run `read` net multiple times and assert that we are reading the
entries the way we stated above.
"""
read_init_net = core.Net('read_init')
read_next_net = core.Net('read_next')
reader = ds.reader(read_init_net)
should_continue, batch = reader.read_record(read_next_net)
workspace.RunNetOnce(read_init_net)
workspace.CreateNet(read_next_net, True)
for entry in entries:
workspace.RunNet(str(read_next_net))
actual = FetchRecord(batch)
_assert_records_equal(actual, entry)
"""
5. Reading/writing in a single plan
If all of operations on the data are expressible as Caffe2 operators,
we don't need to load the data to python, iterating through the dataset
in a single Plan.
Where we will process the dataset a little and store it in a second
dataset. We can reuse the same Reader since it supports reset.
"""
reset_net = core.Net('reset_net')
reader.reset(reset_net)
read_step, batch = reader.execution_step()
""" We will add the line number * 1000 to the feature ids. """
process_net = core.Net('process')
line_no = Const(process_net, 0, dtype=np.int32)
const_one = Const(process_net, 1000, dtype=np.int32)
process_net.Add([line_no, const_one], [line_no])
field = batch.floats.keys.get()
process_net.Print(field, [])
process_net.Add([field, line_no], field, broadcast=1, axis=0)
""" Lets create a second dataset and append to it. """
ds2 = dataset.Dataset(schema, name='dataset2')
ds2.init_empty(reset_net)
writer = ds2.writer(reset_net)
writer.write_record(process_net, batch)
# commit is not necessary for DatasetWriter but will add it for
# generality of the example
commit_net = core.Net('commit')
writer.commit(commit_net)
""" Time to create and run a plan which will do the processing """
plan = core.Plan('process')
plan.AddStep(core.execution_step('reset', reset_net))
plan.AddStep(read_step.AddNet(process_net))
plan.AddStep(core.execution_step('commit', commit_net))
workspace.RunPlan(plan)
"""
Now we should have dataset2 populated.
"""
ds2_data = FetchRecord(ds2.content())
field = ds2_data.floats.keys
field.set(blob=field.get() - [1000, 2000, 2000, 3000, 3000, 3000])
_assert_records_equal(contents, ds2_data)
"""
6. Slicing a dataset
You can create a new schema from pieces of another schema and reuse
the same data.
"""
subschema = Struct(('top_level', schema.int_lists.values))
int_list_contents = contents.int_lists.values.field_names()
self.assertEquals(len(subschema.field_names()), len(int_list_contents))
"""
7. Random Access a dataset
"""
read_init_net = core.Net('read_init')
read_next_net = core.Net('read_next')
idx = np.array([2, 1, 0])
indices_blob = Const(read_init_net, idx, name='indices')
reader = ds.random_reader(read_init_net, indices_blob)
reader.computeoffset(read_init_net)
should_stop, batch = reader.read_record(read_next_net)
workspace.CreateNet(read_init_net, True)
workspace.RunNetOnce(read_init_net)
workspace.CreateNet(read_next_net, True)
for i in range(len(entries)):
k = idx[i] if i in idx else i
entry = entries[k]
workspace.RunNet(str(read_next_net))
actual = FetchRecord(batch)
_assert_records_equal(actual, entry)
workspace.RunNet(str(read_next_net))
self.assertEquals(True, workspace.FetchBlob(should_stop))
"""
8. Random Access a dataset with loop_over = true
"""
read_init_net = core.Net('read_init')
read_next_net = core.Net('read_next')
idx = np.array([2, 1, 0])
indices_blob = Const(read_init_net, idx, name='indices')
reader = ds.random_reader(read_init_net, indices_blob, loop_over=True)
reader.computeoffset(read_init_net)
should_stop, batch = reader.read_record(read_next_net)
workspace.CreateNet(read_init_net, True)
workspace.RunNetOnce(read_init_net)
workspace.CreateNet(read_next_net, True)
for _ in range(len(entries) * 3):
workspace.RunNet(str(read_next_net))
self.assertEquals(False, workspace.FetchBlob(should_stop))
"""
9. Sort and shuffle a dataset
This sort the dataset using the score of a certain column,
and then shuffle within each chunk of size batch_size * shuffle_size
before shuffling the chunks.
"""
read_init_net = core.Net('read_init')
read_next_net = core.Net('read_next')
reader = ds.random_reader(read_init_net)
reader.sort_and_shuffle(read_init_net, 'int_lists:lengths', 1, 2)
reader.computeoffset(read_init_net)
should_continue, batch = reader.read_record(read_next_net)
workspace.CreateNet(read_init_net, True)
workspace.RunNetOnce(read_init_net)
workspace.CreateNet(read_next_net, True)
expected_idx = np.array([2, 1, 0])
for i in range(len(entries)):
k = expected_idx[i] if i in expected_idx else i
entry = entries[k]
workspace.RunNet(str(read_next_net))
actual = FetchRecord(batch)
_assert_records_equal(actual, entry)
"""
Trim a dataset
"""
trim_net = core.Net('trim_ds')
ds.trim(trim_net, multiple_of=2)
workspace.RunNetOnce(trim_net)
trimmed = FetchRecord(ds.content())
EXPECTED_SIZES = [2, 2, 3, 3, 2, 2, 2, 6, 2, 3, 3, 4, 4, 2, 2, 2]
actual_sizes = [d.shape[0] for d in trimmed.field_blobs()]
self.assertEquals(EXPECTED_SIZES, actual_sizes)