def test_dnnlowp_group_norm(self, N, G, K, H, W, order, in_quantized,
out_quantized, weight_quantized, gc, dc):
C = G * K
X = np.random.rand(N, C, H, W).astype(np.float32) * 5.0 - 1.0
if order == "NHWC":
X = np.transpose(X, [0, 2, 3, 1])
gamma = np.random.rand(C).astype(np.float32) * 2.0 - 1.0
beta = np.random.randn(C).astype(np.float32) - 0.5
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [("GroupNorm", ""), ("GroupNorm", "DNNLOWP"),
("Int8GroupNorm", "DNNLOWP")]
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 = (engine == "DNNLOWP" and weight_quantized
and len(outputs) > 0)
if do_quantize:
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
if do_quantize_weight:
int8_given_tensor_fill, gamma_q_param = (
dnnlowp_utils.create_int8_given_tensor_fill(
gamma, "gamma_q"))
net.Proto().op.extend([int8_given_tensor_fill])
X_q_param = hardcode_scale_zp.choose_quantization_params(
X.min(), X.max())
int8_bias_tensor_fill = (
dnnlowp_utils.create_int8_bias_tensor_fill(
beta, "beta_q", X_q_param, gamma_q_param))
net.Proto().op.extend([int8_bias_tensor_fill])
group_norm = core.CreateOperator(
op_type,
[
"X_q" if do_quantize else "X",
"gamma_q" if do_quantize_weight else "gamma",
"beta_q" if do_quantize_weight else "beta"
],
["Y_q" if do_dequantize else "Y"],
dequantize_output=0 if do_dequantize else 1,
group=G,
order=order,
is_test=True,
engine=engine,
device_option=gc,
)
if 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(
group_norm, outputs[0][0])
net.Proto().op.extend([group_norm])
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("gamma").feed(gamma, device_option=gc)
self.ws.create_blob("beta").feed(beta, 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, atol_scale=2.0)
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,
nbits_in_non_outlier,
share_col_buffer,
preserve_activation_sparsity,
preserve_weight_sparsity,
gc,
dc,
):
if group > 1:
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
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
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 = hardcode_scale_zp.choose_quantization_params(
X.min(), X.max())
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,
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:
# 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 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,
):
if group > 1:
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 = hardcode_scale_zp.choose_quantization_params(
X.min(), X.max())
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 test_groupwise_dnnlowp_conv_int(
self,
stride,
pad,
kernel,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
in_quantized,
out_quantized,
preserve_activation_sparsity,
preserve_weight_sparsity,
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,
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:
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 = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
preserve_activation_sparsity=preserve_activation_sparsity,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
conv = core.CreateOperator(
op_type,
["X_q" if do_quantize else "X", "W", "b"],
["Y_q" if do_dequantize else "Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
dequantize_output=not do_dequantize,
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:
# 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])
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 test_dnnlowp_fully_connected_int(
self,
input_channels,
output_channels,
batch_size,
in_quantized,
out_quantized,
weight_quantized,
gc,
dc,
):
# X and W have 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] = X_min
X[0, 1] = X_max
W_min = -100
W_max = W_min + 255
W = np.round(
np.random.rand(output_channels, input_channels) * (W_max - W_min) +
W_min)
W = W.astype(np.float32)
W[0, 0] = W_min
W[1, 0] = W_max
# Make sure we won't have overflows from vpmaddubsw instruction used in
# fbgemm
avoid_vpmaddubsw_overflow_fc(
batch_size,
input_channels,
output_channels,
X,
X_min,
X_max,
W,
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"),
("FC", "DNNLOWP_16"),
("Int8FC", "DNNLOWP"),
]
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 = (engine == "DNNLOWP" and weight_quantized
and len(outputs) > 0)
if do_quantize:
quantize = core.CreateOperator("Quantize", ["X"], ["X_q"],
engine=engine,
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")
net.Proto().op.extend([int8_given_tensor_fill])
# Bias
x_q_param = hardcode_scale_zp.choose_quantization_params(
X.min(), X.max())
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])
fc = 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"],
dequantize_output=not do_dequantize,
engine=engine,
device_option=gc,
)
if 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(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(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(),
op_type=op_type,
engine=engine))
check_quantized_results_close(outputs)
def test_dnnlowp_conv3d_int(
self,
stride,
pad,
temporal_kernels,
spatial_kernels,
dilation,
size,
group,
input_channels_per_group,
output_channels_per_group,
batch_size,
order,
gc,
dc,
):
if group > 1:
dilation = 1
kernels = (temporal_kernels,) + (spatial_kernels,) * 2
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:
net = core.Net("test_net")
fall_back_to_NCHW = "DNNLOWP" not in engine and order == "NHWC"
if fall_back_to_NCHW:
X_nchw = nhwc2nchw(X)
W_nchw = 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
if do_quantize:
quantize = core.CreateOperator(
"Quantize", ["X"], ["X_q"], engine=engine, 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"
)
net.Proto().op.extend([int8_given_tensor_fill])
# Bias
x_q_param = hardcode_scale_zp.choose_quantization_params(
X.min(), X.max()
)
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"],
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:
# 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(net)
Y = self.ws.blobs["Y"].fetch()
if fall_back_to_NCHW:
Y = nchw2nhwc(Y)
outputs.append(Output(Y=Y, op_type=op_type, engine=engine, order=order))
check_quantized_results_close(outputs)
def test_dnnlowp_conv_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,
):
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,
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:
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine and in_quantized
do_dequantize = "DNNLOWP" in engine and out_quantized
# 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
)
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_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 = hardcode_scale_zp.choose_quantization_params(
X.min(), X.max()
)
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_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=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(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_dnnlowp_depthwise_3x3_conv(
self,
stride,
size,
group,
batch_size,
share_col_buffer,
preserve_activation_sparsity,
preserve_weight_sparsity,
relu,
gc,
dc,
):
pad = 1
kernel = 3
dilation = 1
input_channels_per_group = 1
output_channels_per_group = 1
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 = []
if relu:
op_engine_list = [("Conv", ""), ("ConvRelu", "DNNLOWP"),
("Int8ConvRelu", "DNNLOWP")]
else:
op_engine_list = [("Conv", ""), ("Conv", "DNNLOWP"),
("Int8Conv", "DNNLOWP")]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine
do_dequantize = "DNNLOWP" in engine
preserve_activation_sparsity_int = 1 if preserve_activation_sparsity else 0
preserve_weight_sparsity_int = 1 if preserve_weight_sparsity else 0
if do_quantize:
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
preserve_activation_sparsity=
preserve_activation_sparsity_int,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
conv = core.CreateOperator(
op_type,
["X_q" if do_quantize else "X", "W", "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_int,
preserve_weight_sparsity=preserve_weight_sparsity_int,
engine=engine,
group=group,
device_option=gc,
)
if do_dequantize:
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])
elif relu:
relu_op = core.CreateOperator("Relu", ["Y"], ["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([relu_op])
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 test_dnnlowp_depthwise_3x3x3_conv(
self,
stride,
size,
group,
batch_size,
fuse_relu,
share_col_buffer,
preserve_activation_sparsity,
preserve_weight_sparsity,
gc,
dc,
):
pad = 1
kernel = 3
dilation = 1
input_channels_per_group = 1
output_channels_per_group = 1
order = "NHWC"
X, W, b = generate_convnd_inputs(
(stride, ) * 3,
(pad, ) * 3,
(kernel, ) * 3,
(dilation, ) * 3,
(size, ) * 3,
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 = "ConvRelu" if fuse_relu else "Conv"
op_engine_list = [(op, ""), (op, "DNNLOWP"), ("Int8" + op, "DNNLOWP")]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
fall_back_to_NCHW = "DNNLOWP" not in engine
if fall_back_to_NCHW:
X_nchw = nhwc2nchw(X)
W_nchw = nhwc2nchw(W)
do_quantize = "DNNLOWP" in engine
do_dequantize = "DNNLOWP" in engine
preserve_activation_sparsity_int = 1 if preserve_activation_sparsity else 0
preserve_weight_sparsity_int = 1 if preserve_weight_sparsity else 0
if do_quantize:
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
preserve_activation_sparsity=
preserve_activation_sparsity_int,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
conv = core.CreateOperator(
op_type,
["X_q" if do_quantize else "X", "W", "b"],
["Y_q" if do_dequantize else "Y"],
strides=[stride] * 3,
kernels=[kernel] * 3,
dilations=[dilation] * 3,
pads=[pad] * (3 * 2),
order="NCHW" if fall_back_to_NCHW else order,
shared_buffer=(1 if share_col_buffer else 0),
preserve_activation_sparsity=preserve_activation_sparsity_int,
preserve_weight_sparsity=preserve_weight_sparsity_int,
engine=engine,
group=group,
device_option=gc,
)
if do_dequantize:
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_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(net)
Y = self.ws.blobs["Y"].fetch()
if fall_back_to_NCHW:
Y = nchw2nhwc(Y)
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
check_quantized_results_close(outputs,
symmetric=preserve_activation_sparsity)
def test_groupwise_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,
share_col_buffer,
preserve_activation_sparsity,
preserve_weight_sparsity,
gc,
dc,
):
if group > 1:
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[..., 1] = W_min + 128 # "zeros"
for g in range(group):
W[g * output_channels_per_group, 0, 0, 0] = W_min
W[g * output_channels_per_group + 1, 0, 0, 0] = W_max
if not preserve_weight_sparsity:
W[
g * output_channels_per_group : (g + 1) * output_channels_per_group,
] += g
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
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])
conv = core.CreateOperator(
op_type,
["X_q" if do_quantize else "X", "W", "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,
quantize_groupwise=1,
device_option=gc,
)
if do_dequantize:
# 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"], 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 test_groupwise_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,
nbits_in_non_outlier,
share_col_buffer,
gc,
dc,
):
if group > 1:
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,
True, # group-wise
)
else:
X_min = -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
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[..., 1] = W_min + 128 # "zeros"
for g in range(group):
W[g * output_channels_per_group, 0, 0, 0] = W_min
W[g * output_channels_per_group + 1, 0, 0, 0] = W_max
W[
g * output_channels_per_group : (g + 1) * output_channels_per_group,
] += g
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
if do_quantize:
quantize = core.CreateOperator(
"Quantize", ["X"], ["X_q"], engine="DNNLOWP", device_option=gc
)
net.Proto().op.extend([quantize])
conv = core.CreateOperator(
op_type,
["X_q" if do_quantize else "X", "W", "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),
engine=engine,
group=group,
quantize_groupwise=1,
device_option=gc,
)
if do_dequantize:
# 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])
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)
def test_dnnlowp_batch_matmul_int_constant_B(self, m, n, k, C_1, C_2,
A_quantized, B_quantized,
out_quantized, gc, dc):
batch_dims = tuple(np.random.randint(3, size=max(C_1, C_2)))
batch_dims_A = batch_dims[-C_1:]
batch_dims_B = batch_dims[-C_2:]
A = np.zeros(batch_dims_A + (m, k)).astype(np.float32)
B = np.zeros(batch_dims_B + (n, k)).astype(np.float32)
if np.prod(batch_dims) > 0:
for index in np.ndindex(batch_dims_A):
# When both input and output are float, each input of the batch has
# scale 1 but with different offset, so input-wise quantization
# shouldn't have any input quantization error
# A_min = -77 if (A_quantized or out_quantized) else -77 + i
A_min = -77
A_max = A_min + 255
A[index] = np.round(np.random.rand(m, k) * 255 + A_min)
# input channels 0 and 1 are all A_min to avoid overflow from vpmaddubsw
# when multiplied with B_min and B_max
A[index][:, 0] = A_min
A[index][0, 1] = A_max
i = 0
for index in np.ndindex(batch_dims_B):
# When weight is quantized in a lazy manner, each input of the batch has
# scale 1 but with different offset, so input-wise quantization
# shouldn't have any input quantization error when weight is quantized
# in a lazy manner.
B_min = -100 if B_quantized else -100 + i
# B_min = -100
B_max = B_min + 255
B[index] = np.round(np.random.rand(n, k) * 255 + B_min)
B[index][0, 0] = B_min
B[index][1, 0] = B_max
if C_1 > C_2:
# A has more dims
for outer_index in np.ndindex(batch_dims_A[:C_1 - C_2]):
avoid_vpmaddubsw_overflow_fc(
m,
k,
n,
A[outer_index] if C_2 == 0 else A[outer_index +
index],
A_min,
A_max,
B[index],
B_min,
B_max,
)
else:
avoid_vpmaddubsw_overflow_fc(m, k, n, A[index[-C_1:]],
A_min, A_max, B[index], B_min,
B_max)
i += 1
for trans_a, trans_b in product([0, 1], [0, 1]):
Output = collections.namedtuple("Output",
["Y", "op_type", "engine"])
outputs = []
op_engine_list = [
("BatchMatMul", ""),
("BatchMatMul", "DNNLOWP"),
("Int8BatchMatMul", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
do_quantize_A = "DNNLOWP" in engine and A_quantized
do_quantize_B = "DNNLOWP" in engine and B_quantized
do_dequantize = "DNNLOWP" in engine and out_quantized
if do_quantize_A:
quantize_A = core.CreateOperator("Quantize", ["A"],
["A_q"],
engine=engine,
device_option=gc)
net.Proto().op.extend([quantize_A])
if do_quantize_B:
int8_given_tensor_fill, B_q_param = dnnlowp_utils.create_int8_given_tensor_fill(
B if trans_b else B.swapaxes(-1, -2), "B_q")
net.Proto().op.extend([int8_given_tensor_fill])
batch_matmul = core.CreateOperator(
op_type,
[
"A_q" if do_quantize_A else "A",
"B_q" if do_quantize_B else "B"
],
["Y_q" if do_dequantize else "Y"],
trans_a=trans_a,
trans_b=trans_b,
broadcast=True,
constant_B=True,
dequantize_output=not do_dequantize,
engine=engine,
device_option=gc,
)
if do_quantize_B:
# 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(
batch_matmul, outputs[0][0])
net.Proto().op.extend([batch_matmul])
if do_dequantize:
dequantize = core.CreateOperator("Dequantize", ["Y_q"],
["Y"],
engine=engine,
device_option=gc)
net.Proto().op.extend([dequantize])
self.ws.create_blob("A").feed(
A.swapaxes(-1, -2) if trans_a else A, device_option=gc)
self.ws.create_blob("B").feed(
B if trans_b else B.swapaxes(-1, -2), device_option=gc)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(),
op_type=op_type,
engine=engine))
if np.prod(batch_dims) > 0:
check_quantized_results_close(outputs)