def _nd_convolution(
self,
n,
input_channels,
output_channels,
batch_size,
stride,
size,
kernel,
dilation,
pad,
order,
use_bias,
engine,
force_algo_fwd,
force_algo_dgrad,
force_algo_wgrad,
gc,
dc,
):
dkernel = dilation * (kernel - 1) + 1
for op_type in ["Conv", "Conv" + str(n) + "D"]:
op = core.CreateOperator(
op_type,
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
strides=[stride] * n,
kernels=[kernel] * n,
dilations=[dilation] * n,
pads=[pad] * n * 2,
order=order,
engine=engine,
force_algo_fwd=force_algo_fwd,
force_algo_dgrad=force_algo_dgrad,
force_algo_wgrad=force_algo_wgrad,
)
input_dims = [batch_size, input_channels]
input_dims.extend([size] * n)
filter_dims = [output_channels, input_channels]
filter_dims.extend([kernel] * n)
X = np.random.rand(*input_dims).astype(np.float32) - 0.5
w = np.random.rand(*filter_dims).astype(np.float32) - 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NHWC":
X = utils.NCHW2NHWC(X)
w = utils.NCHW2NHWC(w)
inputs = [X, w, b] if use_bias else [X, w]
if size + pad + pad < dkernel or size + pad + pad < dkernel:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
def _conv_2d_shuffle_offsets(
batch_size,
kernel,
dims,
num_deformable_group,
input_channels,
output_channels
):
o = []
w0 = [[0 for x in range(kernel)] for y in range(kernel)]
for y0 in range(0, kernel):
for x0 in range(0, kernel):
x = np.random.randint(0, kernel)
y = np.random.randint(0, kernel)
o.append(y - y0)
o.append(x - x0)
w0[y][x] += 1
o = o * num_deformable_group
e = []
for v in o:
e.append([[v] * int(dims[1])] * int(dims[0]))
w0 = [[w0] * input_channels] * output_channels
return (
np.array([e] * batch_size).astype(np.float32),
utils.NCHW2NHWC(np.array(w0).astype(np.float32))
)
def test_instance_norm_model_helper(self, N, C, H, W, order, epsilon, seed,
is_test):
np.random.seed(seed)
model = model_helper.ModelHelper(name="test_model")
brew.instance_norm(model,
'input',
'output',
C,
epsilon=epsilon,
order=order,
is_test=is_test)
input_blob = np.random.rand(N, C, H, W).astype(np.float32)
if order == 'NHWC':
input_blob = utils.NCHW2NHWC(input_blob)
self.ws.create_blob('input').feed(input_blob)
self.ws.create_net(model.param_init_net).run()
self.ws.create_net(model.net).run()
if is_test:
scale = self.ws.blobs['output_s'].fetch()
assert scale is not None
assert scale.shape == (C, )
bias = self.ws.blobs['output_b'].fetch()
assert bias is not None
assert bias.shape == (C, )
output_blob = self.ws.blobs['output'].fetch()
if order == 'NHWC':
output_blob = utils.NHWC2NCHW(output_blob)
assert output_blob.shape == (N, C, H, W)
def channel_shuffle_ref(X):
if order == "NHWC":
X = utils.NHWC2NCHW(X)
Y_r = X.reshape(X.shape[0], groups, X.shape[1] // groups,
X.shape[2], X.shape[3])
Y_trns = Y_r.transpose((0, 2, 1, 3, 4))
Y_reshaped = Y_trns.reshape(X.shape)
if order == "NHWC":
Y_reshaped = utils.NCHW2NHWC(Y_reshaped)
return Y_reshaped
def _get_inputs(self, N, C, H, W, order):
input_data = np.random.rand(N, C, H, W).astype(np.float32)
if order == 'NHWC':
# Allocate in the same order as NCHW and transpose to make sure
# the inputs are identical on freshly-seeded calls.
input_data = utils.NCHW2NHWC(input_data)
elif order != "NCHW":
raise Exception('unknown order type ({})'.format(order))
scale_data = np.random.rand(C).astype(np.float32)
bias_data = np.random.rand(C).astype(np.float32)
return input_data, scale_data, bias_data
def _get_inputs(self, N, C, H, W, order):
input_data = np.random.rand(N, C, H, W).astype(np.float32) - 0.5
# default step size is 0.05
input_data[np.logical_and(
input_data >= 0, input_data <= 0.051)] = 0.051
input_data[np.logical_and(
input_data <= 0, input_data >= -0.051)] = -0.051
if order == 'NHWC':
input_data = utils.NCHW2NHWC(input_data)
return input_data,
def test_channel_shuffle_fast_path(self, channels_per_group, n, gc, dc):
order = "NHWC"
groups = 4
X = np.round(
np.random.rand(n, channels_per_group * groups, 5, 6) * 255).astype(
np.float32)
if n != 0:
X[0, 0, 0, 0] = 0
X[0, 0, 0, 1] = 255
X = utils.NCHW2NHWC(X)
net = core.Net("test_net")
quantize = core.CreateOperator("Quantize", ["X"], ["X_q"],
engine="DNNLOWP")
channel_shuffle = core.CreateOperator(
"ChannelShuffle",
["X_q"],
["Y_q"],
group=groups,
kernel=1,
order=order,
engine="DNNLOWP",
)
dequantize = core.CreateOperator("Dequantize", ["Y_q"], ["Y"],
engine="DNNLOWP")
net.Proto().op.extend([quantize, channel_shuffle, dequantize])
workspace.FeedBlob("X", X)
workspace.RunNetOnce(net)
Y = workspace.FetchBlob("Y")
def channel_shuffle_ref(X):
if order == "NHWC":
X = utils.NHWC2NCHW(X)
Y_r = X.reshape(X.shape[0], groups, X.shape[1] // groups,
X.shape[2], X.shape[3])
Y_trns = Y_r.transpose((0, 2, 1, 3, 4))
Y_reshaped = Y_trns.reshape(X.shape)
if order == "NHWC":
Y_reshaped = utils.NCHW2NHWC(Y_reshaped)
return Y_reshaped
Y_ref = channel_shuffle_ref(X)
np.testing.assert_allclose(Y, Y_ref)
def test_spatialbn_test_mode_3d(self, size, input_channels, batch_size,
seed, order, epsilon, inplace, engine, gc,
dc):
# Currently MIOPEN SpatialBN only supports 2D
if hiputl.run_in_hip(gc, dc):
assume(engine != "CUDNN")
op = core.CreateOperator(
"SpatialBN",
["X", "scale", "bias", "mean", "var"],
["X" if inplace else "Y"],
order=order,
is_test=True,
epsilon=epsilon,
engine=engine,
)
def reference_spatialbn_test(X, scale, bias, mean, var):
if order == "NCHW":
scale = scale[np.newaxis, :, np.newaxis, np.newaxis,
np.newaxis]
bias = bias[np.newaxis, :, np.newaxis, np.newaxis, np.newaxis]
mean = mean[np.newaxis, :, np.newaxis, np.newaxis, np.newaxis]
var = var[np.newaxis, :, np.newaxis, np.newaxis, np.newaxis]
return ((X - mean) / np.sqrt(var + epsilon) * scale + bias, )
np.random.seed(1701)
scale = np.random.rand(input_channels).astype(np.float32) + 0.5
bias = np.random.rand(input_channels).astype(np.float32) - 0.5
mean = np.random.randn(input_channels).astype(np.float32)
var = np.random.rand(input_channels).astype(np.float32) + 0.5
X = np.random.rand(batch_size, input_channels, size, size, size)\
.astype(np.float32) - 0.5
if order == "NHWC":
X = utils.NCHW2NHWC(X)
self.assertReferenceChecks(gc, op, [X, scale, bias, mean, var],
reference_spatialbn_test)
self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var], [0])
def test_leaky_relu_model_helper_helper(self, N, C, H, W, order, alpha,
seed):
np.random.seed(seed)
arg_scope = {'order': order}
model = model_helper.ModelHelper(name="test_model",
arg_scope=arg_scope)
model.LeakyRelu('input', 'output', alpha=alpha)
input_blob = np.random.rand(N, C, H, W).astype(np.float32)
if order == 'NHWC':
input_blob = utils.NCHW2NHWC(input_blob)
self.ws.create_blob('input').feed(input_blob)
self.ws.create_net(model.param_init_net).run()
self.ws.create_net(model.net).run()
output_blob = self.ws.blobs['output'].fetch()
if order == 'NHWC':
output_blob = utils.NHWC2NCHW(output_blob)
assert output_blob.shape == (N, C, H, W)
def ref(input_blob, scale_blob, bias_blob):
if order == 'NHWC':
input_blob = utils.NHWC2NCHW(input_blob)
mean_blob = input_blob.reshape((N, C, -1)).mean(axis=2)
inv_stdev_blob = 1.0 / \
np.sqrt(input_blob.reshape((N, C, -1)).var(axis=2) + epsilon)
# _bc indicates blobs that are reshaped for broadcast
scale_bc = scale_blob[np.newaxis, :, np.newaxis, np.newaxis]
mean_bc = mean_blob[:, :, np.newaxis, np.newaxis]
inv_stdev_bc = inv_stdev_blob[:, :, np.newaxis, np.newaxis]
bias_bc = bias_blob[np.newaxis, :, np.newaxis, np.newaxis]
normalized_blob = scale_bc * (input_blob - mean_bc) * inv_stdev_bc \
+ bias_bc
if order == 'NHWC':
normalized_blob = utils.NCHW2NHWC(normalized_blob)
if not store_mean and not store_inv_stdev:
return normalized_blob,
elif not store_inv_stdev:
return normalized_blob, mean_blob
else:
return normalized_blob, mean_blob, inv_stdev_blob
def lc_2d_nhwc(X, W, b=None):
XT = utils.NHWC2NCHW(X)
WT = np.transpose(W, [0, 1, 2, 5, 3, 4])
output = lc_2d_nchw(XT, WT, b)
return [utils.NCHW2NHWC(output[0])]
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 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 = utils.NCHW2NHWC(X)
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_min = 0 if X.size == 0 else X.min()
X_max = 0 if X.size == 0 else X.max()
X_q_param = dnnlowp_utils.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 _nd_convolution(
self,
n,
input_channels_per_group,
output_channels_per_group,
batch_size,
stride,
size,
kernel,
dilation,
pad,
group,
order,
use_bias,
engine,
force_algo_fwd,
force_algo_dgrad,
force_algo_wgrad,
gc,
dc,
):
# TODO: Group conv in NHWC not implemented for GPU yet.
# TODO: Group 1D conv in NCHW not implemented for GPU yet.
assume(
group == 1
or (n != 1 and order == "NCHW")
or gc.device_type == caffe2_pb2.CPU
)
if group != 1 and (n == 1 or order == "NHWC"):
dc = [d for d in dc if d.device_type == caffe2_pb2.CPU]
input_channels = group * input_channels_per_group
output_channels = group * output_channels_per_group
dkernel = dilation * (kernel - 1) + 1
for op_type in ["Conv", "Conv" + str(n) + "D"]:
op = core.CreateOperator(
op_type,
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
strides=[stride] * n,
kernels=[kernel] * n,
dilations=[dilation] * n,
pads=[pad] * n * 2,
group=group,
order=order,
engine=engine,
force_algo_fwd=force_algo_fwd,
force_algo_dgrad=force_algo_dgrad,
force_algo_wgrad=force_algo_wgrad,
)
input_dims = [batch_size, input_channels]
input_dims.extend([size] * n)
filter_dims = [output_channels, input_channels // group]
filter_dims.extend([kernel] * n)
X = np.random.rand(*input_dims).astype(np.float32) - 0.5
w = np.random.rand(*filter_dims).astype(np.float32) - 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NHWC":
X = utils.NCHW2NHWC(X)
w = utils.NCHW2NHWC(w)
inputs = [X, w, b] if use_bias else [X, w]
if size + pad + pad < dkernel or size + pad + pad < dkernel:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
def nchw2nhwc_ref(X):
return (utils.NCHW2NHWC(X), )
def test_dnnlowp_depthwise_3x3x3_conv(
self,
stride,
size,
group,
batch_size,
prepack_weight,
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:
init_net = core.Net("test_init_net")
net = core.Net("test_net")
# TODO: no fall back to NCHW
fall_back_to_NCHW = "DNNLOWP" not in engine
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
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_q_param = dnnlowp_utils.choose_quantization_params(
X.min(), X.max(), preserve_activation_sparsity
)
inputs = ["W"]
if do_dequantize:
inputs += ["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", "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,
preserve_weight_sparsity=preserve_weight_sparsity,
engine=engine,
group=group,
device_option=gc,
)
if do_dequantize or do_prepack_weight:
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(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, symmetric=preserve_activation_sparsity)
