def test_spatialbn_train_mode(self, size, input_channels, batch_size, seed,
order, epsilon, momentum, inplace, engine,
gc, dc):
# Currently HIP SpatialBN only supports NCHW
if hiputl.run_in_hip(gc, dc):
assume(order == "NCHW")
op = core.CreateOperator(
"SpatialBN",
["X", "scale", "bias", "running_mean", "running_var"],
[
"X" if inplace else "Y", "running_mean", "running_var",
"saved_mean", "saved_var"
],
order=order,
is_test=False,
epsilon=epsilon,
momentum=momentum,
engine=engine,
)
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).astype(
np.float32) - 0.5
if order == "NHWC":
X = X.swapaxes(1, 2).swapaxes(2, 3)
self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var],
[0, 1, 2, 3, 4])
def test_pooling_3d(self, stride, pad, kernel, size, input_channels,
batch_size, order, op_type, engine, gc, dc):
assume(pad < kernel)
assume(size + pad + pad >= kernel)
# Currently MIOpen Pooling only supports pooling with NCHW order.
if hiputl.run_in_hip(gc, dc) and (workspace.GetHIPVersion() < 303
or order == "NHWC"):
assume(engine != "CUDNN")
# some case here could be calculated with global pooling, but instead
# calculated with general implementation, slower but should still
# be correct.
op = core.CreateOperator(
op_type,
["X"],
["Y"],
strides=[stride] * 3,
kernels=[kernel] * 3,
pads=[pad] * 6,
order=order,
engine=engine,
)
X = np.random.rand(batch_size, size, size, size,
input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0], threshold=0.001)
def test_spatialbn_train_mode_gradient_check_1d(self, size, input_channels,
batch_size, seed, order,
epsilon, momentum, 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"],
["Y", "mean", "var", "saved_mean", "saved_var"],
order=order,
is_test=False,
epsilon=epsilon,
momentum=momentum,
engine=engine,
)
np.random.seed(seed)
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).astype(
np.float32) - 0.5
if order == "NHWC":
X = X.swapaxes(1, 2)
for input_to_check in [0, 1, 2]: # dX, dScale, dBias
self.assertGradientChecks(gc,
op, [X, scale, bias, mean, var],
input_to_check, [0],
stepsize=0.01)
def test_pooling(self, stride, pad, kernel, size,
input_channels, batch_size,
order, op_type, engine, gc, dc):
assume(pad < kernel)
if hiputl.run_in_hip(gc, dc) and engine == "CUDNN":
assume(order == "NCHW" and op_type != "LpPool")
op = core.CreateOperator(
op_type,
["X"],
["Y"],
stride=stride,
kernel=kernel,
pad=pad,
order=order,
engine=engine,
)
X = np.random.rand(
batch_size, size, size, input_channels).astype(np.float32)
if order == "NCHW":
X = X.transpose((0, 3, 1, 2))
self.assertDeviceChecks(dc, op, [X], [0])
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0])
def test_convolution_transpose_layout(
self, stride, pad, kernel, adj,
size, input_channels,
output_channels, batch_size,
engine, shared_buffer, use_bias, gc, dc):
assume(adj < stride)
X = np.random.rand(
batch_size, size, size, input_channels).astype(np.float32) - 0.5
w = np.random.rand(
input_channels, kernel, kernel, output_channels)\
.astype(np.float32) - 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
outputs = {}
for order in ["NCHW", "NHWC"]:
if hiputl.run_in_hip(gc, dc) and order == "NHWC":
# MIOPEN doesn't work with NHWC, fallback to use normal hip
tmp_engine = ""
else:
tmp_engine = engine
op = core.CreateOperator(
"ConvTranspose",
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
strides=[stride] * 2,
kernels=[kernel] * 2,
pads=[pad] * 4,
adjs=[adj] * 2,
order=order,
engine=tmp_engine,
shared_buffer=int(shared_buffer),
device_option=gc,
)
if order == "NCHW":
X_f = utils.NHWC2NCHW(X)
w_f = utils.NHWC2NCHW(w)
else:
X_f = X
w_f = w
self.assertDeviceChecks(
dc,
op,
[X_f, w_f, b] if use_bias else [X_f, w_f],
[0])
self.ws.create_blob("X").feed(X_f, device_option=gc)
self.ws.create_blob("w").feed(w_f, device_option=gc)
self.ws.create_blob("b").feed(b, device_option=gc)
self.ws.run(op)
outputs[order] = self.ws.blobs["Y"].fetch()
output_size = (size - 1) * stride + kernel + adj - 2 * pad
self.assertEqual(
outputs["NCHW"].shape,
(batch_size, output_channels, output_size, output_size))
np.testing.assert_allclose(
outputs["NCHW"],
utils.NHWC2NCHW(outputs["NHWC"]),
atol=1e-4,
rtol=1e-4)
def test_convolution_transpose_layout_legacy_args(
self, stride, pad, kernel, adj,
size, input_channels,
output_channels, batch_size,
engine, shared_buffer, use_bias, gc, dc):
assume(adj < stride)
X = np.random.rand(
batch_size, size, size, input_channels).astype(np.float32) - 0.5
w = np.random.rand(
input_channels, kernel, kernel, output_channels)\
.astype(np.float32) - 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
outputs = {}
for order in ["NCHW", "NHWC"]:
# NHWC not supported in MIOpen, run HIP
if hiputl.run_in_hip(gc, dc) and engine == "CUDNN":
tmp_engine = "CUDNN" if order == "NCHW" else ""
else:
tmp_engine = engine
op = core.CreateOperator(
"ConvTranspose",
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
stride=stride,
kernel=kernel,
pad=pad,
adj=adj,
order=order,
engine=tmp_engine,
shared_buffer=int(shared_buffer),
device_option=gc,
)
if order == "NCHW":
X_f = X.transpose((0, 3, 1, 2))
w_f = w.transpose((0, 3, 1, 2))
else:
X_f = X
w_f = w
self.assertDeviceChecks(
dc,
op,
[X_f, w_f, b] if use_bias else [X_f, w_f],
[0])
self.ws.create_blob("X").feed(X_f, device_option=gc)
self.ws.create_blob("w").feed(w_f, device_option=gc)
self.ws.create_blob("b").feed(b, device_option=gc)
self.ws.run(op)
outputs[order] = self.ws.blobs["Y"].fetch()
output_size = (size - 1) * stride + kernel + adj - 2 * pad
self.assertEqual(
outputs["NCHW"].shape,
(batch_size, output_channels, output_size, output_size))
np.testing.assert_allclose(
outputs["NCHW"],
outputs["NHWC"].transpose((0, 3, 1, 2)),
atol=1e-4,
rtol=1e-4)
def test_sparse_lengths_weightedsum_cpu(
self, batchsize, fptype, fp16asint, blocksize, empty_indices, gc, dc
):
if fptype != np.float32:
assume(gc.device_type == caffe2_pb2.CPU)
assume(not hiputl.run_in_hip(gc, dc))
assume(caffe2_pb2.CUDA not in {d.device_type for d in dc})
print("<test_sparse_lengths_weightedsum_cpu>")
tblsize = 300
if fptype == np.float32:
Tbl = np.random.rand(tblsize, blocksize).astype(np.float32)
atol = 1e-5
else:
if fp16asint:
Tbl = (
(10.0 * np.random.rand(tblsize, blocksize))
.round()
.astype(np.float16)
)
atol = 1e-3
else:
Tbl = np.random.rand(tblsize, blocksize).astype(np.float16)
atol = 1e-1
# array of each row length
if empty_indices:
Lengths = np.zeros(batchsize, dtype=np.int32)
else:
Lengths = np.random.randint(1, 30, size=batchsize, dtype=np.int32)
# flat indices
Indices = np.random.randint(0, tblsize, size=sum(Lengths), dtype=np.int64)
Weights = np.random.rand(sum(Lengths)).astype(np.float32)
op = core.CreateOperator(
"SparseLengthsWeightedSum", ["Tbl", "Weights", "Indices", "Lengths"], "out"
)
def sparse_lengths_weightedsum_ref(Tbl, Weights, Indices, Lengths):
rptr = np.cumsum(np.insert(Lengths, [0], [0]))
out = np.zeros((len(Lengths), blocksize))
for i in range(0, len(rptr[0:-1])):
w = Weights[rptr[i] : rptr[i + 1]]
out[i] = (Tbl[Indices[rptr[i] : rptr[i + 1]]] * w[:, np.newaxis]).sum(
axis=0
)
return [out.astype(np.float32)]
self.assertReferenceChecks(
gc,
op,
[Tbl, Weights, Indices, Lengths],
sparse_lengths_weightedsum_ref,
threshold=1e-3,
atol=atol,
)
def test_1d_convolution_nchw(self, input_channels, output_channels,
batch_size, stride, size, kernel, dilation,
pad, use_bias, engine, force_algo_fwd,
force_algo_dgrad, force_algo_wgrad, gc, dc):
if hiputl.run_in_hip(gc, dc):
# currently miopen only supports 2d conv
assume(engine != 'CUDNN') # CUDNN is aliased to MIOPEN for HIP
self._nd_convolution_nchw(1, input_channels, output_channels,
batch_size, stride, size, kernel, dilation,
pad, use_bias, engine, force_algo_fwd,
force_algo_dgrad, force_algo_wgrad, gc, dc)
def test_max_pool_grad(self, op_type, dim, N, C, D, H, W, kernel, stride,
pad, order, engine, gc, dc):
assume(pad < kernel)
assume(dim > 1 or engine == "")
if hiputl.run_in_hip(gc, dc):
if dim != 2:
assume(engine != "CUDNN")
elif engine == "CUDNN":
assume(order == "NCHW")
if op_type.endswith("ND"):
op_type = op_type.replace("N", str(dim))
op = core.CreateOperator(
op_type,
["X"],
["Y"],
kernels=[kernel] * dim,
strides=[stride] * dim,
pads=[pad] * dim * 2,
order=order,
engine=engine,
)
if dim == 1:
size = W
dims = [N, C, W]
axes = [0, 2, 1]
elif dim == 2:
size = H * W
dims = [N, C, H, W]
axes = [0, 2, 3, 1]
else:
size = D * H * W
dims = [N, C, D, H, W]
axes = [0, 2, 3, 4, 1]
X = np.zeros((N * C, size)).astype(np.float32)
for i in range(N * C):
X[i, :] = np.arange(size, dtype=np.float32) / size
np.random.shuffle(X[i, :])
X = X.reshape(dims)
if order == "NHWC":
X = np.transpose(X, axes)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc,
op, [X],
0, [0],
threshold=0.05,
stepsize=0.005)
def test_group_convolution(self, stride, pad, kernel, size, group,
input_channels_per_group,
output_channels_per_group, batch_size, order,
engine, use_bias, gc, dc):
assume(size >= kernel)
if hiputl.run_in_hip(gc, dc):
if order == "NHWC":
assume(group == 1 and engine != "CUDNN")
else:
# TODO: Group conv in NHWC not implemented for GPU yet.
assume(group == 1 or order == "NCHW"
or gc.device_type == caffe2_pb2.CPU)
if group != 1 and order == "NHWC":
dc = [d for d in dc if d.device_type == caffe2_pb2.CPU]
# Group conv not implemented with EIGEN engine.
assume(group == 1 or engine != "EIGEN")
input_channels = input_channels_per_group * group
output_channels = output_channels_per_group * group
op = core.CreateOperator(
"Conv",
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
stride=stride,
kernel=kernel,
pad=pad,
order=order,
engine=engine,
group=group,
)
X = np.random.rand(batch_size, size, size, input_channels).astype(
np.float32) - 0.5
w = np.random.rand(
output_channels, kernel, kernel,
input_channels_per_group).astype(np.float32)\
- 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NCHW":
X = utils.NHWC2NCHW(X)
w = utils.NHWC2NCHW(w)
inputs = [X, w, b] if use_bias else [X, w]
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
def test_1d_convolution(
self,
input_channels,
output_channels,
batch_size,
stride,
size,
kernel,
dilation,
pad,
group,
order,
use_bias,
engine,
force_algo_fwd,
force_algo_dgrad,
force_algo_wgrad,
gc,
dc,
):
if hiputl.run_in_hip(gc, dc):
# currently miopen only supports 2d conv
assume(engine != "CUDNN") # CUDNN is aliased to MIOPEN for HIP
# TODO: 1D conv in NHWC not implemented for GPU yet.
assume(order == "NCHW" or gc.device_type == caffe2_pb2.CPU)
if order == "NHWC":
dc = [d for d in dc if d.device_type == caffe2_pb2.CPU]
self._nd_convolution(
1,
input_channels,
output_channels,
batch_size,
stride,
size,
kernel,
dilation,
pad,
group,
order,
use_bias,
engine,
force_algo_fwd,
force_algo_dgrad,
force_algo_wgrad,
gc,
dc,
)
def test_convolution_transpose_with_group(self, stride, pad, kernel, adj,
size, input_channels,
output_channels, batch_size,
group, order, engine,
shared_buffer, use_bias, gc, dc):
assume(adj < stride)
# TODO: Group conv_transpose in NHWC not implemented for GPU yet.
assume(group == 1 or order == "NCHW"
or gc.device_type == caffe2_pb2.CPU)
if group != 1 and order == "NHWC":
dc = [d for d in dc if d.device_type == caffe2_pb2.CPU]
if hiputl.run_in_hip(gc, dc) and order == "NHWC":
engine = ""
op = core.CreateOperator(
"ConvTranspose",
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
stride=stride,
kernel=kernel,
pad=pad,
adj=adj,
group=group,
order=order,
engine=engine,
shared_buffer=int(shared_buffer),
device_option=gc,
)
input_channels *= group
output_channels *= group
X = np.random.rand(batch_size, size, size, input_channels).astype(
np.float32) - 0.5
w = np.random.rand(
input_channels, kernel, kernel, int(output_channels / group)) \
.astype(np.float32) - 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NCHW":
X = utils.NHWC2NCHW(X)
w = utils.NHWC2NCHW(w)
inputs = [X, w, b] if use_bias else [X, w]
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
def test_convolution_transpose_gradients(self, stride, pad, kernel, adj,
size, input_channels,
output_channels, batch_size,
order, engine, use_bias,
compute_dX, gc, dc):
assume(adj < stride)
if hiputl.run_in_hip(gc, dc) and engine == "CUDNN":
assume(order == "NCHW")
X = np.random.rand(batch_size, size, size, input_channels).astype(
np.float32) - 0.5
w = np.random.rand(
input_channels, kernel, kernel, output_channels)\
.astype(np.float32) - 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
op = core.CreateOperator(
"ConvTranspose",
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
stride=stride,
kernel=kernel,
pad=pad,
adj=adj,
order=order,
engine=engine,
no_gradient_to_input=not compute_dX,
)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
w = utils.NHWC2NCHW(w)
inputs = [X, w, b] if use_bias else [X, w]
self.assertDeviceChecks(dc, op, inputs, [0])
if use_bias and compute_dX:
# w, b, X
outputs_to_check = [1, 2, 0]
elif use_bias:
# w, b
outputs_to_check = [1, 2]
elif compute_dX:
# w, X
outputs_to_check = [1, 0]
else:
# w
outputs_to_check = [1]
for i in outputs_to_check:
self.assertGradientChecks(gc, op, inputs, i, [0])
def test_avg_pool_count_include_pad(self, op_type, dim, N, C, D, H, W,
kernel, stride, pad, count_include_pad,
order, engine, gc, dc):
assume(pad < kernel)
if hiputl.run_in_hip(gc, dc):
if dim != 2:
assume(engine != "CUDNN")
elif engine == "CUDNN":
assume(order == "NCHW")
if op_type.endswith("ND"):
op_type = op_type.replace("N", str(dim))
op = core.CreateOperator(
op_type,
["X"],
["Y"],
kernels=[kernel] * dim,
strides=[stride] * dim,
pads=[pad] * dim * 2,
count_include_pad=count_include_pad,
order=order,
engine=engine,
)
if dim == 1:
dims = [N, C, W]
axes = [0, 2, 1]
elif dim == 2:
dims = [N, C, H, W]
axes = [0, 2, 3, 1]
else:
dims = [N, C, D, H, W]
axes = [0, 2, 3, 4, 1]
X = np.random.randn(*dims).astype(np.float32)
if order == "NHWC":
X = np.transpose(X, axes)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
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_global_pooling(self, size, input_channels, batch_size, order,
op_type, engine, gc, dc):
# CuDNN 5 does not support deterministic max pooling.
assume(workspace.GetCuDNNVersion() >= 6000 or op_type != "MaxPool")
if hiputl.run_in_hip(gc, dc) and engine == "CUDNN":
assume(order == "NCHW" and op_type != "LpPool")
op = core.CreateOperator(
op_type,
["X"],
["Y"],
order=order,
engine=engine,
global_pooling=True,
)
X = np.random.rand(batch_size, size, size,
input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0])
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0])
def test_global_pooling_3d(self, kernel, size, input_channels, batch_size,
order, op_type, engine, gc, dc):
# Currently MIOpen Pooling only supports 2d pooling
if hiputl.run_in_hip(gc, dc):
assume(engine != "CUDNN")
# pad and stride ignored because they will be infered in global_pooling
op = core.CreateOperator(
op_type,
["X"],
["Y"],
kernels=[kernel] * 3,
order=order,
global_pooling=True,
engine=engine,
)
X = np.random.rand(batch_size, size, size, size,
input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0], threshold=0.001)
def test_1x1_conv(self, op_type, N, G, DX, DY, H, W, use_bias, order,
force_algo_fwd, force_algo_dgrad, force_algo_wgrad, gc,
dc):
if hiputl.run_in_hip(gc, dc):
assume(order == "NCHW")
if order == "NHWC":
G = 1
C = G * DX
M = G * DY
op = core.CreateOperator(
op_type,
["X", "filter", "bias"] if use_bias else ["X", "filter"],
["Y"],
stride_h=1,
stride_w=1,
pad_t=0,
pad_l=0,
pad_b=0,
pad_r=0,
kernel=1,
order=order,
group=G,
force_algo_fwd=force_algo_fwd,
force_algo_dgrad=force_algo_dgrad,
force_algo_wgrad=force_algo_wgrad,
)
if order == "NCHW":
X = np.random.randn(N, C, H, W).astype(np.float32)
filter = np.random.randn(M, DX, 1, 1).astype(np.float32)
else:
X = np.random.randn(N, H, W, C).astype(np.float32)
filter = np.random.randn(M, 1, 1, DX).astype(np.float32)
bias = np.random.randn(M).astype(np.float32)
inputs = [X, filter, bias] if use_bias else [X, filter]
def conv_1x1_nchw_ref(X, filter, bias=None):
X = X.reshape(N, G, DX, -1)
filter = filter.reshape(G, DY, DX)
Y = np.zeros(shape=(N, G, DY, H * W), dtype=np.float32)
for i in range(N):
for j in range(G):
Y[i, j, :, :] = np.dot(filter[j, :, :], X[i, j, :, :])
Y = Y.reshape(N, M, H, W)
if bias is not None:
bias = bias.reshape(1, M, 1, 1)
Y = np.add(Y, bias)
return [Y]
def conv_1x1_nhwc_ref(X, filter, bias=None):
X = X.reshape(N, -1, G, DX)
filter = filter.reshape(G, DY, DX)
Y = np.zeros(shape=(N, H * W, G, DY), dtype=np.float32)
for i in range(N):
for j in range(G):
Y[i, :, j, :] = np.dot(X[i, :, j, :],
filter[j, :, :].transpose())
Y = Y.reshape(N, H, W, M)
if bias is not None:
bias = bias.reshape(1, 1, 1, M)
Y = np.add(Y, bias)
return [Y]
if order == "NCHW":
conv_1x1_ref = conv_1x1_nchw_ref
else:
conv_1x1_ref = conv_1x1_nhwc_ref
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=inputs,
reference=conv_1x1_ref,
)
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
def test_convolution_layout(self, op_type, stride, pad, kernel, dilation,
size, input_channels, output_channels,
batch_size, use_bias, gc, dc):
assume(size >= dilation * (kernel - 1) + 1)
X = np.random.rand(batch_size, size, size, input_channels).astype(
np.float32) - 0.5
w = np.random.rand(output_channels, kernel, kernel,
input_channels).astype(np.float32) - 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
Output = collections.namedtuple("Output", ["Y", "engine", "order"])
outputs = []
for order in ["NCHW", "NHWC"]:
engine_list = ['']
if hiputl.run_in_hip(gc, dc):
if order == 'NCHW':
engine_list.append('MIOPEN')
else:
if _cudnn_supports(dilation=(dilation > 1),
nhwc=(order == 'NHWC')):
engine_list.append('CUDNN')
for engine in engine_list:
op = core.CreateOperator(
op_type,
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
device_option=gc,
exhaustive_search=True,
)
if order == "NCHW":
X_f = X.transpose((0, 3, 1, 2))
w_f = w.transpose((0, 3, 1, 2))
else:
X_f = X
w_f = w
self.assertDeviceChecks(
dc, op, [X_f, w_f, b] if use_bias else [X_f, w_f], [0])
self.ws.create_blob("X").feed(X_f, device_option=gc)
self.ws.create_blob("w").feed(w_f, device_option=gc)
self.ws.create_blob("b").feed(b, device_option=gc)
self.ws.run(op)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(),
engine=engine,
order=order))
def canonical(o):
if o.order == "NHWC":
return o.Y.transpose((0, 3, 1, 2))
else:
return o.Y
for o in outputs:
np.testing.assert_allclose(canonical(outputs[0]),
canonical(o),
atol=1e-4,
rtol=1e-4)
def test_convolution_gradients(self, op_type, stride, pad, kernel,
dilation, size, input_channels,
output_channels, batch_size, group, order,
engine, use_bias, force_algo_fwd,
force_algo_dgrad, force_algo_wgrad, gc, dc):
if order == "NHWC" or engine == "MKLDNN":
group = 1
input_channels *= group
output_channels *= group
dkernel = dilation * (kernel - 1) + 1
if engine == 'CUDNN':
if hiputl.run_in_hip(gc, dc):
assume((order == "NCHW") and not (dilation > 1 and group > 1))
else:
assume(
_cudnn_supports(dilation=(dilation > 1),
nhwc=(order == 'NHWC'),
backward=True))
assume(engine != "MKLDNN" or use_bias is True)
op = core.CreateOperator(
op_type,
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
group=group,
order=order,
engine=engine,
force_algo_fwd=force_algo_fwd,
force_algo_dgrad=force_algo_dgrad,
force_algo_wgrad=force_algo_wgrad,
)
X = np.random.rand(batch_size, size, size, input_channels).astype(
np.float32) - 0.5
w = np.random.rand(output_channels, kernel, kernel,
int(input_channels / group)).astype(
np.float32) - 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NCHW":
X = X.transpose((0, 3, 1, 2))
w = w.transpose((0, 3, 1, 2))
inputs = [X, w, b] if use_bias else [X, w]
# Error handling path.
if size + pad + pad < dkernel or size + pad + pad < dkernel:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
try:
self.assertDeviceChecks(dc, op, inputs, [0])
except RuntimeError as e:
es = str(e)
# CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM should always have
# implementation
if "status == CUDNN_STATUS_SUCCESS" not in es \
or "CUDNN_STATUS_NOT_SUPPORTED" not in es \
or force_algo_fwd == 0:
raise e
for i in range(len(inputs)):
try:
self.assertGradientChecks(gc, op, inputs, i, [0])
except RuntimeError as e:
es = str(e)
if "status == CUDNN_STATUS_SUCCESS" not in es \
or "CUDNN_STATUS_NOT_SUPPORTED" not in es:
raise e
def test_spatialbn_train_mode(self, size, input_channels, batch_size, seed,
order, epsilon, momentum, inplace, engine,
gc, dc):
# Currently HIP SpatialBN only supports NCHW
if hiputl.run_in_hip(gc, dc):
assume(order == "NCHW")
assume(batch_size == 0 or batch_size * size * size > 1)
op = core.CreateOperator(
"SpatialBN",
["X", "scale", "bias", "running_mean", "running_var"],
[
"X" if inplace else "Y", "running_mean", "running_var",
"saved_mean", "saved_var"
],
order=order,
is_test=False,
epsilon=epsilon,
momentum=momentum,
engine=engine,
)
np.random.seed(1701)
scale = np.random.randn(input_channels).astype(np.float32)
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.randn(batch_size, input_channels, size,
size).astype(np.float32)
if order == "NHWC":
X = np.transpose(X, (0, 2, 3, 1))
def batch_norm_ref(X, scale, bias, running_mean, running_var):
if batch_size == 0:
Y = np.zeros(X.shape)
saved_mean = np.zeros(running_mean.shape)
saved_var = np.zeros(running_var.shape)
return (Y, running_mean, running_var, saved_mean, saved_var)
if order == "NHWC":
X = np.transpose(X, (0, 3, 1, 2))
C = X.shape[1]
reduce_size = batch_size * size * size
saved_mean = np.mean(X, (0, 2, 3))
saved_var = np.var(X, (0, 2, 3))
running_mean = momentum * running_mean + (1.0 -
momentum) * saved_mean
running_var = momentum * running_var + (1.0 - momentum) * (
reduce_size / (reduce_size - 1)) * saved_var
std = np.sqrt(saved_var + epsilon)
broadcast_shape = (1, C, 1, 1)
Y = (X - np.reshape(saved_mean, broadcast_shape)) / np.reshape(
std, broadcast_shape) * np.reshape(
scale, broadcast_shape) + np.reshape(
bias, broadcast_shape)
if order == "NHWC":
Y = np.transpose(Y, (0, 2, 3, 1))
return (Y, running_mean, running_var, saved_mean, 1.0 / std)
self.assertReferenceChecks(gc, op, [X, scale, bias, mean, var],
batch_norm_ref)
self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var],
[0, 1, 2, 3, 4])
def test_sparse_lengths_sum_cpu(
self,
batchsize,
fptype,
fp16asint,
blocksize,
normalize_by_lengths,
empty_indices,
gc,
dc,
):
if fptype != np.float32:
assume(gc.device_type == caffe2_pb2.CPU)
assume(not hiputl.run_in_hip(gc, dc))
assume(caffe2_pb2.CUDA not in {d.device_type for d in dc})
if normalize_by_lengths:
print("<test_sparse_lengths_sum_mean_cpu>")
else:
print("<test_sparse_lengths_sum_cpu>")
tblsize = 300
if fptype == np.float32:
Tbl = np.random.rand(tblsize, blocksize).astype(np.float32)
atol = 1e-5
else:
if fp16asint:
Tbl = ((10.0 *
np.random.rand(tblsize, blocksize)).round().astype(
np.float16))
atol = 1e-3
else:
Tbl = np.random.rand(tblsize, blocksize).astype(np.float16)
atol = 1e-1
# array of each row length
if empty_indices:
Lengths = np.zeros(batchsize, dtype=np.int32)
else:
Lengths = np.random.randint(1, 30, size=batchsize, dtype=np.int32)
# flat indices
Indices = np.random.randint(0,
tblsize,
size=sum(Lengths),
dtype=np.int64)
op = core.CreateOperator(
"SparseLengths" + ("Mean" if normalize_by_lengths else "Sum"),
["Tbl", "Indices", "Lengths"],
"out",
)
def sparse_lengths_sum_ref(Tbl, Indices, Lengths):
rptr = np.cumsum(np.insert(Lengths, [0], [0]))
out = np.zeros((len(Lengths), blocksize))
if normalize_by_lengths:
for i in range(0, len(rptr[0:-1])):
if Lengths[i] != 0:
out[i] = (
Tbl[Indices[rptr[i]:rptr[i + 1]]].sum(axis=0) *
1.0 / float(Lengths[i]))
else:
for i in range(0, len(rptr[0:-1])):
out[i] = Tbl[Indices[rptr[i]:rptr[i + 1]]].sum(axis=0)
return [out.astype(np.float32)]
self.assertReferenceChecks(
gc,
op,
[Tbl, Indices, Lengths],
sparse_lengths_sum_ref,
threshold=1e-3,
atol=atol,
)
def test_convolution_gradients(
self,
op_type,
stride,
pad,
kernel,
dilation,
size,
input_channels,
output_channels,
batch_size,
group,
order,
engine,
use_bias,
force_algo_fwd,
force_algo_dgrad,
force_algo_wgrad,
gc,
dc,
):
# TODO: Group conv in NHWC not implemented for GPU yet.
assume(
group == 1
or (order == "NCHW" or gc.device_type == caffe2_pb2.CPU)
and engine != "MKLDNN"
)
if group != 1 and order == "NHWC":
dc = [d for d in dc if d.device_type == caffe2_pb2.CPU]
input_channels *= group
output_channels *= group
dkernel = dilation * (kernel - 1) + 1
if engine == "CUDNN":
if hiputl.run_in_hip(gc, dc):
assume((order == "NCHW") and not (dilation > 1 and group > 1))
else:
assume(
_cudnn_supports(
dilation=(dilation > 1), nhwc=(order == "NHWC"), backward=True
)
)
assume(engine != "MKLDNN" or use_bias is True)
op = core.CreateOperator(
op_type,
["X", "w", "b"] if use_bias else ["X", "w"],
["Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
group=group,
order=order,
engine=engine,
force_algo_fwd=force_algo_fwd,
force_algo_dgrad=force_algo_dgrad,
force_algo_wgrad=force_algo_wgrad,
)
X = (
np.random.rand(batch_size, size, size, input_channels).astype(np.float32)
- 0.5
)
w = (
np.random.rand(
output_channels, kernel, kernel, int(input_channels / group)
).astype(np.float32)
- 0.5
)
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NCHW":
X = utils.NHWC2NCHW(X)
w = utils.NHWC2NCHW(w)
inputs = [X, w, b] if use_bias else [X, w]
# Error handling path.
if size + pad + pad < dkernel or size + pad + pad < dkernel:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
try:
self.assertDeviceChecks(dc, op, inputs, [0])
except RuntimeError as e:
es = str(e)
# CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM should always have
# implementation
if (
"status == CUDNN_STATUS_SUCCESS" not in es
or "CUDNN_STATUS_NOT_SUPPORTED" not in es
or force_algo_fwd == 0
):
raise e
for i in range(len(inputs)):
try:
self.assertGradientChecks(gc, op, inputs, i, [0])
except RuntimeError as e:
es = str(e)
if (
"status == CUDNN_STATUS_SUCCESS" not in es
or "CUDNN_STATUS_NOT_SUPPORTED" not in es
):
raise e
