def check_hybrid_static_memory(**kwargs):
x = mx.nd.random.uniform(shape=(2, 3, 32, 32))
x.attach_grad()
net1 = gluon.model_zoo.vision.get_resnet(
1, 18, pretrained=True, prefix='net_', ctx=mx.context.current_context())
net2 = gluon.model_zoo.vision.get_resnet(
1, 18, pretrained=True, prefix='net_', ctx=mx.context.current_context())
net2.hybridize(**kwargs)
net1(x)
net2(x)
def test(net, x):
with mx.autograd.record():
y = net(x) + net(x)
y.backward()
grads = {k: v.grad() for k, v in net.collect_params().items() if v.grad_req != 'null'}
return y, grads
y1, grads1 = test(net1, x)
y2, grads2 = test(net2, x)
assert_almost_equal(y1.asnumpy(), y2.asnumpy(), rtol=1e-3, atol=1e-5)
for key in grads1:
assert_almost_equal(grads1[key].asnumpy(), grads2[key].asnumpy(), rtol=1e-3, atol=1e-5)
def test_activations():
point_to_validate = mx.nd.array([-0.1, 0.1] * 3)
swish = mx.gluon.nn.Swish()
def swish_test(x):
return x * mx.nd.sigmoid(x)
for test_point, ref_point in zip(swish_test(point_to_validate), swish(point_to_validate)):
assert test_point == ref_point
elu = mx.gluon.nn.ELU()
def elu_test(x):
def elu(x):
return 1.0 * (mx.nd.exp(x) - 1) if x < 0 else x
return [elu(x_i) for x_i in x]
for test_point, ref_point in zip(elu_test(point_to_validate), elu(point_to_validate)):
assert test_point == ref_point
selu = mx.gluon.nn.SELU()
def selu_test(x):
def selu(x):
scale, alpha = 1.0507009873554804934193349852946, 1.6732632423543772848170429916717
return scale * x if x >= 0 else alpha * mx.nd.exp(x) - alpha
return [selu(x_i) for x_i in x]
for test_point, ref_point in zip(selu(point_to_validate), selu(point_to_validate)):
assert test_point == ref_point
prelu = mx.gluon.nn.PReLU()
prelu.initialize()
x = point_to_validate.reshape((1, 3, 2))
assert_almost_equal(prelu(x).asnumpy(), mx.nd.where(x >= 0, x, 0.25 * x).asnumpy())
def check_quantized_pooling(data_shape, kernel, pool_type, pad, stride, global_pool):
with mx.Context('gpu', 0):
data = mx.sym.Variable(name='data', shape=data_shape, dtype='float32')
pooling_fp32 = mx.sym.Pooling(data=data, kernel=kernel, pad=pad, stride=stride,
pool_type=pool_type, global_pool=global_pool, cudnn_off=False)
arg_shapes, _, _ = pooling_fp32.infer_shape(data=data_shape)
arg_names = pooling_fp32.list_arguments()
pooling_fp32_exe = pooling_fp32.simple_bind(ctx=mx.current_context(), grad_req='null')
pooling_fp32_exe.arg_dict[arg_names[0]][:] = mx.nd.random.uniform(low=-127.0, high=127.0,
shape=data_shape).astype('int32')
output = pooling_fp32_exe.forward()[0]
qdata = mx.sym.Variable(name='qdata', shape=data_shape, dtype='int8')
min_data = mx.sym.Variable(name='min_data')
max_data = mx.sym.Variable(name='max_data')
quantized_pooling = mx.sym.contrib.quantized_pooling(data=qdata, min_data=min_data,
max_data=max_data, kernel=kernel,
pad=pad, stride=stride, pool_type=pool_type,
global_pool=global_pool)
pooling_int8_exe = quantized_pooling.simple_bind(ctx=mx.current_context(), grad_req='null')
qarg_names = quantized_pooling.list_arguments()
pooling_int8_exe.arg_dict[qarg_names[0]][:] = pooling_fp32_exe.arg_dict[arg_names[0]].astype('int8')
quantized_range = 127.0
pooling_int8_exe.arg_dict[qarg_names[1]][:] = -quantized_range
pooling_int8_exe.arg_dict[qarg_names[2]][:] = quantized_range
qoutput, min_range, max_range = pooling_int8_exe.forward()
if pool_type == 'max':
assert_almost_equal(output.asnumpy(), qoutput.asnumpy())
elif pool_type == 'avg': # for avg pooling, fp32 and int8 may be different due to rounding errors
diff = mx.nd.abs(output - qoutput.astype(output.dtype))
cond = mx.nd.lesser(2, diff).sum().asscalar()
assert cond == 0
def check_quantized_conv(data_shape, kernel, num_filter, pad, stride, no_bias):
with mx.Context('gpu', 0):
# run fp32 conv
data = mx.sym.Variable(name='data', shape=data_shape, dtype='float32')
conv2d = mx.sym.Convolution(data=data, kernel=kernel, num_filter=num_filter, pad=pad, stride=stride,
no_bias=no_bias, cudnn_off=False, name='conv2d')
arg_shapes, _, _ = conv2d.infer_shape(data=data_shape)
arg_names = conv2d.list_arguments()
conv_exe_fp32 = conv2d.simple_bind(ctx=mx.current_context(), grad_req='null')
conv_exe_fp32.arg_dict[arg_names[0]][:] = mx.nd.random.uniform(low=-127.0, high=127.0,
shape=data_shape).astype('int32')
conv_exe_fp32.arg_dict[arg_names[1]][:] = mx.nd.random.uniform(low=-127.0, high=127.0,
shape=arg_shapes[1]).astype('int32')
if not no_bias:
conv_exe_fp32.arg_dict[arg_names[2]][:] = mx.nd.random.uniform(low=-127.0, high=127.0,
shape=arg_shapes[2]).astype('int32')
output = conv_exe_fp32.forward()[0]
# run quantized conv
qdata = mx.sym.Variable(name='qdata', shape=data_shape, dtype='int8')
qweight = mx.sym.Variable(name='qweight', dtype='int8')
min_data = mx.sym.Variable(name='min_data')
max_data = mx.sym.Variable(name='max_data')
min_weight = mx.sym.Variable(name='min_weight')
max_weight = mx.sym.Variable(name='max_weight')
quantized_conv2d = mx.sym.contrib.quantized_conv(data=qdata, weight=qweight, min_data=min_data,
max_data=max_data, min_weight=min_weight,
max_weight=max_weight, kernel=kernel,
num_filter=num_filter, pad=pad, stride=stride,
no_bias=no_bias)
qarg_names = quantized_conv2d.list_arguments()
type_dict = None
if not no_bias:
type_dict = {qarg_names[2]: 'int8'}
conv_exe_int8 = quantized_conv2d.simple_bind(ctx=mx.current_context(), type_dict=type_dict, grad_req='null')
conv_exe_int8.arg_dict[qarg_names[0]][:] = conv_exe_fp32.arg_dict[arg_names[0]].astype('int8')
conv_exe_int8.arg_dict[qarg_names[1]][:] = conv_exe_fp32.arg_dict[arg_names[1]].astype('int8')
quantized_range = 127.0
if no_bias:
conv_exe_int8.arg_dict[qarg_names[2]][:] = -quantized_range
conv_exe_int8.arg_dict[qarg_names[3]][:] = quantized_range
conv_exe_int8.arg_dict[qarg_names[4]][:] = -quantized_range
conv_exe_int8.arg_dict[qarg_names[5]][:] = quantized_range
else:
conv_exe_int8.arg_dict[qarg_names[2]][:] = conv_exe_fp32.arg_dict[arg_names[2]].astype('int8')
conv_exe_int8.arg_dict[qarg_names[3]][:] = -quantized_range
conv_exe_int8.arg_dict[qarg_names[4]][:] = quantized_range
conv_exe_int8.arg_dict[qarg_names[5]][:] = -quantized_range
conv_exe_int8.arg_dict[qarg_names[6]][:] = quantized_range
conv_exe_int8.arg_dict[qarg_names[7]][:] = -quantized_range
conv_exe_int8.arg_dict[qarg_names[8]][:] = quantized_range
qoutput, min_range, max_range = conv_exe_int8.forward()
if no_bias:
assert_almost_equal(output.asnumpy(), qoutput.asnumpy())
else:
# with adding bias, accuracy loss should not be greater than one
diff = mx.nd.abs(output - qoutput.astype(output.dtype))
cond = mx.nd.lesser(2, diff).sum().asscalar()
assert cond == 0
def check_rsp_pull(kv, ctxs, sparse_pull, is_same_rowid=False, use_slice=False):
count = len(ctxs)
num_rows = shape[0]
row_ids = []
all_row_ids = np.arange(num_rows)
vals = [mx.nd.sparse.zeros(shape=shape, ctx=ctxs[i], stype='row_sparse') for i in range(count)]
if is_same_rowid:
row_id = np.random.randint(num_rows, size=num_rows)
row_ids = [mx.nd.array(row_id)] * count
elif use_slice:
total_row_ids = mx.nd.array(np.random.randint(num_rows, size=count*num_rows))
row_ids = [total_row_ids[i*num_rows : (i+1)*num_rows] for i in range(count)]
else:
for i in range(count):
row_id = np.random.randint(num_rows, size=num_rows)
row_ids.append(mx.nd.array(row_id))
row_ids_to_pull = row_ids[0] if (len(row_ids) == 1 or is_same_rowid) else row_ids
vals_to_pull = vals[0] if len(vals) == 1 else vals
kv.row_sparse_pull('e', out=vals_to_pull, row_ids=row_ids_to_pull)
for val, row_id in zip(vals, row_ids):
retained = val.asnumpy()
excluded_row_ids = np.setdiff1d(all_row_ids, row_id.asnumpy())
for row in range(num_rows):
expected_val = np.zeros_like(retained[row])
expected_val += 0 if row in excluded_row_ids else 2
assert_almost_equal(retained[row], expected_val)
if sparse_pull is True:
kv.pull('e', out=vals_to_pull, ignore_sparse=False)
for val in vals:
retained = val.asnumpy()
expected_val = np.zeros_like(retained)
expected_val[:] = 2
assert_almost_equal(retained, expected_val)
def check_compr_random(kv, threshold, nworker):
# set a seed so all workers generate same data. knowing this helps
# calculate expected value after pull
mx.random.seed(123)
rnd.seed(123)
nrepeat = 5
compr_random_keys_shapes = [('2121', shape),('212221',irregular_shape),('21221', big_shape)]
# use new keys so residual is 0 for calculation of expected
for k,s in compr_random_keys_shapes:
kv.init(k, mx.nd.zeros(s))
for k,s in compr_random_keys_shapes:
curr_residual = np.zeros(s)
for l in range(nrepeat):
orig_val = mx.nd.zeros(s)
kv.pull(k, orig_val)
grad = mx.nd.array(rnd.rand(s[0], s[1]))
# creates a copy because push changes grad because of assignment
grad_cpy = mx.nd.array(grad)
kv.push(k, grad)
val = mx.nd.zeros(s)
kv.pull(k, val)
diff = val - orig_val
# compute expected by using simulation of operator
compr, curr_residual, decompr = compute_expected_2bit_quantization(grad_cpy, curr_residual, threshold)
decompr *= nworker * rate
assert_almost_equal(diff.asnumpy(), decompr)
def test_req():
data = mx.nd.random.uniform(shape=(1,3,224,224))
label = mx.nd.random.uniform(shape=(1))
label[:] = 1
loss = gluon.loss.SoftmaxCrossEntropyLoss()
net = nn.HybridSequential()
net1 = nn.HybridSequential()
net1.add(nn.Dense(4))
net2 = nn.HybridSequential()
net2.add(nn.Dense(3))
net2.add(nn.Dense(2))
net.add(net1)
net.add(net2)
net.initialize()
net.hybridize()
for v in net.collect_params().values():
v.grad_req = 'add'
net.collect_params().zero_grad()
with mx.autograd.record():
pred = net(data)
l = loss(pred, label)
l.backward()
grad = net[0][0].weight.grad().mean().asnumpy()
# run twice to check req = add
pred = net(data)
l = loss(pred, label)
l.backward()
grad_double = net[0][0].weight.grad().mean().asnumpy()
assert_almost_equal(grad * 2, grad_double)
def test_gnmt_encoder():
ctx = mx.Context.default_ctx
for cell_type in ["lstm", "gru", "relu_rnn", "tanh_rnn"]:
for num_layers, num_bi_layers in [(2, 1), (3, 0)]:
for use_residual in [False, True]:
encoder = GNMTEncoder(cell_type=cell_type, num_layers=num_layers,
num_bi_layers=num_bi_layers, hidden_size=8,
dropout=0.0, use_residual=use_residual,
prefix='gnmt_encoder_')
encoder.initialize(ctx=ctx)
encoder.hybridize()
for batch_size in [4]:
for seq_length in [5, 10]:
inputs_nd = mx.nd.random.normal(0, 1, shape=(batch_size, seq_length, 4), ctx=ctx)
valid_length_nd = mx.nd.array(np.random.randint(1, seq_length,
size=(batch_size,)), ctx=ctx)
encoder_outputs, _ = encoder(inputs_nd, valid_length=valid_length_nd)
valid_length_npy = valid_length_nd.asnumpy()
rnn_output = encoder_outputs[0].asnumpy()
for i in range(batch_size):
if valid_length_npy[i] < seq_length - 1:
padded_out = rnn_output[i, int(valid_length_npy[i]):, :]
assert_almost_equal(padded_out, np.zeros_like(padded_out), 1E-6, 1E-6)
assert(encoder_outputs[0].shape == (batch_size, seq_length, 8))
assert(len(encoder_outputs[1]) == num_layers)
def pull_init_test(kv):
# checks that compression is not applied to init of key
out = [mx.nd.zeros(shapes[0], mx.gpu(g)) for g in range(nworker)]
kv.pull(gc_init_test_key, out=out)
exp = np.ones_like(out[0].asnumpy())
for o in out:
assert_almost_equal(o.asnumpy(), exp)
def check_with_uniform(uf, arg_shapes, dim=None, npuf=None, rmin=-10, type_list=[np.float32]):
"""check function consistency with uniform random numbers"""
if isinstance(arg_shapes, int):
assert dim
shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim))
arg_shapes = [shape] * arg_shapes
for dtype in type_list:
ndarray_arg = []
numpy_arg = []
for s in arg_shapes:
npy = np.random.uniform(rmin, 10, s).astype(dtype)
narr = mx.nd.array(npy, dtype=dtype)
ndarray_arg.append(narr)
numpy_arg.append(npy)
out1 = uf(*ndarray_arg)
if npuf is None:
out2 = uf(*numpy_arg).astype(dtype)
else:
out2 = npuf(*numpy_arg).astype(dtype)
assert out1.shape == out2.shape
if isinstance(out1, mx.nd.NDArray):
out1 = out1.asnumpy()
if dtype == np.float16:
assert_almost_equal(out1, out2, rtol=2e-3)
else:
assert_almost_equal(out1, out2)
def _check_subgraph_exe1(sym, subgraph_backend, op_names):
"""Use the partitioned sym to simple_bind an executor and compare the outputs
with those of the original executor"""
out = SymbolHandle()
check_call(_LIB.MXBuildSubgraphByOpNames(sym.handle, c_str(subgraph_backend), mx_uint(len(op_names)),
c_str_array(op_names), ctypes.byref(out)))
partitioned_sym = Symbol(out)
assert partitioned_sym.list_inputs() == sym.list_inputs()
assert partitioned_sym.list_arguments() == sym.list_arguments()
assert partitioned_sym.list_auxiliary_states() == sym.list_auxiliary_states()
exe = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
partitioned_exe = partitioned_sym.simple_bind(ctx=mx.current_context(), grad_req='null')
input_names = sym.list_inputs()
for name in input_names:
if name in exe.arg_dict:
exe.arg_dict[name][:] = mx.nd.random.uniform(shape=exe.arg_dict[name].shape)
partitioned_exe.arg_dict[name][:] = exe.arg_dict[name]
else:
assert name in exe.aux_dict
exe.aux_dict[name][:] = mx.nd.random.uniform(shape=exe.aux_dict[name].shape)
partitioned_exe.aux_dict[name][:] = exe.aux_dict[name]
exe.forward()
partitioned_exe.forward()
assert len(exe.outputs) == len(partitioned_exe.outputs)
for i in range(len(exe.outputs)):
assert_almost_equal((exe.outputs[i] - partitioned_exe.outputs[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
def _check_subgraph_exe4(sym, subgraph_backend, op_names):
"""Use env var MXNET_SUBGRAPH_BACKEND=default to trigger graph partitioning in bind
and compare results of the partitioned sym and the original sym."""
def get_executor(sym, subgraph_backend=None, op_names=None, original_exec=None):
if subgraph_backend is not None:
os.environ['MXNET_SUBGRAPH_BACKEND'] = subgraph_backend
check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend), mx_uint(len(op_names)),
c_str_array(op_names)))
arg_shapes, _, aux_shapes = sym.infer_shape()
if subgraph_backend is None:
arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
else:
arg_array = None
aux_array = None
exe = sym.bind(ctx=mx.current_context(),
args=arg_array if subgraph_backend is None else original_exec.arg_arrays,
aux_states=aux_array if subgraph_backend is None else original_exec.aux_arrays,
grad_req='null')
exe.forward()
if subgraph_backend is not None:
check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
del os.environ['MXNET_SUBGRAPH_BACKEND']
return exe
original_exec = get_executor(sym)
partitioned_exec = get_executor(sym, subgraph_backend, op_names, original_exec)
outputs1 = original_exec.outputs
outputs2 = partitioned_exec.outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
def _check_subgraph_exe2(sym, subgraph_backend, op_names):
"""Use env var MXNET_SUBGRAPH_BACKEND=default to trigger graph partitioning in simple_bind
and compare results of the partitioned sym and the original sym."""
def get_executor(sym, subgraph_backend=None, op_names=None, original_exec=None):
if subgraph_backend is not None:
os.environ['MXNET_SUBGRAPH_BACKEND'] = subgraph_backend
check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend), mx_uint(len(op_names)),
c_str_array(op_names)))
exe = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
input_names = sym.list_inputs()
for name in input_names:
if name in exe.arg_dict:
exe.arg_dict[name][:] = mx.nd.random.uniform(shape=exe.arg_dict[name].shape)\
if original_exec is None else original_exec.arg_dict[name]
else:
assert name in exe.aux_dict
exe.aux_dict[name][:] = mx.nd.random.uniform(shape=exe.aux_dict[name].shape)\
if original_exec is None else original_exec.aux_dict[name]
exe.forward()
if subgraph_backend is not None:
check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
del os.environ['MXNET_SUBGRAPH_BACKEND']
return exe
original_exec = get_executor(sym)
partitioned_exec = get_executor(sym, subgraph_backend, op_names, original_exec)
outputs1 = original_exec.outputs
outputs2 = partitioned_exec.outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
def test_bce_loss_with_pos_weight():
# Suppose it's a multi-label classification
N = np.random.randint(5, 30)
data = mx.nd.random.uniform(-1, 1, shape=(N, 20))
label = mx.nd.array(np.random.randint(2, size=(N, 5)), dtype='float32')
pos_weight = mx.nd.random.uniform(0, 10, shape=(1, 5))
pos_weight = mx.nd.repeat(pos_weight, repeats=N, axis=0)
data_iter = mx.io.NDArrayIter(data, {'label': label, 'pos_w': pos_weight}, batch_size=10, label_name='label')
output = get_net(5)
l = mx.symbol.Variable('label')
pos_w = mx.symbol.Variable('pos_w')
Loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
loss = Loss(output, l, None, pos_w)
loss = mx.sym.make_loss(loss)
mod = mx.mod.Module(loss, data_names=('data',), label_names=('label', 'pos_w'))
mod.fit(data_iter, num_epoch=200, optimizer_params={'learning_rate': 0.01},
eval_metric=mx.metric.Loss(), optimizer='adam',
initializer=mx.init.Xavier(magnitude=2))
assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 0.01
# Test against npy
data = mx.nd.random.uniform(-5, 5, shape=(N, 5))
label = mx.nd.array(np.random.randint(2, size=(N, 5)), dtype='float32')
pos_weight = mx.nd.random.uniform(0, 10, shape=(1, 5))
mx_bce_loss = Loss(data, label, None, pos_weight).asnumpy()
prob_npy = 1.0 / (1.0 + np.exp(-data.asnumpy()))
label_npy = label.asnumpy()
pos_weight_npy = pos_weight.asnumpy()
npy_bce_loss = (- label_npy * np.log(prob_npy)*pos_weight_npy - (1 - label_npy) * np.log(1 - prob_npy)).mean(axis=1)
assert_almost_equal(mx_bce_loss, npy_bce_loss, rtol=1e-4, atol=1e-5)
def _check_subgraph_exe3(sym, subgraph_backend, op_names):
"""Use the partitioned sym to bind an executor and compare the outputs
with those of the original executor"""
out = SymbolHandle()
check_call(_LIB.MXBuildSubgraphByOpNames(sym.handle, c_str(subgraph_backend), mx_uint(len(op_names)),
c_str_array(op_names), ctypes.byref(out)))
partitioned_sym = Symbol(out)
input_names = sym.list_inputs()
arg_names = sym.list_arguments()
aux_names = sym.list_auxiliary_states()
assert partitioned_sym.list_inputs() == input_names
assert partitioned_sym.list_arguments() == arg_names
assert partitioned_sym.list_auxiliary_states() == aux_names
arg_shapes, _, aux_shapes = sym.infer_shape()
arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
exe = sym.bind(ctx=mx.current_context(), args=arg_array, aux_states=aux_array, grad_req='null')
partitioned_exe = partitioned_sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
exe.forward()
partitioned_exe.forward()
assert len(exe.outputs) == len(partitioned_exe.outputs)
for i in range(len(exe.outputs)):
assert_almost_equal((exe.outputs[i] - partitioned_exe.outputs[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
def test_tensorrt_resnet18_feature_vect():
print("downloading sample input")
input_data = get_image(url)
gluon_resnet18 = vision.resnet18_v2(pretrained=True)
gluon_resnet18.hybridize()
gluon_resnet18.forward(input_data)
gluon_resnet18.export(model_file_name)
sym, arg_params, aux_params = mx.model.load_checkpoint(model_file_name, 0)
executor = sym.simple_bind(ctx=mx.gpu(), data=batch_shape,
grad_req='null', force_rebind=True)
executor.copy_params_from(arg_params, aux_params)
y = executor.forward(is_train=False, data=input_data)
trt_sym = sym.get_backend_symbol('TensorRT')
mx.contrib.tensorrt.init_tensorrt_params(trt_sym, arg_params, aux_params)
original_precision_value = mx.contrib.tensorrt.get_use_fp16()
try:
mx.contrib.tensorrt.set_use_fp16(True)
executor = trt_sym.simple_bind(ctx=mx.gpu(), data=batch_shape,
grad_req='null', force_rebind=True)
executor.copy_params_from(arg_params, aux_params)
y_trt = executor.forward(is_train=False, data=input_data)
mx.contrib.tensorrt.set_use_fp16(False)
executor = trt_sym.simple_bind(ctx=mx.gpu(), data=batch_shape,
grad_req='null', force_rebind=True)
executor.copy_params_from(arg_params, aux_params)
y_trt_fp32 = executor.forward(is_train=False, data=input_data)
no_trt_output = y[0].asnumpy()[0]
trt_output = y_trt[0].asnumpy()[0]
trt_fp32_output = y_trt_fp32[0].asnumpy()[0]
assert_almost_equal(no_trt_output, trt_output, 1e-1, 1e-2)
assert_almost_equal(no_trt_output, trt_fp32_output, 1e-4, 1e-4)
finally:
mx.contrib.tensorrt.set_use_fp16(original_precision_value)
def softmax_forward(input_data, true_output):
data = mx.sym.Variable('data')
out1 = data.softmax(axis=1)
exec1 = out1.bind(mx.cpu(), args={'data': input_data})
exec1.forward()[0].wait_to_read()
ndarr = exec1.outputs[0][0][0][0]
nparr = ndarr.asnumpy()
assert_almost_equal(nparr, true_output, rtol=1e-5, atol=1e-5)
def pull_before_push(kv):
for i in range(nrepeat):
for j in range(len(keys)):
out = [mx.nd.ones(shapes[j], mx.gpu(g)) for g in range(nworker)]
kv.pull(keys[j], out=out)
exp = np.zeros_like(out[0].asnumpy())
for o in out:
assert_almost_equal(o.asnumpy(), exp)
def test_bce_equal_ce2():
N = 100
loss1 = gluon.loss.SigmoidBCELoss(from_sigmoid=True)
loss2 = gluon.loss.SoftmaxCELoss(from_logits=True)
out1 = mx.random.uniform(0.1, 0.9, shape=(N, 1))
out2 = mx.nd.log(mx.nd.concat(1-out1, out1, dim=1) + 1e-8)
label = mx.nd.round(mx.random.uniform(0, 1, shape=(N, 1)))
assert_almost_equal(loss1(out1, label).asnumpy(), loss2(out2, label).asnumpy())
def test_smooth_distribution():
assert_exception(lambda: mx.contrib.quant._smooth_distribution(np.zeros((2,)), eps=1e-3), ValueError)
dirac_delta = np.zeros((5,))
dirac_delta[2] = 1
smooth_dirac_delta = dirac_delta.copy()
smooth_dirac_delta += 1e-3
smooth_dirac_delta[2] -= 5e-3
assert_almost_equal(mx.contrib.quant._smooth_distribution(dirac_delta, eps=1e-3), smooth_dirac_delta)
def test_normalize():
data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)
data_in = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
out_nd = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))(data_in)
data_expected = data_in.asnumpy()
data_expected[:][:][0] = data_expected[:][:][0] / 3.0
data_expected[:][:][1] = (data_expected[:][:][1] - 1.0) / 2.0
data_expected[:][:][2] = data_expected[:][:][2] - 2.0
assert_almost_equal(data_expected, out_nd.asnumpy())
def test_logistic_loss_equal_bce():
N = 100
loss_binary = gluon.loss.LogisticLoss(label_format='binary')
loss_signed = gluon.loss.LogisticLoss(label_format='signed')
loss_bce = gluon.loss.SigmoidBCELoss(from_sigmoid=False)
data = mx.random.uniform(-10, 10, shape=(N, 1))
label = mx.nd.round(mx.random.uniform(0, 1, shape=(N, 1)))
assert_almost_equal(loss_binary(data, label).asnumpy(), loss_bce(data, label).asnumpy())
assert_almost_equal(loss_signed(data, 2 * label - 1).asnumpy(), loss_bce(data, label).asnumpy())
def test_zero_grad():
data = mx.nd.random.uniform(shape=(3,3))
net = nn.Embedding(3, 4, sparse_grad=True, prefix='test_zero_grad_')
net.initialize()
with mx.autograd.record():
l = net(data)
l.backward()
net.collect_params().zero_grad()
grad = net.collect_params()['test_zero_grad_weight'].grad()
assert_almost_equal(grad.asnumpy(), grad.asnumpy() * 0)
def bench_dot(lhs_shape, rhs_shape, lhs_stype, rhs_stype,
lhs_den, rhs_den, trans_lhs, ctx, num_repeat=10, fw="mxnet", distribution="uniform"):
set_default_context(ctx)
assert fw == "mxnet" or fw == "scipy"
# Set funcs
dot_func_sparse = mx.nd.sparse.dot if fw == "mxnet" else sp.spmatrix.dot
dot_func_dense = mx.nd.dot if fw == "mxnet" else np.dot
# Create matrix instances
lhs_nd = rand_ndarray(lhs_shape, lhs_stype, density=lhs_den, distribution=distribution)
# only uniform distribution supported for rhs
if rhs_stype == 'csr':
rhs_nd = rand_ndarray(rhs_shape, rhs_stype, density=rhs_den, distribution=distribution)
else:
rhs_nd = rand_ndarray(rhs_shape, rhs_stype, density=rhs_den, distribution="uniform")
lhs_dns = None
rhs_dns = None
dense_cost = None
sparse_cost = None
if fw == "mxnet":
lhs_dns = lhs_nd if lhs_stype == 'default' else lhs_nd.tostype('default')
rhs_dns = rhs_nd if rhs_stype == 'default' else rhs_nd.tostype('default')
# One warm up run, verify correctness
out = dot_func_sparse(lhs_nd, rhs_dns, trans_lhs)
out_expected = dot_func_dense(lhs_dns, rhs_dns, trans_lhs)
assert_almost_equal(out.asnumpy(), out_expected.asnumpy(), rtol=1e-1, atol=1e-1)
sparse_cost = measure_cost(num_repeat, False, False, dot_func_sparse, lhs_nd, rhs_nd, trans_lhs)
dense_cost = measure_cost(num_repeat, False, False, dot_func_dense, lhs_dns, rhs_dns, trans_lhs)
else:
lhs_dns = lhs_nd.asnumpy()
rhs_dns = rhs_nd.asnumpy()
lhs_nd = sp.csr_matrix(lhs_nd.asnumpy())
rhs_nd = rhs_nd.asnumpy()
# One warm up run, verify correctness
lhs_nd_copy = sp.spmatrix.transpose(lhs_nd) if trans_lhs else lhs_nd
out = dot_func_sparse(lhs_nd_copy, rhs_dns)
sparse_cost = measure_cost(num_repeat, trans_lhs, False, dot_func_sparse, lhs_nd, rhs_nd)
dense_cost = measure_cost(num_repeat, trans_lhs, True, dot_func_dense, lhs_dns, rhs_dns)
speedup = dense_cost / sparse_cost
# Print results
m = lhs_shape[0]
k = lhs_shape[1]
n = rhs_shape[1]
result_pattern = '{:15.1f} {:15.1f} {:>10} {:8d} {:8d} {:8d} {:13.2f} {:13.2f} {:8.2f}'
results = result_pattern.format(lhs_den*100,
rhs_den*100,
str(ctx),
m,
k,
n,
sparse_cost*1000,
dense_cost*1000,
speedup)
print(results)
def test_mkldnn_ndarray_slice():
ctx = mx.cpu()
net = gluon.nn.HybridSequential()
with net.name_scope():
net.add(gluon.nn.Conv2D(channels=32, kernel_size=3, activation=None))
net.collect_params().initialize(ctx=ctx)
x = mx.nd.array(np.ones([32, 3, 224, 224]), ctx)
y = net(x)
# trigger computation on ndarray slice
assert_almost_equal(y[0].asnumpy()[0, 0, 0], 0.3376348)
def test_global_norm_clip_multi_device():
for check_isfinite in [True, False]:
x1 = mx.nd.ones((3,3), ctx=mx.gpu(0))
x2 = mx.nd.ones((4,4), ctx=mx.cpu(0))
norm = gluon.utils.clip_global_norm([x1, x2], 1.0, check_isfinite=check_isfinite)
if check_isfinite:
assert norm == 5.0
else:
assert norm.asscalar() == 5.0
assert_almost_equal(x1.asnumpy(), np.ones((3, 3)) / 5)
assert_almost_equal(x2.asnumpy(), np.ones((4, 4)) / 5)
def test_inference():
all_models = ['resnet50_v1', 'vgg19_bn', 'alexnet', #'inceptionv3',
'densenet201', 'squeezenet1.0', 'mobilenet0.25']
batch_size = 10
download_data()
for model_name in all_models:
eprint('testing inference on %s'%model_name)
data_shape = (3, 224, 224) if 'inception' not in model_name else (3, 299, 299)
dataIter = mx.io.ImageRecordIter(
path_imgrec = VAL_DATA,
label_width = 1,
preprocess_threads = 1,
batch_size = batch_size,
data_shape = data_shape,
label_name = 'softmax_label',
rand_crop = False,
rand_mirror = False)
data_batch = dataIter.next()
data = data_batch.data[0]
label = data_batch.label[0]
gpu_data = data.as_in_context(mx.gpu())
gpu_label = label.as_in_context(mx.gpu())
# This is to create a model and run the model once to initialize
# all parameters.
cpu_model = get_model(model_name)
cpu_model.collect_params().initialize(ctx=mx.cpu())
cpu_model(mx.nd.array(data, ctx=mx.cpu()))
gpu_model = get_model(model_name)
gpu_model.collect_params().initialize(ctx=mx.gpu())
gpu_model(mx.nd.array(data, ctx=mx.gpu()))
# Force the two models have the same parameters.
cpu_params = cpu_model.collect_params()
gpu_params = gpu_model.collect_params()
for k in cpu_params.keys():
k = k.replace(cpu_params.prefix, '')
cpu_param = cpu_params.get(k)
gpu_param = gpu_params.get(k)
gpu_param.set_data(cpu_param.data().as_in_context(mx.gpu()))
for i in range(5):
# Run inference.
with autograd.record(train_mode=False):
cpu_out = cpu_model(mx.nd.array(data, ctx=mx.cpu()))
gpu_out = gpu_model(gpu_data)
out = cpu_out.asnumpy()
max_val = np.max(np.abs(out))
gpu_max_val = np.max(np.abs(gpu_out.asnumpy()))
eprint(model_name + ": CPU " + str(max_val) + ", GPU " + str(gpu_max_val))
assert_almost_equal(out / max_val, gpu_out.asnumpy() / max_val, rtol=1e-3, atol=1e-3)
def test_row_sparse_pull_single_device():
kvstore = mx.kv.create('device')
copy = mx.nd.random_normal(shape=(4,4), ctx=mx.gpu(0))
grad = copy.tostype("row_sparse")
key = 0
kvstore.init(key, grad)
idx = grad.indices
kvstore.push(key, grad)
kvstore.row_sparse_pull(key, out=grad, row_ids=idx)
assert_almost_equal(grad.asnumpy(), copy.asnumpy())
def test_mkldnn_sum_inplace_with_cpu_layout():
x_shape = (32, 3, 224, 224)
x_npy = np.ones(x_shape)
y_shape = (32, 32, 222, 222)
y_npy = np.ones(y_shape)
x = mx.sym.Variable("x")
y = mx.sym.Variable("y")
z = mx.symbol.Convolution(data=x, num_filter=32, kernel=(3, 3))
z = mx.sym.add_n(z, y)
exe = z.simple_bind(ctx=mx.cpu(), x=x_shape, y=y_shape)
out = exe.forward(is_train=False, x=x_npy, y=y_npy)[0]
assert_almost_equal(out[0].asnumpy()[0, 0, 0], 1.0)
def test_global_norm_clip():
x1 = mx.nd.ones((3,3))
x2 = mx.nd.ones((4,4))
norm = gluon.utils.clip_global_norm([x1, x2], 1.0)
assert norm == 5.0
assert_almost_equal(x1.asnumpy(), np.ones((3,3))/5)
assert_almost_equal(x2.asnumpy(), np.ones((4,4))/5)
x3 = mx.nd.array([1.0, 2.0, float('nan')])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
gluon.utils.clip_global_norm([x1, x3], 2.0)
assert len(w) == 1
def test_quantize_float32_to_int8():
shape = rand_shape_nd(4)
data = rand_ndarray(shape, 'default', dtype='float32')
min_range = mx.nd.min(data)
max_range = mx.nd.max(data)
qdata, min_val, max_val = mx.nd.contrib.quantize(data,
min_range,
max_range,
out_type='int8')
data_np = data.asnumpy()
min_range = min_range.asscalar()
max_range = max_range.asscalar()
real_range = np.maximum(np.abs(min_range), np.abs(max_range))
quantized_range = 127.0
scale = quantized_range / real_range
assert qdata.dtype == np.int8
assert min_val.dtype == np.float32
assert max_val.dtype == np.float32
assert same(min_val.asscalar(), -real_range)
assert same(max_val.asscalar(), real_range)
qdata_np = (np.sign(data_np) * np.minimum(
np.abs(data_np) * scale + 0.5, quantized_range)).astype(np.int8)
assert_almost_equal(qdata.asnumpy(), qdata_np, atol=1)
def test_random_rotation():
# test exceptions for probability input outside of [0,1]
assertRaises(ValueError,
transforms.RandomRotation, [-10, 10.],
rotate_with_proba=1.1)
assertRaises(ValueError,
transforms.RandomRotation, [-10, 10.],
rotate_with_proba=-0.3)
# test `forward`
transformer = transforms.RandomRotation([-10, 10.])
assertRaises(TypeError, transformer, mx.np.ones((3, 30, 60),
dtype='uint8'))
single_image = mx.np.ones((3, 30, 60), dtype='float32')
single_output = transformer(single_image)
assert same(single_output.shape, (3, 30, 60))
batch_image = mx.np.ones((3, 3, 30, 60), dtype='float32')
batch_output = transformer(batch_image)
assert same(batch_output.shape, (3, 3, 30, 60))
# test identity (rotate_with_proba = 0)
transformer = transforms.RandomRotation([-100., 100.],
rotate_with_proba=0.0)
data = mx.np.random.normal(size=(3, 30, 60))
assert_almost_equal(data.asnumpy(), transformer(data).asnumpy())
def test_bce_loss():
N = 20
data = mx.random.uniform(-1, 1, shape=(N, 20))
label = mx.nd.array(np.random.randint(2, size=(N,)), dtype='float32')
data_iter = mx.io.NDArrayIter(data, label, batch_size=10, label_name='label')
output = get_net(1)
l = mx.symbol.Variable('label')
Loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
loss = Loss(output, l)
loss = mx.sym.make_loss(loss)
mod = mx.mod.Module(loss, data_names=('data',), label_names=('label',))
mod.fit(data_iter, num_epoch=200, optimizer_params={'learning_rate': 0.01},
eval_metric=mx.gluon.metric.Loss(), optimizer='adam',
initializer=mx.init.Xavier(magnitude=2))
assert mod.score(data_iter, eval_metric=mx.gluon.metric.Loss())[0][1] < 0.01
# Test against npy
data = mx.random.uniform(-5, 5, shape=(10,))
label = mx.random.uniform(0, 1, shape=(10,))
mx_bce_loss = Loss(data, label).asnumpy()
prob_npy = 1.0 / (1.0 + np.exp(-data.asnumpy()))
label_npy = label.asnumpy()
npy_bce_loss = - label_npy * np.log(prob_npy) - (1 - label_npy) * np.log(1 - prob_npy)
assert_almost_equal(mx_bce_loss, npy_bce_loss, rtol=1e-4, atol=1e-5)
def test_cosine_loss(hybridize):
#Generating samples
input1 = mx.np.random.randn(3, 2)
input2 = mx.np.random.randn(3, 2)
label = mx.np.sign(mx.np.random.randn(input1.shape[0]))
#Calculating loss from cosine embedding loss function in Gluon
Loss = gluon.loss.CosineEmbeddingLoss()
if hybridize:
Loss.hybridize()
loss = Loss(input1, input2, label)
# Calculating the loss Numpy way
numerator = mx.np.sum(input1 * input2, keepdims=True, axis=1)
denominator = mx.np.sqrt(mx.np.sum(input1**2, axis=1, keepdims=True)) \
* mx.np.sqrt(mx.np.sum(input2**2, axis=1, keepdims=True))
x = numerator / denominator
label = mx.npx.reshape(label, (-1, 1))
numpy_loss = mx.npx.reshape(mx.np.where(label == 1, 1 - x, mx.npx.relu(x)),
(-1, ))
assert_almost_equal(loss.asnumpy(),
numpy_loss.asnumpy(),
rtol=1e-3,
atol=1e-5)
def test_export():
ctx = mx.context.current_context()
model = gluon.model_zoo.vision.resnet18_v1(prefix='resnet',
ctx=ctx,
pretrained=True)
model.hybridize()
data = mx.nd.random.normal(shape=(1, 3, 224, 224))
out = model(data)
model.export('gluon')
module = mx.mod.Module.load('gluon', 0, label_names=None, context=ctx)
module.bind(data_shapes=[('data', data.shape)])
module.forward(mx.io.DataBatch([data], None), is_train=False)
mod_out, = module.get_outputs()
assert_almost_equal(out.asnumpy(), mod_out.asnumpy())
model2 = gluon.model_zoo.vision.resnet18_v1(prefix='resnet', ctx=ctx)
model2.collect_params().load('gluon-0000.params', ctx)
out2 = model2(data)
assert_almost_equal(out.asnumpy(), out2.asnumpy())
def test_np_get_constant():
const_arr = _np.random.uniform(0, 100, size=(10, 10)).astype(_np.float32)
class Foo(gluon.HybridBlock):
def __init__(self):
super(Foo, self).__init__()
self.weight = gluon.Constant(const_arr)
def forward(self, x):
ctx = x.ctx
return x + self.weight.data(ctx).astype(np.float32)
x = np.random.uniform(size=const_arr.shape, dtype=const_arr.dtype)
for hybridize in [False, True]:
foo = Foo()
if hybridize:
foo.hybridize()
foo.initialize()
out = foo(x)
assert_almost_equal(out.asnumpy(), (x.asnumpy() + const_arr),
atol=1e-5,
rtol=1e-4,
use_broadcast=False)
def test_gluon_ctc_consistency():
loss = mx.gluon.loss.CTCLoss()
data = mx.nd.arange(0, 4, repeat=40, ctx=mx.gpu(0)).reshape(
(2, 20, 4)).flip(axis=0)
cpu_label = mx.nd.array([[2, 1, -1, -1], [3, 2, 2, -1]], ctx=mx.cpu(0))
gpu_label = mx.nd.array([[2, 1, -1, -1], [3, 2, 2, -1]], ctx=mx.gpu(0))
cpu_data = data.copy().as_in_context(mx.cpu(0))
cpu_data.attach_grad()
with mx.autograd.record():
l_cpu = loss(cpu_data, cpu_label)
l_cpu.backward()
gpu_data = data.copyto(mx.gpu(0))
gpu_data.attach_grad()
with mx.autograd.record():
l_gpu = loss(gpu_data, gpu_label)
l_gpu.backward()
assert_almost_equal(cpu_data.grad.asnumpy(),
gpu_data.grad.asnumpy(),
atol=1e-3,
rtol=1e-3)
def test_global_norm_clip_multi_device():
for check_isfinite in [True, False]:
x1 = mx.nd.ones((3, 3), ctx=mx.gpu(0))
x2 = mx.nd.ones((4, 4), ctx=mx.cpu(0))
x3 = mx.nd.ones((7, 4), ctx=mx.gpu(0))
x4 = mx.nd.ones((7, 4), ctx=mx.cpu(0))
norm = gluon.utils.clip_global_norm(
[x1, x2, x3, x4], 1.0, check_isfinite=check_isfinite)
if check_isfinite:
assert norm == 9.0
else:
assert norm.asscalar() == 9.0
assert_almost_equal(x1, np.ones((3, 3)) / 9)
assert_almost_equal(x2, np.ones((4, 4)) / 9)
assert_almost_equal(x3, np.ones((7, 4)) / 9)
assert_almost_equal(x4, np.ones((7, 4)) / 9)
def test_get_optimal_thresholds():
# Given an ndarray with elements following a uniform distribution, the optimal threshold
# for quantizing the ndarray should be either abs(min(nd)) or abs(max(nd)).
def get_threshold(nd):
min_nd = mx.nd.min(nd)
max_nd = mx.nd.max(nd)
return mx.nd.maximum(mx.nd.abs(min_nd), mx.nd.abs(max_nd)).asnumpy()
for dtype in ['uint8', 'int8', 'auto']:
nd_dict = {
'layer1':
mx.nd.uniform(low=-10.532,
high=11.3432,
shape=(8, 3, 23, 23),
dtype=np.float64)
}
expected_threshold = get_threshold(nd_dict['layer1'])
th_dict = mx.contrib.quant._get_optimal_thresholds(nd_dict, dtype)
assert 'layer1' in th_dict
assert_almost_equal(np.array([th_dict['layer1'][1]]),
expected_threshold,
rtol=1e-2,
atol=1e-4)
def compare_optimizer(opt1,
opt2,
shape,
dtype,
w_stype='default',
g_stype='default',
rtol=1e-4,
atol=1e-5,
compare_states=True):
"""Compare opt1 and opt2."""
if w_stype == 'default':
w2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
w1 = w2.copyto(default_context())
elif w_stype == 'row_sparse' or w_stype == 'csr':
w2 = rand_ndarray(shape, w_stype, density=1, dtype=dtype)
w1 = w2.copyto(default_context()).tostype('default')
else:
raise Exception("type not supported yet")
if g_stype == 'default':
g2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
g1 = g2.copyto(default_context())
elif g_stype == 'row_sparse' or g_stype == 'csr':
g2 = rand_ndarray(shape, g_stype, dtype=dtype)
g1 = g2.copyto(default_context()).tostype('default')
else:
raise Exception("type not supported yet")
state1 = opt1.create_state_multi_precision(0, w1)
state2 = opt2.create_state_multi_precision(0, w2)
if compare_states:
compare_ndarray_tuple(state1, state2)
opt1.update_multi_precision(0, w1, g1, state1)
opt2.update_multi_precision(0, w2, g2, state2)
if compare_states:
compare_ndarray_tuple(state1, state2, rtol=rtol, atol=atol)
assert_almost_equal(w1.asnumpy(), w2.asnumpy(), rtol=rtol, atol=atol)
def test_np_broadcast_to():
class TestBroadcastTo(HybridBlock):
def __init__(self, dst_shape):
super(TestBroadcastTo, self).__init__()
self._dst_shape = dst_shape
def hybrid_forward(self, F, x):
return F.np.broadcast_to(x, self._dst_shape)
shapes = [((), (1, 2, 4, 5)), ((1, ), (4, 5, 6)), ((1, 0), (2, 4, 0)),
((1, 1), (2, 4, 0)), ((4, 1), (1, 2, 3, 4, 5)),
((4, 1), (1, 0, 3, 4, 5))]
for src_shape, dst_shape in shapes:
for hybridize in [True, False]:
test_broadcast_to = TestBroadcastTo(dst_shape)
if hybridize:
test_broadcast_to.hybridize()
a = _np.random.uniform(size=src_shape).astype(np.float32)
expected_ret = _np.broadcast_to(a, dst_shape)
a_mx = np.array(a, dtype=a.dtype)
a_mx.attach_grad()
with mx.autograd.record():
ret = test_broadcast_to(a_mx)
assert_almost_equal(ret.asnumpy(),
expected_ret,
rtol=1e-5,
atol=1e-6,
use_broadcast=False)
ret.backward()
expected_grad = collapse_sum_like(_np.ones_like(expected_ret),
src_shape)
assert_almost_equal(a_mx.grad.asnumpy(),
expected_grad,
rtol=1e-5,
atol=1e-6,
use_broadcast=False)
def test_subgraph_exe2(sym, subgraph_backend, op_names):
"""Use env var MXNET_SUBGRAPH_BACKEND=default to trigger graph partitioning in _simple_bind
and compare results of the partitioned sym and the original sym."""
def get_executor(sym,
subgraph_backend=None,
op_names=None,
original_exec=None):
exe = sym._simple_bind(ctx=mx.current_context(), grad_req='null')
input_names = sym.list_inputs()
for name in input_names:
if name in exe.arg_dict:
exe.arg_dict[name][:] = mx.nd.random.uniform(shape=exe.arg_dict[name].shape)\
if original_exec is None else original_exec.arg_dict[name]
else:
assert name in exe.aux_dict
exe.aux_dict[name][:] = mx.nd.random.uniform(shape=exe.aux_dict[name].shape)\
if original_exec is None else original_exec.aux_dict[name]
exe.forward()
return exe
sym, _, _ = sym
original_exec = get_executor(sym)
with environment('MXNET_SUBGRAPH_BACKEND', subgraph_backend):
check_call(
_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
partitioned_exec = get_executor(sym, subgraph_backend, op_names,
original_exec)
check_call(
_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
outputs1 = original_exec.outputs
outputs2 = partitioned_exec.outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
onp.zeros(shape=(1, )))
def verify(batch_size):
print('verifying batch size: ', batch_size)
fold = Fold()
num_samples = 100
inputs = []
fold_preds = []
for i in range(num_samples):
# get next batch
l_sent = l_sentences[i]
r_sent = r_sentences[i]
l_tree = l_trees[i]
r_tree = r_trees[i]
inputs.append((l_sent, r_sent, l_tree, r_tree))
z_fold = net.fold_encode(fold, l_sent, r_sent, l_tree, r_tree)
fold_preds.append(z_fold)
if (i + 1) % batch_size == 0 or (i + 1) == num_samples:
fold_outs = fold([fold_preds])[0]
outs = mx.nd.concat(*[
net(l_sent, r_sent, l_tree, r_tree)
for l_sent, r_sent, l_tree, r_tree in inputs
],
dim=0)
if not almost_equal(fold_outs.asnumpy(), outs.asnumpy()):
print(fold_preds)
print('l_sents: ', l_sent, l_sentences[i - 1])
print('r_sents: ', r_sent, r_sentences[i - 1])
print('\n'.join(
(str(l_tree), str_tree(l_tree), str(r_tree),
str_tree(r_tree), str(l_trees[i - 1]),
str_tree(l_trees[i - 1]), str(r_trees[i - 1]),
str_tree(r_trees[i - 1]), str(fold))))
assert_almost_equal(fold_outs.asnumpy(), outs.asnumpy())
fold_preds = []
inputs = []
fold.reset()
def test_instance_norm():
dtype = np.float32
forward_check_eps = 1E-3
axis = -1
eps = 1E-5
in_shape = (LARGE_X, 1, SMALL_Y)
ctx = mx.cpu()
# Implementation of instance normalization using numpy
def npy_instance_norm(data, gamma, beta, axis, eps=1E-5):
if axis < 0:
axis += data.ndim
broadcast_shape = [1 for _ in range(data.ndim)]
broadcast_shape[axis] = data.shape[axis]
mean = data.mean(axis=axis, keepdims=True).astype(dtype)
var = data.var(axis=axis, keepdims=True).astype(dtype)
std = np.sqrt(var + dtype(eps)).astype(dtype)
out = gamma * (data - mean) / std + \
beta
return out
data = np.random.normal(0, 1, in_shape).astype(dtype)
gamma = np.random.normal(0, 1, (1,)).astype(dtype)
beta = np.random.normal(0, 1, (1,)).astype(dtype)
data_s = mx.symbol.Variable('data')
gamma_s = mx.symbol.Variable('gamma')
beta_s = mx.symbol.Variable('beta')
out_s = mx.symbol.InstanceNorm(data=data_s, gamma=gamma_s, beta=beta_s,
eps=eps)
exe = out_s.simple_bind(ctx, data=in_shape)
exe.arg_dict['data'][:] = data
exe.arg_dict['gamma'][:] = gamma
exe.arg_dict['beta'][:] = beta
out_nd = exe.forward()[0]
# Calls implementation of instance norm in numpy and compares the output
out = npy_instance_norm(data, gamma, beta, axis, eps)
assert_almost_equal(out, out_nd.asnumpy(), forward_check_eps,
forward_check_eps)
def test_subgraph_exe1(sym, subgraph_backend, op_names):
"""Use the partitioned sym to _simple_bind an executor and compare the outputs
with those of the original executor"""
sym, _, _ = sym
out = SymbolHandle()
check_call(
_LIB.MXBuildSubgraphByOpNames(sym.handle, c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names),
ctypes.byref(out)))
partitioned_sym = Symbol(out)
assert partitioned_sym.list_inputs() == sym.list_inputs()
assert partitioned_sym.list_arguments() == sym.list_arguments()
assert partitioned_sym.list_auxiliary_states(
) == sym.list_auxiliary_states()
exe = sym._simple_bind(ctx=mx.current_context(), grad_req='null')
partitioned_exe = partitioned_sym._simple_bind(ctx=mx.current_context(),
grad_req='null')
input_names = sym.list_inputs()
for name in input_names:
if name in exe.arg_dict:
exe.arg_dict[name][:] = mx.nd.random.uniform(
shape=exe.arg_dict[name].shape)
partitioned_exe.arg_dict[name][:] = exe.arg_dict[name]
else:
assert name in exe.aux_dict
exe.aux_dict[name][:] = mx.nd.random.uniform(
shape=exe.aux_dict[name].shape)
partitioned_exe.aux_dict[name][:] = exe.aux_dict[name]
exe.forward()
partitioned_exe.forward()
assert len(exe.outputs) == len(partitioned_exe.outputs)
for i in range(len(exe.outputs)):
assert_almost_equal((exe.outputs[i] -
partitioned_exe.outputs[i]).abs().sum().asnumpy(),
onp.zeros(shape=(1, )))
def test_predictor():
prefix = 'test_predictor_simple_dense'
symbol_file = "%s-symbol.json" % prefix
param_file = "%s-0000.params" % prefix
# two inputs with different batch sizes
input1 = np.random.uniform(size=(1, 3))
input2 = np.random.uniform(size=(3, 3))
# define a simple model
block = gluon.nn.HybridSequential()
block.add(gluon.nn.Dense(7))
block.add(gluon.nn.Dense(3))
block.hybridize()
block.initialize()
out1 = block.forward(nd.array(input1))
out2 = block.forward(nd.array(input2))
block.export(prefix)
# create a predictor
predictor = Predictor(
open(symbol_file, "r").read(),
open(param_file, "rb").read(), {'data': input1.shape})
# forward and get output
predictor.forward(data=input1)
predictor_out1 = predictor.get_output(0)
assert_almost_equal(out1.asnumpy(), predictor_out1, rtol=1e-5, atol=1e-6)
# reshape
predictor.reshape({'data': input2.shape})
predictor.forward(data=input2)
predictor_out2 = predictor.get_output(0)
assert_almost_equal(out2.asnumpy(), predictor_out2, rtol=1e-5, atol=1e-6)
# destroy the predictor
del predictor
def test_transformer_encoder():
ctx = mx.current_context()
for num_layers in range(1, 3):
for output_attention in [True, False]:
for use_residual in [False, True]:
encoder = TransformerEncoder(num_layers=num_layers, max_length=10,
units=16, hidden_size=32, num_heads=8,
dropout=0.0, use_residual=use_residual,
output_attention=output_attention, prefix='transformer_encoder_')
encoder.initialize(ctx=ctx)
encoder.hybridize()
for batch_size in [4]:
for seq_length in [5, 10]:
inputs_nd = mx.nd.random.normal(0, 1, shape=(batch_size, seq_length, 16), ctx=ctx)
valid_length_nd = mx.nd.array(np.random.randint(1, seq_length,
size=(batch_size,)), ctx=ctx)
encoder_outputs, additional_outputs = encoder(inputs_nd, valid_length=valid_length_nd)
valid_length_npy = valid_length_nd.asnumpy()
encoder_outputs = encoder_outputs.asnumpy()
for i in range(batch_size):
if valid_length_npy[i] < seq_length - 1:
padded_out = encoder_outputs[i, int(valid_length_npy[i]):, :]
assert_almost_equal(padded_out, np.zeros_like(padded_out), 1E-6, 1E-6)
assert(encoder_outputs.shape == (batch_size, seq_length, 16))
if output_attention:
assert(len(additional_outputs) == num_layers)
attention_out = additional_outputs[0][0].asnumpy()
assert(attention_out.shape == (batch_size, 8, seq_length, seq_length))
for i in range(batch_size):
mem_v_len = int(valid_length_npy[i])
if mem_v_len < seq_length - 1:
assert((attention_out[i, :, :, mem_v_len:] == 0).all())
if mem_v_len > 0:
assert_almost_equal(attention_out[i, :, :, :].sum(axis=-1),
np.ones(attention_out.shape[1:3]))
else:
assert(len(additional_outputs) == 0)
def check_fusion(sym, data_shape, attrs_op):
sym_sg = sym.get_backend_symbol("MKLDNN")
assert ''.join(
sym_sg.get_internals().list_outputs()).find('sg_mkldnn_conv') != -1
for k, v in sym_sg.attr_dict().items():
if k.find('sg_mkldnn_conv') != -1:
for attr_op in attrs_op:
assert v[attr_op] == 'true'
arg_shapes, _, aux_shapes = sym.infer_shape()
arg_array = [
mx.nd.random.uniform(-1, 1, shape=shape) for shape in arg_shapes
]
aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
exe = sym.bind(ctx=mx.current_context(),
args=arg_array,
aux_states=aux_array,
grad_req='null')
exe.forward()
os.environ['MXNET_SUBGRAPH_BACKEND'] = 'MKLDNN'
exe_sg = sym.bind(ctx=mx.current_context(),
args=arg_array,
aux_states=aux_array,
grad_req='null')
exe_sg.forward()
del os.environ['MXNET_SUBGRAPH_BACKEND']
for i in range(len(exe.outputs)):
assert_almost_equal(exe.outputs[i].asnumpy(),
exe_sg.outputs[i].asnumpy(),
rtol=1e-3,
atol=1e-3)
# fp32 to int8
for out_type in ('uint8', 'int8', 'auto'):
check_quantize(sym, data_shape, out_type)
check_quantize(sym, data_shape, out_type, gluon_forward=True)
def test_elemwise_add():
def ref_add(a, b):
return np.add(a, b)
a_sym = mx.sym.Variable("a")
b_sym = mx.sym.Variable("b")
dshape = rand_shape_nd(4)
a_shape = tuple(dshape)
b_shape = tuple(dshape)
z = mx.sym.elemwise_add(a_sym, b_sym)
a = np.random.uniform(-1, 1, a_shape)
b = np.random.uniform(-1, 1, b_shape)
exe = z._simple_bind(ctx=mx.cpu(), a=a_shape, b=b_shape)
out = exe.forward(is_train=False, a=a, b=b)
ref_out = ref_add(a, b)
out = out[0].asnumpy()
assert_almost_equal(out, ref_out, rtol=1e-6, atol=1e-6)
def check_elemwise_add_training(stype):
data_shape = rand_shape_nd(4)
for density in [1.0, 0.5, 0.0]:
a_sym = mx.sym.Variable('a')
b_sym = mx.sym.Variable('b')
sym = mx.sym.elemwise_add(a_sym, b_sym)
a = rand_ndarray(shape=data_shape, stype=stype, density=density)
b = rand_ndarray(shape=data_shape, stype=stype, density=density)
in_location = [a, b]
check_numeric_gradient(sym,
in_location,
numeric_eps=1e-3,
rtol=1e-3,
atol=5e-3)
stypes = ['row_sparse', 'default']
for stype in stypes:
check_elemwise_add_training(stype)
def test_subgraph_backend_gluon(sym, subgraph_backend, op_names, tmp_path):
"""Call hybridize() to partition the graph, and then compare results of the partitioned
sym and the original sym. Here do an inference before hybridizing with the subgraph_backend
which means we'll pass shapes/types"""
# create Gluon block for given symbol
inputs = [mx.sym.var(i, dtype=mx_real_t) for i in sym[1]]
sym_block = nn.SymbolBlock(sym[0], inputs)
sym_block.initialize(ctx=mx.current_context())
x = [
mx.nd.random.uniform(shape=s, ctx=mx.current_context()) for s in sym[2]
]
# hybridize and export to get baseline
sym_block.hybridize()
outputs1 = sym_block(*x)
sym_filename, params_filename = sym_block.export(
str(tmp_path / 'sym-block'))
# load model and partition
sym_block = nn.SymbolBlock.imports(sym_filename,
sym[1],
params_filename,
ctx=mx.current_context())
check_call(
_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
sym_block.optimize_for(*x, backend=subgraph_backend)
outputs2 = sym_block(*x)
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
# compare outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
onp.zeros(shape=(1, )))
def test_subgraph_exe3(sym, subgraph_backend, op_names):
"""Use the partitioned sym to bind an executor and compare the outputs
with those of the original executor"""
sym, _, _ = sym
out = SymbolHandle()
check_call(
_LIB.MXBuildSubgraphByOpNames(sym.handle, c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names),
ctypes.byref(out)))
partitioned_sym = Symbol(out)
input_names = sym.list_inputs()
arg_names = sym.list_arguments()
aux_names = sym.list_auxiliary_states()
assert partitioned_sym.list_inputs() == input_names
assert partitioned_sym.list_arguments() == arg_names
assert partitioned_sym.list_auxiliary_states() == aux_names
arg_shapes, _, aux_shapes = sym.infer_shape()
arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
exe = sym._bind(ctx=mx.current_context(),
args=arg_array,
aux_states=aux_array,
grad_req='null')
partitioned_exe = partitioned_sym._bind(ctx=mx.current_context(),
args=arg_array,
aux_states=aux_array,
grad_req='null')
exe.forward()
partitioned_exe.forward()
assert len(exe.outputs) == len(partitioned_exe.outputs)
for i in range(len(exe.outputs)):
assert_almost_equal((exe.outputs[i] -
partitioned_exe.outputs[i]).abs().sum().asnumpy(),
onp.zeros(shape=(1, )))
def test_mkldnn_engine_threading():
net = gluon.nn.HybridSequential()
with net.name_scope():
net.add(gluon.nn.Conv2D(channels=32, kernel_size=3, activation=None))
net.collect_params().initialize(ctx=mx.cpu())
class Dummy(gluon.data.Dataset):
def __len__(self):
return 2
def __getitem__(self, key):
return key, np.ones((3, 224, 224)), np.ones((10, ))
loader = gluon.data.DataLoader(Dummy(), batch_size=2, num_workers=1)
X = (32, 3, 32, 32)
# trigger mkldnn execution thread
y = net(mx.nd.array(np.ones(X))).asnumpy()
# Use Gluon dataloader to trigger different thread.
# below line triggers different execution thread
for _ in loader:
y = net(mx.nd.array(np.ones(X))).asnumpy()
# output should be 016711406 (non-mkldnn mode output)
assert_almost_equal(y[0, 0, 0, 0], 0.016711406)
break
def test_mkldnn_engine_threading():
"""
This test will trigger mkldnn engine on different thread of execution.
The test will first kickoff simple model calculation, and then uses a
gluon data iterator to trigger different thread context, and executes
the model on this new thread.
"""
import mxnet as mx
from mxnet import gluon, nd
net = gluon.nn.HybridSequential()
with net.name_scope():
net.add(gluon.nn.Conv2D(channels=32, kernel_size=3, activation=None))
net.collect_params().initialize(ctx=mx.cpu())
class Dummy(gluon.data.Dataset):
def __len__(self):
return 2
def __getitem__(self, key):
return key, np.ones((3, 224, 224)), np.ones((10, ))
loader = gluon.data.DataLoader(Dummy(), batch_size=2, num_workers=1)
X = (32, 3, 32, 32)
# trigger mkldnn execution thread
y = net(nd.array(np.ones(X))).asnumpy()
# Use Gluon dataloader to trigger different thread.
# below line triggers different execution thread
for _ in loader:
y = net(nd.array(np.ones(X))).asnumpy()
# output should have 0.3376348
assert_almost_equal(y[0, 0, 0, 0], 0.3376348)
break
def test_seg_broadcast_binary():
for ctx in [mx.cpu(), mx.gpu()]:
for np_func, nd_func, name in [
(npy_seg_broadcast_add, nd.contrib.seg_broadcast_add, 'add'),
(npy_seg_broadcast_mul, nd.contrib.seg_broadcast_mul, 'mul')
]:
for batch_size, seg_num, nnz in [(1, 5, 10), (10, 50, 100),
(4, 1000, 10000)]:
lhs_npy = np.random.normal(0, 1, (batch_size, nnz))
rhs_npy = np.random.normal(0, 1, (batch_size, seg_num))
indptr_npy = rand_indptr(seg_num, nnz)
# Test broadcast_add
print('broadcast_' + name)
gt_npy = np_func(lhs_npy, rhs_npy, indptr_npy)
# Test mx.nd
lhs_nd = nd.array(lhs_npy, dtype=np.float32, ctx=ctx)
rhs_nd = nd.array(rhs_npy, dtype=np.float32, ctx=ctx)
indptr_nd = nd.array(indptr_npy, dtype=np.int32, ctx=ctx)
ret_nd = nd_func(lhs=lhs_nd, rhs=rhs_nd, indptr=indptr_nd)
assert_almost_equal(ret_nd.asnumpy(),
gt_npy,
rtol=1E-4,
atol=1E-4)
def test_subgraph_backend_gluon_ext2(tmpdir):
class Net(gluon.HybridBlock):
def __init__(self, **kwargs):
super(Net, self).__init__(**kwargs)
with self.name_scope():
self.fc1 = nn.Dense(256)
self.fc2 = nn.Dense(128)
self.fc3 = nn.Dense(2)
def hybrid_forward(self, F, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
return self.fc3(x)
# regular inference
x = nd.random.normal(shape=(1, 512),ctx=mx.current_context())
net = Net()
net.collect_params().initialize(ctx=mx.current_context())
outputs1 = net(x)
param_path = os.path.join(str(tmpdir), 'test_subgraph_backend_gluon_ext2.params')
net.save_parameters(param_path)
# after partitioning
net = Net()
net.load_parameters(param_path, ctx=mx.current_context())
subgraph_backend = 'default'
op_names = ['FullyConnected']
check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend), mx_uint(len(op_names)),
c_str_array(op_names)))
net.hybridize(backend = subgraph_backend)
outputs2 = net(x)
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
# compare outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
def test_np_loss_ndarray():
# Ported from test_loss.test_loss_ndarray
output = np.array([1, 2, 3, 4])
label = np.array([1, 3, 5, 7])
weighting = np.array([0.5, 1, 0.5, 1])
loss = gluon.loss.L1Loss()
assert float(np.sum(loss(output, label))) == 6.
loss = gluon.loss.L1Loss(weight=0.5)
assert float(np.sum(loss(output, label))) == 3.
loss = gluon.loss.L1Loss()
assert float(np.sum(loss(output, label, weighting))) == 5.
loss = gluon.loss.L2Loss()
assert float(np.sum(loss(output, label))) == 7.
loss = gluon.loss.L2Loss(weight=0.25)
assert float(np.sum(loss(output, label))) == 1.75
loss = gluon.loss.L2Loss()
assert float(np.sum(loss(output, label, weighting))) == 6
output = np.array([[0, 2], [1, 4]])
label = np.array([0, 1])
weighting = np.array([[0.5], [1.0]])
loss = gluon.loss.SoftmaxCrossEntropyLoss()
L = loss(output, label).asnumpy()
assert_almost_equal(L,
_np.array([2.12692809, 0.04858733]),
use_broadcast=False,
rtol=1e-3)
L = loss(output, label, weighting).asnumpy()
assert_almost_equal(L,
_np.array([1.06346405, 0.04858733]),
use_broadcast=False,
rtol=1e-3)
def test_softmax_cross_entropy():
# dtype of input data, mxnet cross entropy set explicitly to float64
# numpy implicitly takes care of double precision
batch_size = SMALL_Y
num_labels = LARGE_X
input_data = mx.nd.ones((batch_size, num_labels), dtype="float64")
input_label = mx.nd.zeros((batch_size, ), dtype="float64")
true_softmax = np.full((batch_size, num_labels), (1 / num_labels))
# use 1/batch_size when softmax axis=0
# here 1/num_labels since softmax_cross_entropy uses default axis
# by default axis=1
np_one_hot_label = np.zeros((batch_size, num_labels))
np_one_hot_label[:, 0] = 1
true_softmax_cross_entropy = np.sum(-np.log(true_softmax) *
np_one_hot_label)
mx_softmax_cross_entropy = mx.nd.softmax_cross_entropy(input_data,
input_label,
dtype="float64")
assert_almost_equal(mx_softmax_cross_entropy.asnumpy(),
true_softmax_cross_entropy,
rtol=1e-3,
atol=1e-5)
def check_amp_fuse(net, data_example, expected_sym=None, quantized_nodes=[], rtol=0.05):
net.hybridize()
out_ref = net(*data_example)
net.optimize_for(data_example, backend=SG_PASS_NAME) # amp pass works only on oneDNN nodes
lp_net = amp.convert_hybrid_block(net, data_example, target_dtype=AMP_DTYPE,
excluded_sym_names=quantized_nodes, cast_params_offline=True,
device=mx.current_context())
lp_net.optimize_for(data_example, backend=AMP_SG_PASS_NAME)
out_lp_net = lp_net(*data_example)
# check outputs
out_ref = [out_ref] if not isinstance(out_ref, list) else out_ref
out_lp_net = [out_lp_net] if not isinstance(out_ref, list) else out_lp_net
for ref_out, lp_out in zip(out_ref, out_lp_net):
assert_almost_equal(ref_out, lp_out, rtol=rtol, atol=1.0)
# check graph
if expected_sym is not None:
lp_symnet = lp_net.export(None, remove_amp_cast=False)[0]
same_graph_structure(lp_symnet, expected_sym, True)
# check amp with quantization
check_amp_with_quantization(net, data_example, quantized_nodes)
def _check_subgraph_exe8(sym, subgraph_backend, op_names):
"""Call optimize_for to infer shapes, types and dtypes followed by graph partitioning,
then bind and compare results of the partitioned sym and the original sym."""
# bind
arg_shapes, _, aux_shapes = sym.infer_shape()
arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
exe1 = sym.bind(ctx=mx.current_context(),
args=arg_array,
aux_states=aux_array,
grad_req='null')
exe1.forward()
# infer shape/type before partition before bind
check_call(
_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
part_sym = sym.optimize_for(subgraph_backend, arg_array)
check_call(
_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
exe2 = part_sym.bind(ctx=mx.current_context(),
args=arg_array,
aux_states=aux_array,
grad_req='null')
exe2.forward()
# compare outputs
outputs1 = exe1.outputs
outputs2 = exe2.outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal(
(outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1, )))
def test_sparse_aggregator():
"""aggregate sparse ndarray on muliple devices"""
stype = 'row_sparse'
kv = init_kv(stype)
# devices
num_devs = 4
devs = [mx.Context('cpu', i) for i in range(num_devs)]
# single
vals = [rand_ndarray(shape, stype).copyto(devs[i]) for i in range(num_devs)]
expected_sum = np.zeros(shape)
for v in vals:
expected_sum += v.asnumpy()
kv.push(3, vals)
kv.pull(3, out = vals)
result_sum = np.zeros(shape)
for v in vals:
result_sum += v.asnumpy()
assert_almost_equal(result_sum, expected_sum * num_devs)
# list
vals = [[rand_ndarray(shape, stype).copyto(devs[i]) for i in range(num_devs)]] * len(keys)
expected_sum = np.zeros(shape)
for v in vals[0]:
expected_sum += v.asnumpy()
kv.push(keys, vals)
kv.pull(keys, out = vals)
for vv in vals:
result_sum = np.zeros(shape)
for v in vv:
result_sum += v.asnumpy()
assert_almost_equal(result_sum, expected_sum * num_devs)
