def FlowNnBnJob(
x_full_precision: oft.Numpy.Placeholder(x.shape),
mean: oft.Numpy.Placeholder(mean.shape),
variance: oft.Numpy.Placeholder(variance.shape),
offset: oft.Numpy.Placeholder(offset.shape),
scale: oft.Numpy.Placeholder(scale.shape),
):
with flow.scope.placement(device_type, "0:0"):
x_full_precision += flow.get_variable(
name="v1",
shape=(1, ),
dtype=flow.float32,
initializer=flow.zeros_initializer(),
)
if data_type == "float16":
x = flow.cast(x_full_precision, flow.float16)
else:
x = x_full_precision
y = flow.nn.batch_normalization(x,
mean,
variance,
offset,
scale,
epsilon,
axis=axis)
y = flow.cast(y, flow.float32)
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler([],
[0]),
momentum=0).minimize(y)
flow.watch_diff(x_full_precision,
test_global_storage.Setter("x_diff"))
return y
def FlowNnBnJob(
x_full_precision: oft.Numpy.Placeholder(x.shape),
mean: oft.Numpy.Placeholder(mean.shape),
variance: oft.Numpy.Placeholder(variance.shape),
offset: oft.Numpy.Placeholder(offset.shape),
scale: oft.Numpy.Placeholder(scale.shape),
):
with flow.scope.placement("gpu", "0:0"):
x_full_precision += flow.get_variable(
name="v1",
shape=(1,),
dtype=flow.float32,
initializer=flow.zeros_initializer(),
)
if data_type == "float16":
x = flow.cast(x_full_precision, flow.float16)
else:
x = x_full_precision
y = flow.nn.batch_normalization(
x, mean, variance, offset, scale, epsilon, axis=axis
)
y = flow.cast(y, flow.float32)
flow.losses.add_loss(y)
flow.watch_diff(x_full_precision, test_global_storage.Setter("x_diff"))
return y
def diag_job(
input_tensor: tp.Numpy.Placeholder(shape=(input_shape),
dtype=flow.float),
) -> tp.Numpy:
input_var = flow.get_variable(
"input_tensor",
shape=(input_shape),
dtype=flow.float,
initializer=flow.zeros_initializer(),
trainable=True,
)
input_tensor = input_tensor + input_var
input_tensor = flow.cast_to_current_logical_view(input_tensor)
input_tensor = flow.cast(input_tensor, type_name_to_flow_type[dtype])
output = flow.diag(input_tensor, dim)
if (output.dtype == flow.int64 or output.dtype == flow.int8
or output.dtype == flow.int32):
output = flow.cast(output, flow.float)
flow.optimizer.Adam(
flow.optimizer.PiecewiseConstantScheduler([],
[1e-4])).minimize(output)
flow.watch(input_tensor, test_global_storage.Setter("x"))
flow.watch_diff(input_tensor, test_global_storage.Setter("x_diff"))
flow.watch(output, test_global_storage.Setter("output"))
flow.watch_diff(output, test_global_storage.Setter("output_diff"))
return output
def test_masked_fill_fw_bw_job(
x: oft.Numpy.Placeholder(x_shape, dtype=flow_type),
mask: oft.Numpy.Placeholder(mask_shape, dtype=flow_type),
):
with flow.scope.placement(device, "0:0"):
y = flow.get_variable(
name="vx",
shape=(1, ),
dtype=flow.float,
initializer=flow.zeros_initializer(),
)
x += flow.cast(y, flow_type)
mask = flow.cast(mask, dtype=flow.int8)
if type_name == "float16":
out = flow.cast(
flow.masked_fill(flow.cast(x, flow.float16), mask, value),
flow.float,
)
else:
out = flow.masked_fill(x, mask, value)
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler(
[], [1e-4]),
momentum=0).minimize(out)
flow.watch(x, test_global_storage.Setter("x"))
flow.watch_diff(x, test_global_storage.Setter("x_diff"))
flow.watch(out, test_global_storage.Setter("out"))
flow.watch_diff(out, test_global_storage.Setter("out_diff"))
return out
def gather_fn(
params_def: oft.Numpy.Placeholder(input.shape, dtype=flow.float32),
indices_def: oft.Numpy.Placeholder(index.shape, dtype=index_type),
) -> oft.Numpy:
with flow.scope.placement(device_type, "0:0"):
x_var = flow.get_variable(
"input",
shape=input.shape,
dtype=flow.float32,
initializer=flow.constant_initializer(0),
)
x_var = flow.cast_to_current_logical_view(x_var)
x = x_var + params_def
x_f16 = flow.cast(x, flow.float16)
y_f16 = flow.dim_gather(x_f16, dim, indices_def)
x_f32 = flow.cast(x, flow.float32)
y_f32 = flow.cast(y_f16, flow.float32)
y = flow.dim_gather(x, dim, indices_def)
with flow.scope.placement(device_type, "0:0"):
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler(
[], [1e-3]),
momentum=0).minimize(y_f32)
flow.watch_diff(x_f32, _compare_diff)
return y_f32
def build(self, ids, table_ids, embedding_grad):
(
num_unique_matrix,
inverse_unique_partition_indices,
_,
cur_rank_unique_ids,
_,
cur_rank_inverse_indices,
) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables)
if fp16:
embedding_grad = flow.cast(embedding_grad, flow.float16)
cur_rank_unique_embedding_grad = flow._C.one_embedding_embedding_gradient_shuffle(
embedding_grad,
num_unique_matrix,
cur_rank_inverse_indices,
inverse_unique_partition_indices,
)
if fp16:
cur_rank_unique_embedding_grad = flow.cast(
cur_rank_unique_embedding_grad, flow.float32)
return (
cur_rank_unique_embedding_grad,
flow.cast(cur_rank_unique_ids, flow.int32),
flow.cast(cur_rank_inverse_indices, flow.int32),
flow.cast(inverse_unique_partition_indices, flow.int32),
)
def test_element_wise_mul_job(
x: oft.Numpy.Placeholder(shape, dtype=flow.float),
y: oft.Numpy.Placeholder(shape, dtype=flow.float),
):
with flow.scope.placement(device, "0:0"):
x += flow.get_variable(
name="vx",
shape=(1,),
dtype=flow.float,
initializer=flow.zeros_initializer(),
)
y += flow.get_variable(
name="vy",
shape=(1,),
dtype=flow.float,
initializer=flow.zeros_initializer(),
)
x = flow.cast(x, dtype=flow_type)
y = flow.cast(y, dtype=flow_type)
out = flow.math.multiply(x, y)
out = flow.cast(out, dtype=flow.float)
flow.losses.add_loss(out)
flow.watch(x, test_global_storage.Setter("x"))
flow.watch_diff(x, test_global_storage.Setter("x_diff"))
flow.watch(y, test_global_storage.Setter("y"))
flow.watch_diff(y, test_global_storage.Setter("y_diff"))
flow.watch(out, test_global_storage.Setter("out"))
flow.watch_diff(out, test_global_storage.Setter("out_diff"))
return out
def oneflow_hardtanh(
of_input_1: tp.Numpy.Placeholder(shape=input_1.shape,
dtype=flow.float32),
) -> tp.Numpy:
with flow.scope.placement(device_type, "0:0"):
v = flow.get_variable(
shape=input_1.shape,
dtype=flow.float32,
initializer=flow.zeros_initializer(),
name="x_var",
)
x_var = of_input_1 + v
x_f16 = flow.cast(x_var, flow.float16)
of_hardtanh_out_f16 = flow.nn.hardtanh(x_f16, min_val, max_val)
of_hardtanh_out_f32 = flow.cast(of_hardtanh_out_f16, flow.float32)
with flow.scope.placement(device_type, "0:0"):
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler(
[], [1e-3]),
momentum=0).minimize(of_hardtanh_out_f32)
flow.watch_diff(x_var, assert_prediction_grad)
return of_hardtanh_out_f32
def test_fused_scale_tril_fw_bw_job(
x: oft.Numpy.Placeholder(shape, dtype=flow_type), ):
with flow.scope.placement(device, "0:0"):
x_var = flow.get_variable(
name="xv",
shape=(1, ),
dtype=flow.float,
initializer=flow.zeros_initializer(),
)
x += flow.cast(x_var, dtype=flow_type)
if type_name == "float16":
out = flow.cast(
flow.math.fused_scale_tril(flow.cast(x, flow.float16),
diagonal,
scale=scale),
flow.float,
)
else:
out = flow.math.fused_scale_tril(x, diagonal, scale=scale)
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler(
[], [1e-4]),
momentum=0).minimize(out)
flow.watch(x, test_global_storage.Setter("x"))
flow.watch_diff(x, test_global_storage.Setter("x_diff"))
flow.watch(out, test_global_storage.Setter("out"))
flow.watch_diff(out, test_global_storage.Setter("out_diff"))
return out
def DropoutJob():
with flow.scope.placement(device_type, "0:0"):
x = flow.get_variable(
"x",
shape=x_shape,
dtype=dtype,
initializer=flow.random_uniform_initializer(minval=-1,
maxval=1),
trainable=True,
)
if data_type == "float16":
x = flow.cast(flow.cast(x, flow.float16), dtype)
of_out = flow.cast(
flow.nn.dropout(flow.cast(x, flow.float16),
rate=rate,
seed=seed,
name="dropout"),
dtype,
)
else:
of_out = flow.nn.dropout(x,
rate=rate,
seed=seed,
name="dropout")
loss = flow.math.square(of_out)
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler(
[], [1e-4]),
momentum=0).minimize(loss)
flow.watch(x, test_global_storage.Setter("x"))
flow.watch_diff(x, test_global_storage.Setter("x_diff"))
flow.watch(of_out, test_global_storage.Setter("out"))
flow.watch_diff(of_out, test_global_storage.Setter("out_diff"))
return loss
def SoftmaxJob():
with flow.scope.placement(device_type, "0:0"):
x = flow.get_variable(
"x",
shape=x_shape,
dtype=dtype,
initializer=flow.random_uniform_initializer(minval=-0.1,
maxval=0.1),
trainable=True,
)
if data_type == "float16":
loss = flow.cast(
flow.nn.softmax(flow.cast(x, dtype=flow.float16),
axis=axis),
dtype=flow.float,
)
else:
loss = flow.nn.softmax(x, axis=axis)
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler(
[], [1e-4]),
momentum=0).minimize(loss)
flow.watch(x, test_global_storage.Setter("x"))
flow.watch_diff(x, test_global_storage.Setter("x_diff"))
flow.watch(loss, test_global_storage.Setter("loss"))
flow.watch_diff(loss, test_global_storage.Setter("loss_diff"))
return loss
def FusedCastScaleJob():
with flow.scope.placement(device_type, "0:0"):
x = flow.get_variable(
"in",
shape=input_shape,
dtype=flow.float,
initializer=flow.random_uniform_initializer(),
trainable=True,
)
scale = flow.get_variable(
"scale",
shape=(1, ),
dtype=flow.float,
initializer=flow.random_uniform_initializer(),
trainable=False,
)
loss = flow.cast(x, dtype=type_name_to_flow_type[in_dtype])
if test_fuse_cast_scale_pass:
loss = flow.cast(
loss, dtype=type_name_to_flow_type[out_dtype]) * flow.cast(
scale, dtype=type_name_to_flow_type[out_dtype])
else:
loss = fused_cast_scale(
loss,
flow.cast(scale, dtype=type_name_to_flow_type[out_dtype]),
name="fused_cast_scale",
)
loss = flow.cast(loss, dtype=flow.float)
flow.watch(x, test_global_storage.Setter("x"))
flow.watch(scale, test_global_storage.Setter("scale"))
flow.watch(loss, test_global_storage.Setter("loss"))
return loss
def flow_net(var_name, random_mask):
with flow.scope.placement(device_type, "0:0-0"):
x = flow.get_variable(
name=var_name,
shape=x_shape,
dtype=flow.float32,
initializer=flow.ones_initializer(),
trainable=True,
)
constant_val = flow.constant(3.0, dtype=flow.float32, shape=(1, ))
x = x * constant_val
x = x * 2.0
if device_type == "gpu":
x = flow.cast(x, flow.float16)
x = flow.math.relu(x)
x = flow.cast(x, flow.float)
loss = flow.math.reduce_mean(x * random_mask)
flow.optimizer.Adam(
flow.optimizer.PiecewiseConstantScheduler([], [learning_rate]),
beta1=beta1,
beta2=beta2,
epsilon=epsilon,
do_bias_correction=True,
).minimize(loss)
return x
def do_test_dropout_addend_numpy_fp16_p0(test_case, shape):
np_x = np.random.randn(*shape).astype(np.float32)
np_x_fp16 = np_x.astype(np.float16)
np_addend = np.random.randn(*shape).astype(np.float32)
np_addend_fp16 = np_addend.astype(np.float16)
x_tensor = flow.tensor(np_x, requires_grad=True, device="cuda")
x_tensor_fp16 = flow.cast(x_tensor, flow.float16)
addend_tensor = flow.tensor(np_addend, requires_grad=True, device="cuda")
addend_tensor_fp16 = flow.cast(addend_tensor, flow.float16)
np_one_mask = np.ones_like(np_x)
DropoutModule = flow.nn.Dropout(p=0.0)
out = DropoutModule(x_tensor_fp16, addend_tensor_fp16)
out_fp32 = flow.cast(out, flow.float32)
test_case.assertTrue(
np.allclose(out_fp32.numpy(),
np_x_fp16 + np_addend_fp16,
atol=1e-5,
rtol=1e-5))
out_sum = out_fp32.sum()
out_sum.backward()
test_case.assertTrue(
np.allclose(x_tensor.grad.numpy(), np_one_mask, atol=1e-5, rtol=1e-5))
test_case.assertTrue(
np.allclose(addend_tensor.grad.numpy(),
np_one_mask,
atol=1e-5,
rtol=1e-5))
def FlowJob(x_full_precision: oft.Numpy.Placeholder(x.shape, dtype=dtype)):
with flow.scope.placement(device_type, "0:0"):
x_full_precision += flow.get_variable(
name="v1",
shape=(1, ),
dtype=dtype,
initializer=flow.zeros_initializer())
if data_type == "float16":
x = flow.cast(x_full_precision, flow.float16)
else:
x = x_full_precision
y = flow.layers.batch_normalization(x,
*flow_args,
trainable=trainable,
training=training)
y = flow.cast(y, flow.float)
if trainable:
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler(
[], [0.001]),
momentum=0).minimize(y)
flow.watch_diff(x_full_precision,
test_global_storage.Setter("x_diff"))
return y
def FlowJob(
value: oft.Numpy.Placeholder(value.shape),
bias: oft.Numpy.Placeholder(bias.shape),
):
with flow.scope.placement(device_type, "0:0"):
value += flow.get_variable(
name="v1",
shape=(1, ),
dtype=flow.float,
initializer=flow.zeros_initializer(),
)
bias += flow.get_variable(
name="v2",
shape=(1, ),
dtype=flow.float,
initializer=flow.zeros_initializer(),
)
if data_type == "float16":
comp_value = flow.cast(value, dtype=flow.float16)
comp_bias = flow.cast(bias, dtype=flow.float16)
else:
comp_value = value
comp_bias = bias
loss = flow.nn.bias_add(comp_value, comp_bias, *flow_args)
if data_type == "float16":
loss = flow.cast(loss, dtype=flow.float)
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler([],
[0]),
momentum=0).minimize(loss)
flow.watch_diff(value, test_global_storage.Setter("value_diff"))
flow.watch_diff(bias, test_global_storage.Setter("bias_diff"))
return loss
def test_job(x: oft.Numpy.Placeholder(input_shape, dtype=flow.float32),):
v = flow.get_variable(
name="v",
shape=(1,),
dtype=flow.float32,
initializer=flow.zeros_initializer(),
)
x = x + v
x1 = flow.identity(x)
x2 = flow.identity(x)
flow.watch_diff(x1, test_global_storage.Setter("x1_diff"))
flow.watch_diff(x2, test_global_storage.Setter("x2_diff"))
x1 = flow.cast(x1, data_type)
x2 = flow.cast(x2, data_type)
y1 = flow.layers.batch_normalization_relu(x1, axis=axis, name="BN1")
y2 = flow.math.relu(flow.layers.batch_normalization(x2, axis=axis, name="BN2"))
y1 = flow.cast(y1, flow.float32)
y2 = flow.cast(y2, flow.float32)
flow.watch(y1, test_global_storage.Setter("y1"))
flow.watch(y2, test_global_storage.Setter("y2"))
loss = flow.math.reduce_mean(y1 + y2)
flow.optimizer.SGD(
flow.optimizer.PiecewiseConstantScheduler([], [0.001]), momentum=0
).minimize(flow.math.reduce_sum(loss))
return loss
def test_element_wise_mul_job(
x: oft.Numpy.Placeholder(shape, dtype=flow.float),
y: oft.Numpy.Placeholder(shape, dtype=flow.float),
):
with flow.scope.placement(device, "0:0"):
x += flow.get_variable(
name="vx",
shape=(1, ),
dtype=flow.float,
initializer=flow.zeros_initializer(),
)
y += flow.get_variable(
name="vy",
shape=(1, ),
dtype=flow.float,
initializer=flow.zeros_initializer(),
)
x = flow.cast(x, dtype=flow_type)
y = flow.cast(y, dtype=flow_type)
out = flow.math.multiply(x, y)
out = flow.cast(out, dtype=flow.float)
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler(
[], [1e-4]),
momentum=0).minimize(out)
flow.watch(x, test_global_storage.Setter("x"))
flow.watch_diff(x, test_global_storage.Setter("x_diff"))
flow.watch(y, test_global_storage.Setter("y"))
flow.watch_diff(y, test_global_storage.Setter("y_diff"))
flow.watch(out, test_global_storage.Setter("out"))
flow.watch_diff(out, test_global_storage.Setter("out_diff"))
return out
def MatmulJob():
with flow.scope.placement(device_type, "0:0"):
a = flow.get_variable(
"a",
shape=a_shape,
dtype=dtype,
initializer=flow.random_uniform_initializer(minval=0, maxval=1),
trainable=True,
)
b = flow.get_variable(
"b",
shape=b_shape,
dtype=dtype,
initializer=flow.random_uniform_initializer(minval=0, maxval=1),
trainable=True,
)
if data_type == "float16":
out = flow.matmul(
flow.cast(a, dtype=flow.float16),
flow.cast(b, dtype=flow.float16),
transpose_a,
transpose_b,
alpha,
)
c = flow.get_variable(
"c",
shape=out.shape,
dtype=dtype,
initializer=flow.random_uniform_initializer(minval=-1, maxval=1),
trainable=True,
)
loss = flow.cast(
out + flow.cast(c, dtype=flow.float16), dtype=flow.float
)
else:
out = flow.matmul(a, b, transpose_a, transpose_b, alpha)
c = flow.get_variable(
"c",
shape=out.shape,
dtype=dtype,
initializer=flow.random_uniform_initializer(minval=-1, maxval=1),
trainable=True,
)
loss = out + c
flow.optimizer.SGD(
flow.optimizer.PiecewiseConstantScheduler([], [1e-4]), momentum=0
).minimize(loss)
flow.watch(a, test_global_storage.Setter("a"))
flow.watch_diff(a, test_global_storage.Setter("a_diff"))
flow.watch(b, test_global_storage.Setter("b"))
flow.watch_diff(b, test_global_storage.Setter("b_diff"))
flow.watch(c, test_global_storage.Setter("c"))
flow.watch_diff(c, test_global_storage.Setter("c_diff"))
flow.watch(loss, test_global_storage.Setter("loss"))
flow.watch_diff(loss, test_global_storage.Setter("loss_diff"))
return loss
def RepeatAccJob(a: oft.Numpy.Placeholder(shape)):
if dtype == "float16":
return flow.cast(
flow.acc(flow.repeat(flow.cast(a, flow.float16), acc_num),
acc_num),
flow.float,
)
else:
return flow.acc(flow.repeat(a, acc_num), acc_num)
def ReduceSumLikeJob(x: oft.Numpy.Placeholder(input_shape)):
with flow.scope.placement(device_type, "0:0"):
if data_type == "float16":
x = flow.cast(x, dtype=flow.float16)
like = flow.math.reduce_sum(x, axis=axis, keepdims=keepdims)
y = reduce_sum_like(x, like, axis=axis)
y = flow.cast(y, dtype=flow.float32)
else:
like = flow.math.reduce_sum(x, axis=axis, keepdims=keepdims)
y = reduce_sum_like(x, like, axis=axis)
return y
def loss_function(real, pred):
mask = flow.math.not_equal(
real, flow.constant_scalar(0, dtype=flow.int64, name="zero constant"))
real = flow.cast(real, dtype=flow.int32, name="cast_to_int32")
loss_ = flow.nn.sparse_softmax_cross_entropy_with_logits(labels=real,
logits=pred)
mask = flow.cast(mask, dtype=loss_.dtype)
loss_ *= mask
return flow.math.reduce_mean(loss_)
def ReduceSumJob(x: oft.Numpy.Placeholder(input_shape)):
with flow.scope.placement(device_type, "0:0"):
if data_type == "float16":
y = flow.cast(
flow.math.reduce_sum(flow.cast(x, dtype=flow.float16),
axis=axis,
keepdims=keepdims),
dtype=flow.float32,
)
else:
y = flow.math.reduce_sum(x, axis=axis, keepdims=keepdims)
return y
def _CreateAttentionMaskFromInputMask(to_mask_blob, from_seq_length, to_seq_length):
output = flow.cast(to_mask_blob, dtype=flow.float)
output = flow.reshape(output, [-1, 1, to_seq_length])
zeros = flow.constant(0.0, dtype=flow.float, shape=[from_seq_length, to_seq_length])
attention_mask_blob = zeros + output
attention_mask_blob = flow.reshape(
attention_mask_blob, [-1, 1, from_seq_length, to_seq_length]
)
attention_mask_blob = flow.cast(attention_mask_blob, dtype=flow.float)
addr_blob = (attention_mask_blob - 1.0) * 10000.0
return addr_blob
def get_extended_attention_mask(
self, attention_mask, from_seq_length, to_seq_length
):
output = flow.cast(attention_mask, dtype=flow.float32)
output = flow.reshape(output, [-1, 1, to_seq_length])
# broadcast `from_tensor` from 2D to 3D
output = output.expand(-1, from_seq_length, -1)
attention_mask = flow.reshape(output, [-1, 1, from_seq_length, to_seq_length])
attention_mask = flow.cast(attention_mask, dtype=flow.float32)
addr_blob = (attention_mask - 1.0) * 10000.0
return addr_blob
def do_test_dropout_numpy_fp16_p1(test_case, shape):
np_x = np.random.randn(*shape).astype(np.float32)
x_tensor = flow.tensor(np_x, requires_grad=True, device="cuda")
x_tensor_fp16 = flow.cast(x_tensor, flow.float16)
np_zero_mask = np.zeros_like(np_x)
out = flow._C.dropout(x_tensor_fp16, p=1.0)
out_fp32 = flow.cast(out, flow.float32)
test_case.assertTrue(
np.allclose(out_fp32.numpy(), np_zero_mask, atol=1e-5, rtol=1e-5))
out_sum = out_fp32.sum()
out_sum.backward()
test_case.assertTrue(
np.allclose(x_tensor.grad.numpy(), np_zero_mask, atol=1e-5, rtol=1e-5))
def build(self, ids, table_ids):
(
num_unique,
unique_ids,
unique_table_ids,
inverse_indices,
) = flow._C.one_embedding_unique_key_value_pair(
ids, table_ids, num_tables)
return (
flow.cast(num_unique, flow.int32),
flow.cast(unique_ids, flow.int32),
flow.cast(unique_table_ids, flow.int32),
flow.cast(inverse_indices, flow.int32),
)
def _CreateAddrFromAttentionMask(attention_mask_blob, from_seq_length,
to_seq_length):
attention_mask_blob = flow.reshape(attention_mask_blob,
[-1, 1, from_seq_length, to_seq_length])
attention_mask_blob = flow.cast(attention_mask_blob, dtype=flow.float)
addr_blob = (attention_mask_blob - 1.0) * 10000.0
return addr_blob
def cast_forward(
input_def: oft.Numpy.Placeholder(
shape=input_shape, dtype=type_name_to_flow_type[dtype]
)
):
with flow.scope.placement(device_type, "0:0"):
return flow.cast(input_def, dtype=type_name_to_flow_type[dtype])
def logical_xor_op(input, other):
"""
Computes the element-wise logical XOR of the given input tensors.
Zeros are treated as False and nonzeros are treated as True.
Args:
input (oneflow.Tensor): The input Tensor
other (oneflow.Tensor): The Tensor to compute XOR with
Returns:
oneflow.Tensor: The output Tensor
For example:
.. code-block:: python
>>> import numpy as np
>>> import oneflow as flow
>>> input1 = flow.tensor(np.array([1, 0, 1]).astype(np.float32), dtype=flow.float32)
>>> input2 = flow.tensor(np.array([1, 0, 0]).astype(np.float32), dtype=flow.float32)
>>> out = flow.logical_xor(input1, input2)
>>> out
tensor([False, False, True], dtype=oneflow.bool)
"""
if type(input) == type(other): # input and other are tensor.
assert input.shape == other.shape, "shape of input and other should be same"
if other.dtype != input.dtype:
other = flow.cast(other, input.dtype)
return flow._C.logical_xor(input, other)