def l2loss(data):
dtype = data.dtype
check_list = ["float16", "float32"]
if not (dtype.lower() in check_list):
raise RuntimeError("tile_cce only support %s while dtype is %s" % (",".join(check_list), dtype))
vc_util.check_shape(data.shape)
orig_dtype = dtype
if dtype.lower() == "float16":
dtype = "float32"
data = akg.topi.cast(data, dtype)
# code has bug
#shape, axis = simplify_axis_shape(shape, range(len(shape)))
coeff_sqrt = akg.tvm.const(1.0 / (2 ** (0.5)), dtype=dtype)
res = akg.lang.cce.vmuls(data, coeff_sqrt)
res = akg.lang.cce.vmul(res, res)
res, _ = sum.sum_value(res)
if dtype != orig_dtype:
res = akg.topi.cast(res, orig_dtype)
return res
def gather(params_shape,
indices_shape,
params_dtype,
indices_dtype,
axis,
kernel_name,
cce_path="./"):
"""Gather data by indices"""
vc_util.check_shape(params_shape, length=2)
vc_util.check_shape(indices_shape, length=1)
vc_util.ops_dtype_check(params_dtype, vc_util.DtypeForDavinci.ALL_TYPES)
vc_util.ops_dtype_check(indices_dtype, vc_util.DtypeForDavinci.INT32)
vc_util.check_equal("axis", "zero", axis, 0)
# construct compute
o_shape = (indices_shape[0], params_shape[1])
xx = akg.tvm.placeholder(params_shape, dtype=params_dtype, name="X")
yy = akg.tvm.placeholder(indices_shape, dtype=indices_dtype, name="Y")
res = akg.tvm.extern(o_shape, [xx, yy],
lambda ins, outs: kernel_ir(outs[0], ins[0], ins[1]),
name="res",
dtype=params_dtype)
s = akg.tvm.create_schedule(res.op)
# create cce
attrs = {"enable_multicore": False}
with akg.build_config(add_lower_pass=cce.debug_mode(0), dump_pass_ir=True):
mod = akg.build(s, [xx, yy, res], "cce", name=kernel_name, attrs=attrs)
source_code = mod.imported_modules[0].get_source()
utils.create_code(kernel_name, cce_path, source_code)
return mod
def dropout_do_mask(data_tensor, data_mask, keep_prob):
dtype = data_tensor.dtype
shape_tensor = [x.value for x in data_tensor.shape]
vc_util.ops_dtype_check(dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
vc_util.check_shape(shape_tensor)
strides = [1]
for x in reversed(shape_tensor):
strides.append(strides[-1] * x)
if keep_prob < 0 or keep_prob > 1:
raise RuntimeError("keep_prob must in [0,1]")
keep_prob_const = akg.tvm.const(1.0 / keep_prob, dtype=dtype)
data_scale_ub = akg.tvm.compute(
shape_tensor,
lambda *indices: data_tensor(*indices) * keep_prob_const,
name='data_scale_ub')
def get_index(indices):
idx = 0
for i in range(len(indices)):
idx += indices[len(indices) - i - 1] * strides[i]
return idx // 8
if dtype == "float32":
data_scale_ub_16 = akg.topi.cast(data_scale_ub, "float16")
res_ub_16 = akg.tvm.compute(shape_tensor,
lambda *indice: dav.dropout(data_mask[get_index(indice)], data_scale_ub_16(*indice)))
res = akg.topi.cast(res_ub_16, "float32")
else:
res = akg.tvm.compute(shape_tensor, lambda *indice: dav.dropout(data_mask[get_index(indice)], data_scale_ub(*indice)))
return res
def logical_not(inputs):
vc_util.ops_dtype_check(inputs.dtype, vc_util.DtypeForDavinci.BOOL)
vc_util.check_shape(inputs.shape)
res = akg.topi.logical_not(inputs)
return res
def discontinous_mov(data, out_shape):
"""
Extract the element with the odd index from the original data and copy it into a tensor with a dimension of
2 * original dimension/2.
Args:
data (tvm.tensor.Tensor): Tensor of type float16, float32.
out_shape (list): a list of output's shape.
Returns:
tvm.tensor.Tensor, has the same type as data, but it's shape changes to out_shape not data's shape.
Example:
if data = [1,2,3,4,5,6,7,8,9,10] then the output = [[1,3,5,7,9],[1,3,5,7,9]].
"""
# check types
vc_util.ops_dtype_check(data.dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
shape = [x.value for x in data.shape]
vc_util.check_shape(shape)
output = akg.tvm.compute(out_shape,
lambda j, i: data[i * 2],
name="output")
return output
def invert_permutation_run(shape, dtype, attrs):
# check shapes
vc_util.check_shape(shape)
if not (dtype.lower() in "int32"):
raise RuntimeError(
"indices_dtype only support int32 while dtype is %s" % dtype)
A = akg.tvm.placeholder(shape, dtype, name="A")
op = invert_permutation.invert_permutation(A)
s = akg.tvm.create_schedule(op.op)
kernel_name = utils.gen_name_kernel("invert_permutation", dtype, shape)
with akg.build_config(add_lower_pass=cce.debug_mode(0), dump_pass_ir=True):
mod = akg.build(s, [A, op],
"cce",
name=kernel_name,
attrs=attrs,
polyhedral=True)
input_data = np.random.permutation(np.arange(shape[0])).astype(np.int32)
expect = np.full([shape[0]], 0, np.int32)
for i, e in enumerate(input_data):
expect[e] = i
output = np.full([shape[0]], 0, np.int32)
output = utils.mod_launch(mod, (input_data, output), expect=expect)
return (input_data, ), output, expect, compare_tensor(output,
expect,
rtol=5e-03,
equal_nan=True)
def pow(data1, data2):
"""
Computes power(data1,data2) elementwise, broadcast is supported.
Args:
data1 (tvm.tensor.Tensor): Tensor.
data2 (tvm.tensor.Tensor): Tensor of same type as data1, if shape(data2) != shape(data1), broadcast will happen.
Returns:
tvm.tensor.Tensor, powered result, with same type as input tensors and broadcasted shape of data1 and data2.
"""
vc_util.elemwise_dtype_check(data1.dtype, data2.dtype)
vc_util.check_shape(data1.shape)
vc_util.check_shape(data2.shape)
vc_util.auto_broadcast_check(data1.shape, data2.shape)
in_dtype = data1.dtype
if in_dtype == 'float16':
data1 = akg.topi.cast(data1, 'float32')
data2 = akg.topi.cast(data2, 'float32')
res = akg.topi.power(data1, data2)
if in_dtype == 'float16':
res = akg.topi.cast(res, 'float16')
return res
def tanh_grad(data_y, data_dy):
"""
Compute the backpropogation gradient of tanh.
Args:
data_y: Tensor, which equals the output of tanh.
data_dy: Tensor, the initial gradients.
Return:
Tensor, overall gradients.
"""
dtype = data_y.dtype
vc_util.ops_dtype_check(data_y.dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
shape = [x.value for x in data_y.shape]
vc_util.check_shape(shape)
# dx = dy * (1 - y*y)
tmp1 = akg.tvm.const(-1, dtype=dtype)
tmp2 = akg.tvm.const(1, dtype=dtype)
data1_square = akg.lang.cce.vmul(data_y, data_y)
data_tmp = akg.lang.cce.vmuls(data1_square, tmp1)
anuminate = akg.lang.cce.vadds(data_tmp, tmp2)
res = akg.lang.cce.vmul(anuminate, data_dy)
return res
def reduce_prod(data, axis=None, keepdims=False):
"""
Computes the product of elements along specific axis
Args:
data (tvm.tensor.Tensor): indicating the input tensor.
axis (Union[list, tuple, int, None]): indicating the dimensions to reduce at. if it's None, all dimensions
will be reduced.
keepdims (Union[bool, None]): if true, keep the dimensions with length 1.
Returns:
Tensor, the product of elements of input tensor.
"""
shape = [x.value for x in data.shape]
ops_dtype_check(data.dtype, [
DtypeForDavinci.ALL_FLOAT, DtypeForDavinci.INT8, DtypeForDavinci.UINT8
])
if axis is None and keepdims is False:
raise ValueError("keepdims must be True when axis is None!")
axis_new = ft_util.refine_reduce_axis(data, axis)
check_shape(shape)
dtype = data.dtype
if dtype in ["int8", "uint8"]:
data = akg.topi.cast(data, "float16")
vlog_t = akg_log(data)
res = akg.topi.sum(vlog_t, axis=axis_new, keepdims=keepdims)
res = akg_exp(res)
if dtype in ["int8", "uint8"]:
res = akg.topi.cast(res, dtype)
return res
def reciprocal(data, high_precision=True):
"""
Computes the reciprocal of data element-wise.
Args:
data (list[tvm.tensor.Tensor]): a list of tvm.tensor.Tensor of type float16, float32.
high_precision (bool): a bool value, whether to use high-precision version.
Returns:
tvm.tensor.Tensor of same type and shape as data.
"""
vc_util.ops_dtype_check(data.dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
shape = [x.value for x in data.shape]
vc_util.check_shape(shape)
res = akg.tvm.compute(shape, lambda *indice: akg.tvm.const(1, data.dtype) / (data(*indice)), name="res")
# When product is mini, using Newtom iteration method to achieve higher precision.
if utils.product_is_mini() and high_precision:
steps = 1
for _ in range(steps):
temp1 = data * res
temp2 = temp1 * akg.tvm.const(-1, data.dtype)
temp3 = temp2 + akg.tvm.const(2, data.dtype)
res = temp3 * res
return res
def bias_add_ad(head, input_shape, data_format):
"""
Compute gradient for bias_add operator using automatic differentiate.
Args:
head (tvm.tensor.Tensor): Input tensor.
input_shape (Union[list, tuple]): Input shape of head.
data_format (str): Data format of input tensors.
Returns:
tvm.tensor.Tensor of same shape and type as head.
"""
check_list = ["NHWC", "NC1HWC0", "DefaultFormat"]
if data_format not in check_list:
raise RuntimeError("bias_add_grad only support %s while dataformat is %s" % (",".join(check_list), data_format))
vc_util.check_shape(head.shape)
shape1 = [x.value for x in head.shape]
vc_util.davinci_format_check(shape1, data_format)
a = akg.tvm.placeholder(head.shape, head.dtype, "A")
if data_format == "NC1HWC0":
bias_shape = (1, head.shape[1], 1, 1, head.shape[4])
b = akg.tvm.placeholder(bias_shape, head.dtype, "B")
elif data_format == "NHWC":
bias_shape = (input_shape[-1],)
b = akg.tvm.placeholder(bias_shape, head.dtype, "B")
else:
bias_shape = (input_shape[1],)
b = akg.tvm.placeholder(bias_shape, head.dtype, "B")
c = bias_add.bias_add(a, b, data_format)
jacs = list(akg.differentiate(c, [b], head))
attrs = {}
return jacs[0], attrs
def reduce_any_d(x, axis=None, keepdims=False):
"""
Reduce a tensor on a certain axis based on max.
Args:
x (tvm.tensor.Tensor): The input tensor to reduce. Should be of type int8.
axis (Union[list, tuple, int, None]): The dimensions to reduce. If None, all dimensions will be reduced.
each dim must be in the range [-len(data.shape), len(data.shape) - 1].
keepdims (Union[bool, None]): If True, retains reduced dimensions with length 1, defaults to False.
Returns:
tvm.tensor.Tensor of same type as input tensor x.
"""
# check type
vc_util.ops_dtype_check(x.dtype, vc_util.DtypeForDavinci.INT8)
vc_util.check_shape(x.shape)
# check axis
vc_util.reduce_axis_check(x.shape, axis)
refined_axis = refine_reduce_axis(x, axis)
if len(set(refined_axis)) == len(x.shape) and not keepdims:
keepdims = True
res = _reduce_any_d_compute(x, refined_axis, keepdims)
if len(set(refined_axis)) == len(x.shape):
res = topi.reshape(res, (1, ))
return res
def less(data1, data2):
"""
compute tensor with smaller value in data1 and data2 elementwisely.
Args:
data1 (tvm.tensor.Tensor): Tensor of type float16, float32 and int32.
data2 (tvm.tensor.Tensor): Tensor of type float16, float32 and int32.
Returns:
tvm.tensor.Tensor. If data1 less than data2, return True, else return False.
"""
vc_util.check_shape(data1.shape)
vc_util.check_shape(data2.shape)
# check types
vc_util.elemwise_dtype_check(
data1.dtype, data2.dtype,
[vc_util.DtypeForDavinci.ALL_FLOAT, vc_util.DtypeForDavinci.INT32])
# check runtime mode, and change dtype
if utils.product_is_mini() and data1.dtype != "float16":
data1 = akg.topi.cast(data1, "float16")
data2 = akg.topi.cast(data2, "float16")
if (not utils.product_is_mini()) and data1.dtype == "int32":
data1 = akg.topi.cast(data1, "float32")
data2 = akg.topi.cast(data2, "float32")
res = akg.topi.less(data1, data2)
return res
def truncate_div(input_x1, input_x2):
"""
Calculating data's truncate_div, res = floor(x1/x2) if x1/x2>0 else ceil(x1/x2).
Args:
input_x1 (tvm.tensor.Tensor): Input tensor, support float16,
float32 on mini device, while support
int32, int8, uint8, float16, float32 on
cloud ones.
input_x2 (tvm.tensor.Tensor): Input tensor, with same dtype as input_x1.
Returns:
A tvm.tensor.Tensor as result of truncate_div.
"""
vc_util.check_shape(get_shape(input_x1))
vc_util.check_shape(get_shape(input_x2))
vc_util.elemwise_dtype_check(input_x1.dtype, input_x2.dtype)
vc_util.ops_dtype_check(
input_x1.dtype,
(vc_util.DtypeForDavinci.ALL_FLOAT) if utils.product_is_mini() \
else (vc_util.DtypeForDavinci.ALL_FLOAT,
vc_util.DtypeForDavinci.INT32,
vc_util.DtypeForDavinci.INT8,
vc_util.DtypeForDavinci.UINT8))
return truncate_div_compute(input_x1, input_x2)
def broadcast_to(x, shape):
"""
Broadcast an tensor to a compatible shape.
Args:
x (tvm.tensor.Tensor): Tensor of type float32, float16, int8, uint8, int32
shape (list, tuple): The shape of output tensor.
Returns:
An tvm.tensor.Tensor with the same type as x.
"""
# check shape
vc_util.check_shape(x)
vc_util.check_shape(shape)
# check dtype
dtype = x.dtype
vc_util.ops_dtype_check(dtype, vc_util.DtypeForDavinci.ALL_TYPES)
# vector_dup instruction don't support int8 and uint8
# It can be simplified by some methods, such as , "auto cast"
x_shape = get_shape(x)
if len(x_shape) == 1 and x_shape[0] == 1 and dtype in ["int8", "uint8"]:
x = cast(x, "float16")
res = topi.broadcast_to(x, shape)
if res.dtype != dtype:
res = cast(res, dtype)
return res
def floordiv(data1, data2):
"""
Calculate x/y, and always returns an integer which is floored.
Args:
data1 (tvm.tensor.Tensor): Tensor of type float16, float32.
data2 (tvm.tensor.Tensor): Tensor of type float16, float32.
Returns:
tvm.tensor.Tensor, has type of int32.
"""
vc_util.ops_dtype_check([data1.dtype, data2.dtype],
vc_util.DtypeForDavinci.ALL_FLOAT)
shape1 = [x.value for x in data1.shape]
vc_util.check_shape(shape1)
shape2 = [x.value for x in data2.shape]
vc_util.check_shape(shape2)
if utils.product_is_mini():
rec = reciprocal(data2, high_precision=True)
res = data1 * rec
else:
res = akg.topi.divide(data1, data2)
res = akg.lang.cce.floor(res)
return res
def xdivy(data_x1, data_x2):
"""
Calculate data_x1 divided by data_x2.
.. math::
y = \\left\\{
\\begin{aligned}
0, && if \\quad x1 == 0 \\\\
\\dfrac{x1}{x2}, && otherwise
\\end{aligned}
\\right.
Args:
data_x1 (tvm.tensor.Tensor): Tensor of dtype "float16" or "float32"
data_x2 (tvm.tensor.Tensor): Tensor of dtype "float16" or "float32"
Returns:
tvm.tensor.Tensor
"""
shape_x1 = get_shape(data_x1)
shape_x2 = get_shape(data_x2)
vc_util.check_shape(shape_x1)
vc_util.check_shape(shape_x2)
vc_util.elemwise_dtype_check(data_x1.dtype, data_x2.dtype)
dtype = data_x1.dtype
vc_util.ops_dtype_check(dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
return xdivy_compute(data_x1, data_x2)
def clip(data, min_val, max_val):
"""
Clip the data in range(min_val, max_val).
Change values less than min_val in data to min_val, and change values greater than max_val to max_val.
Note:
min_val should be smaller or equal to max_val.
Args:
data: Tensor.
min_val: Float. When data < min_val, set data to min_val.
max_val: Float. When data > max_val, set data to max_val.
Returns:
Tensor, has the same type and shape as data.
"""
dtype = data.dtype
check_list = ["float16", "float32"]
if not dtype.lower() in check_list:
raise RuntimeError("clip only support %s while dtype is %s" %
(",".join(check_list), dtype))
shape = data.shape
vc_util.check_shape(shape)
res = akg.topi.clip(data, min_val, max_val)
return res
def reduce_sum(inputs, axis=None, keepdims=False):
"""
Compute the sum of elements across dimensions of a tensor.
Args:
inputs (tvm.tensor.Tensor): Tensor.
axis (Union[list, tuple, int, None]): If the list or tuple is empty, the axis equal to None.
keepdims (bool): If keepdims equal to True, the result shape length is same to input shape length.
Returns:
tvm.tensor.Tensor, has same type as input. If keepdims is True, all reduced dimensions are retained
with length 1, else these reduced axis will be eliminate.
"""
axis = ft_util.refine_reduce_axis(inputs, axis)
vc_util.check_shape(inputs.shape)
in_dtype = inputs.dtype
if in_dtype == 'float16':
inputs = akg.topi.cast(inputs, 'float32')
output = akg.topi.sum(inputs, axis=axis, keepdims=keepdims)
if in_dtype == 'float16':
output = akg.topi.cast(output, 'float16')
return output
def less_equal(input1, input2):
"""
Check whether input1 lessequals to input2.
Args:
input1 (tvm.tensor.Tensor): Tensor.
input2 (tvm.tensor.Tensor): Tensor.
Returns:
tvm.tensor.Tensor. If input1 lessequal to input2 return True, else return False.
"""
shape1 = [x.value for x in input1.shape]
shape2 = [x.value for x in input2.shape]
vc_util.check_shape(shape1)
vc_util.check_shape(shape2)
shape1, shape2, shape = produce_shapes(shape1, shape2)
vc_util.elemwise_dtype_check(input1.dtype, input2.dtype)
dtype = input1.dtype
# get lessequal compute
t_value = akg.tvm.compute(shape, lambda *indice: akg.tvm.const(1, dtype), "T")
f_value = akg.tvm.compute(shape, lambda *indice: akg.tvm.const(0, dtype), "F")
input1_bro = akg.topi.broadcast_to(input1, shape)
input2_bro = akg.topi.broadcast_to(input2, shape)
c_out = akg.tvm.compute(shape, lambda *indice: akg.tvm.expr.Select(input1_bro[indice] <= input2_bro[indice],
t_value[indice], f_value[indice]), name="C")
res = akg.tvm.compute(shape, lambda *indice: c_out(*indice).astype("bool"), name="res")
return res
def logsoftmax_grad(Y, dY, axis):
"""
Computes the back propagation gradients by chain rule.
Args:
Y: Tensor, holds the logsoftmax activation output.
dY: Tensor, holds the initial gradients.
axis: Integer, on which dimension the softmax is applied.
Returns:
Tensor, the overall gradients.
"""
shape = [x.value for x in Y.shape]
vc_util.check_shape(shape)
dtype = Y.dtype
vc_util.ops_dtype_check(dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
if axis == -1:
axis = len(shape) + axis
if axis >= len(shape):
raise RuntimeError("axis should be less than dimension")
if axis < -1:
raise RuntimeError(
"negative axis only support -1, please specify the axis in positive value"
)
softmax = akg.topi.exp(Y)
dy_sum = akg.lang.cce.sum(dY, axis=axis)
dy_sum_broadcast = akg.lang.cce.broadcast(dy_sum, shape)
mul_result = akg.lang.cce.vmul(softmax, dy_sum_broadcast)
res = akg.lang.cce.vsub(dY, mul_result)
attrs = {"pragma_reschedule": 1, "pragma_modshift": 1}
return res, attrs
def concat(data, axis):
"""
Concatenates data along the dimension set by axis.
Args:
data (Union[list, tuple]): list or tuple of tvm.tensor.Tensor of type float16, float32, int32, int8, uint8
axis (int): Specifies the axis along which to concatenate. Must be in the range [-rank(data), rank(data))
Returns:
tvm.tensor.Tensor of same type as data.
"""
data_size = len(data)
if data_size < min_size:
raise RuntimeError("The size of data must be greater equal 1")
dtype = data[0].dtype
vc_util.ops_dtype_check(dtype, vc_util.DtypeForDavinci.ALL_TYPES)
shape_0 = data[0].shape
vc_util.check_shape(shape_0)
if axis < 0:
axis += len(shape_0)
for i in range(1, data_size):
shape_i = data[i].shape
vc_util.check_shape(shape_i)
if len(shape_i) != len(shape_0):
raise ValueError("Input tensors must have same dimensions.")
res = akg.lang.cce.concat(data, axis)
return res
def acos_grad(x, dy):
"""
Gradient for acos.
.. math:
dx = [\\frac{-1}{(1 - x^2)^0.5} / ] \\cdot dy
Args:
x (tvm.tensor.Tensor): tensor of type float16, float32.
dy (tvm.tensor.Tensor): tensor of type float16, float32.
Returns:
tvm.tensor.Tensor, same type and shape as x.
"""
dtype = x.dtype
vc_util.ops_dtype_check(x.dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
vc_util.ops_dtype_check(dy.dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
vc_util.check_shape(x.shape)
vc_util.check_shape(dy.shape)
one = akg.tvm.const(1.0, dtype=dtype)
mid_square = akg.tvm.compute(x.shape,
lambda *i: (one - x(*i) * x(*i)),
name="mid_square")
rsq = rsqrt.rsqrt(mid_square)
dx = akg.tvm.compute(x.shape, lambda *i: -rsq(*i) * dy(*i), name="dx")
return dx
def blas_axby(x, y, alpha, beta):
r"""
Blas axby.
:math:`\alpha x + \beta y`
Args:
x (tvm.tensor.Tensor): Input `x` of type float16 or float32.
y (tvm.tensor.Tensor): Input `y` of type float16 or float32.
alpha (Union[int, float]): Scale of `x`.
beta (Union[int, float]): Scale of `y`.
Returns:
tvm.tensor.Tensor, has the same shape and type as inputs.
"""
vc_util.ops_dtype_check([x.dtype, y.dtype],
vc_util.DtypeForDavinci.ALL_FLOAT)
vc_util.check_shape(x.shape)
vc_util.check_shape(y.shape)
ax = akg.lang.cce.vmuls(x, alpha)
by = akg.lang.cce.vmuls(y, beta)
res = akg.lang.cce.vadd(ax, by)
return res
def relu_grad(inputs, head):
"""
Computes gradient of inputs for the relu op
Args:
inputs: It is the same with the relu op.
head: Tensor, has the same type and shape as inputs. Back propagation value.
Returns:
Tensor, has the same type and shape as inputs.
"""
check_list = ["float16", "float32"]
dtype = inputs.dtype
if not dtype.lower() in check_list:
raise RuntimeError("relu_grad only support %s while dtype is %s" % (",".join(check_list), dtype))
shape = [x.value for x in inputs.shape]
vc_util.check_shape(shape)
res = akg.tvm.compute(shape,
lambda *i: akg.tvm.if_then_else(
inputs(*i) > akg.tvm.const(0, dtype),
head(*i), akg.tvm.const(0, dtype)
))
return res
def pad(data, paddings, padtype):
"""add paddings to the tensor
:shape: The shape of the tensor, now only support two dimension Tensor
:paddings: The shape of the paddings, shape [N,2], N is the dimension of the tensor,
For each dimension D of input, paddings[D, 0] indicates how many values to add before
the contents of tensor in that dimension, and paddings[D, 1] indicates how many values to
add after the contents of tensor in that dimension.
:dtype: The type of the input, float16, float32
:padtype: One of "CONSTANT", "REFLECT", or "SYMMETRIC".
"""
# check shape
vc_util.check_shape(data.shape)
# check types
vc_util.ops_dtype_check(data.dtype, vc_util.DtypeForDavinci.ALL_TYPES)
# check padding types
ptype_checklist = ['constant']
if not (padtype in ptype_checklist):
raise RuntimeError("pad_cce only support %s while padtype is %s" % (",".join(ptype_checklist), padtype))
dtype = data.dtype
if dtype == 'int8' or dtype == 'uint8':
data = cast(data, "float16")
rank = len(data.shape)
pad_before = []
pad_after = []
for i in range(rank):
pad_before.append(paddings[i][0])
pad_after.append(paddings[i][1])
B = tvm_pad(data, pad_before, pad_after=pad_after, name='B')
if dtype == 'int8' or dtype == 'uint8':
B = cast(B, dtype)
return B
def leaky_relu(data, negative_slop=0):
"""
leaky_relu op for input tensor (N,C,H,W) OR (N,C1,H,W,C0).
..math:`max(x,negative_slop*x)`
Args:
data (tvm.tensor.Tensor): tensor with type float16 or float32.
negative_slop (float): 0<=negative_slop<1
Returns:
tvm.tensor.Tensor.
"""
dtype = data.dtype
vc_util.ops_dtype_check(dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
vc_util.check_shape(data.shape)
if negative_slop >= 1 or negative_slop < 0:
raise RuntimeError(
"leaky_relu only support negative_slop between [0,1)")
slop_tmp = akg.tvm.const(negative_slop, dtype=dtype)
tmp = akg.lang.cce.vmuls(data, slop_tmp)
res = akg.lang.cce.vmax(tmp, data)
return res
def round_value(input):
"""
rounds the values of a akg.tvm.tensor to the nearest even(integer), element-wise
Args:
input: akg.tvm.Tensor of type float16, float32
Returns:
akg.tvm.Tensor of same shape as input, of type int32
Raises:
ValueError: If the type of input is invalid.
"""
dtype = input.dtype
vc_util.ops_dtype_check(dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
shape = input.shape
vc_util.check_shape(shape)
if dtype == "float16":
data_f16 = input
else:
data_f16 = akg.tvm.compute(shape,
lambda *i: input(*i).astype("float16"),
name="data_f16")
res = akg.lang.cce.round(data_f16)
return res
def minimum(input1, input2):
"""
Return the min value of two tensors element-wise.
Note:
minimum supports broadcasting.
Args:
input1: Tensor.
input2: Tensor. Has the same type as input1.
Returns:
Tensor, has the same type as inputs.
"""
vc_util.ops_dtype_check([input1.dtype, input2.dtype], vc_util.DtypeForDavinci.ALL_TYPES)
vc_util.elemwise_dtype_check(input1.dtype, input2.dtype)
dtype = input1.dtype
shape1 = [x.value for x in input1.shape]
shape2 = [x.value for x in input2.shape]
vc_util.check_shape(shape1)
vc_util.check_shape(shape2)
vc_util.auto_broadcast_check(shape1, shape2)
if dtype in ("int8", "uint8"):
input1 = cast(input1, "float16")
input2 = cast(input2, "float16")
res = akg.topi.minimum(input1, input2)
if dtype in ("int8", "uint8"):
res = cast(res, dtype)
return res
def reverse(input_data, axis):
"""
Reverse a tensor on some dimension.
Args:
input_data (tvm.tensor.Tensor): Tensor of float16, float32 and int32.
axis (Union[list, tuple, int]): Because of don't support reverse which contain last dim, so can't equal None.
Returns:
tvm.tensor.Tensor,has the same type and shape as input_data
"""
shape = get_shape(input_data)
dtype = input_data.dtype
# check dtype and shape
vc_util.check_shape(shape)
vc_util.ops_dtype_check(
dtype,
[vc_util.DtypeForDavinci.ALL_FLOAT, vc_util.DtypeForDavinci.INT32])
# check axis
shape_len = len(shape)
if hasattr(axis, 'index'):
axis = list(axis)
if isinstance(axis, int):
axis = [axis]
vc_util.axis_check(shape_len, axis)
_check_axis(axis, shape)
# compute res
res = reverse_compute(input_data, axis)
return res
