def fused_relu_grad_bn_double_update_grad(data_1, data_2, data_3, data_4, data_5, data_6, data_7, layout='NHWC'):
transform_list = [data_2, data_4, data_5, data_6, data_7]
for i in transform_list:
if layout == "NCHW":
i = topi.transpose(i, axes=(0, 2, 3, 1))
elif layout != "NHWC":
raise NotImplementedError( 'Layout not supported {} '.format(layout))
data_tmp1 = topi.full_like(data_7, 0.0)
data_tmp2 = topi.greater(data_7, data_tmp1)
data_tmp3 = topi.add(data_5, data_6)
data_tmp4 = topi.where(data_tmp2, data_tmp3, data_tmp1)
data_tmp5 = topi.cast(data_tmp4, 'float32')
data_tmp7 = topi.sum(data_tmp5, axis=(0, 1, 2))
n, h, w, c = data_7.shape
data_tmp8 = topi.cast(data_2, 'float32')
data_tmp9 = topi.full_like(data_tmp7, 1.0/(n*h*w))
data_tmp10 = topi.multiply(data_1, data_tmp9)
data_tmp11 = topi.broadcast_to(data_tmp10, data_tmp8.shape)
data_tmp12 = topi.subtract(data_tmp8, data_tmp11)
data_tmp13 = topi.multiply(data_tmp5, data_tmp12)
data_tmp15 = topi.sum(data_tmp13, axis=(0, 1, 2))
data_tmp16 = topi.cast(data_4, 'float32')
data_tmp17 = topi.multiply(data_3, data_tmp9)
data_tmp18 = topi.broadcast_to(data_tmp17, data_tmp16.shape)
data_tmp19 = topi.subtract(data_tmp16, data_tmp18)
data_tmp20 = topi.multiply(data_tmp5, data_tmp19)
data_tmp22 = topi.sum(data_tmp20, axis=(0, 1, 2))
return [data_tmp7, data_tmp15, data_tmp22]
def fused_relu_grad_bn_reduce_grad(data_1,
data_2,
data_3,
data_4,
data_5,
data_6,
data_7,
data_8,
data_9,
layout='NHWC',
target=utils.CUDA):
"""
fused_relu_grad_bn_reduce_grad.
Args:
data_1~data_9: tvm.tensor.Tensor.
layout: input layout, only 'NCHW', 'NHWC' supported
Returns:
tvm.tensor.Tensor.
"""
transform_list = [data_7, data_8, data_9]
for i in transform_list:
if layout == "NCHW":
i = topi.transpose(i, axes=(0, 2, 3, 1))
elif layout != "NHWC":
raise NotImplementedError(
'Layout not supported {} '.format(layout))
data_tmp1 = topi.multiply(data_4, data_5)
n, h, w, c = data_9.shape
data_tmp2 = topi.full_like(data_tmp1, 1.0 / (n * h * w))
data_tmp3 = topi.multiply(data_tmp1, data_tmp2)
data_tmp5 = topi.full_like(data_9, 0.0)
data_tmp6 = topi.greater(data_9, data_tmp5)
data_tmp7 = topi.where(data_tmp6, data_8, data_tmp5)
data_tmp8 = topi.cast(data_tmp7, 'float32')
data_tmp9 = topi.full_like(data_tmp8, n * h * w)
data_tmp10 = topi.multiply(data_tmp8, data_tmp9)
data_tmp12 = topi.subtract(data_tmp10, data_3)
data_tmp14 = topi.cast(data_7, 'float32')
data_tmp15 = topi.multiply(data_6, data_tmp2)
data_tmp17 = topi.subtract(data_tmp14, data_tmp15)
data_tmp18 = topi.multiply(data_2, data_tmp17)
data_tmp20 = topi.divide(data_tmp18, data_1)
data_tmp21 = topi.subtract(data_tmp12, data_tmp20)
data_tmp22 = topi.multiply(data_tmp3, data_tmp21)
data_out = topi.cast(data_tmp22, 'float16')
return data_out
def select_compute(condition, x1, x2):
"""select compute implementation"""
shape = get_shape(x1)
con_shape = get_shape(condition)
num_dtype = x1.dtype
bool_dtype = condition.dtype
if num_dtype in ("int8", "uint8"):
x1_dtype = "float32"
ones = akg.lang.cce.broadcast(tvm.const(VALUE_ONE, dtype="float32"),
shape,
output_dtype="float32")
x1 = akg.lang.cce.cast_to(x1, "float32")
x2 = akg.lang.cce.cast_to(x2, "float32")
else:
x1_dtype = num_dtype
ones = akg.lang.cce.broadcast(tvm.const(VALUE_ONE, dtype=num_dtype),
shape,
output_dtype=num_dtype)
if bool_dtype == "int8":
if x1_dtype == "int32":
condition_dtype = akg.lang.cce.ceil(condition)
else:
condition_dtype = akg.lang.cce.cast_to(condition, x1_dtype)
else:
if x1_dtype == "int32":
condition_dtype = condition
else:
condition_dtype = akg.lang.cce.cast_to(condition, x1_dtype)
if list(con_shape) != list(shape):
condition_dtype = akg.lang.cce.broadcast(condition_dtype, shape)
vinsn_support_dtype = ("float16", "float32")
if utils.product_is_mini():
vinsn_support_dtype = ("float16", )
if num_dtype in vinsn_support_dtype:
res = topi.where(condition_dtype, x1, x2)
else:
# For data types that are not supported by the vector instruction (vcmp and vsel),
# if the `topi.where` is directly used, the related instructions generated in the .cce file
# are scalar instructions such as `cond ? x1 : x2`, which is very inefficient.
# Therefore, other equivalent calculation methods are adopted.
condition_opp = akg.lang.cce.vsub(ones, condition_dtype)
temp_x = akg.lang.cce.vmul(x1, condition_dtype)
temp_y = akg.lang.cce.vmul(x2, condition_opp)
res = akg.lang.cce.vadd(temp_x, temp_y)
if num_dtype in ("int8", "uint8"):
res = akg.lang.cce.cast_to(res, num_dtype)
return res
def fused_relu_grad(input1, input2, input3, c1, target=utils.CUDA):
"""
fused_relu_grad.
Args:
input1 ~ input3: tvm.tensor.Tensor.
c1: const.
Returns:
Three output (list of tvm.tensor.Tensor).
"""
data_zero = topi.full_like(input3, c1)
cmp_zero = topi.greater(input3, data_zero)
data_add = topi.add(input1, input2)
return topi.where(cmp_zero, data_add, data_zero)
