def reduce_max_ad_optimized(head, data, axis, keepdims):
def get_shape(pld):
return [d.value for d in pld.shape]
def custom_reduce_max_fdiff(out, inputs, grad, ad_attrs, new_pld_array):
data = inputs[0]
shape = get_shape(data)
max_ = akg.lang.cce.reduce_max(data, axis=axis, keepdims=keepdims)
max_broadcast = akg.lang.cce.broadcast(max_, shape)
return [
akg.tvm.compute(shape,
lambda *indices: akg.tvm.expr.Select(
data(*indices) == max_broadcast(*indices),
grad(*get_reduced_indices(
*indices, axis=axis, keepdims=keepdims)),
akg.tvm.const(0, dtype=data.dtype)),
name="reduce_max_ad2")
]
l = reduce_max.reduce_max(data, axis, keepdims)
[dl_ddata
] = akg.differentiate(l, [data],
head,
None,
None,
override={l: ([data], custom_reduce_max_fdiff)})
return dl_ddata
def softmax_cross_entropy_with_logits(labels,
logits,
axis,
reduction="mean",
scale=1.0):
max_logits = reduce_max(logits, axis, keepdims=True, target=utils.CCE)
data_sub = sub(logits, max_logits, target=utils.CCE)
akg.register_variables("minus_max", [logits], data_sub)
data_exp = Exp(data_sub, target=utils.CCE)
data_expsum = sum(data_exp, axis, keepdims=True, target=utils.CCE)
data_expsum_log = log(data_expsum, target=utils.CCE)
sub_value = sub(data_sub, data_expsum_log, target=utils.CCE)
neg_labels = neg(labels, target=utils.CCE)
cross_entropy = mul(neg_labels, sub_value, target=utils.CCE)
# backprop: prob - labels, where prob = softmax(logits)
prob = Exp(sub_value, target=utils.CCE)
backprop = sub(prob, labels, target=utils.CCE)
if reduction.lower() == "none":
loss = sum_v2(cross_entropy, axis, keepdims=True)
elif reduction.lower() == "mean":
loss = sum_v2(cross_entropy, axis=None)
factor = logits.shape[0].value
loss = loss * akg.tvm.const(1 / factor, logits.dtype)
backprop = backprop * akg.tvm.const(1 / factor, logits.dtype)
elif reduction.lower() == "sum":
loss = sum_v2(cross_entropy, axis=None)
else:
raise ValueError(
"reduction method {0} is not supported".format(reduction))
backprop = akg.topi.multiply(backprop,
akg.tvm.const(scale, backprop.dtype))
return loss, backprop
def segment_max(data, segment_ids, num_segments):
"""
Computes the max value along segment_ids of a akg.tvm.tensor
Args:
data: akg.tvm.Tensor of type "float16", "float32"
segment_ids: akg.tvm.Tensor of type int32, sorted
Returns:
akg.tvm.Tensor of same shape and type as data
"""
d_dtype = data.dtype
vc_util.ops_dtype_check(d_dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
d_shape = [x.value for x in data.shape]
vc_util.check_shape(d_shape)
s_shape = segment_ids.shape
vc_util.check_shape(s_shape)
new_segment_ids, idx = gen_ids(segment_ids)
output_shape = (1, ) + tuple(d_shape[len(s_shape):])
zero_data = akg.tvm.compute(output_shape,
lambda *i: akg.tvm.const(0.0, d_dtype),
name="zero")
data_list = split.split(data, new_segment_ids)
out_n = num_segments
out = []
j = 0
for i in range(0, out_n):
if i in idx:
tmp = reduce_max.reduce_max(data_list[j], 0, True)
out.append(tmp)
j = j + 1
else:
out.append(zero_data)
res = concat.concat(out, 0)
return res
def unsorted_segment_max(data, segment_ids, num_segments):
"""
Computes the max value along segment_ids of a akg.tvm.Tensor
Args:
data: akg.tvm.Tensor of type float16, float32
segment_ids: akg.tvm.Tensor of type int32, shape is a prefix of input_data.shape.
num_segments: the number of classes in segment_ids
Returns:
akg.tvm.Tensor of same type as input_data,
"""
d_dtype = data.dtype
vc_util.ops_dtype_check(d_dtype, vc_util.DtypeForDavinci.ALL_FLOAT)
d_shape = [x.value for x in data.shape]
vc_util.check_shape(d_shape)
s_shape = segment_ids.shape
vc_util.check_shape(s_shape)
new_segment_ids, idx = gen_ids(segment_ids)
output_shape = (1, ) + tuple(d_shape[len(s_shape):])
zero_data = akg.tvm.compute(output_shape,
lambda *i: akg.tvm.const(0.0, d_dtype),
name="zero")
data_list, new_idx = split_new(data, new_segment_ids, idx, num_segments)
out = []
j = 0
for i in range(0, num_segments):
if i in new_idx:
tmp = reduce_max.reduce_max(data_list[j], 0, True)
out.append(tmp)
j = j + 1
else:
out.append(zero_data)
res = concat.concat(out, 0)
return res
def focal_loss(prediction, target, gamma):
"""
Calculate loss by focalloss.
See Source: <a href="https://arxiv.org/abs/1708.02002">Focal Loss for Dense Object Detection;
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár</a>
This op fuses activation function (`softmax`) and loss function (`focalloss`) together.
.. math::
p = softmax(x) \\
FL(p) = -(1-p)^{\\gamma}log(p)
Args:
prediction (tvm.tensor.Tensor): The predicted logits for each class,
type is float32 or float16 and shape is `(batch_size, num_anchors, num_clases)`,
target (tvm.tensor.Tensor): The one-hot encoded classification targets,
type is float32, float16 or int32 and shape is `(batch_size, num_anchors, num_classes)`,
gamma (float): positive float number.
Returns:
tvm.tensor.Tensor, has the same type as inputs with shape `(batch_size, num_anchors)`.
"""
vc_util.check_shape(prediction, length=3, tensor_name="prediction")
vc_util.check_shape(target, length=3, tensor_name="target")
vc_util.ops_dtype_check(prediction.dtype,
vc_util.DtypeForDavinci.ALL_FLOAT)
vc_util.ops_dtype_check(
target.dtype,
[vc_util.DtypeForDavinci.ALL_FLOAT, vc_util.DtypeForDavinci.INT32])
vc_util.check_greater("gamma", "zero", gamma, 0)
dim_info, _ = focal_loss_set_dim_func(prediction, target)
attrs = {"dim": dim_info}
dtype = prediction.dtype
if utils.product_is_mini() and dtype == 'float32':
prediction = akg.topi.cast(prediction, "float16")
target = akg.topi.cast(target, "float16")
axis = -1
shape = get_shape(prediction)
maxv = reduce_max(prediction, axis=axis, keepdims=True)
k1 = akg.tvm.reduce_axis((0, shape[-1]), name="k1")
expsum = akg.tvm.compute(
shape[:-1],
lambda *i: akg.tvm.sum(akg.tvm.exp(prediction(*i, k1) - maxv(*i, 0)),
axis=k1),
name="expsum")
gamma = akg.tvm.const(gamma, prediction.dtype)
one = akg.tvm.const(1, prediction.dtype)
def cal_focalloss(*i):
x = prediction(*i) - maxv(*i[:-1], 0)
pred = akg.tvm.exp(x - akg.tvm.log(expsum(*i[:-1]))) # softmax(x)
log_p = x - akg.tvm.log(expsum(*i[:-1])) # logsoftmax(x)
neg_pred_pow = akg.tvm.exp(akg.tvm.log(one - pred) *
gamma) # (1-pred)^gamma
loss = akg.tvm.const(
-1, prediction.dtype) * target(*i) * neg_pred_pow * log_p
return loss
loss = akg.tvm.compute(shape, cal_focalloss, name="loss")
loss = akg.topi.sum(loss, axis=axis)
if utils.product_is_mini() and dtype == 'float32':
loss = akg.topi.cast(loss, "float32")
return loss, attrs
def reduce_max_ad_optimized_manual_schedule(input_shape,
dtype,
axis,
keepdims,
polyhedral=True,
attrs=None):
def custom_reduce_max_fdiff(out, inputs, head_, ad_attrs, new_pld_array):
data_ = inputs[0]
shape = data_.shape
# reduces maximum value for each column
max_ = akg.lang.cce.reduce_max(data_, axis=axis, keepdims=True)
# copies reduced values to get the original shape
max_broadcast = akg.lang.cce.broadcast(max_, shape)
# head broadcast is needed to generate correct cce code for the selection operation
head_broadcast = akg.tvm.compute(
shape, lambda *indices: head_(*get_reduced_indices(
*indices, axis=axis, keepdims=keepdims)))
# zero all the values that are not max values on the result, remaining is equal to the adjoint of the output
max_values_and_zeros = akg.tvm.compute(
shape,
lambda *indices: akg.tvm.expr.Select(
data_(*indices) == max_broadcast(*indices),
head_broadcast(*indices), akg.tvm.const(0, dtype='float16')),
name="reduce_max_ad2")
# cast data back to the original dtype
if dtype != 'float16':
return [cast(max_values_and_zeros, dtype)]
else:
return [max_values_and_zeros]
# tensor for the input data
data = akg.tvm.placeholder(input_shape, dtype, name="input_data")
# computation of reduce max
# not used on the schedule because this is the diferentiation op
l = reduce_max.reduce_max(data, axis, keepdims)
# adjoint tensor for the differentiation
head = akg.tvm.placeholder(l.shape, name="head", dtype=l.dtype)
# cast input data
if dtype != 'float16':
data_cast = cast(data, "float16")
head_cast = cast(head, "float16")
else:
data_cast = data
head_cast = head
# override differentiation computation with custom function
[dl_ddata] = akg.differentiate(
l, [data_cast],
head_cast,
None,
None,
override={l: ([data_cast], custom_reduce_max_fdiff)})
# get tensors from custom function
if dtype != 'float16':
max_values_and_zeros = dl_ddata.op.input_tensors[0]
max_broadcast = max_values_and_zeros.op.input_tensors[1]
max_ = max_broadcast.op.input_tensors[0]
head_broadcast = max_values_and_zeros.op.input_tensors[2]
else:
max_broadcast = dl_ddata.op.input_tensors[1]
max_ = max_broadcast.op.input_tensors[0]
head_broadcast = dl_ddata.op.input_tensors[2]
# schedule for differetiation operation
# inputs: data and head
s = akg.tvm.create_schedule([dl_ddata.op])
# cache reads of inputs
if dtype != 'float16':
head_ub = s.cache_read(head, "local.UB", [head_cast])
data_ub = s.cache_read(data, "local.UB", [data_cast])
else:
# no cast operation
head_ub = s.cache_read(head_cast, "local.UB", [head_broadcast])
data_ub = s.cache_read(data_cast, "local.UB", [max_, dl_ddata])
# cache write for the output
dl_ddata_ub = s.cache_write(dl_ddata, "local.UB")
# get tiling attributes
if attrs is None:
raise Exception('attrs is None')
tiling_factors = attrs['tile']
split_iterators = []
assert len(tiling_factors) == len(dl_ddata.shape)
# split the final compute and save the iterators
for index, factor in enumerate(tiling_factors):
split_iterators.append(s[dl_ddata].split(dl_ddata.op.axis[index],
factor))
# get iterators
iterator1 = split_iterators[0][0]
# move computation of when there is a cast
if dtype != "float16":
s[data_cast].compute_at(s[dl_ddata], iterator1)
s[data_cast].set_scope("local.UB")
s[head_cast].compute_at(s[dl_ddata], iterator1)
s[head_cast].set_scope("local.UB")
s[max_values_and_zeros].compute_at(s[dl_ddata], iterator1)
s[max_values_and_zeros].set_scope("local.UB")
# move cache reads and writes
s[data_ub].compute_at(s[dl_ddata], iterator1)
s[head_ub].compute_at(s[dl_ddata], iterator1)
s[dl_ddata_ub].compute_at(s[dl_ddata], iterator1)
# move computation of the diferentiation
s[max_].compute_at(s[dl_ddata], iterator1)
s[max_].set_scope("local.UB")
s[max_broadcast].compute_at(s[dl_ddata], iterator1)
s[max_broadcast].set_scope("local.UB")
s[head_broadcast].compute_at(s[dl_ddata], iterator1)
s[head_broadcast].set_scope("local.UB")
with akg.build_config(add_lower_pass=cce.debug_mode(0), dump_pass_ir=True):
mod = akg.build(s, [head, data, dl_ddata],
"cce",
name="reduce_max_ad_manual_schedule",
attrs=attrs,
polyhedral=polyhedral)
source_code = mod.imported_modules[0].get_source()
kernel_name = "reduce_max_ad_manual_schedule"
utils.create_cce(kernel_name, './', source_code)
return mod
def reduce_max_ad(head, data, axis, keepdims):
b = reduce_max.reduce_max(data, axis, keepdims)
_jacs = akg.differentiate(b, [data], head)
return _jacs[0]