Nigel
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Adding API fix for handling dynamic_shape correctly plus STs for SegSum dyn_shape 

Updating InferImpl function

adding check to inferImpl function

lint

lint2
This commit is contained in:
danishnxt 2020-11-26 18:50:36 -05:00
parent c3b057bd69
commit 6f64fffdb4
3 changed files with 185 additions and 26 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

69
mindspore/core/abstract/prim_arrays.cc
View File

@ -205,24 +205,23 @@ AbstractBasePtr InferImplUnsortedSegmentSum(const AnalysisEnginePtr &, const Pri
const AbstractBasePtrList &args_spec_list) {
const std::string op_name = primitive->name();
CheckArgsSize(op_name, args_spec_list, 3);
// input x
auto x = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
MS_EXCEPTION_IF_NULL(x);
MS_EXCEPTION_IF_NULL(x->shape());
auto x_shape = x->shape()->shape();
// segment_ids
auto segment_ids = CheckArg<AbstractTensor>(op_name, args_spec_list, 1);
MS_EXCEPTION_IF_NULL(segment_ids);
MS_EXCEPTION_IF_NULL(segment_ids->shape());
auto segment_ids_shape = segment_ids->shape()->shape();
// checks on Tensors 0 and 1 types
(void)CheckTensorDType(x, {kFloat32, kInt32}, "Input 0 (x) for SequenceMask should be %s");
(void)CheckTensorDType(segment_ids, {kInt32, kInt64}, "Input 1 (segment_ids) for SequenceMask should be %s");
(void)CheckTensorDType(x, {kFloat16, kFloat32, kInt32}, "Input 0 (x) for UnsortedSegmentSum should be %s");
(void)CheckTensorDType(segment_ids, {kInt32, kInt64}, "Input 1 (segment_ids) for UnsortedSegmentSum should be %s");
// check if dynamic shape
bool x_is_dyn = (!x->shape()->min_shape().empty() && !x->shape()->max_shape().empty());
bool ids_is_dyn = (!segment_ids->shape()->min_shape().empty() && !segment_ids->shape()->max_shape().empty());
bool op_is_dynamic = x_is_dyn && ids_is_dyn;
auto x_shape = x->shape()->shape();
ShapeVector shape;
ShapeVector max_shape;
ShapeVector min_shape;
int64_t num_segments_value;
if (args_spec_list[2]->isa<AbstractTensor>()) { // Num segments is Tensor
int64_t num_segments_value = 0;
if (args_spec_list[2]->isa<AbstractTensor>()) { // num_segments is Tensor
auto num_segments = args_spec_list[2]->cast<AbstractTensorPtr>();
MS_EXCEPTION_IF_NULL(num_segments);
auto num_segments_value_ptr = num_segments->BuildValue();
@ -230,26 +229,48 @@ AbstractBasePtr InferImplUnsortedSegmentSum(const AnalysisEnginePtr &, const Pri
auto num_segments_tensor = num_segments_value_ptr->cast<tensor::TensorPtr>();
MS_EXCEPTION_IF_NULL(num_segments_tensor);
num_segments_value = *static_cast<int64_t *>(num_segments_tensor->data_c());
shape.emplace_back(num_segments_value);
} else if (args_spec_list[2]->isa<AbstractScalar>()) { // Num segments is Scalar
} else if (args_spec_list[2]->isa<AbstractScalar>()) { // num_segments is Scalar
auto num_segments = CheckArg<AbstractScalar>(op_name, args_spec_list, 2);
num_segments_value = GetValue<int64_t>(num_segments->BuildValue());
shape.emplace_back(num_segments_value);
} else {
MS_LOG(EXCEPTION) << "num_segments incorrect type in UnsortedSegmentSum";
}
shape.insert(shape.end(), x_shape.begin() + segment_ids_shape.size(), x_shape.end());
// calc max shape
if (!x->shape()->max_shape().empty()) { // copy max shape from x if present
std::copy(x->shape()->max_shape().begin(), x->shape()->max_shape().end(), std::back_inserter(max_shape));
} else { // copy x shape directly if not present
std::copy(x->shape()->shape().begin(), x->shape()->shape().end(), std::back_inserter(max_shape));
if (num_segments_value <= 0) {
MS_LOG(EXCEPTION) << "num_segments must be > 0 in UnsortedSegmentSum";
}
// calc min shape
min_shape.push_back(segment_ids_shape.size());
std::copy(x->shape()->shape().begin() + segment_ids_shape.size(), x->shape()->shape().end(),
back_inserter(min_shape));
// return shape, min shape, max shape
shape.emplace_back(num_segments_value);
shape.insert(shape.end(), x_shape.begin() + segment_ids_shape.size(), x_shape.end());
// dims check
if (!op_is_dynamic) {
for (size_t i = 0; i < segment_ids_shape.size(); i++) {
if (x_shape[i] != segment_ids_shape[i]) {
MS_LOG(EXCEPTION) << "Shape values of segments_ids must match with corresponding x shape values";
}
}
return std::make_shared<AbstractTensor>(x->element(), std::make_shared<Shape>(shape));
}
// is dynamic
ShapeVector min_shape;
ShapeVector max_shape;
min_shape.emplace_back(num_segments_value);
max_shape.emplace_back(num_segments_value);
// only run validation if shape values are known
bool x_any_shape = std::any_of(x_shape.begin(), x_shape.end(), [](int64_t dim) { return dim == Shape::SHP_ANY; });
bool ids_any_shape =
std::any_of(segment_ids_shape.begin(), segment_ids_shape.end(), [](int64_t dim) { return dim == Shape::SHP_ANY; });
if (!x_any_shape && !ids_any_shape) {
for (size_t i = 0; i < segment_ids_shape.size(); i++) {
if (x_shape[i] != segment_ids_shape[i]) {
MS_LOG(EXCEPTION) << "Shape values of segments_ids must match with corresponding x shape values";
}
}
}
ShapeVector x_shape_min;
ShapeVector x_shape_max;
x_shape_min = (x_is_dyn) ? x->shape()->min_shape() : x->shape()->shape();
x_shape_max = (x_is_dyn) ? x->shape()->max_shape() : x->shape()->shape();
min_shape.insert(min_shape.end(), x_shape_min.begin() + segment_ids_shape.size(), x_shape_min.end());
max_shape.insert(max_shape.end(), x_shape_max.begin() + segment_ids_shape.size(), x_shape_max.end());
return std::make_shared<AbstractTensor>(x->element(), std::make_shared<Shape>(shape, min_shape, max_shape));
}

4
mindspore/ops/operations/array_ops.py
View File

@ -1904,7 +1904,9 @@ class UnsortedSegmentSum(PrimitiveWithInfer):
shp += x_shp[segment_ids_shp_len:]
if 'max_shape' in x:
output_max_shape = x['max_shape']
output_incoming = x['max_shape']
output_max_shape = [num_segments_v]
output_max_shape += output_incoming[segment_ids_shp_len:]
else:
output_max_shape = x_shp
out = {'shape': shp,

138
tests/st/ops/gpu/test_unsorted_segment_sum.py
View File

@ -20,11 +20,11 @@ import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.ops.operations import _inner_ops as inner
from mindspore.ops import operations as P
context.set_context(device_target='GPU')
class UnsortedSegmentSumNet(nn.Cell):
def __init__(self, num_segments):
super(UnsortedSegmentSumNet, self).__init__()
@ -108,3 +108,139 @@ def test_3D():
[0., 0., 0.],
[0., 0., 0.]]]
assert (output.asnumpy() == expect).all()
# Testing Dynamic Shape
class UnsortedSegmentSumDynNet(nn.Cell):
def __init__(self, num_segments):
super(UnsortedSegmentSumDynNet, self).__init__()
self.unsorted_segment_sum = P.UnsortedSegmentSum()
self.to_dyn_op = inner.GpuConvertToDynamicShape()
self.num_segments = num_segments
def construct(self, data, ids):
data_dyn = self.to_dyn_op(data)
ids_dyn = self.to_dyn_op(ids)
return self.unsorted_segment_sum(data_dyn, ids_dyn, self.num_segments)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dyn():
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
num_segments = 4
net = UnsortedSegmentSumDynNet(num_segments)
input_x = Tensor([1, 2, 3, 4], mstype.float32)
segment_ids = Tensor([0, 0, 1, 2], mstype.int32)
output = net(input_x, segment_ids)
expect = [3, 3, 4, 0]
assert (output.asnumpy() == expect).all()
input_x = Tensor([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]], mstype.float32)
segment_ids = Tensor([2, 1, 1], mstype.int32)
output = net(input_x, segment_ids)
expect = [[0, 0, 0, 0],
[14, 16, 18, 20],
[1, 2, 3, 4],
[0, 0, 0, 0]]
assert (output.asnumpy() == expect).all()
input_x = Tensor(np.arange(4 * 5 * 3, dtype=np.float32).reshape(4, 5, 3))
segment_ids = Tensor([2, 1, 1, -1], mstype.int32)
output = net(input_x, segment_ids)
expect = [[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]],
[[45., 47., 49.],
[51., 53., 55.],
[57., 59., 61.],
[63., 65., 67.],
[69., 71., 73.]],
[[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.],
[9., 10., 11.],
[12., 13., 14.]],
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]]
assert (output.asnumpy() == expect).all()
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dyn_1():
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
num_segments = 6
net = UnsortedSegmentSumDynNet(num_segments)
input_x = Tensor([1, 2, 3, 4], mstype.float32)
segment_ids = Tensor([0, 0, 1, 2], mstype.int32)
output = net(input_x, segment_ids)
expect = [3, 3, 4, 0, 0, 0]
assert (output.asnumpy() == expect).all()
input_x = Tensor([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]], mstype.float32)
segment_ids = Tensor([2, 1, 1], mstype.int32)
output = net(input_x, segment_ids)
expect = [[0, 0, 0, 0],
[14, 16, 18, 20],
[1, 2, 3, 4],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]]
assert (output.asnumpy() == expect).all()
input_x = Tensor(np.arange(4 * 5 * 3, dtype=np.float32).reshape(4, 5, 3))
segment_ids = Tensor([2, 1, 1, -1], mstype.int32)
output = net(input_x, segment_ids)
expect = [[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]],
[[45., 47., 49.],
[51., 53., 55.],
[57., 59., 61.],
[63., 65., 67.],
[69., 71., 73.]],
[[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.],
[9., 10., 11.],
[12., 13., 14.]],
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]],
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]],
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]]
assert (output.asnumpy() == expect).all()