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!4818 Fix ScatterNdUpdate cpu kernel

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Merge pull request !4818 from huanghui/scatter-nd-update-cpu-kernel
pull/4818/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent bf987de4df b367da88eb
commit be5c455906
5 changed files with 36 additions and 32 deletions
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29
mindspore/ccsrc/backend/kernel_compiler/cpu/scatter_nd_update_cpu_kernel.cc
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@ -25,9 +25,6 @@ void ScatterNdUpdateCPUKernel::InitKernel(const CNodePtr &kernel_node) {
auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
auto updates_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2);
if (indices_shape.size() < 2) {
MS_LOG(EXCEPTION) << "Indices' dimension less than 2";
}
auto indices_unit_rank = indices_shape.back();
if (indices_unit_rank > shape.size()) {
MS_LOG(EXCEPTION) << "Value of last dimension of indices is greater than shape rank";
@ -66,11 +63,11 @@ void ScatterNdUpdateCPUKernel::InitKernel(const CNodePtr &kernel_node) {
bool ScatterNdUpdateCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> & /*workspace*/,
const std::vector<kernel::AddressPtr> &outputs) {
const std::vector<kernel::AddressPtr> & /*outputs*/) {
if (dtype_ == kNumberTypeFloat16) {
LaunchKernel<float16>(inputs, outputs);
LaunchKernel<float16>(inputs);
} else if (dtype_ == kNumberTypeFloat32) {
LaunchKernel<float>(inputs, outputs);
LaunchKernel<float>(inputs);
} else {
MS_LOG(ERROR) << "Only support float16, float32";
return false;
@ -79,30 +76,26 @@ bool ScatterNdUpdateCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inp
}
template <typename T>
void ScatterNdUpdateCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs,
const std::vector<AddressPtr> &outputs) {
void ScatterNdUpdateCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs) {
auto x = reinterpret_cast<T *>(inputs[0]->addr);
auto indices = reinterpret_cast<int *>(inputs[1]->addr);
auto updates = reinterpret_cast<T *>(inputs[2]->addr);
auto y = reinterpret_cast<T *>(outputs[0]->addr);
for (int i = 0; i < num_units_; ++i) {
int offset = 0;
for (int j = 0; j < indices_unit_rank_; ++j) {
offset += indices[i * indices_unit_rank_ + j] * out_strides_[j] * unit_size_;
auto index = indices[i * indices_unit_rank_ + j];
if (index < 0) {
MS_LOG(EXCEPTION) << "Error, Indices exist element which less than 0. element=" << index;
}
offset += index * out_strides_[j] * unit_size_;
}
output_unit_offsets_[i] = offset;
}
auto mem_bits = outputs[0]->size;
auto ret = memcpy_s(y, mem_bits, x, mem_bits);
if (ret != 0) {
MS_LOG(EXCEPTION) << "memcpy_s error, errorno" << ret;
}
for (int i = 0; i < num_units_; i++) {
ret =
memcpy_s(y + output_unit_offsets_[i], unit_size_ * sizeof(T), updates + unit_size_ * i, unit_size_ * sizeof(T));
auto ret =
memcpy_s(x + output_unit_offsets_[i], unit_size_ * sizeof(T), updates + unit_size_ * i, unit_size_ * sizeof(T));
if (ret != 0) {
MS_LOG(EXCEPTION) << "memcpy_s error, errorno" << ret;
}

2
mindspore/ccsrc/backend/kernel_compiler/cpu/scatter_nd_update_cpu_kernel.h
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@ -35,7 +35,7 @@ class ScatterNdUpdateCPUKernel : public CPUKernel {
const std::vector<AddressPtr> &outputs) override;
template <typename T>
void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
void LaunchKernel(const std::vector<AddressPtr> &inputs);
private:
void Check(const CNodePtr &kernel_node);

4
mindspore/ccsrc/backend/kernel_compiler/cpu/unique_with_pad_cpu_kernel.h
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@ -39,8 +39,8 @@ class UniqueWithPadCPUKernel : public CPUKernel {
private:
void CheckParam(const CNodePtr &kernel_node);
int64_t n_;
TypeId dtype_;
int64_t n_{0};
TypeId dtype_{0};
};
MS_REG_CPU_KERNEL(UniqueWithPad,

15
mindspore/core/ir/anf.cc
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@ -224,6 +224,14 @@ std::string GetMaketupleNodeTarget(const CNodePtr &cnode) {
std::string default_target = context_ptr->device_target();
return default_target;
}
std::string GetTupleGetItemTarget(const CNodePtr &cnode, const PrimitivePtr &primitive) {
MS_EXCEPTION_IF_NULL(cnode);
MS_EXCEPTION_IF_NULL(primitive);
auto input_target = GetCNodeTarget(cnode->input(1));
primitive->set_attr("primitive_target", MakeValue(input_target));
return input_target;
}
} // namespace
std::string GetCNodeTarget(const AnfNodePtr &node) {
@ -256,8 +264,8 @@ std::string GetCNodeTarget(const AnfNodePtr &node) {
if (IsPrimitive(attr_input, prim::kPrimImageSummary) || IsPrimitive(attr_input, prim::kPrimScalarSummary) ||
IsPrimitive(attr_input, prim::kPrimTensorSummary) || IsPrimitive(attr_input, prim::kPrimHistogramSummary) ||
IsPrimitive(attr_input, prim::kPrimStateSetItem) || IsPrimitive(attr_input, prim::kPrimDepend) ||
IsPrimitive(attr_input, prim::kPrimTupleGetItem) || IsPrimitive(attr_input, prim::kPrimControlDepend) ||
IsPrimitive(attr_input, prim::kPrimReturn) || IsPrimitive(attr_input, prim::kPrimPartial)) {
IsPrimitive(attr_input, prim::kPrimControlDepend) || IsPrimitive(attr_input, prim::kPrimReturn) ||
IsPrimitive(attr_input, prim::kPrimPartial)) {
primitive->EraseAttr("primitive_target");
return default_target;
}
@ -273,6 +281,9 @@ std::string GetCNodeTarget(const AnfNodePtr &node) {
if (IsPrimitiveCNode(node, prim::kPrimMakeTuple)) {
return GetMaketupleNodeTarget(cnode);
}
if (IsPrimitiveCNode(node, prim::kPrimTupleGetItem)) {
return GetTupleGetItemTarget(cnode, primitive);
}
return default_target;
}
} // namespace mindspore

18
tests/st/ops/cpu/test_scatter_nd_update_op.py
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@ -64,10 +64,10 @@ def test_op1():
update = Tensor(np.array([1.0, 2.2]), mstype.float32)
scatter_nd_update = ScatterNdUpdate1()
output = scatter_nd_update(indices, update)
print("output:\n", output)
scatter_nd_update(indices, update)
print("x:\n", scatter_nd_update.x.default_input)
expect = [[1.0, 0.3, 3.6], [0.4, 2.2, -3.2]]
assert np.allclose(output.asnumpy(), np.array(expect, np.float))
assert np.allclose(scatter_nd_update.x.default_input.asnumpy(), np.array(expect, np.float))
@pytest.mark.level0
@ -78,10 +78,10 @@ def test_op2():
update = Tensor(np.array([9, 10, 11, 12]), mstype.float32)
scatter_nd_update = ScatterNdUpdate2()
output = scatter_nd_update(indices, update)
print("output:\n", output)
scatter_nd_update(indices, update)
print("x:\n", scatter_nd_update.x.default_input)
expect = [1, 11, 3, 10, 9, 6, 7, 12]
assert np.allclose(output.asnumpy(), np.array(expect, dtype=float))
assert np.allclose(scatter_nd_update.x.default_input.asnumpy(), np.array(expect, dtype=float))
@pytest.mark.level0
@ -95,10 +95,10 @@ def test_op3():
[7, 7, 7, 7], [8, 8, 8, 8]]]), mstype.float32)
scatter_nd_update = ScatterNdUpdate3()
output = scatter_nd_update(indices, update)
print("output:\n", output)
scatter_nd_update(indices, update)
print("x:\n", scatter_nd_update.x.default_input)
expect = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]
assert np.allclose(output.asnumpy(), np.array(expect, dtype=float))
assert np.allclose(scatter_nd_update.x.default_input.asnumpy(), np.array(expect, dtype=float))

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