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fix concat

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pull/3977/head
nhussain 5 years ago
parent 1f4222edfb
commit 61769b2dd9
8 changed files with 144 additions and 103 deletions
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67
mindspore/ccsrc/minddata/dataset/core/tensor.cc
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@ -522,16 +522,34 @@ Status Tensor::StartAddrOfIndex(std::vector<dsize_t> ind, uchar **start_addr_of_
return Status::OK();
}
Status Tensor::InsertTensor(const std::vector<dsize_t> &ind, const std::shared_ptr<Tensor> &tensor) {
Status Tensor::InsertTensor(const std::vector<dsize_t> &ind, const std::shared_ptr<Tensor> &tensor,
const bool partial_insert) {
std::string err_msg;
err_msg += (this->type() == DataType::DE_STRING) ? "[Tensor] Cannot batch tensors of type string\n" : "";
err_msg += (!this->shape().known() || !tensor->shape().known()) ? "[Tensor] unknown shape\n" : "";
err_msg += (ind.size() + tensor->Rank() != this->Rank()) ? "[Tensor] incorrect index\n" : "";
err_msg += tensor->type().SizeInBytes() != this->type().SizeInBytes() ? "[Tensor] incorrect datatype\n" : "";
if (partial_insert) {
err_msg += (ind.size() != 1)
? "[Tensor] only supports 1D insertion of elements not along the full length of the axis\n"
: "";
err_msg +=
(ind.at(0) + tensor->shape().NumOfElements() > shape().NumOfElements()) ? "[Tensor] incorrect index\n" : "";
} else {
err_msg += (ind.size() + tensor->Rank() != Rank()) ? "[Tensor] incorrect index\n" : "";
}
err_msg += (type() == DataType::DE_STRING) ? "[Tensor] Cannot insert into a tensor of type string\n" : "";
err_msg += (!shape().known() || !tensor->shape().known()) ? "[Tensor] unknown shape\n" : "";
err_msg += tensor->type().SizeInBytes() != type().SizeInBytes() ? "[Tensor] incorrect datatype\n" : "";
uchar *start_addr_of_ind = nullptr;
TensorShape remaining_shape = TensorShape::CreateUnknownRankShape();
err_msg += (!StartAddrOfIndex(ind, &start_addr_of_ind, &remaining_shape).IsOk()) ? "[Tensor] incorrect index\n" : "";
err_msg += !(remaining_shape == tensor->shape()) ? "[Tensor] memory error\n" : "";
if (partial_insert) {
TensorShape remaining_shape = tensor->shape();
err_msg +=
(!StartAddrOfIndex(ind, &start_addr_of_ind, &remaining_shape).IsOk()) ? "[Tensor] incorrect index\n" : "";
} else {
TensorShape remaining_shape = TensorShape::CreateUnknownRankShape();
err_msg +=
(!StartAddrOfIndex(ind, &start_addr_of_ind, &remaining_shape).IsOk()) ? "[Tensor] incorrect index\n" : "";
err_msg += !(remaining_shape == tensor->shape()) ? "[Tensor] memory error\n" : "";
}
if (!err_msg.empty()) {
MS_LOG(DEBUG) << "Insert tensor message: " << err_msg;
RETURN_STATUS_UNEXPECTED(err_msg);
@ -552,39 +570,6 @@ Status Tensor::InsertTensor(const std::vector<dsize_t> &ind, const std::shared_p
}
}
Status Tensor::Concatenate(const std::vector<dsize_t> &index, const std::shared_ptr<Tensor> &tensor) {
std::string err_msg;
err_msg += (index.size() != 1) ? "[Tensor] only supports 1d concatenation \n" : "";
err_msg += (type() == DataType::DE_STRING) ? "[Tensor] Cannot batch tensors of type string\n" : "";
err_msg += (!shape().known() || !tensor->shape().known()) ? "[Tensor] unknown shape\n" : "";
err_msg +=
(index.at(0) + tensor->shape().NumOfElements() > this->shape().NumOfElements()) ? "[Tensor] incorrect index\n" : "";
err_msg += tensor->type().SizeInBytes() != this->type().SizeInBytes() ? "[Tensor] incorrect datatype\n" : "";
uchar *start_addr_of_ind = nullptr;
TensorShape remaining_shape = tensor->shape();
StartAddrOfIndex(index, &start_addr_of_ind, &remaining_shape);
err_msg += (start_addr_of_ind == nullptr) ? "Failed to create memory for Tensor.\n" : "";
if (!err_msg.empty()) {
MS_LOG(DEBUG) << "Insert tensor message: " << err_msg;
RETURN_STATUS_UNEXPECTED(err_msg);
} else {
int ret_code =
memcpy_s(start_addr_of_ind, tensor->SizeInBytes(), tensor->GetMutableBuffer(), tensor->SizeInBytes());
if (ret_code == 0) {
return Status::OK();
} else {
err_msg += "[Tensor] error in memcpy_s when inserting tensor\n";
MS_LOG(DEBUG) << "Tensor message: " << err_msg;
RETURN_STATUS_UNEXPECTED(err_msg);
}
}
}
Status Tensor::ExpandDim(const dsize_t &axis) {
if (axis > Rank()) {
std::string err = "Axis is out of bound";

7
mindspore/ccsrc/minddata/dataset/core/tensor.h
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@ -319,8 +319,10 @@ class Tensor {
/// Ex: shape <2,3>, inserting into index {0} will replace the first row. index {1,2} will replace the last cell.
/// \param index
/// \param input
/// \param partial_insert: boolean to determine if insertion along the full axis is enforced
/// \return Status code
Status InsertTensor(const std::vector<dsize_t> &index, const std::shared_ptr<Tensor> &input);
Status InsertTensor(const std::vector<dsize_t> &index, const std::shared_ptr<Tensor> &input,
const bool partial_insert = false);
/// Find the address of the given index. Used in InsertTensor.
/// Example:
@ -382,9 +384,6 @@ class Tensor {
static Status GetBufferInfo(Tensor *t, py::buffer_info *out);
#endif
/// Concatenate based on given tensor, can fill in current tensor with a smaller one, unlike InsertTensor
Status Concatenate(const std::vector<dsize_t> &index, const std::shared_ptr<Tensor> &input);
/// TensorIterator is a linear iterator that can be used to iterate over the elements of the Tensor
/// The order elements is as the memory layout (i.e., row-major) [[1,2,3],[4,5,6] --> 1,2,3,4,5,6
/// \tparam T type of values in the Tensor Iterator

7
mindspore/ccsrc/minddata/dataset/include/tensor.h
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@ -319,8 +319,10 @@ class Tensor {
/// Ex: shape <2,3>, inserting into index {0} will replace the first row. index {1,2} will replace the last cell.
/// \param index
/// \param input
/// \param partial_insert: boolean to determine if insertion along the full axis is enforced
/// \return Status code
Status InsertTensor(const std::vector<dsize_t> &index, const std::shared_ptr<Tensor> &input);
Status InsertTensor(const std::vector<dsize_t> &index, const std::shared_ptr<Tensor> &input,
const bool partial_insert = false);
/// Find the address of the given index. Used in InsertTensor.
/// Example:
@ -382,9 +384,6 @@ class Tensor {
static Status GetBufferInfo(Tensor *t, py::buffer_info *out);
#endif
/// Concatenate based on given tensor, can fill in current tensor with a smaller one, unlike InsertTensor
Status Concatenate(const std::vector<dsize_t> &index, const std::shared_ptr<Tensor> &input);
/// TensorIterator is a linear iterator that can be used to iterate over the elements of the Tensor
/// The order elements is as the memory layout (i.e., row-major) [[1,2,3],[4,5,6] --> 1,2,3,4,5,6
/// \tparam T type of values in the Tensor Iterator

82
mindspore/ccsrc/minddata/dataset/kernels/data/data_utils.cc
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@ -580,77 +580,73 @@ Status Mask(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *outpu
Status Concatenate(const TensorRow &input, TensorRow *output, int8_t axis, std::shared_ptr<Tensor> prepend,
std::shared_ptr<Tensor> append) {
CHECK_FAIL_RETURN_UNEXPECTED(input[0]->shape().Rank() == 1, "Only 1D tensors supported");
CHECK_FAIL_RETURN_UNEXPECTED(axis == 0 || axis == -1, "Only concatenation along the last dimension supported");
axis = Tensor::HandleNeg(axis, input[0]->shape().Rank());
CHECK_FAIL_RETURN_UNEXPECTED(axis == 0, "Only axis=0 is supported");
std::shared_ptr<Tensor> out;
TensorShape t = TensorShape::CreateScalar();
DataType first_dtype = input[0]->type();
TensorRow tensor_list;
if (prepend != nullptr) {
CHECK_FAIL_RETURN_UNEXPECTED(first_dtype == prepend->type(), "Tensor types do not match");
CHECK_FAIL_RETURN_UNEXPECTED(prepend->shape().Rank() == 1, "Only 1D tensors supported");
RETURN_IF_NOT_OK(ConcatenateHelper(prepend, &out, axis, input[0]));
} else {
out = input[0];
tensor_list.emplace_back(prepend);
}
for (dsize_t i = 1; i < input.size(); i++) {
std::shared_ptr<Tensor> out_t;
for (dsize_t i = 0; i < input.size(); i++) {
CHECK_FAIL_RETURN_UNEXPECTED(first_dtype == input[i]->type(), "Tensor types do not match");
CHECK_FAIL_RETURN_UNEXPECTED(input[i]->shape().Rank() == 1, "Only 1D tensors supported");
RETURN_IF_NOT_OK(ConcatenateHelper(out, &out_t, axis, input[i]));
out = out_t;
tensor_list.emplace_back(input[i]);
}
std::shared_ptr<Tensor> out_t;
if (append != nullptr) {
CHECK_FAIL_RETURN_UNEXPECTED(first_dtype == append->type(), "Tensor types do not match");
CHECK_FAIL_RETURN_UNEXPECTED(append->shape().Rank() == 1, "Only 1D tensors supported");
RETURN_IF_NOT_OK(ConcatenateHelper(out, &out_t, axis, append));
} else {
out_t = out;
tensor_list.emplace_back(append);
}
output->push_back(out_t);
return Status::OK();
}
Status ConcatenateHelper(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int8_t axis,
std::shared_ptr<Tensor> append) {
CHECK_FAIL_RETURN_UNEXPECTED(input->type() == append->type(), "Tensor types do not match");
TensorShape t({});
for (dsize_t i = 0; i < input->shape().Rank(); i++) {
// create final shape
for (dsize_t i = 0; i < tensor_list[0]->shape().Rank(); i++) {
if (i != axis) {
t = t.AppendDim(input->shape()[i]);
t = t.AppendDim(tensor_list[0]->shape()[i]);
} else {
dsize_t new_shape = input->shape()[i] + append->shape()[i];
dsize_t new_shape = 0;
for (dsize_t j = 0; j < tensor_list.size(); j++) {
new_shape = tensor_list[j]->shape()[i] + new_shape;
}
t = t.AppendDim(new_shape);
}
}
std::shared_ptr<Tensor> out;
if (input->type().IsNumeric()) {
RETURN_IF_NOT_OK(Tensor::CreateEmpty(t, input->type(), &out));
if (input[0]->type().IsNumeric()) {
RETURN_IF_NOT_OK(Tensor::CreateEmpty(t, tensor_list[0]->type(), &out));
std::vector<dsize_t> index(axis + 1, 0);
RETURN_IF_NOT_OK(out->Concatenate({0}, input));
RETURN_IF_NOT_OK(out->Concatenate({input->shape()[0]}, append));
*output = out;
int n = index.size() - 1;
for (dsize_t i = 0; i < tensor_list.size(); i++) {
RETURN_IF_NOT_OK(out->InsertTensor({index}, tensor_list[i], true));
index[n] = index[n] + tensor_list[i]->shape()[axis];
}
} else {
std::vector<std::string> strings;
auto itr = input->begin<std::string_view>();
for (; itr != input->end<std::string_view>(); itr++) {
strings.emplace_back(*itr);
}
itr = append->begin<std::string_view>();
for (; itr != append->end<std::string_view>(); itr++) {
strings.emplace_back(*itr);
for (dsize_t i = 0; i < tensor_list.size(); i++) {
auto itr = tensor_list[i]->begin<std::string_view>();
for (; itr != tensor_list[i]->end<std::string_view>(); itr++) {
strings.emplace_back(*itr);
}
}
RETURN_IF_NOT_OK(Tensor::CreateFromVector(strings, t, &out));
*output = out;
}
output->push_back(out);
return Status::OK();
}
} // namespace dataset
} // namespace mindspore

5
mindspore/ccsrc/minddata/dataset/kernels/data/data_utils.h
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@ -152,11 +152,6 @@ Status Mask(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *outpu
Status Concatenate(const TensorRow &input, TensorRow *output, int8_t axis, std::shared_ptr<Tensor> prepend,
std::shared_ptr<Tensor> append);
// helper for concat, always append to the input, and pass that to the output
Status ConcatenateHelper(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int8_t axis,
std::shared_ptr<Tensor> append);
} // namespace dataset
} // namespace mindspore

71
tests/ut/cpp/dataset/concatenate_op_test.cc
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@ -28,9 +28,8 @@ class MindDataTestConcatenateOp : public UT::Common {
};
TEST_F(MindDataTestConcatenateOp, TestOp) {
MS_LOG(INFO) << "Doing MindDataTestConcatenate-TestOp.";
MS_LOG(INFO) << "Doing MindDataTestConcatenate-TestOp-SingleRowinput.";
std::vector<uint64_t> labels = {1, 1, 2};
TensorShape shape({3});
std::shared_ptr<Tensor> input;
Tensor::CreateFromVector(labels, &input);
@ -57,3 +56,71 @@ TEST_F(MindDataTestConcatenateOp, TestOp) {
MS_LOG(DEBUG) << *expected << std::endl;
ASSERT_TRUE(*output == *expected);
}
TEST_F(MindDataTestConcatenateOp, TestOp2) {
MS_LOG(INFO) << "Doing MindDataTestConcatenate-TestOp2-MultiInput.";
std::vector<uint64_t> labels = {1, 12, 2};
std::shared_ptr<Tensor> row_1;
Tensor::CreateFromVector(labels, &row_1);
std::shared_ptr<Tensor> row_2;
Tensor::CreateFromVector(labels, &row_2);
std::vector<uint64_t> append_labels = {4, 4, 4};
std::shared_ptr<Tensor> append;
Tensor::CreateFromVector(append_labels, &append);
TensorRow tensor_list;
tensor_list.push_back(row_1);
tensor_list.push_back(row_2);
std::shared_ptr<Tensor> output;
std::unique_ptr<ConcatenateOp> op(new ConcatenateOp(0, nullptr, append));
TensorRow out_row;
Status s = op->Compute(tensor_list, &out_row);
std::vector<uint64_t> out = {1, 12, 2, 1, 12, 2, 4, 4, 4};
std::shared_ptr<Tensor> expected;
Tensor::CreateFromVector(out, &expected);
output = out_row[0];
EXPECT_TRUE(s.IsOk());
ASSERT_TRUE(output->shape() == expected->shape());
ASSERT_TRUE(output->type() == expected->type());
MS_LOG(DEBUG) << *output << std::endl;
MS_LOG(DEBUG) << *expected << std::endl;
ASSERT_TRUE(*output == *expected);
}
TEST_F(MindDataTestConcatenateOp, TestOp3) {
MS_LOG(INFO) << "Doing MindDataTestConcatenate-TestOp3-Strings.";
std::vector<std::string> labels = {"hello", "bye"};
std::shared_ptr<Tensor> row_1;
Tensor::CreateFromVector(labels, &row_1);
std::vector<std::string> append_labels = {"1", "2", "3"};
std::shared_ptr<Tensor> append;
Tensor::CreateFromVector(append_labels, &append);
TensorRow tensor_list;
tensor_list.push_back(row_1);
std::shared_ptr<Tensor> output;
std::unique_ptr<ConcatenateOp> op(new ConcatenateOp(0, nullptr, append));
TensorRow out_row;
Status s = op->Compute(tensor_list, &out_row);
std::vector<std::string> out = {"hello", "bye", "1", "2", "3"};
std::shared_ptr<Tensor> expected;
Tensor::CreateFromVector(out, &expected);
output = out_row[0];
EXPECT_TRUE(s.IsOk());
ASSERT_TRUE(output->shape() == expected->shape());
ASSERT_TRUE(output->type() == expected->type());
MS_LOG(DEBUG) << *output << std::endl;
MS_LOG(DEBUG) << *expected << std::endl;
ASSERT_TRUE(*output == *expected);
}

6
tests/ut/cpp/dataset/tensor_test.cc
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@ -432,7 +432,7 @@ TEST_F(MindDataTestTensorDE, TensorSlice) {
ASSERT_EQ(*t2, *t);
}
TEST_F(MindDataTestTensorDE, TensorConcatenate) {
TEST_F(MindDataTestTensorDE, TensorPartialInsert) {
std::vector<uint32_t> values1 = {1, 2, 3, 0, 0, 0};
std::vector<uint32_t> values2 = {4, 5, 6};
std::vector<uint32_t> expected = {1, 2, 3, 4, 5, 6};
@ -445,7 +445,7 @@ TEST_F(MindDataTestTensorDE, TensorConcatenate) {
std::shared_ptr<Tensor> out;
Tensor::CreateFromVector(expected, &out);
Status s = t1->Concatenate({3}, t2);
Status s = t1->InsertTensor({3}, t2, true);
EXPECT_TRUE(s.IsOk());
auto i = out->begin<uint32_t>();
@ -455,7 +455,7 @@ TEST_F(MindDataTestTensorDE, TensorConcatenate) {
}
// should fail if the concatenated vector is too large
s = t1->Concatenate({5}, t2);
s = t1->InsertTensor({5}, t2, true);
EXPECT_FALSE(s.IsOk());
}

2
tests/ut/python/dataset/test_concatenate_op.py
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@ -130,7 +130,7 @@ def test_concatenate_op_incorrect_dim():
def gen():
yield (np.array([["ss", "ad"], ["ss", "ad"]], dtype='S'),)
prepend_tensor = np.array([3, 5], dtype=np.float)
prepend_tensor = np.array(["ss", "ss"], dtype='S')
concatenate_op = data_trans.Concatenate(0, prepend_tensor)
data = ds.GeneratorDataset(gen, column_names=["col"])

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