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Update ops which has addings in internal #675

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Jan 3, 2024
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Update ops which has addings in internal #675

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13 changes: 11 additions & 2 deletions include/tim/vx/ops/scatternd_onnx_v16.h
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Original file line number Diff line number Diff line change
Expand Up @@ -34,14 +34,23 @@ namespace ops {
*
* Scatter updates into a new tensor according to indices.
*
* - shape : The shape of the resulting tensor.
* - reduction: Type of reduction to apply: none (default), add, mul, max, min.
*/

class ScatterND_ONNX_V16 : public BuiltinOp {
public:
ScatterND_ONNX_V16(Graph* graph);
enum ReductionType {
REDUCTION_NONE,
REDUCTION_ADD,
REDUCTION_MUL,
REDUCTION_MAX,
REDUCTION_MIN
};
ScatterND_ONNX_V16(Graph* graph, ReductionType reduction = ReductionType::REDUCTION_NONE);

std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
protected:
ReductionType reduction_;
};

} // namespace ops
Expand Down
10 changes: 5 additions & 5 deletions include/tim/vx/ops/simple_operations.h
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Expand Up @@ -83,14 +83,14 @@ namespace ops {
* returns the largest integer less than or equal to a given number.
*
* ## Ceil
*
*
* returns the largest integer more than or equal to a given number.
*
*
* ## Cast
*
* Change the format from input tensor to output tensor. This operation ignores
* the scale and zeroPoint of quanized tensors.
*
*
* ## Rcp
* Computes the reciprocal of input element-wise.
*/
Expand All @@ -99,8 +99,8 @@ DECLARE_SIMPLE_OP(DataConvert)
DECLARE_SIMPLE_OP(Neg)
DECLARE_SIMPLE_OP(Abs)
DECLARE_SIMPLE_OP(Sin)
// TODO(jiangbo): enable it when internal ops supports `Cos`
//DECLARE_SIMPLE_OP(Cos)
DECLARE_SIMPLE_OP(Cos)
DECLARE_SIMPLE_OP(Tan)
DECLARE_SIMPLE_OP(Exp)
DECLARE_SIMPLE_OP(Log)
DECLARE_SIMPLE_OP(Sqrt)
Expand Down
24 changes: 23 additions & 1 deletion src/tim/vx/ops/bidirectional_sequence_lstm.cc
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Expand Up @@ -109,6 +109,16 @@ class BidirectionalSequenceLstmImpl : public OpImpl {
BI_LSTM_BW_INPUT_LAYERNORM_C = 54,
BI_LSTM_BW_INPUT_LAYERNORM_O = 55,

BI_LSTM_FW_INPUT_BIAS_R2I = 56,
BI_LSTM_FW_INPUT_BIAS_R2F = 57,
BI_LSTM_FW_INPUT_BIAS_R2C = 58,
BI_LSTM_FW_INPUT_BIAS_R2O = 59,

BI_LSTM_BW_INPUT_BIAS_R2I = 60,
BI_LSTM_BW_INPUT_BIAS_R2F = 61,
BI_LSTM_BW_INPUT_BIAS_R2C = 62,
BI_LSTM_BW_INPUT_BIAS_R2O = 63,

INPUT_CNT,

BI_LSTM_FW_OUTPUT_OUTPUT = 0,
Expand Down Expand Up @@ -147,7 +157,7 @@ class BidirectionalSequenceLstmImpl : public OpImpl {
const std::shared_ptr<Tensor>& tensor) override {
in_tensors_[input_tensor_index] = tensor;

if (this->input_tensor_index == INPUT_CNT - 1) {
if (this->input_tensor_index >= INPUT_CNT - 9) {
// Get all input tensor
lstm_forward_->BindInput(in_tensors_[BI_LSTM_INPUT_INPUT]);
reverse_input_->BindInput(in_tensors_[BI_LSTM_INPUT_INPUT]);
Expand Down Expand Up @@ -183,6 +193,12 @@ class BidirectionalSequenceLstmImpl : public OpImpl {
lstm_forward_->BindInput(in_tensors_[BI_LSTM_FW_INPUT_LAYERNORM_F]);
lstm_forward_->BindInput(in_tensors_[BI_LSTM_FW_INPUT_LAYERNORM_C]);
lstm_forward_->BindInput(in_tensors_[BI_LSTM_FW_INPUT_LAYERNORM_O]);
if(this->input_tensor_index == input_cnt_ - 1) {
lstm_forward_->BindInput(in_tensors_[BI_LSTM_FW_INPUT_BIAS_R2I]);
lstm_forward_->BindInput(in_tensors_[BI_LSTM_FW_INPUT_BIAS_R2F]);
lstm_forward_->BindInput(in_tensors_[BI_LSTM_FW_INPUT_BIAS_R2C]);
lstm_forward_->BindInput(in_tensors_[BI_LSTM_FW_INPUT_BIAS_R2O]);
}

lstm_backward_->BindInput(bw_input_tensor_);
lstm_backward_->BindInput(in_tensors_[BI_LSTM_BW_INPUT_H_STATE]);
Expand Down Expand Up @@ -214,6 +230,12 @@ class BidirectionalSequenceLstmImpl : public OpImpl {
lstm_backward_->BindInput(in_tensors_[BI_LSTM_BW_INPUT_LAYERNORM_F]);
lstm_backward_->BindInput(in_tensors_[BI_LSTM_BW_INPUT_LAYERNORM_C]);
lstm_backward_->BindInput(in_tensors_[BI_LSTM_BW_INPUT_LAYERNORM_O]);
if(this->input_tensor_index == input_cnt_ - 1) {
lstm_backward_->BindInput(in_tensors_[BI_LSTM_BW_INPUT_BIAS_R2I]);
lstm_backward_->BindInput(in_tensors_[BI_LSTM_BW_INPUT_BIAS_R2F]);
lstm_backward_->BindInput(in_tensors_[BI_LSTM_BW_INPUT_BIAS_R2C]);
lstm_backward_->BindInput(in_tensors_[BI_LSTM_BW_INPUT_BIAS_R2O]);
}
}
this->input_tensor_index++;
return *this;
Expand Down
24 changes: 22 additions & 2 deletions src/tim/vx/ops/scatternd_onnx_v16.cc
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Expand Up @@ -29,9 +29,29 @@
namespace tim {
namespace vx {
namespace ops {
vsi_nn_reduction_type_e downcast_reduction_type (ScatterND_ONNX_V16::ReductionType type) {
switch (type)
{
case ScatterND_ONNX_V16::ReductionType::REDUCTION_NONE:
return VSI_NN_REDUCTION_TYPE_NONE;
case ScatterND_ONNX_V16::ReductionType::REDUCTION_ADD:
return VSI_NN_REDUCTION_TYPE_ADD;
case ScatterND_ONNX_V16::ReductionType::REDUCTION_MUL:
return VSI_NN_REDUCTION_TYPE_MUL;
case ScatterND_ONNX_V16::ReductionType::REDUCTION_MAX:
return VSI_NN_REDUCTION_TYPE_MAX;
case ScatterND_ONNX_V16::ReductionType::REDUCTION_MIN:
return VSI_NN_REDUCTION_TYPE_MIN;
default:
return VSI_NN_REDUCTION_TYPE_NONE;
}
}

ScatterND_ONNX_V16::ScatterND_ONNX_V16(Graph* graph, ReductionType reduction)
: BuiltinOp(graph, VSI_NN_OP_SCATTER_ND_UPDATE),
reduction_(reduction) {
this->impl()->node()->nn_param.scatter_nd_update.reduction = downcast_reduction_type(reduction_);

ScatterND_ONNX_V16::ScatterND_ONNX_V16(Graph* graph)
: BuiltinOp(graph, VSI_NN_OP_SCATTER_ND_UPDATE) {
}

std::shared_ptr<Operation> ScatterND_ONNX_V16::Clone(std::shared_ptr<Graph>& graph) const {
Expand Down
4 changes: 2 additions & 2 deletions src/tim/vx/ops/simple_operations.cc
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Expand Up @@ -40,8 +40,8 @@ DEFINE_SIMPLE_OP(DataConvert, VSI_NN_OP_DATACONVERT)
DEFINE_SIMPLE_OP(Neg, VSI_NN_OP_NEG)
DEFINE_SIMPLE_OP(Abs, VSI_NN_OP_ABS)
DEFINE_SIMPLE_OP(Sin, VSI_NN_OP_SIN)
// TODO(jiangbo): enable it when ovxlib supports `Cos`
//DEFINE_SIMPLE_OP(Cos, VSI_NN_OP_COS)
DEFINE_SIMPLE_OP(Cos, VSI_NN_OP_COS)
DEFINE_SIMPLE_OP(Tan, VSI_NN_OP_TAN)
DEFINE_SIMPLE_OP(Exp, VSI_NN_OP_EXP)
DEFINE_SIMPLE_OP(Log, VSI_NN_OP_LOG)
DEFINE_SIMPLE_OP(Sqrt, VSI_NN_OP_SQRT)
Expand Down
76 changes: 66 additions & 10 deletions src/tim/vx/ops/simple_operations_test.cc
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Expand Up @@ -47,8 +47,8 @@ TEST(Floor, shape_5_1_fp32) {

EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));

auto add = graph->CreateOperation<tim::vx::ops::Floor>();
(*add).BindInputs({input_tensor}).BindOutputs({output_tensor});
auto op = graph->CreateOperation<tim::vx::ops::Floor>();
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});

EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
Expand Down Expand Up @@ -79,8 +79,8 @@ TEST(Round, shape_15_1_fp32) {

EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));

auto add = graph->CreateOperation<tim::vx::ops::Round>();
(*add).BindInputs({input_tensor}).BindOutputs({output_tensor});
auto op = graph->CreateOperation<tim::vx::ops::Round>();
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});

EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
Expand All @@ -107,8 +107,8 @@ TEST(Ceil, shape_5_1_fp32) {

EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));

auto add = graph->CreateOperation<tim::vx::ops::Ceil>();
(*add).BindInputs({input_tensor}).BindOutputs({output_tensor});
auto op = graph->CreateOperation<tim::vx::ops::Ceil>();
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});

EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
Expand All @@ -135,8 +135,8 @@ TEST(Cast, shape_5_1_fp32_to_int32) {

EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));

auto add = graph->CreateOperation<tim::vx::ops::Cast>();
(*add).BindInputs({input_tensor}).BindOutputs({output_tensor});
auto op = graph->CreateOperation<tim::vx::ops::Cast>();
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});

EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
Expand Down Expand Up @@ -253,12 +253,68 @@ TEST(Rcp, shape_5_1_fp32) {

EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));

auto add = graph->CreateOperation<tim::vx::ops::Rcp>();
(*add).BindInputs({input_tensor}).BindOutputs({output_tensor});
auto op = graph->CreateOperation<tim::vx::ops::Rcp>();
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});

EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(5, 0);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}

TEST(Cos, shape_5_1_fp32) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();

tim::vx::ShapeType io_shape({5, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::OUTPUT);

auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);

std::vector<float> in_data = { 1.0, 0.0, -1.0, 0.5, -0.5};
std::vector<float> golden = {0.5403023, 1, 0.5403023, 0.87758255, 0.87758255};

EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));

auto op = graph->CreateOperation<tim::vx::ops::Cos>();
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});

EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(5);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}

TEST(Tan, shape_5_1_fp32) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();

tim::vx::ShapeType io_shape({5, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::OUTPUT);

auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);

std::vector<float> in_data = { 1, 0, 1.5, 0.5, -0.5};
std::vector<float> golden = { 1.5574077, 0, 14.10142, 0.5463025, -0.5463025};

EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));

auto op = graph->CreateOperation<tim::vx::ops::Tan>();
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});

EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(5);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-4f));
}
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