Change stateful Ops to stateless ones
Bug: 63905942
Updated Ops RNN, LSTM, and SVDF.
Split outputs used for states into inputs and outputs.
Test: NeuralNetworksTest
Change-Id: Ia3d11f640cba4cab1b94d0b9746c46d347c024a4
diff --git a/common/CpuExecutor.cpp b/common/CpuExecutor.cpp
index 82cdef6..9b03d69 100644
--- a/common/CpuExecutor.cpp
+++ b/common/CpuExecutor.cpp
@@ -1157,10 +1157,10 @@
case OperationType::LSTM: {
RunTimeOperandInfo &scratch =
mOperands[outs[LSTMCell::kScratchBufferTensor]];
- RunTimeOperandInfo &outputState =
- mOperands[outs[LSTMCell::kOutputStateTensor]];
- RunTimeOperandInfo &cellState =
- mOperands[outs[LSTMCell::kCellStateTensor]];
+ RunTimeOperandInfo &outputStateOut =
+ mOperands[outs[LSTMCell::kOutputStateOutTensor]];
+ RunTimeOperandInfo &cellStateOut =
+ mOperands[outs[LSTMCell::kCellStateOutTensor]];
RunTimeOperandInfo &output =
mOperands[outs[LSTMCell::kOutputTensor]];
@@ -1171,14 +1171,14 @@
&scratchShape, &outputStateShape,
&cellStateShape, &outputShape) &&
setInfoAndAllocateIfNeeded(&scratch, scratchShape) &&
- setInfoAndAllocateIfNeeded(&outputState, outputStateShape) &&
- setInfoAndAllocateIfNeeded(&cellState, cellStateShape) &&
+ setInfoAndAllocateIfNeeded(&outputStateOut, outputStateShape) &&
+ setInfoAndAllocateIfNeeded(&cellStateOut, cellStateShape) &&
setInfoAndAllocateIfNeeded(&output, outputShape) &&
lstm_cell.Eval();
} break;
case OperationType::RNN: {
- RunTimeOperandInfo &hiddenState =
- mOperands[outs[RNN::kHiddenStateTensor]];
+ RunTimeOperandInfo &hiddenStateOut =
+ mOperands[outs[RNN::kHiddenStateOutTensor]];
RunTimeOperandInfo &output =
mOperands[outs[RNN::kOutputTensor]];
@@ -1187,13 +1187,13 @@
success = RNN::Prepare(operation, mOperands,
&hiddenStateShape, &outputShape) &&
- setInfoAndAllocateIfNeeded(&hiddenState, hiddenStateShape) &&
+ setInfoAndAllocateIfNeeded(&hiddenStateOut, hiddenStateShape) &&
setInfoAndAllocateIfNeeded(&output, outputShape) &&
rnn_cell.Eval();
} break;
case OperationType::SVDF: {
RunTimeOperandInfo &state =
- mOperands[outs[SVDF::kStateTensor]];
+ mOperands[outs[SVDF::kStateOutTensor]];
RunTimeOperandInfo &output =
mOperands[outs[SVDF::kOutputTensor]];
diff --git a/common/operations/LSTM.cpp b/common/operations/LSTM.cpp
index 6a1feed..e7b0dec 100644
--- a/common/operations/LSTM.cpp
+++ b/common/operations/LSTM.cpp
@@ -171,13 +171,16 @@
projection_weights_ = GetInput(operation, operands, kProjectionWeightsTensor); // optional
projection_bias_ = GetInput(operation, operands, kProjectionBiasTensor); // optional
+ output_state_in_ = GetInput(operation, operands, kOutputStateInTensor);
+ cell_state_in_ = GetInput(operation, operands, kCellStateInTensor);
+
params_.activation_ = static_cast<ActivationFn>(getScalarData<int32_t>(
*GetInput(operation, operands, kActivationParam)));
params_.cell_clip_ = getScalarData<float>(*GetInput(operation, operands, kCellClipParam));
params_.proj_clip_ = getScalarData<float>(*GetInput(operation, operands, kProjClipParam));
- output_state_ = GetOutput(operation, operands, kOutputStateTensor);
- cell_state_ = GetOutput(operation, operands, kCellStateTensor);
+ output_state_out_ = GetOutput(operation, operands, kOutputStateOutTensor);
+ cell_state_out_ = GetOutput(operation, operands, kCellStateOutTensor);
output_ = GetOutput(operation, operands, kOutputTensor);
scratch_buffer_ = GetOutput(operation, operands, kScratchBufferTensor);
@@ -338,8 +341,8 @@
Shape *cellStateShape,
Shape *outputShape) {
// Check we have all the inputs and outputs we need.
- NN_CHECK(NumInputsWithValues(operation, operands) >= 13 &&
- NumInputsWithValues(operation, operands) <= 21);
+ NN_CHECK(NumInputsWithValues(operation, operands) >= 15 &&
+ NumInputsWithValues(operation, operands) <= 23);
NN_CHECK_EQ(NumOutputs(operation), 4);
// Inferring batch size, number of outputs and number of cells from the
@@ -463,24 +466,24 @@
if (!use_cifg) {
MatrixBatchVectorMultiplyAccumulate(
GetBuffer<float>(recurrent_to_input_weights_), n_cell, n_output,
- GetBuffer<float>(output_state_), n_batch, input_gate_scratch);
+ GetBuffer<float>(output_state_in_), n_batch, input_gate_scratch);
}
MatrixBatchVectorMultiplyAccumulate(
GetBuffer<float>(recurrent_to_forget_weights_), n_cell, n_output,
- GetBuffer<float>(output_state_), n_batch, forget_gate_scratch);
+ GetBuffer<float>(output_state_in_), n_batch, forget_gate_scratch);
MatrixBatchVectorMultiplyAccumulate(
GetBuffer<float>(recurrent_to_cell_weights_), n_cell, n_output,
- GetBuffer<float>(output_state_), n_batch, cell_scratch);
+ GetBuffer<float>(output_state_in_), n_batch, cell_scratch);
MatrixBatchVectorMultiplyAccumulate(
GetBuffer<float>(recurrent_to_output_weights_), n_cell, n_output,
- GetBuffer<float>(output_state_), n_batch, output_gate_scratch);
+ GetBuffer<float>(output_state_in_), n_batch, output_gate_scratch);
// For each batch and cell: update input gate.
if (!use_cifg) {
if (use_peephole) {
VectorBatchVectorCwiseProductAccumulate(
GetBuffer<float>(cell_to_input_weights_), n_cell,
- GetBuffer<float>(cell_state_), n_batch, input_gate_scratch);
+ GetBuffer<float>(cell_state_in_), n_batch, input_gate_scratch);
}
ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch,
input_gate_scratch);
@@ -490,40 +493,40 @@
if (use_peephole) {
VectorBatchVectorCwiseProductAccumulate(
GetBuffer<float>(cell_to_forget_weights_), n_cell,
- GetBuffer<float>(cell_state_), n_batch, forget_gate_scratch);
+ GetBuffer<float>(cell_state_in_), n_batch, forget_gate_scratch);
}
ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch,
forget_gate_scratch);
// For each batch and cell: update the cell.
- VectorVectorCwiseProduct(forget_gate_scratch, GetBuffer<float>(cell_state_),
- n_batch * n_cell, GetBuffer<float>(cell_state_));
+ VectorVectorCwiseProduct(forget_gate_scratch, GetBuffer<float>(cell_state_in_),
+ n_batch * n_cell, GetBuffer<float>(cell_state_out_));
ApplyActivationToVector(cell_scratch, n_batch * n_cell, params_.activation_,
cell_scratch);
if (use_cifg) {
Sub1Vector(forget_gate_scratch, n_batch * n_cell, forget_gate_scratch);
VectorVectorCwiseProductAccumulate(cell_scratch, forget_gate_scratch,
n_batch * n_cell,
- GetBuffer<float>(cell_state_));
+ GetBuffer<float>(cell_state_out_));
} else {
VectorVectorCwiseProductAccumulate(cell_scratch, input_gate_scratch,
n_batch * n_cell,
- GetBuffer<float>(cell_state_));
+ GetBuffer<float>(cell_state_out_));
}
if (params_.cell_clip_ > 0.0) {
- ClipVector(GetBuffer<float>(cell_state_), n_batch * n_cell,
- params_.cell_clip_, GetBuffer<float>(cell_state_));
+ ClipVector(GetBuffer<float>(cell_state_out_), n_batch * n_cell,
+ params_.cell_clip_, GetBuffer<float>(cell_state_out_));
}
// For each batch and cell: update the output gate.
if (use_peephole) {
VectorBatchVectorCwiseProductAccumulate(
GetBuffer<float>(cell_to_output_weights_), n_cell,
- GetBuffer<float>(cell_state_), n_batch, output_gate_scratch);
+ GetBuffer<float>(cell_state_out_), n_batch, output_gate_scratch);
}
ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell,
output_gate_scratch);
- ApplyActivationToVector(GetBuffer<float>(cell_state_), n_batch * n_cell,
+ ApplyActivationToVector(GetBuffer<float>(cell_state_out_), n_batch * n_cell,
params_.activation_, cell_scratch);
VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, n_batch * n_cell,
output_gate_scratch);
@@ -551,7 +554,7 @@
GetBuffer<float>(output_));
}
CopyVector(GetBuffer<float>(output_), n_batch * n_output,
- GetBuffer<float>(output_state_));
+ GetBuffer<float>(output_state_out_));
return true;
}
diff --git a/common/operations/LSTM.h b/common/operations/LSTM.h
index 430ee61..cc54612 100644
--- a/common/operations/LSTM.h
+++ b/common/operations/LSTM.h
@@ -88,15 +88,17 @@
// Projection bias tensor of size {n_output}
static constexpr int kProjectionBiasTensor = 17; // Optional
- static constexpr int kActivationParam = 18;
- static constexpr int kCellClipParam = 19;
- static constexpr int kProjClipParam = 20;
+ static constexpr int kOutputStateInTensor = 18;
+ static constexpr int kCellStateInTensor = 19;
+
+ static constexpr int kActivationParam = 20;
+ static constexpr int kCellClipParam = 21;
+ static constexpr int kProjClipParam = 22;
// Output tensors.
- // TODO: Do we have to pre-allocate scratch buffer as outputs?
static constexpr int kScratchBufferTensor = 0;
- static constexpr int kOutputStateTensor = 1;
- static constexpr int kCellStateTensor = 2;
+ static constexpr int kOutputStateOutTensor = 1;
+ static constexpr int kCellStateOutTensor = 2;
static constexpr int kOutputTensor = 3;
private:
@@ -130,8 +132,11 @@
const RunTimeOperandInfo *projection_weights_;
const RunTimeOperandInfo *projection_bias_;
- RunTimeOperandInfo *output_state_;
- RunTimeOperandInfo *cell_state_;
+ const RunTimeOperandInfo *output_state_in_;
+ const RunTimeOperandInfo *cell_state_in_;
+
+ RunTimeOperandInfo *output_state_out_;
+ RunTimeOperandInfo *cell_state_out_;
RunTimeOperandInfo *output_;
RunTimeOperandInfo *scratch_buffer_;
diff --git a/common/operations/LSTMTest.cpp b/common/operations/LSTMTest.cpp
index a61ef07..b8a80ec 100644
--- a/common/operations/LSTMTest.cpp
+++ b/common/operations/LSTMTest.cpp
@@ -60,13 +60,15 @@
ACTION(ForgetGateBias) \
ACTION(OutputGateBias) \
ACTION(ProjectionWeights) \
- ACTION(ProjectionBias)
+ ACTION(ProjectionBias) \
+ ACTION(OutputStateIn) \
+ ACTION(CellStateIn)
// For all output and intermediate states
#define FOR_ALL_OUTPUT_TENSORS(ACTION) \
ACTION(Output) \
- ACTION(OutputState) \
- ACTION(CellState) \
+ ACTION(OutputStateOut) \
+ ACTION(CellStateOut) \
ACTION(ScratchBuffer)
class LSTMOpModel {
@@ -75,7 +77,7 @@
uint32_t n_cell, uint32_t n_output, bool use_cifg,
bool use_peephole, bool use_projection_weights,
bool use_projection_bias, float cell_clip, float proj_clip,
- const std::vector<std::vector<uint32_t>>& input_shapes)
+ const std::vector<std::vector<uint32_t>>& input_shapes0)
: n_batch_(n_batch), n_input_(n_input),
n_cell_(n_cell), n_output_(n_output),
use_cifg_(use_cifg), use_peephole_(use_peephole),
@@ -84,7 +86,10 @@
activation_(ActivationFn::kActivationTanh),
cell_clip_(cell_clip), proj_clip_(proj_clip) {
std::vector<uint32_t> inputs;
+ std::vector<std::vector<uint32_t>> input_shapes(input_shapes0.begin(), input_shapes0.end());
auto it = input_shapes.begin();
+ input_shapes.push_back({n_batch, n_output});
+ input_shapes.push_back({n_batch, n_cell});
// Input and weights
#define AddInput(X) \
@@ -154,11 +159,11 @@
#undef DefineSetter
void ResetOutputState() {
- std::fill(OutputState_.begin(), OutputState_.end(), 0.f);
+ std::fill(OutputStateIn_.begin(), OutputStateIn_.end(), 0.f);
}
void ResetCellState() {
- std::fill(CellState_.begin(), CellState_.end(), 0.f);
+ std::fill(CellStateIn_.begin(), CellStateIn_.end(), 0.f);
}
void SetInput(int offset, float *begin, float *end) {
diff --git a/common/operations/RNN.cpp b/common/operations/RNN.cpp
index 4e25a88..8a00734 100644
--- a/common/operations/RNN.cpp
+++ b/common/operations/RNN.cpp
@@ -27,12 +27,13 @@
input_ = GetInput(operation, operands, kInputTensor);
weights_ = GetInput(operation, operands, kWeightsTensor);
recurrent_weights_ = GetInput(operation, operands, kRecurrentWeightsTensor);
+ hidden_state_in_ = GetInput(operation, operands, kHiddenStateInTensor);
bias_ = GetInput(operation, operands, kBiasTensor);
activation_ = static_cast<ActivationFn>(
getScalarData<int32_t>(operands[operation.inputs[kActivationParam]]));
- hidden_state_ = GetOutput(operation, operands, kHiddenStateTensor);
+ hidden_state_out_ = GetOutput(operation, operands, kHiddenStateOutTensor);
output_ = GetOutput(operation, operands, kOutputTensor);
}
@@ -42,7 +43,7 @@
Shape *outputShape) {
// Check we have all the inputs and outputs we need.
const int num_inputs = NumInputsWithValues(operation, operands);
- NN_CHECK(num_inputs == 4 || num_inputs == 5);
+ NN_CHECK(num_inputs == 5 || num_inputs == 6);
NN_CHECK_EQ(NumOutputs(operation), 2);
const RunTimeOperandInfo *input =
@@ -91,10 +92,12 @@
// Initialize the pointer to input, output and bias.
const float* input_ptr_batch =
reinterpret_cast<float*>(input_->buffer) + b * input_size;
+ const float* hidden_state_in_ptr_batch =
+ reinterpret_cast<float*>(hidden_state_in_->buffer) + b * num_units;
float* output_ptr_batch =
reinterpret_cast<float*>(output_->buffer) + b * num_units;
- float* hidden_state_ptr_batch =
- reinterpret_cast<float*>(hidden_state_->buffer) + b * num_units;
+ float* hidden_state_out_ptr_batch =
+ reinterpret_cast<float*>(hidden_state_out_->buffer) + b * num_units;
// Initialize input_weights and recurrent_weights.
const float* input_weights_ptr = reinterpret_cast<float*>(weights_->buffer);
@@ -118,7 +121,7 @@
for (uint32_t o = 0; o < num_units; o++) {
for (uint32_t h = 0; h < num_units; h++) {
output_ptr_batch[o] +=
- hidden_state_ptr_batch[h] * recurrent_weights_ptr[h];
+ hidden_state_in_ptr_batch[h] * recurrent_weights_ptr[h];
}
recurrent_weights_ptr += recurrent_weights_stride;
}
@@ -127,7 +130,7 @@
for (uint32_t o = 0; o < num_units; o++) {
output_ptr_batch[o] =
(ActivationFunctor(activation_))(output_ptr_batch[o]);
- hidden_state_ptr_batch[o] = output_ptr_batch[o];
+ hidden_state_out_ptr_batch[o] = output_ptr_batch[o];
}
}
diff --git a/common/operations/RNN.h b/common/operations/RNN.h
index c6ba7dc..0c6a881 100644
--- a/common/operations/RNN.h
+++ b/common/operations/RNN.h
@@ -50,9 +50,10 @@
static constexpr int kWeightsTensor = 1; // Optional
static constexpr int kRecurrentWeightsTensor = 2;
static constexpr int kBiasTensor = 3;
- static constexpr int kActivationParam = 4;
+ static constexpr int kHiddenStateInTensor = 4;
+ static constexpr int kActivationParam = 5;
- static constexpr int kHiddenStateTensor = 0;
+ static constexpr int kHiddenStateOutTensor = 0;
static constexpr int kOutputTensor = 1;
private:
@@ -62,8 +63,9 @@
const RunTimeOperandInfo *weights_;
const RunTimeOperandInfo *recurrent_weights_;
const RunTimeOperandInfo *bias_;
- const RunTimeOperandInfo *hidden_state_;
+ const RunTimeOperandInfo *hidden_state_in_;
+ RunTimeOperandInfo *hidden_state_out_;
RunTimeOperandInfo *output_;
};
diff --git a/common/operations/RNNTest.cpp b/common/operations/RNNTest.cpp
index d9b2965..7561f33 100644
--- a/common/operations/RNNTest.cpp
+++ b/common/operations/RNNTest.cpp
@@ -140,10 +140,11 @@
ACTION(Weights) \
ACTION(RecurrentWeights) \
ACTION(Bias) \
+ ACTION(HiddenStateIn)
// For all output and intermediate states
#define FOR_ALL_OUTPUT_TENSORS(ACTION) \
- ACTION(HiddenState) \
+ ACTION(HiddenStateOut) \
ACTION(Output)
class BasicRNNOpModel {
@@ -163,18 +164,20 @@
inputs.push_back(model_.addOperand(&RecurrentWeightTy));
OperandType BiasTy(Type::TENSOR_FLOAT32, {units_});
inputs.push_back(model_.addOperand(&BiasTy));
- OperandType ActionParamTy(Type::INT32, {});
+ OperandType HiddenStateTy(Type::TENSOR_FLOAT32, {batches_, units_});
+ inputs.push_back(model_.addOperand(&HiddenStateTy));
+ OperandType ActionParamTy(Type::INT32, {1});
inputs.push_back(model_.addOperand(&ActionParamTy));
std::vector<uint32_t> outputs;
- OperandType HiddenStateTy(Type::TENSOR_FLOAT32, {batches_, units_});
outputs.push_back(model_.addOperand(&HiddenStateTy));
OperandType OutputTy(Type::TENSOR_FLOAT32, {batches_, units_});
outputs.push_back(model_.addOperand(&OutputTy));
Input_.insert(Input_.end(), batches_ * input_size_, 0.f);
- HiddenState_.insert(HiddenState_.end(), batches_ * units_, 0.f);
+ HiddenStateIn_.insert(HiddenStateIn_.end(), batches_ * units_, 0.f);
+ HiddenStateOut_.insert(HiddenStateOut_.end(), batches_ * units_, 0.f);
Output_.insert(Output_.end(), batches_ * units_, 0.f);
model_.addOperation(ANEURALNETWORKS_RNN, inputs, outputs);
@@ -199,7 +202,7 @@
}
void ResetHiddenState() {
- std::fill(HiddenState_.begin(), HiddenState_.end(), 0.f);
+ std::fill(HiddenStateIn_.begin(), HiddenStateIn_.end(), 0.f);
}
const std::vector<float>& GetOutput() const { return Output_; }
diff --git a/common/operations/SVDF.cpp b/common/operations/SVDF.cpp
index 42f1a72..f600b26 100644
--- a/common/operations/SVDF.cpp
+++ b/common/operations/SVDF.cpp
@@ -26,11 +26,12 @@
// TODO: Implement this using circular buffer instead.
// This is here temporarily only to show the logic.
-void svdf_right_shift_state(float* state, int state_len, float shift_value) {
+void svdf_right_shift_state(const float* state_in, int state_len, float shift_value,
+ float* state_out) {
for (int i = 0; i < state_len - 1; i++) {
- state[i] = state[i + 1];
+ state_out[i] = state_in[i + 1];
}
- state[state_len - 1] = shift_value;
+ state_out[state_len - 1] = shift_value;
}
int32_t getInt32ScalarData(RunTimeOperandInfo& info) {
@@ -46,12 +47,13 @@
weights_feature_ = GetInput(operation, operands, kWeightsFeatureTensor);
weights_time_ = GetInput(operation, operands, kWeightsTimeTensor);
bias_ = GetInput(operation, operands, kBiasTensor);
+ state_in_ = GetInput(operation, operands, kStateInTensor);
params_.rank_ = getInt32ScalarData(*GetInput(operation, operands, kRankParam));
params_.activation_ = static_cast<ActivationFn>(getInt32ScalarData(
*GetInput(operation, operands, kActivationParam)));
- state_ = GetOutput(operation, operands, kStateTensor);
+ state_out_ = GetOutput(operation, operands, kStateOutTensor);
output_ = GetOutput(operation, operands, kOutputTensor);
}
@@ -61,7 +63,7 @@
Shape *outputShape) {
// Check we have all the inputs and outputs we need.
const int num_inputs = NumInputsWithValues(operation, operands);
- NN_CHECK(num_inputs == 5 || num_inputs == 6);
+ NN_CHECK(num_inputs == 6 || num_inputs == 7);
NN_CHECK_EQ(NumOutputs(operation), 2);
const RunTimeOperandInfo *input =
@@ -85,11 +87,6 @@
NN_CHECK_EQ(SizeOfDimension(bias, 0), num_units);
}
- const RunTimeOperandInfo *state =
- GetInput(operation, operands, SVDF::kStateTensor);
- const RunTimeOperandInfo *output =
- GetInput(operation, operands, SVDF::kOutputTensor);
-
// Resize state.
const Shape &inputShape = input->shape();
stateShape->type = inputShape.type;
@@ -123,7 +120,8 @@
// Initialize the pointer to input, output and bias.
const float* input_ptr_batch = reinterpret_cast<float *>(input_->buffer) + b * input_size;
float* output_ptr_batch = reinterpret_cast<float*>(output_->buffer) + b * num_units;
- float* state_ptr_batch = reinterpret_cast<float*>(state_->buffer) + b * (memory_size - 1) * num_units;
+ const float* state_in_ptr_batch = reinterpret_cast<const float*>(state_in_->buffer) + b * (memory_size - 1) * num_units;
+ float* state_out_ptr_batch = reinterpret_cast<float*>(state_out_->buffer) + b * (memory_size - 1) * num_units;
// For each unit
for (int c = 0; c < num_units; c++) {
@@ -135,7 +133,8 @@
}
// Initialize state pointer for unit 'c'.
- float* state_ptr = state_ptr_batch + c * (memory_size - 1);
+ const float* state_in_ptr = state_in_ptr_batch + c * (memory_size - 1);
+ float* state_out_ptr = state_out_ptr_batch + c * (memory_size - 1);
// Apply bias if bias tensor exists.
output_ptr_batch[c] = bias_->buffer ? reinterpret_cast<float *>(bias_->buffer)[c] : 0.f;
@@ -143,7 +142,7 @@
// output = tf.matmul(state, weights_time)
output_ptr_batch[c] += weights_time_ptr[memory_size - 1] * activation;
for (int j = 0; j < memory_size - 1; j++) {
- output_ptr_batch[c] += weights_time_ptr[j] * state_ptr[j];
+ output_ptr_batch[c] += weights_time_ptr[j] * state_in_ptr[j];
}
// Apply activation.
@@ -151,7 +150,8 @@
(ActivationFunctor(params_.activation_))(output_ptr_batch[c]);
// Right shift the state and concatenate with activation.
- svdf_right_shift_state(state_ptr, memory_size - 1, activation);
+ svdf_right_shift_state(state_in_ptr, memory_size - 1, activation,
+ state_out_ptr);
// Update weight pointers.
weights_feature_ptr += weights_feature_stride;
diff --git a/common/operations/SVDF.h b/common/operations/SVDF.h
index aafef12..a219fe5 100644
--- a/common/operations/SVDF.h
+++ b/common/operations/SVDF.h
@@ -58,10 +58,11 @@
static constexpr int kWeightsFeatureTensor = 1;
static constexpr int kWeightsTimeTensor = 2;
static constexpr int kBiasTensor = 3; // Optional
- static constexpr int kRankParam = 4;
- static constexpr int kActivationParam = 5;
+ static constexpr int kStateInTensor = 4;
+ static constexpr int kRankParam = 5;
+ static constexpr int kActivationParam = 6;
- static constexpr int kStateTensor = 0;
+ static constexpr int kStateOutTensor = 0;
static constexpr int kOutputTensor = 1;
private:
@@ -71,8 +72,9 @@
const RunTimeOperandInfo *weights_feature_;
const RunTimeOperandInfo *weights_time_;
const RunTimeOperandInfo *bias_;
+ const RunTimeOperandInfo *state_in_;
- RunTimeOperandInfo *state_;
+ RunTimeOperandInfo *state_out_;
RunTimeOperandInfo *output_;
};
diff --git a/common/operations/SVDFTest.cpp b/common/operations/SVDFTest.cpp
index 775d3c1..a3c32e2 100644
--- a/common/operations/SVDFTest.cpp
+++ b/common/operations/SVDFTest.cpp
@@ -108,11 +108,12 @@
ACTION(Input) \
ACTION(WeightsFeature) \
ACTION(WeightsTime) \
- ACTION(Bias)
+ ACTION(Bias) \
+ ACTION(StateIn)
// For all output and intermediate states
#define FOR_ALL_OUTPUT_TENSORS(ACTION) \
- ACTION(State) \
+ ACTION(StateOut) \
ACTION(Output)
// Derived class of SingleOpModel, which is used to test SVDF TFLite op.
@@ -128,7 +129,8 @@
{batches_, input_size_}, // Input tensor
{units_, input_size_}, // weights_feature tensor
{units_, memory_size_}, // weights_time tensor
- {units_} // bias tensor
+ {units_}, // bias tensor
+ {batches_, (memory_size_ - 1) * units_}, // state in
};
std::vector<uint32_t> inputs;
auto it = input_shapes.begin();
@@ -233,7 +235,7 @@
}
// Resets the state of SVDF op by filling it with 0's.
- void ResetState() { std::fill(State_.begin(), State_.end(), 0.f); }
+ void ResetState() { std::fill(StateIn_.begin(), StateIn_.end(), 0.f); }
// Extracts the output tensor from the SVDF op.
const std::vector<float>& GetOutput() const { return Output_; }
diff --git a/runtime/include/NeuralNetworks.h b/runtime/include/NeuralNetworks.h
index 9c932d9..79b6bc5 100644
--- a/runtime/include/NeuralNetworks.h
+++ b/runtime/include/NeuralNetworks.h
@@ -704,17 +704,21 @@
* A 2-D tensor of type T, of shape [output_size, num_units].
* * 17:projection_bias.
* A 1-D tensor of type T, of shape [output_size].
+ * * 18: output_state (in).
+ * A 2-D tensor of type T, of shape [batch_size, output_size].
+ * * 19: cell_state (in).
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
*
* Parameters:
- * * 18:fused_activation_function.
+ * * 20:fused_activation_function.
* An (optional) ActivationFunctionType indicating the activation
* function.
* If “NONE” is specified then it results in a linear activation.
- * * 19:cell_clip.
+ * * 21:cell_clip.
* A clipping threshold for the cell state, such that values are bound
* within [-cell_clip, cell_clip]. If set to 0.0 then clipping is
* disabled.
- * * 20:proj_clip.
+ * * 22:proj_clip.
* A clipping threshold for the output from the projection layer, such
* that values are bound within [-proj_clip, proj_clip]. If set to 0.0
* then clipping is disabled.
@@ -722,9 +726,9 @@
* Outputs:
* * 0: scratch_buffer.
* A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
- * * 1: output_state.
+ * * 1: output_state (out).
* A 2-D tensor of type T, of shape [batch_size, output_size].
- * * 2: cell_state.
+ * * 2: cell_state (out).
* A 2-D tensor of type T, of shape [batch_size, num_units].
* * 3: output.
* A 2-D tensor of type T, of shape [batch_size, output_size]. This is
@@ -945,7 +949,7 @@
* For FLOAT32 input tensor, bias must also be FLOAT32.
* For UINT8 input tensor, bias must be INT32.
*
- * * 4: Hidden state.
+ * * 4: Hidden state (in).
* A 2-D tensor of type T, of shape [batch_size, num_units].
*
* Parameters
@@ -955,7 +959,10 @@
* activation.
*
* Outputs:
- * * 0: output.
+ * * 0: Hidden state (out).
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ *
+ * * 1: output.
* A 2-D tensor of type T, of shape [batch_size, num_units]. This is
* effectively the same as the current state value.
*/
@@ -1074,15 +1081,18 @@
* For FLOAT32 input tensor, bias must also be FLOAT32.
* For UINT8 input tensor, bias must be INT32.
*
+ * * 4: state (in).
+ * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+ *
* Parameters:
- * * 4: rank.
+ * * 5: rank.
* The rank of the SVD approximation.
- * * 5: fused_activation_function.
+ * * 6: fused_activation_function.
* An (optional) ActivationFunctionType indicating the activation function.
* If “NONE” is specified then it results in a linear activation.
*
* Outputs:
- * * 0: state.
+ * * 0: state (out).
* A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
* A 2-D tensor of type T, of shape [batch_size, num_units].
diff --git a/runtime/test/generated/all_generated_tests.cpp b/runtime/test/generated/all_generated_tests.cpp
index d1dcb86..6f51a3d 100644
--- a/runtime/test/generated/all_generated_tests.cpp
+++ b/runtime/test/generated/all_generated_tests.cpp
@@ -757,20 +757,6 @@
max_pool_quant8_3::examples);
}
-namespace mobilenet_quantized {
-std::vector<MixedTypedExample> examples = {
-// Generated mobilenet_quantized test
-#include "generated/examples/mobilenet_quantized.example.cpp"
-};
-// Generated model constructor
-#include "generated/models/mobilenet_quantized.model.cpp"
-} // namespace mobilenet_quantized
-TEST_F(GeneratedTests, mobilenet_quantized) {
- Execute(mobilenet_quantized::CreateModel,
- mobilenet_quantized::is_ignored,
- mobilenet_quantized::examples);
-}
-
namespace mul_broadcast_quant8 {
std::vector<MixedTypedExample> examples = {
// Generated mul_broadcast_quant8 test
diff --git a/runtime/test/generated/examples/lstm.example.cpp b/runtime/test/generated/examples/lstm.example.cpp
index 4e5bdf5..b5313fa 100644
--- a/runtime/test/generated/examples/lstm.example.cpp
+++ b/runtime/test/generated/examples/lstm.example.cpp
@@ -4,16 +4,16 @@
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
- {{0, {2.0f, 3.0f}}, {1, {-0.45018822f, -0.02338299f, -0.0870589f, -0.34550029f, 0.04266912f, -0.15680569f, -0.34856534f, 0.43890524f}}, {2, {0.09701663f, 0.20334584f, -0.50592935f, -0.31343272f, -0.40032279f, 0.44781327f, 0.01387155f, -0.35593212f}}, {3, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, -0.20583314f, 0.44344562f, 0.22077113f, -0.29909778f}}, {4, {-0.25065863f, -0.28290087f, 0.04613829f, 0.40525138f, 0.44272184f, 0.03897077f, -0.1556896f, 0.19487578f}}, {5, {-0.0063535f, -0.2042388f, 0.31454784f, -0.35746509f, 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}}, {6, {-0.48684245f, -0.06655136f, 0.42224967f, 0.2112639f, 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}}, {7, {-0.3407414f, 0.24443203f, -0.2078532f, 0.26320225f, 0.05695659f, -0.00123841f, -0.4744786f, -0.35869038f, -0.06418842f, -0.13502428f, -0.501764f, 0.22830659f, -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}}, {8, {0.43385774f, -0.17194885f, 0.2718237f, 0.09215671f, 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, -0.51818722f, -0.15390486f, 0.0468148f, 0.39922136f}}, {9, {}}, {10, {}}, {11, {}}, {12, {0.0f, 0.0f, 0.0f, 0.0f}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {19, {0.0f}}, {20, {0.0f}}},
+ {{0, {2.0f, 3.0f}}, {1, {-0.45018822f, -0.02338299f, -0.0870589f, -0.34550029f, 0.04266912f, -0.15680569f, -0.34856534f, 0.43890524f}}, {2, {0.09701663f, 0.20334584f, -0.50592935f, -0.31343272f, -0.40032279f, 0.44781327f, 0.01387155f, -0.35593212f}}, {3, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, -0.20583314f, 0.44344562f, 0.22077113f, -0.29909778f}}, {4, {-0.25065863f, -0.28290087f, 0.04613829f, 0.40525138f, 0.44272184f, 0.03897077f, -0.1556896f, 0.19487578f}}, {5, {-0.0063535f, -0.2042388f, 0.31454784f, -0.35746509f, 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}}, {6, {-0.48684245f, -0.06655136f, 0.42224967f, 0.2112639f, 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}}, {7, {-0.3407414f, 0.24443203f, -0.2078532f, 0.26320225f, 0.05695659f, -0.00123841f, -0.4744786f, -0.35869038f, -0.06418842f, -0.13502428f, -0.501764f, 0.22830659f, -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}}, {8, {0.43385774f, -0.17194885f, 0.2718237f, 0.09215671f, 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, -0.51818722f, -0.15390486f, 0.0468148f, 0.39922136f}}, {9, {}}, {10, {}}, {11, {}}, {12, {0.0f, 0.0f, 0.0f, 0.0f}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {0, 0, 0, 0}}, {19, {0, 0, 0, 0}}, {21, {0.0f}}, {22, {0.0f}}},
// int -> INT32 map
- {{18, {4}}},
+ {{20, {4}}},
// int -> QUANT8_ASYMM map
{}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
- {{3, {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {1, {0, 0, 0, 0}}, {2, {0, 0, 0, 0}}},
+ {{1, {0, 0, 0, 0}}, {2, {0, 0, 0, 0}}, {3, {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
diff --git a/runtime/test/generated/examples/lstm2.example.cpp b/runtime/test/generated/examples/lstm2.example.cpp
index a31b1fa..619cad0 100644
--- a/runtime/test/generated/examples/lstm2.example.cpp
+++ b/runtime/test/generated/examples/lstm2.example.cpp
@@ -4,16 +4,16 @@
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
- {{0, {2.0f, 3.0f}}, {1, {}}, {2, {-0.55291498f, -0.42866567f, 0.13056988f, -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f, 0.33826375f}}, {3, {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f, 0.04717243f, 0.48944736f, -0.38535351f, -0.17212132f}}, {4, {0.10725588f, -0.02335852f, -0.55932593f, -0.09426838f, -0.44257352f, 0.54939759f, 0.01533556f, 0.42751634f}}, {5, {}}, {6, {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f, 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}}, {7, {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}}, {8, {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}}, {9, {}}, {10, {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}}, {11, {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}}, {12, {}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {19, {0.0f}}, {20, {0.0f}}},
+ {{0, {2.0f, 3.0f}}, {1, {}}, {2, {-0.55291498f, -0.42866567f, 0.13056988f, -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f, 0.33826375f}}, {3, {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f, 0.04717243f, 0.48944736f, -0.38535351f, -0.17212132f}}, {4, {0.10725588f, -0.02335852f, -0.55932593f, -0.09426838f, -0.44257352f, 0.54939759f, 0.01533556f, 0.42751634f}}, {5, {}}, {6, {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f, 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}}, {7, {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}}, {8, {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}}, {9, {}}, {10, {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}}, {11, {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}}, {12, {}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {0, 0, 0, 0}}, {19, {0, 0, 0, 0}}, {21, {0.0f}}, {22, {0.0f}}},
// int -> INT32 map
- {{18, {4}}},
+ {{20, {4}}},
// int -> QUANT8_ASYMM map
{}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
- {{3, {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {1, {0, 0, 0, 0}}, {2, {0, 0, 0, 0}}},
+ {{1, {0, 0, 0, 0}}, {2, {0, 0, 0, 0}}, {3, {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
diff --git a/runtime/test/generated/examples/lstm3.example.cpp b/runtime/test/generated/examples/lstm3.example.cpp
index 59edc3f..89d3814 100644
--- a/runtime/test/generated/examples/lstm3.example.cpp
+++ b/runtime/test/generated/examples/lstm3.example.cpp
@@ -4,16 +4,16 @@
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
- {{0, {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}}, {1, {0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, -0.06453243f, 0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, -0.1671009f, -0.15519552f, -0.16819797f, -0.13971269f, -0.11953059f, 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f, 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f, -0.028545704f, 0.024939531f, 0.050929718f, 0.0076203286f, -0.0029723682f, -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f, -0.16327988f, -0.2273378f, -0.0993647f, -0.017155107f, 0.0023917493f, 0.049272764f, 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f, -0.04544234f, -0.0497073f, -0.07135631f, -0.048929106f, -0.004042012f, -0.009284026f, 0.018042054f, 0.0036860977f, -0.07427302f, -0.11434604f, -0.018995456f, 0.031487543f, 0.012834908f, 0.019977754f, 0.044256654f, -0.39292613f, -0.18519334f, -0.11651281f, -0.06809892f, 0.011373677f}}, {2, {-0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f, 0.0814421f, -0.12257899f, -0.033945758f, -0.031303465f, 0.045630626f, 0.06843887f, -0.13492945f, -0.012480007f, -0.0811829f, 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0.0059240665f, -0.04995891f, -0.019187413f, 0.0276265f, -0.01628143f, 0.0025863599f, 0.08800015f, 0.035250366f, -0.022165963f, -0.07328642f, -0.009415526f, -0.07455109f, 0.11690406f, 0.0363299f, 0.07411125f, 0.042103454f, -0.009660886f, 0.019076364f, 0.018299393f, -0.046004917f, 0.08891175f, 0.0431396f, -0.026327137f, -0.051502608f, 0.08979574f, -0.051670972f, 0.04940282f, -0.07491107f, -0.021240504f, 0.022596184f, -0.034280192f, 0.060163025f, -0.058211457f, -0.051837247f, -0.01349775f, -0.04639988f, -0.035936575f, -0.011681591f, 0.064818054f, 0.0073146066f, -0.021745546f, -0.043124277f, -0.06471268f, -0.07053354f, -0.029321948f, -0.05330136f, 0.016933719f, -0.053782392f, 0.13747959f, -0.1361751f, -0.11569455f, 0.0033329215f, 0.05693899f, -0.053219706f, 0.063698f, 0.07977434f, -0.07924483f, 0.06936997f, 0.0034815092f, -0.007305279f, -0.037325785f, -0.07251102f, -0.033633437f, -0.08677009f, 0.091591336f, -0.14165086f, 0.021752775f, 0.019683983f, 0.0011612234f, -0.058154266f, 0.049996935f, 0.0288841f, -0.0024567875f, -0.14345716f, 0.010955264f, -0.10234828f, 0.1183656f, -0.0010731248f, -0.023590032f, -0.072285876f, -0.0724771f, -0.026382286f, -0.0014920527f, 0.042667855f, 0.0018776858f, 0.02986552f, 0.009814309f, 0.0733756f, 0.12289186f, 0.018043943f, -0.0458958f, 0.049412545f, 0.033632483f, 0.05495232f, 0.036686596f, -0.013781798f, -0.010036754f, 0.02576849f, -0.08307328f, 0.010112348f, 0.042521734f, -0.05869831f, -0.071689695f, 0.03876447f, -0.13275425f, -0.0352966f, -0.023077697f, 0.10285965f, 0.084736146f, 0.15568255f, -0.00040734606f, 0.027835453f, -0.10292561f, -0.032401145f, 0.10053256f, -0.026142767f, -0.08271222f, -0.0030240538f, -0.016368777f, 0.1070414f, 0.042672627f, 0.013456989f, -0.0437609f, -0.022309763f, 0.11576483f, 0.04108048f, 0.061026827f, -0.0190714f, -0.0869359f, 0.037901703f, 0.0610107f, 0.07202949f, 0.01675338f, 0.086139716f, -0.08795751f, -0.014898893f, -0.023771819f, -0.01965048f, 0.007955471f, -0.043740474f, 0.03346837f, -0.10549954f, 0.090567775f, 0.042013682f, -0.03176985f, 0.12569028f, -0.02421228f, -0.029526481f, 0.023851605f, 0.031539805f, 0.05292009f, -0.02344001f, -0.07811758f, -0.08834428f, 0.10094801f, 0.16594367f, -0.06861939f, -0.021256343f, -0.041093912f, -0.06669611f, 0.035498552f, 0.021757556f, -0.09302526f, -0.015403468f, -0.06614931f, -0.051798206f, -0.013874718f, 0.03630673f, 0.010412845f, -0.08077351f, 0.046185967f, 0.0035662893f, 0.03541868f, -0.094149634f, -0.034814864f, 0.003128424f, -0.020674974f, -0.03944324f, -0.008110165f, -0.11113267f, 0.08484226f, 0.043586485f, 0.040582247f, 0.0968012f, -0.065249965f, -0.028036479f, 0.0050708856f, 0.0017462453f, 0.0326779f, 0.041296225f, 0.09164146f, -0.047743853f, -0.015952192f, -0.034451712f, 0.084197424f, -0.05347844f, -0.11768019f, 0.085926116f, -0.08251791f, -0.045081906f, 0.0948852f, 0.068401024f, 0.024856757f, 0.06978981f, -0.057309967f, -0.012775832f, -0.0032452994f, 0.01977615f, -0.041040014f, -0.024264973f, 0.063464895f, 0.05431621f}}, {9, {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f, -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f, -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f, -0.052169047f, 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f}}, {10, {-0.01998659f, -0.15568835f, -0.24248174f, -0.012770197f, 0.041331276f, -0.072311886f, -0.052123554f, -0.0066330447f, -0.043891653f, 0.036225766f, -0.047248036f, 0.021479502f, 0.033189066f, 0.11952997f, -0.020432774f, 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f}}, {11, {0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f, -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f, -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f, 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f}}, {12, {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f, -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f, -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f, 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f}}, {13, {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f, 0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f, 0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f, -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f, 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f}}, {14, {-0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f, -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f, -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f, -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f, 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f}}, {15, {0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f, 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f, 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f, -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f}}, {16, {-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f, 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f, 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f, -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f, -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f, -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f, 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f, 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f, 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f, 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f, 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f, -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f, 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f, -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f, 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f, -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f, -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f, 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f, -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f, 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f, -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f, -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f, 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f}}, {17, {}}, {18, {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}}, {19, {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}}, {21, {0.0f}}, {22, {0.0f}}},
// int -> INT32 map
- {{18, {4}}},
+ {{20, {4}}},
// int -> QUANT8_ASYMM map
{}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
- {{3, {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.02168f}}, {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, 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, 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, 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}}, {1, {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}}, {2, {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}}},
+ {{1, {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}}, {2, {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}}, {3, {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.02168f}}, {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, 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, 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, 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}}},
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
diff --git a/runtime/test/generated/examples/rnn.example.cpp b/runtime/test/generated/examples/rnn.example.cpp
index 437d722..67e32be 100644
--- a/runtime/test/generated/examples/rnn.example.cpp
+++ b/runtime/test/generated/examples/rnn.example.cpp
@@ -4,9 +4,9 @@
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
- {{0, {0.23689353f, 0.285385f, 0.037029743f, -0.19858193f, -0.27569133f, 0.43773448f, 0.60379338f, 0.35562468f, 0.23689353f, 0.285385f, 0.037029743f, -0.19858193f, -0.27569133f, 0.43773448f, 0.60379338f, 0.35562468f}}, {1, {0.461459f, 0.153381f, 0.529743f, -0.00371218f, 0.676267f, -0.211346f, 0.317493f, 0.969689f, -0.343251f, 0.186423f, 0.398151f, 0.152399f, 0.448504f, 0.317662f, 0.523556f, -0.323514f, 0.480877f, 0.333113f, -0.757714f, -0.674487f, -0.643585f, 0.217766f, -0.0251462f, 0.79512f, -0.595574f, -0.422444f, 0.371572f, -0.452178f, -0.556069f, -0.482188f, -0.685456f, -0.727851f, 0.841829f, 0.551535f, -0.232336f, 0.729158f, -0.00294906f, -0.69754f, 0.766073f, -0.178424f, 0.369513f, -0.423241f, 0.548547f, -0.0152023f, -0.757482f, -0.85491f, 0.251331f, -0.989183f, 0.306261f, -0.340716f, 0.886103f, -0.0726757f, -0.723523f, -0.784303f, 0.0354295f, 0.566564f, -0.485469f, -0.620498f, 0.832546f, 0.697884f, -0.279115f, 0.294415f, -0.584313f, 0.548772f, 0.0648819f, 0.968726f, 0.723834f, -0.0080452f, -0.350386f, -0.272803f, 0.115121f, -0.412644f, -0.824713f, -0.992843f, -0.592904f, -0.417893f, 0.863791f, -0.423461f, -0.147601f, -0.770664f, -0.479006f, 0.654782f, 0.587314f, -0.639158f, 0.816969f, -0.337228f, 0.659878f, 0.73107f, 0.754768f, -0.337042f, 0.0960841f, 0.368357f, 0.244191f, -0.817703f, -0.211223f, 0.442012f, 0.37225f, -0.623598f, -0.405423f, 0.455101f, 0.673656f, -0.145345f, -0.511346f, -0.901675f, -0.81252f, -0.127006f, 0.809865f, -0.721884f, 0.636255f, 0.868989f, -0.347973f, -0.10179f, -0.777449f, 0.917274f, 0.819286f, 0.206218f, -0.00785118f, 0.167141f, 0.45872f, 0.972934f, -0.276798f, 0.837861f, 0.747958f, -0.0151566f, -0.330057f, -0.469077f, 0.277308f, 0.415818f}}, {2, {0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f}}, {3, {0.065691948f, -0.69055247f, 0.1107955f, -0.97084129f, -0.23957068f, -0.23566568f, -0.389184f, 0.47481549f, -0.4791103f, 0.29931796f, 0.10463274f, 0.83918178f, 0.37197268f, 0.61957061f, 0.3956964f, -0.37609905f}}},
+ {{0, {0.23689353f, 0.285385f, 0.037029743f, -0.19858193f, -0.27569133f, 0.43773448f, 0.60379338f, 0.35562468f, 0.23689353f, 0.285385f, 0.037029743f, -0.19858193f, -0.27569133f, 0.43773448f, 0.60379338f, 0.35562468f}}, {1, {0.461459f, 0.153381f, 0.529743f, -0.00371218f, 0.676267f, -0.211346f, 0.317493f, 0.969689f, -0.343251f, 0.186423f, 0.398151f, 0.152399f, 0.448504f, 0.317662f, 0.523556f, -0.323514f, 0.480877f, 0.333113f, -0.757714f, -0.674487f, -0.643585f, 0.217766f, -0.0251462f, 0.79512f, -0.595574f, -0.422444f, 0.371572f, -0.452178f, -0.556069f, -0.482188f, -0.685456f, -0.727851f, 0.841829f, 0.551535f, -0.232336f, 0.729158f, -0.00294906f, -0.69754f, 0.766073f, -0.178424f, 0.369513f, -0.423241f, 0.548547f, -0.0152023f, -0.757482f, -0.85491f, 0.251331f, -0.989183f, 0.306261f, -0.340716f, 0.886103f, -0.0726757f, -0.723523f, -0.784303f, 0.0354295f, 0.566564f, -0.485469f, -0.620498f, 0.832546f, 0.697884f, -0.279115f, 0.294415f, -0.584313f, 0.548772f, 0.0648819f, 0.968726f, 0.723834f, -0.0080452f, -0.350386f, -0.272803f, 0.115121f, -0.412644f, -0.824713f, -0.992843f, -0.592904f, -0.417893f, 0.863791f, -0.423461f, -0.147601f, -0.770664f, -0.479006f, 0.654782f, 0.587314f, -0.639158f, 0.816969f, -0.337228f, 0.659878f, 0.73107f, 0.754768f, -0.337042f, 0.0960841f, 0.368357f, 0.244191f, -0.817703f, -0.211223f, 0.442012f, 0.37225f, -0.623598f, -0.405423f, 0.455101f, 0.673656f, -0.145345f, -0.511346f, -0.901675f, -0.81252f, -0.127006f, 0.809865f, -0.721884f, 0.636255f, 0.868989f, -0.347973f, -0.10179f, -0.777449f, 0.917274f, 0.819286f, 0.206218f, -0.00785118f, 0.167141f, 0.45872f, 0.972934f, -0.276798f, 0.837861f, 0.747958f, -0.0151566f, -0.330057f, -0.469077f, 0.277308f, 0.415818f}}, {2, {0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f}}, {3, {0.065691948f, -0.69055247f, 0.1107955f, -0.97084129f, -0.23957068f, -0.23566568f, -0.389184f, 0.47481549f, -0.4791103f, 0.29931796f, 0.10463274f, 0.83918178f, 0.37197268f, 0.61957061f, 0.3956964f, -0.37609905f}}, {4, {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}}},
// int -> INT32 map
- {{4, {1}}},
+ {{5, {1}}},
// int -> QUANT8_ASYMM map
{}
},
diff --git a/runtime/test/generated/examples/svdf.example.cpp b/runtime/test/generated/examples/svdf.example.cpp
index a2f59c4..949421d 100644
--- a/runtime/test/generated/examples/svdf.example.cpp
+++ b/runtime/test/generated/examples/svdf.example.cpp
@@ -4,16 +4,16 @@
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
- {{0, {0.12609188f, -0.46347019f, -0.89598465f, 0.12609188f, -0.46347019f, -0.89598465f}}, {1, {-0.31930989f, -0.36118156f, 0.0079667f, 0.37613347f, 0.22197971f, 0.12416199f, 0.27901134f, 0.27557442f, 0.3905206f, -0.36137494f, -0.06634006f, -0.10640851f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f}}, {3, {}}},
+ {{0, {0.12609188f, -0.46347019f, -0.89598465f, 0.12609188f, -0.46347019f, -0.89598465f}}, {1, {-0.31930989f, -0.36118156f, 0.0079667f, 0.37613347f, 0.22197971f, 0.12416199f, 0.27901134f, 0.27557442f, 0.3905206f, -0.36137494f, -0.06634006f, -0.10640851f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f}}, {3, {}}, {4, {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, 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}}},
// int -> INT32 map
- {{4, {1}}, {5, {0}}},
+ {{5, {1}}, {6, {0}}},
// int -> QUANT8_ASYMM map
{}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
- {{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, 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}}, {1, {0.014899f, -0.0517661f, -0.143725f, -0.00271883f, 0.014899f, -0.0517661f, -0.143725f, -0.00271883f}}},
+ {{1, {0.014899f, -0.0517661f, -0.143725f, -0.00271883f, 0.014899f, -0.0517661f, -0.143725f, -0.00271883f}}, {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, 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}}},
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
diff --git a/runtime/test/generated/models/lstm.model.cpp b/runtime/test/generated/models/lstm.model.cpp
index 0d6d069..721d484 100644
--- a/runtime/test/generated/models/lstm.model.cpp
+++ b/runtime/test/generated/models/lstm.model.cpp
@@ -3,13 +3,13 @@
OperandType type5(Type::TENSOR_FLOAT32, {0,0});
OperandType type3(Type::TENSOR_FLOAT32, {0});
OperandType type0(Type::TENSOR_FLOAT32, {1, 2});
- OperandType type8(Type::TENSOR_FLOAT32, {1, 4, 4});
- OperandType type9(Type::TENSOR_FLOAT32, {1, 4});
- OperandType type7(Type::TENSOR_FLOAT32, {1});
+ OperandType type9(Type::TENSOR_FLOAT32, {1, 4, 4});
+ OperandType type6(Type::TENSOR_FLOAT32, {1, 4});
+ OperandType type8(Type::TENSOR_FLOAT32, {1});
OperandType type1(Type::TENSOR_FLOAT32, {4, 2});
OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
OperandType type4(Type::TENSOR_FLOAT32, {4});
- OperandType type6(Type::TENSOR_INT32, {1});
+ OperandType type7(Type::TENSOR_INT32, {1});
// Phase 1, operands
auto input = model->addOperand(&type0);
auto input_to_input_weights = model->addOperand(&type1);
@@ -29,23 +29,25 @@
auto output_gate_bias = model->addOperand(&type4);
auto projection_weights = model->addOperand(&type5);
auto projection_bias = model->addOperand(&type3);
- auto activation_param = model->addOperand(&type6);
- auto cell_clip_param = model->addOperand(&type7);
- auto proj_clip_param = model->addOperand(&type7);
- auto scratch_buffer = model->addOperand(&type8);
- auto output_state = model->addOperand(&type9);
- auto cell_state = model->addOperand(&type9);
- auto output = model->addOperand(&type9);
+ auto output_state_in = model->addOperand(&type6);
+ auto cell_state_in = model->addOperand(&type6);
+ auto activation_param = model->addOperand(&type7);
+ auto cell_clip_param = model->addOperand(&type8);
+ auto proj_clip_param = model->addOperand(&type8);
+ auto scratch_buffer = model->addOperand(&type9);
+ auto output_state_out = model->addOperand(&type6);
+ auto cell_state_out = model->addOperand(&type6);
+ auto output = model->addOperand(&type6);
// Phase 2, operations
- model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, activation_param, cell_clip_param, proj_clip_param}, {output, scratch_buffer, output_state, cell_state});
+ model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {output_state_out, cell_state_out, output, scratch_buffer});
// Phase 3, inputs and outputs
model->setInputsAndOutputs(
- {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, activation_param, cell_clip_param, proj_clip_param},
- {output, scratch_buffer, output_state, cell_state});
+ {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param},
+ {output_state_out, cell_state_out, output, scratch_buffer});
assert(model->isValid());
}
bool is_ignored(int i) {
- static std::set<int> ignore = {0, 1, 2};
+ static std::set<int> ignore = {1, 2, 0};
return ignore.find(i) != ignore.end();
}
diff --git a/runtime/test/generated/models/lstm2.model.cpp b/runtime/test/generated/models/lstm2.model.cpp
index 19ab9f8..7580c11 100644
--- a/runtime/test/generated/models/lstm2.model.cpp
+++ b/runtime/test/generated/models/lstm2.model.cpp
@@ -3,13 +3,13 @@
OperandType type5(Type::TENSOR_FLOAT32, {0,0});
OperandType type3(Type::TENSOR_FLOAT32, {0});
OperandType type0(Type::TENSOR_FLOAT32, {1, 2});
- OperandType type8(Type::TENSOR_FLOAT32, {1, 4, 4});
- OperandType type9(Type::TENSOR_FLOAT32, {1, 4});
- OperandType type7(Type::TENSOR_FLOAT32, {1});
+ OperandType type9(Type::TENSOR_FLOAT32, {1, 4, 4});
+ OperandType type6(Type::TENSOR_FLOAT32, {1, 4});
+ OperandType type8(Type::TENSOR_FLOAT32, {1});
OperandType type1(Type::TENSOR_FLOAT32, {4, 2});
OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
OperandType type4(Type::TENSOR_FLOAT32, {4});
- OperandType type6(Type::TENSOR_INT32, {1});
+ OperandType type7(Type::TENSOR_INT32, {1});
// Phase 1, operands
auto input = model->addOperand(&type0);
auto input_to_input_weights = model->addOperand(&type1);
@@ -29,23 +29,25 @@
auto output_gate_bias = model->addOperand(&type4);
auto projection_weights = model->addOperand(&type5);
auto projection_bias = model->addOperand(&type3);
- auto activation_param = model->addOperand(&type6);
- auto cell_clip_param = model->addOperand(&type7);
- auto proj_clip_param = model->addOperand(&type7);
- auto scratch_buffer = model->addOperand(&type8);
- auto output_state = model->addOperand(&type9);
- auto cell_state = model->addOperand(&type9);
- auto output = model->addOperand(&type9);
+ auto output_state_in = model->addOperand(&type6);
+ auto cell_state_in = model->addOperand(&type6);
+ auto activation_param = model->addOperand(&type7);
+ auto cell_clip_param = model->addOperand(&type8);
+ auto proj_clip_param = model->addOperand(&type8);
+ auto scratch_buffer = model->addOperand(&type9);
+ auto output_state_out = model->addOperand(&type6);
+ auto cell_state_out = model->addOperand(&type6);
+ auto output = model->addOperand(&type6);
// Phase 2, operations
- model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, activation_param, cell_clip_param, proj_clip_param}, {output, scratch_buffer, output_state, cell_state});
+ model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {output_state_out, cell_state_out, output, scratch_buffer});
// Phase 3, inputs and outputs
model->setInputsAndOutputs(
- {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, activation_param, cell_clip_param, proj_clip_param},
- {output, scratch_buffer, output_state, cell_state});
+ {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param},
+ {output_state_out, cell_state_out, output, scratch_buffer});
assert(model->isValid());
}
bool is_ignored(int i) {
- static std::set<int> ignore = {0, 1, 2};
+ static std::set<int> ignore = {1, 2, 0};
return ignore.find(i) != ignore.end();
}
diff --git a/runtime/test/generated/models/lstm3.model.cpp b/runtime/test/generated/models/lstm3.model.cpp
index b6ec737..d87bce6 100644
--- a/runtime/test/generated/models/lstm3.model.cpp
+++ b/runtime/test/generated/models/lstm3.model.cpp
@@ -2,15 +2,15 @@
void CreateModel(Model *model) {
OperandType type5(Type::TENSOR_FLOAT32, {0});
OperandType type4(Type::TENSOR_FLOAT32, {16,20});
- OperandType type7(Type::TENSOR_FLOAT32, {1});
- OperandType type9(Type::TENSOR_FLOAT32, {2, 16});
- OperandType type8(Type::TENSOR_FLOAT32, {2, 20, 4});
- OperandType type10(Type::TENSOR_FLOAT32, {2, 20});
+ OperandType type9(Type::TENSOR_FLOAT32, {1});
+ OperandType type6(Type::TENSOR_FLOAT32, {2, 16});
+ OperandType type10(Type::TENSOR_FLOAT32, {2, 20, 4});
+ OperandType type7(Type::TENSOR_FLOAT32, {2, 20});
OperandType type0(Type::TENSOR_FLOAT32, {2, 5});
OperandType type2(Type::TENSOR_FLOAT32, {20, 16});
OperandType type1(Type::TENSOR_FLOAT32, {20, 5});
OperandType type3(Type::TENSOR_FLOAT32, {20});
- OperandType type6(Type::TENSOR_INT32, {1});
+ OperandType type8(Type::TENSOR_INT32, {1});
// Phase 1, operands
auto input = model->addOperand(&type0);
auto input_to_input_weights = model->addOperand(&type1);
@@ -30,23 +30,25 @@
auto output_gate_bias = model->addOperand(&type3);
auto projection_weights = model->addOperand(&type4);
auto projection_bias = model->addOperand(&type5);
- auto activation_param = model->addOperand(&type6);
- auto cell_clip_param = model->addOperand(&type7);
- auto proj_clip_param = model->addOperand(&type7);
- auto scratch_buffer = model->addOperand(&type8);
- auto output_state = model->addOperand(&type9);
- auto cell_state = model->addOperand(&type10);
- auto output = model->addOperand(&type9);
+ auto output_state_in = model->addOperand(&type6);
+ auto cell_state_in = model->addOperand(&type7);
+ auto activation_param = model->addOperand(&type8);
+ auto cell_clip_param = model->addOperand(&type9);
+ auto proj_clip_param = model->addOperand(&type9);
+ auto scratch_buffer = model->addOperand(&type10);
+ auto output_state_out = model->addOperand(&type6);
+ auto cell_state_out = model->addOperand(&type7);
+ auto output = model->addOperand(&type6);
// Phase 2, operations
- model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, activation_param, cell_clip_param, proj_clip_param}, {output, scratch_buffer, output_state, cell_state});
+ model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {output_state_out, cell_state_out, output, scratch_buffer});
// Phase 3, inputs and outputs
model->setInputsAndOutputs(
- {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, activation_param, cell_clip_param, proj_clip_param},
- {output, scratch_buffer, output_state, cell_state});
+ {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param},
+ {output_state_out, cell_state_out, output, scratch_buffer});
assert(model->isValid());
}
bool is_ignored(int i) {
- static std::set<int> ignore = {0, 1, 2};
+ static std::set<int> ignore = {1, 2, 0};
return ignore.find(i) != ignore.end();
}
diff --git a/runtime/test/generated/models/rnn.model.cpp b/runtime/test/generated/models/rnn.model.cpp
index 5ef8094..a5788c6 100644
--- a/runtime/test/generated/models/rnn.model.cpp
+++ b/runtime/test/generated/models/rnn.model.cpp
@@ -3,23 +3,24 @@
OperandType type2(Type::TENSOR_FLOAT32, {16, 16});
OperandType type1(Type::TENSOR_FLOAT32, {16, 8});
OperandType type3(Type::TENSOR_FLOAT32, {16});
- OperandType type5(Type::TENSOR_FLOAT32, {2, 16});
+ OperandType type4(Type::TENSOR_FLOAT32, {2, 16});
OperandType type0(Type::TENSOR_FLOAT32, {2, 8});
- OperandType type4(Type::TENSOR_INT32, {1});
+ OperandType type5(Type::TENSOR_INT32, {1});
// Phase 1, operands
auto input = model->addOperand(&type0);
auto weights = model->addOperand(&type1);
auto recurrent_weights = model->addOperand(&type2);
auto bias = model->addOperand(&type3);
- auto activation_param = model->addOperand(&type4);
- auto hidden_state = model->addOperand(&type5);
- auto output = model->addOperand(&type5);
+ auto hidden_state_in = model->addOperand(&type4);
+ auto activation_param = model->addOperand(&type5);
+ auto hidden_state_out = model->addOperand(&type4);
+ auto output = model->addOperand(&type4);
// Phase 2, operations
- model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, activation_param}, {hidden_state, output});
+ model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
// Phase 3, inputs and outputs
model->setInputsAndOutputs(
- {input, weights, recurrent_weights, bias, activation_param},
- {hidden_state, output});
+ {input, weights, recurrent_weights, bias, hidden_state_in, activation_param},
+ {hidden_state_out, output});
assert(model->isValid());
}
diff --git a/runtime/test/generated/models/svdf.model.cpp b/runtime/test/generated/models/svdf.model.cpp
index 674b25f..074ec77 100644
--- a/runtime/test/generated/models/svdf.model.cpp
+++ b/runtime/test/generated/models/svdf.model.cpp
@@ -1,27 +1,28 @@
// Generated file (from: svdf.mod.py). Do not edit
void CreateModel(Model *model) {
- OperandType type5(Type::TENSOR_FLOAT32, {2, 36});
+ OperandType type4(Type::TENSOR_FLOAT32, {2, 36});
OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
OperandType type6(Type::TENSOR_FLOAT32, {2, 4});
OperandType type2(Type::TENSOR_FLOAT32, {4, 10});
OperandType type1(Type::TENSOR_FLOAT32, {4, 3});
OperandType type3(Type::TENSOR_FLOAT32, {4});
- OperandType type4(Type::TENSOR_INT32, {1});
+ OperandType type5(Type::TENSOR_INT32, {1});
// Phase 1, operands
auto input = model->addOperand(&type0);
auto weights_feature = model->addOperand(&type1);
auto weights_time = model->addOperand(&type2);
auto bias = model->addOperand(&type3);
- auto rank_param = model->addOperand(&type4);
- auto activation_param = model->addOperand(&type4);
- auto state = model->addOperand(&type5);
+ auto state_in = model->addOperand(&type4);
+ auto rank_param = model->addOperand(&type5);
+ auto activation_param = model->addOperand(&type5);
+ auto state_out = model->addOperand(&type4);
auto output = model->addOperand(&type6);
// Phase 2, operations
- model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, rank_param, activation_param}, {state, output});
+ model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {output, state_out});
// Phase 3, inputs and outputs
model->setInputsAndOutputs(
- {input, weights_feature, weights_time, bias, rank_param, activation_param},
- {state, output});
+ {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param},
+ {output, state_out});
assert(model->isValid());
}
diff --git a/runtime/test/specs/lstm.mod.py b/runtime/test/specs/lstm.mod.py
index c0d88a5..4791c68 100644
--- a/runtime/test/specs/lstm.mod.py
+++ b/runtime/test/specs/lstm.mod.py
@@ -48,13 +48,16 @@
projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}")
projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
+output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+
activation_param = Input("activation_param", "TENSOR_INT32", "{1}")
cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d, %d}" % (n_batch, n_cell, 4))
-output_state = IgnoredOutput("output_state", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-cell_state = IgnoredOutput("cell_state", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
model = model.Operation("LSTM",
@@ -82,10 +85,13 @@
projection_weights,
projection_bias,
+ output_state_in,
+ cell_state_in,
+
activation_param,
cell_clip_param,
proj_clip_param
-).To([scratch_buffer, output_state, cell_state, output])
+).To([scratch_buffer, output_state_out, cell_state_out, output])
# Example 1. Input in operand 0,
input0 = {input_to_input_weights: [-0.45018822, -0.02338299, -0.0870589, -0.34550029, 0.04266912, -0.15680569, -0.34856534, 0.43890524],
@@ -145,9 +151,11 @@
for (input_tensor, output_tensor) in zip(test_inputs, golden_outputs):
output0 = {
scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
- cell_state: [ 0 for x in range(n_batch * n_cell) ],
- output_state: [ 0 for x in range(n_batch * n_output) ],
+ cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
+ output_state_out: [ 0 for x in range(n_batch * n_output) ],
output: output_tensor
}
input0[input] = input_tensor
+ input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
+ input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]
Example((input0, output0))
diff --git a/runtime/test/specs/lstm2.mod.py b/runtime/test/specs/lstm2.mod.py
index ccab307..f5a219f 100644
--- a/runtime/test/specs/lstm2.mod.py
+++ b/runtime/test/specs/lstm2.mod.py
@@ -48,13 +48,16 @@
projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}")
projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
+output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+
activation_param = Input("activation_param", "TENSOR_INT32", "{1}");
cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d, %d}" % (n_batch, n_cell, 4))
-output_state = IgnoredOutput("output_state", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-cell_state = IgnoredOutput("cell_state", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
model = model.Operation("LSTM",
@@ -82,10 +85,13 @@
projection_weights,
projection_bias,
+ output_state_in,
+ cell_state_in,
+
activation_param,
cell_clip_param,
proj_clip_param
-).To([scratch_buffer, output_state, cell_state, output])
+).To([scratch_buffer, output_state_out, cell_state_out, output])
input0 = {input_to_input_weights:[],
input_to_cell_weights: [-0.49770179, -0.27711356, -0.09624726, 0.05100781, 0.04717243, 0.48944736, -0.38535351, -0.17212132],
@@ -128,8 +134,8 @@
output0 = {
scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
- cell_state: [ 0 for x in range(n_batch * n_cell) ],
- output_state: [ 0 for x in range(n_batch * n_output) ],
+ cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
+ output_state_out: [ 0 for x in range(n_batch * n_output) ],
}
# Instantiate examples
@@ -146,5 +152,7 @@
for (input_tensor, output_tensor) in zip(test_inputs, golden_outputs):
input0[input] = input_tensor
+ input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]
+ input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
output0[output] = output_tensor
Example((input0, output0))
diff --git a/runtime/test/specs/lstm3.mod.py b/runtime/test/specs/lstm3.mod.py
index 8387022..05d3f34 100644
--- a/runtime/test/specs/lstm3.mod.py
+++ b/runtime/test/specs/lstm3.mod.py
@@ -48,13 +48,16 @@
projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
+output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+
activation_param = Input("activation_param", "TENSOR_INT32", "{1}");
cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d, %d}" % (n_batch, n_cell, 4))
-output_state = IgnoredOutput("output_state", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-cell_state = IgnoredOutput("cell_state", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
# TODO: need support for more than one output
@@ -83,10 +86,13 @@
projection_weights,
projection_bias,
+ output_state_in,
+ cell_state_in,
+
activation_param,
cell_clip_param,
proj_clip_param
-).To([scratch_buffer, output_state, cell_state, output])
+).To([scratch_buffer, output_state_out, cell_state_out, output])
input0 = {input_to_input_weights: [
0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463,
@@ -671,11 +677,13 @@
for (input_tensor0, input_tensor1, output_tensor0, output_tensor1) in zip(test_inputs_batch0, test_inputs_batch1, golden_outputs_batch0, golden_outputs_batch1):
input_tensor0.extend(input_tensor1)
input0[input] = input_tensor0
+ input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]
+ input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
output_tensor0.extend(output_tensor1)
output0 = {
scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
- cell_state: [ 0 for x in range(n_batch * n_cell) ],
- output_state: [ 0 for x in range(n_batch * n_output) ],
+ cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
+ output_state_out: [ 0 for x in range(n_batch * n_output) ],
output: output_tensor0
}
Example((input0, output0))
diff --git a/runtime/test/specs/rnn.mod.py b/runtime/test/specs/rnn.mod.py
index f5d5a37..7964f24 100644
--- a/runtime/test/specs/rnn.mod.py
+++ b/runtime/test/specs/rnn.mod.py
@@ -24,14 +24,15 @@
weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size))
recurrent_weights = Input("recurrent_weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, units))
bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units))
+hidden_state_in = Input("hidden_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
activation_param = Input("activation_param", "TENSOR_INT32", "{1}")
-hidden_state = IgnoredOutput("hidden_state", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
+hidden_state_out = IgnoredOutput("hidden_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
-model = model.Operation("RNN", input, weights, recurrent_weights, bias,
- activation_param).To([hidden_state, output])
+model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
+ activation_param).To([hidden_state_out, output])
input0 = {
weights: [
@@ -190,8 +191,9 @@
input_end = input_begin + input_size
input0[input] = test_inputs[input_begin:input_end]
input0[input].extend(input0[input])
+ input0[hidden_state_in] = [0 for x in range(batches * units)]
output0 = {
- hidden_state: [0 for x in range(batches * units)],
+ hidden_state_out: [0 for x in range(batches * units)],
}
golden_start = i * units
golden_end = golden_start + units
diff --git a/runtime/test/specs/svdf.mod.py b/runtime/test/specs/svdf.mod.py
index d716b12..3a96fe3 100644
--- a/runtime/test/specs/svdf.mod.py
+++ b/runtime/test/specs/svdf.mod.py
@@ -25,13 +25,14 @@
weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size))
weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (units, memory_size))
bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units))
+state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, (memory_size-1)*units))
rank_param = Input("rank_param", "TENSOR_INT32", "{1}")
activation_param = Input("activation_param", "TENSOR_INT32", "{1}")
-state = IgnoredOutput("state", "TENSOR_FLOAT32", "{%d, %d}" % (batches, (memory_size-1)*units))
+state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, (memory_size-1)*units))
output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
-model = model.Operation("SVDF", input, weights_feature, weights_time, bias,
- rank_param, activation_param).To([state, output])
+model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
+ rank_param, activation_param).To([state_out, output])
input0 = {
weights_feature: [
@@ -130,7 +131,8 @@
batch_start = i * input_size * batches
batch_end = batch_start + input_size * batches
input0[input] = test_inputs[batch_start:batch_end]
- output0 = {state:[0 for x in range(batches * (memory_size - 1) * units)],
+ input0[state_in] = [0 for _ in range(batches * (memory_size - 1) * units)]
+ output0 = {state_out:[0 for x in range(batches * (memory_size - 1) * units)],
output: []}
golden_start = i * units * batches
golden_end = golden_start + units * batches
