diff options
Diffstat (limited to 'src/runtime/NEON/functions/NELSTMLayerQuantized.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NELSTMLayerQuantized.cpp | 177 |
1 files changed, 168 insertions, 9 deletions
diff --git a/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp b/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp index 6cfa9887ff..264cca0bf2 100644 --- a/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp +++ b/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp @@ -147,15 +147,30 @@ void NELSTMLayerQuantized::configure(const ITensor *input, _bias.allocator()->allocate(); // Get the gate tensors - _memory_group.manage(&_input_gate_input); - _slice_input_tensor.configure(&_output_lowp, &_input_gate_input, { 0, 0 }, { output_size, batch_size }); - _memory_group.manage(&_forget_gate_input); - _slice_forget_tensor.configure(&_output_lowp, &_forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size }); - _memory_group.manage(&_input_modulation_gate_input); - _slice_cell_tensor.configure(&_output_lowp, &_input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size }); - _memory_group.manage(&_output_gate_input); - _slice_output_tensor.configure(&_output_lowp, &_output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size }); - _output_lowp.allocator()->allocate(); + if(batch_size > 1) + { + _memory_group.manage(&_input_gate_input); + _slice_input_tensor.configure(&_output_lowp, &_input_gate_input, { 0, 0 }, { output_size, batch_size }); + _memory_group.manage(&_forget_gate_input); + _slice_forget_tensor.configure(&_output_lowp, &_forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size }); + _memory_group.manage(&_input_modulation_gate_input); + _slice_cell_tensor.configure(&_output_lowp, &_input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size }); + _memory_group.manage(&_output_gate_input); + _slice_output_tensor.configure(&_output_lowp, &_output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size }); + _output_lowp.allocator()->allocate(); + } + else + { + _memory_group.manage(&_input_gate_input); + _slice_input_tensor.configure(&_output_lowp, &_input_gate_input, { 0 }, { output_size }); + _memory_group.manage(&_forget_gate_input); + _slice_forget_tensor.configure(&_output_lowp, &_forget_gate_input, { output_size }, { 2 * output_size }); + _memory_group.manage(&_input_modulation_gate_input); + _slice_cell_tensor.configure(&_output_lowp, &_input_modulation_gate_input, { 2 * output_size }, { 3 * output_size }); + _memory_group.manage(&_output_gate_input); + _slice_output_tensor.configure(&_output_lowp, &_output_gate_input, { 3 * output_size }, { 4 * output_size }); + _output_lowp.allocator()->allocate(); + } // Forget gate _memory_group.manage(&_forget_gate_output); @@ -265,6 +280,150 @@ Status NELSTMLayerQuantized::validate(const ITensorInfo *input, ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&cell_state_info, cell_state_in); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&output_state_info, output_state_in); + // Validate internal functions + // _concat_input_weights + std::vector<const ITensorInfo *> inputs_weights_vector; + inputs_weights_vector.emplace_back(input_to_input_weights); + inputs_weights_vector.emplace_back(input_to_forget_weights); + inputs_weights_vector.emplace_back(input_to_cell_weights); + inputs_weights_vector.emplace_back(input_to_output_weights); + const QuantizationInfo qweights = input_to_input_weights->quantization_info(); // Weights quantization + const TensorInfo input_weights(TensorShape(input_size, 4 * output_size), 1, DataType::QASYMM8, qweights); + ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(inputs_weights_vector, &input_weights, Window::DimY)); + + // _concat_recurrent_weights + std::vector<const ITensorInfo *> recurrent_weights_vector; + recurrent_weights_vector.emplace_back(recurrent_to_input_weights); + recurrent_weights_vector.emplace_back(recurrent_to_forget_weights); + recurrent_weights_vector.emplace_back(recurrent_to_cell_weights); + recurrent_weights_vector.emplace_back(recurrent_to_output_weights); + const TensorInfo recurrent_weights(TensorShape(output_size, 4 * output_size), 1, DataType::QASYMM8, qweights); + ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(recurrent_weights_vector, &recurrent_weights, Window::DimY)); + + // _concat_weights + std::vector<const ITensorInfo *> weights_vector; + weights_vector.emplace_back(&recurrent_weights); + weights_vector.emplace_back(&input_weights); + const TensorInfo weights(TensorShape(input_size + output_size, 4 * output_size), 1, DataType::QASYMM8, qweights); + ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(weights_vector, &weights, Window::DimX)); + // _transpose_weights + const TensorShape weights_transposed_shape(weights.tensor_shape()[1], weights.tensor_shape()[0]); + TensorInfo weights_transposed = weights.clone()->set_is_resizable(true).set_tensor_shape(weights_transposed_shape); + ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(&weights, &weights_transposed)); + + // _concat_inputs + std::vector<const ITensorInfo *> input_vector; + input_vector.emplace_back(input); + input_vector.emplace_back(output_state_in); + TensorInfo input_concatenated(TensorShape(output_size + input_size, batch_size), 1, DataType::QASYMM8, qasymm); + ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(input_vector, &input_concatenated, Window::DimX)); + + // _concat_bias + std::vector<const ITensorInfo *> bias_vector; + bias_vector.emplace_back(input_gate_bias); + bias_vector.emplace_back(forget_gate_bias); + bias_vector.emplace_back(cell_bias); + bias_vector.emplace_back(output_gate_bias); + + const TensorInfo bias_concatenated(TensorShape(4 * output_size), 1, DataType::S32); + ARM_COMPUTE_RETURN_ON_ERROR(NEConcatenateLayer::validate(bias_vector, &bias_concatenated, Window::DimX)); + + // Invert the offset for gemmlowp + input_concatenated.set_quantization_info(QuantizationInfo(qasymm.uniform().scale, -qasymm.uniform().offset)); + weights_transposed.set_quantization_info(QuantizationInfo(qweights.uniform().scale, -qweights.uniform().offset)); + + // _gemmlowp + const TensorInfo output_highp(TensorShape(4 * output_size, batch_size), 1, DataType::S32); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(&input_concatenated, &weights_transposed, nullptr, &output_highp)); + + // Set the offset back + input_concatenated.set_quantization_info(QuantizationInfo(qasymm.uniform().scale, qasymm.uniform().offset)); + weights_transposed.set_quantization_info(QuantizationInfo(qweights.uniform().scale, qweights.uniform().offset)); + + // multiplier = (input_scale * weights_scale) / output_scale (2 ^ (-12)) + const TensorInfo output_lowp(output_highp.tensor_shape(), 1, DataType::QSYMM16, qsymm_3); + + const float multiplier = 4096.f * qasymm.uniform().scale * qweights.uniform().scale; + ARM_COMPUTE_UNUSED(multiplier); + ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f); + // _output_stage + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::validate(&output_highp, &bias_concatenated, &output_lowp)); + + TensorInfo input_gate_input; + TensorInfo forget_gate_input; + TensorInfo input_modulation_gate_input; + TensorInfo output_gate_input; + + if(batch_size > 1) + { + // _slice_input_tensor + input_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3); + ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &input_gate_input, { 0, 0 }, { output_size, batch_size })); + // _slice_forget_tensor + forget_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3); + ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &forget_gate_input, { output_size, 0 }, { 2 * output_size, batch_size })); + // _slice_cell_tensor + input_modulation_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3); + ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &input_modulation_gate_input, { 2 * output_size, 0 }, { 3 * output_size, batch_size })); + // _slice_output_tensor + output_gate_input = TensorInfo(TensorShape(output_size, batch_size), 1, DataType::QSYMM16, qsymm_3); + ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &output_gate_input, { 3 * output_size, 0 }, { 4 * output_size, batch_size })); + } + else + { + // _slice_input_tensor + input_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3); + ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &input_gate_input, { 0 }, { output_size })); + // _slice_forget_tensor + forget_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3); + ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &forget_gate_input, { output_size }, { 2 * output_size })); + // _slice_cell_tensor + input_modulation_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3); + ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &input_modulation_gate_input, { 2 * output_size }, { 3 * output_size })); + // _slice_output_tensor + output_gate_input = TensorInfo(TensorShape(output_size), 1, DataType::QSYMM16, qsymm_3); + ARM_COMPUTE_RETURN_ON_ERROR(NESlice::validate(&output_lowp, &output_gate_input, { 3 * output_size }, { 4 * output_size })); + } + + // _sigmoid_forget_gate + const TensorInfo forget_gate_output(forget_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0); + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&forget_gate_input, &forget_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); + // _sigmoid_input_gate + const TensorInfo input_gate_output(input_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0); + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&input_gate_input, &input_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); + // _tanh_modulation_gate + const TensorInfo input_modulation_gate_output(input_modulation_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0); + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&input_modulation_gate_input, &input_modulation_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f))); + // _sigmoid_output_gate + const TensorInfo output_gate_output(output_gate_input.tensor_shape(), 1, DataType::QSYMM16, qsymm_0); + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&output_gate_input, &output_gate_output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))); + + // _mul_forget_gate_cell_state + const TensorInfo cell_state_tmp1(forget_gate_output.tensor_shape(), 1, DataType::QSYMM16, qsymm_4); + ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&forget_gate_output, cell_state_in, &cell_state_tmp1, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); + + // _mul_input_gate_input_mod_gate + const TensorInfo cell_state_tmp2(input_gate_output.tensor_shape(), 1, DataType::QSYMM16, qsymm_4); + ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&input_gate_output, &input_modulation_gate_output, &cell_state_tmp2, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); + + // _add_cell_state_tmps + ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAddition::validate(&cell_state_tmp1, &cell_state_tmp2, cell_state_out, ConvertPolicy::SATURATE)); + + // _tanh_modulation_gate + const TensorInfo output_state_tmp(cell_state_out->tensor_shape(), 1, DataType::QSYMM16, qsymm_0); + ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(cell_state_out, &output_state_tmp, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f))); + + // _mul_output_state_tmp_output_gate + const TensorInfo output_state_out_symm(output_gate_output.tensor_shape(), 1, DataType::QSYMM16, qsymm_0); + ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplication::validate(&output_state_tmp, &output_gate_output, &output_state_out_symm, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO)); + + // _dequantize + const TensorInfo output_state_out_f32(output_state_out_symm.tensor_shape(), 1, DataType::F32); + ARM_COMPUTE_RETURN_ON_ERROR(NEDequantizationLayer::validate(&output_state_out_symm, &output_state_out_f32)); + + // _quantize + ARM_COMPUTE_RETURN_ON_ERROR(NEQuantizationLayer::validate(&output_state_out_f32, output_state_out)); + if(cell_state_out->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&cell_state_info, cell_state_out); |