COMPMID-3239: Fix projection and peephole in NEQLSTMLayer

- Peephole and projection has been fixed to be working
- Small internal kernel copying data between tensors to
  cover the case where num_units and output_size is different
  is added.

Below is strictly outside of this patch's scope but are changes
helping this patch working (directly or indirectly) or making
NEQLSTM more complete.

- Consideration for layer normalization is added to InfoHelpers
- QSYMM8 data type is added to helper function to
  print out tensors.
- NE/CLLSTMLayer::validate() logic has been modified to use correct
  value for shape validation.

Change-Id: I40b4e71dfdbe8432caa2fe4a9af60a725362cc33
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3157
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

6 files changed, 251 insertions, 39 deletions

diff --git a/arm_compute/core/utils/misc/InfoHelpers.h b/arm_compute/core/utils/misc/InfoHelpers.h
index 8cf701c124..6ecda7a0dd 100644
--- a/arm_compute/core/utils/misc/InfoHelpers.h
+++ b/arm_compute/core/utils/misc/InfoHelpers.h
@@ -90,6 +90,23 @@ inline void build_lstm_params_tensor_info(const LSTMParams<T>     &lstm_params,
         lstm_params_info->set_cifg_params(lstm_params.input_to_input_weights()->info(), lstm_params.recurrent_to_input_weights()->info(),
                                           cell_to_input_weights_info, lstm_params.input_gate_bias()->info());
     }
+    if(lstm_params.use_layer_norm())
+    {
+        ARM_COMPUTE_ERROR_ON_NULLPTR(lstm_params.forget_layer_norm_weights(),
+                                     lstm_params.output_layer_norm_weights(),
+                                     lstm_params.cell_layer_norm_weights());
+        if(!lstm_params.has_cifg_opt())
+        {
+            ARM_COMPUTE_ERROR_ON_NULLPTR(lstm_params.input_layer_norm_weights());
+        }
+
+        const ITensorInfo *forget_info = lstm_params.forget_layer_norm_weights()->info();
+        const ITensorInfo *cell_info   = lstm_params.cell_layer_norm_weights()->info();
+        const ITensorInfo *output_info = lstm_params.output_layer_norm_weights()->info();
+        const ITensorInfo *input_info  = lstm_params.has_cifg_opt() ? nullptr : lstm_params.input_layer_norm_weights()->info();
+
+        lstm_params_info->set_layer_normalization_params(input_info, forget_info, cell_info, output_info);
+    }
 }
 } // namespace info_helpers
 } // namespace utils
diff --git a/arm_compute/runtime/NEON/functions/NEQLSTMLayer.h b/arm_compute/runtime/NEON/functions/NEQLSTMLayer.h
index 0553e4f266..9eb0654cfe 100644
--- a/arm_compute/runtime/NEON/functions/NEQLSTMLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEQLSTMLayer.h
@@ -178,7 +178,8 @@ private:
         Output,
         Count
     };
-    static constexpr uint8_t _layer_norm_count = static_cast<uint8_t>(LayerNormGate::Count);
+    static constexpr uint8_t  _layer_norm_count                    = static_cast<uint8_t>(LayerNormGate::Count);
+    static constexpr uint32_t _out_state_output_size_dimension_idx = 0;
 
     /** Internal method to configure matrix multiplication plus output stage of each gate.
      *
@@ -201,6 +202,35 @@ private:
 
     MemoryGroup _memory_group{};
 
+    /** A small internel kernel do the copy between two tensors */
+    class TensorCopyKernel
+    {
+        static constexpr uint32_t max_dimension_supported = 2;
+
+        ITensor *_src{ nullptr };
+        ITensor *_dst{ nullptr };
+        size_t   _row_size{};
+        Window   _window{};
+
+    public:
+        /** Static function to check if given info will lead to a valid configuration of @ref NEQLSTMLayer::TensorCopyKernel
+         *
+         * @param[in] src Source tensor info.
+         * @param[in] dst Destination tensor info
+         *
+         * @return a status
+         */
+        static Status validate(const ITensorInfo &src, const ITensorInfo &dst);
+        /** Set the input and output tensors.
+         *
+         * @param[in]  src Source tensor
+         * @param[out] dst Destination tensor
+         */
+        void configure(ITensor &src, ITensor &dst);
+        /** run the kernel */
+        void run();
+    };
+
     // Functions used
     NETranspose                      _transpose_input_to_forget_weights{};
     NETranspose                      _transpose_input_to_cell_weights{};
@@ -245,7 +275,7 @@ private:
     NEPixelWiseMultiplicationKernel  _pixelwise_mul_cell_to_input{};
     NEGEMMLowpOutputStage            _cell_to_input_outstage{};
     NEArithmeticAdditionKernel       _accumulate_cell_input{};
-    NEActivationLayer                _input_gate_tanh{};
+    NEActivationLayer                _input_gate_sigmoid{};
     NEPixelWiseMultiplicationKernel  _pixelwise_mul_forget_cell{};
     NEPixelWiseMultiplicationKernel  _pixelwise_mul_input_cell{};
     NEArithmeticAdditionKernel       _add_forget_cell{};
@@ -256,6 +286,7 @@ private:
     NEGEMMLowpOutputStage            _recurrent_to_output_outstage{};
     NEArithmeticAdditionKernel       _accumulate_input_recurrent_output{};
     NEPixelWiseMultiplicationKernel  _pixelwise_mul_cell_to_output{};
+    NEGEMMLowpOutputStage            _cell_to_output_outstage{};
     NEArithmeticAdditionKernel       _accumulate_cell_to_output{};
     NEActivationLayer                _output_gate_sigmoid{};
     NEActivationLayer                _hidden_tanh{};
@@ -265,6 +296,12 @@ private:
     NEGEMMLowpOutputStage            _projection_outstage{};
     NEArithmeticAdditionKernel       _accumulate_projection{};
     NEActivationLayer                _projection_clip{};
+
+    TensorCopyKernel _projection_bias_copy{};
+    TensorCopyKernel _projection_output_to_accumulate_copy{};
+    TensorCopyKernel _projection_accumulate_to_output_copy{};
+    TensorCopyKernel _hidden_to_output_copy{};
+
     std::array<NEQLSTMLayerNormalizationKernel, _layer_norm_count> _layer_norms{ {} };
 
     // Tensor pointers
@@ -375,11 +412,16 @@ private:
     Tensor _input_to_output_outstage_res{ nullptr };
     Tensor _mm_recurrent_to_output_res{ nullptr };
     Tensor _mul_cell_to_output_res{ nullptr };
+    Tensor _cell_to_output_outstage_res{ nullptr };
     Tensor _recurrent_to_output_outstage_res{ nullptr };
     Tensor _output_gate{ nullptr };
     Tensor _hidden_mul_res{ nullptr };
+    Tensor _hidden_gate{ nullptr };
     Tensor _mm_projection_res{ nullptr };
     Tensor _projection_outstage_res{ nullptr };
+    Tensor _projection_out_res{ nullptr };
+    Tensor _projection_eff_bias_adjusted{ nullptr };
+    Tensor _projection_accumulate_res{ nullptr };
     Tensor _ones{ nullptr };
     std::array<Tensor, _layer_norm_count> _layer_norm_output{ {} };
 
@@ -395,6 +437,7 @@ private:
     bool _has_projection_clipping{ false };
     bool _has_peephole{ false };
     bool _has_layer_norm{ false };
+    bool _projection_tensor_copy_required{ false };
 };
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_NEQLSTMLAYER_H */
diff --git a/src/core/Utils.cpp b/src/core/Utils.cpp
index 892cbcd41c..bdde082a1f 100644
--- a/src/core/Utils.cpp
+++ b/src/core/Utils.cpp
@@ -493,6 +493,7 @@ void arm_compute::print_consecutive_elements(std::ostream &s, DataType dt, const
             print_consecutive_elements_impl<uint8_t>(s, ptr, n, stream_width, element_delim);
             break;
         case DataType::S8:
+        case DataType::QSYMM8:
         case DataType::QASYMM8_SIGNED:
         case DataType::QSYMM8_PER_CHANNEL:
             print_consecutive_elements_impl<int8_t>(s, reinterpret_cast<const int8_t *>(ptr), n, stream_width, element_delim);
@@ -533,6 +534,7 @@ int arm_compute::max_consecutive_elements_display_width(std::ostream &s, DataTyp
         case DataType::QASYMM8:
             return max_consecutive_elements_display_width_impl<uint8_t>(s, ptr, n);
         case DataType::S8:
+        case DataType::QSYMM8:
         case DataType::QASYMM8_SIGNED:
         case DataType::QSYMM8_PER_CHANNEL:
             return max_consecutive_elements_display_width_impl<int8_t>(s, reinterpret_cast<const int8_t *>(ptr), n);
diff --git a/src/runtime/CL/functions/CLLSTMLayer.cpp b/src/runtime/CL/functions/CLLSTMLayer.cpp
index 32ff813f43..56f22e2fe0 100644
--- a/src/runtime/CL/functions/CLLSTMLayer.cpp
+++ b/src/runtime/CL/functions/CLLSTMLayer.cpp
@@ -444,7 +444,7 @@ Status CLLSTMLayer::validate(const ITensorInfo *input,
         {
             ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_layer_norm_weights());
             ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->num_dimensions() > 1);
-            ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->dimension(0) != num_batches);
+            ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->dimension(0) != num_cells);
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, lstm_params.input_layer_norm_weights());
         }
 
@@ -453,9 +453,9 @@ Status CLLSTMLayer::validate(const ITensorInfo *input,
         ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->num_dimensions() > 1);
         ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->num_dimensions() > 1);
         ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->num_dimensions() > 1);
-        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->dimension(0) != num_batches);
-        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->dimension(0) != num_batches);
-        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->dimension(0) != num_batches);
+        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->dimension(0) != num_cells);
+        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->dimension(0) != num_cells);
+        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->dimension(0) != num_cells);
     }
 
     // Check peephole optimization
diff --git a/src/runtime/NEON/functions/NELSTMLayer.cpp b/src/runtime/NEON/functions/NELSTMLayer.cpp
index aac63e7643..f9d445fe71 100644
--- a/src/runtime/NEON/functions/NELSTMLayer.cpp
+++ b/src/runtime/NEON/functions/NELSTMLayer.cpp
@@ -420,7 +420,7 @@ Status NELSTMLayer::validate(const ITensorInfo *input,
         {
             ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_layer_norm_weights());
             ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->num_dimensions() > 1);
-            ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->dimension(0) != num_batches);
+            ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.input_layer_norm_weights()->dimension(0) != num_cells);
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, lstm_params.input_layer_norm_weights());
         }
 
@@ -429,9 +429,9 @@ Status NELSTMLayer::validate(const ITensorInfo *input,
         ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->num_dimensions() > 1);
         ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->num_dimensions() > 1);
         ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->num_dimensions() > 1);
-        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->dimension(0) != num_batches);
-        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->dimension(0) != num_batches);
-        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->dimension(0) != num_batches);
+        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.forget_layer_norm_weights()->dimension(0) != num_cells);
+        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_layer_norm_weights()->dimension(0) != num_cells);
+        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.output_layer_norm_weights()->dimension(0) != num_cells);
     }
 
     // Check peephole optimization
diff --git a/src/runtime/NEON/functions/NEQLSTMLayer.cpp b/src/runtime/NEON/functions/NEQLSTMLayer.cpp
index a279bba2ab..9c78ea8b75 100644
--- a/src/runtime/NEON/functions/NEQLSTMLayer.cpp
+++ b/src/runtime/NEON/functions/NEQLSTMLayer.cpp
@@ -46,6 +46,36 @@ Status validate_mm(GEMMLowpOutputStageInfo &gemmlowp_info, const ITensorInfo *mm
 }
 } // namespace
 
+Status NEQLSTMLayer::TensorCopyKernel::validate(const ITensorInfo &src, const ITensorInfo &dst)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON(src.tensor_shape().num_dimensions() > max_dimension_supported);
+    ARM_COMPUTE_RETURN_ERROR_ON(dst.tensor_shape().num_dimensions() > max_dimension_supported);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &dst);
+    ARM_COMPUTE_RETURN_ERROR_ON(dst.tensor_shape().y() != src.tensor_shape().y());
+    return Status{};
+}
+
+void NEQLSTMLayer::TensorCopyKernel::configure(ITensor &src, ITensor &dst)
+{
+    ARM_COMPUTE_ERROR_THROW_ON(NEQLSTMLayer::TensorCopyKernel::validate(*src.info(), *dst.info()));
+    _src      = &src;
+    _dst      = &dst;
+    _row_size = std::min(_src->info()->tensor_shape().x(), _dst->info()->tensor_shape().x());
+    _window   = calculate_max_window(*_src->info(), Steps());
+}
+
+void NEQLSTMLayer::TensorCopyKernel::run()
+{
+    Iterator input_iter{ _src, _window };
+    Iterator output_iter{ _dst, _window };
+
+    execute_window_loop(_window, [&](const Coordinates &)
+    {
+        memcpy(output_iter.ptr(), input_iter.ptr(), _row_size);
+    },
+    input_iter, output_iter);
+}
+
 NEQLSTMLayer::NEQLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager)
 {
     _memory_group = MemoryGroup(std::move(memory_manager));
@@ -93,8 +123,9 @@ void NEQLSTMLayer::configure(const ITensor *input,
                                                       forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(),
                                                       cell_state_in->info(), output_state_in->info(), cell_state_out->info(), output_state_out->info(), lstm_params_info));
 
-    const int batch_size = input->info()->dimension(1);
-    const int num_units  = input_to_output_weights->info()->dimension(1);
+    const int batch_size  = input->info()->dimension(1);
+    const int num_units   = input_to_output_weights->info()->dimension(1);
+    const int output_size = output_state_out->info()->dimension(_out_state_output_size_dimension_idx);
 
     const UniformQuantizationInfo qinput           = input->info()->quantization_info().uniform();
     const UniformQuantizationInfo qcell_state_in   = cell_state_in->info()->quantization_info().uniform();
@@ -154,10 +185,9 @@ void NEQLSTMLayer::configure(const ITensor *input,
     _recurrent_to_cell_reduction.configure(recurrent_to_cell_weights, &_recurrent_to_cell_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
     _input_to_output_reduction.configure(input_to_output_weights, &_input_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
     _recurrent_to_output_reduction.configure(recurrent_to_output_weights, &_recurrent_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
-    if(_projection_bias != nullptr)
+    if(_has_projection)
     {
-        _projection_reduction.configure(_projection_weights, &_projection_reduction_res, GEMMLowpReductionKernelInfo(num_units, false, lstm_params.hidden_state_zero(), true));
-        _projection_bias_add.configure(_projection_bias, &_projection_reduction_res, &_projection_eff_bias, ConvertPolicy::SATURATE);
+        _projection_reduction.configure(_projection_weights, &_projection_eff_bias, GEMMLowpReductionKernelInfo(output_size, false, lstm_params.hidden_state_zero(), true));
     }
 
     // Pre-transpose weights to be used in GEMM.
@@ -203,6 +233,7 @@ void NEQLSTMLayer::configure(const ITensor *input,
 
     if(_has_peephole)
     {
+        _mul_cell_to_forget_res.allocator()->init(TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32));
         _memory_group.manage(&_mul_cell_to_forget_res);
         _pixelwise_mul_cell_to_forget.configure(cell_state_in, lstm_params.cell_to_forget_weights(), &_mul_cell_to_forget_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
         _cell_to_forget_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_forget_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0)));
@@ -287,7 +318,7 @@ void NEQLSTMLayer::configure(const ITensor *input,
 
         const float recurrent_to_input_scale = _recurrent_to_input_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale();
         configure_mm(_mm_recurrent_to_input, _recurrent_to_input_outstage, gemmlowp_info,
-                     input, &_recurrent_to_input_weights_transposed, &_recurrent_to_input_eff_bias,
+                     output_state_in, &_recurrent_to_input_weights_transposed, &_recurrent_to_input_eff_bias,
                      &_mm_recurrent_to_input_res, &_recurrent_to_input_outstage_res, recurrent_to_input_scale,
                      mm_out_info, input_outstage_info);
         _accumulate_input_recurrent_input.configure(&_input_to_input_outstage_res, &_recurrent_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE);
@@ -295,6 +326,7 @@ void NEQLSTMLayer::configure(const ITensor *input,
 
         if(_has_peephole)
         {
+            _mul_cell_to_input_res.allocator()->init(TensorInfo(cell_state_in->info()->tensor_shape(), 1, DataType::S32));
             _memory_group.manage(&_mul_cell_to_input_res);
             _pixelwise_mul_cell_to_input.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_mul_cell_to_input_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
             const float cell_to_input_scale = std::pow(2, cell_shift) * lstm_params.cell_to_input_weights()->info()->quantization_info().uniform().scale / lstm_params.input_intermediate_scale();
@@ -316,7 +348,7 @@ void NEQLSTMLayer::configure(const ITensor *input,
             input_activation_input = &get_layer_norm_output(LayerNormGate::Input);
         }
 
-        _input_gate_tanh.configure(input_activation_input, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+        _input_gate_sigmoid.configure(input_activation_input, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
         input_activation_input->allocator()->allocate();
     }
     // Cell.
@@ -357,12 +389,19 @@ void NEQLSTMLayer::configure(const ITensor *input,
     {
         // TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplicationKernel
         // Here we are not using the output stage because all operations are done in float
-        // const float cell_to_output_scale = std::pow(2, cell_shift) * lstm_params.cell_to_output_weights()->info()->quantization_info().uniform().scale / lstm_params.output_intermediate_scale();
-        // quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
+        _mul_cell_to_output_res.allocator()->init(TensorInfo(cell_state_out->info()->tensor_shape(), 1, DataType::S32));
         _memory_group.manage(&_mul_cell_to_output_res);
         _pixelwise_mul_cell_to_output.configure(cell_state_out, lstm_params.cell_to_output_weights(), &_mul_cell_to_output_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
-        _accumulate_cell_to_output.configure(&_recurrent_to_output_outstage_res, &_mul_cell_to_output_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE);
+
+        const float cell_to_output_scale = std::pow(2, cell_shift) * lstm_params.cell_to_output_weights()->info()->quantization_info().uniform().scale / lstm_params.output_intermediate_scale();
+        quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
+        _cell_to_output_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_output_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0)));
+        _memory_group.manage(&_cell_to_output_outstage_res);
+        _cell_to_output_outstage.configure(&_mul_cell_to_output_res, nullptr, &_cell_to_output_outstage_res, gemmlowp_info);
         _mul_cell_to_output_res.allocator()->allocate();
+
+        _accumulate_cell_to_output.configure(&_recurrent_to_output_outstage_res, &_cell_to_output_outstage_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE);
+        _cell_to_output_outstage_res.allocator()->allocate();
     }
 
     Tensor *output_activation_input = &_recurrent_to_output_outstage_res;
@@ -393,13 +432,24 @@ void NEQLSTMLayer::configure(const ITensor *input,
     quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true);
     gemmlowp_info.gemmlowp_offset  = lstm_params.hidden_state_zero();
     gemmlowp_info.output_data_type = output_state_in->info()->data_type();
-    _hidden_outstage.configure(&_hidden_mul_res, nullptr, output_state_out, gemmlowp_info);
+
+    _projection_tensor_copy_required = (num_units != output_size);
+    ITensor *hidden_gate_result      = output_state_out;
+
+    if(_projection_tensor_copy_required)
+    {
+        _hidden_gate.allocator()->init(*output_state_out->info());
+        _hidden_gate.info()->set_tensor_shape(_hidden_mul_res.info()->tensor_shape());
+        hidden_gate_result = &_hidden_gate;
+    }
+
+    _hidden_outstage.configure(&_hidden_mul_res, nullptr, hidden_gate_result, gemmlowp_info);
     _hidden_mul_res.allocator()->allocate();
 
     // Projection.
     if(_has_projection)
     {
-        const TensorInfo              projection_outstage_info(*output_state_out->info());
+        const TensorInfo              projection_outstage_info(*hidden_gate_result->info());
         const UniformQuantizationInfo qprojection      = _projection_weights->info()->quantization_info().uniform();
         const float                   projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale;
         gemmlowp_info.gemmlowp_offset                  = qoutput_state_in.offset;
@@ -407,14 +457,44 @@ void NEQLSTMLayer::configure(const ITensor *input,
         gemmlowp_info.gemmlowp_max_bound               = std::numeric_limits<int8_t>::max();
         gemmlowp_info.output_data_type                 = DataType::QASYMM8_SIGNED;
 
+        _memory_group.manage(&_projection_eff_bias_adjusted);
+        ITensor *bias_to_use = &_projection_eff_bias;
+
+        if(_projection_tensor_copy_required)
+        {
+            _projection_eff_bias_adjusted.allocator()->init(*_projection_eff_bias.info());
+            _projection_eff_bias_adjusted.info()->set_tensor_shape(TensorShape(num_units));
+            _projection_bias_copy.configure(_projection_eff_bias, _projection_eff_bias_adjusted);
+            bias_to_use = &_projection_eff_bias_adjusted;
+        }
+
         configure_mm(_mm_projection, _projection_outstage, gemmlowp_info,
-                     output_state_out, &_projection_weights_transposed, &_projection_eff_bias,
+                     hidden_gate_result, &_projection_weights_transposed, bias_to_use,
                      &_mm_projection_res, &_projection_outstage_res, projection_scale,
                      mm_out_info, projection_outstage_info);
 
-        _accumulate_projection.configure(&_projection_outstage_res, output_state_out, output_state_out, ConvertPolicy::SATURATE);
+        _projection_eff_bias_adjusted.allocator()->allocate();
+
+        ITensor *accumulate_destination = output_state_out;
+
+        if(_projection_tensor_copy_required)
+        {
+            _hidden_gate.allocator()->allocate();
+            _projection_accumulate_res.allocator()->init(*output_state_out->info());
+            _projection_accumulate_res.info()->set_tensor_shape(_projection_outstage_res.info()->tensor_shape());
+            _projection_output_to_accumulate_copy.configure(*output_state_out, _projection_accumulate_res);
+            accumulate_destination = &_projection_accumulate_res;
+        }
+
+        _accumulate_projection.configure(&_projection_outstage_res, accumulate_destination, accumulate_destination, ConvertPolicy::SATURATE);
         _projection_outstage_res.allocator()->allocate();
 
+        if(_projection_tensor_copy_required)
+        {
+            _projection_accumulate_to_output_copy.configure(_projection_accumulate_res, *output_state_out);
+            _projection_accumulate_res.allocator()->allocate();
+        }
+
         int8_t quantized_projection_clip{ 0 };
         if(lstm_params.projection_clip() > 0.0f)
         {
@@ -427,6 +507,14 @@ void NEQLSTMLayer::configure(const ITensor *input,
             _has_projection_clipping = true;
         }
     }
+    else
+    {
+        if(_projection_tensor_copy_required)
+        {
+            _hidden_to_output_copy.configure(_hidden_gate, *output_state_out);
+            _hidden_gate.allocator()->allocate();
+        }
+    }
 }
 
 Status NEQLSTMLayer::validate(const ITensorInfo *input,
@@ -446,7 +534,7 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input,
     const unsigned int input_size  = input->dimension(0);
     const unsigned int batch_size  = input->dimension(1);
     const unsigned int num_units   = input_to_output_weights->dimension(1);
-    const unsigned int output_size = recurrent_to_output_weights->dimension(0);
+    const unsigned int output_size = output_state_out->dimension(_out_state_output_size_dimension_idx);
 
     ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->num_dimensions() != 2);
     ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->dimension(0) != input_size);
@@ -509,6 +597,7 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input,
 
     // Precompute effective bias for optimizing the matmul computations.
     const TensorInfo eff_bias_info(TensorShape(num_units), 1, DataType::S32);
+    const TensorInfo projection_eff_bias_info(TensorShape(output_size), 1, DataType::S32);
     if(!lstm_params.has_cifg_opt())
     {
         ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(lstm_params.input_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
@@ -521,11 +610,11 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input,
     ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(recurrent_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
     ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(input_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
     ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(recurrent_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
-    if(lstm_params.projection_bias() != nullptr)
+    if(lstm_params.has_projection())
     {
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(lstm_params.projection_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, lstm_params.hidden_state_zero(),
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(lstm_params.projection_weights(), &projection_eff_bias_info, GEMMLowpReductionKernelInfo(output_size, false,
+                                                                               lstm_params.hidden_state_zero(),
                                                                                true)));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(lstm_params.projection_bias(), &eff_bias_info, &eff_bias_info, ConvertPolicy::SATURATE));
     }
 
     const TensorInfo input_weights_transposed(TensorShape(num_units, input_size), 1, input_to_forget_weights->data_type(), input_to_forget_weights->quantization_info());
@@ -545,7 +634,8 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input,
     }
     if(lstm_params.has_projection())
     {
-        ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.projection_weights(), &recurrent_weights_transposed));
+        const TensorInfo projection_weights_transposed(TensorShape(output_size, num_units), 1, lstm_params.projection_weights()->data_type(), lstm_params.projection_weights()->quantization_info());
+        ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(lstm_params.projection_weights(), &projection_weights_transposed));
     }
 
     GEMMLowpOutputStageInfo gemmlowp_info;
@@ -627,23 +717,22 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input,
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(forget_gate_bias, lstm_params.input_gate_bias());
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(forget_gate_bias, lstm_params.input_gate_bias());
 
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(input, lstm_params.input_to_input_weights(), nullptr, &mm_out_info));
         const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0));
         const float      input_to_input_scale = lstm_params.input_to_input_weights()->quantization_info().uniform().scale * qinput.scale / lstm_params.input_intermediate_scale();
-        validate_mm(gemmlowp_info, input, lstm_params.input_to_input_weights(), &eff_bias_info, input_to_input_scale, &mm_out_info, &input_outstage_info);
+        validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_input_scale, &mm_out_info, &input_outstage_info);
 
         const float recurrent_to_input_scale = lstm_params.recurrent_to_input_weights()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale();
-        validate_mm(gemmlowp_info, input, lstm_params.recurrent_to_input_weights(), &eff_bias_info, recurrent_to_input_scale, &mm_out_info, &input_outstage_info);
+        validate_mm(gemmlowp_info, input, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_input_scale, &mm_out_info, &input_outstage_info);
 
         ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE));
 
         if(lstm_params.has_peephole_opt())
         {
-            ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_input_weights(), &input_outstage_info, 1.f, ConvertPolicy::SATURATE,
+            ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_input_weights(), &mm_out_info, 1.f, ConvertPolicy::SATURATE,
                                                                                   RoundingPolicy::TO_ZERO));
             const float cell_to_input_scale = std::pow(2, cell_shift) * lstm_params.cell_to_input_weights()->quantization_info().uniform().scale / lstm_params.input_intermediate_scale();
             ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
-            ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&input_outstage_info, &eff_bias_info, &input_outstage_info, gemmlowp_info));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&mm_out_info, &eff_bias_info, &input_outstage_info, gemmlowp_info));
             ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(&input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE));
         }
 
@@ -654,7 +743,7 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input,
             ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(input_outstage_info, *w_info, *b_info));
         }
 
-        ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&input_outstage_info, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(&input_outstage_info, &input_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
     }
     // Cell.
     ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&forget_gate_info, cell_state_in, &forget_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
@@ -699,11 +788,14 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input,
     // Hidden.
     ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(cell_state_out, &input_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
     const TensorInfo hidden_mul_res(TensorShape(num_units, batch_size), 1, DataType::S32);
+    const TensorInfo hidden_out_info(TensorShape(num_units, batch_size), 1, DataType::QASYMM8_SIGNED);
     ARM_COMPUTE_RETURN_ON_ERROR(NEPixelWiseMultiplicationKernel::validate(&output_gate_info, &input_gate_info, &hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
     const float hidden_state_scale = std::pow(2, -15) / lstm_params.hidden_state_scale() * std::pow(2, -15);
     ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true));
     gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero();
-    ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&hidden_mul_res, nullptr, output_state_out, gemmlowp_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&hidden_mul_res, nullptr, &hidden_out_info, gemmlowp_info));
+
+    const bool projection_tensor_copy_required = num_units != output_size;
 
     // Projection.
     if(lstm_params.has_projection())
@@ -719,11 +811,30 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input,
         gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max();
         gemmlowp_info.output_data_type   = DataType::QASYMM8_SIGNED;
 
-        const TensorInfo projection_outstage_info(*output_state_out);
-        validate_mm(gemmlowp_info, output_state_out, &recurrent_weights_transposed, &eff_bias_info, input_to_output_scale, &mm_out_info, &projection_outstage_info);
+        TensorInfo projection_outstage_info(hidden_out_info);
+
+        if(projection_tensor_copy_required)
+        {
+            TensorInfo projection_eff_bias_adjusted_info{ projection_eff_bias_info };
+            projection_eff_bias_adjusted_info.set_tensor_shape(TensorShape(num_units));
+
+            ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(projection_eff_bias_info, projection_eff_bias_adjusted_info));
+        }
+
+        validate_mm(gemmlowp_info, output_state_out, &recurrent_weights_transposed, &projection_eff_bias_info, input_to_output_scale, &mm_out_info, &projection_outstage_info);
+
+        if(projection_tensor_copy_required)
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(*output_state_out, projection_outstage_info));
+        }
 
         ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(output_state_out, output_state_out, output_state_out, ConvertPolicy::SATURATE));
 
+        if(projection_tensor_copy_required)
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(projection_outstage_info, *output_state_out));
+        }
+
         int8_t quantized_projection_clip{ 0 };
         if(lstm_params.projection_clip() > 0.0f)
         {
@@ -736,6 +847,13 @@ Status NEQLSTMLayer::validate(const ITensorInfo *input,
                                                                                                                    quantized_projection_clip)));
         }
     }
+    else
+    {
+        if(projection_tensor_copy_required)
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(NEQLSTMLayer::TensorCopyKernel::validate(hidden_out_info, *output_state_out));
+        }
+    }
 
     if(cell_state_out->total_size() > 0)
     {
@@ -821,7 +939,7 @@ void NEQLSTMLayer::run()
             NEScheduler::get().schedule(&get_layer_norm(LayerNormGate::Input), Window::DimY);
         }
 
-        _input_gate_tanh.run();
+        _input_gate_sigmoid.run();
     }
 
     // Cell.
@@ -842,6 +960,7 @@ void NEQLSTMLayer::run()
     if(_has_peephole)
     {
         NEScheduler::get().schedule(&_pixelwise_mul_cell_to_output, Window::DimY);
+        _cell_to_output_outstage.run();
         NEScheduler::get().schedule(&_accumulate_cell_to_output, Window::DimY);
     }
 
@@ -860,14 +979,38 @@ void NEQLSTMLayer::run()
     // Projection.
     if(_has_projection)
     {
+        if(_projection_tensor_copy_required)
+        {
+            _projection_bias_copy.run();
+        }
+
         _mm_projection.run();
         _projection_outstage.run();
+
+        if(_projection_tensor_copy_required)
+        {
+            _projection_output_to_accumulate_copy.run();
+        }
+
         NEScheduler::get().schedule(&_accumulate_projection, Window::DimY);
+
+        if(_projection_tensor_copy_required)
+        {
+            _projection_accumulate_to_output_copy.run();
+        }
+
         if(_has_projection_clipping)
         {
             _projection_clip.run();
         }
     }
+    else
+    {
+        if(_projection_tensor_copy_required)
+        {
+            _hidden_to_output_copy.run();
+        }
+    }
 }
 
 void NEQLSTMLayer::prepare()
@@ -932,6 +1075,13 @@ void NEQLSTMLayer::prepare()
             _projection_weights_transposed.allocator()->allocate();
             _transpose_projection_weights.run();
             _projection_weights->mark_as_unused();
+
+            if(!_projection_tensor_copy_required)
+            {
+                _hidden_gate.mark_as_unused();
+                _projection_eff_bias_adjusted.mark_as_unused();
+                _projection_accumulate_res.mark_as_unused();
+            }
         }
 
         // Mark weights as unused