COMPMID-2336: Validate multiplier and offset calculation in LSTMQuantized and DepthwiseConvolution functions
This patch also adds validation of internal functions in LSTMQuantizedLayer.
Change-Id: Id8dbbfbb421f7d053410476b4bb4ef7d85e5f41e
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1794
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
index c91dcec..42e5fbc 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
@@ -80,6 +80,17 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
}
+ if(is_qasymm)
+ {
+ const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
+ const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info;
+
+ float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
+ ARM_COMPUTE_UNUSED(multiplier);
+ ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f);
+ }
+
return Status{};
}
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
index c78ad1a..b8b144d 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
@@ -96,6 +96,17 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
}
+ if(is_qasymm)
+ {
+ const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
+ const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info;
+
+ float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
+ ARM_COMPUTE_UNUSED(multiplier);
+ ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f);
+ }
+
return Status{};
}
diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
index 451ccc4..f01b58a 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
@@ -142,9 +142,10 @@
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
+ const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
const bool is_nhwc = input->data_layout() == DataLayout::NHWC;
const bool needs_permute = is_nhwc && (depth_multiplier > 1);
- const bool needs_weights_reshape = is_nhwc && (depth_multiplier == 1);
+ const bool needs_weights_reshape = is_nhwc && (depth_multiplier == 1) && is_quantized;
const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1);
const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());
@@ -152,6 +153,17 @@
info.c0 = 4;
info.transpose = is_stride_1_dilation_1 && is_dot8_supported;
+ if(is_quantized)
+ {
+ const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
+ const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = (output->total_size() == 0) ? iq_info : output->quantization_info().uniform();
+
+ const float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
+ ARM_COMPUTE_UNUSED(multiplier);
+ ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f);
+ }
+
if(needs_permute)
{
TensorShape permuted_input_shape = input->tensor_shape();
@@ -177,7 +189,10 @@
ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, &weights->clone()->set_tensor_shape(reshaped_weights_shape), biases, output, conv_info, depth_multiplier,
act_info, dilation));
}
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation));
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation));
+ }
}
else
{
@@ -373,7 +388,7 @@
const bool can_run_optimised_3x3_kernel = (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3);
- if(can_run_optimised_3x3_kernel)
+ if(!can_run_optimised_3x3_kernel)
{
const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
@@ -415,6 +430,13 @@
if(is_quantized)
{
+ const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
+ const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = (output->total_size() == 0) ? iq_info : output->quantization_info().uniform();
+
+ const float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
+ ARM_COMPUTE_UNUSED(multiplier);
+ ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f);
ARM_COMPUTE_RETURN_ON_ERROR(CLDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output));
}
@@ -426,7 +448,7 @@
}
else
{
- CLDepthwiseConvolutionLayer3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, GPUTarget::MIDGARD, dilation);
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, GPUTarget::MIDGARD, dilation));
}
return Status{};
}
diff --git a/src/runtime/CL/functions/CLLSTMLayerQuantized.cpp b/src/runtime/CL/functions/CLLSTMLayerQuantized.cpp
index e0006a7..11cf85e 100644
--- a/src/runtime/CL/functions/CLLSTMLayerQuantized.cpp
+++ b/src/runtime/CL/functions/CLLSTMLayerQuantized.cpp
@@ -168,15 +168,30 @@
_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);
@@ -286,6 +301,150 @@
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(CLConcatenateLayer::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(CLConcatenateLayer::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(CLConcatenateLayer::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(CLTranspose::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(CLConcatenateLayer::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(CLConcatenateLayer::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(CLGEMMLowpMatrixMultiplyCore::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(CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint::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(CLSlice::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(CLSlice::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(CLSlice::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(CLSlice::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(CLSlice::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(CLSlice::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(CLSlice::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(CLSlice::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(CLActivationLayer::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(CLActivationLayer::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(CLActivationLayer::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(CLActivationLayer::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(CLPixelWiseMultiplication::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(CLPixelWiseMultiplication::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(CLArithmeticAddition::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(CLActivationLayer::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(CLPixelWiseMultiplication::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(CLDequantizationLayer::validate(&output_state_out_symm, &output_state_out_f32));
+
+ // _quantize
+ ARM_COMPUTE_RETURN_ON_ERROR(CLQuantizationLayer::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);
diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
index cdd278b..fbdee84 100644
--- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
@@ -583,10 +583,22 @@
ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(channel_idx));
}
+ const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+
+ if(is_quantized)
+ {
+ const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
+ const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = output->quantization_info().uniform();
+
+ float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale;
+ ARM_COMPUTE_UNUSED(multiplier);
+ ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f);
+ }
+
if(!NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input, weights, conv_info, depth_multiplier, dilation))
{
- const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
- TensorInfo accumulator = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
+ TensorInfo accumulator = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayer3x3Kernel::validate(input, weights, is_quantized ? &accumulator : output, conv_info, depth_multiplier, dilation));
if(is_quantized)
diff --git a/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp b/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp
index 6cfa988..264cca0 100644
--- a/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp
+++ b/src/runtime/NEON/functions/NELSTMLayerQuantized.cpp
@@ -147,15 +147,30 @@
_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 @@
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);
diff --git a/tests/validation/CL/DepthwiseConvolutionLayer.cpp b/tests/validation/CL/DepthwiseConvolutionLayer.cpp
index 4db363f..1c3dc30 100644
--- a/tests/validation/CL/DepthwiseConvolutionLayer.cpp
+++ b/tests/validation/CL/DepthwiseConvolutionLayer.cpp
@@ -48,7 +48,7 @@
constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(0); /**< Tolerance value for comparing reference's output against implementation's output for DataType::QASYMM8 */
constexpr float tolerance_num = 0.05f; /**< Tolerance number */
-const auto depth_multipliers = framework::dataset::make("DepthMultiplier", { 1, 2, 5 });
+const auto depth_multipliers = framework::dataset::make("DepthMultiplier", { 1, 2, 5 });
const auto large_depth_multipliers = framework::dataset::make("DepthMultiplier", { 1, 2, 5, 8 });
//Activation Functions
@@ -273,7 +273,7 @@
framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, true, true })),
input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, dilation, expected)
{
- bool is_valid = bool(CLDepthwiseConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier,ActivationLayerInfo(), dilation));
+ bool is_valid = bool(CLDepthwiseConvolutionLayer::validate(&input_info.clone()->set_is_resizable(true), &weights_info.clone()->set_is_resizable(true), &biases_info.clone()->set_is_resizable(true), &output_info.clone()->set_is_resizable(true), conv_info, depth_multiplier,ActivationLayerInfo(), dilation));
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
// clang-format on