reference_model/src/generate/generate_dot_product.cc - tosa/reference_model - Gitiles

 // Copyright (c) 2023, ARM Limited.
 //
 //    Licensed under the Apache License, Version 2.0 (the "License");
 //    you may not use this file except in compliance with the License.
 //    You may obtain a copy of the License at
 //
 //         http://www.apache.org/licenses/LICENSE-2.0
 //
 //    Unless required by applicable law or agreed to in writing, software
 //    distributed under the License is distributed on an "AS IS" BASIS,
 //    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 //    See the License for the specific language governing permissions and
 //    limitations under the License.

 #include "generate_dot_product.h"
 #include "half.hpp"

 namespace
 {
 //---------------------------------------------------------------------------//
 //                              MatMul                                       //
 //---------------------------------------------------------------------------//

 template <typename DataType>
 void generateMatMulA(const TosaReference::GenerateConfig& cfg,
                      TosaReference::IDotProductGenerator& generator,
                      DataType* data,
                      size_t size)
 {
     const uint32_t T = cfg.shape[0] * cfg.shape[1] * cfg.shape[2];
     const uint32_t C = cfg.shape[2];

     for (uint32_t t = 0; t < T; ++t)
     {
         data[t] = static_cast<DataType>(generator(t % C));    // k = c
     }
 }

 template <typename DataType>
 void generateMatMulB(const TosaReference::GenerateConfig& cfg,
                      TosaReference::IDotProductGenerator& generator,
                      DataType* data,
                      size_t size)
 {
     const uint32_t T = cfg.shape[0] * cfg.shape[1] * cfg.shape[2];
     const uint32_t C = cfg.shape[1];
     const uint32_t W = cfg.shape[2];

     for (uint32_t t = 0; t < T; ++t)
     {
         data[t] = static_cast<DataType>(generator((t / W) % C));    // k = c
     }
 }

 bool generateMatMul(const TosaReference::GenerateConfig& cfg,
                     TosaReference::IDotProductGenerator& generator,
                     void* data,
                     size_t size)
 {
     if (cfg.shape.size() != 3)
     {
         WARNING("[Generator][DP][MatMul] Tensor shape expected 3 dimensions.");
         return false;
     }
     if (cfg.inputPos > 1 || cfg.inputPos < 0)
     {
         WARNING("[Generator][DP][MatMul] Invalid input tensor slot position to operator.");
         return false;
     }

     switch (cfg.dataType)
     {
         case DType::DType_FP32: {
             float* outData = reinterpret_cast<float*>(data);
             (cfg.inputPos == 0) ? generateMatMulA(cfg, generator, outData, size)
                                 : generateMatMulB(cfg, generator, outData, size);
             break;
         }
         case DType::DType_FP16: {
             half_float::half* outData = reinterpret_cast<half_float::half*>(data);
             (cfg.inputPos == 0) ? generateMatMulA(cfg, generator, outData, size)
                                 : generateMatMulB(cfg, generator, outData, size);
             break;
         }
         default:
             WARNING("[Generator][DP][MatMul] Only supports FP32 or FP16.");
             return false;
     }

     return true;
 }
 //---------------------------------------------------------------------------//
 //                              Conv2D                                       //
 //---------------------------------------------------------------------------//

 template <typename DataType>
 bool generateConv2DInput(const TosaReference::GenerateConfig& cfg,
                          TosaReference::IDotProductGenerator& generator,
                          DataType* data,
                          size_t size)
 {
     if (cfg.dotProductInfo.kernel.size() != 2 || cfg.dotProductInfo.kernel[0] <= 0 || cfg.dotProductInfo.kernel[1] <= 0)
     {
         WARNING("[Generator][DP][Conv2D][Input] Missing or incorrect kernel size information.");
         return false;
     }
     if (cfg.shape.size() != 4)
     {
         WARNING("[Generator][DP][Conv2D][Input] Tensor shape expected 4 dimensions.");
         return false;
     }

     const int64_t T   = TosaReference::numElementsFromShape(cfg.shape);
     const uint32_t IH = cfg.shape[1];
     const uint32_t IW = cfg.shape[2];
     const uint32_t IC = cfg.shape[3];
     const uint32_t KH = cfg.dotProductInfo.kernel[0];
     const uint32_t KW = cfg.dotProductInfo.kernel[1];

     for (int64_t t = 0; t < T; ++t)
     {
         uint32_t ic = t % IC;
         uint32_t ix = (t / IC) % IW;
         uint32_t iy = ((t / IC) / IW) % IH;
         uint32_t k  = ((iy % KH) * KW + (ix % KW)) * IC + ic;

         data[t] = static_cast<DataType>(generator(k));
     }
     return true;
 }

 template <typename DataType>
 bool generateConv2DWeight(const TosaReference::GenerateConfig& cfg,
                           TosaReference::IDotProductGenerator& generator,
                           DataType* data,
                           size_t size)
 {
     if (cfg.shape.size() != 4)
     {
         WARNING("[Generator][DP][Conv2D][Weight] Tensor shape expected 4 dimensions.");
         return false;
     }

     const int64_t T   = TosaReference::numElementsFromShape(cfg.shape);
     const uint32_t KH = cfg.shape[1];
     const uint32_t KW = cfg.shape[2];
     const uint32_t IC = cfg.shape[3];

     for (int64_t t = 0; t < T; ++t)
     {
         uint32_t ic = t % IC;
         uint32_t kx = (t / IC) % KW;
         uint32_t ky = ((t / IC) / KW) % KH;
         uint32_t k  = (ky + KW * kx) * IC + ic;

         data[t] = static_cast<DataType>(generator(k));
     }
     return true;
 }

 template <typename DataType>
 bool generateConv2DBias(const TosaReference::GenerateConfig& cfg,
                         TosaReference::IDotProductGenerator& generator,
                         DataType* data,
                         size_t size)
 {
     if (cfg.shape.size() != 1)
     {
         WARNING("[Generator][DP][Conv2D][Bias] Tensor shape expected 1 dimension.");
         return false;
     }

     const uint32_t T = cfg.shape[0];

     for (uint32_t t = 0; t < T; ++t)
     {
         data[t] = static_cast<DataType>(generator(2));
     }
     return true;
 }

 bool generateConv2D(const TosaReference::GenerateConfig& cfg,
                     TosaReference::IDotProductGenerator& generator,
                     void* data,
                     size_t size)
 {
     switch (cfg.dataType)
     {
         case DType::DType_FP32: {
             float* outData = reinterpret_cast<float*>(data);
             switch (cfg.inputPos)
             {
                 case 0:
                     return generateConv2DInput(cfg, generator, outData, size);
                 case 1:
                     return generateConv2DWeight(cfg, generator, outData, size);
                 case 2:
                     return generateConv2DBias(cfg, generator, outData, size);
                 default:
                     WARNING("[Generator][DP][Conv2D] Invalid input tensor slot position to operator.");
                     return false;
             }
             break;
         }
         case DType::DType_FP16: {
             half_float::half* outData = reinterpret_cast<half_float::half*>(data);
             switch (cfg.inputPos)
             {
                 case 0:
                     return generateConv2DInput(cfg, generator, outData, size);
                 case 1:
                     return generateConv2DWeight(cfg, generator, outData, size);
                 case 2:
                     return generateConv2DBias(cfg, generator, outData, size);
                 default:
                     WARNING("[Generator][DP][Conv2D] Invalid input tensor slot position to operator.");
                     return false;
             }
             break;
         }
         default:
             WARNING("[Generator][DP][Conv2D] Only supports FP32 or FP16.");
             return false;
     }
 }
 //---------------------------------------------------------------------------//
 //                              Reduce Sum                                   //
 //---------------------------------------------------------------------------//

 template <typename DataType>
 void generateReduceSumData(const TosaReference::GenerateConfig& cfg,
                            TosaReference::IDotProductGenerator& generator,
                            DataType* data,
                            size_t size)
 {
     const int64_t T     = TosaReference::numElementsFromShape(cfg.shape);
     const uint32_t axis = cfg.dotProductInfo.axis;

     for (int64_t t = 0; t < T; ++t)
     {
         uint64_t k = t;
         for (uint32_t d = cfg.shape.size() - 1; d > axis; --d)
         {
             k = k / cfg.shape[d];
         }
         k = k % cfg.shape[axis];

         data[t] = static_cast<DataType>(generator(static_cast<int32_t>(k)));
     }
 }

 bool generateReduceSum(const TosaReference::GenerateConfig& cfg,
                        TosaReference::IDotProductGenerator& generator,
                        void* data,
                        size_t size)
 {
     if (cfg.inputPos != 0)
     {
         WARNING("[Generator][DP][ReduceSum] Invalid input tensor slot position to operator.");
         return false;
     }
     if (cfg.dotProductInfo.axis < 0 || static_cast<size_t>(cfg.dotProductInfo.axis) >= cfg.shape.size())
     {
         WARNING("[Generator][DP][ReduceSum] Invalid axis %d.", cfg.dotProductInfo.axis);
         return false;
     }

     switch (cfg.dataType)
     {
         case DType::DType_FP32: {
             float* outData = reinterpret_cast<float*>(data);
             generateReduceSumData(cfg, generator, outData, size);
             break;
         }
         case DType::DType_FP16: {
             half_float::half* outData = reinterpret_cast<half_float::half*>(data);
             generateReduceSumData(cfg, generator, outData, size);
             break;
         }
         default:
             WARNING("[Generator][DP][ReduceSum] Only supports FP32 or FP16.");
             return false;
     }

     return true;
 }
 //---------------------------------------------------------------------------//
 //                              Fully Connected                              //
 //---------------------------------------------------------------------------//

 template <typename DataType>
 bool generateFullyConnectedInput(const TosaReference::GenerateConfig& cfg,
                                  TosaReference::IDotProductGenerator& generator,
                                  DataType* data,
                                  size_t size)
 {
     if (cfg.shape.size() != 2)
     {
         WARNING("[Generator][DP][FullyConnected][Input] Tensor shape expected 2 dimensions.");
         return false;
     }

     const int64_t T   = TosaReference::numElementsFromShape(cfg.shape);
     const uint32_t IC = cfg.shape[1];

     for (int64_t t = 0; t < T; ++t)
     {
         uint32_t k = t % IC;

         data[t] = static_cast<DataType>(generator(k));
     }
     return true;
 }

 template <typename DataType>
 bool generateFullyConnectedWeight(const TosaReference::GenerateConfig& cfg,
                                   TosaReference::IDotProductGenerator& generator,
                                   DataType* data,
                                   size_t size)
 {
     if (cfg.shape.size() != 2)
     {
         WARNING("[Generator][DP][FullyConnected][Weight] Tensor shape expected 2 dimensions.");
         return false;
     }

     const int64_t T   = TosaReference::numElementsFromShape(cfg.shape);
     const uint32_t IC = cfg.shape[1];

     for (int64_t t = 0; t < T; ++t)
     {
         uint32_t k = t % IC;

         data[t] = static_cast<DataType>(generator(k));
     }
     return true;
 }

 template <typename DataType>
 bool generateFullyConnectedBias(const TosaReference::GenerateConfig& cfg,
                                 TosaReference::IDotProductGenerator& generator,
                                 DataType* data,
                                 size_t size)
 {
     if (cfg.shape.size() != 1)
     {
         WARNING("[Generator][DP][FullyConnected][Bias] Tensor shape expected 1 dimension.");
         return false;
     }

     const uint32_t T = cfg.shape[0];

     for (uint32_t t = 0; t < T; ++t)
     {
         data[t] = static_cast<DataType>(generator(2));
     }
     return true;
 }

 bool generateFullyConnected(const TosaReference::GenerateConfig& cfg,
                             TosaReference::IDotProductGenerator& generator,
                             void* data,
                             size_t size)
 {
     switch (cfg.dataType)
     {
         case DType::DType_FP32: {
             float* outData = reinterpret_cast<float*>(data);
             switch (cfg.inputPos)
             {
                 case 0:
                     return generateFullyConnectedInput(cfg, generator, outData, size);
                 case 1:
                     return generateFullyConnectedWeight(cfg, generator, outData, size);
                 case 2:
                     return generateFullyConnectedBias(cfg, generator, outData, size);
                 default:
                     WARNING("[Generator][DP][FullyConnected] Invalid input tensor slot position to operator.");
                     return false;
             }
             break;
         }
         case DType::DType_FP16: {
             half_float::half* outData = reinterpret_cast<half_float::half*>(data);
             switch (cfg.inputPos)
             {
                 case 0:
                     return generateFullyConnectedInput(cfg, generator, outData, size);
                 case 1:
                     return generateFullyConnectedWeight(cfg, generator, outData, size);
                 case 2:
                     return generateFullyConnectedBias(cfg, generator, outData, size);
                 default:
                     WARNING("[Generator][DP][FullyConnected] Invalid input tensor slot position to operator.");
                     return false;
             }
             break;
         }
         default:
             WARNING("[Generator][DP][FullyConnected] Only supports FP32 or FP16.");
             return false;
     }
 }
 //---------------------------------------------------------------------------//
 //                              Avg Pool 2D                                   //
 //---------------------------------------------------------------------------//

 template <typename DataType>
 void generateAvgPool2DData(const TosaReference::GenerateConfig& cfg,
                            TosaReference::IDotProductGenerator& generator,
                            DataType* data,
                            size_t size)
 {
     const int64_t T   = TosaReference::numElementsFromShape(cfg.shape);
     const uint32_t IH = cfg.shape[1];
     const uint32_t IW = cfg.shape[2];
     const uint32_t C  = cfg.shape[3];
     const uint32_t KY = cfg.dotProductInfo.kernel[0];
     const uint32_t KX = cfg.dotProductInfo.kernel[1];

     for (int64_t t = 0; t < T; ++t)
     {
         uint32_t c  = t % C;
         uint32_t ix = (t / C) % IW;
         uint32_t iy = ((t / C) / IW) % IH;
         uint32_t k  = ((iy % KY) * KX + (ix % KX)) * C + c;

         data[t] = static_cast<DataType>(generator(k));
     }
 }

 bool generateAvgPool2D(const TosaReference::GenerateConfig& cfg,
                        TosaReference::IDotProductGenerator& generator,
                        void* data,
                        size_t size)
 {
     if (cfg.inputPos != 0)
     {
         WARNING("[Generator][DP][AvgPool2D] Invalid input tensor slot position to operator.");
         return false;
     }
     if (cfg.dotProductInfo.kernel.size() != 2 || cfg.dotProductInfo.kernel[0] <= 0 || cfg.dotProductInfo.kernel[1] <= 0)
     {
         WARNING("[Generator][DP][AvgPool2D] Missing or incorrect kernel size information.");
         return false;
     }
     if (cfg.shape.size() != 4)
     {
         WARNING("[Generator][DP][AvgPool2D] Tensor shape expected 4 dimensions.");
         return false;
     }

     switch (cfg.dataType)
     {
         case DType::DType_FP32: {
             float* outData = reinterpret_cast<float*>(data);
             generateAvgPool2DData(cfg, generator, outData, size);
             break;
         }
         case DType::DType_FP16: {
             half_float::half* outData = reinterpret_cast<half_float::half*>(data);
             generateAvgPool2DData(cfg, generator, outData, size);
             break;
         }
         default:
             WARNING("[Generator][DP][AvgPool2D] Only supports FP32 or FP16.");
             return false;
     }

     return true;
 }
 }    // namespace

 namespace TosaReference
 {

 bool generateDotProduct(const GenerateConfig& cfg, void* data, size_t size)
 {
     auto generator = pickDotProductGenerator(cfg);
     if (!generator)
     {
         WARNING("[Generator][DP] Requested generator could not be created!");
         return false;
     }
     if (cfg.dotProductInfo.ks <= 0)
     {
         WARNING("[Generator][DP] Invalid test set kernel size %d.", cfg.dotProductInfo.ks);
         return false;
     }

     // Select which generator to use
     switch (cfg.opType)
     {
         case tosa::Op_MATMUL:
             return generateMatMul(cfg, *generator, data, size);
         case tosa::Op_CONV2D:
             return generateConv2D(cfg, *generator, data, size);
         case tosa::Op_REDUCE_SUM:
             return generateReduceSum(cfg, *generator, data, size);
         case tosa::Op_FULLY_CONNECTED:
             return generateFullyConnected(cfg, *generator, data, size);
         case tosa::Op_AVG_POOL2D:
             return generateAvgPool2D(cfg, *generator, data, size);
         default:
             WARNING("[Generator][DP] Unsupported operator.");
             return false;
     }

     return false;
 }
 }    // namespace TosaReference
	// Copyright (c) 2023, ARM Limited.
	//
	// Licensed under the Apache License, Version 2.0 (the "License");
	// you may not use this file except in compliance with the License.
	// You may obtain a copy of the License at
	//
	// http://www.apache.org/licenses/LICENSE-2.0
	//
	// Unless required by applicable law or agreed to in writing, software
	// distributed under the License is distributed on an "AS IS" BASIS,
	// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	// See the License for the specific language governing permissions and
	// limitations under the License.

	#include "generate_dot_product.h"
	#include "half.hpp"

	namespace
	{
	//---------------------------------------------------------------------------//
	// MatMul //
	//---------------------------------------------------------------------------//

	template <typename DataType>
	void generateMatMulA(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	DataType* data,
	size_t size)
	{
	const uint32_t T = cfg.shape[0] * cfg.shape[1] * cfg.shape[2];
	const uint32_t C = cfg.shape[2];

	for (uint32_t t = 0; t < T; ++t)
	{
	data[t] = static_cast<DataType>(generator(t % C)); // k = c
	}
	}

	template <typename DataType>
	void generateMatMulB(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	DataType* data,
	size_t size)
	{
	const uint32_t T = cfg.shape[0] * cfg.shape[1] * cfg.shape[2];
	const uint32_t C = cfg.shape[1];
	const uint32_t W = cfg.shape[2];

	for (uint32_t t = 0; t < T; ++t)
	{
	data[t] = static_cast<DataType>(generator((t / W) % C)); // k = c
	}
	}

	bool generateMatMul(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	void* data,
	size_t size)
	{
	if (cfg.shape.size() != 3)
	{
	WARNING("[Generator][DP][MatMul] Tensor shape expected 3 dimensions.");
	return false;
	}
	if (cfg.inputPos > 1 \|\| cfg.inputPos < 0)
	{
	WARNING("[Generator][DP][MatMul] Invalid input tensor slot position to operator.");
	return false;
	}

	switch (cfg.dataType)
	{
	case DType::DType_FP32: {
	float* outData = reinterpret_cast<float*>(data);
	(cfg.inputPos == 0) ? generateMatMulA(cfg, generator, outData, size)
	: generateMatMulB(cfg, generator, outData, size);
	break;
	}
	case DType::DType_FP16: {
	half_float::half* outData = reinterpret_cast<half_float::half*>(data);
	(cfg.inputPos == 0) ? generateMatMulA(cfg, generator, outData, size)
	: generateMatMulB(cfg, generator, outData, size);
	break;
	}
	default:
	WARNING("[Generator][DP][MatMul] Only supports FP32 or FP16.");
	return false;
	}

	return true;
	}
	//---------------------------------------------------------------------------//
	// Conv2D //
	//---------------------------------------------------------------------------//

	template <typename DataType>
	bool generateConv2DInput(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	DataType* data,
	size_t size)
	{
	if (cfg.dotProductInfo.kernel.size() != 2 \|\| cfg.dotProductInfo.kernel[0] <= 0 \|\| cfg.dotProductInfo.kernel[1] <= 0)
	{
	WARNING("[Generator][DP][Conv2D][Input] Missing or incorrect kernel size information.");
	return false;
	}
	if (cfg.shape.size() != 4)
	{
	WARNING("[Generator][DP][Conv2D][Input] Tensor shape expected 4 dimensions.");
	return false;
	}

	const int64_t T = TosaReference::numElementsFromShape(cfg.shape);
	const uint32_t IH = cfg.shape[1];
	const uint32_t IW = cfg.shape[2];
	const uint32_t IC = cfg.shape[3];
	const uint32_t KH = cfg.dotProductInfo.kernel[0];
	const uint32_t KW = cfg.dotProductInfo.kernel[1];

	for (int64_t t = 0; t < T; ++t)
	{
	uint32_t ic = t % IC;
	uint32_t ix = (t / IC) % IW;
	uint32_t iy = ((t / IC) / IW) % IH;
	uint32_t k = ((iy % KH) * KW + (ix % KW)) * IC + ic;

	data[t] = static_cast<DataType>(generator(k));
	}
	return true;
	}

	template <typename DataType>
	bool generateConv2DWeight(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	DataType* data,
	size_t size)
	{
	if (cfg.shape.size() != 4)
	{
	WARNING("[Generator][DP][Conv2D][Weight] Tensor shape expected 4 dimensions.");
	return false;
	}

	const int64_t T = TosaReference::numElementsFromShape(cfg.shape);
	const uint32_t KH = cfg.shape[1];
	const uint32_t KW = cfg.shape[2];
	const uint32_t IC = cfg.shape[3];

	for (int64_t t = 0; t < T; ++t)
	{
	uint32_t ic = t % IC;
	uint32_t kx = (t / IC) % KW;
	uint32_t ky = ((t / IC) / KW) % KH;
	uint32_t k = (ky + KW * kx) * IC + ic;

	data[t] = static_cast<DataType>(generator(k));
	}
	return true;
	}

	template <typename DataType>
	bool generateConv2DBias(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	DataType* data,
	size_t size)
	{
	if (cfg.shape.size() != 1)
	{
	WARNING("[Generator][DP][Conv2D][Bias] Tensor shape expected 1 dimension.");
	return false;
	}

	const uint32_t T = cfg.shape[0];

	for (uint32_t t = 0; t < T; ++t)
	{
	data[t] = static_cast<DataType>(generator(2));
	}
	return true;
	}

	bool generateConv2D(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	void* data,
	size_t size)
	{
	switch (cfg.dataType)
	{
	case DType::DType_FP32: {
	float* outData = reinterpret_cast<float*>(data);
	switch (cfg.inputPos)
	{
	case 0:
	return generateConv2DInput(cfg, generator, outData, size);
	case 1:
	return generateConv2DWeight(cfg, generator, outData, size);
	case 2:
	return generateConv2DBias(cfg, generator, outData, size);
	default:
	WARNING("[Generator][DP][Conv2D] Invalid input tensor slot position to operator.");
	return false;
	}
	break;
	}
	case DType::DType_FP16: {
	half_float::half* outData = reinterpret_cast<half_float::half*>(data);
	switch (cfg.inputPos)
	{
	case 0:
	return generateConv2DInput(cfg, generator, outData, size);
	case 1:
	return generateConv2DWeight(cfg, generator, outData, size);
	case 2:
	return generateConv2DBias(cfg, generator, outData, size);
	default:
	WARNING("[Generator][DP][Conv2D] Invalid input tensor slot position to operator.");
	return false;
	}
	break;
	}
	default:
	WARNING("[Generator][DP][Conv2D] Only supports FP32 or FP16.");
	return false;
	}
	}
	//---------------------------------------------------------------------------//
	// Reduce Sum //
	//---------------------------------------------------------------------------//

	template <typename DataType>
	void generateReduceSumData(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	DataType* data,
	size_t size)
	{
	const int64_t T = TosaReference::numElementsFromShape(cfg.shape);
	const uint32_t axis = cfg.dotProductInfo.axis;

	for (int64_t t = 0; t < T; ++t)
	{
	uint64_t k = t;
	for (uint32_t d = cfg.shape.size() - 1; d > axis; --d)
	{
	k = k / cfg.shape[d];
	}
	k = k % cfg.shape[axis];

	data[t] = static_cast<DataType>(generator(static_cast<int32_t>(k)));
	}
	}

	bool generateReduceSum(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	void* data,
	size_t size)
	{
	if (cfg.inputPos != 0)
	{
	WARNING("[Generator][DP][ReduceSum] Invalid input tensor slot position to operator.");
	return false;
	}
	if (cfg.dotProductInfo.axis < 0 \|\| static_cast<size_t>(cfg.dotProductInfo.axis) >= cfg.shape.size())
	{
	WARNING("[Generator][DP][ReduceSum] Invalid axis %d.", cfg.dotProductInfo.axis);
	return false;
	}

	switch (cfg.dataType)
	{
	case DType::DType_FP32: {
	float* outData = reinterpret_cast<float*>(data);
	generateReduceSumData(cfg, generator, outData, size);
	break;
	}
	case DType::DType_FP16: {
	half_float::half* outData = reinterpret_cast<half_float::half*>(data);
	generateReduceSumData(cfg, generator, outData, size);
	break;
	}
	default:
	WARNING("[Generator][DP][ReduceSum] Only supports FP32 or FP16.");
	return false;
	}

	return true;
	}
	//---------------------------------------------------------------------------//
	// Fully Connected //
	//---------------------------------------------------------------------------//

	template <typename DataType>
	bool generateFullyConnectedInput(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	DataType* data,
	size_t size)
	{
	if (cfg.shape.size() != 2)
	{
	WARNING("[Generator][DP][FullyConnected][Input] Tensor shape expected 2 dimensions.");
	return false;
	}

	const int64_t T = TosaReference::numElementsFromShape(cfg.shape);
	const uint32_t IC = cfg.shape[1];

	for (int64_t t = 0; t < T; ++t)
	{
	uint32_t k = t % IC;

	data[t] = static_cast<DataType>(generator(k));
	}
	return true;
	}

	template <typename DataType>
	bool generateFullyConnectedWeight(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	DataType* data,
	size_t size)
	{
	if (cfg.shape.size() != 2)
	{
	WARNING("[Generator][DP][FullyConnected][Weight] Tensor shape expected 2 dimensions.");
	return false;
	}

	const int64_t T = TosaReference::numElementsFromShape(cfg.shape);
	const uint32_t IC = cfg.shape[1];

	for (int64_t t = 0; t < T; ++t)
	{
	uint32_t k = t % IC;

	data[t] = static_cast<DataType>(generator(k));
	}
	return true;
	}

	template <typename DataType>
	bool generateFullyConnectedBias(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	DataType* data,
	size_t size)
	{
	if (cfg.shape.size() != 1)
	{
	WARNING("[Generator][DP][FullyConnected][Bias] Tensor shape expected 1 dimension.");
	return false;
	}

	const uint32_t T = cfg.shape[0];

	for (uint32_t t = 0; t < T; ++t)
	{
	data[t] = static_cast<DataType>(generator(2));
	}
	return true;
	}

	bool generateFullyConnected(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	void* data,
	size_t size)
	{
	switch (cfg.dataType)
	{
	case DType::DType_FP32: {
	float* outData = reinterpret_cast<float*>(data);
	switch (cfg.inputPos)
	{
	case 0:
	return generateFullyConnectedInput(cfg, generator, outData, size);
	case 1:
	return generateFullyConnectedWeight(cfg, generator, outData, size);
	case 2:
	return generateFullyConnectedBias(cfg, generator, outData, size);
	default:
	WARNING("[Generator][DP][FullyConnected] Invalid input tensor slot position to operator.");
	return false;
	}
	break;
	}
	case DType::DType_FP16: {
	half_float::half* outData = reinterpret_cast<half_float::half*>(data);
	switch (cfg.inputPos)
	{
	case 0:
	return generateFullyConnectedInput(cfg, generator, outData, size);
	case 1:
	return generateFullyConnectedWeight(cfg, generator, outData, size);
	case 2:
	return generateFullyConnectedBias(cfg, generator, outData, size);
	default:
	WARNING("[Generator][DP][FullyConnected] Invalid input tensor slot position to operator.");
	return false;
	}
	break;
	}
	default:
	WARNING("[Generator][DP][FullyConnected] Only supports FP32 or FP16.");
	return false;
	}
	}
	//---------------------------------------------------------------------------//
	// Avg Pool 2D //
	//---------------------------------------------------------------------------//

	template <typename DataType>
	void generateAvgPool2DData(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	DataType* data,
	size_t size)
	{
	const int64_t T = TosaReference::numElementsFromShape(cfg.shape);
	const uint32_t IH = cfg.shape[1];
	const uint32_t IW = cfg.shape[2];
	const uint32_t C = cfg.shape[3];
	const uint32_t KY = cfg.dotProductInfo.kernel[0];
	const uint32_t KX = cfg.dotProductInfo.kernel[1];

	for (int64_t t = 0; t < T; ++t)
	{
	uint32_t c = t % C;
	uint32_t ix = (t / C) % IW;
	uint32_t iy = ((t / C) / IW) % IH;
	uint32_t k = ((iy % KY) * KX + (ix % KX)) * C + c;

	data[t] = static_cast<DataType>(generator(k));
	}
	}

	bool generateAvgPool2D(const TosaReference::GenerateConfig& cfg,
	TosaReference::IDotProductGenerator& generator,
	void* data,
	size_t size)
	{
	if (cfg.inputPos != 0)
	{
	WARNING("[Generator][DP][AvgPool2D] Invalid input tensor slot position to operator.");
	return false;
	}
	if (cfg.dotProductInfo.kernel.size() != 2 \|\| cfg.dotProductInfo.kernel[0] <= 0 \|\| cfg.dotProductInfo.kernel[1] <= 0)
	{
	WARNING("[Generator][DP][AvgPool2D] Missing or incorrect kernel size information.");
	return false;
	}
	if (cfg.shape.size() != 4)
	{
	WARNING("[Generator][DP][AvgPool2D] Tensor shape expected 4 dimensions.");
	return false;
	}

	switch (cfg.dataType)
	{
	case DType::DType_FP32: {
	float* outData = reinterpret_cast<float*>(data);
	generateAvgPool2DData(cfg, generator, outData, size);
	break;
	}
	case DType::DType_FP16: {
	half_float::half* outData = reinterpret_cast<half_float::half*>(data);
	generateAvgPool2DData(cfg, generator, outData, size);
	break;
	}
	default:
	WARNING("[Generator][DP][AvgPool2D] Only supports FP32 or FP16.");
	return false;
	}

	return true;
	}
	} // namespace

	namespace TosaReference
	{

	bool generateDotProduct(const GenerateConfig& cfg, void* data, size_t size)
	{
	auto generator = pickDotProductGenerator(cfg);
	if (!generator)
	{
	WARNING("[Generator][DP] Requested generator could not be created!");
	return false;
	}
	if (cfg.dotProductInfo.ks <= 0)
	{
	WARNING("[Generator][DP] Invalid test set kernel size %d.", cfg.dotProductInfo.ks);
	return false;
	}

	// Select which generator to use
	switch (cfg.opType)
	{
	case tosa::Op_MATMUL:
	return generateMatMul(cfg, *generator, data, size);
	case tosa::Op_CONV2D:
	return generateConv2D(cfg, *generator, data, size);
	case tosa::Op_REDUCE_SUM:
	return generateReduceSum(cfg, *generator, data, size);
	case tosa::Op_FULLY_CONNECTED:
	return generateFullyConnected(cfg, *generator, data, size);
	case tosa::Op_AVG_POOL2D:
	return generateAvgPool2D(cfg, *generator, data, size);
	default:
	WARNING("[Generator][DP] Unsupported operator.");
	return false;
	}

	return false;
	}
	} // namespace TosaReference