Improved selection of block size for ConvBuffer1x1

PiperOrigin-RevId: 300460634
Change-Id: I1c5ef87614c84a1f638d9f48225f411dfa79fc96

tensorflow/lite/delegates/gpu/cl/cl_device.cc

@@ -297,10 +297,19 @@ bool MaliInfo::IsMidgard() const {
   return IsMaliT6xx() || IsMaliT7xx() || IsMaliT8xx();
 }
 
-bool MaliInfo::IsBifrost() const {
+bool MaliInfo::IsBifrostGen1() const {
   return gpu_version == MaliGPU::G31 || gpu_version == MaliGPU::G51 ||
-         gpu_version == MaliGPU::G71 || gpu_version == MaliGPU::G52 ||
+         gpu_version == MaliGPU::G71;
-         gpu_version == MaliGPU::G72 || gpu_version == MaliGPU::G76;
+}
+
+bool MaliInfo::IsBifrostGen2() const {
+  return gpu_version == MaliGPU::G52 || gpu_version == MaliGPU::G72;
+}
+
+bool MaliInfo::IsBifrostGen3() const { return gpu_version == MaliGPU::G76; }
+
+bool MaliInfo::IsBifrost() const {
+  return IsBifrostGen1() || IsBifrostGen2() || IsBifrostGen3();
 }
 
 bool MaliInfo::IsValhall() const {

tensorflow/lite/delegates/gpu/cl/cl_device.h

@@ -94,6 +94,9 @@ struct MaliInfo {
   bool IsMaliT7xx() const;
   bool IsMaliT8xx() const;
   bool IsMidgard() const;
+  bool IsBifrostGen1() const;
+  bool IsBifrostGen2() const;
+  bool IsBifrostGen3() const;
   bool IsBifrost() const;
   bool IsValhall() const;
 };

tensorflow/lite/delegates/gpu/cl/kernels/conv_buffer_1x1.cc

@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/lite/delegates/gpu/cl/kernels/conv_buffer_1x1.h"
 
 #include <array>
+#include <cfloat>
 #include <string>
 #include <utility>
 
@@ -201,6 +202,74 @@ std::string GenerateConvBuffer1x1(
   return c;
 }
 
+// task_size as amount of FLT4 processed elements.
+int GetRecommendedBlockSizeForConv(const CLDevice& device,
+                                   const OperationDef& definition,
+                                   int task_size) {
+  const float task_size_per_cu =
+      task_size / static_cast<float>(device.GetInfo().compute_units_count);
+  int block_size = 1;
+  float threshold_1 = FLT_MAX;
+  float threshold_2 = FLT_MAX;
+  float threshold_4 = FLT_MAX;
+  if (!device.IsMali()) {
+    return 1;
+  }
+  MaliInfo mali_info = device.GetInfo().mali_info;
+  switch (definition.precision) {
+    case CalculationsPrecision::F16:
+      if (mali_info.IsBifrostGen1()) {
+        threshold_1 = 256.0f;
+        threshold_2 = 256.0f * 4.0f;
+        threshold_4 = 256.0f * 8.0f;
+      } else if (mali_info.IsBifrostGen2()) {
+        threshold_1 = 256.0f * 2.0f;
+        threshold_2 = 256.0f * 8.0f;
+        threshold_4 = 256.0f * 16.0f;
+      } else if (mali_info.IsBifrostGen3()) {
+        threshold_1 = 256.0f;
+        threshold_2 = 256.0f * 6.0f;
+        threshold_4 = 256.0f * 16.0f;
+      }
+      break;
+    case CalculationsPrecision::F32_F16:
+      if (mali_info.IsBifrostGen1()) {
+        threshold_1 = 256.0f;
+        threshold_2 = 256.0f * 3.0f;
+        threshold_4 = 256.0f * 32.0f;
+      } else if (mali_info.IsBifrostGen2()) {
+        threshold_1 = 256.0f * 2.0f;
+        threshold_2 = 256.0f * 8.0f;
+      } else if (mali_info.IsBifrostGen3()) {
+        threshold_1 = 256.0f;
+        threshold_2 = 256.0f * 8.0f;
+      }
+      break;
+    case CalculationsPrecision::F32:
+      if (mali_info.IsBifrostGen1()) {
+        threshold_1 = 256.0f;
+        threshold_2 = 256.0f * 4.0f;
+      } else if (mali_info.IsBifrostGen2()) {
+        threshold_1 = 128.0f;
+        threshold_2 = 256.0f * 4.0f;
+      } else if (mali_info.IsBifrostGen3()) {
+        threshold_1 = 256.0f;
+        threshold_2 = 256.0f * 12.0f;
+      }
+      break;
+  }
+  if (task_size_per_cu <= threshold_1) {
+    block_size = 1;
+  } else if (task_size_per_cu <= threshold_2) {
+    block_size = 2;
+  } else if (task_size_per_cu <= threshold_4) {
+    block_size = 4;
+  } else {
+    block_size = 8;
+  }
+  return block_size;
+}
+
 ConvBuffer1x1::ConvParams GetBestParams(const CLDevice& device,
                                         const OperationDef& definition,
                                         const BHWC& shape, int src_depth,
@@ -211,15 +280,46 @@ ConvBuffer1x1::ConvParams GetBestParams(const CLDevice& device,
   if (!device.IsMali()) {
     return conv_params;
   }
-  const int width = shape.w * shape.b;
+  bool can_use_flt8 = (shape.w * shape.b) % 2 == 0 &&
-  if (width % 2 == 0) {
+                      definition.precision != CalculationsPrecision::F32;
-    conv_params.element_size = 8;
+  bool is_midgard = device.IsMali() && device.GetInfo().mali_info.IsMidgard();
+  if (is_midgard) {
+    if (can_use_flt8) {
+      conv_params.element_size = 8;
+    }
+    if (definition.precision == CalculationsPrecision::F16 || !can_use_flt8) {
+      conv_params.block_size.x = 2;
+    }
+    return conv_params;
   }
-  if (device.GetInfo().compute_units_count <= 4) {
+
-    if (definition.precision == CalculationsPrecision::F16) {
+  int task_size = shape.w * shape.b * shape.h * dst_depth;
-      conv_params.block_size.x *= 2;
+  int block_size =
+      GetRecommendedBlockSizeForConv(device, definition, task_size);
+
+  if (!can_use_flt8 && block_size > 4) {
+    block_size = 4;
+  }
+
+  if (can_use_flt8 && block_size >= 2) {
+    conv_params.element_size = 8;
+    block_size /= 2;
+  }
+  if (block_size == 4) {
+    conv_params.block_size.x = 2;
+    if (definition.precision == CalculationsPrecision::F32 && dst_depth < 32) {
+      conv_params.block_size.y = 2;
+    } else {
+      conv_params.block_size.z = 2;
+    }
+  } else if (block_size == 2) {
+    if (dst_depth >= 32) {
+      conv_params.block_size.z = 2;
+    } else {
+      conv_params.block_size.x = 2;
     }
   }
+
   return conv_params;
 }
 

tensorflow/lite/delegates/gpu/cl/kernels/convolution_transposed.cc

@@ -324,7 +324,9 @@ ConvolutionTransposed::ConvolutionTransposed(
   }
   const int dst_depth = IntegralDivideRoundUp(attr.weights.shape.o, 4);
   if (dst_depth == 1 || dst_depth == 3) {
-    block_size_.y *= block_size_.z;
+    if (!device.IsMali()) {
+      block_size_.y *= block_size_.z;
+    }
     block_size_.z = 1;
   }
 }