add thneed optimizer (#23772)

* add thneed optimizer

* local work group opt

* kernels and final mods

* release files

* build system touchups

* fix kernel path, rand inputs for self test

* broken since extra is gone

* update model replay ref

Co-authored-by: Comma Device <device@comma.ai>

release/files_common

@@ -426,7 +426,9 @@ selfdrive/modeld/transforms/transform.cl
 selfdrive/modeld/thneed/thneed.*
 selfdrive/modeld/thneed/serialize.cc
 selfdrive/modeld/thneed/compile.cc
+selfdrive/modeld/thneed/optimizer.cc
 selfdrive/modeld/thneed/include/*
+selfdrive/modeld/thneed/kernels/*.cl
 
 selfdrive/modeld/runners/snpemodel.cc
 selfdrive/modeld/runners/snpemodel.h

selfdrive/modeld/SConscript

@@ -1,3 +1,5 @@
+import os
+
 Import('env', 'arch', 'cereal', 'messaging', 'common', 'gpucommon', 'visionipc')
 lenv = env.Clone()
 
@@ -23,6 +25,7 @@ common_src = [
 thneed_src = [
   "thneed/thneed.cc",
   "thneed/serialize.cc",
+  "thneed/optimizer.cc",
   "runners/thneedmodel.cc",
 ]
 
@@ -62,12 +65,16 @@ common_model = lenv.Object(common_src)
 
 # build thneed model
 if use_thneed and arch in ("aarch64", "larch64"):
+  fn = "../../models/supercombo"
   compiler = lenv.Program('thneed/compile', ["thneed/compile.cc"]+common_model, LIBS=libs)
-  cmd = f"cd {Dir('.').abspath} && {compiler[0].abspath} ../../models/supercombo.dlc ../../models/supercombo.thneed --binary"
+  cmd = f"cd {Dir('.').abspath} && {compiler[0].abspath} {fn}.dlc {fn}.thneed --binary"
 
   lib_paths = ':'.join(Dir(p).abspath for p in lenv["LIBPATH"])
-  cenv = Environment(ENV={'LD_LIBRARY_PATH': f"{lib_paths}:{lenv['ENV']['LD_LIBRARY_PATH']}"})
-  cenv.Command("../../models/supercombo.thneed", ["../../models/supercombo.dlc", compiler], cmd)
+  kernel_path = os.path.join(Dir('.').abspath, "thneed", "kernels")
+  cenv = Environment(ENV={'LD_LIBRARY_PATH': f"{lib_paths}:{lenv['ENV']['LD_LIBRARY_PATH']}", 'KERNEL_PATH': kernel_path})
+
+  kernels = [os.path.join(kernel_path, x) for x in os.listdir(kernel_path) if x.endswith(".cl")]
+  cenv.Command(fn + ".thneed", [fn + ".dlc", kernels, compiler], cmd)
 
 lenv.Program('_dmonitoringmodeld', [
     "dmonitoringmodeld.cc",

selfdrive/modeld/runners/snpemodel.cc

@@ -7,6 +7,7 @@
 #include <cstring>
 
 #include "selfdrive/common/util.h"
+#include "selfdrive/common/timing.h"
 
 void PrintErrorStringAndExit() {
   std::cerr << zdl::DlSystem::getLastErrorString() << std::endl;
@@ -158,8 +159,14 @@ void SNPEModel::execute(float *net_input_buf, int buf_size) {
       float *outputs_golden = (float *)malloc(output_size*sizeof(float));
       memcpy(outputs_golden, output, output_size*sizeof(float));
       memset(output, 0, output_size*sizeof(float));
+
+      for (int i = 0; i < 5; i++) {
         memset(recurrent, 0, recurrent_size*sizeof(float));
+        uint64_t start_time = nanos_since_boot();
         thneed->execute(inputs, output);
+        uint64_t elapsed_time = nanos_since_boot() - start_time;
+        printf("ran model in %.2f ms\n", float(elapsed_time)/1e6);
+      }
 
       if (memcmp(output, outputs_golden, output_size*sizeof(float)) == 0) {
         printf("thneed selftest passed\n");

selfdrive/modeld/thneed/kernels/convolution_horizontal_reduced_reads.cl

@@ -0,0 +1,129 @@
+__kernel void convolution_horizontal_reduced_reads(
+    read_only image2d_t input,
+    short startPackedInputChannel,
+    short numPackedInputChannelsForGroup, short totalNumPackedInputChannels,
+    short packedOuputChannelOffset, short totalNumPackedOutputChannels,
+    read_only image2d_t weights, __constant float *biases,
+    short filterSizeX, short filterSizeY,
+    write_only image2d_t output,
+    short paddingX, short paddingY, short strideX, short strideY,
+    short dilationX, short dilationY,
+    short neuron, float a, float b, float min_clamp, float max_clamp,
+    __constant float *parameters, __constant float *batchNormBiases,
+    short numOutputColumns) {
+
+  // init
+  const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+  short packedOutputChannel = get_global_id(0);
+  short startOutputColumn = mul24((short)get_global_id(1), 4);
+  short outputRow = get_global_id(2);
+  short startX = mad24(mad24(startOutputColumn, strideX, -paddingX),
+                       totalNumPackedInputChannels, startPackedInputChannel);
+  short strideWithChannels = mul24(strideX, totalNumPackedInputChannels);
+
+  float4 outputValues[4];
+  for (short i = 0; i < 4; ++i) {
+    outputValues[i] = (float4)(0, 0, 0, 0);
+  }
+
+  int2 inputLocation;
+  inputLocation.y = mad24(outputRow, strideY, -paddingY);
+
+  int2 weightLocation;
+  weightLocation.x = 0;
+  weightLocation.y = packedOutputChannel;
+
+  // convolution
+  for (short rfRow = 0; rfRow < filterSizeY; ++rfRow) {
+    for (short packedInputChannel = 0;
+         packedInputChannel < numPackedInputChannelsForGroup;
+         ++packedInputChannel) {
+      short startXForChannel = startX + packedInputChannel;
+      for (short rfColumn = 0; rfColumn < filterSizeX; ++rfColumn) {
+        float4 weightValues[4];
+        for (short outChIdx = 0; outChIdx < 4; ++outChIdx) {
+          weightValues[outChIdx] = read_imagef(weights, smp, weightLocation);
+          ++weightLocation.x;
+        }
+        short dilatedStepX = mul24(totalNumPackedInputChannels, dilationX);
+        inputLocation.x = mad24(rfColumn, dilatedStepX, startXForChannel);
+        float4 inputValues[4];
+        for (short i = 0; i < 4; ++i) {
+          inputValues[i] = read_imagef(input, smp, inputLocation);
+          inputLocation.x += strideWithChannels;
+        }
+        for (short i = 0; i < 4; ++i) {
+          float4 curOutputValues = outputValues[i];
+          curOutputValues.x += inputValues[i].x * weightValues[0].x;
+          curOutputValues.x += inputValues[i].y * weightValues[0].y;
+          curOutputValues.x += inputValues[i].z * weightValues[0].z;
+          curOutputValues.x += inputValues[i].w * weightValues[0].w;
+          curOutputValues.y += inputValues[i].x * weightValues[1].x;
+          curOutputValues.y += inputValues[i].y * weightValues[1].y;
+          curOutputValues.y += inputValues[i].z * weightValues[1].z;
+          curOutputValues.y += inputValues[i].w * weightValues[1].w;
+          curOutputValues.z += inputValues[i].x * weightValues[2].x;
+          curOutputValues.z += inputValues[i].y * weightValues[2].y;
+          curOutputValues.z += inputValues[i].z * weightValues[2].z;
+          curOutputValues.z += inputValues[i].w * weightValues[2].w;
+          curOutputValues.w += inputValues[i].x * weightValues[3].x;
+          curOutputValues.w += inputValues[i].y * weightValues[3].y;
+          curOutputValues.w += inputValues[i].z * weightValues[3].z;
+          curOutputValues.w += inputValues[i].w * weightValues[3].w;
+          outputValues[i] = curOutputValues;
+        }
+      }
+    }
+    inputLocation.y += dilationY;
+  }
+
+  // bias
+  packedOutputChannel += packedOuputChannelOffset;
+  short outputChannel = mul24(packedOutputChannel, 4);
+  float4 biasValues = vload4(0, biases + outputChannel);
+  for (short i = 0; i < 4; ++i) {
+    outputValues[i] += biasValues;
+  }
+
+  // activation
+  switch (neuron) {
+  case 1:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], 0.0f);
+    }
+    break;
+  case 2:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = a * tanh(b * outputValues[i]);
+    }
+    break;
+  case 3:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = native_recip(1.0f + native_exp(-a * outputValues[i] + b));
+    }
+    break;
+  case 4:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], min_clamp);
+      outputValues[i] = min(outputValues[i], max_clamp);
+    }
+    break;
+  case 5:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], 0.0f) + a * (native_exp(min(outputValues[i], 0.0f)) - 1.0f);
+    }
+    break;
+  }
+
+  // output
+  int2 outputLocation;
+  short outputColumn = startOutputColumn;
+  outputLocation.y = outputRow;
+  for (short i = 0; i < 4; ++i) {
+    outputLocation.x = mad24(outputColumn, totalNumPackedOutputChannels, packedOutputChannel);
+    if (outputColumn < numOutputColumns) {
+      write_imagef(output, outputLocation, outputValues[i]);
+    }
+    ++outputColumn;
+  }
+}

selfdrive/modeld/thneed/kernels/convolution_horizontal_reduced_reads_1x1.cl

@@ -0,0 +1,140 @@
+__kernel void convolution_horizontal_reduced_reads_1x1(
+    read_only image2d_t input,
+    short startPackedInputChannel,
+    short numPackedInputChannelsForGroup, short totalNumPackedInputChannels,
+    short packedOuputChannelOffset, short totalNumPackedOutputChannels,
+    read_only image2d_t weights, __constant float *biases,
+    short filterSizeX, short filterSizeY,
+    write_only image2d_t output,
+    short paddingX, short paddingY, short strideX, short strideY,
+    short neuron, float a, float b, float min_clamp, float max_clamp,
+    __constant float *parameters, __constant float *batchNormBiases,
+    short numOutputColumns,
+    short doAccumulate, read_only image2d_t accumulator) {
+
+  // init
+  const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+  short packedOutputChannel = get_global_id(0);
+  short startOutputColumn = mul24((short)get_global_id(1), 4);
+  short outputRow = get_global_id(2);
+  short endPackedInputChannel = startPackedInputChannel + numPackedInputChannelsForGroup;
+  short startX = mad24(mad24(startOutputColumn, strideX, -paddingX),
+                       totalNumPackedInputChannels, startPackedInputChannel);
+  short strideWithChannels = mul24(strideX, totalNumPackedInputChannels);
+
+  float4 outputValues[4];
+  for (short i = 0; i < 4; ++i) {
+    outputValues[i] = (float4)(0, 0, 0, 0);
+  }
+
+  int2 inputLocation;
+  inputLocation.y = mad24(outputRow, strideY, -paddingY);
+
+  int2 weightLocation;
+  weightLocation.x = 0;
+  weightLocation.y = packedOutputChannel;
+
+  // convolution
+  for (short packedInputChannel = startPackedInputChannel;
+       packedInputChannel < endPackedInputChannel; ++packedInputChannel) {
+
+    float4 weightValues[4];
+    for (short outChIdx = 0; outChIdx < 4; ++outChIdx) {
+      weightValues[outChIdx] = read_imagef(weights, smp, weightLocation);
+      ++weightLocation.x;
+    }
+
+    inputLocation.x = startX + packedInputChannel;
+    float4 inputValues[4];
+    for (short i = 0; i < 4; ++i) {
+      inputValues[i] = read_imagef(input, smp, inputLocation);
+      inputLocation.x += strideWithChannels;
+    }
+
+    for (short i = 0; i < 4; ++i) {
+      float4 curOutputValues = outputValues[i];
+      curOutputValues.x += inputValues[i].x * weightValues[0].x;
+      curOutputValues.x += inputValues[i].y * weightValues[0].y;
+      curOutputValues.x += inputValues[i].z * weightValues[0].z;
+      curOutputValues.x += inputValues[i].w * weightValues[0].w;
+      curOutputValues.y += inputValues[i].x * weightValues[1].x;
+      curOutputValues.y += inputValues[i].y * weightValues[1].y;
+      curOutputValues.y += inputValues[i].z * weightValues[1].z;
+      curOutputValues.y += inputValues[i].w * weightValues[1].w;
+      curOutputValues.z += inputValues[i].x * weightValues[2].x;
+      curOutputValues.z += inputValues[i].y * weightValues[2].y;
+      curOutputValues.z += inputValues[i].z * weightValues[2].z;
+      curOutputValues.z += inputValues[i].w * weightValues[2].w;
+      curOutputValues.w += inputValues[i].x * weightValues[3].x;
+      curOutputValues.w += inputValues[i].y * weightValues[3].y;
+      curOutputValues.w += inputValues[i].z * weightValues[3].z;
+      curOutputValues.w += inputValues[i].w * weightValues[3].w;
+      outputValues[i] = curOutputValues;
+    }
+  }
+
+  // bias
+  packedOutputChannel += packedOuputChannelOffset;
+  short outputChannel = mul24(packedOutputChannel, 4);
+  float4 biasValues = vload4(0, biases + outputChannel);
+  for (short i = 0; i < 4; ++i) {
+    outputValues[i] += biasValues;
+  }
+
+  // accumulate
+  if (doAccumulate) {
+    int2 outputLocation;
+    short outputColumn = startOutputColumn;
+    outputLocation.y = outputRow;
+    for (short i = 0; i < 4; ++i) {
+      outputLocation.x = mad24(outputColumn, totalNumPackedOutputChannels, packedOutputChannel);
+      if (outputColumn < numOutputColumns) {
+        outputValues[i] += read_imagef(accumulator, smp, outputLocation);
+      }
+      ++outputColumn;
+    }
+  }
+
+  // activation
+  switch (neuron) {
+  case 1:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], 0.0f);
+    }
+    break;
+  case 2:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = a * tanh(b * outputValues[i]);
+    }
+    break;
+  case 3:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = native_recip(1.0f + native_exp(-a * outputValues[i] + b));
+    }
+    break;
+  case 4:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], min_clamp);
+      outputValues[i] = min(outputValues[i], max_clamp);
+    }
+    break;
+  case 5:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], 0.0f) + a * (native_exp(min(outputValues[i], 0.0f)) - 1.0f);
+    }
+    break;
+  }
+
+  // output
+  int2 outputLocation;
+  short outputColumn = startOutputColumn;
+  outputLocation.y = outputRow;
+  for (short i = 0; i < 4; ++i) {
+    outputLocation.x = mad24(outputColumn, totalNumPackedOutputChannels, packedOutputChannel);
+    if (outputColumn < numOutputColumns) {
+      write_imagef(output, outputLocation, outputValues[i]);
+    }
+    ++outputColumn;
+  }
+}
+

selfdrive/modeld/thneed/kernels/convolution_horizontal_reduced_reads_5_outputs.cl

@@ -0,0 +1,130 @@
+__kernel void convolution_horizontal_reduced_reads_5_outputs(
+    read_only image2d_t input,
+    short startPackedInputChannel,
+    short numPackedInputChannelsForGroup, short totalNumPackedInputChannels,
+    short packedOuputChannelOffset, short totalNumPackedOutputChannels,
+    read_only image2d_t weights, __constant float *biases,
+    short filterSizeX, short filterSizeY,
+    write_only image2d_t output,
+    short paddingX, short paddingY, short strideX, short strideY,
+    short neuron, float a, float b, float min_clamp, float max_clamp,
+    __constant float *parameters, __constant float *batchNormBiases,
+    short numOutputColumns) {
+
+  // init
+  const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+  short packedOutputChannel = get_global_id(0);
+  short startOutputColumn = mul24((short)get_global_id(1), 5);
+  short outputRow = get_global_id(2);
+  short startX = mad24(mad24(startOutputColumn, strideX, -paddingX),
+                       totalNumPackedInputChannels, startPackedInputChannel);
+  short strideWithChannels = mul24(strideX, totalNumPackedInputChannels);
+
+  float4 outputValues[5];
+  for (short i = 0; i < 5; ++i) {
+    outputValues[i] = (float4)(0, 0, 0, 0);
+  }
+
+  int2 inputLocation;
+  inputLocation.y = mad24(outputRow, strideY, -paddingY);
+
+  int2 weightLocation;
+  weightLocation.x = 0;
+  weightLocation.y = packedOutputChannel;
+
+  // convolution
+  for (short rfRow = 0; rfRow < filterSizeY; ++rfRow) {
+    for (short packedInputChannel = 0;
+         packedInputChannel < numPackedInputChannelsForGroup;
+         ++packedInputChannel) {
+      short startXForChannel = startX + packedInputChannel;
+      for (short rfColumn = 0; rfColumn < filterSizeX; ++rfColumn) {
+        float4 weightValues[4];
+        for (short outChIdx = 0; outChIdx < 4; ++outChIdx) {
+          weightValues[outChIdx] = read_imagef(weights, smp, weightLocation);
+          ++weightLocation.x;
+        }
+        inputLocation.x =
+            mad24(rfColumn, totalNumPackedInputChannels, startXForChannel);
+
+        float4 inputValues[5];
+        for (short i = 0; i < 5; ++i) {
+          inputValues[i] = read_imagef(input, smp, inputLocation);
+          inputLocation.x += strideWithChannels;
+        }
+
+        for (short i = 0; i < 5; ++i) {
+          float4 curOutputValues = outputValues[i];
+          curOutputValues.x += inputValues[i].x * weightValues[0].x;
+          curOutputValues.x += inputValues[i].y * weightValues[0].y;
+          curOutputValues.x += inputValues[i].z * weightValues[0].z;
+          curOutputValues.x += inputValues[i].w * weightValues[0].w;
+          curOutputValues.y += inputValues[i].x * weightValues[1].x;
+          curOutputValues.y += inputValues[i].y * weightValues[1].y;
+          curOutputValues.y += inputValues[i].z * weightValues[1].z;
+          curOutputValues.y += inputValues[i].w * weightValues[1].w;
+          curOutputValues.z += inputValues[i].x * weightValues[2].x;
+          curOutputValues.z += inputValues[i].y * weightValues[2].y;
+          curOutputValues.z += inputValues[i].z * weightValues[2].z;
+          curOutputValues.z += inputValues[i].w * weightValues[2].w;
+          curOutputValues.w += inputValues[i].x * weightValues[3].x;
+          curOutputValues.w += inputValues[i].y * weightValues[3].y;
+          curOutputValues.w += inputValues[i].z * weightValues[3].z;
+          curOutputValues.w += inputValues[i].w * weightValues[3].w;
+          outputValues[i] = curOutputValues;
+        }
+      }
+    }
+    ++inputLocation.y;
+  }
+
+  // bias
+  packedOutputChannel += packedOuputChannelOffset;
+  short outputChannel = mul24(packedOutputChannel, 4);
+  float4 biasValues = vload4(0, biases + outputChannel);
+  for (short i = 0; i < 5; ++i) {
+    outputValues[i] += biasValues;
+  }
+
+  // activation
+  switch (neuron) {
+  case 1:
+    for (short i = 0; i < 5; ++i) {
+      outputValues[i] = max(outputValues[i], 0.0f);
+    }
+    break;
+  case 2:
+    for (short i = 0; i < 5; ++i) {
+      outputValues[i] = a * tanh(b * outputValues[i]);
+    }
+    break;
+  case 3:
+    for (short i = 0; i < 5; ++i) {
+      outputValues[i] = native_recip(1.0f + native_exp(-a * outputValues[i] + b));
+    }
+    break;
+  case 4:
+    for (short i = 0; i < 5; ++i) {
+      outputValues[i] = max(outputValues[i], min_clamp);
+      outputValues[i] = min(outputValues[i], max_clamp);
+    }
+    break;
+  case 5:
+    for (short i = 0; i < 5; ++i) {
+      outputValues[i] = max(outputValues[i], 0.0f) + a * (native_exp(min(outputValues[i], 0.0f)) - 1.0f);
+    }
+    break;
+  }
+
+  // output
+  int2 outputLocation;
+  short outputColumn = startOutputColumn;
+  outputLocation.y = outputRow;
+  for (short i = 0; i < 5; ++i) {
+    outputLocation.x = mad24(outputColumn, totalNumPackedOutputChannels, packedOutputChannel);
+    if (outputColumn < numOutputColumns) {
+      write_imagef(output, outputLocation, outputValues[i]);
+    }
+    ++outputColumn;
+  }
+}

selfdrive/modeld/thneed/kernels/convolution_horizontal_reduced_reads_depthwise.cl

@@ -0,0 +1,101 @@
+__kernel void convolution_horizontal_reduced_reads_depthwise(
+    read_only image2d_t input,
+    short totalNumPackedChannels,
+    read_only image2d_t weights, __constant float *biases,
+    short filterSizeX, short filterSizeY,
+    write_only image2d_t output,
+    short paddingX, short paddingY, short strideX, short strideY,
+    short dilationX, short dilationY,
+    short neuron, float a, float b, float min_clamp, float max_clamp,
+    short numOutputColumns) {
+
+  // init
+  const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+  short packedChannel = get_global_id(0);
+  short startOutputColumn = mul24((short)get_global_id(1), 4);
+  short outputRow = get_global_id(2);
+  short startXForChannel = mad24(mad24(startOutputColumn, strideX, -paddingX),
+                                 totalNumPackedChannels, packedChannel);
+  short strideWithChannels = mul24(strideX, totalNumPackedChannels);
+
+  float4 outputValues[4];
+  for (short i = 0; i < 4; ++i) {
+    outputValues[i] = (float4)(0, 0, 0, 0);
+  }
+
+  int2 inputLocation;
+  inputLocation.y = mad24(outputRow, strideY, -paddingY);
+
+  int2 weightLocation;
+  weightLocation.x = 0;
+  weightLocation.y = packedChannel;
+
+  // convolution
+  for (short rfRow = 0; rfRow < filterSizeY; ++rfRow) {
+    for (short rfColumn = 0; rfColumn < filterSizeX; ++rfColumn) {
+      short dilatedStepX = mul24(totalNumPackedChannels, dilationX);
+      inputLocation.x = mad24(rfColumn, dilatedStepX, startXForChannel);
+      float4 inputValues[4];
+      for (short i = 0; i < 4; ++i) {
+        inputValues[i] = read_imagef(input, smp, inputLocation);
+        inputLocation.x += strideWithChannels;
+      }
+      float4 weightValues = read_imagef(weights, smp, weightLocation);
+      ++weightLocation.x;
+      outputValues[0] += inputValues[0] * weightValues;
+      outputValues[1] += inputValues[1] * weightValues;
+      outputValues[2] += inputValues[2] * weightValues;
+      outputValues[3] += inputValues[3] * weightValues;
+    }
+    inputLocation.y += dilationY;
+  }
+
+  // bias
+  short outputChannel = mul24(packedChannel, 4);
+  float4 biasValues = vload4(0, biases + outputChannel);
+  for (short i = 0; i < 4; ++i) {
+    outputValues[i] += biasValues;
+  }
+
+  // activation
+  switch (neuron) {
+  case 1:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], 0.0f);
+    }
+    break;
+  case 2:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = a * tanh(b * outputValues[i]);
+    }
+    break;
+  case 3:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = native_recip(1.0f + native_exp(-a * outputValues[i] + b));
+    }
+    break;
+  case 4:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], min_clamp);
+      outputValues[i] = min(outputValues[i], max_clamp);
+    }
+    break;
+  case 5:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], 0.0f) + a * (native_exp(min(outputValues[i], 0.0f)) - 1.0f);
+    }
+    break;
+  }
+
+  // output
+  int2 outputLocation;
+  short outputColumn = startOutputColumn;
+  outputLocation.y = outputRow;
+  for (short i = 0; i < 4; ++i) {
+    outputLocation.x = mad24(outputColumn, totalNumPackedChannels, packedChannel);
+    if (outputColumn < numOutputColumns) {
+      write_imagef(output, outputLocation, outputValues[i]);
+    }
+    ++outputColumn;
+  }
+}

selfdrive/modeld/thneed/kernels/convolution_horizontal_reduced_reads_depthwise_stride_1.cl

@@ -0,0 +1,103 @@
+__kernel void convolution_horizontal_reduced_reads_depthwise_stride_1(
+    read_only image2d_t input,
+    short totalNumPackedChannels,
+    read_only image2d_t weights, __constant float *biases,
+    short filterSizeX, short filterSizeY,
+    write_only image2d_t output,
+    short paddingX, short paddingY, short strideX, short strideY,
+    short neuron, float a, float b, float min_clamp, float max_clamp,
+    short numOutputColumns) {
+
+  // init
+  const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
+  short packedChannel = get_global_id(0);
+  short startOutputColumn = mul24((short)get_global_id(1), 4);
+  short outputRow = get_global_id(2);
+  short startXForChannel = mad24(mad24(startOutputColumn, strideX, -paddingX),
+                                 totalNumPackedChannels, packedChannel);
+
+  float4 outputValues[4];
+  for (short i = 0; i < 4; ++i) {
+    outputValues[i] = (float4)(0, 0, 0, 0);
+  }
+
+  int2 inputLocation;
+  inputLocation.y = mad24(outputRow, strideY, -paddingY);
+
+  int2 weightLocation;
+  weightLocation.x = 0;
+  weightLocation.y = packedChannel;
+
+  // convolution
+  for (short rfRow = 0; rfRow < filterSizeY; ++rfRow) {
+    float4 inputValues[4];
+    inputLocation.x = startXForChannel;
+    for (short i = 1; i < 4; ++i) {
+      inputValues[i] = read_imagef(input, smp, inputLocation);
+      inputLocation.x += totalNumPackedChannels;
+    }
+    for (short rfColumn = 0; rfColumn < filterSizeX; ++rfColumn) {
+      inputValues[0] = inputValues[1];
+      inputValues[1] = inputValues[2];
+      inputValues[2] = inputValues[3];
+      inputValues[3] = read_imagef(input, smp, inputLocation);
+      inputLocation.x += totalNumPackedChannels;
+      float4 weightValues = read_imagef(weights, smp, weightLocation);
+      ++weightLocation.x;
+      outputValues[0] += inputValues[0] * weightValues;
+      outputValues[1] += inputValues[1] * weightValues;
+      outputValues[2] += inputValues[2] * weightValues;
+      outputValues[3] += inputValues[3] * weightValues;
+    }
+    ++inputLocation.y;
+  }
+
+  // bias
+  short outputChannel = mul24(packedChannel, 4);
+  float4 biasValues = vload4(0, biases + outputChannel);
+  for (short i = 0; i < 4; ++i) {
+    outputValues[i] += biasValues;
+  }
+
+  // activation
+  switch (neuron) {
+  case 1:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], 0.0f);
+    }
+    break;
+  case 2:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = a * tanh(b * outputValues[i]);
+    }
+    break;
+  case 3:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = native_recip(1.0f + native_exp(-a * outputValues[i] + b));
+    }
+    break;
+  case 4:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], min_clamp);
+      outputValues[i] = min(outputValues[i], max_clamp);
+    }
+    break;
+  case 5:
+    for (short i = 0; i < 4; ++i) {
+      outputValues[i] = max(outputValues[i], 0.0f) + a * (native_exp(min(outputValues[i], 0.0f)) - 1.0f);
+    }
+    break;
+  }
+
+  // output
+  int2 outputLocation;
+  short outputColumn = startOutputColumn;
+  outputLocation.y = outputRow;
+  for (short i = 0; i < 4; ++i) {
+    outputLocation.x = mad24(outputColumn, totalNumPackedChannels, packedChannel);
+    if (outputColumn < numOutputColumns) {
+      write_imagef(output, outputLocation, outputValues[i]);
+    }
+    ++outputColumn;
+  }
+}

selfdrive/modeld/thneed/optimizer.cc

@@ -0,0 +1,259 @@
+#include <map>
+#include <string>
+#include <string.h>
+#include <assert.h>
+#include "thneed.h"
+
+extern map<cl_program, string> g_program_source;
+
+static int is_same_size_image(cl_mem a, cl_mem b) {
+  size_t a_width, a_height, a_depth, a_array_size, a_row_pitch, a_slice_pitch;
+  clGetImageInfo(a, CL_IMAGE_WIDTH, sizeof(a_width), &a_width, NULL);
+  clGetImageInfo(a, CL_IMAGE_HEIGHT, sizeof(a_height), &a_height, NULL);
+  clGetImageInfo(a, CL_IMAGE_DEPTH, sizeof(a_depth), &a_depth, NULL);
+  clGetImageInfo(a, CL_IMAGE_ARRAY_SIZE, sizeof(a_array_size), &a_array_size, NULL);
+  clGetImageInfo(a, CL_IMAGE_ROW_PITCH, sizeof(a_row_pitch), &a_row_pitch, NULL);
+  clGetImageInfo(a, CL_IMAGE_SLICE_PITCH, sizeof(a_slice_pitch), &a_slice_pitch, NULL);
+
+  size_t b_width, b_height, b_depth, b_array_size, b_row_pitch, b_slice_pitch;
+  clGetImageInfo(b, CL_IMAGE_WIDTH, sizeof(b_width), &b_width, NULL);
+  clGetImageInfo(b, CL_IMAGE_HEIGHT, sizeof(b_height), &b_height, NULL);
+  clGetImageInfo(b, CL_IMAGE_DEPTH, sizeof(b_depth), &b_depth, NULL);
+  clGetImageInfo(b, CL_IMAGE_ARRAY_SIZE, sizeof(b_array_size), &b_array_size, NULL);
+  clGetImageInfo(b, CL_IMAGE_ROW_PITCH, sizeof(b_row_pitch), &b_row_pitch, NULL);
+  clGetImageInfo(b, CL_IMAGE_SLICE_PITCH, sizeof(b_slice_pitch), &b_slice_pitch, NULL);
+
+  return (a_width == b_width) && (a_height == b_height) &&
+    (a_depth == b_depth) && (a_array_size == b_array_size) &&
+    (a_row_pitch == b_row_pitch) && (a_slice_pitch == b_slice_pitch);
+}
+
+static cl_mem make_image_like(cl_context context, cl_mem val) {
+  cl_image_format format;
+  size_t width, height, row_pitch;
+  clGetImageInfo(val, CL_IMAGE_FORMAT, sizeof(format), &format, NULL);
+  assert(format.image_channel_order == CL_RGBA);
+  assert(format.image_channel_data_type == CL_HALF_FLOAT);
+  clGetImageInfo(val, CL_IMAGE_WIDTH, sizeof(width), &width, NULL);
+  clGetImageInfo(val, CL_IMAGE_HEIGHT, sizeof(height), &height, NULL);
+  clGetImageInfo(val, CL_IMAGE_ROW_PITCH, sizeof(row_pitch), &row_pitch, NULL);
+
+  cl_image_desc desc = {0};
+  desc.image_type = CL_MEM_OBJECT_IMAGE2D;
+  desc.image_width = width;
+  desc.image_height = height;
+  desc.image_row_pitch = row_pitch;
+
+  cl_mem buf = clCreateBuffer(context, CL_MEM_READ_WRITE, row_pitch*height, NULL, NULL);
+  assert(buf != NULL);
+  desc.buffer = buf;
+
+  cl_int err;
+  cl_mem tmp = clCreateImage(context, CL_MEM_READ_WRITE, &format, &desc, NULL, &err);
+  //printf("got %d for image %zux%zu %zu\n", err, width, height, row_pitch);
+  assert(tmp != NULL);
+
+  return tmp;
+}
+
+// convolution_horizontal_reduced_reads_1x1 is 66% of the model runtime
+// make that faster and the model gets faster
+
+// this cuts ~2 ms off the model runtime right now
+int Thneed::optimize() {
+  const char *kernel_path = getenv("KERNEL_PATH");
+  if (!kernel_path) { kernel_path = "/data/openpilot/selfdrive/modeld/thneed/kernels"; printf("no KERNEL_PATH set, defaulting to %s\n", kernel_path); }
+  // load custom kernels
+  map<string, cl_program> g_programs;
+  for (auto &k : kq) {
+    // replace program?
+    if (g_programs.find(k->name) == g_programs.end()) {
+      char fn[0x100];
+      snprintf(fn, sizeof(fn), "%s/%s.cl", kernel_path, k->name.c_str());
+      FILE *g = fopen(fn, "rb");
+      if (g != NULL) {
+        char *src[0x10000];
+        const char *srcs[1]; srcs[0] = (const char *)src;
+        memset(src, 0, sizeof(src));
+        size_t length = fread(src, 1, sizeof(src), g);
+        fclose(g);
+
+        printf("building kernel %s\n", k->name.c_str());
+        k->program = clCreateProgramWithSource(context, 1, srcs, &length, NULL);
+        int err = clBuildProgram(k->program, 1, &device_id, "", NULL, NULL);
+
+        if (err != 0) {
+          printf("got err %d\n", err);
+          size_t err_length;
+          char buffer[2048];
+          clGetProgramBuildInfo(k->program, device_id, CL_PROGRAM_BUILD_LOG, sizeof(buffer), buffer, &err_length);
+          buffer[err_length] = '\0';
+          printf("%s\n", buffer);
+        }
+        assert(err == 0);
+
+        // save in cache
+        g_programs[k->name] = k->program;
+        g_program_source[k->program] = string((char *)src, length);
+      } else {
+        g_programs[k->name] = NULL;
+      }
+    } else {
+      // cached replacement
+      if (g_programs[k->name] != NULL) {
+        k->program = g_programs[k->name];
+      }
+    }
+
+    // hack in accumulator to convolution_horizontal_reduced_reads_1x1
+    if (k->name == "convolution_horizontal_reduced_reads_1x1") {
+      k->arg_names.push_back("doAccumulate");
+      short doAccumulate = 0;
+      k->args.push_back(string((char *)&doAccumulate, sizeof(doAccumulate)));
+      k->args_size.push_back(2);
+      k->arg_names.push_back("accumulator");
+      k->args.push_back(k->args[k->get_arg_num("output")]);
+      k->args_size.push_back(8);
+      k->num_args += 2;
+    }
+
+    // assert that parameters + batchNormBiases are not used
+    // since they aren't supported in custom replacement kernels
+    if (k->name == "convolution_horizontal_reduced_reads_1x1" ||
+        k->name == "convolution_horizontal_reduced_reads" ||
+        k->name == "convolution_horizontal_reduced_reads_5_outputs") {
+      string p1 = k->args[k->get_arg_num("parameters")];
+      string p2 = k->args[k->get_arg_num("batchNormBiases")];
+      assert(p1.length() == 8 && *((uint64_t*)p1.data()) == 0);
+      assert(p2.length() == 8 && *((uint64_t*)p2.data()) == 0);
+    }
+  }
+
+  // optimizer
+  size_t start_size;
+  do {
+    start_size = kq.size();
+
+    // get optimizations
+    map<string, string> replacements;
+    for (int i = 0; i < kq.size(); i++) {
+      // fusing elementwise_sum + activate_image will save 3 enqueues
+
+      // delete useless copy layers
+      // saves ~0.7 ms
+      if (kq[i]->name == "concatenation" || kq[i]->name == "flatten") {
+        string in = kq[i]->args[kq[i]->get_arg_num("input")];
+        string out = kq[i]->args[kq[i]->get_arg_num("output")];
+        if (is_same_size_image(*(cl_mem*)in.data(), *(cl_mem*)out.data())) {
+          cl_mem tmp = make_image_like(context, *(cl_mem *)in.data());
+          replacements[in] = string((char *)&tmp, sizeof(tmp));
+          replacements[out] = string((char *)&tmp, sizeof(tmp));
+
+          kq.erase(kq.begin()+i); --i;
+        }
+      }
+
+      // NOTE: if activations/accumulation are done in the wrong order, this will be wrong
+
+      // fuse activations into convs and fc_Wtx
+      // saves ~1.5 ms
+      // NOTE: this changes the outputs because of rounding, should be better now!
+      if (i != 0 && kq[i]->name == "activate_image") {
+        if (kq[i-1]->name == "convolution_horizontal_reduced_reads_1x1" ||
+            kq[i-1]->name == "convolution_horizontal_reduced_reads_5_outputs" ||
+            kq[i-1]->name == "convolution_horizontal_reduced_reads" ||
+            kq[i-1]->name == "convolution_horizontal_reduced_reads_depthwise" ||
+            kq[i-1]->name == "convolution_horizontal_reduced_reads_depthwise_stride_1" ||
+            kq[i-1]->name == "fc_Wtx") {
+          string lastout = kq[i-1]->args[kq[i-1]->get_arg_num("output")];
+          string in = kq[i]->args[kq[i]->get_arg_num("input")];
+          string out = kq[i]->args[kq[i]->get_arg_num("output")];
+
+          if (lastout == in) {
+            short neuron = *(int*)kq[i]->args[kq[i]->get_arg_num("neuron")].data();
+            kq[i-1]->args[kq[i-1]->get_arg_num("neuron")] = string((char *)&neuron, sizeof(neuron));
+
+            cl_mem tmp = make_image_like(context, *(cl_mem *)lastout.data());
+            replacements[in] = string((char *)&tmp, sizeof(tmp));
+            replacements[out] = string((char *)&tmp, sizeof(tmp));
+
+            kq.erase(kq.begin()+i); --i;
+          }
+        }
+      }
+
+      // fuse accumulation into convs and fc_Wtx
+      if (i != 0 && kq[i]->name == "elementwise_sum") {
+        if (kq[i-1]->name == "convolution_horizontal_reduced_reads_1x1" ||
+            kq[i-1]->name == "fc_Wtx") {
+          string lastout = kq[i-1]->args[kq[i-1]->get_arg_num("output")];
+          string a = kq[i]->args[kq[i]->get_arg_num("a")];
+          string b = kq[i]->args[kq[i]->get_arg_num("b")];
+          string out = kq[i]->args[kq[i]->get_arg_num("output")];
+
+          if (lastout == a) {
+            kq[i-1]->args[kq[i-1]->get_arg_num("accumulator")] = b;
+          } else if (lastout == b) {
+            kq[i-1]->args[kq[i-1]->get_arg_num("accumulator")] = a;
+          } else {
+            continue;
+          }
+
+          cl_mem tmp = make_image_like(context, *(cl_mem *)lastout.data());
+          replacements[lastout] = string((char *)&tmp, sizeof(tmp));
+          replacements[out] = string((char *)&tmp, sizeof(tmp));
+
+          short doAccumulate = 1;
+          kq[i-1]->args[kq[i-1]->get_arg_num("doAccumulate")] = string((char *)&doAccumulate, sizeof(doAccumulate));
+
+          kq.erase(kq.begin()+i); --i;
+        }
+      }
+    }
+
+    // remap inputs and outputs, and clear the kernels
+    for (int i = 0; i < kq.size(); i++) {
+      kq[i]->kernel = NULL;
+      for (int j = 0; j < kq[i]->num_args; j++) {
+        if (replacements.find(kq[i]->args[j]) != replacements.end()) {
+          kq[i]->args[j] = replacements[kq[i]->args[j]];
+        }
+      }
+    }
+
+    printf("optimize %lu -> %lu\n", start_size, kq.size());
+  } while (kq.size() != start_size);
+
+  size_t work_group_size = 0;
+  clGetDeviceInfo(device_id, CL_DEVICE_MAX_WORK_GROUP_SIZE, sizeof(work_group_size), &work_group_size, NULL);
+  printf("max work group size %lu\n", work_group_size);
+
+  // local work group optimizer
+  for (auto &k : kq) {
+    // only do it for convs, since others might share memory
+    if (k->name.rfind("convolution_", 0) == 0) {
+      int best = -1;
+      if (k->local_work_size[0] * k->local_work_size[1] * k->local_work_size[2] < work_group_size/2) {
+        uint64_t base_time = k->benchmark();
+        uint64_t best_time = base_time;
+        for (int i = 0; i < 3; i++) {
+          k->local_work_size[i] *= 2;
+          uint64_t this_time = k->benchmark();
+          if (this_time < best_time) {
+            best = i;
+            best_time = this_time;
+          }
+          k->local_work_size[i] /= 2;
+        }
+        if (best != -1) {
+          k->local_work_size[best] *= 2;
+          //printf("%s %.2f ms doubled %d to %.2f ms\n", k->name.c_str(), base_time/1e6, best, best_time/1e6);
+        }
+      }
+
+    }
+  }
+
+  return 0;
+}
+

selfdrive/modeld/thneed/thneed.cc

@@ -12,7 +12,7 @@
 #include "selfdrive/common/clutil.h"
 #include "selfdrive/common/timing.h"
 //#define RUN_DISASSEMBLER
-//#define RUN_OPTIMIZER
+#define RUN_OPTIMIZER
 
 Thneed *g_thneed = NULL;
 int g_fd = -1;
@@ -528,6 +528,23 @@ cl_int CLQueuedKernel::exec() {
     kernel, work_dim, NULL, global_work_size, local_work_size, 0, NULL, NULL);
 }
 
+uint64_t CLQueuedKernel::benchmark() {
+  uint64_t ret = 0;
+  int old_record = thneed->record;
+  thneed->record = 0;
+  clFinish(thneed->command_queue);
+  // TODO: benchmarking at a lower level will make this more accurate
+  for (int i = 0; i < 10; i++) {
+    uint64_t sb = nanos_since_boot();
+    exec();
+    clFinish(thneed->command_queue);
+    uint64_t et = nanos_since_boot() - sb;
+    if (ret == 0 || et < ret) ret = et;
+  }
+  thneed->record = old_record;
+  return ret;
+}
+
 void CLQueuedKernel::debug_print(bool verbose) {
   printf("%p %56s -- ", kernel, name.c_str());
   for (int i = 0; i < work_dim; i++) {

selfdrive/modeld/thneed/thneed.h

@@ -44,6 +44,7 @@ class CLQueuedKernel {
                    const size_t *_global_work_size,
                    const size_t *_local_work_size);
     cl_int exec();
+    uint64_t benchmark();
     void debug_print(bool verbose);
     int get_arg_num(const char *search_arg_name);
     cl_program program;

selfdrive/test/process_replay/model_replay_ref_commit

@@ -1 +1 @@
-7d3ad941bc4ba4c923af7a1d7b48544bfc0d3e13
+0c94f7c258bcdabc34c7f7be6cb6c2502afbb339