Add abs calculations under precise_mode
This adds a second run of reference model under precise_mode
when test_desc.json contains a "compliance" dictionary which
contains a "mode" entry with value "dot product".
In this second run, abs_mode will be set to true, which causes:
1. evaluation will take absolute values of inputs for these operators:
conv2d, conv3d, depthwise_conv2d, fully_connected,
matmul, transpose_conv2d, fft2d, rfft2d
reduce_sum, avg_pool2d
2. output files will have prefix "bounds_" prepended to them
Signed-off-by: Tai Ly <tai.ly@arm.com>
Change-Id: I7070ecc7ead2d2ea3375c44663d653c6772b88e0
diff --git a/reference_model/include/func_config.h b/reference_model/include/func_config.h
index b92845b..900ebab 100644
--- a/reference_model/include/func_config.h
+++ b/reference_model/include/func_config.h
@@ -49,6 +49,7 @@
uint32_t dump_intermediates = 0;
std::string fp_format = "0.5";
uint32_t precise_mode = 0;
+ bool abs_mode = 0; // set in main as second run of precise_mode
bool float_is_big_endian = false; // Set in arith_util.h by float_is_big_endian()
tosa_level_t tosa_level;
diff --git a/reference_model/src/main.cpp b/reference_model/src/main.cpp
index aad07cb..55e6d67 100644
--- a/reference_model/src/main.cpp
+++ b/reference_model/src/main.cpp
@@ -33,10 +33,12 @@
using json = nlohmann::json;
int initTestDesc(json& test_desc);
-int readInputTensors(SubgraphTraverser& gt, json test_desc);
-int writeFinalTensors(SubgraphTraverser& gt, json test_desc);
-int loadGraph(TosaSerializationHandler& tsh, json test_desc);
+int readInputTensors(SubgraphTraverser& gt, json& test_desc);
+int writeFinalTensors(SubgraphTraverser& gt, json& test_desc, const std::string& filename_prefix);
+int loadGraph(TosaSerializationHandler& tsh, json& test_desc);
void parse_value(const std::string& text, tosa_level_t& value);
+const std::string getResultFilenamePrefix();
+bool isComplianceModeDotProduct(json& test_desc);
int main(int argc, char** argv)
{
@@ -84,44 +86,51 @@
FATAL_ERROR("Unable to load graph");
}
- SubgraphTraverser main_gt(tsh.GetMainRegion()->GetBlockByName("main"), &tsh, nullptr);
+ GraphStatus status = GraphStatus::TOSA_VALID;
- if (main_gt.initializeGraph())
+ // max of 2 runs, second run only happens when precise_mode is set, to do an abs_mode run
+ for (int run = 0; run < 2; run++)
{
- WARNING("Unable to initialize main graph traverser.");
- goto done;
- }
+ SubgraphTraverser main_gt(tsh.GetMainRegion()->GetBlockByName("main"), &tsh, nullptr);
- if (main_gt.linkTensorsAndNodes())
- {
- WARNING("Failed to link tensors and nodes");
- goto done;
- }
+ if (main_gt.initializeGraph())
+ {
+ WARNING("Unable to initialize main graph traverser.");
+ goto done;
+ }
- if (main_gt.validateGraph())
- {
- WARNING("Failed to validate graph. Evaluation aborted.");
- goto done;
- }
+ if (main_gt.linkTensorsAndNodes())
+ {
+ WARNING("Failed to link tensors and nodes");
+ goto done;
+ }
- if (main_gt.allocateTensor())
- {
- WARNING("Failed to allocate tensor. Evaluation aborted.");
- goto done;
- }
+ if (main_gt.validateGraph())
+ {
+ WARNING("Failed to validate graph. Evaluation aborted.");
+ goto done;
+ }
- if (g_func_config.validate_only)
- {
- goto done;
- }
+ if (main_gt.allocateTensor())
+ {
+ WARNING("Failed to allocate tensor. Evaluation aborted.");
+ goto done;
+ }
- if (readInputTensors(main_gt, test_desc))
- {
- FATAL_ERROR("Unable to read input tensors");
- }
+ if (g_func_config.validate_only)
+ {
+ goto done;
+ }
- if (g_func_config.eval)
- {
+ if (readInputTensors(main_gt, test_desc))
+ {
+ FATAL_ERROR("Unable to read input tensors");
+ }
+
+ if (!g_func_config.eval)
+ {
+ goto done;
+ }
// evaluateAll() returns 1 if graph evaluation is forced to be terminated earlier.
if (main_gt.evaluateAll())
@@ -165,36 +174,47 @@
if (g_func_config.output_tensors)
{
- if (writeFinalTensors(main_gt, test_desc))
+ if (writeFinalTensors(main_gt, test_desc, getResultFilenamePrefix()))
{
WARNING("Errors encountered in saving output tensors");
}
}
}
- }
-done:
- switch (main_gt.getGraphStatus())
- {
- case GraphStatus::TOSA_VALID:
- // Result is valid.
- break;
- case GraphStatus::TOSA_UNPREDICTABLE:
- fprintf(stderr, "Graph result: UNPREDICTABLE.\n");
- break;
- case GraphStatus::TOSA_ERROR:
- fprintf(stderr, "Graph result: ERROR.\n");
- break;
- default:
- fprintf(stderr, "Unknown graph status code=%d.\n", (int)main_gt.getGraphStatus());
+ done:
+ status = main_gt.getGraphStatus();
+ switch (status)
+ {
+ case GraphStatus::TOSA_VALID:
+ // Result is valid.
+ break;
+ case GraphStatus::TOSA_UNPREDICTABLE:
+ fprintf(stderr, "Graph result: UNPREDICTABLE.\n");
+ break;
+ case GraphStatus::TOSA_ERROR:
+ fprintf(stderr, "Graph result: ERROR.\n");
+ break;
+ default:
+ fprintf(stderr, "Unknown graph status code=%d.\n", (int)main_gt.getGraphStatus());
+ }
+
+ if (status == GraphStatus::TOSA_VALID && g_func_config.eval && g_func_config.precise_mode &&
+ isComplianceModeDotProduct(test_desc))
+ {
+ // first run result is valid, in precise mode and eval is true: turn on abs_mode for second run
+ g_func_config.abs_mode = true;
+ continue;
+ }
+
+ // otherwise, do only one run
+ break;
}
g_func_debug.fini_debug();
-
- return (int)main_gt.getGraphStatus();
+ return (int)status;
}
-int loadGraph(TosaSerializationHandler& tsh, json test_desc)
+int loadGraph(TosaSerializationHandler& tsh, json& test_desc)
{
char graph_fullname[1024];
const std::string error_msg1 = "Check \"tosa_file\" in .json specified by --tosa_desc";
@@ -248,7 +268,7 @@
return 0;
}
-int readInputTensors(SubgraphTraverser& gt, json test_desc)
+int readInputTensors(SubgraphTraverser& gt, json& test_desc)
{
int tensorCount = gt.getNumInputTensors();
Tensor* tensor;
@@ -314,7 +334,24 @@
return 0;
}
-int writeFinalTensors(SubgraphTraverser& gt, json test_desc)
+const std::string getResultFilenamePrefix()
+{
+ return g_func_config.abs_mode ? "bounds_" : "";
+}
+
+// returns true iff test_desc contains a dictionay, "compliance",
+// which contains entry "mode" whose value is "dot product"
+bool isComplianceModeDotProduct(json& test_desc)
+{
+ if (test_desc.contains("compliance") && test_desc["compliance"].contains("mode") &&
+ test_desc["compliance"]["mode"] == "dot product")
+ {
+ return true;
+ }
+ return false;
+}
+
+int writeFinalTensors(SubgraphTraverser& gt, json& test_desc, const std::string& filename_prefix)
{
int tensorCount = gt.getNumOutputTensors();
const Tensor* tensor;
@@ -338,7 +375,7 @@
return 1;
}
- snprintf(filename, sizeof(filename), "%s/%s", g_func_config.output_dir.c_str(),
+ snprintf(filename, sizeof(filename), "%s/%s%s", g_func_config.output_dir.c_str(), filename_prefix.c_str(),
test_desc["ofm_file"][i].get<std::string>().c_str());
DEBUG_MED(GT, "Writing output tensor[%d] %s to filename: %s", i, tensor->getName().c_str(), filename);
diff --git a/reference_model/src/ops/reduction.cc b/reference_model/src/ops/reduction.cc
index fd48472..f07ffd7 100644
--- a/reference_model/src/ops/reduction.cc
+++ b/reference_model/src/ops/reduction.cc
@@ -192,7 +192,10 @@
{
case TOSA_REF_TYPE_FP16:
case TOSA_REF_TYPE_BF16:
- this->out->getTensor() = this->in->getTensor().sum(this->dims).reshape(this->out->getTensor().dimensions()).unaryExpr([](float f){return fpTrunc<Dtype>(f);});
+ this->out->getTensor() = this->in->getTensor()
+ .sum(this->dims)
+ .reshape(this->out->getTensor().dimensions())
+ .unaryExpr([](float f) { return fpTrunc<Dtype>(f); });
break;
case TOSA_REF_TYPE_FP32:
case TOSA_REF_TYPE_INT32:
@@ -225,7 +228,9 @@
template <int Rank, TOSA_REF_TYPE Dtype>
int OpReduceSumInt<Rank, Dtype>::eval()
{
- this->out->getTensor() = this->in->getTensor().reduce(this->dims, SumRequiresReducer(this->parent_sgt)).reshape(this->out->getTensor().dimensions());
+ this->out->getTensor() = this->in->getTensor()
+ .reduce(this->dims, SumRequiresReducer(this->parent_sgt))
+ .reshape(this->out->getTensor().dimensions());
return GraphNode::eval();
}
@@ -250,12 +255,17 @@
template <int Rank, TOSA_REF_TYPE Dtype>
int OpReduceSumDouble<Rank, Dtype>::eval()
{
+ typename ReduceNode<Rank, Dtype>::TIn in_val = this->in->getTensor();
+ if (g_func_config.abs_mode)
+ {
+ // in abs_mode: take abs values of in value
+ in_val = in_val.abs();
+ }
switch (Dtype)
{
case TOSA_REF_TYPE_FP64:
- this->out->getTensor() = this->in->getTensor()
- .reduce(this->dims, SumDoubleReducer())
- .reshape(this->out->getTensor().dimensions());
+ this->out->getTensor() =
+ in_val.reduce(this->dims, SumDoubleReducer()).reshape(this->out->getTensor().dimensions());
break;
default:
ERROR_IF(true, "unsupported TOSA_REF_TYPE %s", EnumNameTOSAREFTYPE(Dtype));
diff --git a/reference_model/src/ops/tensor_ops.cc b/reference_model/src/ops/tensor_ops.cc
index a60819d..5ba7a83 100644
--- a/reference_model/src/ops/tensor_ops.cc
+++ b/reference_model/src/ops/tensor_ops.cc
@@ -572,6 +572,12 @@
ETensor4<InEigenType> input_padded = input_val.pad(pad);
+ if (g_func_config.abs_mode)
+ {
+ // in abs_mode: take abs values of input_padded
+ input_padded = input_padded.abs();
+ }
+
// assuming input and output have same scales
// so input and output scaling is not required
// TODO: check if this assumption TOSA made
@@ -799,6 +805,16 @@
ETensor4<InEigenType> input_padded = input_val.pad(pad);
+ TBias bias_val = this->bias->getTensor();
+
+ if (g_func_config.abs_mode)
+ {
+ // in abs_mode: take abs values of conv operands
+ input_padded = input_padded.abs();
+ weight_val = weight_val.abs();
+ bias_val = bias_val.abs();
+ }
+
// extract_image_patches() output [N, KH, KW, H * W, C]
// need to transpose to [N, H * W, KH, KW, C]
ETensor5<InEigenType> input_extract_patches =
@@ -815,7 +831,8 @@
// don't need to apply bias_multiplier ( * bias_scale and >> bias_shift) since tflite already scale it
// and reshaped from [C] to [1, C], and broadcast to [N * H * W, C]
- ETensor2<OutEigenType> bias_2d = (this->bias->getTensor().reshape(bias_reshaped_dims).broadcast(bias_bcast_dims)).template cast<OutEigenType>();
+ ETensor2<OutEigenType> bias_2d =
+ (bias_val.reshape(bias_reshaped_dims).broadcast(bias_bcast_dims)).template cast<OutEigenType>();
// output matrix is [N * H * W, C]
ETensor2<OutEigenType> contracted_result =
@@ -977,6 +994,16 @@
ETensor5<InEigenType> input_padded = input_val.pad(pad);
+ TBias bias_val = this->bias->getTensor();
+
+ if (g_func_config.abs_mode)
+ {
+ // in abs_mode: take abs values of conv operands
+ input_padded = input_padded.abs();
+ weight_val = weight_val.abs();
+ bias_val = bias_val.abs();
+ }
+
// 1. initialize with bias
Eigen::array<Eigen::Index, 5> reshape_dim;
reshape_dim.fill(1);
@@ -988,7 +1015,7 @@
bcast[2] = out_height;
bcast[3] = out_width;
bcast[4] = 1;
- this->output->getTensor() = this->bias->getTensor().reshape(reshape_dim).broadcast(bcast);
+ this->output->getTensor() = bias_val.reshape(reshape_dim).broadcast(bcast);
// 2. direct convolution
AccEigenType acc(0.0);
@@ -1167,6 +1194,16 @@
ETensor4<InEigenType> input_padded = input_val.pad(pad);
+ TBias bias_val = this->bias->getTensor();
+
+ if (g_func_config.abs_mode)
+ {
+ // in abs_mode: take abs values of conv operands
+ input_padded = input_padded.abs();
+ weight_val = weight_val.abs();
+ bias_val = bias_val.abs();
+ }
+
// GEMM doesn't fit well with DepthwiseConv2d
// 1. use extract_image_patches() to handle stride/dilation/pad
// 2. perform direct convolution
@@ -1186,7 +1223,7 @@
bcast[3] = 1;
// initialize with bias
- this->output->getTensor() = this->bias->getTensor().reshape(reshape_dim).broadcast(bcast);
+ this->output->getTensor() = bias_val.reshape(reshape_dim).broadcast(bcast);
// 2. direct depthwise convolution
for (int ob = 0; ob < out_batch; ob++)
@@ -1307,9 +1344,20 @@
weight_val = weight_val - (WeightEigenType)attribute->weight_zp();
}
- this->output->getTensor() =
- input_val.template cast<AccEigenType>().contract(weight_val.template cast<AccEigenType>(), dims).template cast<OutEigenType>() +
- this->bias->getTensor().reshape(bias_reshape).broadcast(bias_bcast);
+ TBias bias_val = this->bias->getTensor();
+
+ if (g_func_config.abs_mode)
+ {
+ // in abs_mode: take abs values of conv operands
+ input_val = input_val.abs();
+ weight_val = weight_val.abs();
+ bias_val = bias_val.abs();
+ }
+
+ this->output->getTensor() = input_val.template cast<AccEigenType>()
+ .contract(weight_val.template cast<AccEigenType>(), dims)
+ .template cast<OutEigenType>() +
+ bias_val.reshape(bias_reshape).broadcast(bias_bcast);
if (OutDtype == TOSA_REF_TYPE_INT48)
{
@@ -1416,6 +1464,13 @@
b_val = b_val - (InEigenType)attribute->b_zp();
}
+ if (g_func_config.abs_mode)
+ {
+ // in abs_mode: take abs values of matmul operands
+ a_val = a_val.abs();
+ b_val = b_val.abs();
+ }
+
Eigen::array<Eigen::Index, 2> a_rank2_shape({ H, C });
Eigen::array<Eigen::Index, 2> b_rank2_shape({ C, W });
Eigen::array<Eigen::Index, 3> output_rank3_shape({ 1, H, W });
@@ -1692,6 +1747,16 @@
sign_val = -1.0;
}
+ TIn in_real_val = this->in_real->getTensor();
+ TIn in_imag_val = this->in_imag->getTensor();
+
+ if (g_func_config.abs_mode)
+ {
+ // in abs_mode: take abs values of real and imag operands
+ in_real_val = in_real_val.abs();
+ in_imag_val = in_imag_val.abs();
+ }
+
for (int n = 0; n < in_real_batch; n++)
{
for (int oy = 0; oy < out_real_height; oy++)
@@ -1704,8 +1769,8 @@
{
for (int ix = 0; ix < in_real_width; ix++)
{
- OutEigenType val_real = this->in_real->getTensor()(n, iy, ix);
- OutEigenType val_imag = this->in_imag->getTensor()(n, iy, ix);
+ OutEigenType val_real = in_real_val(n, iy, ix);
+ OutEigenType val_imag = in_imag_val(n, iy, ix);
// Use explicit cast to ensure intermmediate calculations are completed using OutEigenType
a = sign_val * 2 * M_PI * ((iy * (OutEigenType)oy) / in_real_height + (ix * (OutEigenType)ox) / in_real_width);
sum_real += val_real * cos(a) + val_imag * sin(a);
@@ -1800,6 +1865,14 @@
OutEigenType sum_real, sum_imag, a;
+ TIn in_val = this->in->getTensor();
+
+ if (g_func_config.abs_mode)
+ {
+ // in abs_mode: take abs values of in operand
+ in_val = in_val.abs();
+ }
+
for (int n = 0; n < in_batch; n++)
{
for (int oy = 0; oy < out_real_height; oy++)
@@ -1814,8 +1887,8 @@
{
// Use explicit cast to ensure intermmediate calculations are completed using OutEigenType
a = 2 * M_PI * ((iy * (OutEigenType)oy) / in_height + (ix * (OutEigenType)ox) / in_width);
- sum_real += this->in->getTensor()(n, iy, ix) * cos(a);
- sum_imag += -this->in->getTensor()(n, iy, ix) * sin(a);
+ sum_real += in_val(n, iy, ix) * cos(a);
+ sum_imag += -in_val(n, iy, ix) * sin(a);
}
}
this->out_real->getTensor()(n, oy, ox) = sum_real;
@@ -2005,6 +2078,16 @@
weight_val = weight_val - (WeightEigenType)attribute->weight_zp();
}
+ TBias bias_val = this->bias->getTensor();
+
+ if (g_func_config.abs_mode)
+ {
+ // in abs_mode: take abs values of conv operands
+ input_val = input_val.abs();
+ weight_val = weight_val.abs();
+ bias_val = bias_val.abs();
+ }
+
Eigen::array<Eigen::Index, 4> reshape_dim;
reshape_dim.fill(1);
reshape_dim[3] = b_out_channels;
@@ -2016,7 +2099,7 @@
bcast[3] = 1;
// initialize with bias
- this->output->getTensor() = this->bias->getTensor().reshape(reshape_dim).broadcast(bcast);
+ this->output->getTensor() = bias_val.reshape(reshape_dim).broadcast(bcast);
int out_x_origin, out_y_origin;
int out_x, out_y;