Main Compliance: REDUCE_PRODUCT support
Update and fix REDUCE_PRODUCT compliance verify lib support.
Added compliance test generation with data range to not cause infs.
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: I3b3004c6caa80d97e330a6393f435f5270b56e21
diff --git a/reference_model/src/generate/generate_utils.cc b/reference_model/src/generate/generate_utils.cc
index d31048f..b94b888 100644
--- a/reference_model/src/generate/generate_utils.cc
+++ b/reference_model/src/generate/generate_utils.cc
@@ -1,4 +1,4 @@
-// Copyright (c) 2023, ARM Limited.
+// Copyright (c) 2023-2024, ARM Limited.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
@@ -69,6 +69,7 @@
{ Op::Op_RSQRT, "RSQRT" },
{ Op::Op_REDUCE_MAX, "REDUCE_MAX" },
{ Op::Op_REDUCE_MIN, "REDUCE_MIN" },
+ { Op::Op_REDUCE_PRODUCT, "REDUCE_PRODUCT" },
{ Op::Op_REDUCE_SUM, "REDUCE_SUM" },
{ Op::Op_SCATTER, "SCATTER" },
{ Op::Op_SIGMOID, "SIGMOID" },
diff --git a/reference_model/src/verify/verifiers.h b/reference_model/src/verify/verifiers.h
index 152cd6a..6830115 100644
--- a/reference_model/src/verify/verifiers.h
+++ b/reference_model/src/verify/verifiers.h
@@ -45,11 +45,12 @@
///
/// \param referenceTensor Reference tensor
/// \param implementationTensor Implementation resulting tensor
-/// \param m Number of manisa bits in the floating point representation
-/// \param n Number of elements in the product
+/// \param rpInfo Reduce-product verification meta-data
///
/// \return True if compliant else false
-bool verifyReduceProduct(const CTensor* referenceTensor, const CTensor* implementationTensor, uint64_t m, uint64_t n);
+bool verifyReduceProduct(const CTensor* referenceTensor,
+ const CTensor* implementationTensor,
+ const ReduceProductVerifyInfo& rpInfo);
/// \brief Perform ULP result verification
///
diff --git a/reference_model/src/verify/verify_entry.cc b/reference_model/src/verify/verify_entry.cc
index 2b318d1..afc5916 100644
--- a/reference_model/src/verify/verify_entry.cc
+++ b/reference_model/src/verify/verify_entry.cc
@@ -35,7 +35,7 @@
return verifyExact(ref, imp);
}
case VerifyMode::ReduceProduct: {
- return verifyReduceProduct(ref, imp, cfg.reduceProductInfo.m, cfg.reduceProductInfo.n);
+ return verifyReduceProduct(ref, imp, cfg.reduceProductInfo);
}
case VerifyMode::Ulp: {
return verifyULP(ref, imp, cfg.ulpInfo);
diff --git a/reference_model/src/verify/verify_reduce_product.cc b/reference_model/src/verify/verify_reduce_product.cc
index 625e2cf..5306ef7 100644
--- a/reference_model/src/verify/verify_reduce_product.cc
+++ b/reference_model/src/verify/verify_reduce_product.cc
@@ -1,5 +1,5 @@
-// Copyright (c) 2023, ARM Limited.
+// Copyright (c) 2023-2024, ARM Limited.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
@@ -19,64 +19,60 @@
#include "verifiers.h"
#include "verify/verify_utils.h"
-namespace
-{
-
-auto calculateError(uint64_t M, uint64_t N)
-{
- return std::pow(1 + std::pow(2, -static_cast<int64_t>(M) - 1), N) - 1;
-}
-
-template <typename FP>
-auto calculateTolerance(uint64_t M, uint64_t N, FP value)
-{
- return std::abs(value) * calculateError(M, N);
-}
-} // namespace
-
namespace TosaReference
{
-bool verifyReduceProduct(const CTensor* referenceTensor, const CTensor* implementationTensor, uint64_t m, uint64_t n)
+namespace
+{
+template <typename OutDtype>
+bool validateData(const double* ref,
+ const OutDtype* imp,
+ const std::vector<int32_t>& shape,
+ const ReduceProductVerifyInfo& cfg)
+{
+ const size_t T = static_cast<size_t>(numElements(shape));
+ TOSA_REF_REQUIRE(T > 0, "[RP] Invalid shape for reference tensor");
+
+ for (size_t i = 0; i < T; ++i)
+ {
+ double errBound =
+ std::abs(ref[i]) * (std::pow(1 + std::pow(2, -AccPrecision<OutDtype>::normal_frac - 1), cfg.n) - 1);
+ bool valid = tosaCheckFloatBound(imp[i], ref[i], errBound);
+ if (!valid)
+ {
+ auto pos = indexToPosition(T, shape);
+ WARNING("[Verifier][RP] Location %s", positionToString(pos).c_str());
+ return false;
+ }
+ }
+ return true;
+}
+} // namespace
+
+bool verifyReduceProduct(const CTensor* referenceTensor,
+ const CTensor* implementationTensor,
+ const ReduceProductVerifyInfo& rpInfo)
{
// Validate that tensors are provided
TOSA_REF_REQUIRE(referenceTensor != nullptr, "[RP] Reference tensor is missing");
TOSA_REF_REQUIRE(implementationTensor != nullptr, "[RP] Implementation tensor is missing");
- // Get number of elements
- const auto elementCount =
- numElements(std::vector<int32_t>(referenceTensor->shape, referenceTensor->shape + referenceTensor->num_dims));
- TOSA_REF_REQUIRE(elementCount > 0, "[RP] Invalid shape for reference tensor");
+ const std::vector<int32_t> refShape(referenceTensor->shape, referenceTensor->shape + referenceTensor->num_dims);
+
+ const double* refData = reinterpret_cast<const double*>(referenceTensor->data);
+ TOSA_REF_REQUIRE(refData != nullptr, "[RP] Missing data for reference");
switch (implementationTensor->data_type)
{
case tosa_datatype_fp32_t: {
- const auto* refData = reinterpret_cast<const float*>(referenceTensor->data);
- TOSA_REF_REQUIRE(refData != nullptr, "[RP] Missing data for reference");
-
const auto* impData = reinterpret_cast<const float*>(implementationTensor->data);
TOSA_REF_REQUIRE(impData != nullptr, "[RP] Missing data for implementation");
-
- return std::equal(refData, std::next(refData, elementCount), impData, std::next(impData, elementCount),
- [m, n](const auto& referenceValue, const auto& implementationValue) {
- // Result overflows must be set to zero of the correct sign.
- if (std::isinf(implementationValue))
- {
- return implementationValue == referenceValue;
- }
-
- // Result underflows must be set to a zero of the correct sign.
- if (implementationValue == 0.f || implementationValue == -0.f)
- {
- return implementationValue == referenceValue;
- }
-
- // Otherwise we are in the normal range.
- const auto absoulteError = (referenceValue < implementationValue)
- ? implementationValue - referenceValue
- : referenceValue - implementationValue;
- return absoulteError <= calculateTolerance(m, n, implementationValue);
- });
+ return validateData(refData, impData, refShape, rpInfo);
+ }
+ case tosa_datatype_fp16_t: {
+ const auto* impData = reinterpret_cast<const half_float::half*>(implementationTensor->data);
+ TOSA_REF_REQUIRE(impData != nullptr, "[RP] Missing data for implementation");
+ return validateData(refData, impData, refShape, rpInfo);
}
default:
WARNING("[Verifier][RP] Data-type not supported.");
diff --git a/reference_model/src/verify/verify_utils.cc b/reference_model/src/verify/verify_utils.cc
index 5ce646c..6f53c63 100644
--- a/reference_model/src/verify/verify_utils.cc
+++ b/reference_model/src/verify/verify_utils.cc
@@ -66,7 +66,6 @@
void from_json(const nlohmann::json& j, ReduceProductVerifyInfo& reduceProduceInfo)
{
- j.at("m").get_to(reduceProduceInfo.m);
j.at("n").get_to(reduceProduceInfo.n);
}
diff --git a/reference_model/test/verify_tests.cpp b/reference_model/test/verify_tests.cpp
index f92792a..ba18af1 100644
--- a/reference_model/test/verify_tests.cpp
+++ b/reference_model/test/verify_tests.cpp
@@ -1,4 +1,4 @@
-// Copyright (c) 2023, ARM Limited.
+// Copyright (c) 2023-2024, ARM Limited.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
@@ -136,13 +136,13 @@
template <typename FP>
auto reduceProductTolerance(uint64_t M, uint64_t N, const std::vector<FP>& results)
{
- const auto error = reduceProductError(M, N);
- auto tolerances = std::vector<FP>(results.size());
+ const auto error = reduceProductError(M, N);
+ auto tolerances_fp64 = std::vector<FP>(results.size());
for (unsigned i = 0, end = results.size(); i < end; ++i)
{
- tolerances[i] = std::abs(results[i]) * error;
+ tolerances_fp64[i] = std::abs(results[i]) * error;
}
- return tolerances;
+ return tolerances_fp64;
}
} // namespace
@@ -299,8 +299,7 @@
"mode": "REDUCE_PRODUCT",
"data_type": "FP32",
"reduce_product_info": {
- "m": 23,
- "n": 8
+ "n": 8
}
}
}
@@ -313,48 +312,49 @@
// Generate some random floats using the full range of fp32. This will be the "result" of our
// dot product. Here we "reduced" over the z-axis of our shape.
- auto data = generateRandomTensorData<float>(elementCount / reductionSize, false);
- // Calculate the tolerances for each element in the result.
+ auto data_fp32 = generateRandomTensorData<float>(elementCount / reductionSize, false);
+ std::vector<double> data_fp64(data_fp32.begin(), data_fp32.end());
+ // Calculate the tolerances_fp64 for each element in the result.
// A float has 23 bit dedicated to the fraction.
constexpr uint64_t mantisa_count = 23;
- const auto tolerances = reduceProductTolerance(mantisa_count, reductionSize, data);
+ const auto tolerances_fp64 = reduceProductTolerance(mantisa_count, reductionSize, data_fp64);
SUBCASE("same")
{
- // TODO: Generate some new floats that are as far away as possible from each result without
+ // Generate some new floats that are as far away as possible from each result without
// exceeding the tolerance.
- auto otherData = std::vector<float>(elementCount / reductionSize);
- for (unsigned i = 0; i < data.size(); ++i)
+ auto otherData_fp32 = std::vector<float>(elementCount / reductionSize);
+ for (unsigned i = 0; i < data_fp32.size(); ++i)
{
- auto newValue = data[i];
- auto oldValue = newValue;
- const auto target = tolerances[i] + newValue;
+ auto newValue = data_fp32[i];
+ const double target = tolerances_fp64[i] + newValue;
// Here we just increment the value until we exceed the tolerance. For simplicity we go up.
+ auto previousValue = newValue;
while (newValue < target)
{
- oldValue = newValue;
- newValue = std::nextafter(newValue, std::numeric_limits<float>::infinity());
+ previousValue = newValue;
+ newValue = std::nextafter(newValue, std::numeric_limits<float>::infinity());
}
- otherData[i] = oldValue;
+ otherData_fp32[i] = previousValue;
}
const auto referenceTensor =
- TosaTensor("out1", tosa_datatype_fp64_t, outputShape, reinterpret_cast<uint8_t*>(data.data()));
+ TosaTensor("out1", tosa_datatype_fp64_t, outputShape, reinterpret_cast<uint8_t*>(data_fp64.data()));
const auto implementationTensor =
- TosaTensor("out1", tosa_datatype_fp32_t, outputShape, reinterpret_cast<uint8_t*>(otherData.data()));
+ TosaTensor("out1", tosa_datatype_fp32_t, outputShape, reinterpret_cast<uint8_t*>(otherData_fp32.data()));
REQUIRE(tvf_verify_data(referenceTensor.cTensor(), nullptr, implementationTensor.cTensor(), jsonCfg.c_str()));
}
SUBCASE("different")
{
- // TODO: Generate some new floats that exceed the tolerance.
- auto otherData = std::vector<float>(elementCount / reductionSize);
- for (unsigned i = 0; i < data.size(); ++i)
+ // Generate some new floats that exceed the tolerance.
+ auto otherData_fp32 = std::vector<float>(elementCount / reductionSize);
+ for (unsigned i = 0; i < data_fp32.size(); ++i)
{
- auto newValue = data[i];
- const auto target = tolerances[i] + newValue;
+ auto newValue = data_fp32[i];
+ const double target = tolerances_fp64[i] + newValue;
// Here we just increment the value until we exceed the tolerance. For simplicity we go up.
while (newValue < target)
@@ -362,13 +362,13 @@
newValue = std::nextafter(newValue, std::numeric_limits<float>::infinity());
}
- otherData[i] = newValue;
+ otherData_fp32[i] = newValue;
}
const auto referenceTensor =
- TosaTensor("out1", tosa_datatype_fp64_t, outputShape, reinterpret_cast<uint8_t*>(data.data()));
+ TosaTensor("out1", tosa_datatype_fp64_t, outputShape, reinterpret_cast<uint8_t*>(data_fp64.data()));
const auto implementationTensor =
- TosaTensor("out1", tosa_datatype_fp32_t, outputShape, reinterpret_cast<uint8_t*>(otherData.data()));
+ TosaTensor("out1", tosa_datatype_fp32_t, outputShape, reinterpret_cast<uint8_t*>(otherData_fp32.data()));
REQUIRE_FALSE(
tvf_verify_data(referenceTensor.cTensor(), nullptr, implementationTensor.cTensor(), jsonCfg.c_str()));
}
diff --git a/scripts/schemavalidation/compliance-config.schema.json b/scripts/schemavalidation/compliance-config.schema.json
index dd62404..c0a479d 100644
--- a/scripts/schemavalidation/compliance-config.schema.json
+++ b/scripts/schemavalidation/compliance-config.schema.json
@@ -73,6 +73,21 @@
}
},
"additionalProperties": false
+ },
+ "reduce_product_info": {
+ "description": "info required for the REDUCE_PRODUCT mode",
+ "type": "object",
+ "properties":
+ {
+ "n": {
+ "description": "number of products in the operation",
+ "type": "integer"
+ }
+ },
+ "additionalProperties": false,
+ "required": [
+ "n"
+ ]
}
},
"additionalProperties": false,
diff --git a/verif/conformance/tosa_main_profile_ops_info.json b/verif/conformance/tosa_main_profile_ops_info.json
index fb25622..2b99ed9 100644
--- a/verif/conformance/tosa_main_profile_ops_info.json
+++ b/verif/conformance/tosa_main_profile_ops_info.json
@@ -2507,6 +2507,7 @@
"profile": [
"tosa-mi"
],
+ "support_for": [ "lazy_data_gen" ],
"generation": {
"standard": {
"generator_args": [
@@ -2518,13 +2519,15 @@
"--target-dtype",
"bf16",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--tensor-dim-range",
"1,34"
],
[
"--target-dtype",
"fp16",
+ "--fp-values-range",
+ "-max,max",
"--target-shape",
"2,65527,3,1",
"--target-shape",
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 1e23822..8641499 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2023, ARM Limited.
+# Copyright (c) 2021-2024, ARM Limited.
# SPDX-License-Identifier: Apache-2.0
import itertools
import math
@@ -1274,6 +1274,30 @@
testGen, opName, dtypeList, shapeList, argsDict, error_name
)
+ @staticmethod
+ def tvgReduceProduct(
+ testGen, opName, dtypeList, shapeList, argsDict, error_name=None
+ ):
+ dtype = dtypeList[0]
+ if error_name is None:
+ # Limit ranges for (non error) tests by using
+ # values that can be multiplied on any axis to not hit infinity
+ highval_lookup = {
+ dtype: math.pow(
+ TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE[dtype],
+ 1 / max(shapeList[0]),
+ )
+ }
+ data_range = TosaTensorValuesGen._get_data_range(
+ testGen, dtype, highval_lookup
+ )
+ assert data_range is not None
+ argsDict["data_range"] = data_range
+
+ return TosaTensorValuesGen.tvgLazyGenDefault(
+ testGen, opName, dtypeList, shapeList, argsDict, error_name
+ )
+
# Set the POW exponent high data range
TVG_FLOAT_HIGH_VALUE_POW_EXP = {
DType.FP32: 10.0,
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 5129e24..0d072ac 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -347,6 +347,7 @@
compliance_tens["ulp_info"] = {"ulp": op["compliance"]["ulp"]}
elif op["op"] == Op.REDUCE_PRODUCT:
mode = gtu.ComplianceMode.REDUCE_PRODUCT
+ compliance_tens["reduce_product_info"] = {"n": argsDict["n"]}
elif op["op"] in (Op.EXP, Op.POW, Op.TANH, Op.SIGMOID):
mode = gtu.ComplianceMode.ABS_ERROR
if "compliance" in op and "abs_error_lower_bound" in op["compliance"]:
@@ -1251,13 +1252,13 @@
self.ser.addOperator(op["op"], input_list, output_list, attr)
- if op["op"] == Op.REDUCE_PRODUCT:
- # TODO: Add compliance support!
- compliance = None
- else:
- compliance = self.tensorComplianceMetaData(
- op, a.dtype, args_dict, result_tensor, error_name
- )
+ if error_name is None and op["op"] == Op.REDUCE_PRODUCT:
+ # Number of products - needed for compliance
+ args_dict["n"] = a.shape[axis]
+
+ compliance = self.tensorComplianceMetaData(
+ op, a.dtype, args_dict, result_tensor, error_name
+ )
return TosaTestGen.BuildInfo(result_tensor, compliance)
@@ -4066,7 +4067,7 @@
"build_fcn": (
build_reduce,
TosaTensorGen.tgBasic,
- TosaTensorValuesGen.tvgLazyGenDefault,
+ TosaTensorValuesGen.tvgReduceProduct,
TosaArgGen.agAxis,
),
"types": TYPE_FP,
@@ -4080,6 +4081,9 @@
TosaErrorValidator.evWrongInputList,
TosaErrorValidator.evWrongOutputList,
),
+ "data_gen": {
+ "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+ },
},
"reduce_sum": {
"op": Op.REDUCE_SUM,