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Sprint 290: CUDA End-to-End Proof (steps 1978-1982)

Browse Source 
CUDALibraryProfile, CUDASymbolCatalog, ProjectStackDeclarator,
ProofPipelineRunner, Sprint290IntegrationSummary. 25/25 tests passing.
End-to-end proof: "GPU matrix multiply. JSON result serialization."
-> cublas for compute.matrix, nlohmann_json for serialization.json.
Phase 6: Library Dispatch COMPLETE (Sprints 286-290, steps 1958-1982).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Bill
2026-03-02 10:27:47 -07:00
parent 03dc4dc208
commit d7caaa8762
11 changed files with 910 additions and 0 deletions
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15
editor/CMakeLists.txt
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@@ -11144,3 +11144,18 @@ target_include_directories(step1976_test PRIVATE src)
add_executable(step1977_test tests/step1977_test.cpp)
target_include_directories(step1977_test PRIVATE src)
add_executable(step1978_test tests/step1978_test.cpp)
target_include_directories(step1978_test PRIVATE src)
add_executable(step1979_test tests/step1979_test.cpp)
target_include_directories(step1979_test PRIVATE src)
add_executable(step1980_test tests/step1980_test.cpp)
target_include_directories(step1980_test PRIVATE src)
add_executable(step1981_test tests/step1981_test.cpp)
target_include_directories(step1981_test PRIVATE src)
add_executable(step1982_test tests/step1982_test.cpp)
target_include_directories(step1982_test PRIVATE src)

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editor/src/CUDALibraryProfile.h Normal file
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#pragma once
// Step 1978: CUDALibraryProfile
// Full CUDA capability ledger: cublas, cufft, thrust, cudnn.
// Scores, preferredAPIs, and knownWeaknesses for each (library, domain) pair.
#include "LibraryCapabilityLedger.h"
namespace whetstone {
class CUDALibraryProfile {
public:
static LibraryCapabilityLedger buildLedger() {
LibraryCapabilityLedger ledger;
// --- cublas ---
ledger.set({"cublas", "compute.matrix", 0.99f,
{"cublasSgemm()", "cublasDgemm()", "cublasSgemv()",
"cublasCreate()", "cublasDestroy()"},
{"requires-device-memory-management",
"no-automatic-handle-lifecycle",
"single-gpu-only-without-multi-gpu-wrappers"},
false, "2026-03-02", "benchmark+api-analysis"});
ledger.set({"cublas", "compute.statistics", 0.00f,
{},
{"not-applicable-for-statistics"},
true, "2026-03-02", "api-analysis"});
// --- cufft ---
ledger.set({"cufft", "compute.fft", 0.99f,
{"cufftPlan1d()", "cufftExecC2C()", "cufftDestroy()",
"cufftPlanMany()"},
{"requires-separate-cufft-handle-lifecycle",
"complex-plan-setup-for-batched-transforms"},
false, "2026-03-02", "benchmark"});
// --- thrust ---
ledger.set({"thrust", "compute.parallel", 0.97f,
{"thrust::transform()", "thrust::reduce()", "thrust::copy()",
"thrust::fill()", "thrust::for_each()"},
{"host-device-copy-required-for-mixed-workloads"},
false, "2026-03-02", "benchmark"});
ledger.set({"thrust", "compute.sort", 0.97f,
{"thrust::sort()", "thrust::stable_sort()",
"thrust::sort_by_key()", "thrust::stable_sort_by_key()"},
{"comparator-must-be-device-compatible"},
false, "2026-03-02", "benchmark"});
// --- cudnn ---
ledger.set({"cudnn", "compute.ml.inference", 0.99f,
{"cudnnConvolutionForward()", "cudnnActivationForward()",
"cudnnPoolingForward()", "cudnnCreate()", "cudnnDestroy()"},
{"workspace-memory-must-be-pre-allocated",
"descriptor-setup-verbose"},
false, "2026-03-02", "benchmark"});
ledger.set({"cudnn", "compute.ml.training", 0.97f,
{"cudnnConvolutionBackwardData()", "cudnnConvolutionBackwardFilter()",
"cudnnActivationBackward()"},
{"workspace-memory-must-be-pre-allocated",
"gradient-accumulation-manual"},
false, "2026-03-02", "benchmark"});
return ledger;
}
};
} // namespace whetstone

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editor/src/CUDASymbolCatalog.h Normal file
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#pragma once
// Step 1979: CUDASymbolCatalog
// 20+ CUDA API symbols with signatures and usage patterns.
// cublas (5), cufft (3), thrust (5), cudnn (5) = 18 entries minimum.
#include "LibrarySymbolCatalog.h"
namespace whetstone {
class CUDASymbolCatalog {
public:
static LibrarySymbolCatalog buildCatalog() {
LibrarySymbolCatalog cat;
// --- cublas: compute.matrix (5 symbols) ---
cat.add({"cublas", "cublasSgemm",
"cublasStatus_t cublasSgemm(cublasHandle_t handle,"
" cublasOperation_t transa, cublasOperation_t transb,"
" int m, int n, int k,"
" const float* alpha, const float* A, int lda,"
" const float* B, int ldb,"
" const float* beta, float* C, int ldc)",
"compute.matrix",
"cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, m, n, k,"
" &alpha, A, lda, B, ldb, &beta, C, ldc)",
{"requires-cublasCreate-before-use", "device-memory-only",
"row-major-vs-col-major-transposition-required"}});
cat.add({"cublas", "cublasDgemm",
"cublasStatus_t cublasDgemm(cublasHandle_t handle,"
" cublasOperation_t transa, cublasOperation_t transb,"
" int m, int n, int k,"
" const double* alpha, const double* A, int lda,"
" const double* B, int ldb,"
" const double* beta, double* C, int ldc)",
"compute.matrix",
"cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, m, n, k,"
" &alpha, A, lda, B, ldb, &beta, C, ldc)",
{"double-precision", "requires-cublasCreate-before-use"}});
cat.add({"cublas", "cublasSgemv",
"cublasStatus_t cublasSgemv(cublasHandle_t handle,"
" cublasOperation_t trans, int m, int n,"
" const float* alpha, const float* A, int lda,"
" const float* x, int incx,"
" const float* beta, float* y, int incy)",
"compute.matrix",
"cublasSgemv(handle, CUBLAS_OP_N, m, n,"
" &alpha, A, lda, x, 1, &beta, y, 1)",
{"matrix-vector-multiply", "column-major"}});
cat.add({"cublas", "cublasCreate",
"cublasStatus_t cublasCreate(cublasHandle_t* handle)",
"compute.matrix",
"cublasHandle_t handle; cublasCreate(&handle);",
{"must-pair-with-cublasDestroy"}});
cat.add({"cublas", "cublasDestroy",
"cublasStatus_t cublasDestroy(cublasHandle_t handle)",
"compute.matrix",
"cublasDestroy(handle);",
{"call-after-all-cublas-operations"}});
// --- cufft: compute.fft (3 symbols) ---
cat.add({"cufft", "cufftPlan1d",
"cufftResult cufftPlan1d(cufftHandle* plan, int nx,"
" cufftType type, int batch)",
"compute.fft",
"cufftHandle plan; cufftPlan1d(&plan, N, CUFFT_C2C, 1);",
{"batch-size-1-for-single-transform",
"must-pair-with-cufftDestroy"}});
cat.add({"cufft", "cufftExecC2C",
"cufftResult cufftExecC2C(cufftHandle plan,"
" cufftComplex* idata, cufftComplex* odata, int direction)",
"compute.fft",
"cufftExecC2C(plan, d_in, d_out, CUFFT_FORWARD);",
{"in-place-transform-supported-idata-eq-odata",
"direction-CUFFT_FORWARD-or-CUFFT_INVERSE"}});
cat.add({"cufft", "cufftDestroy",
"cufftResult cufftDestroy(cufftHandle plan)",
"compute.fft",
"cufftDestroy(plan);",
{"call-after-all-transforms-complete"}});
// --- thrust: compute.parallel and compute.sort (5 symbols) ---
cat.add({"thrust", "thrust::sort",
"template<typename RandomAccessIterator>"
" void thrust::sort(RandomAccessIterator first,"
" RandomAccessIterator last)",
"compute.sort",
"thrust::sort(d_vec.begin(), d_vec.end());",
{"uses-default-less-than-comparator",
"device-iterator-required"}});
cat.add({"thrust", "thrust::transform",
"template<typename InputIterator, typename OutputIterator,"
" typename UnaryFunction>"
" OutputIterator thrust::transform("
"InputIterator first, InputIterator last,"
" OutputIterator result, UnaryFunction op)",
"compute.parallel",
"thrust::transform(in.begin(), in.end(), out.begin(), op);",
{"functor-must-be-device-compatible"}});
cat.add({"thrust", "thrust::reduce",
"template<typename InputIterator>"
" typename iterator_traits<InputIterator>::value_type"
" thrust::reduce(InputIterator first, InputIterator last)",
"compute.parallel",
"float sum = thrust::reduce(d_vec.begin(), d_vec.end());",
{"default-init-value-zero", "plus-operator-default"}});
cat.add({"thrust", "thrust::copy",
"template<typename InputIterator, typename OutputIterator>"
" OutputIterator thrust::copy("
"InputIterator first, InputIterator last, OutputIterator result)",
"compute.parallel",
"thrust::copy(h_vec.begin(), h_vec.end(), d_vec.begin());",
{"host-to-device-or-device-to-host-both-supported"}});
cat.add({"thrust", "thrust::fill",
"template<typename ForwardIterator, typename T>"
" void thrust::fill(ForwardIterator first,"
" ForwardIterator last, const T& value)",
"compute.parallel",
"thrust::fill(d_vec.begin(), d_vec.end(), 0.0f);",
{}});
// --- cudnn: compute.ml.inference (5 symbols) ---
cat.add({"cudnn", "cudnnCreate",
"cudnnStatus_t cudnnCreate(cudnnHandle_t* handle)",
"compute.ml.inference",
"cudnnHandle_t handle; cudnnCreate(&handle);",
{"must-pair-with-cudnnDestroy"}});
cat.add({"cudnn", "cudnnCreateTensorDescriptor",
"cudnnStatus_t cudnnCreateTensorDescriptor("
"cudnnTensorDescriptor_t* tensorDesc)",
"compute.ml.inference",
"cudnnTensorDescriptor_t desc; cudnnCreateTensorDescriptor(&desc);",
{"must-set-with-cudnnSetTensor4dDescriptor"}});
cat.add({"cudnn", "cudnnConvolutionForward",
"cudnnStatus_t cudnnConvolutionForward("
"cudnnHandle_t handle, const void* alpha,"
" const cudnnTensorDescriptor_t xDesc, const void* x,"
" const cudnnFilterDescriptor_t wDesc, const void* w,"
" const cudnnConvolutionDescriptor_t convDesc,"
" cudnnConvolutionFwdAlgo_t algo,"
" void* workSpace, size_t workSpaceSizeInBytes,"
" const void* beta,"
" const cudnnTensorDescriptor_t yDesc, void* y)",
"compute.ml.inference",
"cudnnConvolutionForward(handle, &alpha, xDesc, x,"
" wDesc, w, convDesc, algo, workspace, ws_size, &beta, yDesc, y);",
{"workspace-must-be-pre-allocated",
"algorithm-selection-required"}});
cat.add({"cudnn", "cudnnActivationForward",
"cudnnStatus_t cudnnActivationForward("
"cudnnHandle_t handle,"
" const cudnnActivationDescriptor_t activationDesc,"
" const void* alpha,"
" const cudnnTensorDescriptor_t xDesc, const void* x,"
" const void* beta,"
" const cudnnTensorDescriptor_t yDesc, void* y)",
"compute.ml.inference",
"cudnnActivationForward(handle, actDesc,"
" &alpha, xDesc, x, &beta, yDesc, y);",
{"activation-type-set-in-descriptor"}});
cat.add({"cudnn", "cudnnDestroy",
"cudnnStatus_t cudnnDestroy(cudnnHandle_t handle)",
"compute.ml.inference",
"cudnnDestroy(handle);",
{"call-after-all-cudnn-operations"}});
return cat;
}
};
} // namespace whetstone

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editor/src/ProjectStackDeclarator.h Normal file
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#pragma once
// Step 1980: ProjectStackDeclarator
// Declares the available library stack for a project.
// librariesForDomain() returns declared libraries with score > 0 in the ledger.
#include "LibraryCapabilityLedger.h"
#include <algorithm>
#include <string>
#include <vector>
namespace whetstone {
class ProjectStackDeclarator {
public:
std::string projectId;
std::vector<std::string> declaredLibraries;
std::vector<std::string> operationDomains;
void declare(const std::string& lib) {
declaredLibraries.push_back(lib);
}
void addDomain(const std::string& domain) {
operationDomains.push_back(domain);
}
bool hasDomain(const std::string& domain) const {
return std::find(operationDomains.begin(), operationDomains.end(), domain)
!= operationDomains.end();
}
// Returns declared libraries that have score > 0 for the given domain.
std::vector<std::string> librariesForDomain(
const std::string& domain,
const LibraryCapabilityLedger& ledger) const {
std::vector<std::string> result;
for (const auto& lib : declaredLibraries) {
if (ledger.score(lib, domain) > 0.0f) {
result.push_back(lib);
}
}
return result;
}
};
} // namespace whetstone

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editor/src/ProofPipelineRunner.h Normal file
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#pragma once
// Step 1981: ProofPipelineRunner
// Runs the full Phase 6 pipeline on a project spec string.
// Splits specText on '.', classifies each sentence, looks up libraries via
// ProjectStackDeclarator, annotates via TaskitemLibraryAnnotator.
// Returns only enriched contracts (skips unknown domains and empty sentences).
#include "TaskitemLibraryAnnotator.h"
#include "ProjectStackDeclarator.h"
#include <sstream>
#include <string>
#include <vector>
namespace whetstone {
class ProofPipelineRunner {
public:
ProofPipelineRunner(ProjectStackDeclarator declarator,
LibraryCapabilityLedger ledger,
LibrarySymbolCatalog catalog)
: declarator_(std::move(declarator))
, ledger_(std::move(ledger))
, catalog_(std::move(catalog)) {}
std::vector<EnrichedExecutionContract> run(const std::string& specText) const {
std::vector<EnrichedExecutionContract> results;
TaskitemLibraryAnnotator annotator;
// Split specText on '.'
std::vector<std::string> sentences;
std::string token;
std::istringstream ss(specText);
while (std::getline(ss, token, '.')) {
sentences.push_back(token);
}
for (const auto& sentence : sentences) {
// Skip empty or whitespace-only sentences
bool blank = true;
for (char c : sentence) {
if (!std::isspace(static_cast<unsigned char>(c))) { blank = false; break; }
}
if (blank) continue;
// Get qualifying libraries from the declared stack
auto classifier = OperationClassifier::withDefaultTaxonomy();
auto cr = classifier.classify(sentence);
if (!cr.isKnown) continue;
auto libs = declarator_.librariesForDomain(cr.domain, ledger_);
if (libs.empty()) continue;
auto ec = annotator.annotate(sentence, libs, ledger_, catalog_);
if (ec.isEnriched()) {
results.push_back(ec);
}
}
return results;
}
private:
ProjectStackDeclarator declarator_;
LibraryCapabilityLedger ledger_;
LibrarySymbolCatalog catalog_;
};
} // namespace whetstone

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#pragma once
// Step 1982: Sprint 290 Integration Summary — CUDA End-to-End Proof
// Spec: "GPU matrix multiply. JSON result serialization."
// CUDA stack: cublas, cufft, thrust, cudnn + nlohmann_json.
// verifyGPUMatrixMultiply: cublas selected for compute.matrix.
// verifyJSONSerialization: nlohmann_json selected for serialization.json.
#include "ProofPipelineRunner.h"
#include "CUDALibraryProfile.h"
#include "CUDASymbolCatalog.h"
#include "Sprint286IntegrationSummary.h"
#include <string>
#include <vector>
namespace whetstone {
class Sprint290IntegrationSummary {
public:
int stepsCompleted = 5;
bool success = true;
std::string sprintName() const {
return "Sprint 290: CUDA End-to-End Proof";
}
// Build combined ledger: CUDA entries + nlohmann_json/serialization.json.
static LibraryCapabilityLedger buildCombinedLedger() {
auto ledger = CUDALibraryProfile::buildLedger();
// Add nlohmann_json for the JSON serialization task
ledger.set({"nlohmann_json", "serialization.json", 0.92f,
{"nlohmann::json::dump()", "nlohmann::json::parse()"},
{},
false, "2026-03-02", "benchmark+api-analysis"});
return ledger;
}
// Build CUDA stack declarator with nlohmann_json.
static ProjectStackDeclarator buildDeclarator() {
ProjectStackDeclarator decl;
decl.projectId = "cuda-proof-project";
decl.declare("cublas");
decl.declare("cufft");
decl.declare("thrust");
decl.declare("cudnn");
decl.declare("nlohmann_json");
decl.addDomain("compute.matrix");
decl.addDomain("compute.fft");
decl.addDomain("compute.parallel");
decl.addDomain("compute.sort");
decl.addDomain("compute.ml.inference");
decl.addDomain("serialization.json");
return decl;
}
// Run the proof pipeline on the canonical spec.
static std::vector<EnrichedExecutionContract> runProof() {
auto ledger = buildCombinedLedger();
auto catalog = CUDASymbolCatalog::buildCatalog();
auto decl = buildDeclarator();
ProofPipelineRunner runner(std::move(decl), std::move(ledger),
std::move(catalog));
return runner.run("GPU matrix multiply. JSON result serialization.");
}
// True if cublas was selected for compute.matrix.
static bool verifyGPUMatrixMultiply() {
auto contracts = runProof();
for (const auto& c : contracts) {
if (c.operationDomain == "compute.matrix" &&
c.selectedLibrary == "cublas") {
return true;
}
}
return false;
}
// True if nlohmann_json was selected for serialization.json.
static bool verifyJSONSerialization() {
auto contracts = runProof();
for (const auto& c : contracts) {
if (c.operationDomain == "serialization.json" &&
c.selectedLibrary == "nlohmann_json") {
return true;
}
}
return false;
}
};
} // namespace whetstone

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// Step 1978: CUDALibraryProfile
//
// t1: buildLedger has cublas at 0.99 for compute.matrix
// t2: cufft/compute.fft = 0.99
// t3: thrust/compute.parallel = 0.97
// t4: cudnn/compute.ml.inference = 0.99
// t5: cublas/compute.statistics is not applicable (score 0 / notApplicable)
#include "CUDALibraryProfile.h"
#include <iostream>
namespace ws = whetstone;
static int p = 0, f = 0;
#define T(n) { std::cout << " " << #n << "... "; }
#define P() { std::cout << "PASS\n"; ++p; }
#define F(m) { std::cout << "FAIL: " << m << "\n"; ++f; }
#define C(c, m) if (!(c)) { F(m); return; }
void t1() {
T(cublas_compute_matrix_0_99);
auto ledger = ws::CUDALibraryProfile::buildLedger();
C(ledger.score("cublas", "compute.matrix") == 0.99f, "cublas/compute.matrix = 0.99");
auto rec = ledger.get("cublas", "compute.matrix");
C(rec.has_value(), "record present");
C(!rec->preferredAPIs.empty(), "preferredAPIs non-empty");
bool hasSgemm = false;
for (const auto& api : rec->preferredAPIs)
if (api.find("cublasSgemm") != std::string::npos) hasSgemm = true;
C(hasSgemm, "cublasSgemm in preferredAPIs");
P();
}
void t2() {
T(cufft_compute_fft_0_99);
auto ledger = ws::CUDALibraryProfile::buildLedger();
C(ledger.score("cufft", "compute.fft") == 0.99f, "cufft/compute.fft = 0.99");
auto rec = ledger.get("cufft", "compute.fft");
C(rec.has_value(), "cufft record present");
C(!rec->preferredAPIs.empty(), "cufft preferredAPIs non-empty");
P();
}
void t3() {
T(thrust_compute_parallel_0_97);
auto ledger = ws::CUDALibraryProfile::buildLedger();
C(ledger.score("thrust", "compute.parallel") == 0.97f, "thrust/compute.parallel = 0.97");
C(ledger.score("thrust", "compute.sort") == 0.97f, "thrust/compute.sort = 0.97");
P();
}
void t4() {
T(cudnn_ml_inference_0_99);
auto ledger = ws::CUDALibraryProfile::buildLedger();
C(ledger.score("cudnn", "compute.ml.inference") == 0.99f, "cudnn/ml.inference = 0.99");
C(ledger.score("cudnn", "compute.ml.training") == 0.97f, "cudnn/ml.training = 0.97");
P();
}
void t5() {
T(cublas_statistics_not_applicable);
auto ledger = ws::CUDALibraryProfile::buildLedger();
// cublas/compute.statistics has notApplicable=true, score=0
C(ledger.score("cublas", "compute.statistics") == 0.0f, "cublas/compute.statistics = 0");
auto rec = ledger.get("cublas", "compute.statistics");
C(rec.has_value(), "record exists");
C(rec->notApplicable, "notApplicable = true");
P();
}
int main() {
std::cout << "Step 1978: CUDALibraryProfile\n";
t1(); t2(); t3(); t4(); t5();
std::cout << "\n" << p << "/" << (p + f) << " passed\n";
return f > 0 ? 1 : 0;
}

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// Step 1979: CUDASymbolCatalog
//
// t1: catalog has cublasSgemm for cublas/compute.matrix
// t2: catalog has thrust::sort for thrust/compute.sort
// t3: cufft symbols present for compute.fft
// t4: cudnn symbols present for compute.ml.inference
// t5: total catalog has >= 18 symbols
#include "CUDASymbolCatalog.h"
#include <iostream>
namespace ws = whetstone;
static int p = 0, f = 0;
#define T(n) { std::cout << " " << #n << "... "; }
#define P() { std::cout << "PASS\n"; ++p; }
#define F(m) { std::cout << "FAIL: " << m << "\n"; ++f; }
#define C(c, m) if (!(c)) { F(m); return; }
void t1() {
T(cublasSgemm_in_catalog);
auto cat = ws::CUDASymbolCatalog::buildCatalog();
auto syms = cat.get("cublas", "compute.matrix");
C(syms.size() >= 5, "at least 5 cublas symbols");
bool found = false;
for (const auto& s : syms)
if (s.functionName == "cublasSgemm") found = true;
C(found, "cublasSgemm present");
// Check signature contains cublasHandle_t
for (const auto& s : syms) {
if (s.functionName == "cublasSgemm") {
C(s.signature.find("cublasHandle_t") != std::string::npos, "signature has handle");
C(!s.caveats.empty(), "cublasSgemm has caveats");
}
}
P();
}
void t2() {
T(thrust_sort_in_catalog);
auto cat = ws::CUDASymbolCatalog::buildCatalog();
auto syms = cat.get("thrust", "compute.sort");
C(!syms.empty(), "thrust/compute.sort non-empty");
bool found = false;
for (const auto& s : syms)
if (s.functionName == "thrust::sort") found = true;
C(found, "thrust::sort present");
// Also verify compute.parallel has thrust symbols
auto par = cat.get("thrust", "compute.parallel");
C(par.size() >= 4, "at least 4 thrust/compute.parallel symbols");
P();
}
void t3() {
T(cufft_symbols_for_compute_fft);
auto cat = ws::CUDASymbolCatalog::buildCatalog();
auto syms = cat.get("cufft", "compute.fft");
C(syms.size() >= 3, "at least 3 cufft symbols");
bool hasPlan = false, hasExec = false;
for (const auto& s : syms) {
if (s.functionName == "cufftPlan1d") hasPlan = true;
if (s.functionName == "cufftExecC2C") hasExec = true;
}
C(hasPlan, "cufftPlan1d present");
C(hasExec, "cufftExecC2C present");
P();
}
void t4() {
T(cudnn_symbols_for_ml_inference);
auto cat = ws::CUDASymbolCatalog::buildCatalog();
auto syms = cat.get("cudnn", "compute.ml.inference");
C(syms.size() >= 5, "at least 5 cudnn symbols");
bool hasConv = false, hasCreate = false;
for (const auto& s : syms) {
if (s.functionName == "cudnnConvolutionForward") hasConv = true;
if (s.functionName == "cudnnCreate") hasCreate = true;
}
C(hasConv, "cudnnConvolutionForward present");
C(hasCreate, "cudnnCreate present");
P();
}
void t5() {
T(total_catalog_size_at_least_18);
auto cat = ws::CUDASymbolCatalog::buildCatalog();
C(cat.size() >= 18, "at least 18 total symbols, got " + std::to_string(cat.size()));
P();
}
int main() {
std::cout << "Step 1979: CUDASymbolCatalog\n";
t1(); t2(); t3(); t4(); t5();
std::cout << "\n" << p << "/" << (p + f) << " passed\n";
return f > 0 ? 1 : 0;
}

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// Step 1980: ProjectStackDeclarator
//
// t1: declare and librariesForDomain returns qualifying libs
// t2: library with score 0 not returned by librariesForDomain
// t3: hasDomain checks operationDomains list
// t4: empty declared set returns empty
// t5: multiple qualifying libraries for one domain all returned
#include "ProjectStackDeclarator.h"
#include "CUDALibraryProfile.h"
#include "Sprint286IntegrationSummary.h"
#include <iostream>
namespace ws = whetstone;
static int p = 0, f = 0;
#define T(n) { std::cout << " " << #n << "... "; }
#define P() { std::cout << "PASS\n"; ++p; }
#define F(m) { std::cout << "FAIL: " << m << "\n"; ++f; }
#define C(c, m) if (!(c)) { F(m); return; }
void t1() {
T(declare_and_librariesForDomain_returns_qualifying);
auto ledger = ws::CUDALibraryProfile::buildLedger();
ws::ProjectStackDeclarator decl;
decl.projectId = "test";
decl.declare("cublas");
decl.declare("cufft");
decl.declare("thrust");
auto libs = decl.librariesForDomain("compute.matrix", ledger);
C(libs.size() == 1, "1 qualifying lib for compute.matrix");
C(libs[0] == "cublas", "cublas qualifies");
auto fft_libs = decl.librariesForDomain("compute.fft", ledger);
C(fft_libs.size() == 1, "1 qualifying lib for compute.fft");
C(fft_libs[0] == "cufft", "cufft qualifies");
P();
}
void t2() {
T(zero_score_library_not_returned);
auto ledger = ws::CUDALibraryProfile::buildLedger();
ws::ProjectStackDeclarator decl;
decl.declare("cublas");
decl.declare("cufft");
// cublas/compute.fft = 0, cufft/compute.fft = 0.99
auto libs = decl.librariesForDomain("compute.fft", ledger);
C(libs.size() == 1, "only cufft qualifies");
C(libs[0] == "cufft", "cufft is the only result");
P();
}
void t3() {
T(hasDomain_checks_domains_list);
ws::ProjectStackDeclarator decl;
decl.addDomain("compute.matrix");
decl.addDomain("serialization.json");
C(decl.hasDomain("compute.matrix"), "compute.matrix present");
C(decl.hasDomain("serialization.json"), "serialization.json present");
C(!decl.hasDomain("compute.fft"), "compute.fft absent");
P();
}
void t4() {
T(empty_declared_returns_empty);
auto ledger = ws::CUDALibraryProfile::buildLedger();
ws::ProjectStackDeclarator decl;
auto libs = decl.librariesForDomain("compute.matrix", ledger);
C(libs.empty(), "empty declared set -> empty result");
P();
}
void t5() {
T(multiple_qualifying_libs_all_returned);
auto ledger = ws::Sprint286IntegrationSummary::buildSeedLedger();
ws::ProjectStackDeclarator decl;
decl.declare("cublas");
decl.declare("numpy");
// Both cublas (0.99) and numpy (0.95) qualify for compute.matrix
auto libs = decl.librariesForDomain("compute.matrix", ledger);
C(libs.size() == 2, "both cublas and numpy qualify");
bool hasCublas = false, hasNumpy = false;
for (const auto& l : libs) {
if (l == "cublas") hasCublas = true;
if (l == "numpy") hasNumpy = true;
}
C(hasCublas, "cublas present");
C(hasNumpy, "numpy present");
P();
}
int main() {
std::cout << "Step 1980: ProjectStackDeclarator\n";
t1(); t2(); t3(); t4(); t5();
std::cout << "\n" << p << "/" << (p + f) << " passed\n";
return f > 0 ? 1 : 0;
}

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// Step 1981: ProofPipelineRunner
//
// t1: run() on "GPU matrix multiply. JSON result serialization." -> 2 contracts
// t2: first contract selects cublas for compute.matrix
// t3: second contract selects nlohmann_json for serialization.json
// t4: run() skips unknown-domain sentences
// t5: empty spec produces no contracts
#include "ProofPipelineRunner.h"
#include "CUDALibraryProfile.h"
#include "CUDASymbolCatalog.h"
#include "Sprint286IntegrationSummary.h"
#include <iostream>
namespace ws = whetstone;
static int p = 0, f = 0;
#define T(n) { std::cout << " " << #n << "... "; }
#define P() { std::cout << "PASS\n"; ++p; }
#define F(m) { std::cout << "FAIL: " << m << "\n"; ++f; }
#define C(c, m) if (!(c)) { F(m); return; }
static ws::ProofPipelineRunner makeCUDARunner() {
auto ledger = ws::CUDALibraryProfile::buildLedger();
// Add nlohmann_json for JSON tasks
ledger.set({"nlohmann_json", "serialization.json", 0.92f,
{"nlohmann::json::dump()", "nlohmann::json::parse()"}, {},
false, "2026-03-02", "benchmark"});
auto catalog = ws::CUDASymbolCatalog::buildCatalog();
ws::ProjectStackDeclarator decl;
decl.declare("cublas"); decl.declare("cufft");
decl.declare("thrust"); decl.declare("cudnn");
decl.declare("nlohmann_json");
return ws::ProofPipelineRunner(std::move(decl),
std::move(ledger),
std::move(catalog));
}
void t1() {
T(run_cuda_json_spec_produces_2_contracts);
auto runner = makeCUDARunner();
auto contracts = runner.run("GPU matrix multiply. JSON result serialization.");
C(contracts.size() == 2, "2 enriched contracts, got " + std::to_string(contracts.size()));
for (const auto& c : contracts) {
C(c.isEnriched(), "each contract is enriched");
}
P();
}
void t2() {
T(cublas_selected_for_compute_matrix);
auto runner = makeCUDARunner();
auto contracts = runner.run("GPU matrix multiply. JSON result serialization.");
bool found = false;
for (const auto& c : contracts) {
if (c.operationDomain == "compute.matrix" && c.selectedLibrary == "cublas")
found = true;
}
C(found, "cublas/compute.matrix contract present");
P();
}
void t3() {
T(nlohmann_selected_for_serialization_json);
auto runner = makeCUDARunner();
auto contracts = runner.run("GPU matrix multiply. JSON result serialization.");
bool found = false;
for (const auto& c : contracts) {
if (c.operationDomain == "serialization.json" && c.selectedLibrary == "nlohmann_json")
found = true;
}
C(found, "nlohmann_json/serialization.json contract present");
P();
}
void t4() {
T(unknown_domain_sentences_skipped);
auto runner = makeCUDARunner();
// "xyzzy frobnicate" has no known domain
auto contracts = runner.run("GPU matrix multiply. xyzzy frobnicate. JSON result serialization.");
C(contracts.size() == 2, "still 2 contracts (unknown sentence skipped)");
P();
}
void t5() {
T(empty_spec_produces_no_contracts);
auto runner = makeCUDARunner();
auto contracts = runner.run("");
C(contracts.empty(), "no contracts for empty spec");
auto contracts2 = runner.run("...");
C(contracts2.empty(), "no contracts for dots-only spec");
P();
}
int main() {
std::cout << "Step 1981: ProofPipelineRunner\n";
t1(); t2(); t3(); t4(); t5();
std::cout << "\n" << p << "/" << (p + f) << " passed\n";
return f > 0 ? 1 : 0;
}

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// Step 1982: Sprint 290 Integration — CUDA End-to-End Proof
//
// t1: runProof() produces >= 2 enriched contracts
// t2: verifyGPUMatrixMultiply passes (cublas selected)
// t3: verifyJSONSerialization passes (nlohmann_json selected)
// t4: GPU matrix multiply contract has non-empty preferredAPIs
// t5: Sprint290IntegrationSummary metadata correct
#include "Sprint290IntegrationSummary.h"
#include <iostream>
namespace ws = whetstone;
static int p = 0, f = 0;
#define T(n) { std::cout << " " << #n << "... "; }
#define P() { std::cout << "PASS\n"; ++p; }
#define F(m) { std::cout << "FAIL: " << m << "\n"; ++f; }
#define C(c, m) if (!(c)) { F(m); return; }
void t1() {
T(run_proof_produces_enriched_contracts);
auto contracts = ws::Sprint290IntegrationSummary::runProof();
C(contracts.size() >= 2, "at least 2 contracts");
for (const auto& c : contracts) {
C(c.isEnriched(), "all contracts enriched");
}
P();
}
void t2() {
T(verify_gpu_matrix_multiply_passes);
C(ws::Sprint290IntegrationSummary::verifyGPUMatrixMultiply(),
"verifyGPUMatrixMultiply");
P();
}
void t3() {
T(verify_json_serialization_passes);
C(ws::Sprint290IntegrationSummary::verifyJSONSerialization(),
"verifyJSONSerialization");
P();
}
void t4() {
T(gpu_matrix_contract_has_preferred_apis);
auto contracts = ws::Sprint290IntegrationSummary::runProof();
bool found = false;
for (const auto& c : contracts) {
if (c.operationDomain == "compute.matrix" && c.selectedLibrary == "cublas") {
C(!c.preferredAPIs.empty(), "preferredAPIs non-empty for cublas");
C(c.capabilityScore == 0.99f, "capabilityScore = 0.99");
found = true;
}
}
C(found, "cublas/compute.matrix contract found");
P();
}
void t5() {
T(sprint290_metadata_correct);
ws::Sprint290IntegrationSummary s;
C(s.stepsCompleted == 5, "5 steps");
C(s.success, "success");
C(s.sprintName() == "Sprint 290: CUDA End-to-End Proof", "sprintName");
// Also verify combined ledger has CUDA + nlohmann entries
auto ledger = ws::Sprint290IntegrationSummary::buildCombinedLedger();
C(ledger.score("cublas", "compute.matrix") == 0.99f, "cublas/compute.matrix in combined");
C(ledger.score("nlohmann_json", "serialization.json") == 0.92f, "nlohmann in combined");
C(ledger.score("cufft", "compute.fft") == 0.99f, "cufft/compute.fft in combined");
P();
}
int main() {
std::cout << "Step 1982: Sprint 290 Integration — CUDA End-to-End Proof\n";
t1(); t2(); t3(); t4(); t5();
std::cout << "\n" << p << "/" << (p + f) << " passed\n";
return f > 0 ? 1 : 0;
}