ClaudeBot/llama.cpp
0
0
Fork 1
mirror of https://github.com/ggml-org/llama.cpp.git synced 2026-09-17 20:31:47 +02:00
Code Issues Packages Projects Releases Wiki Activity

tests : drop SYCL special-casing in test-backend-ops.cpp (#28688)

Browse Source
This commit is contained in:
Georgi Gerganov
2026-09-10 13:43:43 +03:00
committed by GitHub
parent 3ff67eb43d
commit e5a8d439ce
1 changed files with 15 additions and 124 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/test-backend-ops.cpp
+15 -124
View File
@@ -462,17 +462,9 @@ static std::string var_to_str(ggml_scale_mode mode) {
#define VARS_TO_STR16(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p) VAR_TO_STR(a) + "," + VARS_TO_STR15(b, c, d, e, f, g, h, i, j, k, l, m, n, o, p) #define VARS_TO_STR16(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p) VAR_TO_STR(a) + "," + VARS_TO_STR15(b, c, d, e, f, g, h, i, j, k, l, m, n, o, p)
#define VARS_TO_STR17(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q) VAR_TO_STR(a) + "," + VARS_TO_STR16(b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q) #define VARS_TO_STR17(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q) VAR_TO_STR(a) + "," + VARS_TO_STR16(b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q)
#ifdef GGML_USE_SYCL
static bool inline _isinf(float f) {
return (*(uint32_t *)&f & 0x7fffffff) == 0x7f800000;
}
#else
static bool inline _isinf(float f) { return std::isinf(f); }
#endif
// accept FLT_MAX as infinity // accept FLT_MAX as infinity
static bool isinf_or_max(float f) { static bool isinf_or_max(float f) {
return _isinf(f) || f == FLT_MAX || f == -FLT_MAX; return std::isinf(f) || f == FLT_MAX || f == -FLT_MAX;
} }
static bool ggml_is_view_op(enum ggml_op op) { static bool ggml_is_view_op(enum ggml_op op) {
@@ -4831,51 +4823,6 @@ struct test_mul_mat : public test_case {
} }
}; };
#define P 1.0f
#define N -1.0f
// constant Hadamard matrix via Paley I construction
static constexpr float H12[12][12] = {
{ P, P, P, P, P, P, P, P, P, P, P, P },
{ P, N, P, N, P, P, P, N, N, N, P, N },
{ P, N, N, P, N, P, P, P, N, N, N, P },
{ P, P, N, N, P, N, P, P, P, N, N, N },
{ P, N, P, N, N, P, N, P, P, P, N, N },
{ P, N, N, P, N, N, P, N, P, P, P, N },
{ P, N, N, N, P, N, N, P, N, P, P, P },
{ P, P, N, N, N, P, N, N, P, N, P, P },
{ P, P, P, N, N, N, P, N, N, P, N, P },
{ P, P, P, P, N, N, N, P, N, N, P, N },
{ P, N, P, P, P, N, N, N, P, N, N, P },
{ P, P, N, P, P, P, N, N, N, P, N, N }
};
static constexpr float H20[20][20] = {
{ P, P, P, P, P, P, P, P, P, P, P, P, P, P, P, P, P, P, P, P },
{ P, N, P, N, N, P, P, P, P, N, P, N, P, N, N, N, N, P, P, N },
{ P, N, N, P, N, N, P, P, P, P, N, P, N, P, N, N, N, N, P, P },
{ P, P, N, N, P, N, N, P, P, P, P, N, P, N, P, N, N, N, N, P },
{ P, P, P, N, N, P, N, N, P, P, P, P, N, P, N, P, N, N, N, N },
{ P, N, P, P, N, N, P, N, N, P, P, P, P, N, P, N, P, N, N, N },
{ P, N, N, P, P, N, N, P, N, N, P, P, P, P, N, P, N, P, N, N },
{ P, N, N, N, P, P, N, N, P, N, N, P, P, P, P, N, P, N, P, N },
{ P, N, N, N, N, P, P, N, N, P, N, N, P, P, P, P, N, P, N, P },
{ P, P, N, N, N, N, P, P, N, N, P, N, N, P, P, P, P, N, P, N },
{ P, N, P, N, N, N, N, P, P, N, N, P, N, N, P, P, P, P, N, P },
{ P, P, N, P, N, N, N, N, P, P, N, N, P, N, N, P, P, P, P, N },
{ P, N, P, N, P, N, N, N, N, P, P, N, N, P, N, N, P, P, P, P },
{ P, P, N, P, N, P, N, N, N, N, P, P, N, N, P, N, N, P, P, P },
{ P, P, P, N, P, N, P, N, N, N, N, P, P, N, N, P, N, N, P, P },
{ P, P, P, P, N, P, N, P, N, N, N, N, P, P, N, N, P, N, N, P },
{ P, P, P, P, P, N, P, N, P, N, N, N, N, P, P, N, N, P, N, N },
{ P, N, P, P, P, P, N, P, N, P, N, N, N, N, P, P, N, N, P, N },
{ P, N, N, P, P, P, P, N, P, N, P, N, N, N, N, P, P, N, N, P },
{ P, P, N, N, P, P, P, P, N, P, N, P, N, N, N, N, P, P, N, N }
};
#undef P
#undef N
// GGML_HINT_SRC0_IS_HADAMARD // GGML_HINT_SRC0_IS_HADAMARD
struct test_mul_mat_hadamard : public test_mul_mat { struct test_mul_mat_hadamard : public test_mul_mat {
test_mul_mat_hadamard(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32, test_mul_mat_hadamard(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
@@ -4900,58 +4847,20 @@ struct test_mul_mat_hadamard : public test_mul_mat {
void initialize_tensors(ggml_context * ctx) override { void initialize_tensors(ggml_context * ctx) override {
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (strcmp(t->name, "a") == 0) { if (strcmp(t->name, "a") == 0) {
const int64_t n_cols = t->ne[0]; const int64_t n_cols = t->ne[0];
const int64_t n_rows = ggml_nrows(t); const int64_t n_rows = ggml_nrows(t);
std::vector<float> data(n_cols * n_rows); std::vector<float> data(n_cols * n_rows);
float scale = 1.0f / sqrtf((float) n_cols); float scale = 1.0f / sqrtf((float)n_cols);
for (int64_t r = 0; r < n_rows; r++) {
auto is_pow2 = [](const int64_t a) { float * row_data = data.data() + r * n_cols;
return (a > 0) && ((a & (a - 1)) == 0); for (int64_t i = 0; i < n_cols; i++) {
}; int pop = 0;
#ifdef GGML_USE_SYCL int64_t val = r & i;
const bool is_kronecker = while (val) {
((n_cols % 12 == 0) && is_pow2(n_cols / 12)) || ((n_cols % 20 == 0) && is_pow2(n_cols / 20)); pop += (val & 1);
#else val >>= 1;
const bool is_kronecker = false;
#endif
if (is_kronecker) {
const int64_t B = (n_cols % 12 == 0 && is_pow2(n_cols / 12)) ? 12 : 20;
for (int64_t r = 0; r < n_rows; r++) {
float * row_data = data.data() + r * n_cols;
const int64_t r_mod = r % n_cols;
const int64_t r_b = r_mod / B;
const int64_t r_m = r_mod % B;
for (int64_t i = 0; i < n_cols; i++) {
const int64_t c_b = i / B;
const int64_t c_m = i % B;
int pop = 0;
int64_t val = r_b & c_b;
while (val) {
pop += (val & 1);
val >>= 1;
}
const float sign_m = (pop % 2 == 0) ? 1.0f : -1.0f;
const float sign_b = (B == 12) ? H12[c_m][r_m] : H20[c_m][r_m];
row_data[i] = scale * sign_b * sign_m;
}
}
}
else if (is_pow2(n_cols)) {
for (int64_t r = 0; r < n_rows; r++) {
float * row_data = data.data() + r * n_cols;
for (int64_t i = 0; i < n_cols; i++) {
int pop_cnt = 0;
int64_t val = r & i;
while (val) {
pop_cnt += (val & 1);
val >>= 1;
}
row_data[i] = (pop_cnt % 2 == 0) ? scale : -scale;
} }
row_data[i] = (pop % 2 == 0) ? scale : -scale;
} }
} }
ggml_backend_tensor_set(t, data.data(), 0, data.size() * sizeof(float)); ggml_backend_tensor_set(t, data.data(), 0, data.size() * sizeof(float));
@@ -9716,16 +9625,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 256, 512, 256)); // many rows test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 256, 512, 256)); // many rows
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 32, 1, 32)); // too small (N<64) test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 32, 1, 32)); // too small (N<64)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 1024, 1, 1024)); // too big (N>512) test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 1024, 1, 1024)); // too big (N>512)
#ifdef GGML_USE_SYCL
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 384, 1, 384)); // m=12 (N=384)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 384, 32, 384)); // m=12 (batch)
test_cases.emplace_back(
new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 384, 4, 384, { 2, 3 })); // m=12 (multi-dim)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 768, 1, 768)); // m=12 (N=768)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 640, 1, 640)); // m=20 (N=640)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 640, 32, 640)); // m=20 (batch)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 1280, 1, 1280)); // m=20 (N=1280)
#endif
#if 0 #if 0
// > 4GB A matrix. Too slow to be enabled by default. // > 4GB A matrix. Too slow to be enabled by default.
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F16, 900000, 3, 2592, {1, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F16, 900000, 3, 2592, {1, 1}, {1, 1}));
@@ -11011,16 +10911,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 128, 2048, 128)); test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 128, 2048, 128));
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 256, 2048, 256)); test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 256, 2048, 256));
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 512, 2048, 512)); test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 512, 2048, 512));
#ifdef GGML_USE_SYCL
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 384, 1, 384)); // m=12 (N=384)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 384, 32, 384)); // m=12 (batch)
test_cases.emplace_back(
new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 384, 4, 384, { 2, 3 })); // m=12 (multi-dim)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 768, 1, 768)); // m=12 (N=768)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 640, 1, 640)); // m=20 (N=640)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 640, 32, 640)); // m=20 (batch)
test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 1280, 1, 1280)); // m=20 (N=1280)
#endif
test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 32, 64, 4, 4 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 32, 64, 4, 4 }));
test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 2 }, { 32, 128, 4, 2 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 2 }, { 32, 128, 4, 2 }));
// qwen3next with CHUNK_SIZE 64 // qwen3next with CHUNK_SIZE 64