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llama.cpp/tests/test-backend-ops.cpp
Kawrakow 55c1b2a3bb
IQ1_M: 1.75 bpw quantization (#6302) 
* iq1_m: basics

* iq1_m: basics-2

* iq1_m: CUDA dequantize works

Very 1st shot I get PPL = 9.76 for LLaMA-v2-7B.

* iq1_m: separate shifts for each group of 8 in a block

We get
PPL(LLaMA-v2-7B ) = 9.2810
PPL(LLaMA-v2-13B) = 6.8105

Not bad, but slightly higher than
  sqrt(PPL(IQ1_S) * PPL(IQ2_XXS))
which is the expected outcome given that IQ1_M is
halfway between IQ1_S and IQ2_XXS in terms of bpw.
From this, we would expect
 PPL = 9.14 for LLaMA-v2-7B
 PPL = 6.63 for LLaMA-v2-13B

* iq1_m: go to 3-bit scales

There is slight increase in PPL, but the 0.0625 bpw reduction
in size is totally worth it.

We now have
PPL(LLaMA-v2-7B ) = 9.4469 at 1.96 bpw
PPL(LLaMA-v2-13B) = 6.8717 at 1.93 bpw
PPL(LLaMA-v2-70B) = 4.8568 at 1.85 bpw

* iq1_m: scalar dot product

* iq1_m: AVX2 dot product

* iq1_m: very slightly faster AVX2 dot product

* iq1_m: ARM_NEON dot product

Works, but very slow (10.5 t/s)

* iq1_m: Metal - dequantize works, dot product does not

* iq1_m: Metal now works

About the same performance as iq1_s.

* iq1_m: minor

* iq1_m: checking pure iq1_m quantization

It is pretty bad: PPL(LLaMA-v2-7B) = 34 if we quantize output.weight
with Q4_K.

* iiq1_m: slightly faster ARM_NEON dot product

10.5 t/s -> 11.65 t/s

* iq1_m: faster ARM_NEON dot product

11.65 t/s -> 14.9 t/s

* iq1_m: another minor ARM_NEON dot product improvement

14.9 -> 15.0 t/s

* iq1_m: small PPL improvement via super-block scale adjustment

After quantizing block scales redo the super-block scale fit.

PPL(LLaMA-v2-7B ) = 9.3346
PPL(LLaMA-v2-13B) = 6.8419
PPL(LLaMA-v2-70B) = 4.8294
PPL(Mistral-7B  ) = 8.1624

* iq1_m: adapt to CUDA refactoring

* iq1_m: remove unused variable

We have progressed to warnings being errors.

* iq1_m: add to backend-ops tests

* iq1_m: fix Windows ARM

* iq1_m: use common definition of iq1m_scale_t

* cuda: assert -> NO_DEVICE_CODE

* iq1_M: PR comments

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-03-26 15:21:27 +01:00

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#include <ggml.h>
#include <ggml-alloc.h>
#include <ggml-backend.h>
#include <ggml-backend-impl.h>
#include <algorithm>
#include <array>
#include <cfloat>
#include <cstring>
#include <functional>
#include <memory>
#include <random>
#include <stdio.h>
#include <stdlib.h>
#include <string>
#include <thread>
#include <vector>
static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
// static RNG initialization (revisit if n_threads stops being constant)
static const size_t n_threads = std::thread::hardware_concurrency();
static std::vector<std::default_random_engine> generators = []() {
std::random_device rd;
std::vector<std::default_random_engine> vec;
vec.reserve(n_threads);
//for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(1234 + i); } // fixed seed
for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(rd()); }
return vec;
}();
size_t size = ggml_nelements(tensor);
std::vector<float> data(size);
auto init_thread = [&](size_t ith, size_t start, size_t end) {
std::uniform_real_distribution<float> distribution(min, max);
for (size_t i = start; i < end; i++) {
data[i] = distribution(generators[ith]);
}
};
std::vector<std::thread> threads;
threads.reserve(n_threads);
for (size_t i = 0; i < n_threads; i++) {
size_t start = i*size/n_threads;
size_t end = (i+1)*size/n_threads;
threads.emplace_back(init_thread, i, start, end);
}
for (auto & t : threads) {
t.join();
}
if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
} else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16) {
GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0);
std::vector<uint8_t> dataq(ggml_row_size(tensor->type, size));
std::vector<float> imatrix(tensor->ne[0], 1.0f); // dummy importance matrix
const float * im = imatrix.data();
if (!ggml_quantize_requires_imatrix(tensor->type)) {
// when the imatrix is optional, we want to test both quantization with and without imatrix
// use one of the random numbers to decide
if (data[0] > 0.5f*(min + max)) {
im = nullptr;
}
}
ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
} else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
// This is going to create some weird integers though.
ggml_backend_tensor_set(tensor, data.data(), 0, ggml_nbytes(tensor));
} else {
GGML_ASSERT(false);
}
}
static std::vector<float> tensor_to_float(const ggml_tensor * t) {
std::vector<float> tv;
tv.reserve(ggml_nelements(t));
std::vector<uint8_t> buf(ggml_nbytes(t));
ggml_backend_tensor_get(t, buf.data(), 0, ggml_nbytes(t));
ggml_type_traits_t tt = ggml_internal_get_type_traits(t->type);
size_t bs = ggml_blck_size(t->type);
std::vector<float> vq(ggml_blck_size(t->type));
bool quantized = ggml_is_quantized(t->type);
// access elements by index to avoid gaps in views
for (int64_t i3 = 0; i3 < t->ne[3]; i3++) {
for (int64_t i2 = 0; i2 < t->ne[2]; i2++) {
for (int64_t i1 = 0; i1 < t->ne[1]; i1++) {
for (int64_t i0 = 0; i0 < t->ne[0]; i0 += bs) {
size_t i = i3*t->nb[3] + i2*t->nb[2] + i1*t->nb[1] + i0/bs*t->nb[0];
if (t->type == GGML_TYPE_F16) {
tv.push_back(ggml_fp16_to_fp32(*(ggml_fp16_t*)&buf[i]));
} else if (t->type == GGML_TYPE_F32) {
tv.push_back(*(float *) &buf[i]);
} else if (t->type == GGML_TYPE_I32) {
tv.push_back((float)*(int32_t *) &buf[i]);
} else if (t->type == GGML_TYPE_I16) {
tv.push_back((float)*(int16_t *) &buf[i]);
} else if (t->type == GGML_TYPE_I8) {
tv.push_back((float)*(int8_t *) &buf[i]);
} else if (quantized) {
tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type));
tv.insert(tv.end(), vq.begin(), vq.end());
} else {
GGML_ASSERT(false);
}
}
}
}
}
return tv;
}
/*
static double cosine_similarity(const float * v1, const float * v2, size_t n) {
double dot = 0.0;
double mag1 = 0.0;
double mag2 = 0.0;
for (size_t i = 0; i < n; i++) {
if (std::isnan(v1[i]) || std::isnan(v2[i])) {
return -1.0f;
}
if (std::isinf(v1[i]) && std::isinf(v2[i])) {
continue;
}
dot += v1[i]*v2[i];
mag1 += v1[i]*v1[i];
mag2 += v2[i]*v2[i];
}
return dot/sqrt(mag1*mag2);
}
static float distance(const float * v1, const float * v2, size_t n) {
double d = 0.0;
for (size_t i = 0; i < n; i++) {
if (std::isnan(v1[i]) || std::isnan(v2[i])) {
return INFINITY;
}
if (std::isinf(v1[i]) && std::isinf(v2[i])) {
continue;
}
d += (v1[i] - v2[i])*(v1[i] - v2[i]);
}
return sqrt(d);
}
static float vec_len(const float * v, size_t n) {
double d = 0.0;
for (size_t i = 0; i < n; i++) {
if (std::isnan(v[i])) {
return INFINITY;
}
if (std::isinf(v[i])) {
continue;
}
d += v[i]*v[i];
}
return sqrt(d);
}
*/
// normalized mean squared error = mse(a, b) / mse(a, 0)
static double nmse(const float * a, const float * b, size_t n) {
double mse_a_b = 0.0;
double mse_a_0 = 0.0;
for (size_t i = 0; i < n; i++) {
float a_i = a[i];
float b_i = b[i];
mse_a_b += (a_i - b_i) * (a_i - b_i);
mse_a_0 += a_i * a_i;
}
return mse_a_b / mse_a_0;
}
// utils for printing the variables of the test cases
#define VAR_TO_STR(x) (#x "=" + var_to_str(x))
template<typename T>
static std::string var_to_str(const T & x) {
return std::to_string(x);
}
template<typename T, size_t N>
static std::string var_to_str(const T (&x)[N]) {
std::string s = "[";
for (size_t i = 0; i < N; i++) {
if (i > 0) {
s += ",";
}
s += var_to_str(x[i]);
}
s += "]";
return s;
}
template<typename T, size_t N>
static std::string var_to_str(const std::array<T, N> & x) {
std::string s = "[";
for (size_t i = 0; i < N; i++) {
if (i > 0) {
s += ",";
}
s += var_to_str(x[i]);
}
s += "]";
return s;
}
//static std::string var_to_str(ggml_unary_op unary_op) {
// return ggml_unary_op_name(unary_op);
//}
static std::string var_to_str(ggml_type type) {
return ggml_type_name(type);
}
static std::string var_to_str(ggml_op_pool pool) {
switch (pool) {
case GGML_OP_POOL_AVG: return "avg";
case GGML_OP_POOL_MAX: return "max";
default: return std::to_string(pool);
}
}
#define VARS_TO_STR1(a) VAR_TO_STR(a)
#define VARS_TO_STR2(a, b) VAR_TO_STR(a) + "," + VAR_TO_STR(b)
#define VARS_TO_STR3(a, b, c) VAR_TO_STR(a) + "," + VARS_TO_STR2(b, c)
#define VARS_TO_STR4(a, b, c, d) VAR_TO_STR(a) + "," + VARS_TO_STR3(b, c, d)
#define VARS_TO_STR5(a, b, c, d, e) VAR_TO_STR(a) + "," + VARS_TO_STR4(b, c, d, e)
#define VARS_TO_STR6(a, b, c, d, e, f) VAR_TO_STR(a) + "," + VARS_TO_STR5(b, c, d, e, f)
#define VARS_TO_STR7(a, b, c, d, e, f, g) VAR_TO_STR(a) + "," + VARS_TO_STR6(b, c, d, e, f, g)
#define VARS_TO_STR8(a, b, c, d, e, f, g, h) VAR_TO_STR(a) + "," + VARS_TO_STR7(b, c, d, e, f, g, h)
#define VARS_TO_STR9(a, b, c, d, e, f, g, h, i) VAR_TO_STR(a) + "," + VARS_TO_STR8(b, c, d, e, f, g, h, i)
#define VARS_TO_STR10(a, b, c, d, e, f, g, h, i, j) VAR_TO_STR(a) + "," + VARS_TO_STR9(b, c, d, e, f, g, h, i, j)
#define VARS_TO_STR11(a, b, c, d, e, f, g, h, i, j, k) VAR_TO_STR(a) + "," + VARS_TO_STR10(b, c, d, e, f, g, h, i, j, k)
#define VARS_TO_STR12(a, b, c, d, e, f, g, h, i, j, k, l) VAR_TO_STR(a) + "," + VARS_TO_STR11(b, c, d, e, f, g, h, i, j, k, l)
#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
static bool isinf_or_max(float f) {
return _isinf(f) || f == FLT_MAX || f == -FLT_MAX;
}
static bool ggml_is_view_op(enum ggml_op op) {
return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE;
}
enum test_mode {
MODE_TEST,
MODE_PERF,
};
struct test_case {
virtual ~test_case() {}
virtual std::string op_desc(ggml_tensor * t) {
return ggml_op_desc(t);
}
virtual std::string vars() {
return "";
}
virtual ggml_tensor * build_graph(ggml_context * ctx) = 0;
virtual double max_nmse_err() {
return 1e-7;
}
virtual void initialize_tensors(ggml_context * ctx) {
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
init_tensor_uniform(t);
}
}
virtual size_t op_size(ggml_tensor * t) {
size_t size = ggml_nbytes(t);
// add source tensors
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (t->src[i] != NULL) {
size += ggml_nbytes(t->src[i]);
}
}
return size;
}
ggml_cgraph * gf = nullptr;
static const int sentinel_size = 1024;
test_mode mode;
std::vector<ggml_tensor *> sentinels;
void add_sentinel(ggml_context * ctx) {
if (mode == MODE_PERF) {
return;
}
ggml_tensor * sentinel = ::ggml_new_tensor_1d(ctx, GGML_TYPE_F32, sentinel_size);
ggml_format_name(sentinel, "sent_%zu", sentinels.size());
sentinels.push_back(sentinel);
}
// hijack ggml_new_tensor to add sentinels after each tensor to check for overflows in the backend
ggml_tensor * ggml_new_tensor(ggml_context * ctx, ggml_type type, int n_dims, const int64_t * ne) {
ggml_tensor * t = ::ggml_new_tensor(ctx, type, n_dims, ne);
add_sentinel(ctx);
return t;
}
ggml_tensor * ggml_new_tensor_1d(ggml_context * ctx, ggml_type type, int64_t ne0) {
ggml_tensor * t = ::ggml_new_tensor_1d(ctx, type, ne0);
add_sentinel(ctx);
return t;
}
ggml_tensor * ggml_new_tensor_2d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1) {
ggml_tensor * t = ::ggml_new_tensor_2d(ctx, type, ne0, ne1);
add_sentinel(ctx);
return t;
}
ggml_tensor * ggml_new_tensor_3d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2) {
ggml_tensor * t = ::ggml_new_tensor_3d(ctx, type, ne0, ne1, ne2);
add_sentinel(ctx);
return t;
}
ggml_tensor * ggml_new_tensor_4d(ggml_context * ctx, ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
ggml_tensor * t = ::ggml_new_tensor_4d(ctx, type, ne0, ne1, ne2, ne3);
add_sentinel(ctx);
return t;
}
bool eval(ggml_backend_t backend1, ggml_backend_t backend2, const char * op_name) {
mode = MODE_TEST;
ggml_init_params params = {
/* .mem_size = */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
/* .mem_base = */ NULL,
/* .no_alloc = */ true,
};
ggml_context * ctx = ggml_init(params);
gf = ggml_new_graph(ctx);
// pre-graph sentinel
add_sentinel(ctx);
ggml_tensor * out = build_graph(ctx);
if (op_name != nullptr && op_desc(out) != op_name) {
//printf(" %s: skipping\n", op_desc(out).c_str());
ggml_free(ctx);
return true;
}
printf(" %s(%s): ", op_desc(out).c_str(), vars().c_str());
fflush(stdout);
// check if the backends support the ops
bool supported = true;
for (ggml_backend_t backend : {backend1, backend2}) {
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (!ggml_backend_supports_op(backend, t)) {
printf("not supported [%s] ", ggml_backend_name(backend));
supported = false;
break;
}
}
}
if (!supported) {
printf("\n");
ggml_free(ctx);
return true;
}
// post-graph sentinel
add_sentinel(ctx);
// allocate
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend1);
if (buf == NULL) {
printf("failed to allocate tensors [%s] ", ggml_backend_name(backend1));
ggml_free(ctx);
return false;
}
// build graph
ggml_build_forward_expand(gf, out);
// add sentinels as graph nodes so that they are checked in the callback
for (ggml_tensor * sentinel : sentinels) {
gf->nodes[gf->n_nodes++] = sentinel;
}
// randomize tensors
initialize_tensors(ctx);
// compare
struct callback_userdata {
bool ok;
double max_err;
ggml_backend_t backend1;
ggml_backend_t backend2;
};
callback_userdata ud {
true,
max_nmse_err(),
backend1,
backend2
};
auto callback = [](int index, ggml_tensor * t1, ggml_tensor * t2, void * user_data) -> bool {
callback_userdata * ud = (callback_userdata *) user_data;
const char * bn1 = ggml_backend_name(ud->backend1);
const char * bn2 = ggml_backend_name(ud->backend2);
if (t1->op == GGML_OP_NONE) {
// sentinels must be unchanged
std::vector<uint8_t> t1_data(ggml_nbytes(t1));
std::vector<uint8_t> t2_data(ggml_nbytes(t2));
ggml_backend_tensor_get(t1, t1_data.data(), 0, ggml_nbytes(t1));
ggml_backend_tensor_get(t2, t2_data.data(), 0, ggml_nbytes(t2));
if (memcmp(t1_data.data(), t2_data.data(), ggml_nbytes(t1)) != 0) {
printf("sentinel mismatch: %s ", t1->name);
ud->ok = false;
return true;
}
}
std::vector<float> f1 = tensor_to_float(t1);
std::vector<float> f2 = tensor_to_float(t2);
for (size_t i = 0; i < f1.size(); i++) {
// check for nans
if (std::isnan(f1[i]) || std::isnan(f2[i])) {
printf("[%s] NaN at index %zu (%s=%f %s=%f) ", ggml_op_desc(t1), i, bn1, f1[i], bn2, f2[i]);
ud->ok = false;
return true;
}
// check for infs: both must be inf of the same sign, or both must be finite
if (isinf_or_max(f1[i]) || isinf_or_max(f2[i])) {
if (isinf_or_max(f1[i]) && isinf_or_max(f2[i])) {
if (std::signbit(f1[i]) != std::signbit(f2[i])) {
printf("[%s] inf sign mismatch: %s=%f %s=%f ", ggml_op_desc(t1), bn1, f1[i], bn2, f2[i]);
ud->ok = false;
return true;
}
} else {
printf("[%s] inf mismatch: %s=%f %s=%f ", ggml_op_desc(t1), bn1, f1[i], bn2, f2[i]);
ud->ok = false;
return true;
}
}
}
double err = nmse(f1.data(), f2.data(), f1.size());
if (err > ud->max_err) {
printf("[%s] NMSE = %.9f > %.9f ", ggml_op_desc(t1), err, ud->max_err);
//for (int i = 0; i < (int) f1.size(); i++) {
// printf("%5d %9.6f %9.6f, diff = %9.6f\n", i, f1[i], f2[i], f1[i] - f2[i]);
//}
//printf("\n");
//exit(1);
ud->ok = false;
}
return true;
GGML_UNUSED(index);
};
const bool cmp_ok = ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud);
if (!cmp_ok) {
printf("compare failed ");
}
ggml_backend_buffer_free(buf);
ggml_free(ctx);
if (ud.ok && cmp_ok) {
printf("\033[1;32mOK\033[0m\n");
return true;
}
printf("\033[1;31mFAIL\033[0m\n");
return false;
}
bool eval_perf(ggml_backend_t backend, const char * op_name) {
mode = MODE_PERF;
static const size_t graph_nodes = 8192;
ggml_init_params params = {
/* .mem_size = */ ggml_tensor_overhead()*128 + ggml_graph_overhead_custom(graph_nodes, false),
/* .mem_base = */ NULL,
/* .no_alloc = */ true,
};
ggml_context * ctx = ggml_init(params);
ggml_tensor * out = build_graph(ctx);
if (op_name != nullptr && op_desc(out) != op_name) {
//printf(" %s: skipping\n", op_desc(out).c_str());
ggml_free(ctx);
return true;
}
int len = printf(" %s(%s): ", op_desc(out).c_str(), vars().c_str());
fflush(stdout);
// check if backends support op
if (!ggml_backend_supports_op(backend, out)) {
printf("not supported\n");
ggml_free(ctx);
return true;
}
// align while also leaving some margin for variations in parameters
int align = 20;
int last = (len + align - 1) / align * align;
if (last - len < 5) {
last += align;
}
last = std::max(last, 60);
printf("%*s", last - len, "");
// allocate
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend);
if (buf == NULL) {
printf("failed to allocate tensors\n");
ggml_free(ctx);
return false;
}
// randomize tensors
initialize_tensors(ctx);
// build graph
ggml_cgraph * gf = ggml_new_graph_custom(ctx, graph_nodes, false);
ggml_build_forward_expand(gf, out);
// warmup run
ggml_backend_graph_compute(backend, gf);
// duplicate the op
size_t target_size = ggml_backend_is_cpu(backend) ? 1ULL << 33 : 1ULL << 35; // 8 GB CPU, 32 GB GPU
int n_runs = std::min((size_t)gf->size - gf->n_nodes, target_size / op_size(out)) + 1;
for (int i = 1; i < n_runs; i++) {
gf->nodes[gf->n_nodes++] = out;
}
// calculate memory
size_t mem = n_runs * op_size(out);
auto tensor_op_size = [](ggml_tensor * t) {
size_t size = ggml_nbytes(t);
// add source tensors
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (t->src[i] != NULL) {
size += ggml_nbytes(t->src[i]);
}
}
return size;
};
for (int i = 0; i < gf->n_nodes; i++) {
if (ggml_is_view_op(gf->nodes[i]->op) || gf->nodes[i] == out) {
continue;
}
mem += tensor_op_size(gf->nodes[i]);
}
// run
ggml_backend_synchronize(backend);
int64_t start_time = ggml_time_us();
ggml_backend_graph_compute(backend, gf);
ggml_backend_synchronize(backend);
int64_t end_time = ggml_time_us();
double time_us = end_time - start_time;
printf(" %5d runs - %8.2f us/run - %8zu kB/run - \033[1;34m%7.2f GB/s\033[0m\n",
n_runs,
time_us / n_runs,
op_size(out) / 1024,
mem / (time_us/1e6) / 1024.0 / 1024.0 / 1024.0);
ggml_backend_buffer_free(buf);
ggml_free(ctx);
return true;
}
};
// GGML_OP_UNARY
struct test_unary : public test_case {
const ggml_unary_op op;
const ggml_type type;
const std::array<int64_t, 4> ne;
std::string vars() override {
return VARS_TO_STR2(type, ne);
}
test_unary(ggml_unary_op op,
ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {128, 10, 10, 10})
: op(op), type(type), ne(ne) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * in = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_unary(ctx, in, op);
return out;
}
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)) {
// test extended range of values to check for NaNs in GELU
init_tensor_uniform(t, -150.f, 150.f);
}
}
};
// GGML_OP_GET_ROWS
struct test_get_rows : public test_case {
const ggml_type type;
const int n; // cols
const int m; // rows
const int r; // rows to get
const int b; // batch size
const bool v; // view (non-contiguous src1)
std::string vars() override {
return VARS_TO_STR6(type, n, m, r, b, v);
}
test_get_rows(ggml_type type = GGML_TYPE_F32, int n = 10, int m = 5, int r = 3, int b = 1, bool v = false)
: type(type), n(n), m(m), r(r), b(b), v(v) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * in = ggml_new_tensor_3d(ctx, type, n, m, b);
ggml_tensor * rows = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, r, b);
if (v) {
rows = ggml_view_2d(ctx, rows, r/2, b, rows->nb[1], 0);
}
ggml_tensor * out = ggml_get_rows(ctx, in, rows);
return out;
}
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)) {
if (t->type == GGML_TYPE_I32) {
if (ggml_is_view_op(t->op)) { continue; }
// rows
std::vector<int> data(r*b);
for (int i = 0; i < r*b; i++) {
data[i] = rand() % m;
}
ggml_backend_tensor_set(t, data.data(), 0, r * b * sizeof(int));
} else {
init_tensor_uniform(t);
}
}
}
};
// GGML_OP_REPEAT
struct test_repeat : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
const std::array<int, 4> nr;
std::string vars() override {
return VARS_TO_STR3(type, ne, nr);
}
size_t op_size(ggml_tensor * t) override {
return ggml_nbytes(t) * 2;
}
test_repeat(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10},
std::array<int, 4> nr = {2, 2, 2, 2})
: type(type), ne(ne), nr(nr) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * target = ggml_new_tensor_4d(ctx, type, ne[0]*nr[0], ne[1]*nr[1], ne[2]*nr[2], ne[3]*nr[3]);
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_repeat(ctx, src, target);
return out;
}
};
// GGML_OP_DUP
struct test_dup : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
const std::array<int64_t, 4> permute;
bool _use_permute;
std::string vars() override {
std::string v = VARS_TO_STR2(type, ne);
if (_use_permute) v += "," + VAR_TO_STR(permute);
return v;
}
test_dup(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 1},
std::array<int64_t, 4> permute = {0, 0, 0, 0})
: type(type), ne(ne), permute(permute),
_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
if (_use_permute) {
src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
}
ggml_tensor * out = ggml_dup(ctx, src);
return out;
}
};
// GGML_OP_CPY
struct test_cpy : public test_case {
const ggml_type type_src;
const ggml_type type_dst;
const std::array<int64_t, 4> ne;
std::string vars() override {
return VARS_TO_STR3(type_src, type_dst, ne);
}
size_t op_size(ggml_tensor * t) override {
return ggml_nbytes(t) + ggml_nbytes(t->src[0]);
}
test_cpy(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 1})
: type_src(type_src), type_dst(type_dst), ne(ne) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, ne.data());
ggml_tensor * out = ggml_cpy(ctx, src, dst);
return out;
}
};
// GGML_OP_CONT
struct test_cont : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
std::string vars() override {
return VARS_TO_STR2(type, ne);
}
test_cont(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 1})
: type(type), ne(ne) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
src = ggml_transpose(ctx, src);
ggml_tensor * out = ggml_cont(ctx, src);
return out;
}
};
// GGML_OP_ADD
// GGML_OP_MUL
// GGML_OP_DIV
struct test_bin_bcast : public test_case {
using op_t = ggml_tensor * (*) (ggml_context *, ggml_tensor *, ggml_tensor *);
op_t op;
const ggml_type type;
const std::array<int64_t, 4> ne;
const std::array<int, 4> nr;
std::string vars() override {
return VARS_TO_STR3(type, ne, nr);
}
size_t op_size(ggml_tensor * t) override {
return ggml_nbytes(t) * 3;
}
test_bin_bcast(op_t op, ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 1, 1},
std::array<int, 4> nr = {1, 2, 1, 1})
: op(op), type(type), ne(ne), nr(nr) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0]*nr[0], ne[1]*nr[1], ne[2]*nr[2], ne[3]*nr[3]);
ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = op(ctx, a, b);
return out;
}
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)) {
if (op == ggml_div) {
// avoid division by zero
init_tensor_uniform(t, 1.0f, 2.0f);
} else {
init_tensor_uniform(t);
}
}
}
};
// GGML_OP_SCALE
struct test_scale : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
float scale;
std::string vars() override {
return VARS_TO_STR3(type, ne, scale);
}
test_scale(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10},
float scale = 2.0f)
: type(type), ne(ne), scale(scale) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_scale(ctx, a, scale);
return out;
}
};
// GGML_OP_NORM
struct test_norm : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
float eps;
std::string vars() override {
return VARS_TO_STR3(type, ne, eps);
}
test_norm(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {64, 10, 10, 10},
float eps = 1e-6f)
: type(type), ne(ne), eps(eps) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_norm(ctx, a, eps);
return out;
}
};
// GGML_OP_RMS_NORM
struct test_rms_norm : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
float eps;
std::string vars() override {
return VARS_TO_STR3(type, ne, eps);
}
test_rms_norm(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {64, 10, 10, 10},
float eps = 1e-6f)
: type(type), ne(ne), eps(eps) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_rms_norm(ctx, a, eps);
return out;
}
};
// GGML_OP_MUL_MAT
struct test_mul_mat : public test_case {
const ggml_type type_a;
const ggml_type type_b;
const int64_t m;
const int64_t n;
const int64_t k;
const std::array<int64_t, 2> bs; // dims 3 and 4
const std::array<int64_t, 2> nr; // repeat in dims 3 and 4
std::string vars() override {
return VARS_TO_STR7(type_a, type_b, m, n, k, bs, nr);
}
double max_nmse_err() override {
return 5e-4;
}
size_t op_size(ggml_tensor * t) override {
size_t a = ggml_nbytes(t->src[0]) * n * nr[0] * nr[1];
size_t b = ggml_nbytes(t->src[1]) * m;
size_t c = ggml_nbytes(t);
return a + b + c;
GGML_UNUSED(t);
}
test_mul_mat(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
int64_t m = 32, int64_t n = 32, int64_t k = 32,
std::array<int64_t, 2> bs = {10, 10},
std::array<int64_t, 2> nr = {2, 2})
: type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
// C^T = A * B^T: (k, m) * (k, n) => (m, n)
ggml_tensor * a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0] , bs[1]);
ggml_tensor * b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
ggml_tensor * out = ggml_mul_mat(ctx, a, b);
return out;
}
};
// GGML_OP_MUL_MAT_ID
struct test_mul_mat_id : public test_case {
const ggml_type type_a;
const ggml_type type_b;
const int n_mats;
const int id;
const int64_t m;
const int64_t n;
const int64_t k;
const bool v; // view (non-contiguous ids)
std::string vars() override {
return VARS_TO_STR8(type_a, type_b, n_mats, id, m, n, k, v);
}
double max_nmse_err() override {
return 5e-4;
}
size_t op_size(ggml_tensor * t) override {
size_t a = ggml_nbytes(t->src[2]) * n;
size_t b = ggml_nbytes(t->src[1]) * m;
size_t c = ggml_nbytes(t);
return a + b + c;
GGML_UNUSED(t);
}
test_mul_mat_id(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
int n_mats = 2, int id = 0,
int64_t m = 32, int64_t n = 32, int64_t k = 32, bool v = false)
: type_a(type_a), type_b(type_b), n_mats(n_mats), id(id),
m(m), n(n), k(k), v(v) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
// C^T = A * B^T: (k, m) * (k, n) => (m, n)
std::vector<ggml_tensor *> mats;
for (int i = 0; i < n_mats; i++) {
ggml_tensor * a = ggml_new_tensor_2d(ctx, type_a, k, m);
mats.push_back(a);
}
ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n);
if (v) {
ids = ggml_view_2d(ctx, ids, n_mats/2, ids->ne[1], ids->nb[1], 0);
}
ggml_tensor * b = ggml_new_tensor_2d(ctx, type_b, k, n);
ggml_tensor * out = ggml_mul_mat_id(ctx, mats.data(), n_mats, ids, v ? id/2 : id, b);
return out;
}
void initialize_tensors(ggml_context * ctx) override {
std::random_device rd;
std::default_random_engine rng(rd());
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->type == GGML_TYPE_I32) {
if (ggml_is_view_op(t->op)) { continue; }
// ids
for (int64_t r = 0; r < ggml_nrows(t); r++) {
std::vector<int32_t> data(t->ne[0]);
for (int i = 0; i < t->ne[0]; i++) {
data[i] = i % n_mats;
}
std::shuffle(data.begin(), data.end(), rng);
ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
}
} else {
init_tensor_uniform(t);
}
}
}
};
// GGML_OP_SQR
struct test_sqr : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
std::string vars() override {
return VARS_TO_STR2(type, ne);
}
test_sqr(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10})
: type(type), ne(ne) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_sqr(ctx, a);
return out;
}
};
// GGML_OP_CLAMP
struct test_clamp : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
float min;
float max;
std::string vars() override {
return VARS_TO_STR4(type, ne, min, max);
}
test_clamp(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10},
float min = -0.5f, float max = 0.5f)
: type(type), ne(ne), min(min), max(max) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_clamp(ctx, a, min, max);
return out;
}
};
// GGML_OP_DIAG_MASK_INF
struct test_diag_mask_inf : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
const int n_past;
std::string vars() override {
return VARS_TO_STR3(type, ne, n_past);
}
test_diag_mask_inf(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10},
int n_past = 5)
: type(type), ne(ne), n_past(n_past) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_diag_mask_inf(ctx, a, n_past);
return out;
}
};
// GGML_OP_SOFT_MAX
struct test_soft_max : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
const bool mask;
const float scale;
const float max_bias;
std::string vars() override {
return VARS_TO_STR5(type, ne, mask, scale, max_bias);
}
test_soft_max(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10},
bool mask = false,
float scale = 1.0f,
float max_bias = 0.0f)
: type(type), ne(ne), mask(mask), scale(scale), max_bias(max_bias) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * mask = nullptr;
if (this->mask) {
mask = ggml_new_tensor_2d(ctx, type, ne[0], ne[1]);
}
ggml_tensor * pos = nullptr;
if (max_bias > 0.0f) {
pos = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ne[0]);
}
ggml_tensor * out = ggml_soft_max_ext(ctx, a, mask, pos, scale, max_bias);
return out;
}
};
// GGML_OP_ROPE
struct test_rope : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
int n_dims;
int mode;
int n_ctx;
std::string vars() override {
return VARS_TO_STR5(type, ne, n_dims, mode, n_ctx);
}
test_rope(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 1},
int n_dims = 10, int mode = 0, int n_ctx = 512)
: type(type), ne(ne), n_dims(n_dims), mode(mode), n_ctx(n_ctx) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne[2]);
ggml_tensor * out = ggml_rope(ctx, a, pos, n_dims, mode, n_ctx);
return out;
}
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)) {
if (t->type == GGML_TYPE_I32) {
// pos
std::vector<int> data(ne[2]);
for (int i = 0; i < ne[2]; i++) {
data[i] = rand() % n_ctx;
}
ggml_backend_tensor_set(t, data.data(), 0, ne[2] * sizeof(int));
} else {
init_tensor_uniform(t);
}
}
}
};
// GGML_OP_POOL2D
struct test_pool2d : public test_case {
enum ggml_op_pool pool_type;
const ggml_type type_input;
const std::array<int64_t, 4> ne_input;
// kernel size
const int k0;
const int k1;
// stride
const int s0;
const int s1;
// padding
const int p0;
const int p1;
std::string vars() override {
return VARS_TO_STR9(pool_type, type_input, ne_input, k0, k1, s0, s1, p0, p1);
}
test_pool2d(ggml_op_pool pool_type = GGML_OP_POOL_AVG,
ggml_type type_input = GGML_TYPE_F32,
std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
int k0 = 3, int k1 = 3,
int s0 = 1, int s1 = 1,
int p0 = 1, int p1 = 1)
: pool_type(pool_type), type_input(type_input), ne_input(ne_input), k0(k0), k1(k1), s0(s0), s1(s1), p0(p0), p1(p1) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
ggml_tensor * out = ggml_pool_2d(ctx, input, pool_type, k0, k1, s0, s1, p0, p1);
return out;
}
};
// GGML_OP_IM2COL
struct test_im2col : public test_case {
const ggml_type type_input;
const ggml_type type_kernel;
const ggml_type dst_type;
const std::array<int64_t, 4> ne_input;
const std::array<int64_t, 4> ne_kernel;
// stride
const int s0;
const int s1;
// padding
const int p0;
const int p1;
// dilatation
const int d0;
const int d1;
// mode
const bool is_2D;
std::string vars() override {
return VARS_TO_STR12(type_input, type_kernel, dst_type, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D);
}
test_im2col(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16, ggml_type dst_type = GGML_TYPE_F32,
std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
int s0 = 1, int s1 = 1,
int p0 = 1, int p1 = 1,
int d0 = 1, int d1 = 1,
bool is_2D = true)
: type_input(type_input), type_kernel(type_kernel), dst_type(dst_type), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data());
ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D, dst_type);
return out;
}
};
// GGML_OP_CONCAT
struct test_concat : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
const int64_t b_ne2;
std::string vars() override {
return VARS_TO_STR3(type, ne, b_ne2);
}
test_concat(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10},
int64_t b_ne2 = 10)
: type(type), ne(ne), b_ne2(b_ne2) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * b = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], b_ne2, ne[3]);
ggml_tensor * out = ggml_concat(ctx, a, b);
return out;
}
};
// GGML_OP_ARGSORT
struct test_argsort : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
ggml_sort_order order;
std::string vars() override {
return VARS_TO_STR3(type, ne, order);
}
test_argsort(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {16, 10, 10, 10},
ggml_sort_order order = GGML_SORT_ORDER_ASC)
: type(type), ne(ne), order(order) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_argsort(ctx, a, order);
return out;
}
void initialize_tensors(ggml_context * ctx) override {
std::random_device rd;
std::default_random_engine rng(rd());
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->type == GGML_TYPE_I32) {
// indices
std::vector<int> data(ggml_nelements(t));
for (int i = 0; i < ggml_nelements(t); i++) {
data[i] = rand();
}
std::shuffle(data.begin(), data.end(), rng);
ggml_backend_tensor_set(t, data.data(), 0, ne[0]*ne[1]*ne[2]*ne[3] * sizeof(int));
} else if (t->type == GGML_TYPE_F32) {
// initialize with unique values to avoid ties
for (int64_t r = 0; r < ggml_nrows(t); r++) {
std::vector<float> data(t->ne[0]);
for (int i = 0; i < t->ne[0]; i++) {
data[i] = i;
}
std::shuffle(data.begin(), data.end(), rng);
ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(float));
}
} else {
GGML_ASSERT(false);
}
}
}
};
// GGML_OP_SUM_ROWS
struct test_sum_rows : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
std::string vars() override {
return VARS_TO_STR2(type, ne);
}
test_sum_rows(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10})
: type(type), ne(ne) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_sum_rows(ctx, a);
return out;
}
};
// GGML_OP_UPSCALE
struct test_upscale : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
const int32_t scale_factor;
std::string vars() override {
return VARS_TO_STR3(type, ne, scale_factor);
}
test_upscale(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {512, 512, 3, 1},
int32_t scale_factor = 2)
: type(type), ne(ne), scale_factor(scale_factor) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_upscale(ctx, a, scale_factor);
return out;
}
};
// GGML_OP_GROUP_NORM
struct test_group_norm : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
const int32_t num_groups;
std::string vars() override {
return VARS_TO_STR3(type, ne, num_groups);
}
test_group_norm(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {64, 64, 320, 1},
int32_t num_groups = 32)
: type(type), ne(ne), num_groups(num_groups) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_group_norm(ctx, a, num_groups);
return out;
}
};
// GGML_OP_ACC
struct test_acc : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne_a;
const std::array<int64_t, 4> ne_b;
std::string vars() override {
return VARS_TO_STR3(type, ne_a, ne_b);
}
test_acc(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne_a = {1024, 577, 1, 1},
std::array<int64_t, 4> ne_b = {1024, 576, 1, 1})
: type(type), ne_a(ne_a), ne_b(ne_b) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
ggml_tensor * out = ggml_acc(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], b->nb[1]);
return out;
}
};
// GGML_OP_PAD
struct test_pad : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne_a;
const int pad_0;
const int pad_1;
std::string vars() override {
return VARS_TO_STR4(type, ne_a, pad_0, pad_1);
}
test_pad(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne_a = {512, 512, 1, 1},
int pad_0 = 1, int pad_1 = 1)
: type(type), ne_a(ne_a), pad_0(pad_0), pad_1(pad_1) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
ggml_tensor * out = ggml_pad(ctx, a, pad_0, pad_1, 0, 0);
return out;
}
};
// GGML_OP_ARANGE
struct test_arange : public test_case {
const ggml_type type;
const float start;
const float stop;
const float step;
std::string vars() override {
return VARS_TO_STR4(type, start, stop, step);
}
test_arange(ggml_type type = GGML_TYPE_F32,
float start = 0.f, float stop = 10.f, float step = 1.f)
: type(type), start(start), stop(stop), step(step) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * out = ggml_arange(ctx, start, stop, step);
return out;
}
};
// GGML_OP_TIMESTEP_EMBEDDING
struct test_timestep_embedding : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne_a;
const int dim;
const int max_period;
std::string vars() override {
return VARS_TO_STR4(type, ne_a, dim, max_period);
}
test_timestep_embedding(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne_a = {2, 1, 1, 1},
int dim = 320, int max_period=10000)
: type(type), ne_a(ne_a), dim(dim), max_period(max_period) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
ggml_tensor * out = ggml_timestep_embedding(ctx, a, dim, max_period);
return out;
}
};
// GGML_OP_LEAKY_RELU
struct test_leaky_relu : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne_a;
const float negative_slope;
std::string vars() override {
return VARS_TO_STR3(type, ne_a, negative_slope);
}
test_leaky_relu(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne_a = {10, 10, 10, 10},
float negative_slope = 0.1f)
: type(type), ne_a(ne_a), negative_slope(negative_slope) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
ggml_tensor * out = ggml_leaky_relu(ctx, a, negative_slope, true);
return out;
}
};
// Mixtral MOE
struct test_moe : public test_case {
const int n_experts;
const int n_experts_per_tok;
const int n_tokens;
const int n_embd;
const int n_ff;
std::string op_desc(ggml_tensor * t) override {
return "MOE";
GGML_UNUSED(t);
}
std::string vars() override {
return VARS_TO_STR5(n_experts, n_experts_per_tok, n_tokens, n_embd, n_ff);
}
test_moe(int n_experts = 8, int n_experts_per_tok = 2, int n_tokens = 1, int n_embd = 4096, int n_ff = 14336)
: n_experts(n_experts), n_experts_per_tok(n_experts_per_tok), n_tokens(n_tokens), n_embd(n_embd), n_ff(n_ff) {
}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * ffn_gate_inp = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_experts);
std::vector<ggml_tensor *> ffn_up_exp(n_experts);
std::vector<ggml_tensor *> ffn_gate_exp(n_experts);
std::vector<ggml_tensor *> ffn_down_exp(n_experts);
for (int i = 0; i < n_experts; ++i) {
ffn_up_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
ffn_gate_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
ffn_down_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
}
ggml_tensor * cur = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens);
ggml_tensor * logits = ggml_mul_mat(ctx, ffn_gate_inp, cur);
ggml_tensor * probs = ggml_soft_max_ext(ctx, logits, nullptr, nullptr, 1.0f/sqrtf(n_embd), 0.0f);
// select experts
ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_experts_per_tok);
ggml_tensor * weights = ggml_get_rows(ctx,
ggml_reshape_3d(ctx, probs, 1, n_experts, n_tokens), selected_experts);
weights = ggml_reshape_2d(ctx, weights, n_experts_per_tok, n_tokens);
ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights);
weights = ggml_div(ctx, weights, weights_sum);
// compute expert outputs
ggml_tensor * moe_out = nullptr;
for (int i = 0; i < n_experts_per_tok; ++i) {
ggml_tensor * cur_expert;
ggml_tensor * cur_up = ggml_mul_mat_id(ctx, ffn_up_exp.data(), n_experts, selected_experts, i, cur);
ggml_tensor * cur_gate = ggml_mul_mat_id(ctx, ffn_gate_exp.data(), n_experts, selected_experts, i, cur);
cur_gate = ggml_silu(ctx, cur_gate);
cur_expert = ggml_mul(ctx, cur_up, cur_gate);
cur_expert = ggml_mul_mat_id(ctx, ffn_down_exp.data(), n_experts, selected_experts, i, cur_expert);
cur_expert = ggml_mul(ctx, cur_expert,
ggml_view_2d(ctx, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
if (i == 0) {
moe_out = cur_expert;
} else {
moe_out = ggml_add(ctx, moe_out, cur_expert);
}
}
cur = moe_out;
return cur;
}
};
enum llm_norm_type {
LLM_NORM,
LLM_NORM_RMS,
};
struct llama_hparams {
uint32_t n_vocab;
uint32_t n_embd;
uint32_t n_head;
uint32_t n_head_kv;
static constexpr uint32_t n_layer = 1;
uint32_t n_rot;
uint32_t n_embd_head; // dimension of values (d_v)
uint32_t n_ff;
float f_norm_eps;
float f_norm_rms_eps;
// cparams
static constexpr uint32_t n_ctx = 512; // user-specified context size
static constexpr uint32_t n_orig_ctx = n_ctx;
// batch
int32_t n_tokens;
// llm_build_context
static constexpr int32_t n_kv = 32; // size of KV cache to consider (n_kv <= n_ctx
static constexpr int32_t kv_head = 1; // index of where we store new KV data in the cache
uint32_t n_embd_gqa() const { // dimension of key embeddings across all k-v heads
return n_embd_head * n_head_kv;
}
};
// LLM base class
struct test_llm : public test_case {
llama_hparams hp;
protected:
test_llm(llama_hparams hp)
: hp(std::move(hp)) {
}
public:
struct ggml_tensor * llm_build_norm(
struct ggml_context * ctx,
struct ggml_tensor * cur,
struct ggml_tensor * mw,
struct ggml_tensor * mb,
llm_norm_type type) {
switch (type) {
case LLM_NORM: cur = ggml_norm (ctx, cur, hp.f_norm_eps); break;
case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hp.f_norm_rms_eps); break;
}
cur = ggml_mul(ctx, cur, mw);
if (mb) {
cur = ggml_add(ctx, cur, mb);
}
return cur;
}
void llm_build_kv_store(
struct ggml_context * ctx,
struct ggml_tensor * k_l,
struct ggml_tensor * v_l,
struct ggml_tensor * k_cur,
struct ggml_tensor * v_cur) {
// compute the transposed [n_tokens, n_embd] V matrix
struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, hp.n_embd_gqa(), hp.n_tokens));
struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, k_l, hp.n_tokens*hp.n_embd_gqa(),
(ggml_row_size(k_l->type, hp.n_embd_gqa()))*hp.kv_head);
struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, v_l, hp.n_tokens, hp.n_embd_gqa(),
( hp.n_ctx)*ggml_element_size(v_l),
(hp.kv_head)*ggml_element_size(v_l));
// important: storing RoPE-ed version of K in the KV cache!
ggml_cpy(ctx, k_cur, k_cache_view);
ggml_cpy(ctx, v_cur_t, v_cache_view);
}
struct ggml_tensor * llm_build_kqv(
struct ggml_context * ctx,
struct ggml_tensor * k_l,
struct ggml_tensor * v_l,
struct ggml_tensor * q_cur,
struct ggml_tensor * kq_mask,
float kq_scale) {
struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
struct ggml_tensor * k =
ggml_view_3d(ctx, k_l,
hp.n_embd_head, hp.n_kv, hp.n_head_kv,
ggml_row_size(k_l->type, hp.n_embd_gqa()),
ggml_row_size(k_l->type, hp.n_embd_head),
0);
struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
kq = ggml_soft_max_ext(ctx, kq, kq_mask, nullptr, kq_scale, 0.0f);
// split cached v into n_head heads
struct ggml_tensor * v =
ggml_view_3d(ctx, v_l,
hp.n_kv, hp.n_embd_head, hp.n_head_kv,
ggml_element_size(v_l)*hp.n_ctx,
ggml_element_size(v_l)*hp.n_ctx*hp.n_embd_head,
0);
struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, hp.n_embd_head*hp.n_head, hp.n_tokens);
struct ggml_tensor * wo = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd);
cur = ggml_mul_mat(ctx, wo, cur);
return cur;
}
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)) {
if (t->type == GGML_TYPE_I32) {
// pos
std::vector<int> data(hp.n_tokens);
for (int i = 0; i < hp.n_tokens; i++) {
data[i] = rand() % hp.n_ctx;
}
ggml_backend_tensor_set(t, data.data(), 0, hp.n_tokens * sizeof(int));
} else {
init_tensor_uniform(t);
}
}
}
};
// Llama
struct test_llama : public test_llm {
static constexpr float freq_base = 10000.0f;
static constexpr float freq_scale = 1.0f;
static constexpr float ext_factor = 0.0f;
static constexpr float attn_factor = 1.0f;
static constexpr float beta_fast = 32.0f;
static constexpr float beta_slow = 1.0f;
std::string op_desc(ggml_tensor * t) override {
GGML_UNUSED(t);
return "LLAMA";
}
std::string vars() override {
auto n_tokens = hp.n_tokens;
return VARS_TO_STR1(n_tokens);
}
double max_nmse_err() override {
return 2e-3;
}
test_llama(int n_tokens = 1)
: test_llm({
/*n_vocab =*/ 32000,
/*n_embd =*/ 3200,
/*n_head =*/ 32,
/*n_head_kv =*/ 32,
/*n_rot =*/ 100,
/*n_embd_head =*/ 100,
/*n_ff =*/ 8640,
/*f_norm_eps =*/ 0.f,
/*f_norm_rms_eps =*/ 1e-5f,
/*n_tokens =*/ n_tokens,
}) {
}
ggml_tensor * build_graph(ggml_context * ctx) override {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.n_embd, hp.n_tokens);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1);
ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
for (uint32_t il = 0; il < hp.n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
ggml_tensor * attn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
cur = llm_build_norm(ctx, inpL, attn_norm, nullptr, LLM_NORM_RMS);
// self-attention
{
ggml_tensor * wq = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd);
ggml_tensor * wk = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd_gqa());
ggml_tensor * wv = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd_gqa());
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_mul_mat(ctx, wq, cur);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx, wk, cur);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx, wv, cur);
Qcur = ggml_rope_custom(
ctx, ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head, hp.n_tokens), inp_pos,
hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_custom(
ctx, ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens), inp_pos,
hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
llm_build_kv_store(ctx, k_l, v_l, Kcur, Vcur);
cur = llm_build_kqv(ctx, k_l, v_l, Qcur, KQ_mask, 1.0f/sqrtf(float(hp.n_embd_head)));
}
struct ggml_tensor * ffn_inp = ggml_add(ctx, cur, inpSA);
// feed-forward network
ggml_tensor * ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
cur = llm_build_norm(ctx, ffn_inp, ffn_norm, nullptr, LLM_NORM_RMS);
ggml_tensor * ffn_gate = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff);
ggml_tensor * ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_ff, hp.n_embd);
ggml_tensor * ffn_up = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff);
struct ggml_tensor * tmp = ggml_mul_mat(ctx, ffn_up, cur);
cur = ggml_mul_mat(ctx, ffn_gate, cur);
cur = ggml_silu(ctx, cur);
cur = ggml_mul(ctx, cur, tmp);
cur = ggml_mul_mat(ctx, ffn_down, cur);
cur = ggml_add(ctx, cur, ffn_inp);
// input for next layer
inpL = cur;
}
cur = inpL;
ggml_tensor * output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
cur = llm_build_norm(ctx, cur, output_norm, nullptr, LLM_NORM_RMS);
// lm_head
ggml_tensor * output = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_vocab);
cur = ggml_mul_mat(ctx, output, cur);
return cur;
}
};
// Falcon
struct test_falcon : public test_llm {
static constexpr float freq_base = 10000.0f;
static constexpr float freq_scale = 1.0f;
static constexpr float ext_factor = 0.0f;
static constexpr float attn_factor = 1.0f;
static constexpr float beta_fast = 32.0f;
static constexpr float beta_slow = 1.0f;
std::string op_desc(ggml_tensor * t) override {
GGML_UNUSED(t);
return "FALCON";
}
std::string vars() override {
auto n_tokens = hp.n_tokens;
return VARS_TO_STR1(n_tokens);
}
double max_nmse_err() override {
return 2e-3;
}
test_falcon(int n_tokens = 1)
: test_llm({
/*n_vocab =*/ 32000,
/*n_embd =*/ 3200,
/*n_head =*/ 50,
/*n_head_kv =*/ 1,
/*n_rot =*/ 64,
/*n_embd_head =*/ 64,
/*n_ff =*/ 8640,
/*f_norm_eps =*/ 1e-5f,
/*f_norm_rms_eps =*/ 0.f,
/*n_tokens =*/ n_tokens,
}) {
}
ggml_tensor * build_graph(ggml_context * ctx) override {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.n_embd, hp.n_tokens);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1);
ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
for (uint32_t il = 0; il < hp.n_layer; ++il) {
// norm
ggml_tensor * attn_norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
ggml_tensor * attn_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
ggml_tensor * attn_norm = llm_build_norm(ctx, inpL, attn_norm_w, attn_norm_b, LLM_NORM);
// self-attention
{
cur = attn_norm;
ggml_tensor * wqkv = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd + 2*hp.n_embd_gqa());
cur = ggml_mul_mat(ctx, wqkv, cur);
struct ggml_tensor * Qcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd, hp.n_tokens, cur->nb[1], 0*sizeof(float)*(hp.n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd_gqa(), hp.n_tokens, cur->nb[1], 1*sizeof(float)*(hp.n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd_gqa(), hp.n_tokens, cur->nb[1], 1*sizeof(float)*(hp.n_embd + hp.n_embd_gqa())));
Qcur = ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head, hp.n_tokens);
Kcur = ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens);
// using mode = 2 for neox mode
Qcur = ggml_rope_custom(
ctx, Qcur, inp_pos, hp.n_rot, 2, 0, hp.n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_custom(
ctx, Kcur, inp_pos, hp.n_rot, 2, 0, hp.n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
llm_build_kv_store(ctx, k_l, v_l, Kcur, Vcur);
cur = llm_build_kqv(ctx, k_l, v_l, Qcur, KQ_mask, 1.0f/sqrtf(float(hp.n_embd_head)));
}
struct ggml_tensor * ffn_inp = cur;
// feed forward
{
ggml_tensor * ffn_up = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff);
ggml_tensor * ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_ff, hp.n_embd);
cur = attn_norm;
cur = ggml_mul_mat(ctx, ffn_up, cur);
cur = ggml_gelu(ctx, cur);
cur = ggml_mul_mat(ctx, ffn_down, cur);
}
cur = ggml_add(ctx, cur, ffn_inp);
cur = ggml_add(ctx, cur, inpL);
// input for next layer
inpL = cur;
}
cur = inpL;
ggml_tensor * output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
ggml_tensor * output_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
cur = llm_build_norm(ctx, cur, output_norm, output_norm_b, LLM_NORM);
// lm_head
ggml_tensor * output = ggml_new_tensor_2d(ctx, GGML_TYPE_Q8_0, hp.n_embd, hp.n_vocab);
cur = ggml_mul_mat(ctx, output, cur);
return cur;
}
};
static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name) {
std::vector<std::unique_ptr<test_case>> test_cases;
std::default_random_engine rng(0);
const ggml_type all_types[] = {
GGML_TYPE_F32, GGML_TYPE_F16,
GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
GGML_TYPE_Q8_0,
GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
GGML_TYPE_Q6_K,
GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
};
// unary ops
for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
test_cases.emplace_back(new test_unary((ggml_unary_op) op));
}
test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false));
for (ggml_type type : all_types) {
for (int b : {1, 7}) {
for (bool v : {false, true}) {
test_cases.emplace_back(new test_get_rows(type, 256, 5, 4, b, v));
}
}
}
for (int b : {1, 7}) {
for (bool v : {false, true}) {
test_cases.emplace_back(new test_get_rows(GGML_TYPE_I32, 256, 5, 4, b, v));
}
}
for (ggml_type type_input : {GGML_TYPE_F32}) {
for (ggml_op_pool pool_type : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) {
for (int k0 : {1, 3}) {
for (int k1 : {1, 3}) {
for (int s0 : {1, 2}) {
for (int s1 : {1, 2}) {
for (int p0 : {0, 1}) {
for (int p1 : {0, 1}) {
test_cases.emplace_back(new test_pool2d(pool_type, type_input, {10, 10, 3, 1}, k0, k1, s0, s1, p0, p1));
}
}
}
}
}
}
}
}
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 2, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 2}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_I32, {10, 10, 10, 10}, {2, 1, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 10, 10, 10}, {1, 1, 1, 2}));
test_cases.emplace_back(new test_dup(GGML_TYPE_F32));
test_cases.emplace_back(new test_dup(GGML_TYPE_F16));
test_cases.emplace_back(new test_dup(GGML_TYPE_I32));
test_cases.emplace_back(new test_dup(GGML_TYPE_I16));
test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3}));
test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));
for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
for (ggml_type type_dst : all_types) {
test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
}
}
test_cases.emplace_back(new test_cont());
auto add_test_bin_bcast = [&](ggml_type type, std::array<int64_t, 4> ne, std::array<int, 4> nr) {
for (auto op : {ggml_add, ggml_mul, ggml_div}) {
test_cases.emplace_back(new test_bin_bcast(op, type, ne, nr));
}
};
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 8, 1}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1, 1}, {32, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 320, 320}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 1, 1}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 1}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {2, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 2, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 1, 2, 1});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 1, 1, 2});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 1, 2, 2});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {1, 2, 2, 2});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 10}, {2, 2, 2, 2});
// stable diffusion
add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 16, 16, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1280, 16, 16, 1}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 256, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1280, 1}, {16, 16, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {16, 16, 1280, 1}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1920, 1}, {16, 16, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 2560, 1}, {16, 16, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1280, 1}, {32, 32, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1920, 1}, {32, 32, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 640, 1}, {32, 32, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {5120, 1, 1, 1}, {1, 256, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {640, 1, 1, 1}, {1, 1, 1, 1});
//add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {1, 1, 1, 1});
//add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {2, 1, 1, 1});
test_cases.emplace_back(new test_scale());
for (float eps : {1e-6f, 1e-5f, 1e-3f, 1e-1f}) {
test_cases.emplace_back(new test_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
}
for (ggml_type type_a : all_types) {
for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 2}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 2}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, { 1, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 1}, {2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {1, 2}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 2}));
}
}
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 2, 128, { 8, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 83, 2, 128, { 8, 1}, {4, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 2, 64, { 8, 1}, {4, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 83, 2, 64, { 8, 1}, {4, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 45, 128, { 8, 1}, {4, 1}));
test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45, 64, { 8, 1}, {4, 1}));
for (ggml_type type_a : all_types) {
for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
for (int n_mats : {2, 4, 8}) {
for (int id = 0; id < n_mats; id++) {
for (bool v : {false, true}) {
test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, id, 16, 16, 256, v));
}
}
}
}
}
test_cases.emplace_back(new test_sqr());
test_cases.emplace_back(new test_clamp());
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 1, 1}, 5));
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 10, 1}, 5));
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 10, 10}, 5));
#if 0
std::uniform_int_distribution<> dist_ne1(1, 50);
int exponent = 1;
while (exponent < (1 << 17)) {
std::uniform_int_distribution<> dist_ne0(exponent, 2*exponent);
for (int n = 0; n < 10; ++n) {
int64_t ne0 = dist_ne0(rng);
int64_t ne1 = dist_ne1(rng);
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, n/2 == 0, 0.1f, ne0 < 1000 ? 4.0f : 0.0f));
}
exponent <<= 1;
}
#endif
for (bool mask : {false, true}) {
for (float max_bias : {0.0f, 8.0f}) {
for (float scale : {1.0f, 0.1f}) {
for (int64_t ne0 : {16, 1024}) {
for (int64_t ne1 : {16, 1024}) {
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, mask, scale, max_bias));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, scale, max_bias));
}
}
}
}
}
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 8.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 8.0f));
for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512)); // llama 7B
test_cases.emplace_back(new test_rope(type, {128, 40, 10, 1}, 128, 0, 512)); // llama 13B
test_cases.emplace_back(new test_rope(type, {128, 52, 10, 1}, 128, 0, 512)); // llama 30B
test_cases.emplace_back(new test_rope(type, {128, 64, 10, 1}, 128, 0, 512)); // llama 65B
test_cases.emplace_back(new test_rope(type, { 64, 1, 10, 1}, 64, 2, 512)); // neox (falcon 7B)
test_cases.emplace_back(new test_rope(type, { 64, 71, 10, 1}, 64, 2, 512)); // neox (falcon 7B)
test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512)); // neox (falcon 40B)
test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512)); // neox (falcon 40B)
test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512)); // neox (stablelm)
test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512)); // neox (phi-2)
}
test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) {
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order));
}
test_cases.emplace_back(new test_sum_rows());
test_cases.emplace_back(new test_upscale());
test_cases.emplace_back(new test_group_norm());
test_cases.emplace_back(new test_acc());
test_cases.emplace_back(new test_pad());
test_cases.emplace_back(new test_arange());
test_cases.emplace_back(new test_timestep_embedding());
test_cases.emplace_back(new test_leaky_relu());
// these tests are disabled to save execution time, but they can be handy for debugging
#if 0
#if !defined(__SANITIZE_THREAD__)
// FIXME: these tests use too much memory with thread sanitizer
test_cases.emplace_back(new test_moe(8, 2, 1, 4096, 8*1024));
//test_cases.emplace_back(new test_moe(8, 2, 8, 4096, 14336));
#endif
test_cases.emplace_back(new test_llama(1));
test_cases.emplace_back(new test_llama(2));
test_cases.emplace_back(new test_falcon(1));
test_cases.emplace_back(new test_falcon(2));
#endif
// run tests
if (mode == MODE_TEST) {
ggml_backend_t backend_cpu = ggml_backend_cpu_init();
size_t n_ok = 0;
for (auto & test : test_cases) {
if (test->eval(backend, backend_cpu, op_name)) {
n_ok++;
}
}
printf(" %zu/%zu tests passed\n", n_ok, test_cases.size());
ggml_backend_free(backend_cpu);
return n_ok == test_cases.size();
}
if (mode == MODE_PERF) {
for (auto & test : test_cases) {
test->eval_perf(backend, op_name);
}
return true;
}
GGML_ASSERT(false);
return false;
}
static void usage(char ** argv) {
printf("Usage: %s [mode] [-o op] [-b backend]\n", argv[0]);
printf(" valid modes are: test (compare with CPU backend for correctness) or perf (performance evaluation)\n");
printf(" op names are as given by ggml_op_desc()\n");
}
int main(int argc, char ** argv) {
test_mode mode = MODE_TEST;
const char * op_name_filter = NULL;
const char * backend_filter = NULL;
for (int i = 1; i < argc; i++) {
if (strcmp(argv[i], "test") == 0) {
mode = MODE_TEST;
} else if (strcmp(argv[i], "perf") == 0) {
mode = MODE_PERF;
} else if (strcmp(argv[i], "-o") == 0) {
if (i + 1 < argc) {
op_name_filter = argv[++i];
} else {
usage(argv);
return 1;
}
} else if (strcmp(argv[i], "-b") == 0) {
if (i + 1 < argc) {
backend_filter = argv[++i];
} else {
usage(argv);
return 1;
}
} else {
usage(argv);
return 1;
}
}
// enumerate backends
printf("Testing %zu backends\n\n", ggml_backend_reg_get_count());
size_t n_ok = 0;
for (size_t i = 0; i < ggml_backend_reg_get_count(); i++) {
printf("Backend %zu/%zu (%s)\n", i + 1, ggml_backend_reg_get_count(), ggml_backend_reg_get_name(i));
if (backend_filter != NULL && strcmp(backend_filter, ggml_backend_reg_get_name(i)) != 0) {
printf(" Skipping\n");
n_ok++;
continue;
}
ggml_backend_t backend = ggml_backend_reg_init_backend(i, NULL);
GGML_ASSERT(backend != NULL);
if (backend_filter == NULL && ggml_backend_is_cpu(backend)) {
printf(" Skipping CPU backend\n");
ggml_backend_free(backend);
n_ok++;
continue;
}
printf(" Backend name: %s\n", ggml_backend_name(backend));
bool ok = test_backend(backend, mode, op_name_filter);
printf(" Backend %s: ", ggml_backend_name(backend));
if (ok) {
printf("\033[1;32mOK\033[0m\n");
n_ok++;
} else {
printf("\033[1;31mFAIL\033[0m\n");
}
printf("\n");
ggml_backend_free(backend);
}
printf("%zu/%zu backends passed\n", n_ok, ggml_backend_reg_get_count());
if (n_ok != ggml_backend_reg_get_count()) {
printf("\033[1;31mFAIL\033[0m\n");
return 1;
}
ggml_quantize_free();
printf("\033[1;32mOK\033[0m\n");
return 0;
}
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