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ggml : remove ggml_task_type and GGML_PERF (#8017)

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* ggml : remove ggml_task_type and GGML_PERF

* check abort_callback on main thread only

* vulkan : remove usage of ggml_compute_params

* remove LLAMA_PERF
This commit is contained in:
slaren 2024-06-24 03:07:59 +02:00 committed by GitHub
parent e112b610a1
commit 95f57bb5d5
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GPG Key ID: B5690EEEBB952194
8 changed files with 402 additions and 1082 deletions
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7
CMakeLists.txt
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@ -144,9 +144,6 @@ option(LLAMA_BUILD_SERVER "llama: build server example"
option(LLAMA_LASX "llama: enable lasx" ON)
option(LLAMA_LSX "llama: enable lsx" ON)
# add perf arguments
option(LLAMA_PERF "llama: enable perf" OFF)
# Required for relocatable CMake package
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
@ -870,10 +867,6 @@ if (LLAMA_CPU_HBM)
target_link_libraries(ggml PUBLIC memkind)
endif()
if (LLAMA_PERF)
add_compile_definitions(GGML_PERF)
endif()
function(get_flags CCID CCVER)
set(C_FLAGS "")
set(CXX_FLAGS "")

3
Makefile
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@ -344,9 +344,6 @@ ifdef LLAMA_GPROF
MK_CFLAGS += -pg
MK_CXXFLAGS += -pg
endif
ifdef LLAMA_PERF
MK_CPPFLAGS += -DGGML_PERF
endif
# Architecture specific
# TODO: probably these flags need to be tweaked on some architectures

36
ggml-vulkan.cpp
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@ -513,8 +513,8 @@ static size_t vk_skip_checks;
static size_t vk_output_tensor;
static void ggml_vk_print_tensor(ggml_backend * ctx, const ggml_tensor * tensor, const char * name);
static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor);
static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor);
static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor * tensor);
static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_tensor * tensor);
#endif
typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
@ -5644,7 +5644,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
}
}
static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){
static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * tensor){
ggml_tensor_extra_gpu * extra = nullptr;
switch (tensor->op) {
@ -5697,17 +5697,10 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_
return false;
}
if (params->ith != 0) {
return true;
}
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return true;
}
VK_LOG_DEBUG("ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")");
#ifdef GGML_VULKAN_CHECK_RESULTS
ggml_vk_check_results_0(ctx, params, tensor);
ggml_vk_check_results_0(ctx, tensor);
#endif
vk_context& subctx = ctx->gc.contexts[extra->ctx_idx];
@ -6214,9 +6207,6 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen
ggml_vk_build_graph(ctx,cgraph->nodes[i], i == last_node);
}
ggml_compute_params params = {};
params.type = GGML_TASK_TYPE_COMPUTE;
params.ith = 0;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
@ -6224,13 +6214,13 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen
continue;
}
bool ok = ggml_vk_compute_forward(ctx, &params, node);
bool ok = ggml_vk_compute_forward(ctx, node);
if (!ok) {
fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
}
#ifdef GGML_VULKAN_CHECK_RESULTS
else {
ggml_vk_check_results_1(ctx, &params, node);
ggml_vk_check_results_1(ctx, node);
}
#endif
GGML_ASSERT(ok);
@ -6600,11 +6590,8 @@ void * comp_result;
size_t comp_size;
size_t comp_nb[GGML_MAX_DIMS];
size_t check_counter = 0;
static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) {
if (params->ith != 0) {
return;
}
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor * tensor) {
if (tensor->op == GGML_OP_TRANSPOSE) {
return;
}
@ -6908,11 +6895,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
ggml_free(ggml_ctx);
}
static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) {
if (params->ith != 0) {
return;
}
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_tensor * tensor) {
if (tensor->op == GGML_OP_TRANSPOSE) {
return;
}
if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {

1254
ggml.c
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File diff suppressed because it is too large Load Diff

35
ggml.h
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@ -591,11 +591,7 @@ extern "C" {
struct ggml_tensor * grad;
struct ggml_tensor * src[GGML_MAX_SRC];
// performance
int perf_runs;
int64_t perf_cycles;
int64_t perf_time_us;
// source tensor and offset for views
struct ggml_tensor * view_src;
size_t view_offs;
@ -605,7 +601,7 @@ extern "C" {
void * extra; // extra things e.g. for ggml-cuda.cu
char padding[8];
// char padding[4];
};
static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
@ -652,11 +648,6 @@ extern "C" {
struct ggml_hash_set visited_hash_table;
enum ggml_cgraph_eval_order order;
// performance
int perf_runs;
int64_t perf_cycles;
int64_t perf_time_us;
};
// scratch buffer
@ -673,28 +664,6 @@ extern "C" {
bool no_alloc; // don't allocate memory for the tensor data
};
// compute types
// NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
// This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
enum ggml_task_type {
GGML_TASK_TYPE_INIT = 0,
GGML_TASK_TYPE_COMPUTE,
GGML_TASK_TYPE_FINALIZE,
};
struct ggml_compute_params {
enum ggml_task_type type;
// ith = thread index, nth = number of threads
int ith, nth;
// work buffer for all threads
size_t wsize;
void * wdata;
};
// numa strategies
enum ggml_numa_strategy {
GGML_NUMA_STRATEGY_DISABLED = 0,

6
llama.cpp
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@ -12785,12 +12785,6 @@ static int llama_decode_internal(
}
}
#ifdef GGML_PERF
// print timing information per ggml operation (for debugging purposes)
// requires GGML_PERF to be defined
ggml_graph_print(gf);
#endif
// plot the computation graph in dot format (for debugging purposes)
//if (n_past%100 == 0) {
// ggml_graph_dump_dot(gf, NULL, "llama.dot");

37
sgemm.cpp
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@ -249,8 +249,7 @@ class tinyBLAS {
: A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
}
void matmul(int64_t m, int64_t n, int task) {
if (task == GGML_TASK_TYPE_COMPUTE)
void matmul(int64_t m, int64_t n) {
mnpack(0, m, 0, n);
}
@ -458,8 +457,7 @@ class tinyBLAS_Q0_ARM {
: A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
}
void matmul(int64_t m, int64_t n, int task) {
if (task == GGML_TASK_TYPE_COMPUTE)
void matmul(int64_t m, int64_t n) {
mnpack(0, m, 0, n);
}
@ -596,8 +594,7 @@ class tinyBLAS_Q0_AVX {
: A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
}
void matmul(int64_t m, int64_t n, int task) {
if (task == GGML_TASK_TYPE_COMPUTE)
void matmul(int64_t m, int64_t n) {
mnpack(0, m, 0, n);
}
@ -829,7 +826,7 @@ class tinyBLAS_Q0_AVX {
* For example, for single-threaded single-precision GEMM you can say
*
* llamafile_sgemm(m, n, k, A, lda, B, ldb, C, ldc,
* 0, 1, GGML_TASK_TYPE_COMPUTE,
* 0, 1,
* GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32);
*
* @param m is rows in `A` and `C`
@ -843,14 +840,13 @@ class tinyBLAS_Q0_AVX {
* @param ldc is row stride of `C`
* @param ith is thread id (must be less than `nth`)
* @param nth is number of threads (must be greater than zero)
* @param task is GGML task type
* @param Atype is GGML data type of `A`
* @param Btype is GGML data type of `B`
* @param Ctype is GGML data type of `C`
* @return true if this function was able to service the matmul request
*/
bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda, const void *B, int64_t ldb, void *C,
int64_t ldc, int ith, int nth, int task, int Atype, int Btype, int Ctype) {
int64_t ldc, int ith, int nth, int Atype, int Btype, int Ctype) {
assert(m >= 0);
assert(n >= 0);
@ -877,7 +873,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const float *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#elif defined(__AVX__) || defined(__AVX2__)
if (k % 8)
@ -887,7 +883,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const float *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#elif defined(__ARM_NEON)
if (n < 4)
@ -899,7 +895,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const float *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#else
return false;
@ -917,7 +913,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const float *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#elif (defined(__AVX__) || defined(__AVX2__)) && defined(__F16C__)
if (k % 8)
@ -929,7 +925,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const float *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#elif defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER)
if (n < 8)
@ -943,7 +939,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const ggml_fp16_t *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#elif defined(__ARM_NEON) && !defined(_MSC_VER)
if (k % 4)
@ -955,7 +951,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const float *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#else
return false;
@ -971,7 +967,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const block_q8_0 *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#elif defined(__ARM_FEATURE_DOTPROD)
tinyBLAS_Q0_ARM<block_q8_0> tb{
@ -979,7 +975,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const block_q8_0 *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#else
return false;
@ -995,7 +991,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const block_q8_0 *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#elif defined(__ARM_FEATURE_DOTPROD)
tinyBLAS_Q0_ARM<block_q4_0> tb{
@ -1003,7 +999,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(const block_q8_0 *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n, task);
tb.matmul(m, n);
return true;
#else
return false;
@ -1025,7 +1021,6 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
(void)ldc;
(void)ith;
(void)nth;
(void)task;
(void)Atype;
(void)Btype;
(void)Ctype;

2
sgemm.h
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@ -7,7 +7,7 @@ extern "C" {
bool llamafile_sgemm(int64_t, int64_t, int64_t, const void *, int64_t,
const void *, int64_t, void *, int64_t, int, int,
int, int, int, int);
int, int, int);
#ifdef __cplusplus
}