aditya/llama.cpp
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Clblast fixes + enhancements to save VRAM and offload more layers (#1675)

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* Use events instead of clFinish, where possible

* OpenCL: Don't load gpu layers into RAM, add mul_f32 kernel

* Reduce queueing overhead for contiguous tensors by using single mul kernel call

* Adapt to #1612 cl_mem malloc changes

* Reduce code duplication between cuda and opencl branches

* Improve implementation

* Clblast fixes + enhancements to save VRAM:

1. Change all Clblast buffers to CL_MEM_READ_WRITE, as the pool malloc currently doesn't properly handle them.
2. When recycling buffers in pool malloc, always assign the SMALLEST available buffer that fits, instead of the FIRST available buffer
3. When failing to recycle a buffer in pool malloc (all too small), instead recycle the largest available free buffer by resizing it.

* change max value size_t to use limits

* removed flags from the CL pool malloc, apply code tidying suggestions.
This commit is contained in:
LostRuins 2023-06-07 01:00:01 +08:00 committed by GitHub
parent 2d43387daf
commit d5b111f53d
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GPG key ID: 4AEE18F83AFDEB23
1 changed files with 46 additions and 21 deletions
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67
ggml-opencl.cpp
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@ -4,6 +4,7 @@
#include <atomic>
#include <sstream>
#include <vector>
#include <limits>
#define CL_TARGET_OPENCL_VERSION 110
#include <clblast.h>
@ -604,21 +605,44 @@ struct cl_buffer {
static cl_buffer g_cl_buffer_pool[MAX_CL_BUFFERS];
static std::atomic_flag g_cl_pool_lock = ATOMIC_FLAG_INIT;
static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size, cl_mem_flags flags) {
static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size) {
scoped_spin_lock lock(g_cl_pool_lock);
cl_int err;
int best_i = -1;
size_t best_size = std::numeric_limits<size_t>::max(); //smallest unused buffer that fits our needs
int worst_i = -1;
size_t worst_size = 0; //largest unused buffer seen so far
for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
cl_buffer& b = g_cl_buffer_pool[i];
if (b.size > 0 && b.size >= size) {
cl_mem mem = b.mem;
*actual_size = b.size;
b.size = 0;
return mem;
cl_buffer &b = g_cl_buffer_pool[i];
if (b.size > 0 && b.size >= size && b.size < best_size)
{
best_i = i;
best_size = b.size;
}
if (b.size > 0 && b.size > worst_size)
{
worst_i = i;
worst_size = b.size;
}
}
if(best_i!=-1) //found the smallest buffer that fits our needs
{
cl_buffer& b = g_cl_buffer_pool[best_i];
cl_mem mem = b.mem;
*actual_size = b.size;
b.size = 0;
return mem;
}
if(worst_i!=-1) //no buffer that fits our needs, resize largest one to save memory
{
cl_buffer& b = g_cl_buffer_pool[worst_i];
cl_mem mem = b.mem;
b.size = 0;
clReleaseMemObject(mem);
}
cl_mem mem;
CL_CHECK((mem = clCreateBuffer(context, flags, size, NULL, &err), err));
CL_CHECK((mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err), err));
*actual_size = size;
return mem;
}
@ -692,9 +716,10 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1,
size_t x_size;
size_t d_size;
cl_mem d_X = ggml_cl_pool_malloc(ne0 * sizeof(float), &x_size, CL_MEM_READ_ONLY); // src0
cl_mem d_X = ggml_cl_pool_malloc(ne0 * sizeof(float), &x_size); // src0
cl_mem d_Y = (cl_mem) src1->data; // src1 is already on device, broadcasted.
cl_mem d_D = ggml_cl_pool_malloc(ne0 * sizeof(float), &d_size, CL_MEM_WRITE_ONLY); // dst
cl_mem d_D = ggml_cl_pool_malloc(ne0 * sizeof(float), &d_size); // dst
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
@ -792,10 +817,10 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
if (src0->backend == GGML_BACKEND_CL) {
d_X = (cl_mem) src0->data;
} else {
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size, CL_MEM_READ_ONLY);
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
}
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size, CL_MEM_READ_ONLY);
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
@ -868,10 +893,10 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
if (src0->backend == GGML_BACKEND_CL) {
d_X = (cl_mem) src0->data;
} else {
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size, CL_MEM_READ_ONLY);
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
}
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size, CL_MEM_READ_ONLY);
cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size);
cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size);
bool src1_cont_rows = nb10 == sizeof(float);
bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
@ -970,13 +995,13 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
size_t q_size;
cl_mem d_X;
if (!mul_mat_vec) {
d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size, CL_MEM_READ_WRITE);
d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
}
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size, CL_MEM_READ_ONLY);
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
cl_mem d_Q;
if (src0->backend == GGML_BACKEND_CPU) {
d_Q = ggml_cl_pool_malloc(q_sz, &q_size, CL_MEM_READ_ONLY);
d_Q = ggml_cl_pool_malloc(q_sz, &q_size);
}
cl_kernel* to_fp32_cl = ggml_get_to_fp32_cl(type);
@ -1143,7 +1168,7 @@ void ggml_cl_transform_tensor(ggml_tensor * tensor) {
const size_t q_sz = ggml_type_size(type) * ne0 * ne1 * ne2 * ne3 / ggml_blck_size(type);
size_t q_size;
cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size, CL_MEM_READ_ONLY);
cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size);
// copy tensor to device
for (int64_t i3 = 0; i3 < ne3; i3++) {