haraldwolff/llama.cpp
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CLBlast: Add broadcast support for matrix multiplication (#3402)

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Broadcast src0 into src1 across dimensions 2 and 3 when needed.
This is required for models that use GQA.
This commit is contained in:
shibe2 2023-10-02 23:26:15 +04:00 committed by GitHub
parent 29a404a951
commit 665018c749
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GPG key ID: 4AEE18F83AFDEB23
2 changed files with 67 additions and 28 deletions
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90
ggml-opencl.cpp
View file

@ -1476,10 +1476,15 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int64_t ne12 = src1->ne[2];
const int64_t ne13 = src1->ne[3];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const int64_t r2 = ne12 / ne02;
const int64_t r3 = ne13 / ne03;
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
@ -1498,13 +1503,22 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
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++) {
int64_t pi02 = -1;
int64_t pi03 = -1;
for (int64_t i13 = 0; i13 < ne13; i13++) {
int64_t i03 = i13 / r3;
for (int64_t i12 = 0; i12 < ne12; i12++) {
int64_t i02 = i12 / r2;
// copy data to device
if (src0->backend != GGML_BACKEND_GPU) {
if (src0->backend != GGML_BACKEND_GPU && (i02 != pi02 || i03 != pi03)) {
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
pi02 = i02;
pi03 = i03;
}
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL));
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
CL_CHECK(clFinish(queue));
@ -1525,7 +1539,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
}
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
}
}
@ -1547,6 +1561,8 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int64_t ne12 = src1->ne[2];
const int64_t ne13 = src1->ne[3];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
@ -1556,6 +1572,9 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const int64_t r2 = ne12 / ne02;
const int64_t r3 = ne13 / ne03;
const ggml_fp16_t alpha = ggml_fp32_to_fp16(1.0f);
const ggml_fp16_t beta = ggml_fp32_to_fp16(0.0f);
const int x_ne = ne01 * ne00;
@ -1577,32 +1596,41 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
bool src1_cont_rows = nb10 == sizeof(float);
bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int64_t pi02 = -1;
int64_t pi03 = -1;
for (int64_t i13 = 0; i13 < ne13; i13++) {
int64_t i03 = i13 / r3;
for (int64_t i12 = 0; i12 < ne12; i12++) {
int64_t i02 = i12 / r2;
// copy src0 to device
if (src0->backend != GGML_BACKEND_GPU) {
if (src0->backend != GGML_BACKEND_GPU && (i02 != pi02 || i03 != pi03)) {
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
pi02 = i02;
pi03 = i03;
}
// convert src1 to fp16
// TODO: use multiple threads
ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata + (ne11 * ne10) * (i03 * ne02 + i02);
char * src1i = (char *) src1->data + i03*nb13 + i02*nb12;
ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata + (ne11 * ne10) * (i13 * ne12 + i12);
char * src1i = (char *) src1->data + i13*nb13 + i12*nb12;
if (src1_cont_rows) {
if (src1_cont_cols) {
ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
}
else {
for (int64_t i01 = 0; i01 < ne11; i01++) {
ggml_fp32_to_fp16_row((float *) (src1i + i01*nb11), tmp + i01*ne10, ne10);
for (int64_t i11 = 0; i11 < ne11; i11++) {
ggml_fp32_to_fp16_row((float *) (src1i + i11*nb11), tmp + i11*ne10, ne10);
}
}
}
else {
for (int64_t i01 = 0; i01 < ne11; i01++) {
for (int64_t i00 = 0; i00 < ne10; i00++) {
for (int64_t i11 = 0; i11 < ne11; i11++) {
for (int64_t i10 = 0; i10 < ne10; i10++) {
// very slow due to no inlining
tmp[i01*ne10 + i00] = ggml_fp32_to_fp16(*(float *) (src1i + i01*nb11 + i00*nb10));
tmp[i11*ne10 + i10] = ggml_fp32_to_fp16(*(float *) (src1i + i11*nb11 + i10*nb10));
}
}
}
@ -1631,7 +1659,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
// copy dst to host, then convert to float
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
ggml_fp16_to_fp32_row(tmp, d, d_ne);
}
@ -1652,12 +1680,17 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int64_t ne12 = src1->ne[2];
const int64_t ne13 = src1->ne[3];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const ggml_type type = src0->type;
const bool mul_mat_vec = ne11 == 1;
const int64_t r2 = ne12 / ne02;
const int64_t r3 = ne13 / ne03;
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
@ -1690,12 +1723,23 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
size_t ev_idx = 0;
std::vector<cl_event> events;
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int64_t pi02 = -1;
int64_t pi03 = -1;
for (int64_t i13 = 0; i13 < ne13; i13++) {
int64_t i03 = i13 / r3;
for (int64_t i12 = 0; i12 < ne12; i12++) {
int64_t i02 = i12 / r2;
// copy src0 to device if necessary
if (src0->backend == GGML_BACKEND_CPU) {
events.emplace_back();
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
if (i02 != pi02 || i03 != pi03) {
events.emplace_back();
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
pi02 = i02;
pi03 = i03;
}
} else if (src0->backend == GGML_BACKEND_GPU) {
d_Q = (cl_mem) src0->extra;
} else {
@ -1704,7 +1748,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel
// copy src1 to device
events.emplace_back();
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, events.data() + ev_idx++));
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, events.data() + ev_idx++));
// compute
const size_t global = ne01 * CL_DMMV_BLOCK_SIZE;
@ -1725,7 +1769,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
// copy src1 to device
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL));
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
events.emplace_back();
@ -1749,7 +1793,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
}
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &events[events.size() - 1], NULL));
for (auto *event : events) {
clReleaseEvent(event);

5
ggml.c
View file

@ -11621,11 +11621,6 @@ static void ggml_compute_forward_mul_mat(
#if defined(GGML_USE_CLBLAST)
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
// TODO: handle case when src0 is broadcast-able into src1 across 2nd,3rd dimension
// ref: https://github.com/ggerganov/ggml/pull/224
GGML_ASSERT(ne02 == ne12);
GGML_ASSERT(ne03 == ne13);
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
}