}
#endif
-// off1 = offset in i11 and i1
-// cne1 = ne11 and ne1
-// in a normal matrix multiplication, off1 = 0 and cne1 = ne1
-// during GGML_TASK_INIT, the full src1 is converted regardless of off1 and cne1
static void ggml_compute_forward_mul_mat(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
- struct ggml_tensor * dst,
- int64_t off1, int64_t cne1) {
+ struct ggml_tensor * dst) {
int64_t t0 = ggml_perf_time_us();
UNUSED(t0);
const int64_t i03 = i13/r3;
const int64_t i02 = i12/r2;
- const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
- const float * y = (float *) ((char *) src1->data + off1*nb11 + i12*nb12 + i13*nb13);
- float * d = (float *) ((char *) dst->data + off1*nb1 + i12*nb2 + i13*nb3);
+ const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
+ const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
+ float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
if (type != GGML_TYPE_F32) {
float * const wdata = params->wdata;
}
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
- cne1, ne01, ne10,
+ ne1, ne01, ne10,
1.0f, y, ne10,
x, ne00,
0.0f, d, ne01);
const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
- const int64_t nr0 = ne01; // src0 rows
- const int64_t nr1 = cne1*ne12*ne13; // src1 rows
+ const int64_t nr0 = ne01; // src0 rows
+ const int64_t nr1 = ne1*ne12*ne13; // src1 rows
//printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
- const int64_t i13 = (ir1/(ne12*cne1));
- const int64_t i12 = (ir1 - i13*ne12*cne1)/cne1;
- const int64_t i11 = (ir1 - i13*ne12*cne1 - i12*cne1) + off1;
+ const int64_t i13 = (ir1/(ne12*ne1));
+ const int64_t i12 = (ir1 - i13*ne12*ne1)/ne1;
+ const int64_t i11 = (ir1 - i13*ne12*ne1 - i12*ne1);
// broadcast src0 into src1
const int64_t i03 = i13/r3;
static void ggml_compute_forward_mul_mat_id(
const struct ggml_compute_params * params,
- const struct ggml_tensor * src0,
+ const struct ggml_tensor * ids,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
- if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
- // during GGML_TASK_INIT the entire src1 is converted to vec_dot_type
- ggml_compute_forward_mul_mat(params, dst->src[2], src1, dst, 0, dst->ne[1]);
- return;
- }
+ const struct ggml_tensor * src0 = dst->src[2]; // only for GGML_TENSOR_BINARY_OP_LOCALS
+
+ GGML_TENSOR_BINARY_OP_LOCALS
+
+ const int ith = params->ith;
+ const int nth = params->nth;
+
+ const enum ggml_type type = src0->type;
+
+ const bool src1_cont = ggml_is_contiguous(src1);
+
+ ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
+ enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
+ ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
+
+ GGML_ASSERT(ne0 == ne01);
+ GGML_ASSERT(ne1 == ne11);
+ GGML_ASSERT(ne2 == ne12);
+ GGML_ASSERT(ne3 == ne13);
+
+ // we don't support permuted src0 or src1
+ GGML_ASSERT(nb00 == ggml_type_size(type));
+ GGML_ASSERT(nb10 == ggml_type_size(src1->type));
+
+ // dst cannot be transposed or permuted
+ GGML_ASSERT(nb0 == sizeof(float));
+ GGML_ASSERT(nb0 <= nb1);
+ GGML_ASSERT(nb1 <= nb2);
+ GGML_ASSERT(nb2 <= nb3);
- const struct ggml_tensor * ids = src0;
+ // broadcast factors
+ const int64_t r2 = ne12/ne02;
+ const int64_t r3 = ne13/ne03;
+
+ // row groups
const int id = ggml_get_op_params_i32(dst, 0);
const int n_as = ggml_get_op_params_i32(dst, 1);
- for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
- const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
+ char * wdata_src1_end = (src1->type == vec_dot_type) ?
+ (char *) params->wdata :
+ (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));
+
+ int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
+ int64_t * matrix_rows = matrix_row_counts + n_as; // [n_as][ne11]
+
+ #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
- GGML_ASSERT(row_id >= 0 && row_id < n_as);
+ if (params->type == GGML_TASK_INIT) {
+ char * wdata = params->wdata;
+ if (src1->type != vec_dot_type) {
+ const size_t row_size = ggml_row_size(vec_dot_type, ne10);
- const struct ggml_tensor * src0_row = dst->src[row_id + 2];
- ggml_compute_forward_mul_mat(params, src0_row, src1, dst, i01, 1);
+ assert(params->wsize >= ne11*ne12*ne13*row_size);
+ assert(src1->type == GGML_TYPE_F32);
+
+ for (int64_t i13 = 0; i13 < ne13; ++i13) {
+ for (int64_t i12 = 0; i12 < ne12; ++i12) {
+ for (int64_t i11 = 0; i11 < ne11; ++i11) {
+ from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
+ wdata += row_size;
+ }
+ }
+ }
+ }
+
+ // initialize matrix_row_counts
+ GGML_ASSERT(wdata == wdata_src1_end);
+ memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
+
+ // group rows by src0 matrix
+ for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
+ const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
+
+ GGML_ASSERT(row_id >= 0 && row_id < n_as);
+ MMID_MATRIX_ROW(row_id, matrix_row_counts[row_id]) = i01;
+ matrix_row_counts[row_id] += 1;
+ }
+
+ return;
}
+
+ if (params->type == GGML_TASK_FINALIZE) {
+ return;
+ }
+
+ // compute each matrix multiplication in sequence
+ for (int cur_a = 0; cur_a < n_as; ++cur_a) {
+ const int64_t cne1 = matrix_row_counts[cur_a];
+
+ if (cne1 == 0) {
+ continue;
+ }
+
+ const struct ggml_tensor * src0_cur = dst->src[cur_a + 2];
+
+ const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
+ const size_t row_size = ggml_row_size(vec_dot_type, ne10);
+
+ const int64_t nr0 = ne01; // src0 rows
+ const int64_t nr1 = cne1*ne12*ne13; // src1 rows
+
+ //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
+
+ // distribute the thread work across the inner or outer loop based on which one is larger
+
+ const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
+ const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows
+
+ const int64_t ith0 = ith % nth0;
+ const int64_t ith1 = ith / nth0;
+
+ const int64_t dr0 = (nr0 + nth0 - 1)/nth0;
+ const int64_t dr1 = (nr1 + nth1 - 1)/nth1;
+
+ const int64_t ir010 = dr0*ith0;
+ const int64_t ir011 = MIN(ir010 + dr0, nr0);
+
+ const int64_t ir110 = dr1*ith1;
+ const int64_t ir111 = MIN(ir110 + dr1, nr1);
+
+ //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);
+
+ // threads with no work simply yield (not sure if it helps)
+ if (ir010 >= ir011 || ir110 >= ir111) {
+ sched_yield();
+ continue;
+ }
+
+ assert(ne12 % ne02 == 0);
+ assert(ne13 % ne03 == 0);
+
+ // block-tiling attempt
+ const int64_t blck_0 = 16;
+ const int64_t blck_1 = 16;
+
+ // attempt to reduce false-sharing (does not seem to make a difference)
+ float tmp[16];
+
+ for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
+ for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
+ for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
+ const int64_t i13 = (ir1/(ne12*cne1)); // Note: currently, src1 is always a matrix
+ const int64_t i12 = (ir1 - i13*ne12*cne1)/cne1;
+ const int64_t _i11 = (ir1 - i13*ne12*cne1 - i12*cne1);
+ const int64_t i11 = MMID_MATRIX_ROW(cur_a, _i11);
+
+ // broadcast src0 into src1
+ const int64_t i03 = i13/r3;
+ const int64_t i02 = i12/r2;
+
+ const int64_t i1 = i11;
+ const int64_t i2 = i12;
+ const int64_t i3 = i13;
+
+ const char * src0_row = (const char *) src0_cur->data + (0 + i02*nb02 + i03*nb03);
+
+ // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
+ // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
+ // the original src1 data pointer, so we should index using the indices directly
+ // TODO: this is a bit of a hack, we should probably have a better way to handle this
+ const char * src1_col = (const char *) wdata +
+ (src1_cont || src1->type != vec_dot_type
+ ? (i11 + i12*ne11 + i13*ne12*ne11)*row_size
+ : (i11*nb11 + i12*nb12 + i13*nb13));
+
+ float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
+
+ //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
+ // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
+ //}
+
+ for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
+ vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col);
+ }
+ memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
+ }
+ }
+ }
+ }
+
+ #undef MMID_MATRIX_ROW
}
// ggml_compute_forward_out_prod
} break;
case GGML_OP_MUL_MAT:
{
- ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor, 0, tensor->ne[1]);
+ ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
} break;
case GGML_OP_MUL_MAT_ID:
{
} break;
case GGML_OP_MUL_MAT_ID:
{
- // FIXME: blas
n_tasks = n_threads;
} break;
case GGML_OP_OUT_PROD:
} break;
case GGML_OP_MUL_MAT_ID:
{
- const struct ggml_tensor * a = node->src[2];
- const struct ggml_tensor * b = node->src[1];
- const enum ggml_type vec_dot_type = type_traits[a->type].vec_dot_type;
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
- if (ggml_compute_forward_mul_mat_use_blas(a, b, node)) {
- if (a->type != GGML_TYPE_F32) {
- // here we need memory just for single 2D matrix from src0
- cur = ggml_type_size(GGML_TYPE_F32)*(a->ne[0]*a->ne[1]);
- }
- } else
-#endif
- if (b->type != vec_dot_type) {
- cur = ggml_row_size(vec_dot_type, ggml_nelements(b));
+ const struct ggml_tensor * src0 = node->src[2];
+ const struct ggml_tensor * src1 = node->src[1];
+ const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type;
+ if (src1->type != vec_dot_type) {
+ cur = ggml_row_size(vec_dot_type, ggml_nelements(src1));
}
+ const int n_as = ggml_get_op_params_i32(node, 1);
+ cur = GGML_PAD(cur, sizeof(int64_t)); // align
+ cur += n_as * sizeof(int64_t); // matrix_row_counts
+ cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows
} break;
case GGML_OP_OUT_PROD:
{
overview / pkg / ggml / sources / llama.cpp / commitdiff