#include "ggml-cpu-impl.h"
#include "ggml-cpu.h"
#include "ggml-impl.h"
-#include "ggml-quants.h"
#include "ggml-cpu-quants.h"
#include "ggml-threading.h"
-#include "amx/amx.h"
#include "ggml.h"
#if defined(_MSC_VER) || defined(__MINGW32__)
atomic_int n_graph; // incremented when there is work to be done (i.e each graph)
atomic_int GGML_CACHE_ALIGN n_barrier;
atomic_int GGML_CACHE_ALIGN n_barrier_passed;
- atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads.
+ atomic_int GGML_CACHE_ALIGN current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads.
// these are atomic as an annotation for thread-sanitizer
atomic_bool stop; // Used for stopping the threadpool altogether
if (src1->type != vec_dot_type) {
char * wdata = params->wdata;
+ const size_t nbw0 = ggml_type_size(vec_dot_type);
const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
const size_t nbw2 = nbw1*ne11;
const size_t nbw3 = nbw2*ne12;
assert(params->wsize >= ne13*nbw3);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
+ #if 0
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
}
}
}
+ #else
+ for (int64_t i13 = 0; i13 < ne13; ++i13) {
+ for (int64_t i12 = 0; i12 < ne12; ++i12) {
+ for (int64_t i11 = 0; i11 < ne11; ++i11) {
+ size_t bs = ggml_blck_size(vec_dot_type);
+ int64_t ne10_block_start = (ith * ne10/bs) / nth;
+ int64_t ne10_block_end = ((ith + 1) * ne10/bs) / nth;
+ from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + ne10_block_start*bs*nb10),
+ (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1 + ne10_block_start*nbw0),
+ (ne10_block_end - ne10_block_start) * bs);
+ }
+ }
+ }
+ #endif
}
if (ith == 0) {
if ((nr0 % 2 != 0) || (ne11 % 2 != 0) || ((ir0_end - ir0_start) % 2 != 0) || ((ir1_end - ir1_start) % 2 != 0)) {
num_rows_per_vec_dot = 1;
}
-
ggml_compute_forward_mul_mat_one_chunk(params, dst, src0->type, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end);
if (nth >= nchunk0 * nchunk1) {
// ggml_compute_forward_mul_mat_id
+#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ids->ne[0]*ids->ne[1] + (i1)]
+
+struct mmid_row_mapping {
+ int32_t i1;
+ int32_t i2;
+};
+
+static void ggml_compute_forward_mul_mat_id_one_chunk(
+ struct ggml_tensor * dst,
+ const struct ggml_tensor * src0,
+ const struct ggml_tensor * src1,
+ const struct ggml_tensor * ids,
+ const int64_t cur_a,
+ const int64_t ir0_start,
+ const int64_t ir0_end,
+ const int64_t ir1_start,
+ const int64_t ir1_end,
+ const char * src0_cur,
+ const struct mmid_row_mapping * matrix_rows,
+ const size_t row_size,
+ const bool src1_cont,
+ const void * wdata) {
+
+ GGML_TENSOR_BINARY_OP_LOCALS
+
+ const enum ggml_type type = src0->type;
+
+ ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot;
+ enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;
+
+ const int64_t blck_0 = 16;
+ const int64_t blck_1 = 16;
+
+ float tmp[16];
+
+ for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
+ for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
+ for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ++ir1) {
+ const int64_t _i12 = ir1; // logical row index for this expert
+
+ struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12);
+ const int id = row_mapping.i1; // selected expert index
+
+ const int64_t i11 = id % ne11;
+ const int64_t i12 = row_mapping.i2; // row index in src1
+
+ const int64_t i1 = id; // selected expert index
+ const int64_t i2 = i12; // row
+
+ // 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)*row_size
+ : (i11*nb11 + i12*nb12));
+
+ float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2));
+
+ for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) {
+ vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1);
+ }
+
+ memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir0_end) - iir0)*sizeof(float));
+ }
+ }
+ }
+}
+
+static void * incr_ptr_aligned(void ** p, size_t size, size_t align) {
+
+ void * ptr = *p;
+ ptr = (void *) GGML_PAD((uintptr_t) ptr, align);
+ *p = (void *) ((char *) ptr + size);
+ return ptr;
+}
+
static void ggml_compute_forward_mul_mat_id(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const bool src1_cont = ggml_is_contiguous(src1);
- ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot;
enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;
ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float;
const int n_ids = ids->ne[0]; // n_expert_used
const int n_as = ne02; // n_expert
- 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));
+ void * wdata_cur = params->wdata;
- struct mmid_row_mapping {
- int32_t i1;
- int32_t i2;
- };
+ if (src1->type != vec_dot_type) {
+ incr_ptr_aligned(&wdata_cur, ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));
+ }
+
+ int64_t * matrix_row_counts = // [n_as]
+ incr_ptr_aligned(&wdata_cur, n_as*sizeof(int64_t), sizeof(int64_t));
+
+ struct mmid_row_mapping * matrix_rows = // [n_as][ids->ne[0]*ids->ne[1]]
+ incr_ptr_aligned(&wdata_cur, n_as*ids->ne[0]*ids->ne[1]*sizeof(struct mmid_row_mapping), sizeof(int64_t));
- int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
- struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11]
+ char (*atomic_current_chunk)[CACHE_LINE_SIZE] = // [n_as]
+ incr_ptr_aligned(&wdata_cur, CACHE_LINE_SIZE * n_as, CACHE_LINE_SIZE);
+
+ GGML_ASSERT(params->wsize >= (size_t)((char *) wdata_cur - (char *) params->wdata));
if (src1->type != vec_dot_type) {
char * wdata = params->wdata;
+ const size_t nbw0 = ggml_type_size(vec_dot_type);
const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
const size_t nbw2 = nbw1*ne11;
const size_t nbw3 = nbw2*ne12;
assert(params->wsize >= ne13*nbw3);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
+#if 0
for (int64_t i13 = 0; i13 < ne13; ++i13) {
- for (int64_t i12 = 0; i12 < ne12; ++i12) {
- for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
+ for (int64_t i12 = ith; i12 < ne12; i12 += nth) {
+ for (int64_t i11 = 0; i11 < ne11; ++i11) {
from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
}
}
}
+#else
+ for (int64_t i13 = 0; i13 < ne13; ++i13) {
+ for (int64_t i12 = 0; i12 < ne12; ++i12) {
+ for (int64_t i11 = 0; i11 < ne11; ++i11) {
+ size_t bs = ggml_blck_size(vec_dot_type);
+ int64_t ne10_block_start = (ith * ne10/bs) / nth;
+ int64_t ne10_block_end = ((ith + 1) * ne10/bs) / nth;
+ from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + ne10_block_start*bs*nb10),
+ (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1 + ne10_block_start*nbw0),
+ (ne10_block_end - ne10_block_start) * bs);
+ }
+ }
+ }
+#endif
}
-#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)]
-
if (ith == 0) {
// initialize matrix_row_counts
memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
}
}
+ // reset current_chunk
+ for (int cur_a = ith; cur_a < n_as; cur_a += nth) {
+ atomic_int * current_chunk_ctr = (atomic_int *)(atomic_current_chunk + cur_a);
+ *current_chunk_ctr = nth;
+ }
+
ggml_barrier(params->threadpool);
- // 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];
continue;
}
- const char * src0_cur = (const char *) src0->data + cur_a*nb02;
-
- const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
+ const char * src0_cur = (const char *) src0->data + cur_a * nb02;
+ 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; // src1 rows
-
- // 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 nr0 = ne01;
+ const int64_t nr1 = cne1;
- const int64_t ir110 = dr1*ith1;
- const int64_t ir111 = MIN(ir110 + dr1, nr1);
-
- // threads with no work simply yield (not sure if it helps)
- //if (ir010 >= ir011 || ir110 >= ir111) {
- // sched_yield();
- // continue;
- //}
+ int chunk_size = 16;
+ if (nr0 == 1 || nr1 == 1) {
+ chunk_size = 64;
+ }
- // block-tiling attempt
- const int64_t blck_0 = 16;
- const int64_t blck_1 = 16;
+#if defined(__aarch64__)
+ // disable for ARM
+ const bool disable_chunking = true;
+#else
+ // disable for NUMA
+ const bool disable_chunking = ggml_is_numa();
+#endif // defined(__aarch64__)
- // attempt to reduce false-sharing (does not seem to make a difference)
- float tmp[16];
+ int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size;
+ int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size;
- 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 _i12 = ir1; // logical row index for this expert
+ if (nchunk0 * nchunk1 < nth * 4 || disable_chunking) {
+ nchunk0 = nr0 > nr1 ? nth : 1;
+ nchunk1 = nr0 > nr1 ? 1 : nth;
+ }
- struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12);
- const int id = row_mapping.i1; // selected expert index
+ const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
+ const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1;
- const int64_t i11 = id % ne11;
- const int64_t i12 = row_mapping.i2; // row index in src1
+ int current_chunk = ith;
- const int64_t i1 = id; // selected expert index
- const int64_t i2 = i12; // row
+ atomic_int * current_chunk_ctr = (atomic_int *)(atomic_current_chunk + cur_a);
- // 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)*row_size
- : (i11*nb11 + i12*nb12));
+ while (current_chunk < nchunk0 * nchunk1) {
+ const int64_t ith0 = current_chunk % nchunk0;
+ const int64_t ith1 = current_chunk / nchunk0;
- float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2));
+ const int64_t ir0_start = dr0 * ith0;
+ const int64_t ir0_end = MIN(ir0_start + dr0, nr0);
- //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
- // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
- //}
+ const int64_t ir1_start = dr1 * ith1;
+ const int64_t ir1_end = MIN(ir1_start + dr1, nr1);
- for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
- vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1);
- }
+ ggml_compute_forward_mul_mat_id_one_chunk(
+ dst, src0, src1, ids, cur_a,
+ ir0_start, ir0_end, ir1_start, ir1_end,
+ src0_cur, matrix_rows, row_size, src1_cont, wdata
+ );
- memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
- }
+ if (nth >= nchunk0 * nchunk1) {
+ break;
}
+
+ current_chunk = atomic_fetch_add_explicit(current_chunk_ctr, 1, memory_order_relaxed);
}
}
-
-#undef MMID_MATRIX_ROW
}
// ggml_compute_forward_out_prod
cur = 0;
const struct ggml_tensor * src0 = node->src[0];
const struct ggml_tensor * src1 = node->src[1];
+ const struct ggml_tensor * ids = node->src[2];
const enum ggml_type vec_dot_type = type_traits_cpu[src0->type].vec_dot_type;
+ const int n_as = src0->ne[2];
+ // src1
if (src1->type != vec_dot_type) {
- cur += ggml_row_size(vec_dot_type, ggml_nelements(src1));
+ cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)) + sizeof(int64_t);
}
- const int n_as = src0->ne[2];
- cur += GGML_PAD(cur, sizeof(int64_t)); // align
- cur += n_as * sizeof(int64_t); // matrix_row_counts
- cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows
+ // matrix_row_counts
+ cur += n_as * sizeof(int64_t) + sizeof(int64_t);
+ // matrix_rows
+ cur += n_as*ids->ne[0]*ids->ne[1]*sizeof(struct mmid_row_mapping) + sizeof(int64_t);
+ // atomic_current_chunk
+ cur += CACHE_LINE_SIZE*n_as + CACHE_LINE_SIZE;
} break;
case GGML_OP_OUT_PROD:
{
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