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ggml-cpu : add chunking support to mul_mat_id (llama/11666)
* ggml-cpu : add chunking support to mul_mat_id * allocate chunk counter in wdata parallelize src1 quantization by column to allows parallelization even when there is only one row * disable for arm * cleanup * better way to disable for arm * fix uninitialized counter when using 1 thread only * revert test-backend-ops changes
This commit is contained in:
Diego Devesa
Georgi Gerganov
parent
4fac43fe00
commit
d2c5154bb5
@ -7,10 +7,8 @@
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#include "ggml-cpu-impl.h"
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#include "ggml-cpu.h"
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#include "ggml-impl.h"
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#include "ggml-quants.h"
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#include "ggml-cpu-quants.h"
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#include "ggml-threading.h"
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#include "amx/amx.h"
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#include "ggml.h"
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#if defined(_MSC_VER) || defined(__MINGW32__)
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@ -1291,7 +1289,7 @@ struct ggml_threadpool {
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atomic_int n_graph; // incremented when there is work to be done (i.e each graph)
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atomic_int GGML_CACHE_ALIGN n_barrier;
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atomic_int GGML_CACHE_ALIGN n_barrier_passed;
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atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads.
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atomic_int GGML_CACHE_ALIGN current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads.
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// these are atomic as an annotation for thread-sanitizer
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atomic_bool stop; // Used for stopping the threadpool altogether
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@ -7490,6 +7488,7 @@ UseGgmlGemm1:;
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if (src1->type != vec_dot_type) {
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char * wdata = params->wdata;
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const size_t nbw0 = ggml_type_size(vec_dot_type);
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const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
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const size_t nbw2 = nbw1*ne11;
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const size_t nbw3 = nbw2*ne12;
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@ -7497,6 +7496,7 @@ UseGgmlGemm1:;
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assert(params->wsize >= ne13*nbw3);
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GGML_ASSERT(src1->type == GGML_TYPE_F32);
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#if 0
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for (int64_t i13 = 0; i13 < ne13; ++i13) {
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for (int64_t i12 = 0; i12 < ne12; ++i12) {
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for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
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@ -7506,6 +7506,20 @@ UseGgmlGemm1:;
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}
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}
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}
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#else
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for (int64_t i13 = 0; i13 < ne13; ++i13) {
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for (int64_t i12 = 0; i12 < ne12; ++i12) {
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for (int64_t i11 = 0; i11 < ne11; ++i11) {
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size_t bs = ggml_blck_size(vec_dot_type);
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int64_t ne10_block_start = (ith * ne10/bs) / nth;
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int64_t ne10_block_end = ((ith + 1) * ne10/bs) / nth;
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from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + ne10_block_start*bs*nb10),
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(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1 + ne10_block_start*nbw0),
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(ne10_block_end - ne10_block_start) * bs);
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}
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}
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}
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#endif
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}
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if (ith == 0) {
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@ -7593,7 +7607,6 @@ UseGgmlGemm2:;
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if ((nr0 % 2 != 0) || (ne11 % 2 != 0) || ((ir0_end - ir0_start) % 2 != 0) || ((ir1_end - ir1_start) % 2 != 0)) {
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num_rows_per_vec_dot = 1;
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}
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ggml_compute_forward_mul_mat_one_chunk(params, dst, src0->type, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end);
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if (nth >= nchunk0 * nchunk1) {
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@ -7606,6 +7619,84 @@ UseGgmlGemm2:;
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// ggml_compute_forward_mul_mat_id
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#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ids->ne[0]*ids->ne[1] + (i1)]
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struct mmid_row_mapping {
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int32_t i1;
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int32_t i2;
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};
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static void ggml_compute_forward_mul_mat_id_one_chunk(
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struct ggml_tensor * dst,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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const struct ggml_tensor * ids,
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const int64_t cur_a,
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const int64_t ir0_start,
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const int64_t ir0_end,
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const int64_t ir1_start,
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const int64_t ir1_end,
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const char * src0_cur,
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const struct mmid_row_mapping * matrix_rows,
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const size_t row_size,
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const bool src1_cont,
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const void * wdata) {
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GGML_TENSOR_BINARY_OP_LOCALS
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const enum ggml_type type = src0->type;
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ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot;
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enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;
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const int64_t blck_0 = 16;
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const int64_t blck_1 = 16;
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float tmp[16];
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for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
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for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
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for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ++ir1) {
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const int64_t _i12 = ir1; // logical row index for this expert
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struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12);
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const int id = row_mapping.i1; // selected expert index
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const int64_t i11 = id % ne11;
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const int64_t i12 = row_mapping.i2; // row index in src1
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const int64_t i1 = id; // selected expert index
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const int64_t i2 = i12; // row
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// desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
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// if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
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// the original src1 data pointer, so we should index using the indices directly
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// TODO: this is a bit of a hack, we should probably have a better way to handle this
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const char * src1_col = (const char *) wdata +
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(src1_cont || src1->type != vec_dot_type
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? (i11 + i12*ne11)*row_size
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: (i11*nb11 + i12*nb12));
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float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2));
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for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) {
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vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1);
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}
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memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir0_end) - iir0)*sizeof(float));
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}
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}
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}
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}
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static void * incr_ptr_aligned(void ** p, size_t size, size_t align) {
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void * ptr = *p;
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ptr = (void *) GGML_PAD((uintptr_t) ptr, align);
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*p = (void *) ((char *) ptr + size);
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return ptr;
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}
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static void ggml_compute_forward_mul_mat_id(
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const struct ggml_compute_params * params,
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struct ggml_tensor * dst) {
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@ -7623,7 +7714,6 @@ static void ggml_compute_forward_mul_mat_id(
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const bool src1_cont = ggml_is_contiguous(src1);
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ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot;
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enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;
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ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float;
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@ -7641,21 +7731,27 @@ static void ggml_compute_forward_mul_mat_id(
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const int n_ids = ids->ne[0]; // n_expert_used
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const int n_as = ne02; // n_expert
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char * wdata_src1_end = (src1->type == vec_dot_type) ?
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(char *) params->wdata :
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(char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));
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void * wdata_cur = params->wdata;
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struct mmid_row_mapping {
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int32_t i1;
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int32_t i2;
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};
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if (src1->type != vec_dot_type) {
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incr_ptr_aligned(&wdata_cur, ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));
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}
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int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
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struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11]
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int64_t * matrix_row_counts = // [n_as]
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incr_ptr_aligned(&wdata_cur, n_as*sizeof(int64_t), sizeof(int64_t));
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struct mmid_row_mapping * matrix_rows = // [n_as][ids->ne[0]*ids->ne[1]]
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incr_ptr_aligned(&wdata_cur, n_as*ids->ne[0]*ids->ne[1]*sizeof(struct mmid_row_mapping), sizeof(int64_t));
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char (*atomic_current_chunk)[CACHE_LINE_SIZE] = // [n_as]
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incr_ptr_aligned(&wdata_cur, CACHE_LINE_SIZE * n_as, CACHE_LINE_SIZE);
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GGML_ASSERT(params->wsize >= (size_t)((char *) wdata_cur - (char *) params->wdata));
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if (src1->type != vec_dot_type) {
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char * wdata = params->wdata;
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const size_t nbw0 = ggml_type_size(vec_dot_type);
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const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
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const size_t nbw2 = nbw1*ne11;
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const size_t nbw3 = nbw2*ne12;
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@ -7663,19 +7759,32 @@ static void ggml_compute_forward_mul_mat_id(
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assert(params->wsize >= ne13*nbw3);
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GGML_ASSERT(src1->type == GGML_TYPE_F32);
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#if 0
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for (int64_t i13 = 0; i13 < ne13; ++i13) {
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for (int64_t i12 = 0; i12 < ne12; ++i12) {
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for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
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for (int64_t i12 = ith; i12 < ne12; i12 += nth) {
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for (int64_t i11 = 0; i11 < ne11; ++i11) {
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from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
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(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
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ne10);
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}
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}
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}
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#else
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for (int64_t i13 = 0; i13 < ne13; ++i13) {
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for (int64_t i12 = 0; i12 < ne12; ++i12) {
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for (int64_t i11 = 0; i11 < ne11; ++i11) {
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size_t bs = ggml_blck_size(vec_dot_type);
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int64_t ne10_block_start = (ith * ne10/bs) / nth;
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int64_t ne10_block_end = ((ith + 1) * ne10/bs) / nth;
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from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + ne10_block_start*bs*nb10),
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(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1 + ne10_block_start*nbw0),
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(ne10_block_end - ne10_block_start) * bs);
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}
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}
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}
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#endif
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}
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#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)]
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if (ith == 0) {
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// initialize matrix_row_counts
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memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
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@ -7693,9 +7802,14 @@ static void ggml_compute_forward_mul_mat_id(
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}
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}
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// reset current_chunk
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for (int cur_a = ith; cur_a < n_as; cur_a += nth) {
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atomic_int * current_chunk_ctr = (atomic_int *)(atomic_current_chunk + cur_a);
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*current_chunk_ctr = nth;
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}
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ggml_barrier(params->threadpool);
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// compute each matrix multiplication in sequence
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for (int cur_a = 0; cur_a < n_as; ++cur_a) {
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const int64_t cne1 = matrix_row_counts[cur_a];
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@ -7703,84 +7817,64 @@ static void ggml_compute_forward_mul_mat_id(
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continue;
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}
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const char * src0_cur = (const char *) src0->data + cur_a*nb02;
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const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
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const char * src0_cur = (const char *) src0->data + cur_a * nb02;
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const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
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const size_t row_size = ggml_row_size(vec_dot_type, ne10);
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const int64_t nr0 = ne01; // src0 rows
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const int64_t nr1 = cne1; // src1 rows
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const int64_t nr0 = ne01;
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const int64_t nr1 = cne1;
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// distribute the thread work across the inner or outer loop based on which one is larger
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int chunk_size = 16;
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if (nr0 == 1 || nr1 == 1) {
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chunk_size = 64;
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}
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const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
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const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows
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#if defined(__aarch64__)
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// disable for ARM
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const bool disable_chunking = true;
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#else
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// disable for NUMA
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const bool disable_chunking = ggml_is_numa();
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#endif // defined(__aarch64__)
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const int64_t ith0 = ith % nth0;
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const int64_t ith1 = ith / nth0;
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int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size;
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int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size;
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const int64_t dr0 = (nr0 + nth0 - 1)/nth0;
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const int64_t dr1 = (nr1 + nth1 - 1)/nth1;
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if (nchunk0 * nchunk1 < nth * 4 || disable_chunking) {
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nchunk0 = nr0 > nr1 ? nth : 1;
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nchunk1 = nr0 > nr1 ? 1 : nth;
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}
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const int64_t ir010 = dr0*ith0;
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const int64_t ir011 = MIN(ir010 + dr0, nr0);
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const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
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const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1;
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const int64_t ir110 = dr1*ith1;
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const int64_t ir111 = MIN(ir110 + dr1, nr1);
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int current_chunk = ith;
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// threads with no work simply yield (not sure if it helps)
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//if (ir010 >= ir011 || ir110 >= ir111) {
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// sched_yield();
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// continue;
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//}
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atomic_int * current_chunk_ctr = (atomic_int *)(atomic_current_chunk + cur_a);
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// block-tiling attempt
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const int64_t blck_0 = 16;
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const int64_t blck_1 = 16;
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while (current_chunk < nchunk0 * nchunk1) {
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const int64_t ith0 = current_chunk % nchunk0;
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const int64_t ith1 = current_chunk / nchunk0;
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// attempt to reduce false-sharing (does not seem to make a difference)
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float tmp[16];
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const int64_t ir0_start = dr0 * ith0;
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const int64_t ir0_end = MIN(ir0_start + dr0, nr0);
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for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
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for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
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for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
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const int64_t _i12 = ir1; // logical row index for this expert
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const int64_t ir1_start = dr1 * ith1;
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const int64_t ir1_end = MIN(ir1_start + dr1, nr1);
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struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12);
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const int id = row_mapping.i1; // selected expert index
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ggml_compute_forward_mul_mat_id_one_chunk(
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dst, src0, src1, ids, cur_a,
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ir0_start, ir0_end, ir1_start, ir1_end,
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src0_cur, matrix_rows, row_size, src1_cont, wdata
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);
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const int64_t i11 = id % ne11;
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const int64_t i12 = row_mapping.i2; // row index in src1
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const int64_t i1 = id; // selected expert index
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const int64_t i2 = i12; // row
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// desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
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// if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
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// the original src1 data pointer, so we should index using the indices directly
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// TODO: this is a bit of a hack, we should probably have a better way to handle this
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const char * src1_col = (const char *) wdata +
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(src1_cont || src1->type != vec_dot_type
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? (i11 + i12*ne11)*row_size
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: (i11*nb11 + i12*nb12));
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float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2));
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//for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
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// vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
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//}
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for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
|
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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, 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
|
||||
@ -13713,14 +13807,19 @@ struct ggml_cplan ggml_graph_plan(
|
||||
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;
|
||||
if (src1->type != vec_dot_type) {
|
||||
cur += ggml_row_size(vec_dot_type, ggml_nelements(src1));
|
||||
}
|
||||
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
|
||||
// src1
|
||||
if (src1->type != vec_dot_type) {
|
||||
cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)) + sizeof(int64_t);
|
||||
}
|
||||
// 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:
|
||||
{
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user