ggml : group mul_mat_id rows by matrix (cpu only) (#4480)
* ggml : group mul_mat_id rows by matrix (cpu only) * remove mmid parameters from mm forward * store row groups in wdata and calculate only once in GGML_TASK_INIT ggml-ci
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slaren
GitHub
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6744dbe924
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ee4725a686
237
ggml.c
237
ggml.c
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@ -9580,16 +9580,11 @@ static bool ggml_compute_forward_mul_mat_use_blas(
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}
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#endif
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// off1 = offset in i11 and i1
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// cne1 = ne11 and ne1
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// in a normal matrix multiplication, off1 = 0 and cne1 = ne1
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// during GGML_TASK_INIT, the full src1 is converted regardless of off1 and cne1
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static void ggml_compute_forward_mul_mat(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst,
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int64_t off1, int64_t cne1) {
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struct ggml_tensor * dst) {
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int64_t t0 = ggml_perf_time_us();
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UNUSED(t0);
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@ -9657,9 +9652,9 @@ static void ggml_compute_forward_mul_mat(
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const int64_t i03 = i13/r3;
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const int64_t i02 = i12/r2;
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const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
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const float * y = (float *) ((char *) src1->data + off1*nb11 + i12*nb12 + i13*nb13);
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float * d = (float *) ((char *) dst->data + off1*nb1 + i12*nb2 + i13*nb3);
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const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
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const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
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float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
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if (type != GGML_TYPE_F32) {
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float * const wdata = params->wdata;
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@ -9676,7 +9671,7 @@ static void ggml_compute_forward_mul_mat(
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}
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cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
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cne1, ne01, ne10,
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ne1, ne01, ne10,
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1.0f, y, ne10,
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x, ne00,
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0.0f, d, ne01);
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@ -9717,8 +9712,8 @@ static void ggml_compute_forward_mul_mat(
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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*ne12*ne13; // src1 rows
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const int64_t nr0 = ne01; // src0 rows
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const int64_t nr1 = ne1*ne12*ne13; // src1 rows
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//printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
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@ -9760,9 +9755,9 @@ static void ggml_compute_forward_mul_mat(
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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 i13 = (ir1/(ne12*cne1));
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const int64_t i12 = (ir1 - i13*ne12*cne1)/cne1;
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const int64_t i11 = (ir1 - i13*ne12*cne1 - i12*cne1) + off1;
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const int64_t i13 = (ir1/(ne12*ne1));
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const int64_t i12 = (ir1 - i13*ne12*ne1)/ne1;
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const int64_t i11 = (ir1 - i13*ne12*ne1 - i12*ne1);
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// broadcast src0 into src1
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const int64_t i03 = i13/r3;
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@ -9802,28 +9797,191 @@ static void ggml_compute_forward_mul_mat(
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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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const struct ggml_tensor * src0,
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const struct ggml_tensor * ids,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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// during GGML_TASK_INIT the entire src1 is converted to vec_dot_type
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ggml_compute_forward_mul_mat(params, dst->src[2], src1, dst, 0, dst->ne[1]);
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return;
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}
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const struct ggml_tensor * src0 = dst->src[2]; // only for GGML_TENSOR_BINARY_OP_LOCALS
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const struct ggml_tensor * ids = src0;
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GGML_TENSOR_BINARY_OP_LOCALS
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const int ith = params->ith;
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const int nth = params->nth;
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const enum ggml_type type = src0->type;
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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[type].vec_dot;
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enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
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ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
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GGML_ASSERT(ne0 == ne01);
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GGML_ASSERT(ne1 == ne11);
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GGML_ASSERT(ne2 == ne12);
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GGML_ASSERT(ne3 == ne13);
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// we don't support permuted src0 or src1
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GGML_ASSERT(nb00 == ggml_type_size(type));
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GGML_ASSERT(nb10 == ggml_type_size(src1->type));
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// dst cannot be transposed or permuted
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GGML_ASSERT(nb0 == sizeof(float));
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GGML_ASSERT(nb0 <= nb1);
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GGML_ASSERT(nb1 <= nb2);
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GGML_ASSERT(nb2 <= nb3);
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// broadcast factors
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const int64_t r2 = ne12/ne02;
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const int64_t r3 = ne13/ne03;
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// row groups
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const int id = ggml_get_op_params_i32(dst, 0);
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const int n_as = ggml_get_op_params_i32(dst, 1);
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for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
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const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
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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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GGML_ASSERT(row_id >= 0 && row_id < n_as);
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int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
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int64_t * matrix_rows = matrix_row_counts + n_as; // [n_as][ne11]
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const struct ggml_tensor * src0_row = dst->src[row_id + 2];
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ggml_compute_forward_mul_mat(params, src0_row, src1, dst, i01, 1);
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#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
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if (params->type == GGML_TASK_INIT) {
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char * wdata = params->wdata;
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if (src1->type != vec_dot_type) {
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const size_t row_size = ggml_row_size(vec_dot_type, ne10);
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assert(params->wsize >= ne11*ne12*ne13*row_size);
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assert(src1->type == GGML_TYPE_F32);
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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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from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
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wdata += row_size;
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}
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}
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}
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}
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// initialize matrix_row_counts
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GGML_ASSERT(wdata == wdata_src1_end);
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memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
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// group rows by src0 matrix
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for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
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const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
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GGML_ASSERT(row_id >= 0 && row_id < n_as);
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MMID_MATRIX_ROW(row_id, matrix_row_counts[row_id]) = i01;
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matrix_row_counts[row_id] += 1;
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}
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return;
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}
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if (params->type == GGML_TASK_FINALIZE) {
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return;
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}
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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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if (cne1 == 0) {
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continue;
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}
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const struct ggml_tensor * src0_cur = dst->src[cur_a + 2];
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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*ne12*ne13; // src1 rows
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//printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
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// distribute the thread work across the inner or outer loop based on which one is larger
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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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const int64_t ith0 = ith % nth0;
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const int64_t ith1 = ith / nth0;
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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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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 ir110 = dr1*ith1;
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const int64_t ir111 = MIN(ir110 + dr1, nr1);
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//printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);
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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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assert(ne12 % ne02 == 0);
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assert(ne13 % ne03 == 0);
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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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// attempt to reduce false-sharing (does not seem to make a difference)
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float tmp[16];
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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 i13 = (ir1/(ne12*cne1)); // Note: currently, src1 is always a matrix
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const int64_t i12 = (ir1 - i13*ne12*cne1)/cne1;
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const int64_t _i11 = (ir1 - i13*ne12*cne1 - i12*cne1);
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const int64_t i11 = MMID_MATRIX_ROW(cur_a, _i11);
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// broadcast src0 into src1
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const int64_t i03 = i13/r3;
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const int64_t i02 = i12/r2;
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const int64_t i1 = i11;
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const int64_t i2 = i12;
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const int64_t i3 = i13;
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const char * src0_row = (const char *) src0_cur->data + (0 + i02*nb02 + i03*nb03);
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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 + i13*ne12*ne11)*row_size
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: (i11*nb11 + i12*nb12 + i13*nb13));
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float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
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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], src0_row + ir0*nb01, src1_col);
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}
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memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
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}
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}
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}
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}
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#undef MMID_MATRIX_ROW
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}
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// ggml_compute_forward_out_prod
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@ -14191,7 +14349,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
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} break;
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case GGML_OP_MUL_MAT:
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{
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ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor, 0, tensor->ne[1]);
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ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
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} break;
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case GGML_OP_MUL_MAT_ID:
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{
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@ -15991,7 +16149,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
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} break;
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case GGML_OP_MUL_MAT_ID:
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{
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// FIXME: blas
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n_tasks = n_threads;
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} break;
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case GGML_OP_OUT_PROD:
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@ -16325,20 +16482,16 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
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} break;
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case GGML_OP_MUL_MAT_ID:
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{
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const struct ggml_tensor * a = node->src[2];
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const struct ggml_tensor * b = node->src[1];
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const enum ggml_type vec_dot_type = type_traits[a->type].vec_dot_type;
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#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
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if (ggml_compute_forward_mul_mat_use_blas(a, b, node)) {
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if (a->type != GGML_TYPE_F32) {
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// here we need memory just for single 2D matrix from src0
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cur = ggml_type_size(GGML_TYPE_F32)*(a->ne[0]*a->ne[1]);
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}
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} else
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#endif
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if (b->type != vec_dot_type) {
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cur = ggml_row_size(vec_dot_type, ggml_nelements(b));
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const struct ggml_tensor * src0 = node->src[2];
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const struct ggml_tensor * src1 = node->src[1];
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const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type;
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if (src1->type != vec_dot_type) {
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cur = ggml_row_size(vec_dot_type, ggml_nelements(src1));
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}
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const int n_as = ggml_get_op_params_i32(node, 1);
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cur = GGML_PAD(cur, sizeof(int64_t)); // align
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cur += n_as * sizeof(int64_t); // matrix_row_counts
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cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows
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} break;
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case GGML_OP_OUT_PROD:
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{
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