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ggml : mul mat tweaks (#2372)
* ggml : mul mat wip ggml-ci * ggml : alternative thread distribution for mul_mat ggml-ci * ggml : mul_mat block tiling attempt * ggml : mul_mat threads yield ggml-ci
This commit is contained in:
Georgi Gerganov
GitHub
parent
60baff7c85
commit
93356bdb7a
1 changed files with 74 additions and 50 deletions
124
ggml.c
124
ggml.c
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@ -10731,72 +10731,96 @@ static void ggml_compute_forward_mul_mat(
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return;
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return;
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}
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}
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// parallelize by src0 rows
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const int64_t dr = (ne01 + nth - 1)/nth;
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const int64_t ir10 = dr*ith;
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const int64_t ir11 = MIN(ir10 + dr, ne01);
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// src1 rows
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const int64_t nr1 = ne11*ne12*ne13;
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const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
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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 = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
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const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
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for (int64_t ir1 = 0; ir1 < nr1; ++ir1) {
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const int64_t nr0 = ne01; // src0 rows
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const int64_t i13 = (ir1/(ne12*ne11));
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const int64_t nr1 = ne11*ne12*ne13; // src1 rows
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const int64_t i12 = (ir1 - i13*ne12*ne11)/ne11;
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const int64_t i11 = (ir1 - i13*ne12*ne11 - i12*ne11);
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const int64_t ir0 = (ir1/ne11)%(ne02*ne03);
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//printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
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const int64_t i03 = (ir0/(ne02));
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// Hack for "Falcon multi-query-attention key stutter" / alternative to ggml_repeat2.
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// See https://github.com/ggerganov/llama.cpp/issues/1602#issuecomment-1606087470:
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// GG: this is likely the correct way to broadcast, though need some more thought
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// therefore leaving the comments to remind us for now
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const int64_t i02 = (i12 / (ne12 / ne02));
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// Original from PR/224 (and also essential/correct for non-broadcast matmuls in Falcon)
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// const int64_t i02 = (ir0 - i03*ne02);
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const int64_t i1 = i11;
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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 i2 = i12;
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const int64_t i3 = i13;
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const char * src0_row = (const char *) src0->data + ( 0 + i02*nb02 + i03*nb03 );
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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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// desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
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const int64_t ith0 = ith % nth0;
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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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const int64_t ith1 = ith / nth0;
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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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const int64_t dr0 = (nr0 + nth0 - 1)/nth0;
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const int64_t dr1 = (nr1 + nth1 - 1)/nth1;
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for (int64_t ir = ir10; ir < ir11; ++ir) {
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const int64_t ir010 = dr0*ith0;
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vec_dot(ne00, &dst_col[ir], src0_row + ir*nb01, src1_col);
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const int64_t ir011 = MIN(ir010 + dr0, nr0);
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}
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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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return;
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}
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}
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//int64_t t1 = ggml_time_us();
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assert(ne12 % ne02 == 0);
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//static int64_t acc = 0;
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assert(ne13 % ne03 == 0);
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//acc += t1 - t0;
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//if (t1 - t0 > 10) {
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// printf("\n");
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// printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
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// printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
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// printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
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// printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
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// broadcast factors
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//}
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const int64_t r2 = ne12/ne02;
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const int64_t r3 = ne13/ne03;
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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*ne11));
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const int64_t i12 = (ir1 - i13*ne12*ne11)/ne11;
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const int64_t i11 = (ir1 - i13*ne12*ne11 - i12*ne11);
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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->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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}
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// ggml_compute_forward_out_prod
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// ggml_compute_forward_out_prod
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static void ggml_compute_forward_out_prod_f32(
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static void ggml_compute_forward_out_prod_f32(
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const struct ggml_compute_params * params,
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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 * src0,
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