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hexagon: generic dims collapse/reshape for unary, binary and matmul ops (#31071)

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* hex-tools: support for --compare option in the profile.py

* hex-unary: improve collapse logic to also do splits if nrows is small

* hex-colapse: handle all colapsing at the host and make it generic

* hex-colapse: remove leftover unary helper
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Max Krasnyansky authored and GitHub committed 2026-10-25 11:11:48 +02:00
1 parent 9a4580b61c
commit f0fb5984b0
9 files changed
+441 -200
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ggml/src/ggml-hexagon/ggml-hexagon.cpp
+1 -1
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@@ -2397,7 +2397,7 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
}
}
GGML_ASSERT(htp_kparams_solve(sess, node.node, node.kernel_params));
GGML_ASSERT(htp_kparams_solve(sess, node));
if (node.opcode == HTP_OP_CPY && node.kparams<htp_copy_kernel_params>()->total_elems == 0) {
continue;
}
ggml/src/ggml-hexagon/htp-fusion.cpp
+25 -26
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@@ -192,15 +192,20 @@ static bool fuse_mul_mat_add(const ggml_hexagon_session * sess, htp_opnode & pre
if (!htp_match_consumer(prev, cur, /*commutative=*/ true, &src2)) return false;
if (src2->type != GGML_TYPE_F32) return false;
const ggml_tensor * src0 = prev.src0();
const ggml_tensor * src1 = prev.src1();
const ggml_tensor * dst = cur.dst();
if (!ggml_are_same_shape(prev.dst(), dst)) return false;
const ggml_tensor * src0 = prev.node->src[0];
const ggml_tensor * src1 = prev.node->src[1];
const ggml_tensor * dst = cur.node;
if (!ggml_are_same_shape(prev.node, dst)) return false;
const bool prev_collapsed = htp_matmul_can_collapse(src0, src1, prev.node);
const bool cur_collapsed = htp_matmul_add_can_collapse(src0, src1, src2, dst);
if (prev_collapsed != cur_collapsed) {
return false;
}
const auto * orig_kparams = prev.kparams<htp_mm_kernel_params>();
struct htp_mm_kernel_params kparams;
if (!htp_kparams_solve_matmul_add(sess, src0, src1, src2, dst, &kparams) ||
orig_kparams->collapse != kparams.collapse) {
if (!htp_kparams_solve_matmul_add(sess, src0, src1, src2, dst, &kparams)) {
return false;
}
@@ -222,6 +227,7 @@ static bool fuse_mul_mat_add(const ggml_hexagon_session * sess, htp_opnode & pre
prev.opcode = HTP_OP_MUL_MAT_ADD;
prev.inputs = { src0, src1, src2 };
prev.outputs = { dst };
htp_opnode_collapse(sess, prev);
prev.fused.push_back(cur.node);
prev.set_kparams(kparams);
return true;
@@ -233,36 +239,31 @@ static bool fuse_mul_mat_nx(const ggml_hexagon_session * sess, htp_opnode & prev
const uint32_t curr_n = (uint32_t) prev.outputs.size();
if (curr_n >= HTP_OP_MAX_OUTPUTS || curr_n + 1 >= HTP_OP_MAX_INPUTS) return false;
const ggml_tensor * w0 = prev.inputs[0];
const ggml_tensor * x = prev.inputs[curr_n];
const ggml_tensor * w_in = cur.src0();
const ggml_tensor * x_in = cur.src1();
const ggml_tensor * d_in = cur.dst();
const ggml_tensor * w0 = prev.inputs[0];
const ggml_tensor * x_orig = prev.node->src[1];
const ggml_tensor * w_in = cur.node->src[0];
const ggml_tensor * x_in = cur.node->src[1];
const ggml_tensor * d_in = cur.node;
auto * orig_kparams = prev.kparams<htp_mm_kernel_params>();
const auto * cur_kparams = cur.kparams<htp_mm_kernel_params>();
if (x_in != x || w_in->type != w0->type || w_in->ne[0] != w0->ne[0] ||
if (x_in != x_orig || w_in->type != w0->type || w_in->ne[0] != w0->ne[0] ||
w0->ne[2] != 1 || w0->ne[3] != 1 || w_in->ne[2] != 1 || w_in->ne[3] != 1 ||
htp_tensor_is_tiled(w_in) != htp_tensor_is_tiled(w0) ||
orig_kparams->n_hmx != cur_kparams->n_hmx) {
return false;
}
const bool collapse = orig_kparams->collapse && htp_matmul_can_collapse(w_in, x, d_in);
if ((x->ne[2] > 1 || x->ne[3] > 1) && !collapse) return false;
const bool collapse = htp_matmul_can_collapse(w0, x_orig, prev.node) && htp_matmul_can_collapse(w_in, x_in, d_in);
if ((x_orig->ne[2] > 1 || x_orig->ne[3] > 1) && !collapse) return false;
if (prev.opcode == HTP_OP_MUL_MAT_NX) {
orig_kparams->n_weights = curr_n + 1;
} else {
struct htp_mm_kernel_params kparams;
if (collapse) {
const ggml_tensor x_collapsed = htp_tensor_collapse_rows(x);
if (!htp_kparams_solve_mmnx(sess, w0, &x_collapsed, curr_n + 1, &kparams)) return false;
kparams.collapse = 1;
} else if (!htp_kparams_solve_mmnx(sess, w0, x, curr_n + 1, &kparams)) {
return false;
}
const ggml_tensor x_collapsed = collapse ? htp_tensor_collapse_rows(x_orig) : *x_orig;
if (!htp_kparams_solve_mmnx(sess, w0, &x_collapsed, curr_n + 1, &kparams)) return false;
if (!is_supported_mul_mat_nx_kernel(w0, &kparams)) return false;
prev.set_kparams(kparams);
@@ -270,7 +271,7 @@ static bool fuse_mul_mat_nx(const ggml_hexagon_session * sess, htp_opnode & prev
}
prev.inputs.insert(prev.inputs.begin() + curr_n, w_in);
prev.outputs.push_back(d_in);
prev.outputs.push_back(collapse ? prev.add_dummy(htp_tensor_collapse_rows(d_in)) : d_in);
prev.fused.push_back(cur.node);
return true;
}
@@ -415,15 +416,13 @@ static bool fuse_unary_mul_glu(const ggml_hexagon_session * sess, htp_opnode & p
const ggml_tensor * dst = cur.dst();
if (htp_inputs_overlap_unsafe(prev, dst, up)) return false;
struct htp_glu_kernel_params kparams;
if (!htp_kparams_solve_unary_mul_glu(sess, glu_op, gate, up, dst, &kparams)) return false;
prev.opcode = glu_op;
prev.inputs = { gate, up };
prev.outputs = { dst };
if (!htp_kparams_solve(sess, prev)) return false;
prev.fused.push_back(cur.node);
memset(prev.params, 0, sizeof(prev.params));
prev.set_kparams(kparams);
return true;
}
ggml/src/ggml-hexagon/htp-kparams.cpp
+222 -52
View File
@@ -1,6 +1,7 @@
#define _USE_MATH_DEFINES
#include "htp-kparams.h"
#include "htp-opnode.h"
#include "ggml-impl.h"
#include <algorithm>
@@ -1023,14 +1024,12 @@ static bool htp_kparams_solve_matmul(
if (htp_matmul_can_collapse(src0, src1, dst)) {
const ggml_tensor src1_collapsed = htp_tensor_collapse_rows(src1);
const ggml_tensor dst_collapsed = htp_tensor_collapse_rows(dst);
const bool ok = htp_kparams_solve_matmul_impl(sess, src0, &src1_collapsed, &dst_collapsed, 0, 0, kparams);
kparams->collapse = 1;
return ok;
return htp_kparams_solve_matmul_impl(sess, src0, &src1_collapsed, &dst_collapsed, 0, 0, kparams);
}
return htp_kparams_solve_matmul_impl(sess, src0, src1, dst, 0, 0, kparams);
}
static bool htp_matmul_add_can_collapse(
bool htp_matmul_add_can_collapse(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
const struct ggml_tensor * src2,
@@ -1068,9 +1067,7 @@ bool htp_kparams_solve_matmul_add(
: (src2 ? *src2 : ggml_tensor{});
const struct ggml_tensor * p_src2 = src2 ? &src2_collapsed : nullptr;
const size_t src2_size = p_src2 ? hex_round_up(ggml_nbytes(p_src2), 128) : 0;
const bool ok = htp_kparams_solve_matmul_impl(sess, src0, &src1_collapsed, &dst_collapsed, p_src2 ? p_src2->nb[1] : 0, src2_size, kparams);
kparams->collapse = 1;
return ok;
return htp_kparams_solve_matmul_impl(sess, src0, &src1_collapsed, &dst_collapsed, p_src2 ? p_src2->nb[1] : 0, src2_size, kparams);
}
const size_t src2_size = src2 ? hex_round_up(ggml_nbytes(src2), 128) : 0;
return htp_kparams_solve_matmul_impl(sess, src0, src1, dst, src2 ? src2->nb[1] : 0, src2_size, kparams);
@@ -1405,47 +1402,103 @@ static bool htp_kparams_solve_binary(
}
// collapse contiguous dims of elementwise ops so that rows fill at least one HVX vector
static void htp_unary_collapse_dims(struct ggml_tensor * src, struct ggml_tensor * dst, size_t elem_size) {
if (src->nb[0] != elem_size || dst->nb[0] != elem_size) {
static void htp_collapse_dims(struct ggml_tensor ** tensors, size_t n_tensors, size_t elem_size, uint32_t n_threads) {
if (n_tensors == 0 || !tensors[0]) {
return;
}
for (size_t t = 0; t < n_tensors; t++) {
if (!tensors[t] || tensors[t]->nb[0] != elem_size) {
return;
}
if (t > 0 && !ggml_are_same_shape(tensors[0], tensors[t])) {
return;
}
}
while (src->ne[1] > 1 || src->ne[2] > 1 || src->ne[3] > 1) {
const size_t row_size = (size_t) src->ne[0] * elem_size;
const bool rows_contig = src->nb[1] == row_size && dst->nb[1] == (size_t) dst->ne[0] * elem_size;
const bool can_merge = (src->ne[1] == 1 && dst->ne[1] == 1) ||
(rows_contig && (src->ne[0] == 1 || (row_size < 128 && row_size * src->ne[1] <= 2048)));
struct ggml_tensor * t0 = tensors[0];
while (t0->ne[1] > 1 || t0->ne[2] > 1 || t0->ne[3] > 1) {
const size_t row_size = (size_t) t0->ne[0] * elem_size;
bool rows_contig = true;
for (size_t t = 0; t < n_tensors; t++) {
if (tensors[t]->nb[1] != (size_t) tensors[t]->ne[0] * elem_size) {
rows_contig = false;
break;
}
}
bool all_ne1_one = true;
for (size_t t = 0; t < n_tensors; t++) {
if (tensors[t]->ne[1] != 1) {
all_ne1_one = false;
break;
}
}
const bool can_merge = all_ne1_one ||
(rows_contig && (t0->ne[0] == 1 || (row_size < 128 && row_size * t0->ne[1] <= 2048)));
if (!can_merge) {
break;
}
src->ne[0] *= src->ne[1];
src->ne[1] = src->ne[2];
src->ne[2] = src->ne[3];
src->ne[3] = 1;
dst->ne[0] *= dst->ne[1];
dst->ne[1] = dst->ne[2];
dst->ne[2] = dst->ne[3];
dst->ne[3] = 1;
src->nb[1] = src->nb[2];
src->nb[2] = src->nb[3];
dst->nb[1] = dst->nb[2];
dst->nb[2] = dst->nb[3];
for (size_t t = 0; t < n_tensors; t++) {
tensors[t]->ne[0] *= tensors[t]->ne[1];
tensors[t]->ne[1] = tensors[t]->ne[2];
tensors[t]->ne[2] = tensors[t]->ne[3];
tensors[t]->ne[3] = 1;
tensors[t]->nb[1] = tensors[t]->nb[2];
tensors[t]->nb[2] = tensors[t]->nb[3];
tensors[t]->nb[3] = tensors[t]->nb[2] * tensors[t]->ne[2];
}
}
// ne1 too big to merge in full: merge it in chunks of k rows (k divides ne1)
const size_t row_size = (size_t) src->ne[0] * elem_size;
if (src->ne[1] > 1 && row_size < 128 && src->nb[1] == row_size && dst->nb[1] == row_size) {
const size_t row_size = (size_t) t0->ne[0] * elem_size;
bool rows_contig = true;
for (size_t t = 0; t < n_tensors; t++) {
if (tensors[t]->nb[1] != row_size) {
rows_contig = false;
break;
}
}
if (t0->ne[1] > 1 && row_size < 128 && rows_contig) {
int64_t k = 2048 / row_size;
while (k > 1 && src->ne[1] % k != 0) {
while (k > 1 && t0->ne[1] % k != 0) {
k--;
}
if (k > 1) {
src->ne[0] *= k;
src->ne[1] /= k;
src->nb[1] *= k;
dst->ne[0] *= k;
dst->ne[1] /= k;
dst->nb[1] *= k;
for (size_t t = 0; t < n_tensors; t++) {
tensors[t]->ne[0] *= k;
tensors[t]->ne[1] /= k;
tensors[t]->nb[1] *= k;
tensors[t]->nb[2] = tensors[t]->nb[1] * tensors[t]->ne[1];
tensors[t]->nb[3] = tensors[t]->nb[2] * tensors[t]->ne[2];
}
}
}
// nrows too small to saturate threads: split ne0 into k rows (k divides ne0)
const size_t row_size_curr = (size_t) t0->ne[0] * elem_size;
const uint32_t nrows = (uint32_t) (t0->ne[1] * t0->ne[2] * t0->ne[3]);
rows_contig = true;
for (size_t t = 0; t < n_tensors; t++) {
if (tensors[t]->ne[1] != 1 && tensors[t]->nb[1] != row_size_curr) {
rows_contig = false;
break;
}
}
if (nrows < n_threads && rows_contig && row_size_curr >= 4096) {
const size_t vec_elems = 128 / elem_size;
int64_t k = std::max<int64_t>(row_size_curr / 16384, (n_threads * 4 + nrows - 1) / nrows);
k = std::min<int64_t>(k, row_size_curr / 2048);
while (k > 1 && t0->ne[0] % (k * vec_elems) != 0) {
k--;
}
if (k > 1) {
for (size_t t = 0; t < n_tensors; t++) {
tensors[t]->ne[0] /= k;
tensors[t]->ne[1] *= k;
tensors[t]->nb[1] = tensors[t]->ne[0] * elem_size;
tensors[t]->nb[2] = tensors[t]->nb[1] * tensors[t]->ne[1];
tensors[t]->nb[3] = tensors[t]->nb[2] * tensors[t]->ne[2];
}
}
}
}
@@ -1507,19 +1560,6 @@ static bool htp_kparams_solve_unary_impl(
const size_t elem_size = ggml_type_size(src0->type);
struct ggml_tensor src0_c = *src0;
struct ggml_tensor dst_c = *dst;
if (htp_unary_is_elementwise(op)) {
htp_unary_collapse_dims(&src0_c, &dst_c, elem_size);
src0 = &src0_c;
dst = &dst_c;
for (int i = 0; i < HTP_OP_MAX_DIMS; i++) {
kparams->ne[i] = (uint32_t) src0->ne[i];
kparams->src0_nb[i] = (uint32_t) src0->nb[i];
kparams->dst_nb[i] = (uint32_t) dst->nb[i];
}
}
const size_t dst_data_row_size = dst->ne[0] * ggml_type_size(dst->type);
const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3];
@@ -1533,9 +1573,14 @@ static bool htp_kparams_solve_unary_impl(
const bool broadcast_weight = (op == HTP_OP_RMS_NORM_MUL) && (src1->ne[1] * src1->ne[2] * src1->ne[3] == 1);
kparams->broadcast_weight = broadcast_weight;
const bool src0_contig = ggml_is_contiguous_1(src0);
const bool src1_contig = (op == HTP_OP_RMS_NORM_MUL && !broadcast_weight) ? ggml_is_contiguous_1(src1) : true;
const uint32_t max_block = (src0_contig && src1_contig) ? src0_nrows : (uint32_t) src0->ne[1];
const bool src0_contig = ggml_is_contiguous_1(src0);
const bool src1_contig = (op == HTP_OP_RMS_NORM_MUL && !broadcast_weight) ? ggml_is_contiguous_1(src1) : true;
const uint32_t src0_nrows_per_thread = (src0_nrows + n_threads - 1) / n_threads;
const uint32_t min_block_rows = (uint32_t) (std::max<size_t>(1, 2048 / dst_data_row_size));
uint32_t max_block = (src0_contig && src1_contig) ? src0_nrows : (uint32_t) src0->ne[1];
if (src0_nrows_per_thread >= 2 * min_block_rows) {
max_block = (std::min)(max_block, (std::max)(min_block_rows, src0_nrows_per_thread / 4));
}
kparams->src0_contig = src0_contig ? 1 : 0;
kparams->src1_contig = src1_contig ? 1 : 0;
@@ -2000,11 +2045,17 @@ bool htp_kparams_solve_unary_mul_glu(
const size_t vtcm_size = htp_session_vtcm_size(sess);
const size_t row_size_aligned = hex_round_up(dst_row_size, 128);
const uint32_t vtcm_row_per_thread = (uint32_t) (vtcm_size / (n_threads * 3 * row_size_aligned));
uint32_t vtcm_row_per_thread = (uint32_t) (vtcm_size / (n_threads * 3 * row_size_aligned));
if (vtcm_row_per_thread < 2) {
return false;
}
const uint32_t src0_nrows_per_thread = (src0_nrows + n_threads - 1) / n_threads;
if (src0_nrows_per_thread >= 2 && vtcm_row_per_thread > src0_nrows_per_thread) {
uint32_t target_rpt = (src0_nrows_per_thread >= 4) ? (src0_nrows_per_thread / 2) & ~1u : 2;
vtcm_row_per_thread = (std::max)(2u, (std::min)(vtcm_row_per_thread, target_rpt));
}
struct htp_glu_vtcm_layout L;
htp_glu_vtcm_layout_build(&L, dst_row_size, n_threads, vtcm_row_per_thread);
@@ -3246,12 +3297,131 @@ static inline bool htp_kparams_invoke(
return SolveFn(sess, op, kparams ? (KParams *) kparams : &tmp);
}
void htp_opnode_collapse(const struct ggml_hexagon_session * sess, htp_opnode & node) {
const uint32_t n_threads = htp_session_n_threads(sess);
if (htp_op_is_unary(node.opcode) && htp_unary_is_elementwise(node.opcode)) {
if (node.inputs.empty() || !node.inputs[0] || node.outputs.empty() || !node.outputs[0]) {
return;
}
if (!ggml_are_same_shape(node.inputs[0], node.outputs[0])) {
return;
}
const size_t elem_size = ggml_type_size(node.inputs[0]->type);
struct ggml_tensor src0_c = *node.inputs[0];
struct ggml_tensor dst_c = *node.outputs[0];
struct ggml_tensor * tensors[2] = { &src0_c, &dst_c };
htp_collapse_dims(tensors, 2, elem_size, n_threads);
if (src0_c.ne[0] != node.inputs[0]->ne[0] || src0_c.ne[1] != node.inputs[0]->ne[1]) {
node.inputs[0] = node.add_dummy(src0_c);
node.outputs[0] = node.add_dummy(dst_c);
}
return;
}
if (htp_op_is_glu(node.opcode)) {
if (node.inputs.size() < 2 || !node.inputs[0] || !node.inputs[1] || node.outputs.empty() || !node.outputs[0]) {
return;
}
if (!ggml_are_same_shape(node.inputs[0], node.outputs[0]) ||
!ggml_are_same_shape(node.inputs[1], node.outputs[0])) {
return;
}
const size_t elem_size = ggml_type_size(node.inputs[0]->type);
struct ggml_tensor src0_c = *node.inputs[0];
struct ggml_tensor src1_c = *node.inputs[1];
struct ggml_tensor dst_c = *node.outputs[0];
struct ggml_tensor * tensors[3] = { &src0_c, &src1_c, &dst_c };
htp_collapse_dims(tensors, 3, elem_size, n_threads);
if (src0_c.ne[0] != node.inputs[0]->ne[0] || src0_c.ne[1] != node.inputs[0]->ne[1]) {
node.inputs[0] = node.add_dummy(src0_c);
node.inputs[1] = node.add_dummy(src1_c);
node.outputs[0] = node.add_dummy(dst_c);
}
return;
}
if (node.opcode == HTP_OP_MUL_MAT) {
if (node.inputs.size() < 2 || !node.inputs[0] || !node.inputs[1] || node.outputs.empty() || !node.outputs[0]) {
return;
}
const struct ggml_tensor * src0 = node.inputs[0];
const struct ggml_tensor * src1 = node.inputs[1];
const struct ggml_tensor * dst = node.outputs[0];
if (htp_matmul_can_collapse(src0, src1, dst)) {
node.inputs[1] = node.add_dummy(htp_tensor_collapse_rows(src1));
node.outputs[0] = node.add_dummy(htp_tensor_collapse_rows(dst));
}
return;
}
if (node.opcode == HTP_OP_MUL_MAT_ADD) {
if (node.inputs.size() < 2 || !node.inputs[0] || !node.inputs[1] || node.outputs.empty() || !node.outputs[0]) {
return;
}
const struct ggml_tensor * src0 = node.inputs[0];
const struct ggml_tensor * src1 = node.inputs[1];
const struct ggml_tensor * src2 = node.inputs.size() > 2 ? node.inputs[2] : nullptr;
const struct ggml_tensor * dst = node.outputs[0];
if (htp_matmul_add_can_collapse(src0, src1, src2, dst)) {
node.inputs[1] = node.add_dummy(htp_tensor_collapse_rows(src1));
node.outputs[0] = node.add_dummy(htp_tensor_collapse_rows(dst));
if (src2 && (src2->ne[1] * src2->ne[2] * src2->ne[3] > 1)) {
node.inputs[2] = node.add_dummy(htp_tensor_collapse_rows(src2));
}
}
return;
}
}
bool htp_kparams_solve(
const struct ggml_hexagon_session * sess,
htp_opnode & node
) {
htp_opnode_collapse(sess, node);
const ggml_tensor * src0 = node.inputs.empty() ? nullptr : node.inputs[0];
const ggml_tensor * src1 = node.inputs.size() > 1 ? node.inputs[1] : nullptr;
const ggml_tensor * dst = node.dst();
if (htp_op_is_unary(node.opcode)) {
return htp_kparams_solve_unary_impl(sess, node.opcode, src0, src1, dst, node.kparams<htp_unary_kernel_params>());
}
if (htp_op_is_glu(node.opcode)) {
return htp_kparams_solve_unary_mul_glu(sess, node.opcode, src0, src1, dst, node.kparams<htp_glu_kernel_params>());
}
if (node.opcode == HTP_OP_MUL_MAT) {
if (!htp_matmul_check_types(src0, src1, dst)) {
return false;
}
return htp_kparams_solve_matmul_impl(sess, src0, src1, dst, 0, 0, node.kparams<htp_mm_kernel_params>());
}
if (node.opcode == HTP_OP_MUL_MAT_ADD) {
const ggml_tensor * src2 = node.inputs.size() > 2 ? node.inputs[2] : nullptr;
const size_t src2_size = src2 ? hex_round_up(ggml_nbytes(src2), 128) : 0;
return htp_kparams_solve_matmul_impl(sess, src0, src1, dst, src2 ? src2->nb[1] : 0, src2_size, node.kparams<htp_mm_kernel_params>());
}
return htp_kparams_solve(sess, node.node, node.kernel_params);
}
bool htp_kparams_solve(
const struct ggml_hexagon_session * sess,
const struct ggml_tensor * op,
void * kparams
) {
const htp_op_code opcode = htp_op_remap(op);
if (htp_op_is_unary(opcode) || htp_op_is_glu(opcode)) {
htp_opnode tmp(opcode, op);
if (!htp_kparams_solve(sess, tmp)) {
return false;
}
if (kparams) {
memcpy(kparams, tmp.kernel_params, sizeof(tmp.kernel_params));
}
return true;
}
switch (opcode) {
case HTP_OP_MUL_MAT:
case HTP_OP_MUL_MAT_ID:
ggml/src/ggml-hexagon/htp-kparams.h
+13
View File
@@ -34,6 +34,7 @@
bool htp_rows_stride(const int64_t * ne, const size_t * nb, size_t * stride);
bool htp_matmul_can_collapse(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst);
bool htp_matmul_add_can_collapse(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * src2, const struct ggml_tensor * dst);
ggml_tensor htp_tensor_collapse_rows(const struct ggml_tensor * t);
template <typename... Args>
@@ -45,6 +46,18 @@ static inline bool htp_kparams_check(Args &&... args) {
return SolveFn(std::forward<Args>(args)..., &kparams);
}
struct htp_opnode;
bool htp_kparams_solve(
const struct ggml_hexagon_session * sess,
struct htp_opnode & node
);
void htp_opnode_collapse(
const struct ggml_hexagon_session * sess,
struct htp_opnode & node
);
bool htp_kparams_solve(
const struct ggml_hexagon_session * sess,
const struct ggml_tensor * op,
ggml/src/ggml-hexagon/htp/matmul-ops.c
-70
View File
@@ -3775,16 +3775,6 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k
return HTP_STATUS_OK;
}
static inline void htp_mm_tensor_collapse_rows(struct htp_tensor * c, const struct htp_tensor * t, uint32_t stride) {
*c = *t;
c->ne[1] = t->ne[1] * t->ne[2] * t->ne[3];
c->ne[2] = 1;
c->ne[3] = 1;
c->nb[1] = stride;
c->nb[2] = c->nb[1] * c->ne[1];
c->nb[3] = c->nb[2];
}
static int op_matmul_impl(struct htp_ops_context * octx) {
const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
@@ -3801,36 +3791,6 @@ static int op_matmul_impl(struct htp_ops_context * octx) {
}
int op_matmul(struct htp_ops_context * octx) {
const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
if (kparams->collapse) {
const struct htp_tensor * act = octx->src[1];
const struct htp_tensor * dst = octx->dst;
const uint32_t s_act = (act->ne[1] > 1) ? act->nb[1] : ((act->ne[2] > 1) ? act->nb[2] : act->nb[3]);
const uint32_t sd = (dst->ne[1] > 1) ? dst->nb[1] : ((dst->ne[2] > 1) ? dst->nb[2] : dst->nb[3]);
struct htp_tensor act_collapsed, dst_collapsed;
htp_mm_tensor_collapse_rows(&act_collapsed, act, s_act);
htp_mm_tensor_collapse_rows(&dst_collapsed, dst, sd);
octx->src[1] = &act_collapsed;
octx->dst = &dst_collapsed;
struct htp_tensor src2_collapsed;
const struct htp_tensor * src2 = octx->src[2];
if (src2 && (src2->ne[1] * src2->ne[2] * src2->ne[3] > 1)) {
const uint32_t s2 = (src2->ne[1] > 1) ? src2->nb[1] : ((src2->ne[2] > 1) ? src2->nb[2] : src2->nb[3]);
htp_mm_tensor_collapse_rows(&src2_collapsed, src2, s2);
octx->src[2] = &src2_collapsed;
}
const int status = op_matmul_impl(octx);
octx->src[1] = act;
octx->dst = dst;
if (src2) {
octx->src[2] = src2;
}
return status;
}
return op_matmul_impl(octx);
}
@@ -4562,35 +4522,5 @@ static int op_matmul_nx_impl(struct htp_ops_context * octx) {
}
int op_matmul_nx(struct htp_ops_context * octx) {
const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
if (kparams->collapse) {
const uint32_t n_weights = kparams->n_weights;
const struct htp_tensor * act = octx->src[n_weights];
const uint32_t s1 = (act->ne[1] > 1) ? act->nb[1] : ((act->ne[2] > 1) ? act->nb[2] : act->nb[3]);
struct htp_tensor act_collapsed;
struct htp_tensor dsts_collapsed[HTP_OP_MAX_OUTPUTS];
const struct htp_tensor * orig_dsts[HTP_OP_MAX_OUTPUTS];
htp_mm_tensor_collapse_rows(&act_collapsed, act, s1);
octx->src[n_weights] = &act_collapsed;
for (uint32_t p = 0; p < n_weights; p++) {
orig_dsts[p] = octx->dsts[p];
const struct htp_tensor * d = octx->dsts[p];
const uint32_t sd = (d->ne[1] > 1) ? d->nb[1] : ((d->ne[2] > 1) ? d->nb[2] : d->nb[3]);
htp_mm_tensor_collapse_rows(&dsts_collapsed[p], d, sd);
octx->dsts[p] = &dsts_collapsed[p];
}
const int status = op_matmul_nx_impl(octx);
octx->src[n_weights] = act;
for (uint32_t p = 0; p < n_weights; p++) {
octx->dsts[p] = orig_dsts[p];
}
return status;
}
return op_matmul_nx_impl(octx);
}
ggml/src/ggml-hexagon/htp/matmul-ops.h
+1 -1
View File
@@ -61,7 +61,7 @@ enum htp_mm_kernel_type {
struct htp_mm_kernel_params {
uint8_t kernel_type; // enum htp_mm_kernel_type
uint8_t pipeline; // 1 = pipelined execution, 0 = standard
uint8_t collapse; // 1 = collapse outer dims into 2D, 0 = standard
uint8_t unused; // reserved
uint8_t n_hmx; // 1 = use HMX, 0 = use HVX
uint8_t n_threads; // Number of threads to spawn
ggml/src/ggml-hexagon/htp/unary-ops.c
+2 -23
View File
@@ -1984,29 +1984,8 @@ static int execute_op_unary(struct htp_ops_context * octx) {
int op_unary(struct htp_ops_context * octx) {
switch (octx->src[0]->type) {
case HTP_TYPE_F32:
case HTP_TYPE_F16: {
if (!htp_unary_is_elementwise(octx->op)) {
return execute_op_unary(octx);
}
const struct htp_unary_kernel_params * kparams = (const struct htp_unary_kernel_params *) octx->kernel_params;
struct htp_tensor src0_c = *octx->src[0];
struct htp_tensor dst_c = *octx->dst;
memcpy(src0_c.ne, kparams->ne, sizeof(src0_c.ne));
memcpy(dst_c.ne, kparams->ne, sizeof(dst_c.ne));
memcpy(src0_c.nb, kparams->src0_nb, sizeof(src0_c.nb));
memcpy(dst_c.nb, kparams->dst_nb, sizeof(dst_c.nb));
const struct htp_tensor * orig_src0 = octx->src[0];
const struct htp_tensor * orig_dst = octx->dst;
octx->src[0] = &src0_c;
octx->dst = &dst_c;
const int status = execute_op_unary(octx);
octx->src[0] = orig_src0;
octx->dst = orig_dst;
return status;
}
case HTP_TYPE_F16:
return execute_op_unary(octx);
default:
return HTP_STATUS_NO_SUPPORT;
ggml/src/ggml-hexagon/htp/unary-ops.h
+3 -5
View File
@@ -24,11 +24,6 @@ struct htp_unary_kernel_params {
struct fastdiv_values div_ne02;
struct fastdiv_values div_ne012;
struct fastdiv_values div_tpr;
// collapsed shape of elementwise ops
uint32_t ne[HTP_OP_MAX_DIMS];
uint32_t src0_nb[HTP_OP_MAX_DIMS];
uint32_t dst_nb[HTP_OP_MAX_DIMS];
};
#if defined(__cplusplus)
@@ -72,6 +67,9 @@ static inline bool htp_op_is_unary(uint32_t opcode) {
}
static inline bool htp_unary_is_elementwise(uint32_t opcode) {
if (!htp_op_is_unary(opcode)) {
return false;
}
switch (opcode) {
case HTP_OP_NORM:
case HTP_OP_RMS_NORM:
scripts/snapdragon/ggml-hexagon-profile.py
+174 -22
View File
@@ -215,6 +215,7 @@ def parse_log(file_path, pmu_index=None, limit=None, device_filter=None, op_filt
'unwrapped_cycles_start': unwrapped_cycles_start,
'pmu_val': pmu_val,
'evt_val': evt_val,
'mhz': float(op_match.group('mhz')) if op_match.group('mhz') else None,
'abs_usec': abs_usec,
'trace_events': [],
'device': device
@@ -630,6 +631,165 @@ def generate_report(ops, top_n, width_overrides, sort_col, pmu_name=None):
logger.info("| " + " | ".join(row_vals) + " |")
def has_perf_print(file_path):
if file_path == "-":
return True
with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
return any("common_perf_print" in line for line in f)
def pct_delta(a, b):
return (b - a) * 100.0 / a if a else None
def fmt_pct(p):
return f"{p:+.2f}%" if p is not None else "n/a"
def print_table(title, cols, rows, width_overrides):
# cols: list of (cli name, header); rows: list of dicts keyed by cli name
logger.info(f"\n# {title}\n")
if not rows:
logger.info("None.")
return
headers, keys, widths = [], [], []
for key, header in cols:
natural_width = max([len(str(row[key])) for row in rows] + [len(header)])
target_width = width_overrides.get(key, natural_width)
if target_width == 0:
continue
headers.append(header)
keys.append(key)
widths.append(target_width)
logger.info("| " + " | ".join(f"{h:<{widths[i]}}" for i, h in enumerate(headers)) + " |")
logger.info("| " + " | ".join("-" * widths[i] for i in range(len(headers))) + " |")
for row in rows:
row_vals = []
for i, key in enumerate(keys):
val = str(row[key])
if len(val) > widths[i]:
val = val[:widths[i] - 3] + "..."
row_vals.append(f"{val:<{widths[i]}}")
logger.info("| " + " | ".join(row_vals) + " |")
def generate_compare_report(ops_a, ops_b, file_a, file_b, top_n, width_overrides, min_delta):
def summarize(ops):
batches = [o for o in ops if o['name'] == "OPBATCH"]
others = [o for o in ops if o['name'] != "OPBATCH"]
mhz = [o['mhz'] for o in others if o['mhz'] is not None]
return {
'n_ops': len(others),
'tot_usec': sum(o['usec'] for o in others),
'n_batch': len(batches),
'batch_usec': sum(o['usec'] for o in batches),
'avg_mhz': statistics.mean(mhz) if mhz else 0.0,
}
sa, sb = summarize(ops_a), summarize(ops_b)
logger.info("\n# Compare Summary (OPBATCH excluded from op totals)\n")
logger.info(f" A (baseline) : {file_a}")
logger.info(f" B (new) : {file_b}")
logger.info(" Deltas are B - A: negative means B is faster\n")
logger.info(f" Ops : A {sa['n_ops']} B {sb['n_ops']}")
logger.info(f" Tot op usec : A {sa['tot_usec']} B {sb['tot_usec']} delta {sb['tot_usec'] - sa['tot_usec']:+d} ({fmt_pct(pct_delta(sa['tot_usec'], sb['tot_usec']))})")
logger.info(f" OPBATCH : A {sa['n_batch']} / {sa['batch_usec']} usec B {sb['n_batch']} / {sb['batch_usec']} usec")
logger.info(f" Avg op mhz : A {sa['avg_mhz']:.1f} B {sb['avg_mhz']:.1f}")
if sa['n_batch'] != sb['n_batch']:
logger.warning(f" WARNING: OPBATCH counts differ (A {sa['n_batch']}, B {sb['n_batch']}), one log may be incomplete")
for label, path in (("A", file_a), ("B", file_b)):
if not has_perf_print(path):
logger.warning(f" WARNING: log {label} has no common_perf_print line, run may be incomplete")
# Per-op-name rollup
by_name = defaultdict(lambda: [0, 0, 0, 0])
for idx, ops in ((0, ops_a), (1, ops_b)):
for op in ops:
if op['name'] == "OPBATCH":
continue
by_name[op['name']][idx] += 1
by_name[op['name']][idx + 2] += op['usec']
rows = []
for name, (cnt_a, cnt_b, tot_a, tot_b) in by_name.items():
p = pct_delta(tot_a, tot_b)
if min_delta and p is not None and abs(p) < min_delta:
continue
rows.append({'op': name, 'count-a': cnt_a, 'count-b': cnt_b, 'tot-usec-a': tot_a, 'tot-usec-b': tot_b,
'delta-usec': f"{tot_b - tot_a:+d}", 'delta-pct': fmt_pct(p), '_sort': abs(tot_b - tot_a)})
rows.sort(key=lambda r: r['_sort'], reverse=True)
print_table("Per-Op Rollup (delta = B - A, sorted by abs delta usec)", [
("op", "Op"), ("count-a", "Count A"), ("count-b", "Count B"), ("tot-usec-a", "Tot usec A"),
("tot-usec-b", "Tot usec B"), ("delta-usec", "Delta usec"), ("delta-pct", "Delta %")], rows, width_overrides)
# Per-group compare keyed on (op, dims, dtypes)
def group(ops):
grouped = defaultdict(list)
for op in ops:
if op['name'] != "OPBATCH":
grouped[(op['name'], op['dims'], op['types'])].append(op['usec'])
return grouped
ga, gb = group(ops_a), group(ops_b)
rows = []
for key in ga.keys() & gb.keys():
ua, ub = ga[key], gb[key]
tot_a, tot_b = sum(ua), sum(ub)
avg_a, avg_b = tot_a / len(ua), tot_b / len(ub)
p = pct_delta(avg_a, avg_b)
if min_delta and p is not None and abs(p) < min_delta:
continue
rows.append({'op': key[0], 'dims': key[1], 'dtypes': key[2], 'count-a': len(ua), 'count-b': len(ub),
'avg-usec-a': f"{avg_a:.2f}", 'avg-usec-b': f"{avg_b:.2f}", 'delta-pct': fmt_pct(p),
'tot-usec-a': tot_a, 'tot-usec-b': tot_b, 'delta-usec': f"{tot_b - tot_a:+d}", '_sort': abs(tot_b - tot_a)})
rows.sort(key=lambda r: r['_sort'], reverse=True)
print_table(f"Per-Group Compare (delta = B - A, top {top_n} sorted by abs delta usec)", [
("op", "Op"), ("dims", "Dims"), ("dtypes", "DTypes"), ("count-a", "Count A"), ("count-b", "Count B"),
("avg-usec-a", "Avg usec A"), ("avg-usec-b", "Avg usec B"), ("delta-pct", "Delta %"),
("tot-usec-a", "Tot usec A"), ("tot-usec-b", "Tot usec B"), ("delta-usec", "Delta usec")], rows[:top_n], width_overrides)
# Groups present in only one log
for label, g, other in (("A", ga, gb), ("B", gb, ga)):
rows = [{'op': k[0], 'dims': k[1], 'dtypes': k[2], 'count': len(v), 'tot-usec': sum(v)} for k, v in g.items() if k not in other]
rows.sort(key=lambda r: r['tot-usec'], reverse=True)
print_table(f"Groups only in {label} (Top {top_n} sorted by tot usec)", [
("op", "Op"), ("dims", "Dims"), ("dtypes", "DTypes"), ("count", "Count"), ("tot-usec", "Tot usec")], rows[:top_n], width_overrides)
def filter_ops(ops, args):
if args.filter:
try:
filter_re = re.compile(args.filter)
except re.error as e:
logger.error(f"Invalid regex filter: {e}")
sys.exit(1)
ops = [op for op in ops if filter_re.search(op['op_text'])]
if args.head is not None or args.tail is not None:
ops_by_dev = defaultdict(list)
for op in ops:
ops_by_dev[op['device']].append(op)
filtered_ops = []
for dev in sorted(ops_by_dev.keys()):
dev_ops = ops_by_dev[dev]
if args.head is not None:
dev_ops = dev_ops[:args.head]
elif args.tail is not None:
dev_ops = dev_ops[-args.tail:]
filtered_ops.extend(dev_ops)
ops = filtered_ops
return ops
def main():
parser = argparse.ArgumentParser(description="Post-process Op profile info.")
parser.add_argument("logfile")
@@ -642,6 +802,8 @@ def main():
help="Output ASCII art event summary or thread idle bubble analysis (default: summary)")
parser.add_argument("--filter", type=str, help="Regex filter matching against the original profile-op line")
parser.add_argument("--device", type=str, help="Device to filter by (e.g. HTP0, HTP0:0) or 'split' to generate separate reports per device")
parser.add_argument("--compare", type=str, metavar="LOGFILE", help="Compare against another log (logfile is baseline A, this is B)")
parser.add_argument("--min-delta", type=float, default=0, metavar="PCT", help="In compare mode hide rows with abs(Delta %%) below PCT")
group = parser.add_mutually_exclusive_group()
group.add_argument("--head", type=int, help="Limit to first N ops")
@@ -655,6 +817,10 @@ def main():
logger.error(f"Cannot sort by '{args.sort}' without --pmu-index.")
sys.exit(1)
if args.compare and (args.timeline or args.device == "split"):
logger.error("--compare cannot be used with --timeline or --device split.")
sys.exit(1)
overrides = {}
if args.width:
for w in args.width:
@@ -681,6 +847,13 @@ def main():
if args.device and args.device != "split":
ops = [op for op in ops if device_matches(op['device'], args.device)]
if args.compare:
ops_b = parse_log(args.compare, pmu_index=args.pmu_index, limit=limit, device_filter=device_filter, op_filter_re=op_filter_re)
if device_filter:
ops_b = [op for op in ops_b if device_matches(op['device'], device_filter)]
generate_compare_report(filter_ops(ops, args), filter_ops(ops_b, args), args.logfile, args.compare, args.top, overrides, args.min_delta)
return
if args.device == "split":
unique_devices = sorted(list(set(op['device'] for op in ops)))
for dev in unique_devices:
@@ -712,28 +885,7 @@ def main():
else:
generate_report(dev_ops, args.top, overrides, args.sort, pmu_name=final_pmu_name)
else:
if args.filter:
try:
filter_re = re.compile(args.filter)
except re.error as e:
logger.error(f"Invalid regex filter: {e}")
sys.exit(1)
ops = [op for op in ops if filter_re.search(op['op_text'])]
if args.head is not None or args.tail is not None:
ops_by_dev = defaultdict(list)
for op in ops:
ops_by_dev[op['device']].append(op)
filtered_ops = []
for dev in sorted(ops_by_dev.keys()):
dev_ops = ops_by_dev[dev]
if args.head is not None:
dev_ops = dev_ops[:args.head]
elif args.tail is not None:
dev_ops = dev_ops[-args.tail:]
filtered_ops.extend(dev_ops)
ops = filtered_ops
ops = filter_ops(ops, args)
if args.timeline:
for op in ops: