Updating infra to enable op fusion and using RMS_NORM+MUL as the use-case.
#include "ggml-hexagon.h"
#include "ggml-impl.h"
#include "ggml-quants.h"
-#include "op-desc.h"
+#include "htp-opnode.h"
#include "htp-ops.h"
#include "htp_iface.h"
#include "htp-drv.h"
// ** debug helpers
-static void ggml_hexagon_dump_op_exec(const std::string &sess_name, const ggml_tensor * op, const uint32_t req_flags) {
+static void ggml_hexagon_dump_op_exec(const std::string &sess_name, const htp_opnode & node, const uint32_t req_flags) {
if (!opt_verbose) return;
- op_desc desc(op);
+ htp_opformat fmt(node);
GGML_LOG_DEBUG("ggml-hex: %s execute-op %s: %s : %s : %s : %s : %s : flags 0x%x\n", sess_name.c_str(),
- ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, req_flags);
+ node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.buffs, req_flags);
}
static void ggml_hexagon_dump_op_supp(const std::string &sess_name, const struct ggml_tensor * op, bool supp) {
if (!opt_verbose) return;
- op_desc desc(op);
+ htp_opformat fmt(htp_opformat(htp_opnode{const_cast<ggml_tensor*>(op), {}, HTP_OP_INVALID}));
GGML_LOG_DEBUG("ggml-hex: %s supports-op %s: %s : %s : %s : %s : %s : %s\n", sess_name.c_str(),
- ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, supp ? "yes" : "no");
+ ggml_op_desc(op), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.buffs, supp ? "yes" : "no");
}
-static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const ggml_tensor * op,
+static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const htp_opnode & node,
uint32_t op_usec, uint32_t op_cycles, const uint32_t pmu[]) {
if (!opt_profile) return;
pmu[0], pmu[1], pmu[2], pmu[3], pmu[4], pmu[5], pmu[6], pmu[7]);
}
- op_desc desc(op);
+ htp_opformat fmt(node);
GGML_LOG_DEBUG("ggml-hex: %s profile-op %s: %s : %s : %s : %s : usec %u cycles %u%s\n", sess_name.c_str(),
- ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, op_usec, op_cycles, pmu_str);
+ node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, op_usec, op_cycles, pmu_str);
}
// ** backend sessions
struct ggml_hexagon_opbatch;
struct ggml_hexagon_opqueue;
+struct htp_opnode;
struct ggml_hexagon_session {
std::string name;
void allocate(int dev_id) noexcept(false);
void release() noexcept(true);
- void enqueue_op(htp_op_code opcode, const ggml_tensor *op);
+ void enqueue_op(const htp_opnode & node);
void flush(bool all = true);
void flush_pending(bool all = false);
/* .is_host = */ ggml_backend_hexagon_repack_buffer_type_is_host,
};
-// Backend session implementation
-
struct ggml_hexagon_opbatch {
ggml_hexagon_session* sess;
- std::vector<const ggml_tensor*> ops; // pointers to original ops
+ std::vector<htp_opnode> ops; // htp_opnode of ops
std::vector<htp_buf_desc> h_bufs; // htp buffer descriptors
std::vector<htp_tensor> h_tens; // htp tensor descriptors
return ti;
}
- bool fit_op(const struct ggml_tensor *t) const {
+ bool fit_op(const htp_opnode & node) const {
if (n_ops >= n_ops_max ) return false;
// check how much extras we will need
}
};
- for (unsigned int i=0; i < HTP_OP_MAX_INPUTS && t->src[i]; i++) {
- fit_tensor(t->src[i]);
+ for (const auto * src : node.get_inputs()) {
+ fit_tensor(src);
}
- fit_tensor(t);
+ fit_tensor(node.dst());
if ((extra_bufs + n_bufs) > n_bufs_max) return false;
if ((extra_tens + n_tens) > n_tens_max) return false;
}
// assumes that fit_op() was called first and returned true
- void add_op(htp_op_code opcode, const struct ggml_tensor * t) {
+ void add_op(const htp_opnode & node) {
// Add new op
unsigned int n = n_ops++;
GGML_ASSERT(n_ops <= n_ops_max);
- ops[n] = t;
+ ops[n] = node;
htp_op_desc &o = h_ops[n];
- memcpy(&o.params, &t->op_params, sizeof(t->op_params));
- o.opcode = opcode;
+ memcpy(&o.params, &node.node->op_params, sizeof(node.node->op_params));
+ o.opcode = node.opcode;
o.flags = 0;
if (!(opt_opstage & HTP_OPSTAGE_COMPUTE)) {
o.flags |= HTP_OPFLAGS_SKIP_COMPUTE;
}
- ggml_hexagon_dump_op_exec(sess->c_name(), t, o.flags);
+ ggml_hexagon_dump_op_exec(sess->c_name(), node, o.flags);
+ auto inputs = node.get_inputs();
for (unsigned int i=0; i < HTP_OP_MAX_INPUTS; i++) {
- o.src[i] = t->src[i] ? add_tensor(t->src[i]) : 0xffff;
+ o.src[i] = (i < inputs.size() && inputs[i]) ? add_tensor(inputs[i]) : 0xffff;
}
- o.dst = add_tensor(t);
+ o.dst = add_tensor(node.dst());
}
};
ggml_hexagon_shared_buffer *shm_buf;
size_t shm_blk_size;
- using opvec = std::vector<const ggml_tensor*>;
+ using opvec = std::vector<htp_opnode>;
std::queue<unsigned int> done; // completed batch ids
std::vector<opvec> op_cache; // per batch op cache
}
}
-void ggml_hexagon_session::enqueue_op(htp_op_code opcode, const ggml_tensor *op) {
- if (!op_batch->fit_op(op)) {
+void ggml_hexagon_session::enqueue_op(const htp_opnode & node) {
+ if (!op_batch->fit_op(node)) {
flush_batch();
}
- op_batch->add_op(opcode, op);
+ op_batch->add_op(node);
}
// Flush HTP response queue i.e wait for all outstanding requests to complete
HEX_VERBOSE("ggml-hex: %s graph-compute n_nodes %d\n", sess->c_name(), graph->n_nodes);
+ std::vector<htp_opnode> nodes;
+ nodes.reserve(graph->n_nodes);
+
+ // Fusion
for (int i = 0; i < graph->n_nodes; ++i) {
ggml_tensor * n = graph->nodes[i];
- if (op_is_compute(n) && (opt_opstage & HTP_OPSTAGE_QUEUE)) {
- sess->enqueue_op(op_remap_to_htp(n), n);
+ if (!op_is_compute(n)) {
+ continue;
+ }
+
+ ggml_tensor * next_node = (i + 1 < graph->n_nodes) ? graph->nodes[i + 1] : nullptr;
+
+ htp_opnode node = {
+ /*.node =*/ n,
+ /*.fused =*/ {},
+ /*.opcode =*/ HTP_OP_INVALID
+ };
+
+ if (n->op == GGML_OP_RMS_NORM && next_node) {
+ if (next_node->op == GGML_OP_MUL && op_is_compute(next_node) && ggml_can_fuse(graph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+ node.add_fused(next_node);
+ node.opcode = HTP_OP_RMS_NORM_MUL;
+ i++; // skip the fused MUL node
+ }
+ }
+
+ if (node.opcode == HTP_OP_INVALID) {
+ node.opcode = op_remap_to_htp(n);
+ }
+
+ nodes.push_back(std::move(node));
+ }
+
+ // Queue and execute
+ if (opt_opstage & HTP_OPSTAGE_QUEUE) {
+ for (const auto & node : nodes) {
+ sess->enqueue_op(node);
}
}
sess->flush();
}
-struct node_info {
- ggml_tensor * node;
-
- std::vector<ggml_tensor *> fused;
-
- ggml_op op() const {
- return node->op;
- }
-
- const ggml_tensor * dst() const {
- return fused.empty() ? node : fused.back();
- }
-
- const ggml_tensor * src0() const {
- return node->src[0];
- }
-
- const ggml_tensor * src1() const {
- return node->src[1];
- }
-
- bool is_empty() const {
- return ggml_op_is_empty(node->op);
- }
-
- void add_fused(ggml_tensor * t) {
- fused.push_back(t);
- }
-
- bool stackable() const {
- switch (this->op()) {
- case GGML_OP_MUL_MAT:
- case GGML_OP_MUL_MAT_ID:
- return ggml_is_quantized(this->src0()->type);
- default:
- return false;
- }
- }
-
- bool same_input(const node_info& n) const {
- return n.src1() == this->src1();
- }
-};
-
-static std::vector<int> ggml_hexagon_graph_optimize_reorder(const std::vector<node_info> & nodes) {
+static std::vector<int> ggml_hexagon_graph_optimize_reorder(const std::vector<htp_opnode> & nodes) {
const int n = nodes.size();
std::vector<int> res;
enum ggml_op ops[MAX_FUSE];
- std::vector<node_info> nodes;
+ std::vector<htp_opnode> nodes;
nodes.reserve(gf->n_nodes);
// fuse nodes:
// we don't want to make reorders that break fusing, so we first pack all fusable tensors
// and perform the reorder over the fused nodes. after the reorder is done, we unfuse
for (int i = 0; i < n; i++) {
- node_info node = {
+ htp_opnode node = {
/*.node =*/gf->nodes[i],
/*.fused =*/{},
};
--- /dev/null
+#ifndef HTP_OPNODE_H
+#define HTP_OPNODE_H
+
+#define GGML_COMMON_IMPL_CPP
+#include "ggml-backend-impl.h"
+#include "ggml-common.h"
+
+#include <string>
+#include <vector>
+#include <stdio.h>
+#include "htp-ops.h"
+
+struct htp_opnode {
+ ggml_tensor * node = nullptr;
+
+ std::vector<ggml_tensor *> fused;
+
+ htp_op_code opcode = HTP_OP_INVALID;
+
+ ggml_op op() const {
+ return node->op;
+ }
+
+ const ggml_tensor * dst() const {
+ return fused.empty() ? node : fused.back();
+ }
+
+ const ggml_tensor * src0() const {
+ return node->src[0];
+ }
+
+ const ggml_tensor * src1() const {
+ return node->src[1];
+ }
+
+ bool is_empty() const {
+ return ggml_op_is_empty(node->op);
+ }
+
+ void add_fused(ggml_tensor * t) {
+ fused.push_back(t);
+ }
+
+ bool stackable() const {
+ switch (this->op()) {
+ case GGML_OP_MUL_MAT:
+ case GGML_OP_MUL_MAT_ID:
+ return ggml_is_quantized(this->src0()->type);
+ default:
+ return false;
+ }
+ }
+
+ bool same_input(const htp_opnode& n) const {
+ return n.src1() == this->src1();
+ }
+
+ std::vector<const ggml_tensor *> get_inputs() const {
+ std::vector<const ggml_tensor *> inputs;
+ std::vector<const ggml_tensor *> outputs;
+ outputs.push_back(node);
+ for (const auto * f : fused) {
+ outputs.push_back(f);
+ }
+
+ auto contains = [&](const std::vector<const ggml_tensor *> & vec, const ggml_tensor * t) {
+ for (const auto * x : vec) {
+ if (x == t) return true;
+ }
+ return false;
+ };
+
+ auto add_input = [&](const ggml_tensor * t) {
+ if (t && !contains(outputs, t) && !contains(inputs, t)) {
+ inputs.push_back(t);
+ }
+ };
+
+ for (int i = 0; i < GGML_MAX_SRC && node->src[i]; i++) {
+ add_input(node->src[i]);
+ }
+ for (const auto * f : fused) {
+ for (int i = 0; i < GGML_MAX_SRC && f->src[i]; i++) {
+ add_input(f->src[i]);
+ }
+ }
+ return inputs;
+ }
+
+ std::string op_name() const {
+ if (fused.empty()) {
+ return ggml_op_desc(node);
+ }
+ std::string name = ggml_op_desc(node);
+ for (const auto * f : fused) {
+ name += "+";
+ name += ggml_op_desc(f);
+ }
+ return name;
+ }
+};
+
+struct htp_opformat {
+ char strides[64 * GGML_MAX_SRC];
+ char dims[64 * GGML_MAX_SRC];
+ char types[16 * GGML_MAX_SRC];
+ char buffs[64 * GGML_MAX_SRC];
+ char names[64 * GGML_MAX_SRC];
+
+ int format_tensor_dims(char * str, const struct ggml_tensor * t) {
+ if (t->ne[2] == 1 && t->ne[3] == 1) {
+ return sprintf(str, "%d:%d", (int) t->ne[0], (int) t->ne[1]);
+ } else {
+ return sprintf(str, "%d:%d:%d:%d", (int) t->ne[0], (int) t->ne[1], (int) t->ne[2], (int) t->ne[3]);
+ }
+ }
+
+ void format_op_dims(char * str, const htp_opnode & node) {
+ char * p = str;
+ auto inputs = node.get_inputs();
+
+ if (!inputs.empty()) {
+ p += format_tensor_dims(p, inputs[0]);
+
+ for (size_t i = 1; i < inputs.size(); i++) {
+ p += sprintf(p, " x ");
+ p += format_tensor_dims(p, inputs[i]);
+ }
+
+ p += sprintf(p, " -> ");
+ }
+
+ char self[64];
+ format_tensor_dims(self, node.dst());
+ p += sprintf(p, "%s", self);
+ }
+
+ int format_tensor_strides(char * str, const struct ggml_tensor * t) {
+ const char * c = ggml_is_contiguous(t) ? "" : "!";
+
+ if (t->ne[2] == 1 && t->ne[3] == 1) {
+ return sprintf(str, "%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], c);
+ } else {
+ return sprintf(str, "%zu:%zu:%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], (size_t) t->nb[2], (size_t) t->nb[3], c);
+ }
+ }
+
+ void format_op_strides(char * str, const htp_opnode & node) {
+ char * p = str;
+ auto inputs = node.get_inputs();
+
+ if (!inputs.empty()) {
+ p += format_tensor_strides(p, inputs[0]);
+
+ for (size_t i = 1; i < inputs.size(); i++) {
+ p += sprintf(p, " x ");
+ p += format_tensor_strides(p, inputs[i]);
+ }
+
+ p += sprintf(p, " -> ");
+ }
+
+ char self[64];
+ format_tensor_strides(self, node.dst());
+ p += sprintf(p, "%s", self);
+ }
+
+ void format_op_types(char * str, const htp_opnode & node) {
+ char * p = str;
+ auto inputs = node.get_inputs();
+
+ if (!inputs.empty()) {
+ p += sprintf(p, "%s", ggml_type_name(inputs[0]->type));
+
+ for (size_t i = 1; i < inputs.size(); i++) {
+ p += sprintf(p, " x ");
+ p += sprintf(p, "%s", ggml_type_name(inputs[i]->type));
+ }
+
+ p += sprintf(p, " -> ");
+ }
+
+ p += sprintf(p, "%s", ggml_type_name(node.dst()->type));
+ }
+
+ const char * tensor_buff_name(const struct ggml_tensor * t) {
+ if (t->buffer) {
+ return ggml_backend_buffer_name(t->buffer);
+ }
+ return "NONE";
+ }
+
+ void format_op_buffs(char * str, const htp_opnode & node) {
+ char * p = str;
+ auto inputs = node.get_inputs();
+
+ if (!inputs.empty()) {
+ p += sprintf(p, "%s", tensor_buff_name(inputs[0]));
+
+ for (size_t i = 1; i < inputs.size(); i++) {
+ p += sprintf(p, " x ");
+ p += sprintf(p, "%s", tensor_buff_name(inputs[i]));
+ }
+
+ p += sprintf(p, " -> ");
+ }
+
+ p += sprintf(p, "%s", tensor_buff_name(node.dst()));
+ }
+
+ void format_op_names(char * str, const htp_opnode & node) {
+ char * p = str;
+ auto inputs = node.get_inputs();
+
+ if (!inputs.empty()) {
+ p += sprintf(p, "%s", inputs[0]->name);
+
+ for (size_t i = 1; i < inputs.size(); i++) {
+ p += sprintf(p, " x ");
+ p += sprintf(p, "%s", inputs[i]->name);
+ }
+
+ p += sprintf(p, " -> ");
+ }
+
+ p += sprintf(p, "%s", node.dst()->name);
+ }
+
+ void format(const htp_opnode & node) {
+ format_op_dims(dims, node);
+ format_op_strides(strides, node);
+ format_op_types(types, node);
+ format_op_buffs(buffs, node);
+ format_op_names(names, node);
+ }
+
+ htp_opformat() {}
+ htp_opformat(const htp_opnode & node) { format(node); }
+};
+
+#endif // HTP_OPNODE_H
HTP_OP_MUL_MAT,
HTP_OP_MUL_MAT_ID,
HTP_OP_RMS_NORM,
+ HTP_OP_RMS_NORM_MUL,
HTP_OP_UNARY_SILU,
HTP_OP_UNARY_GELU,
HTP_OP_UNARY_SIGMOID,
case HTP_OP_NORM:
case HTP_OP_RMS_NORM:
+ case HTP_OP_RMS_NORM_MUL:
case HTP_OP_SCALE:
case HTP_OP_SQR:
case HTP_OP_SQRT:
// Precomputed values
const uint8_t * data_src0;
+ const uint8_t * data_src1; // weight/scale tensor for RMS_NORM_MUL
uint8_t * data_dst;
size_t src0_data_row_size; // actual data bytes per row
+ size_t src1_data_row_size;
size_t dst_data_row_size; // actual data bytes per row
size_t src0_row_size_aligned;
+ size_t src1_row_size_aligned;
size_t dst_row_size_aligned;
size_t src0_spad_half_size;
+ size_t src1_spad_half_size;
size_t dst_spad_half_size;
uint32_t block;
uint32_t src0_nrows;
uint32_t src0_nrows_per_thread;
uint32_t nc;
+ bool broadcast_weight;
};
// Convert flat row index to DDR byte offset using the tensor's actual strides.
}
}
+static void hvx_fast_rms_norm_mul_f32(const uint8_t * restrict src,
+ const uint8_t * restrict weight,
+ uint8_t * restrict dst,
+ const int num_elems,
+ float epsilon) {
+ const HVX_Vector * restrict v_src = (const HVX_Vector *) src;
+ const HVX_Vector * restrict v_weight = (const HVX_Vector *) weight;
+ HVX_Vector * restrict v_dst = (HVX_Vector *) dst;
+
+ const int nvec = num_elems / VLEN_FP32; // number of full vectors
+ const int nloe = num_elems % VLEN_FP32; // leftover elements
+
+ // Compute sum of squares for full vectors
+ HVX_Vector sum_v = Q6_V_vsplat_R(0x00000000);
+ HVX_Vector epsilon_v = hvx_vec_splat_f32(epsilon);
+
+ #pragma unroll(4)
+ for (int i = 0; i < nvec; i++) {
+ HVX_Vector v1 = v_src[i];
+ HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, v1);
+ sum_v = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, v2);
+ }
+
+ // Handle tail elements using vectorized ops with masking
+ if (nloe > 0) {
+ HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+ HVX_Vector v1 = Q6_V_vand_QV(bmask, v_src[nvec]);
+ HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, v1);
+ sum_v = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, v2);
+ }
+
+ // Reduce HVX sum
+ sum_v = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_v));
+
+ HVX_Vector t_v = hvx_vec_splat_f32((float) num_elems);
+ HVX_Vector denom_v = hvx_vec_inverse_f32(t_v);
+ HVX_Vector mean_v = Q6_Vqf32_vmpy_VsfVsf(sum_v, denom_v);
+ HVX_Vector mean_epsilon_v = Q6_Vqf32_vadd_Vqf32Vsf(mean_v, epsilon_v);
+
+ // Scale and multiply
+ HVX_Vector scale_v = hvx_vec_rsqrt_f32(Q6_Vsf_equals_Vqf32(mean_epsilon_v));
+
+ #pragma unroll(4)
+ for (int i = 0; i < nvec; i++) {
+ HVX_Vector v1 = v_src[i];
+ HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, scale_v);
+ HVX_Vector v3 = Q6_Vsf_equals_Vqf32(v2);
+ HVX_Vector result = Q6_Vqf32_vmpy_VsfVsf(v3, v_weight[i]);
+ v_dst[i] = Q6_Vsf_equals_Vqf32(result);
+ }
+
+ // Handle tail elements using vectorized ops with masking
+ if (nloe > 0) {
+ HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+ HVX_Vector v1 = Q6_V_vand_QV(bmask, v_src[nvec]);
+ HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, scale_v);
+ HVX_Vector v3 = Q6_Vsf_equals_Vqf32(v2);
+ HVX_Vector result = Q6_Vqf32_vmpy_VsfVsf(v3, v_weight[nvec]);
+ HVX_Vector res_v = Q6_Vsf_equals_Vqf32(result);
+
+ // Store with masking to avoid overwriting memory beyond the tensor
+ hvx_vec_store_a(&v_dst[nvec], nloe * 4, res_v);
+ }
+}
+
static void hvx_fast_norm_f32(const uint8_t * restrict src,
uint8_t * restrict dst,
uint8_t * restrict pad,
}
}
+static void rms_norm_mul_f32(const float * restrict src,
+ const float * restrict weight,
+ float * restrict dst,
+ const uint32_t num_rows,
+ const uint32_t row_elems,
+ const size_t row_size,
+ const size_t weight_row_size,
+ int32_t * op_params,
+ bool broadcast_weight) {
+ float epsilon = 0.f;
+ memcpy(&epsilon, op_params, sizeof(float));
+
+ for (uint32_t ir = 0; ir < num_rows; ir++) {
+ const uint8_t * restrict src_local = (const uint8_t *)src + (ir * row_size);
+ const uint8_t * restrict w_local = (const uint8_t *)weight + (broadcast_weight ? 0 : ir * weight_row_size);
+ uint8_t * restrict dst_local = (uint8_t *)dst + (ir * row_size);
+
+ hvx_fast_rms_norm_mul_f32(src_local, w_local, dst_local, row_elems, epsilon);
+ }
+}
+
static void norm_f32(const float * restrict src,
float * restrict dst,
uint8_t * restrict spad,
t1 = HAP_perf_get_qtimer_count();
const uint8_t * restrict data_src = uctx->data_src0;
+ const uint8_t * restrict data_src1 = uctx->data_src1;
uint8_t * restrict data_dst = uctx->data_dst;
uint8_t * src0_spad_data = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
+ uint8_t * src1_spad_data = octx->src1_spad.data + (ith * octx->src1_spad.size_per_thread);
uint8_t * dst_spad_data = octx->dst_spad.data + (ith * octx->dst_spad.size_per_thread);
size_t src0_spad_half_size = uctx->src0_spad_half_size;
+ size_t src1_spad_half_size = uctx->src1_spad_half_size;
size_t dst_spad_half_size = uctx->dst_spad_half_size;
// Non-contiguous tensors have gaps at dim-2/3 boundaries that a single-stride
dma_queue * dma_queue = octx->ctx->dma[ith];
+ // If weight is broadcasted, load it once per thread at the beginning of execution
+ if (htp_op == HTP_OP_RMS_NORM_MUL && uctx->broadcast_weight) {
+ dma_queue_push(dma_queue, dma_make_ptr(src1_spad_data, data_src1), uctx->src1_row_size_aligned, 0, uctx->src1_data_row_size, 1);
+ dma_queue_flush(dma_queue);
+ }
+
for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; spad_idx++) {
const uint32_t block_size = unary_block_size(ir, src0_end_row, BLOCK, src0_contig, dst_contig, ne01, ne1);
dma_queue_push(dma_queue,
dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src + src0_off),
src0_row_size_aligned, nb01, src0_data_row_size, block_size);
+
+ if (htp_op == HTP_OP_RMS_NORM_MUL && !uctx->broadcast_weight) {
+ const size_t src1_off = unary_row_offset(ir, ne01, ne02, nb01, nb02, nb03);
+ dma_queue_push(dma_queue,
+ dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + src1_off),
+ uctx->src1_row_size_aligned, nb01, uctx->src1_data_row_size, block_size);
+ }
+
ir += block_size;
}
float * dst_spad = (float *) dma_queue_pop(dma_queue).src;
float * src0_spad = (float *) dma_queue_pop(dma_queue).dst;
+ float * src1_spad = NULL;
+ if (htp_op == HTP_OP_RMS_NORM_MUL && !uctx->broadcast_weight) {
+ src1_spad = (float *) dma_queue_pop(dma_queue).dst;
+ }
// Process block in VTCM
switch (htp_op) {
case HTP_OP_RMS_NORM:
rms_norm_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params);
break;
+ case HTP_OP_RMS_NORM_MUL:
+ {
+ const float * w_ptr = uctx->broadcast_weight ? (const float *) src1_spad_data : src1_spad;
+ rms_norm_mul_f32(src0_spad, w_ptr, dst_spad, block_size, ne0, src0_row_size_aligned, uctx->src1_row_size_aligned, op_params, uctx->broadcast_weight);
+ }
+ break;
case HTP_OP_SCALE:
scale_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params);
break;
if (pref_ir < src0_end_row) {
const uint32_t pref_block_size = unary_block_size(pref_ir, src0_end_row, BLOCK, src0_contig, dst_contig, ne01, ne1);
const size_t src0_pref_off = unary_row_offset(pref_ir, ne01, ne02, nb01, nb02, nb03);
- dma_queue_push(dma_queue,
- dma_make_ptr(src0_spad, data_src + src0_pref_off),
- src0_row_size_aligned, nb01, src0_data_row_size, pref_block_size);
+ dma_queue_push(dma_queue,
+ dma_make_ptr(src0_spad, data_src + src0_pref_off),
+ src0_row_size_aligned, nb01, src0_data_row_size, pref_block_size);
+
+ if (htp_op == HTP_OP_RMS_NORM_MUL && !uctx->broadcast_weight) {
+ const size_t src1_pref_off = unary_row_offset(pref_ir, ne01, ne02, nb01, nb02, nb03);
+ dma_queue_push(dma_queue,
+ dma_make_ptr(src1_spad, data_src1 + src1_pref_off),
+ uctx->src1_row_size_aligned, nb01, uctx->src1_data_row_size, pref_block_size);
+ }
}
}
ir += block_size;
case HTP_OP_RMS_NORM:
op_type = "rmsnorm-f32";
break;
+ case HTP_OP_RMS_NORM_MUL:
+ op_type = "rmsnorm-mul-f32";
+ break;
case HTP_OP_SCALE:
op_type = "scale-f32";
break;
const size_t src0_row_size_aligned = hex_round_up(src0_data_row_size, VLEN);
const size_t dst_row_size_aligned = hex_round_up(dst_data_row_size, VLEN);
+ size_t src1_data_row_size = 0;
+ size_t src1_row_size_aligned = 0;
+ bool broadcast_weight = false;
+ const struct htp_tensor * src1 = NULL;
+
+ if (octx->op == HTP_OP_RMS_NORM_MUL) {
+ src1 = octx->src[1];
+ src1_data_row_size = src1->ne[0] * sizeof(float);
+ src1_row_size_aligned = hex_round_up(src1_data_row_size, VLEN);
+ broadcast_weight = (src1->ne[1] * src1->ne[2] * src1->ne[3] == 1);
+ }
+
// VTCM scratchpads for all tensors
// N rows per thread, padded to HVX vector size
// Double buffering requires 2x size per buffer
- size_t spad_size_per_row = 2 * (src0_row_size_aligned + dst_row_size_aligned);
- size_t vtcm_row_per_thread = (octx->ctx->vtcm_size)/ (n_threads * spad_size_per_row);
+ size_t spad_size_per_row = 0;
+ size_t vtcm_row_per_thread = 0;
+
+ if (octx->op == HTP_OP_RMS_NORM_MUL) {
+ if (broadcast_weight) {
+ size_t available_vtcm = octx->ctx->vtcm_size;
+ size_t src1_spad_total = n_threads * src1_row_size_aligned;
+ if (available_vtcm > src1_spad_total) {
+ available_vtcm -= src1_spad_total;
+ } else {
+ available_vtcm = 0;
+ }
+ spad_size_per_row = 2 * (src0_row_size_aligned + dst_row_size_aligned);
+ vtcm_row_per_thread = available_vtcm / (n_threads * spad_size_per_row);
+ } else {
+ spad_size_per_row = 2 * (src0_row_size_aligned + dst_row_size_aligned + src1_row_size_aligned);
+ vtcm_row_per_thread = (octx->ctx->vtcm_size) / (n_threads * spad_size_per_row);
+ }
+ } else {
+ spad_size_per_row = 2 * (src0_row_size_aligned + dst_row_size_aligned);
+ vtcm_row_per_thread = (octx->ctx->vtcm_size)/ (n_threads * spad_size_per_row);
+ }
// Make sure the reserved vtcm size is sufficient
if (vtcm_row_per_thread == 0) {
octx->src0_spad.size = n_threads * octx->src0_spad.size_per_thread;
octx->dst_spad.size = n_threads * octx->dst_spad.size_per_thread;
+ if (octx->op == HTP_OP_RMS_NORM_MUL) {
+ if (broadcast_weight) {
+ octx->src1_spad.size_per_thread = src1_row_size_aligned;
+ } else {
+ octx->src1_spad.size_per_thread = src1_row_size_aligned * vtcm_row_per_thread * 2;
+ }
+ octx->src1_spad.size = n_threads * octx->src1_spad.size_per_thread;
+ } else {
+ octx->src1_spad.size = 0;
+ octx->src1_spad.size_per_thread = 0;
+ }
+
octx->src0_spad.data = octx->ctx->vtcm_base;
- octx->dst_spad.data = octx->src0_spad.data + octx->src0_spad.size;
+ if (octx->op == HTP_OP_RMS_NORM_MUL) {
+ octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size;
+ octx->dst_spad.data = octx->src1_spad.data + octx->src1_spad.size;
+ } else {
+ octx->dst_spad.data = octx->src0_spad.data + octx->src0_spad.size;
+ }
FARF(HIGH, "%s: (%ux%ux%ux%u) -> (%ux%ux%ux%u) : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n", op_type,
src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
.src0_nrows = src0_nrows,
.data_src0 = (const uint8_t *)src0->data,
+ .data_src1 = (octx->op == HTP_OP_RMS_NORM_MUL) ? (const uint8_t *)src1->data : NULL,
.data_dst = (uint8_t *)dst->data,
.src0_data_row_size = src0_data_row_size,
+ .src1_data_row_size = src1_data_row_size,
.dst_data_row_size = dst_data_row_size,
.src0_row_size_aligned = src0_row_size_aligned,
+ .src1_row_size_aligned = src1_row_size_aligned,
.dst_row_size_aligned = dst_row_size_aligned,
.src0_spad_half_size = octx->src0_spad.size_per_thread / 2,
+ .src1_spad_half_size = (octx->op == HTP_OP_RMS_NORM_MUL) ? (octx->src1_spad.size_per_thread / (broadcast_weight ? 1 : 2)) : 0,
.dst_spad_half_size = octx->dst_spad.size_per_thread / 2,
.block = (octx->src0_spad.size_per_thread / 2) / src0_row_size_aligned,
.nc = src0->ne[0],
+ .broadcast_weight = broadcast_weight,
};
worker_pool_run_func(octx->ctx->worker_pool, unary_job_f32_per_thread, &uctx, n_threads);
+++ /dev/null
-#ifndef OP_DESC_H
-#define OP_DESC_H
-
-#define GGML_COMMON_IMPL_CPP
-#include "ggml-backend-impl.h"
-#include "ggml-common.h"
-
-#include <string>
-#include <stdio.h>
-
-struct op_desc {
- char strides[64 * GGML_MAX_SRC];
- char dims[64 * GGML_MAX_SRC];
- char types[16 * GGML_MAX_SRC];
- char buffs[64 * GGML_MAX_SRC];
- char names[64 * GGML_MAX_SRC];
-
- int format_tensor_dims(char * str, const struct ggml_tensor * t) {
- if (t->ne[2] == 1 && t->ne[3] == 1) {
- return sprintf(str, "%d:%d", (int) t->ne[0], (int) t->ne[1]);
- } else {
- return sprintf(str, "%d:%d:%d:%d", (int) t->ne[0], (int) t->ne[1], (int) t->ne[2], (int) t->ne[3]);
- }
- }
-
- void format_op_dims(char * str, const struct ggml_tensor * t) {
- char * p = str;
-
- // append src0 and src1 (if any)
- if (t->src[0]) {
- p += format_tensor_dims(p, t->src[0]);
-
- for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) {
- p += sprintf(p, " x ");
- p += format_tensor_dims(p, t->src[i]);
- }
-
- p += sprintf(p, " -> ");
- }
-
- // format self dims separately for better visual alignment
- char self[64];
- format_tensor_dims(self, t);
-
- p += sprintf(p, "%s", self);
- }
-
- int format_tensor_strides(char * str, const struct ggml_tensor * t) {
- const char * c = ggml_is_contiguous(t) ? "" : "!";
-
- if (t->ne[2] == 1 && t->ne[3] == 1) {
- return sprintf(str, "%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], c);
- } else {
- return sprintf(str, "%zu:%zu:%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], (size_t) t->nb[2], (size_t) t->nb[3], c);
- }
- }
-
- void format_op_strides(char * str, const struct ggml_tensor * t) {
- char * p = str;
-
- // append src0 and src1 (if any)
- if (t->src[0]) {
- p += format_tensor_strides(p, t->src[0]);
-
- for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) {
- p += sprintf(p, " x ");
- p += format_tensor_strides(p, t->src[i]);
- }
-
- p += sprintf(p, " -> ");
- }
-
- // format self dims separately for better visual alignment
- char self[64];
- format_tensor_strides(self, t);
-
- p += sprintf(p, "%s", self);
- }
-
- void format_op_types(char * str, const struct ggml_tensor * t) {
- char * p = str;
-
- // append src0 and src1 (if any)
- if (t->src[0]) {
- p += sprintf(p, "%s", ggml_type_name(t->src[0]->type));
-
- for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) {
- p += sprintf(p, " x ");
- p += sprintf(p, "%s", ggml_type_name(t->src[i]->type));
- }
-
- p += sprintf(p, " -> ");
- }
-
- p += sprintf(p, "%s", ggml_type_name(t->type));
- }
-
- const char * tensor_buff_name(const struct ggml_tensor * t) {
- if (t->buffer) {
- return ggml_backend_buffer_name(t->buffer);
- }
- return "NONE";
- }
-
- void format_op_buffs(char * str, const struct ggml_tensor * t) {
- char * p = str;
-
- // append src0 and src1 (if any)
- if (t->src[0]) {
- p += sprintf(p, "%s", tensor_buff_name(t->src[0]));
-
- for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) {
- p += sprintf(p, " x ");
- p += sprintf(p, "%s", tensor_buff_name(t->src[i]));
- }
-
- p += sprintf(p, " -> ");
- }
-
- p += sprintf(p, "%s", tensor_buff_name(t));
- }
-
- void format_op_names(char * str, const struct ggml_tensor * t) {
- char * p = str;
-
- // append src0 and src1 (if any)
- if (t->src[0]) {
- p += sprintf(p, "%s", t->src[0]->name);
-
- for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) {
- p += sprintf(p, " x ");
- p += sprintf(p, "%s", t->src[i]->name);
- }
-
- p += sprintf(p, " -> ");
- }
-
- p += sprintf(p, "%s", t->name);
- }
-
- void format(const ggml_tensor * op) {
- format_op_dims(dims, op);
- format_op_strides(strides, op);
- format_op_types(types, op);
- format_op_buffs(buffs, op);
- format_op_names(names, op);
- }
-
- op_desc() {}
- op_desc(const ggml_tensor * op) { format(op); }
-};
-
-#endif // OP_DESC_H
}
op_pattern = re.compile(
- r"profile-op\s+(?P<op_name>[A-Z_0-9]+):\s+.*?\s+:\s+(?P<dims>[\d:x\s\->!]+)\s+:\s+(?P<types>[a-z\d_\s\->x]+)\s+:\s+.*?\s+usec\s+(?P<usec>\d+)\s+cycles\s+(?P<cycles>\d+)(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?"
+ r"profile-op\s+(?P<op_name>[A-Z_0-9
+]+):\s+.*?\s+:\s+(?P<dims>[\d:x\s\->!]+)\s+:\s+(?P<types>[a-z\d_\s\->x]+)\s+:\s+.*?\s+usec\s+(?P<usec>\d+)\s+cycles\s+(?P<cycles>\d+)(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?"
)
logger = logging.getLogger("ggml-hexagon-profile")
overview / pkg / ggml / sources / llama.cpp / commitdiff