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add unary op template and more ops

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This commit is contained in:
hongruichen 2024-07-13 00:06:58 +08:00
parent 7cbc4fbd8c
commit 100ccd5e7f
4 changed files with 225 additions and 74 deletions
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35
ggml/src/ggml-qnn.cpp
View file

@ -1,6 +1,5 @@
#include "ggml-qnn.h"
#include <list>
#include <stdatomic.h>
#include <stdio.h>
#include <stdlib.h>
@ -15,6 +14,7 @@
#include <fstream>
#include <functional>
#include <iostream>
#include <list>
#include <memory>
#include <mutex>
#include <queue>
@ -142,7 +142,8 @@ struct ggml_backend_qnn_buffer_type_context {
// =================================================================================================
static bool ggml_qnn_can_handle_op(ggml_backend_qnn_context *ctx, const struct ggml_tensor *tensor,
bool b_dump_tensor_info) {
if (ggml_is_empty(tensor) || !qnn::ggml_qnn_op_array()[tensor->op]) {
if (ggml_is_empty(tensor) ||
(!qnn::ggml_qnn_unary_op_array()[tensor->op] && !qnn::ggml_qnn_binary_op_array()[tensor->op])) {
return false;
}
@ -161,19 +162,6 @@ static bool ggml_qnn_can_handle_op(ggml_backend_qnn_context *ctx, const struct g
return false;
}
// TODO: support other GGML OPs using QNN API
// a GENERAL approach could fix this problem in a standalone PR of refine ggml backend
// subsystem for hybrid inference between CPU&GPU / CPU&NPU easily(less the 100 LoC and no
// side-effect to the existing codes) for ANY ggml backends which the backend's
// ggml_backend_xxx_buffer_is_host return true. this approach could be found at:
// https://github.com/ggerganov/llama.cpp/pull/7641
bool supported_op = false;
supported_op = (tensor->op == GGML_OP_ADD);
supported_op = ((tensor->op == GGML_OP_ADD) || (tensor->op == GGML_OP_MUL_MAT));
if (!supported_op) {
return false;
}
// TODO: support other quantized data type
if (ggml_is_quantized(src0->type)) {
if (src0->type != GGML_TYPE_Q8_0 && src0->type != GGML_TYPE_Q4_0) {
@ -192,14 +180,18 @@ static bool ggml_qnn_can_handle_op(ggml_backend_qnn_context *ctx, const struct g
}
bool ggml_qnn_compute_forward(ggml_backend_qnn_context *ctx, struct ggml_tensor *tensor) {
auto func = qnn::ggml_qnn_op_array()[tensor->op];
if (!func) {
QNN_LOG_WARN("unsupported op %d", tensor->op);
return false;
auto unary_op = qnn::ggml_qnn_unary_op_array()[tensor->op];
if (unary_op) {
return unary_op(ctx, tensor->src[0], tensor);
}
func(ctx, tensor->src[0], tensor->src[1], tensor);
return true;
auto binary_op = qnn::ggml_qnn_binary_op_array()[tensor->op];
if (binary_op) {
return binary_op(ctx, tensor->src[0], tensor->src[1], tensor);
}
QNN_LOG_WARN("unsupported op %d", tensor->op);
return false;
}
static const char *ggml_backend_qnn_buffer_get_name(ggml_backend_buffer_t buffer) {
@ -343,6 +335,7 @@ GGML_CALL static void ggml_backend_qnn_free(ggml_backend_t backend) {
auto instance = g_qnn_mgr[ctx->device].instance;
if (instance) {
ctx->qnn_unary_graph_cache.clear();
for (const auto &graph_item : ctx->qnn_binary_graph_cache) {
QNN_LOG_INFO("graph type:%s", graph_item.first.c_str());
}

243
ggml/src/ggml-qnn/backend-ops.cpp
View file

@ -12,6 +12,23 @@
namespace {
bool qnn_is_valid_params(ggml_backend_qnn_context *ctx, const ggml_tensor *src, ggml_tensor *dst) {
if (!ctx || !src || !dst) {
QNN_LOG_WARN("invalid params\n");
return false;
}
auto instance = ctx->instance;
auto *tensor0 = qnn::ggml_qnn_tensor::from_ggml_tensor(src);
auto *tensor1 = qnn::ggml_qnn_tensor::from_ggml_tensor(dst);
if (!instance || !tensor0 || !tensor1) {
QNN_LOG_WARN("invalid tensors\n");
return false;
}
return true;
}
bool qnn_is_valid_params(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst) {
if (!ctx || !src0 || !src1 || !dst) {
@ -33,15 +50,13 @@ bool qnn_is_valid_params(ggml_backend_qnn_context *ctx, const ggml_tensor *src0,
} // namespace
#define CHECK_PARAMS(ctx, src0, src1, dst) \
do { \
if (!qnn_is_valid_params((ctx), (src0), (src1), (dst))) { \
return; \
} \
} while (0)
#define CHECK_PARAMS(ctx, ...) \
if (!qnn_is_valid_params((ctx), __VA_ARGS__)) { \
return false; \
}
#else
#define CHECK_PARAMS(ctx, src0, src1, dst)
#define CHECK_PARAMS(ctx, ...)
#endif
namespace {
@ -125,15 +140,33 @@ bool execute_graph(qnn::ggml_qnn_graph<_InputSize, _OutputSize> *graph,
return true;
}
qnn::ggml_qnn_unary_graph_cache_t &get_qnn_graph_cache(ggml_backend_qnn_context *ctx,
const std::array<const ggml_tensor *, 1> &inputs,
const std::array<ggml_tensor *, 1> &outputs) {
GGML_UNUSED(inputs);
GGML_UNUSED(outputs);
return ctx->qnn_unary_graph_cache;
}
qnn::ggml_qnn_binary_graph_cache_t &get_qnn_graph_cache(ggml_backend_qnn_context *ctx,
const std::array<const ggml_tensor *, 2> &inputs,
const std::array<ggml_tensor *, 1> &outputs) {
GGML_UNUSED(inputs);
GGML_UNUSED(outputs);
return ctx->qnn_binary_graph_cache;
}
template <size_t _InputSize, size_t _OutputSize>
qnn::ggml_qnn_graph_binary *get_qnn_graph_from_cache(ggml_backend_qnn_context *ctx, ggml_op op,
const std::string &qnn_op,
const std::array<const ggml_tensor *, _InputSize> &inputs,
const std::array<ggml_tensor *, _OutputSize> &outputs) {
qnn::ggml_qnn_graph<_InputSize, _OutputSize> *get_qnn_graph_from_cache(
ggml_backend_qnn_context *ctx, ggml_op op, const std::string &qnn_op,
const std::array<const ggml_tensor *, _InputSize> &inputs, const std::array<ggml_tensor *, _OutputSize> &outputs) {
using graph_t = qnn::ggml_qnn_graph<_InputSize, _OutputSize>;
auto &graph_cache = get_qnn_graph_cache(ctx, inputs, outputs);
const std::string graph_key(ggml_op_name(op));
auto it = ctx->qnn_binary_graph_cache.find(graph_key);
qnn::ggml_qnn_graph_binary *graph_ptr = nullptr;
if (it != ctx->qnn_binary_graph_cache.end()) {
auto it = graph_cache.find(graph_key);
graph_t *graph_ptr = nullptr;
if (it != graph_cache.end()) {
graph_ptr = it->second.get();
} else {
std::string graph_name = graph_key + "_" + std::to_string(ctx->threads);
@ -141,49 +174,49 @@ qnn::ggml_qnn_graph_binary *get_qnn_graph_from_cache(ggml_backend_qnn_context *c
graph_name += "_";
graph_name += input->name;
}
auto graph = std::make_unique<qnn::ggml_qnn_graph_binary>(graph_name, (QNNBackend)(ctx->device),
ctx->instance->get_qnn_context_handle(),
ctx->raw_interface, ctx->socinfo.vtcm_size_in_mb);
auto graph =
std::make_unique<graph_t>(graph_name, (QNNBackend)(ctx->device), ctx->instance->get_qnn_context_handle(),
ctx->raw_interface, ctx->socinfo.vtcm_size_in_mb);
if (!graph->is_valid()) {
return nullptr;
}
if (!qnn_bind_tensors_to_graph<2, 1>(graph.get(), qnn_op.c_str(), inputs, outputs)) {
if (!qnn_bind_tensors_to_graph<_InputSize, _OutputSize>(graph.get(), qnn_op.c_str(), inputs, outputs)) {
return nullptr;
}
graph_ptr = graph.get();
ctx->qnn_binary_graph_cache[graph_key] = std::move(graph);
graph_cache[graph_key] = std::move(graph);
}
return graph_ptr;
}
constexpr const char *kGgmlOpToQnnOp[] = {
nullptr, // GGML_OP_NONE
nullptr, // GGML_OP_DUP
QNN_OP_ELEMENT_WISE_ADD, // GGML_OP_ADD
nullptr, // GGML_OP_ADD1
nullptr, // GGML_OP_ACC
nullptr, // GGML_OP_SUB
QNN_OP_ELEMENT_WISE_MULTIPLY, // GGML_OP_MUL
nullptr, // GGML_OP_DIV
nullptr, // GGML_OP_SQR
nullptr, // GGML_OP_SQRT
nullptr, // GGML_OP_LOG
nullptr, // GGML_OP_SUM
nullptr, // GGML_OP_SUM_ROWS
nullptr, // GGML_OP_MEAN
nullptr, // GGML_OP_ARGMAX
nullptr, // GGML_OP_REPEAT
nullptr, // GGML_OP_REPEAT_BACK
nullptr, // GGML_OP_CONCAT
nullptr, // GGML_OP_SILU_BACK
nullptr, // GGML_OP_NORM
nullptr, // GGML_OP_RMS_NORM
nullptr, // GGML_OP_RMS_NORM_BACK
nullptr, // GGML_OP_GROUP_NORM
nullptr, // GGML_OP_NONE
nullptr, // GGML_OP_DUP
QNN_OP_ELEMENT_WISE_ADD, // GGML_OP_ADD
nullptr, // GGML_OP_ADD1
nullptr, // GGML_OP_ACC
QNN_OP_ELEMENT_WISE_SUBTRACT, // GGML_OP_SUB
QNN_OP_ELEMENT_WISE_MULTIPLY, // GGML_OP_MUL
QNN_OP_ELEMENT_WISE_DIVIDE, // GGML_OP_DIV
nullptr, // GGML_OP_SQR
QNN_OP_ELEMENT_WISE_SQUARE_ROOT, // GGML_OP_SQRT
nullptr, // GGML_OP_LOG
nullptr, // GGML_OP_SUM
nullptr, // GGML_OP_SUM_ROWS
nullptr, // GGML_OP_MEAN
nullptr, // GGML_OP_ARGMAX
nullptr, // GGML_OP_REPEAT
nullptr, // GGML_OP_REPEAT_BACK
nullptr, // GGML_OP_CONCAT
nullptr, // GGML_OP_SILU_BACK
nullptr, // GGML_OP_NORM
nullptr, // GGML_OP_RMS_NORM
nullptr, // GGML_OP_RMS_NORM_BACK
nullptr, // GGML_OP_GROUP_NORM
QNN_OP_MAT_MUL, // GGML_OP_MUL_MAT
nullptr, // GGML_OP_MUL_MAT_ID
@ -249,7 +282,7 @@ static_assert(sizeof(kGgmlOpToQnnOp) / sizeof(kGgmlOpToQnnOp[0]) == GGML_OP_COUN
"GGML_OP_COUNT does not match the size of the ops table");
template <ggml_op _GgmlOp>
void qnn_binary_op_impl(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
bool qnn_binary_op_impl(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst) {
static_assert(kGgmlOpToQnnOp[_GgmlOp] != nullptr, "GGML_OP does not have a corresponding QNN_OP");
@ -270,20 +303,136 @@ void qnn_binary_op_impl(ggml_backend_qnn_context *ctx, const ggml_tensor *src0,
print_ggml_tensor(src1);
print_ggml_tensor(dst);
}
return succeed;
}
template <ggml_op _GgmlOp>
bool qnn_unary_op_impl(ggml_backend_qnn_context *ctx, const ggml_tensor *src, ggml_tensor *dst) {
static_assert(kGgmlOpToQnnOp[_GgmlOp] != nullptr, "GGML_OP does not have a corresponding QNN_OP");
CHECK_PARAMS(ctx, src, dst);
qnn::qnn_perf perf(ggml_op_name(_GgmlOp));
perf.start();
bool succeed = false;
auto *graph_ptr = get_qnn_graph_from_cache<1, 1>(ctx, _GgmlOp, kGgmlOpToQnnOp[_GgmlOp], { src }, { dst });
if (graph_ptr) {
succeed = execute_graph<1, 1>(graph_ptr, { src }, { dst });
}
if (!succeed) {
print_ggml_tensor(src);
print_ggml_tensor(dst);
}
return succeed;
}
} // namespace
qnn::ggml_qnn_op_array_t qnn::ggml_qnn_op_array() {
static constexpr const qnn::ggml_qnn_op_t kQnnOpsTable[] = {
qnn::ggml_qnn_unary_op_array_t qnn::ggml_qnn_unary_op_array() {
static constexpr const qnn::ggml_qnn_unary_op_t kQnnOpsTable[] = {
nullptr, // GGML_OP_NONE
nullptr, // GGML_OP_DUP
nullptr, // GGML_OP_ADD
nullptr, // GGML_OP_ADD1
nullptr, // GGML_OP_ACC
nullptr, // GGML_OP_SUB
nullptr, // GGML_OP_MUL
nullptr, // GGML_OP_DIV
nullptr, // GGML_OP_SQR
qnn_unary_op_impl<GGML_OP_SQRT>, // GGML_OP_SQRT
nullptr, // GGML_OP_LOG
nullptr, // GGML_OP_SUM
nullptr, // GGML_OP_SUM_ROWS
nullptr, // GGML_OP_MEAN
nullptr, // GGML_OP_ARGMAX
nullptr, // GGML_OP_REPEAT
nullptr, // GGML_OP_REPEAT_BACK
nullptr, // GGML_OP_CONCAT
nullptr, // GGML_OP_SILU_BACK
nullptr, // GGML_OP_NORM
nullptr, // GGML_OP_RMS_NORM
nullptr, // GGML_OP_RMS_NORM_BACK
nullptr, // GGML_OP_GROUP_NORM
nullptr, // GGML_OP_MUL_MAT
nullptr, // GGML_OP_MUL_MAT_ID
nullptr, // GGML_OP_OUT_PROD
nullptr, // GGML_OP_SCALE
nullptr, // GGML_OP_SET
nullptr, // GGML_OP_CPY
nullptr, // GGML_OP_CONT
nullptr, // GGML_OP_RESHAPE
nullptr, // GGML_OP_VIEW
nullptr, // GGML_OP_PERMUTE
nullptr, // GGML_OP_TRANSPOSE
nullptr, // GGML_OP_GET_ROWS
nullptr, // GGML_OP_GET_ROWS_BACK
nullptr, // GGML_OP_DIAG
nullptr, // GGML_OP_DIAG_MASK_INF
nullptr, // GGML_OP_DIAG_MASK_ZERO
nullptr, // GGML_OP_SOFT_MAX
nullptr, // GGML_OP_SOFT_MAX_BACK
nullptr, // GGML_OP_ROPE
nullptr, // GGML_OP_ROPE_BACK
nullptr, // GGML_OP_CLAMP
nullptr, // GGML_OP_CONV_TRANSPOSE_1D
nullptr, // GGML_OP_IM2COL
nullptr, // GGML_OP_CONV_TRANSPOSE_2D
nullptr, // GGML_OP_POOL_1D
nullptr, // GGML_OP_POOL_2D
nullptr, // GGML_OP_UPSCALE
nullptr, // GGML_OP_PAD
nullptr, // GGML_OP_ARANGE
nullptr, // GGML_OP_TIMESTEP_EMBEDDING
nullptr, // GGML_OP_ARGSORT
nullptr, // GGML_OP_LEAKY_RELU
nullptr, // GGML_OP_FLASH_ATTN_EXT
nullptr, // GGML_OP_FLASH_ATTN_BACK
nullptr, // GGML_OP_SSM_CONV
nullptr, // GGML_OP_SSM_SCAN
nullptr, // GGML_OP_WIN_PART
nullptr, // GGML_OP_WIN_UNPART
nullptr, // GGML_OP_GET_REL_POS
nullptr, // GGML_OP_ADD_REL_POS
nullptr, // GGML_OP_UNARY
nullptr, // GGML_OP_MAP_UNARY
nullptr, // GGML_OP_MAP_BINARY
nullptr, // GGML_OP_MAP_CUSTOM1_F32
nullptr, // GGML_OP_MAP_CUSTOM2_F32
nullptr, // GGML_OP_MAP_CUSTOM3_F32
nullptr, // GGML_OP_MAP_CUSTOM1
nullptr, // GGML_OP_MAP_CUSTOM2
nullptr, // GGML_OP_MAP_CUSTOM3
nullptr, // GGML_OP_CROSS_ENTROPY_LOSS
nullptr, // GGML_OP_CROSS_ENTROPY_LOSS_BACK
};
static_assert(sizeof(kQnnOpsTable) / sizeof(kQnnOpsTable[0]) == GGML_OP_COUNT,
"GGML_OP_COUNT does not match the size of the ops table");
return kQnnOpsTable;
}
qnn::ggml_qnn_binary_op_array_t qnn::ggml_qnn_binary_op_array() {
static constexpr const qnn::ggml_qnn_binary_op_t kQnnOpsTable[] = {
nullptr, // GGML_OP_NONE
nullptr, // GGML_OP_DUP
qnn_binary_op_impl<GGML_OP_ADD>, // GGML_OP_ADD
nullptr, // GGML_OP_ADD1
nullptr, // GGML_OP_ACC
nullptr, // GGML_OP_SUB
qnn_binary_op_impl<GGML_OP_SUB>, // GGML_OP_SUB
qnn_binary_op_impl<GGML_OP_MUL>, // GGML_OP_MUL
nullptr, // GGML_OP_DIV
qnn_binary_op_impl<GGML_OP_DIV>, // GGML_OP_DIV
nullptr, // GGML_OP_SQR
nullptr, // GGML_OP_SQRT
nullptr, // GGML_OP_LOG

11
ggml/src/ggml-qnn/backend-ops.hpp
View file

@ -6,11 +6,14 @@
namespace qnn {
typedef void (*ggml_qnn_op_t)(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst);
typedef bool (*ggml_qnn_unary_op_t)(ggml_backend_qnn_context *ctx, const ggml_tensor *src, ggml_tensor *dst);
typedef bool (*ggml_qnn_binary_op_t)(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst);
typedef const ggml_qnn_op_t (&ggml_qnn_op_array_t)[GGML_OP_COUNT];
typedef const ggml_qnn_unary_op_t (&ggml_qnn_unary_op_array_t)[GGML_OP_COUNT];
typedef const ggml_qnn_binary_op_t (&ggml_qnn_binary_op_array_t)[GGML_OP_COUNT];
ggml_qnn_op_array_t ggml_qnn_op_array();
ggml_qnn_unary_op_array_t ggml_qnn_unary_op_array();
ggml_qnn_binary_op_array_t ggml_qnn_binary_op_array();
} // namespace qnn

8
ggml/src/ggml-qnn/backend.hpp
View file

@ -11,6 +11,11 @@
#include "graph.hpp"
#include "qnn.hpp"
namespace qnn {
typedef std::unordered_map<std::string, std::unique_ptr<qnn::ggml_qnn_graph_unary>> ggml_qnn_unary_graph_cache_t;
typedef std::unordered_map<std::string, std::unique_ptr<qnn::ggml_qnn_graph_binary>> ggml_qnn_binary_graph_cache_t;
} // namespace qnn
struct ggml_backend_qnn_context {
int device;
int threads;
@ -21,5 +26,6 @@ struct ggml_backend_qnn_context {
QNN_INTERFACE_VER_TYPE raw_interface;
QNN_SYSTEM_INTERFACE_VER_TYPE raw_system_interface;
qnn::qcom_socinfo socinfo;
std::unordered_map<std::string, std::unique_ptr<qnn::ggml_qnn_graph_binary>> qnn_binary_graph_cache;
qnn::ggml_qnn_unary_graph_cache_t qnn_unary_graph_cache;
qnn::ggml_qnn_binary_graph_cache_t qnn_binary_graph_cache;
};