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feat: add GGML_UNARY_OP_GELU

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hongruichen 2024-07-18 10:25:45 +08:00
parent ce199b2de7
commit d82b3a0bdb
3 changed files with 60 additions and 25 deletions
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25
ggml/src/ggml-qnn.cpp
View file

@ -135,14 +135,19 @@ struct ggml_backend_qnn_buffer_type_context {
//
// =================================================================================================
static bool ggml_qnn_can_handle_op(ggml_backend_qnn_context *ctx, const struct ggml_tensor *tensor) {
if (ggml_is_empty(tensor) ||
(!qnn::ggml_qnn_unary_op_array()[tensor->op] && !qnn::ggml_qnn_binary_op_array()[tensor->op])) {
if (ggml_is_empty(tensor)) {
return false;
}
if (!qnn::ggml_qnn_unary_op_array()[tensor->op] && !qnn::ggml_qnn_binary_op_array()[tensor->op] &&
(tensor->op != GGML_OP_UNARY ||
qnn::ggml_qnn_unary_op_array()[qnn::kGgmlUnaryOpStart + ggml_get_unary_op(tensor)])) {
return false;
}
const struct ggml_tensor *src0 = tensor->src[0];
const struct ggml_tensor *src1 = tensor->src[1];
if (nullptr == src0 || nullptr == src1) {
if (!src0 || !src1) {
return false;
}
@ -162,18 +167,16 @@ static bool ggml_qnn_can_handle_op(ggml_backend_qnn_context *ctx, const struct g
}
}
if (tensor->op == GGML_OP_MUL_MAT) {
if (ne00 <= 32 || ne01 <= 32 || ne10 <= 32 || ne11 <= 32) {
// comment it for make UT of mul_mat with QNN RPC happy
// return false;
}
}
return true;
}
static bool ggml_qnn_compute_forward(ggml_backend_qnn_context *ctx, struct ggml_tensor *tensor) {
auto unary_op = qnn::ggml_qnn_unary_op_array()[tensor->op];
size_t unary_op_idx = tensor->op;
if (tensor->op == GGML_OP_UNARY) {
unary_op_idx = qnn::kGgmlUnaryOpStart + ggml_get_unary_op(tensor);
}
auto unary_op = qnn::ggml_qnn_unary_op_array()[unary_op_idx];
if (unary_op) {
return unary_op(ctx, tensor->src[0], tensor);
}

56
ggml/src/ggml-qnn/backend-ops.cpp
View file

@ -158,12 +158,16 @@ qnn::ggml_qnn_binary_graph_cache_t &get_qnn_graph_cache(ggml_backend_qnn_context
template <size_t _InputSize, size_t _OutputSize>
qnn::ggml_qnn_graph<_InputSize, _OutputSize> *get_qnn_graph_from_cache(
ggml_backend_qnn_context *ctx, ggml_op op, const std::string &qnn_op,
ggml_backend_qnn_context *ctx, size_t 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>;
GGML_ASSERT(op < (GGML_OP_COUNT + GGML_UNARY_OP_COUNT));
auto &graph_cache = get_qnn_graph_cache(ctx, inputs, outputs);
const std::string graph_key(ggml_op_name(op));
const auto *op_name = op < qnn::kGgmlUnaryOpStart ? ggml_op_name(ggml_op(op))
: ggml_unary_op_name(ggml_unary_op(op - qnn::kGgmlUnaryOpStart));
const std::string graph_key(op_name);
auto it = graph_cache.find(graph_key);
graph_t *graph_ptr = nullptr;
if (it != graph_cache.end()) {
@ -276,10 +280,27 @@ constexpr const char *kGgmlOpToQnnOp[] = {
nullptr, // GGML_OP_CROSS_ENTROPY_LOSS
nullptr, // GGML_OP_CROSS_ENTROPY_LOSS_BACK
// ggml_unary_op
nullptr, // GGML_UNARY_OP_ABS
nullptr, // GGML_UNARY_OP_SGN
nullptr, // GGML_UNARY_OP_NEG
nullptr, // GGML_UNARY_OP_STEP
nullptr, // GGML_UNARY_OP_TANH
nullptr, // GGML_UNARY_OP_ELU
nullptr, // GGML_UNARY_OP_RELU
nullptr, // GGML_UNARY_OP_SIGMOID
QNN_OP_GELU, // GGML_UNARY_OP_GELU
nullptr, // GGML_UNARY_OP_GELU_QUICK
nullptr, // GGML_UNARY_OP_SILU
nullptr, // GGML_UNARY_OP_HARDSWISH
nullptr, // GGML_UNARY_OP_HARDSIGMOID
};
static_assert(sizeof(kGgmlOpToQnnOp) / sizeof(kGgmlOpToQnnOp[0]) == GGML_OP_COUNT,
"GGML_OP_COUNT does not match the size of the ops table");
static_assert(sizeof(kGgmlOpToQnnOp) / sizeof(kGgmlOpToQnnOp[0]) == (GGML_OP_COUNT + GGML_UNARY_OP_COUNT),
"GGML_OP_COUNT does not match the size of the kGgmlOpToQnnOp table");
static_assert(kGgmlOpToQnnOp[GGML_UNARY_OP_GELU + qnn::kGgmlUnaryOpStart] != nullptr,
"GGML_UNARY_OP_GELU does not correspond to QNN_OP_GELU");
template <ggml_op _GgmlOp>
bool qnn_binary_op_impl(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
@ -288,9 +309,6 @@ bool qnn_binary_op_impl(ggml_backend_qnn_context *ctx, const ggml_tensor *src0,
CHECK_PARAMS(ctx, src0, src1, dst);
qnn::qnn_perf perf(ggml_op_name(_GgmlOp));
perf.start();
bool succeed = false;
qnn::ggml_qnn_graph_binary *graph_ptr =
get_qnn_graph_from_cache<2, 1>(ctx, _GgmlOp, kGgmlOpToQnnOp[_GgmlOp], { src0, src1 }, { dst });
@ -307,15 +325,12 @@ bool qnn_binary_op_impl(ggml_backend_qnn_context *ctx, const ggml_tensor *src0,
return succeed;
}
template <ggml_op _GgmlOp>
template <size_t _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) {
@ -416,10 +431,25 @@ qnn::ggml_qnn_unary_op_array_t qnn::ggml_qnn_unary_op_array() {
nullptr, // GGML_OP_CROSS_ENTROPY_LOSS
nullptr, // GGML_OP_CROSS_ENTROPY_LOSS_BACK
// ggml_unary_op
nullptr, // GGML_UNARY_OP_ABS
nullptr, // GGML_UNARY_OP_SGN
nullptr, // GGML_UNARY_OP_NEG
nullptr, // GGML_UNARY_OP_STEP
nullptr, // GGML_UNARY_OP_TANH
nullptr, // GGML_UNARY_OP_ELU
nullptr, // GGML_UNARY_OP_RELU
nullptr, // GGML_UNARY_OP_SIGMOID
qnn_unary_op_impl<GGML_UNARY_OP_GELU + qnn::kGgmlUnaryOpStart>, // GGML_UNARY_OP_GELU
nullptr, // GGML_UNARY_OP_GELU_QUICK
nullptr, // GGML_UNARY_OP_SILU
nullptr, // GGML_UNARY_OP_HARDSWISH
nullptr, // GGML_UNARY_OP_HARDSIGMOID
};
static_assert(sizeof(kQnnOpsTable) / sizeof(kQnnOpsTable[0]) == GGML_OP_COUNT,
"GGML_OP_COUNT does not match the size of the ops table");
static_assert(sizeof(kQnnOpsTable) / sizeof(kQnnOpsTable[0]) == (GGML_OP_COUNT + GGML_UNARY_OP_COUNT),
"GGML_OP_COUNT does not match the size of the kQnnOpsTable table");
return kQnnOpsTable;
}

4
ggml/src/ggml-qnn/backend-ops.hpp
View file

@ -10,9 +10,11 @@ typedef bool (*ggml_qnn_unary_op_t)(ggml_backend_qnn_context *ctx, const ggml_te
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_unary_op_t (&ggml_qnn_unary_op_array_t)[GGML_OP_COUNT];
typedef const ggml_qnn_unary_op_t (&ggml_qnn_unary_op_array_t)[GGML_OP_COUNT + GGML_UNARY_OP_COUNT];
typedef const ggml_qnn_binary_op_t (&ggml_qnn_binary_op_array_t)[GGML_OP_COUNT];
constexpr const size_t kGgmlUnaryOpStart = GGML_OP_COUNT;
ggml_qnn_unary_op_array_t ggml_qnn_unary_op_array();
ggml_qnn_binary_op_array_t ggml_qnn_binary_op_array();