vbatts/llama.cpp
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use tensor wrapper in matmul

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This commit is contained in:
hongruichen 2024-06-16 21:46:15 +08:00
parent 37bb9263dd
commit 36e41a1055
1 changed files with 6 additions and 53 deletions
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59
ggml-qnn.cpp
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@ -2394,17 +2394,14 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
qnn_instance * instance = nullptr;
std::string graph_name = "ggml_op_qnn_mul_mat";
Qnn_GraphHandle_t graph_handle = nullptr;
Qnn_Tensor_t * tensor_0 = nullptr;
Qnn_Tensor_t * tensor_1 = nullptr;
Qnn_Tensor_t * tensor_2 = nullptr;
Qnn_Param_t qnn_params[] = {};
enum ggml_op ggmlop = GGML_OP_MUL_MAT;
Qnn_DataType_t src0_qnn_type = QNN_DATATYPE_FLOAT_32;
Qnn_DataType_t src1_qnn_type = QNN_DATATYPE_FLOAT_32;
Qnn_DataType_t dst_qnn_type = QNN_DATATYPE_FLOAT_32;
CHECK_PARAMS(ctx, src0, src1, dst);
tensor_0 = (Qnn_Tensor_t *) src0->extra;
tensor_1 = (Qnn_Tensor_t *) src1->extra;
tensor_2 = (Qnn_Tensor_t *) dst->extra;
instance = ctx->instance;
@ -2413,22 +2410,16 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
qnn_perf perf("ggml_qnn_mul_mat");
perf.start();
tensor_0 = (Qnn_Tensor_t *) src0->extra;
tensor_1 = (Qnn_Tensor_t *) src1->extra;
tensor_2 = (Qnn_Tensor_t *) dst->extra;
instance = ctx->instance;
QNN_VER_PTR(*tensor_0)->type = QNN_TENSOR_TYPE_APP_WRITE;
QNN_VER_PTR(*tensor_1)->type = QNN_TENSOR_TYPE_APP_WRITE;
QNN_VER_PTR(*tensor_2)->type = QNN_TENSOR_TYPE_APP_READ;
src0_qnn_type = qnn_datatype_from_ggml_datatype(src0->type);
src1_qnn_type = qnn_datatype_from_ggml_datatype(src1->type);
dst_qnn_type = qnn_datatype_from_ggml_datatype(dst->type);
uint32_t dimensions_input_0[] = {
(uint32_t) src0->ne[0], (uint32_t) src0->ne[1], (uint32_t) src0->ne[2],
(uint32_t) src0->ne[3]};
uint32_t dimensions_input_1[] = {
(uint32_t) src1->ne[0], (uint32_t) src1->ne[1], (uint32_t) src1->ne[2],
(uint32_t) src1->ne[3]};
@ -2444,7 +2435,6 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
graph_handle = std::get<0>(graph_item);
}
uint32_t * tensor_0_dimensions = QNN_VER_PTR(*tensor_0)->dimensions;
uint32_t * tensor_1_dimensions = QNN_VER_PTR(*tensor_1)->dimensions;
uint32_t * tensor_2_dimensions = QNN_VER_PTR(*tensor_2)->dimensions;
@ -2508,9 +2498,6 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
}
if (ctx->device == QNN_BACKEND_NPU) {
QNN_VER_PTR(*tensor_0)->memType = QNN_TENSORMEMTYPE_MEMHANDLE;
QNN_VER_PTR(*tensor_0)->clientBuf= {.data=nullptr, .dataSize=0};
QNN_VER_PTR(*tensor_1)->memType = QNN_TENSORMEMTYPE_MEMHANDLE;
QNN_VER_PTR(*tensor_1)->clientBuf= {.data=nullptr, .dataSize=0};
@ -2518,9 +2505,8 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
QNN_VER_PTR(*tensor_2)->clientBuf= {.data=nullptr, .dataSize=0};
}
error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_0);
if (QNN_SUCCESS != error) {
QNN_LOG_INFO("error = %d\n", error);
ggml_qnn_tensor_writer tensor_writer0(src0, graph_handle, ctx);
if (!tensor_writer0.is_valid()) {
goto failure;
}
error = qnn_raw_interface.tensorCreateGraphTensor(graph_handle, tensor_1);
@ -2534,9 +2520,6 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
goto failure;
}
QNN_VER_PTR(*tensor_0)->dimensions = dimensions_input_0;
QNN_VER_PTR(*tensor_0)->rank = qnn_get_ggml_tensor_rank(src0);
QNN_VER_PTR(*tensor_0)->dataType = src0_qnn_type;
QNN_VER_PTR(*tensor_1)->dimensions = dimensions_input_1;
QNN_VER_PTR(*tensor_1)->rank = qnn_get_ggml_tensor_rank(src1);
QNN_VER_PTR(*tensor_1)->dataType = src1_qnn_type;
@ -2545,29 +2528,15 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
QNN_VER_PTR(*tensor_2)->dataType = dst_qnn_type;
if (ctx->device != QNN_BACKEND_NPU) {
QNN_VER_PTR(*tensor_0)->clientBuf = {src0->data,
qnn_get_ggml_tensor_data_size(src0)};
QNN_VER_PTR(*tensor_1)->clientBuf = {src1->data,
qnn_get_ggml_tensor_data_size(src1)};
QNN_VER_PTR(*tensor_2)->clientBuf = {dst->data,
qnn_get_ggml_tensor_data_size(dst)};
} else {
uint8_t * qnn_buffer_0 = nullptr;
uint8_t * qnn_buffer_1 = nullptr;
uint8_t * qnn_buffer_2 = nullptr;
qnn_instance * instance = ctx->instance;
qnn_buffer_0 = static_cast<uint8_t *>(instance->alloc_rpcmem(
ggml_nbytes(src0), 4));
if (nullptr == qnn_buffer_0) {
QNN_LOG_WARN("alloc rpcmem failure, %s\n", strerror(errno));
goto failure;
} else {
QNN_LOG_INFO("alloc rpcmem successfully\n");
}
instance->register_rpcmem(qnn_buffer_0, tensor_0);
memcpy(qnn_buffer_0, src0->data, ggml_nbytes(src0));
qnn_buffer_1 = static_cast<uint8_t *>(instance->alloc_rpcmem(
ggml_nbytes(src1), 4));
if (nullptr == qnn_buffer_1) {
@ -2590,7 +2559,7 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
instance->register_rpcmem(qnn_buffer_2, tensor_2);
}
Qnn_Tensor_t tensor_inputs[] = {*tensor_0, *tensor_1};
Qnn_Tensor_t tensor_inputs[] = {*tensor_writer0.get_qnn_tensor(), *tensor_1};
Qnn_Tensor_t tensor_outputs[] = {*tensor_2};
Qnn_OpConfig_t op_config = {
(Qnn_OpConfigVersion_t) 1,
@ -2632,27 +2601,20 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
memcpy(dst->data, qnn_buffer_2, ggml_nbytes(dst));
}
auto graph_item = std::make_tuple(graph_handle, tensor_0, tensor_1, tensor_2);
auto graph_item = std::make_tuple(graph_handle, tensor_writer0.get_qnn_tensor(), tensor_1, tensor_2);
instance->_qnn_graph_map[map_entry] = graph_item;
} else {
auto & graph_item= instance->_qnn_graph_map[map_entry];
graph_handle = std::get<0>(graph_item);
tensor_0 = std::get<1>(graph_item);
ggml_qnn_tensor_writer tensor_writer0(src0, std::get<1>(graph_item), ctx);
tensor_1 = std::get<2>(graph_item);
tensor_2 = std::get<3>(graph_item);
uint32_t dimensions_input_0[] = {
(uint32_t) src0->ne[0], (uint32_t) src0->ne[1],
(uint32_t) src0->ne[2], (uint32_t) src0->ne[3]};
uint32_t dimensions_input_1[] = {
(uint32_t) src1->ne[0], (uint32_t) src1->ne[1],
(uint32_t) src1->ne[2], (uint32_t) src1->ne[3]};
uint32_t dimensions_output[] = {
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],
(uint32_t) dst->ne[3]};
QNN_VER_PTR(*tensor_0)->dimensions = dimensions_input_0;
QNN_VER_PTR(*tensor_0)->rank = qnn_get_ggml_tensor_rank(src0);
QNN_VER_PTR(*tensor_0)->dataType = src0_qnn_type;
QNN_VER_PTR(*tensor_1)->dimensions = dimensions_input_1;
QNN_VER_PTR(*tensor_1)->rank = qnn_get_ggml_tensor_rank(src1);
QNN_VER_PTR(*tensor_1)->dataType = src1_qnn_type;
@ -2661,25 +2623,18 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
QNN_VER_PTR(*tensor_2)->dataType = dst_qnn_type;
if (ctx->device != QNN_BACKEND_NPU) {
QNN_VER_PTR(*tensor_0)->clientBuf = {src0->data,
qnn_get_ggml_tensor_data_size(src0)};
QNN_VER_PTR(*tensor_1)->clientBuf = {src1->data,
qnn_get_ggml_tensor_data_size(src1)};
QNN_VER_PTR(*tensor_2)->clientBuf = {dst->data,
qnn_get_ggml_tensor_data_size(dst)};
} else {
uint8_t * qnn_buffer_0 = static_cast<uint8_t *>(ctx->instance->get_rpcmem_from_memhandle(
QNN_VER_PTR(*tensor_0)->memHandle));
if (nullptr != qnn_buffer_0)
memcpy(qnn_buffer_0, src0->data, ggml_nbytes(src0));
uint8_t * qnn_buffer_1 = static_cast<uint8_t *>(ctx->instance->get_rpcmem_from_memhandle(
QNN_VER_PTR(*tensor_1)->memHandle));
if (nullptr != qnn_buffer_1)
memcpy(qnn_buffer_1, src1->data, ggml_nbytes(src1));
}
Qnn_Tensor_t tensor_inputs[] = {*tensor_0, *tensor_1};
Qnn_Tensor_t tensor_inputs[] = {*tensor_writer0.get_qnn_tensor(), *tensor_1};
Qnn_Tensor_t tensor_outputs[] = {*tensor_2};
error = qnn_raw_interface.graphExecute(graph_handle,
tensor_inputs, 2,
@ -2705,7 +2660,6 @@ static void ggml_qnn_mul_mat(ggml_backend_qnn_context * ctx,
failure:
if (QNN_SUCCESS != error) {
QNN_LOG_DEBUG("tensor0 name %s", QNN_TENSOR_GET_NAME(*tensor_0));
QNN_LOG_DEBUG("tensor1 name %s", QNN_TENSOR_GET_NAME(*tensor_1));
QNN_LOG_DEBUG("tensor2 name %s", QNN_TENSOR_GET_NAME(*tensor_2));
QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64
@ -2724,7 +2678,6 @@ failure:
dst->ne[1], dst->ne[2], dst->nb[0], dst->nb[1], dst->nb[2]);
}
QNN_VER_PTR(*tensor_0)->dimensions = tensor_0_dimensions;
QNN_VER_PTR(*tensor_1)->dimensions = tensor_1_dimensions;
QNN_VER_PTR(*tensor_2)->dimensions = tensor_2_dimensions;
perf.info();