vbatts/llama.cpp
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possibly slower, but cannot use larger batches without modifying ggml library.

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Concedo 2023-07-04 00:35:02 +08:00
parent bfeb3471d7
commit ca9a11697c
2 changed files with 53 additions and 11 deletions
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22
gpttype_adapter.cpp
View file

@ -432,10 +432,10 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
{
rwkv_ctx_v3 = rwkv_init_from_file(modelname.c_str(), n_threads);
// if(inputs.gpulayers>0)
// {
// rwkv_gpu_offload_layers(rwkv_ctx_v3,inputs.gpulayers);
// }
if(inputs.gpulayers>0)
{
rwkv_gpu_offload_layers(rwkv_ctx_v3,inputs.gpulayers);
}
const struct rwkv_file_header & header = rwkv_ctx_v3->instance->model.header;
const size_t n_vocab = header.n_vocab;
@ -1066,15 +1066,15 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
}
else
{
// if(embd.size()>1)
// {
// evalres = rwkv_eval_sequence(rwkv_ctx_v3, (uint32_t*)embd.data(), embd.size(), rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
// }
// else
// {
if(embd.size()>1)
{
evalres = rwkv_eval_sequence(rwkv_ctx_v3, (uint32_t*)embd.data(), embd.size(), rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
}
else
{
bool ignoreLogits = (!startedsampling && ((int)embd_inp.size() > input_consumed + 2));
evalres = rwkv_eval(rwkv_ctx_v3, embd[0], rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, ignoreLogits?nullptr:rwkv_ctx_v3->logits_out);
//}
}
memcpy(logits.data(), rwkv_ctx_v3->logits_out, sizeof(float) * rwkv_vocab.size());
rwkv_ctx_v3->state_in = rwkv_ctx_v3->state_out;

42
otherarch/rwkv_v3.cpp
View file

@ -6,6 +6,13 @@
#include "rwkv_v3.h"
#include "ggml.h"
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#endif
#if defined(GGML_USE_CLBLAST)
#include "ggml-opencl.h"
#endif
#include <string>
#include <vector>
#include <cstring>
@ -1058,7 +1065,11 @@ struct rwkv_future_tensor rwkv_future_graph_work(struct rwkv_future_ctx & ctx,
const size_t n_threads,
const size_t sequence_len = 1
) {
#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUBLAS)
enum ggml_type mul_mat_type = type == GGML_TYPE_F32 ? GGML_TYPE_F32 : GGML_TYPE_F16;
#else
enum ggml_type mul_mat_type = ggml_is_quantized(type) ? GGML_TYPE_Q8_1 : type;
#endif
return ctx.alloc(GGML_TYPE_I8, rwkv_future_tensor::size(mul_mat_type, ffn_key_height, sequence_len) * n_threads + 64 * (n_threads - 1));
}
@ -1545,7 +1556,38 @@ struct rwkv_context * rwkv_clone_context(struct rwkv_context * ctx, const uint32
}
bool rwkv_gpu_offload_layers(struct rwkv_context * ctx, const uint32_t n_layers) {
#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUBLAS)
printf("\nOffloading %u (or fewer) layers...",n_layers);
const auto offload = [&](struct ggml_tensor * tensor) {
// TODO support multi-GPU
tensor->backend = GGML_BACKEND_GPU;
#if defined(GGML_USE_CLBLAST)
ggml_cl_transform_tensor(tensor->data, tensor);
#else
ggml_cuda_transform_tensor(tensor->data, tensor);
#endif
};
const size_t n_gpu = std::min(n_layers, ctx->instance->model.header.n_layer);
if (ctx->gpu_layers < n_gpu) {
for (size_t & i = ctx->gpu_layers; i < n_gpu; i++) {
const struct rwkv_layer & layer = ctx->instance->model.layers[i];
// TODO also offload other operations to GPU with ggml_cuda_assign_buffers
offload(layer.att_key);
offload(layer.att_value);
offload(layer.att_receptance);
offload(layer.att_output);
offload(layer.ffn_key);
offload(layer.ffn_value);
offload(layer.ffn_receptance);
}
return true;
}
#endif
return false;
}