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Implemented basic GPU offloading for MPT, GPT-2, GPT-J and GPT-NeoX

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Concedo 2023-06-22 00:43:25 +08:00
parent b1f00fa9cc
commit 1b71752a9f
6 changed files with 99 additions and 8 deletions
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4
gpttype_adapter.cpp
View file

@ -671,7 +671,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
{
if(file_format==FileFormat::NEOX_6|| file_format==FileFormat::NEOX_7)
{
ModelLoadResult res = gpt_neox_model_load(params.model, neox_ctx_v3, vocab, file_format);
ModelLoadResult res = gpt_neox_model_load(params.model, neox_ctx_v3, vocab, file_format, inputs.gpulayers);
if(res==ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
@ -733,7 +733,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
}
else if(file_format==FileFormat::MPT_1)
{
bool res = mpt_model_load(params.model, mpt_ctx_v3, vocab);
bool res = mpt_model_load(params.model, mpt_ctx_v3, vocab, inputs.gpulayers);
if(res==false)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());

22
otherarch/gpt2_v3.cpp
View file

@ -345,6 +345,28 @@ ModelLoadResult gpt2_model_load(const std::string & fname, gpt2_model & model, g
fin.close();
//gpu offload
#if defined(GGML_USE_CLBLAST)
{
const auto & hparams = model.hparams;
size_t vram_total = 0;
const int n_gpu = std::min(gpulayers, int(hparams.n_layer));
fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu);
for (int i = 0; i < n_gpu; ++i) {
const auto & layer = model.layers[i];
layer.c_attn_attn_w->backend = GGML_BACKEND_GPU;
layer.c_attn_proj_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU;
ggml_cl_transform_tensor(layer.c_attn_attn_w->data,layer.c_attn_attn_w); vram_total += ggml_nbytes(layer.c_attn_attn_w);
ggml_cl_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
ggml_cl_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
ggml_cl_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
}
fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
}
#endif
return ModelLoadResult::SUCCESS;
}

30
otherarch/gptj_v3.cpp
View file

@ -15,7 +15,9 @@
#include "model_adapter.h"
#if defined(GGML_USE_CLBLAST)
#include "ggml-opencl.h"
#endif
// load the model's weights from a file
ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, gpt_vocab & vocab, int gpulayers) {
@ -331,7 +333,31 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
fin.close();
//gpu offload
#if defined(GGML_USE_CLBLAST)
{
const auto & hparams = model.hparams;
size_t vram_total = 0;
const int n_gpu = std::min(gpulayers, int(hparams.n_layer));
fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu);
for (int i = 0; i < n_gpu; ++i) {
const auto & layer = model.layers[i];
layer.c_attn_q_proj_w->backend = GGML_BACKEND_GPU;
layer.c_attn_k_proj_w->backend = GGML_BACKEND_GPU;
layer.c_attn_v_proj_w->backend = GGML_BACKEND_GPU;
layer.c_attn_proj_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU;
ggml_cl_transform_tensor(layer.c_attn_q_proj_w->data,layer.c_attn_q_proj_w); vram_total += ggml_nbytes(layer.c_attn_q_proj_w);
ggml_cl_transform_tensor(layer.c_attn_k_proj_w->data,layer.c_attn_k_proj_w); vram_total += ggml_nbytes(layer.c_attn_k_proj_w);
ggml_cl_transform_tensor(layer.c_attn_v_proj_w->data,layer.c_attn_v_proj_w); vram_total += ggml_nbytes(layer.c_attn_v_proj_w);
ggml_cl_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
ggml_cl_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
ggml_cl_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
}
fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
}
#endif
return ModelLoadResult::SUCCESS;
}

26
otherarch/mpt_v3.cpp
View file

@ -18,7 +18,7 @@
// load the model's weights from a file
bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vocab) {
bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vocab, int gpulayers) {
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
auto fin = std::ifstream(fname, std::ios::binary);
@ -75,7 +75,7 @@ bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vo
std::string word;
std::vector<char> buf(128);
for (int i = 0; i < n_vocab; i++) {
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
fin.read((char *) &len, sizeof(len));
@ -278,6 +278,28 @@ bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vo
fin.close();
//gpu offload
#if defined(GGML_USE_CLBLAST)
{
const auto & hparams = model.hparams;
size_t vram_total = 0;
const int n_gpu = std::min(gpulayers, int(hparams.n_layers));
fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu);
for (int i = 0; i < n_gpu; ++i) {
const auto & layer = model.layers[i];
layer.ffn_up_proj->backend = GGML_BACKEND_GPU;
layer.ffn_down_proj->backend = GGML_BACKEND_GPU;
layer.c_attn_wqkv_weight->backend = GGML_BACKEND_GPU;
layer.c_attn_out_proj_weight->backend = GGML_BACKEND_GPU;
ggml_cl_transform_tensor(layer.ffn_up_proj->data,layer.ffn_up_proj); vram_total += ggml_nbytes(layer.ffn_up_proj);
ggml_cl_transform_tensor(layer.ffn_down_proj->data,layer.ffn_down_proj); vram_total += ggml_nbytes(layer.ffn_down_proj);
ggml_cl_transform_tensor(layer.c_attn_wqkv_weight->data,layer.c_attn_wqkv_weight); vram_total += ggml_nbytes(layer.c_attn_wqkv_weight);
ggml_cl_transform_tensor(layer.c_attn_out_proj_weight->data,layer.c_attn_out_proj_weight); vram_total += ggml_nbytes(layer.c_attn_out_proj_weight);
}
fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
}
#endif
return true;
}

24
otherarch/neox_v3.cpp
View file

@ -16,7 +16,7 @@
// load the model's weights from a file
ModelLoadResult gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt_vocab & vocab, FileFormat file_format) {
ModelLoadResult gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt_vocab & vocab, FileFormat file_format, int gpulayers) {
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
auto fin = std::ifstream(fname, std::ios::binary);
@ -318,6 +318,28 @@ ModelLoadResult gpt_neox_model_load(const std::string & fname, gpt_neox_model &
fin.close();
//gpu offload
#if defined(GGML_USE_CLBLAST)
{
const auto & hparams = model.hparams;
size_t vram_total = 0;
const int n_gpu = std::min(gpulayers, int(hparams.n_layer));
fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu);
for (int i = 0; i < n_gpu; ++i) {
const auto & layer = model.layers[i];
layer.c_attn_attn_w->backend = GGML_BACKEND_GPU;
layer.c_attn_proj_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU;
ggml_cl_transform_tensor(layer.c_attn_attn_w->data,layer.c_attn_attn_w); vram_total += ggml_nbytes(layer.c_attn_attn_w);
ggml_cl_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
ggml_cl_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
ggml_cl_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
}
fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
}
#endif
return ModelLoadResult::SUCCESS;
}

1
otherarch/otherarch.h
View file

@ -43,7 +43,6 @@ struct gptj_layer {
struct ggml_tensor * c_mlp_fc_b;
struct ggml_tensor * c_mlp_proj_w;
struct ggml_tensor * c_mlp_proj_w_trans; //for backwards compatibility
struct ggml_tensor * c_mlp_proj_b;
};
struct gptj_layer_v2 {