vbatts/llama.cpp
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change GPT-J and GPT2 KVs to use fp16 instead

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Concedo 2023-04-05 15:53:07 +08:00
parent 57e9f929ee
commit 1490cdd71d
5 changed files with 18 additions and 12 deletions
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koboldcpp.dll
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10
otherarch/gpt2_v1.cpp
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@ -85,6 +85,8 @@ ModelLoadResult gpt2_model_load(const std::string & fname, gpt2_model & model, g
auto & ctx = model.ctx;
auto memory_type = GGML_V1_TYPE_F16;
size_t ctx_size = 0;
{
@ -119,8 +121,8 @@ ModelLoadResult gpt2_model_load(const std::string & fname, gpt2_model & model, g
ctx_size += n_layer*(4*n_embd*n_embd*ggml_v1_type_size(wtype)); // c_mlp_proj_w
ctx_size += n_layer*( n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // c_mlp_proj_b
ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32); // memory_k
ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32); // memory_v
ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_size(memory_type); // memory_k
ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_size(memory_type); // memory_v
ctx_size += (6 + 12*n_layer)*256; // object overhead
@ -218,8 +220,8 @@ ModelLoadResult gpt2_model_load(const std::string & fname, gpt2_model & model, g
const int n_mem = n_layer*n_ctx;
const int n_elements = n_embd*n_mem;
model.memory_k = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_elements);
model.memory_v = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_elements);
model.memory_k = ggml_v1_new_tensor_1d(ctx, memory_type, n_elements);
model.memory_v = ggml_v1_new_tensor_1d(ctx, memory_type, n_elements);
const size_t memory_size = ggml_v1_nbytes(model.memory_k) + ggml_v1_nbytes(model.memory_v);

10
otherarch/gptj_v1.cpp
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@ -103,6 +103,8 @@ ModelLoadResult legacy_gptj_model_load(const std::string & fname, gptj_model_v1
auto & ctx = model.ctx;
auto memory_type = GGML_V1_TYPE_F16;
size_t ctx_size = 0;
{
@ -136,8 +138,8 @@ ModelLoadResult legacy_gptj_model_load(const std::string & fname, gptj_model_v1
ctx_size += n_layer*(4*n_embd*n_embd*ggml_v1_type_sizef(wtype)); // c_mlp_proj_w_trans
ctx_size += n_layer*( n_embd*ggml_v1_type_sizef(GGML_V1_TYPE_F32)); // c_mlp_proj_b
ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_sizef(GGML_V1_TYPE_F32); // memory_k
ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_sizef(GGML_V1_TYPE_F32); // memory_v
ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_sizef(memory_type); // memory_k
ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_sizef(memory_type); // memory_v
ctx_size += (5 + 10*n_layer)*256; // object overhead
@ -240,8 +242,8 @@ ModelLoadResult legacy_gptj_model_load(const std::string & fname, gptj_model_v1
const int n_mem = n_layer*n_ctx;
const int n_elements = n_embd*n_mem;
model.memory_k = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_elements);
model.memory_v = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_elements);
model.memory_k = ggml_v1_new_tensor_1d(ctx, memory_type, n_elements);
model.memory_v = ggml_v1_new_tensor_1d(ctx, memory_type, n_elements);
const size_t memory_size = ggml_v1_nbytes(model.memory_k) + ggml_v1_nbytes(model.memory_v);

10
otherarch/gptj_v2.cpp
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@ -103,6 +103,8 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
auto & ctx = model.ctx;
auto memory_type = GGML_TYPE_F16;
size_t ctx_size = 0;
{
@ -136,8 +138,8 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w
ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_k
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_v
ctx_size += (5 + 10*n_layer)*256; // object overhead
@ -234,8 +236,8 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
const int n_mem = n_layer*n_ctx;
const int n_elements = n_embd*n_mem;
model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements);
model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements);
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);