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Make stablelm support compatible with pre-layer refactor
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* undoing more semantic renames in ggerganov/llama.cpp#3837
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@brittlewis12
brittlewis12 committed Nov 18, 2023
1 parent 18dadad commit 6c0c7df
Showing 1 changed file with 14 additions and 14 deletions.
28 changes: 14 additions & 14 deletions Sources/llmfarm_core_cpp/llama/llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2991,7 +2991,7 @@ static void llm_load_tensors(
} break;
case LLM_ARCH_STABLELM:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);

// output
{
Expand All @@ -3002,12 +3002,12 @@ static void llm_load_tensors(
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
backend_norm = GGML_BACKEND_GPU;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : GGML_BACKEND_GPU;
#endif // _WIN32

backend_output = llama_backend_offload_split;
backend_output = GGML_BACKEND_GPU_SPLIT;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
Expand Down Expand Up @@ -3035,8 +3035,8 @@ static void llm_load_tensors(
/*
llama_model_loader: - tensor 4: blk.0.attn_output.weight f16 [ 2560, 2560, 1, 1 ]
*/
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : GGML_BACKEND_GPU; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : GGML_BACKEND_GPU_SPLIT; // NOLINT

auto & layer = model.layers[i];

Expand All @@ -3051,15 +3051,15 @@ static void llm_load_tensors(
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);

layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);

if (backend == GGML_BACKEND_GPU) {
vram_weights +=
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
}
}
} break;
Expand Down Expand Up @@ -5943,7 +5943,7 @@ struct ggml_cgraph * build_stablelm() {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;

inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embeddings, cb);
cb(inpL, "inp_embd", -1);

// inp_pos - contains the positions
Expand Down Expand Up @@ -6076,9 +6076,9 @@ struct ggml_cgraph * build_stablelm() {
cb(cur, "ffn_norm", il);

cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
model.layers[il].w3, NULL,
model.layers[il].w1, NULL,
model.layers[il].w2, NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
Expand Down

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