stablelm : StableLM support (#3586)
* Add support for stablelm-3b-4e1t * Supports GPU offloading of (n-1) layers
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36eed0c42c
6 changed files with 322 additions and 12 deletions
284
llama.cpp
284
llama.cpp
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@ -192,6 +192,7 @@ enum llm_arch {
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LLM_ARCH_PERSIMMON,
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LLM_ARCH_REFACT,
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LLM_ARCH_BLOOM,
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LLM_ARCH_STABLELM,
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LLM_ARCH_UNKNOWN,
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};
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@ -207,6 +208,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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{ LLM_ARCH_PERSIMMON, "persimmon" },
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{ LLM_ARCH_REFACT, "refact" },
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{ LLM_ARCH_BLOOM, "bloom" },
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{ LLM_ARCH_STABLELM, "stablelm" },
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};
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enum llm_kv {
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@ -495,6 +497,25 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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},
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},
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{
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LLM_ARCH_STABLELM,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_UNKNOWN,
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{
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@ -2216,6 +2237,16 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_STABLELM:
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{
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GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_3B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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default: (void)0;
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}
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@ -3087,6 +3118,81 @@ static void llm_load_tensors(
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}
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}
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} break;
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case LLM_ARCH_STABLELM:
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{
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model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
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// output
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{
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ggml_backend_type backend_norm;
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ggml_backend_type backend_output;
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if (n_gpu_layers > int(n_layer)) {
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// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
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// on Windows however this is detrimental unless everything is on the GPU
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#ifndef _WIN32
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backend_norm = llama_backend_offload;
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#else
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backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
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#endif // _WIN32
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backend_output = llama_backend_offload_split;
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} else {
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backend_norm = GGML_BACKEND_CPU;
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backend_output = GGML_BACKEND_CPU;
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}
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model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
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model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
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model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
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if (backend_norm == GGML_BACKEND_GPU) {
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vram_weights += ggml_nbytes(model.output_norm);
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}
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if (backend_output == GGML_BACKEND_GPU_SPLIT) {
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vram_weights += ggml_nbytes(model.output);
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}
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}
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const uint32_t n_ff = hparams.n_ff;
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const int i_gpu_start = n_layer - n_gpu_layers;
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model.layers.resize(n_layer);
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for (uint32_t i = 0; i < n_layer; ++i) {
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/*
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llama_model_loader: - tensor 4: blk.0.attn_output.weight f16 [ 2560, 2560, 1, 1 ]
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*/
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const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
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const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
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layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
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layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
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layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
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layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
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layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
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layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
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layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
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layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
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layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
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layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
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if (backend == GGML_BACKEND_GPU) {
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vram_weights +=
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ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
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ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
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ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
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}
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}
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -4565,6 +4671,177 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_stablelm() {
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struct ggml_cgraph * gf = ggml_new_graph(ctx0);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
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cb(inpL, "inp_embd", -1);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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cb(inp_pos, "inp_pos", -1);
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// KQ_scale
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struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
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cb(KQ_scale, "KQ_scale", -1);
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
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cb(KQ_mask, "KQ_mask", -1);
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// shift the entire K-cache if needed
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if (do_rope_shift) {
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llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb);
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}
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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// norm
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm,
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model.layers[il].attn_norm_b,
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LLM_NORM, cb, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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// compute Q and K and RoPE them
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struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(tmpq, "tmpq", il);
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struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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cb(tmpk, "tmpk", il);
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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// RoPE the first n_rot of q/k, pass the other half, and concat.
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struct ggml_tensor * qrot = ggml_cont(ctx0, ggml_view_3d(
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ctx0, tmpq, hparams.n_rot, n_head, n_tokens,
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ggml_element_size(tmpq) * n_embd_head,
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ggml_element_size(tmpq) * n_embd_head * n_head,
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0
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));
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cb(qrot, "qrot", il);
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struct ggml_tensor * krot = ggml_cont(ctx0, ggml_view_3d(
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ctx0, tmpk, hparams.n_rot, n_head, n_tokens,
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ggml_element_size(tmpk) * n_embd_head,
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ggml_element_size(tmpk) * n_embd_head * n_head_kv,
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0
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));
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cb(krot, "krot", il);
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// get the second half of tmpq, e.g tmpq[n_rot:, :, :]
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struct ggml_tensor * qpass = ggml_view_3d(
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ctx0, tmpq, (n_embd_head - hparams.n_rot), n_head, n_tokens,
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ggml_element_size(tmpq) * n_embd_head,
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ggml_element_size(tmpq) * n_embd_head * n_head,
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ggml_element_size(tmpq) * hparams.n_rot
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);
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cb(qpass, "qpass", il);
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struct ggml_tensor * kpass = ggml_view_3d(
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ctx0, tmpk, (n_embd_head - hparams.n_rot), n_head_kv, n_tokens,
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ggml_element_size(tmpk) * (n_embd_head),
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ggml_element_size(tmpk) * (n_embd_head) * n_head_kv,
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ggml_element_size(tmpk) * hparams.n_rot
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);
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cb(kpass, "kpass", il);
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struct ggml_tensor * qrotated = ggml_rope_custom(
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ctx0, qrot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
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freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(qrotated, "qrotated", il);
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struct ggml_tensor * krotated = ggml_rope_custom(
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ctx0, krot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
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freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(krotated, "krotated", il);
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// ggml currently only supports concatenation on dim=2
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// so we need to permute qrot, qpass, concat, then permute back.
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qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
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cb(qrotated, "qrotated", il);
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krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
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cb(krotated, "krotated", il);
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qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
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cb(qpass, "qpass", il);
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kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
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cb(kpass, "kpass", il);
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struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
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cb(Qcur, "Qcur", il);
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struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
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cb(Kcur, "Kcur", il);
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struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
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cb(Q, "Q", il);
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Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
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cb(Kcur, "Kcur", il);
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llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
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cur = llm_build_kqv(ctx0, hparams, kv_self,
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model.layers[il].wo, NULL,
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Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
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cb(cur, "kqv_out", il);
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}
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struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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{
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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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model.layers[il].ffn_norm,
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model.layers[il].ffn_norm_b,
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LLM_NORM, cb, il);
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cb(cur, "ffn_norm", il);
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cur = llm_build_ffn(ctx0, cur,
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "l_out", il);
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cur = llm_build_norm(ctx0, cur, hparams,
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model.output_norm,
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model.output_norm_b,
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LLM_NORM, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = ggml_mul_mat(ctx0, model.output, cur);
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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};
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//
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@ -5034,6 +5311,10 @@ static struct ggml_cgraph * llama_build_graph(
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{
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result = llm.build_mpt();
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} break;
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case LLM_ARCH_STABLELM:
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{
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result = llm.build_stablelm();
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} break;
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default:
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GGML_ASSERT(false);
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}
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@ -5209,7 +5490,8 @@ static int llama_decode_internal(
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model.arch == LLM_ARCH_FALCON ||
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model.arch == LLM_ARCH_REFACT ||
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model.arch == LLM_ARCH_MPT ||
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model.arch == LLM_ARCH_STARCODER;
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model.arch == LLM_ARCH_STARCODER ||
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model.arch == LLM_ARCH_STABLELM;
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const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
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if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
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