llama : add grok-1 support (#6204)
* Add support for Grok model architecture * Revert convert-hf-to-gguf to default options * Fixed f_norm_rms_eps bug * Fix whitespaces * llama : fix grok rope type * llama : minor --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Julius Arkenberg
GitHub
parent
21cad01b6e
commit
476b0251b2
4 changed files with 384 additions and 20 deletions
299
llama.cpp
299
llama.cpp
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@ -195,6 +195,7 @@ enum llm_arch {
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LLM_ARCH_LLAMA,
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LLM_ARCH_FALCON,
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LLM_ARCH_BAICHUAN,
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LLM_ARCH_GROK,
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LLM_ARCH_GPT2,
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LLM_ARCH_GPTJ,
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LLM_ARCH_GPTNEOX,
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@ -224,6 +225,7 @@ enum llm_arch {
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static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_LLAMA, "llama" },
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{ LLM_ARCH_FALCON, "falcon" },
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{ LLM_ARCH_GROK, "grok" },
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{ LLM_ARCH_GPT2, "gpt2" },
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{ LLM_ARCH_GPTJ, "gptj" },
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{ LLM_ARCH_GPTNEOX, "gptneox" },
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@ -494,6 +496,28 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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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_GROK,
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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_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
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{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
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{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
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{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
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{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
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{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
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},
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},
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{
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LLM_ARCH_GPT2,
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{
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@ -1635,6 +1659,7 @@ enum e_model {
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MODEL_40B,
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MODEL_65B,
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MODEL_70B,
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MODEL_314B,
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MODEL_SMALL,
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MODEL_MEDIUM,
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MODEL_LARGE,
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@ -3419,6 +3444,7 @@ static const char * llama_model_type_name(e_model type) {
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case MODEL_40B: return "40B";
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case MODEL_65B: return "65B";
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case MODEL_70B: return "70B";
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case MODEL_314B: return "314B";
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case MODEL_SMALL: return "0.1B";
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case MODEL_MEDIUM: return "0.4B";
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case MODEL_LARGE: return "0.8B";
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@ -3557,6 +3583,15 @@ 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_GROK:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_layer) {
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case 64: model.type = e_model::MODEL_314B; 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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case LLM_ARCH_FALCON:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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@ -4394,6 +4429,54 @@ static bool llm_load_tensors(
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}
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}
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} break;
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case LLM_ARCH_GROK:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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// output
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{
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
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// if output is NULL, init from the input tok embed
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if (model.output == NULL) {
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model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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ml.n_created--; // artificial tensor
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ml.size_data += ggml_nbytes(model.output);
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}
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}
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
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layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd});
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GGML_ASSERT(hparams.n_expert > 0);
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GGML_ASSERT(hparams.n_expert_used > 0);
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// MoE branch
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for (uint32_t x = 0; x < hparams.n_expert; ++x) {
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layer.ffn_gate_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff});
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layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd});
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layer.ffn_up_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff});
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}
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layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
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}
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} break;
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case LLM_ARCH_BAICHUAN:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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@ -5621,6 +5704,20 @@ static struct ggml_tensor * llm_build_kqv(
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ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
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}
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if (model.arch == LLM_ARCH_GROK) {
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// need to do the following:
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// multiply by attn_output_multiplyer of 0.08838834764831845
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// and then :
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// kq = 30 * tanh(kq / 30)
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// before the softmax below
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//try from phi2
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//ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
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kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
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kq = ggml_scale(ctx, kq, 30);
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}
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#if defined(GGML_USE_KOMPUTE)
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#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute")
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#pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024")
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@ -6395,6 +6492,203 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_grok() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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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, lctx, hparams, batch, model.tok_embd, cb);
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// multiply by embedding_multiplier_scale of 78.38367176906169
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inpL = ggml_scale(ctx0, inpL, 78.38367176906169f);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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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 = build_inp_KQ_mask();
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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, NULL,
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LLM_NORM_RMS, 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 * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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if (model.layers[il].bq) {
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Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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cb(Qcur, "Qcur", il);
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}
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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if (model.layers[il].bk) {
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Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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cb(Kcur, "Kcur", il);
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}
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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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if (model.layers[il].bv) {
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Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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cb(Vcur, "Vcur", il);
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}
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Qcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
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n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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Kcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
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n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Kcur, "Kcur", il);
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cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
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model.layers[il].wo, model.layers[il].bo,
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Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
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}
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// Grok
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// if attn_out_norm is present then apply it before adding the input
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if (model.layers[il].attn_out_norm) {
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cur = llm_build_norm(ctx0, cur, hparams,
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model.layers[il].attn_out_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "attn_out_norm", 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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// MoE branch
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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
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cb(logits, "ffn_moe_logits", il);
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ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
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cb(probs, "ffn_moe_probs", il);
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// select experts
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ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
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cb(selected_experts->src[0], "ffn_moe_argsort", il);
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ggml_tensor * weights = ggml_get_rows(ctx0,
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ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
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cb(weights, "ffn_moe_weights", il);
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weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
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ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
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cb(weights_sum, "ffn_moe_weights_sum", il);
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weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
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cb(weights, "ffn_moe_weights_norm", il);
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// compute expert outputs
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ggml_tensor * moe_out = nullptr;
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for (int i = 0; i < n_expert_used; ++i) {
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ggml_tensor * cur_expert;
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ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exp, n_expert, selected_experts, i, cur);
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cb(cur_up, "ffn_moe_up", il);
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ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exp, n_expert, selected_experts, i, cur);
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cb(cur_gate, "ffn_moe_gate", il);
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//GeLU
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cur_gate = ggml_gelu(ctx0, cur_gate);
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cb(cur_gate, "ffn_moe_gelu", il);
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cur_expert = ggml_mul(ctx0, cur_up, cur_gate); // [n_tokens, n_embd]
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cb(cur_expert, "ffn_moe_gate_par", il);
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cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exp, n_expert, selected_experts, i, cur_expert); // [n_tokens, n_embd]
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cb(cur_expert, "ffn_moe_down", il);
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cur_expert = ggml_mul(ctx0, cur_expert,
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ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
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cb(cur_expert, "ffn_moe_weighted", il);
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if (i == 0) {
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moe_out = cur_expert;
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} else {
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moe_out = ggml_add(ctx0, moe_out, cur_expert);
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cb(moe_out, "ffn_moe_out", il);
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}
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}
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cur = moe_out;
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// Grok
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// if layer_out_norm is present then apply it before adding the input
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// Idea: maybe ffn_out_norm is a better name
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if (model.layers[il].layer_out_norm) {
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cur = llm_build_norm(ctx0, cur, hparams,
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model.layers[il].layer_out_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "layer_out_norm", il);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "ffn_out", il);
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ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
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if (layer_dir != nullptr) {
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cur = ggml_add(ctx0, cur, layer_dir);
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}
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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, NULL,
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LLM_NORM_RMS, 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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// Grok
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// multiply logits by output_multiplier_scale of 0.5773502691896257
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cur = ggml_scale(ctx0, cur, 0.5773502691896257f);
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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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struct ggml_cgraph * build_starcoder() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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@ -8818,6 +9112,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm.build_falcon();
|
||||
} break;
|
||||
case LLM_ARCH_GROK:
|
||||
{
|
||||
result = llm.build_grok();
|
||||
} break;
|
||||
case LLM_ARCH_STARCODER:
|
||||
{
|
||||
result = llm.build_starcoder();
|
||||
|
|
@ -13561,6 +13859,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
|||
|
||||
// the pairs of head values are offset by n_rot/2
|
||||
case LLM_ARCH_FALCON:
|
||||
case LLM_ARCH_GROK:
|
||||
case LLM_ARCH_PERSIMMON:
|
||||
case LLM_ARCH_BERT:
|
||||
case LLM_ARCH_NOMIC_BERT:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue