2bf318fd2f
* model: add JAIS-2 architecture support Add support for the JAIS-2 family of Arabic-English bilingual models from Inception AI (https://huggingface.co/inceptionai/Jais-2-8B-Chat). Architecture characteristics: - LayerNorm (not RMSNorm) with biases - ReLU² (ReLU squared) activation function - Separate Q/K/V projections with biases - Simple MLP without gate projection (up -> act -> down) - RoPE positional embeddings - GPT-2 BPE tokenizer Supported model sizes: - Jais-2-8B (32 layers, 26 heads, 3328 hidden) - Jais-2-70B (68 layers, 56 heads, 7168 hidden) Tested with quantizations: BF16, Q8_0, Q6_K, Q5_K_M, Q5_0, Q4_K_M, Q4_0, Q3_K_M, Q2_K Note: JAIS-2 requires F32 precision accumulators for numerical stability and uses standard attention (not flash attention) on CUDA backends. * fix: run convert_hf_to_gguf_update.py for jais-2 tokenizer hash * fix: use NEOX RoPE type for JAIS2 * fix: remove Q/K permutation (NEOX RoPE doesn't need it) * fix: enable flash attention for JAIS2 (fixed by #19115) * fix: add dedicated JAIS2 pre-tokenizer type and control vector support - Add LLAMA_VOCAB_PRE_TYPE_JAIS2 with cascading whitespace regex - Include original regex from tokenizer.json as comment - Add build_cvec call for control vector support * no longer necessary to override set_vocab --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
123 lines
4.1 KiB
C++
123 lines
4.1 KiB
C++
#include "models.h"
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// JAIS-2 model graph builder
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// Uses: LayerNorm (not RMSNorm), relu2 activation, separate Q/K/V, RoPE embeddings
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llm_build_jais2::llm_build_jais2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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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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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = build_inp_embd(model.tok_embd);
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// inp_pos - contains the positions
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ggml_tensor * inp_pos = build_inp_pos();
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// KV input for attention
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auto * inp_attn = build_attn_inp_kv();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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for (int il = 0; il < n_layer; ++il) {
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// Pre-attention LayerNorm
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cur = build_norm(inpL,
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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, il);
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cb(cur, "attn_norm", il);
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// Self-attention with separate Q, K, V projections
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{
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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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cb(Qcur, "Qcur_bias", il);
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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cb(Kcur, "Kcur_bias", il);
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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cb(Vcur, "Vcur_bias", il);
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// Reshape for attention
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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// Apply RoPE
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, 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_rope", il);
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cb(Kcur, "Kcur_rope", il);
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cur = build_attn(inp_attn,
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model.layers[il].wo, model.layers[il].bo,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
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}
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if (il == n_layer - 1 && inp_out_ids) {
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
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}
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// Residual connection
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
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cb(ffn_inp, "ffn_inp", il);
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// Pre-FFN LayerNorm
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cur = build_norm(ffn_inp,
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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, il);
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cb(cur, "ffn_norm", il);
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// FFN with relu2 activation (ReLU squared) - no gate projection
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// up -> relu2 -> down
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cur = build_ffn(cur,
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model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
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NULL, NULL, NULL, // no gate
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
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NULL,
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LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);
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cb(cur, "ffn_out", il);
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// Residual connection
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inpL = ggml_add(ctx0, cur, ffn_inp);
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inpL = build_cvec(inpL, il);
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cb(inpL, "l_out", il);
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}
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// Final LayerNorm
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cur = build_norm(inpL,
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model.output_norm,
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model.output_norm_b,
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LLM_NORM, -1);
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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// Output projection
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cur = build_lora_mm(model.output, cur);
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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ggml_build_forward_expand(gf, cur);
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}
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