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Add LLaDA 8b Diffusion model (#14771)
* Add support for Llada-8b: diffusion model * Add README * Fix README and convert_hf_to_gguf * convert_hf_to_gguf.py: address review comments * Make everything in a single example * Remove model-specific sampling * Remove unused argmax * Remove braced initializers, improve README.md a bit * Add diffusion specific gguf params in set_vocab, remove setting rope_theta and rms_norm_eps * Remove adding the mask token * Move add_add_bos_token to set_vocab * use add_bool in gguf_writer.py
This commit is contained in:
Aman Gupta
GitHub
parent
11490b3672
commit
8a4a856277
12 changed files with 931 additions and 385 deletions
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@ -869,6 +869,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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hparams.causal_attn = false;
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}
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break;
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case LLM_ARCH_LLADA:
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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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// LLaDA-8B has 32 layers, similar to LLaMA but for diffusion
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switch (hparams.n_layer) {
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case 32:
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type = LLM_TYPE_8B;
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break;
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default:
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type = LLM_TYPE_UNKNOWN;
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}
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// Set non-causal attention for diffusion models
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hparams.causal_attn = false;
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}
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break;
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case LLM_ARCH_QWEN2MOE:
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{
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
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@ -2149,6 +2164,53 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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}
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}
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} break;
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case LLM_ARCH_LLADA:
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{
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
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// output
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
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// if output is NULL, init from the input tok embed
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if (output == NULL) {
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output =
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create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
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}
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
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// Use separate Q, K, V projections without bias, matching LLaDALlamaBlock
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layer.wq =
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create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0);
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// No bias for QKV projections as per config: include_bias=false, include_qkv_bias=false
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layer.wo =
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create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
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layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
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layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_rot / 2 },
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TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
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// optional MLP bias
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layer.ffn_gate_b =
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create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), { n_ff }, TENSOR_NOT_REQUIRED);
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layer.ffn_down_b =
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create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED);
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layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), { n_ff }, TENSOR_NOT_REQUIRED);
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}
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}
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break;
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case LLM_ARCH_LLAMA4:
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{
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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@ -8042,6 +8104,106 @@ struct llm_build_dream : public llm_graph_context {
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}
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};
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struct llm_build_llada : public llm_graph_context {
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llm_build_llada(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context(params) {
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// LLaDA is similar to LLaMA but uses non-causal attention for diffusion
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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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// Non-causal attention for diffusion
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auto * inp_attn = build_attn_inp_no_cache();
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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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ggml_tensor * inpSA = inpL;
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// norm
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cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, 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 separate Q, K, V projections without bias, matching LLaDALlamaBlock
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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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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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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, 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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Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, 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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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr,
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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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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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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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cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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cur = ggml_add(ctx0, cur, ffn_inp);
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cur = build_cvec(cur, il);
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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 = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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// lm_head
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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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};
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struct llm_build_qwen2vl : public llm_graph_context {
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llm_build_qwen2vl(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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@ -17201,6 +17363,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
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case LLM_ARCH_NEO_BERT:
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case LLM_ARCH_WAVTOKENIZER_DEC:
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case LLM_ARCH_DREAM:
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case LLM_ARCH_LLADA:
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{
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res = nullptr;
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} break;
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@ -17367,6 +17530,11 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
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llm = std::make_unique<llm_build_dream>(*this, params);
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}
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break;
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case LLM_ARCH_LLADA:
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{
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llm = std::make_unique<llm_build_llada>(*this, params);
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}
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break;
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case LLM_ARCH_QWEN2VL:
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{
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llm = std::make_unique<llm_build_qwen2vl>(*this, params);
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@ -17765,6 +17933,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
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// use what we call a normal RoPE, operating on pairs of consecutive head values
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case LLM_ARCH_LLAMA:
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case LLM_ARCH_LLADA:
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case LLM_ARCH_LLAMA4:
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case LLM_ARCH_DECI:
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case LLM_ARCH_BAICHUAN:
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@ -17943,6 +18112,10 @@ bool llama_model_is_recurrent(const llama_model * model) {
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return llm_arch_is_recurrent(model->arch);
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}
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bool llama_model_is_diffusion(const llama_model * model) {
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return llm_arch_is_diffusion(model->arch);
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}
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const std::vector<std::pair<std::string, ggml_tensor *>> & llama_internal_get_tensor_map(const llama_model * model) {
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return model->tensors_by_name;
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}
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