diff --git a/common/arg.cpp b/common/arg.cpp index 060053595dbfd..74137d2db959d 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -3438,12 +3438,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_SERVER})); - // diffusion parameters add_opt(common_arg( { "--diffusion-steps" }, "N", string_format("number of diffusion steps (default: %d)", params.diffusion.steps), [](common_params & params, int value) { params.diffusion.steps = value; } ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "--diffusion-visual" }, + string_format("enable visual diffusion mode (show progressive generation) (default: %s)", + params.diffusion.visual_mode ? "true" : "false"), + [](common_params & params) { params.diffusion.visual_mode = true; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( { "--diffusion-eps" }, "F", string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps), @@ -3451,21 +3457,32 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); add_opt(common_arg( { "--diffusion-algorithm" }, "N", - string_format("diffusion algorithm: 0=ORIGIN, 1=MASKGIT_PLUS, 2=TOPK_MARGIN, 3=ENTROPY (default: %d)", + string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)", params.diffusion.algorithm), [](common_params & params, int value) { params.diffusion.algorithm = value; } ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); add_opt(common_arg( { "--diffusion-alg-temp" }, "F", - string_format("algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp), + string_format("dream algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp), [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); } ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( - { "--diffusion-visual" }, - string_format("enable visual diffusion mode (show progressive generation) (default: %s)", - params.diffusion.visual_mode ? "true" : "false"), - [](common_params & params) { params.diffusion.visual_mode = true; } + { "--diffusion-block-length" }, "N", + string_format("llada block length for generation (default: %d)", params.diffusion.block_length), + [](common_params & params, int value) { params.diffusion.block_length = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "--diffusion-cfg-scale" }, "F", + string_format("llada classifier-free guidance scale (default: %.3f)", (double) params.diffusion.cfg_scale), + [](common_params & params, const std::string & value) { params.diffusion.cfg_scale = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "--diffusion-add-gumbel-noise" }, "F", + string_format("add gumbel noise to the logits if temp > 0.0 (default: %s)", params.diffusion.add_gumbel_noise ? "true" : "false"), + [](common_params & params, const std::string & value) { params.diffusion.add_gumbel_noise = std::stof(value); } ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + return ctx_arg; } diff --git a/common/common.h b/common/common.h index 00f42694eafa8..38129b99d511f 100644 --- a/common/common.h +++ b/common/common.h @@ -220,11 +220,17 @@ struct common_params_vocoder { }; struct common_params_diffusion { - int32_t steps = 64; // number of diffusion steps - float eps = 1e-3f; // epsilon for timesteps - int32_t algorithm = 0; // diffusion algorithm (0=ORIGIN, 1=MASKGIT_PLUS, 2=TOPK_MARGIN, 3=ENTROPY) - float alg_temp = 0.0f; // algorithm temperature - bool visual_mode = false; // show progressive diffusion on screen + int32_t steps = 128; + bool visual_mode = false; + + float eps = 0; // epsilon for timesteps + int32_t block_length = 32; // block length for generation + + int32_t algorithm = 4; // default algorithm: low-confidence + float alg_temp = 0.0f; // algorithm temperature + + float cfg_scale = 0; // classifier-free guidance scale + bool add_gumbel_noise = false; // add gumbel noise to the logits if temp > 0.0 }; enum common_reasoning_format { diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 3f5cefe007cca..db4112318d487 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2904,6 +2904,107 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter yield from super().modify_tensors(data_torch, name, bid) +@ModelBase.register("LLaDAModelLM") +class LLaDAModel(TextModel): + model_arch = gguf.MODEL_ARCH.LLADA + undo_permute = True + + def get_vocab_base(self) -> tuple[list[str], list[int], str]: + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True) + + vocab_dict = tokenizer.get_vocab() + vocab_size = self.hparams.get("vocab_size", len(vocab_dict)) + assert max(vocab_dict.values()) < vocab_size + + tokpre = self.get_vocab_base_pre(tokenizer) + + reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()} + added_vocab = tokenizer.get_added_vocab() + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + elif reverse_vocab[i] in added_vocab: + tokens.append(reverse_vocab[i]) + # Check if it's a special token - treat special tokens as CONTROL tokens + if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder: + if tokenizer.added_tokens_decoder[i].special: + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.USER_DEFINED) + else: + # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|> + toktypes.append(gguf.TokenType.CONTROL) + else: + tokens.append(reverse_vocab[i]) + toktypes.append(gguf.TokenType.NORMAL) + + return tokens, toktypes, tokpre + + def set_vocab(self): + self._set_vocab_gpt2() + + # LLaDA specific parameters + self.gguf_writer.add_add_bos_token(True) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self._try_set_pooling_type() + + # Add parameters similar to LlamaModel + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if (rope_dim := hparams.get("head_dim")) is None: + n_heads = hparams.get("num_attention_heads", hparams.get("n_heads")) + rope_dim = hparams.get("hidden_size", hparams.get("d_model")) // n_heads + self.gguf_writer.add_rope_dimension_count(rope_dim) + + # Set context length for LLaDA + context_length = self.hparams.get("max_sequence_length", 4096) + self.gguf_writer.add_context_length(context_length) + + # Set embedding length (dimension size) + embedding_length = self.hparams.get("d_model", 4096) + self.gguf_writer.add_embedding_length(embedding_length) + + # Set feed forward length (MLP hidden size) + feed_forward_length = self.hparams.get("mlp_hidden_size", 12288) + self.gguf_writer.add_feed_forward_length(feed_forward_length) + + # LLaDA models use non-causal attention for diffusion, similar to Dream + self.gguf_writer.add_causal_attention(False) + + # LLaDA models don't shift their logits + self.gguf_writer.add_diffusion_shift_logits(False) + + @staticmethod + def permute(weights: Tensor, n_head: int, n_head_kv: int | None): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams.get("num_attention_heads", self.hparams.get("n_heads")) + n_kv_head = self.hparams.get("num_key_value_heads", self.hparams.get("n_kv_heads")) + + if self.undo_permute: + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LLaDAModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LLaDAModel.permute(data_torch, n_head, n_kv_head) + + # LLaDA model tensors should be mapped directly since it's the base model + yield from super().modify_tensors(data_torch, name, bid) + + @ModelBase.register("Ernie4_5_ForCausalLM") class Ernie4_5Model(TextModel): model_arch = gguf.MODEL_ARCH.ERNIE4_5 diff --git a/examples/diffusion/README.md b/examples/diffusion/README.md new file mode 100644 index 0000000000000..26de5668aa8e6 --- /dev/null +++ b/examples/diffusion/README.md @@ -0,0 +1,13 @@ +# Diffusion Text Generation + +This directory contains implementations for Diffusion LLMs (DLLMs) + +More Info: +- https://github.com/ggml-org/llama.cpp/pull/14644 +- https://github.com/ggml-org/llama.cpp/pull/14771 + + +Example of using Dream architechture: `llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual` + +Example of using LLaDA architechture: `llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual` + diff --git a/examples/diffusion/diffusion-cli.cpp b/examples/diffusion/diffusion-cli.cpp index 3e11ce1160b05..8431dcea8fe2a 100644 --- a/examples/diffusion/diffusion-cli.cpp +++ b/examples/diffusion/diffusion-cli.cpp @@ -5,67 +5,212 @@ #include "log.h" #include -#include -#include + #include #include +#include #include #include +#include +#include -typedef bool (*diffusion_step_callback_t)(int32_t step, - int32_t total_steps, - const llama_token * tokens, - int32_t n_tokens, - void * user_data); - -enum diffusion_alg { - DIFFUSION_ALG_ORIGIN = 0, - DIFFUSION_ALG_MASKGIT_PLUS = 1, - DIFFUSION_ALG_TOPK_MARGIN = 2, - DIFFUSION_ALG_ENTROPY = 3, +enum diffusion_algorithm { ORIGIN = 0, ENTROPY_BASED = 1, MARGIN_BASED = 2, RANDOM = 3, CONFIDENCE_BASED = 4 }; + +// Unified transfer scheduling methods +enum transfer_schedule { + TIMESTEP_BASED = 0, // Dream-style: (1.0 - s/t) * remaining + BLOCK_BASED = 1, // LLaDA-style: process in blocks with get_num_transfer_tokens }; +typedef bool (*diffusion_step_callback_t)(int32_t step, + int32_t total_steps, + const llama_token * tokens, + int32_t n_tokens, + void * user_data); + struct diffusion_params { - int32_t steps; - float eps; - float temperature; - float top_p; - int32_t top_k; - llama_token mask_token_id; - enum diffusion_alg algorithm; - float alg_temp; - diffusion_step_callback_t step_callback; - void * step_callback_user_data; - int32_t seed; + int32_t steps = 0; + float temperature = 0; + llama_token mask_token_id = LLAMA_TOKEN_NULL; + diffusion_step_callback_t step_callback = nullptr; + void * step_callback_user_data = nullptr; + int32_t seed = 0; + bool visual_mode = false; + bool shift_logits = false; // Shift logits by -1 after decode + + float top_p = 0.; + int32_t top_k = 0.; + + diffusion_algorithm algorithm = CONFIDENCE_BASED; + transfer_schedule schedule = TIMESTEP_BASED; + + float cfg_scale = 0.; // Config scale for classifier-free guidance + float eps = 0.; // Timestep scheduling + int32_t block_length = 0; // Block size (for block scheduling) + float alg_temp = 0; // algorithm temperature (0.0 = deterministic) + bool add_gumbel_noise = false; // Add gumbel noise to the logits if temp > 0.0 + + int32_t max_length = 0; // Maximum sequence length }; +struct callback_data { + diffusion_params * diff_params; + const llama_vocab * vocab; + int32_t n_input; +}; + +static float calculate_confidence(const llama_token_data_array & cur_p, + diffusion_algorithm algorithm, + std::mt19937 & rng) { + switch (algorithm) { + case CONFIDENCE_BASED: + return cur_p.data[cur_p.selected].p; // Selected token probability + + case ENTROPY_BASED: + { + float entropy = 0.0f; + const float epsilon = 1e-10f; + for (size_t i = 0; i < cur_p.size; i++) { + float prob = cur_p.data[i].p; + entropy += prob * logf(prob + epsilon); + } + return -entropy; // Higher entropy = lower confidence + } + + case MARGIN_BASED: + return (cur_p.size > 1) ? cur_p.data[0].p - cur_p.data[1].p : cur_p.data[0].p; + + case RANDOM: + { + std::uniform_real_distribution uniform(0.0f, 1.0f); + return uniform(rng); // Random confidence + } + + case ORIGIN: + return cur_p.data[cur_p.selected].p; + + default: + return 0.0f; + } +} + +// Unified transfer count calculation function +static int32_t calculate_transfer_count(int32_t step, + int32_t total_steps, + int32_t remaining_masked, + transfer_schedule schedule, + float eps, + const std::vector & num_transfer_tokens = {}) { + switch (schedule) { + case TIMESTEP_BASED: + { + float t = 1.0f - (float) step / total_steps * (1.0f - eps); + float s = 1.0f - (float) (step + 1) / total_steps * (1.0f - eps); + float p_transfer = (step < total_steps - 1) ? (1.0f - s / t) : 1.0f; + return (int32_t) (remaining_masked * p_transfer); + } + + case BLOCK_BASED: + if (!num_transfer_tokens.empty() && step < (int32_t) num_transfer_tokens.size()) { + return num_transfer_tokens[step]; + } + return remaining_masked / (total_steps - step); // Fallback + + default: + return remaining_masked / (total_steps - step); + } +} + +static bool diffusion_step_callback(int32_t step, + int32_t total_steps, + const llama_token * tokens, + int32_t n_tokens, + void * user_data) { + (void) user_data; + + callback_data * data = static_cast(user_data); + + auto print_progress_bar = [](int32_t step, int32_t total_steps) { + int progress_percent = (step * 100) / total_steps; + int progress_bars = (step * 50) / total_steps; + LOG_INF("\rdiffusion step: %d/%d [%s%s] %d%%", + step, + total_steps, + std::string(progress_bars, '=').c_str(), + std::string(50 - progress_bars, ' ').c_str(), + progress_percent); + }; + + if (data->diff_params->visual_mode) { + // Visual mode: clear + LOG_INF("\033[2J\033[H"); // Clear screen and move cursor to top-left + + print_progress_bar(step, total_steps); + + LOG_INF("\n"); + + std::string current_text = " "; + + for (int32_t i = data->n_input; i < n_tokens; i++) { + std::string token_str; + if (tokens[i] != llama_vocab_mask(data->vocab)) { + char piece[256]; + int n_chars = llama_token_to_piece(data->vocab, tokens[i], piece, sizeof(piece), 0, false); + if (n_chars > 0) { + piece[n_chars] = '\0'; + token_str = piece; + } + } else { + token_str = " "; + } + + current_text += token_str; + } -static diffusion_params diffusion_default_params() { - diffusion_params params = {}; - params.steps = 64; - params.eps = 1e-3f; - params.temperature = 0.2f; - params.top_p = 0.95f; - params.top_k = 0; - params.mask_token_id = LLAMA_TOKEN_NULL; - params.algorithm = DIFFUSION_ALG_ORIGIN; - params.alg_temp = 0.0f; - params.step_callback = nullptr; - params.step_callback_user_data = nullptr; - params.seed = 0; - return params; + LOG_INF("%s\n", current_text.c_str()); + } else { + print_progress_bar(step, total_steps); + } + + return true; } -static void diffusion_generate(llama_context * ctx, - const llama_token * input_tokens, - llama_token * output_tokens, - int32_t n_input, - int32_t max_length, - struct diffusion_params params, - int32_t & n_generated) { +static void add_gumbel_noise(float * logits, int32_t n_vocab, float temperature, std::mt19937 & rng) { + if (temperature == 0.0f) { + return; + } + + std::uniform_real_distribution uniform(0.0, 1.0); + for (int32_t i = 0; i < n_vocab; i++) { + double noise = uniform(rng); + // Prevent log(0) + noise = std::max(noise, 1e-20); + double gumbel_noise = std::pow(-std::log(noise), temperature); + logits[i] = std::exp(logits[i]) / gumbel_noise; + } +} + +static std::vector get_num_transfer_tokens(int32_t mask_count, int32_t steps) { + std::vector num_transfer_tokens(steps); + + int32_t base = mask_count / steps; + int32_t remainder = mask_count % steps; + + for (int32_t i = 0; i < steps; i++) { + num_transfer_tokens[i] = base + (i < remainder ? 1 : 0); + } + + return num_transfer_tokens; +} +static void diffusion_generate(llama_context * ctx, + const llama_token * input_tokens, + llama_token * output_tokens, + int32_t n_input, + const diffusion_params & params, + int32_t & n_generated) { n_generated = 0; - if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || max_length <= n_input) { + if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || params.max_length <= n_input) { return; } @@ -73,27 +218,21 @@ static void diffusion_generate(llama_context * ctx, // Initialize with input and pad with mask tokens std::copy(input_tokens, input_tokens + n_input, output_tokens); - std::fill(output_tokens + n_input, output_tokens + max_length, params.mask_token_id); + std::fill(output_tokens + n_input, output_tokens + params.max_length, params.mask_token_id); std::mt19937 rng(params.seed); - std::vector timesteps(params.steps + 1); - for (int32_t i = 0; i <= params.steps; i++) { - timesteps[i] = 1.0f - (float) i / params.steps * (1.0f - params.eps); - } - llama_set_causal_attn(ctx, false); int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model)); std::vector candidates(n_vocab); - std::vector conf_candidates; - conf_candidates.reserve(max_length); - + conf_candidates.reserve(params.max_length); std::vector mask_positions; - mask_positions.reserve(max_length); + mask_positions.reserve(params.max_length); + // Setup sampler chain struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params()); if (params.top_k > 0) { llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k)); @@ -108,210 +247,269 @@ static void diffusion_generate(llama_context * ctx, struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed); - llama_batch batch = llama_batch_init(max_length, 0, 1); - batch.n_tokens = max_length; + llama_batch batch = llama_batch_init(params.max_length, 0, 1); + batch.n_tokens = params.max_length; - int64_t total_sampling_time = 0; - int64_t total_time = 0; + // Pre-allocate buffers for CFG if needed + int32_t logits_size = n_vocab * params.max_length; + std::vector cond_logits_buffer; + std::vector un_x_buffer; + if (params.cfg_scale > 0.0f) { + cond_logits_buffer.resize(logits_size); + un_x_buffer.resize(params.max_length); + } - int64_t time_start = ggml_time_us(); - for (int32_t step = 0; step < params.steps; step++) { - if (params.step_callback) { - if (!params.step_callback(step, params.steps, output_tokens, max_length, params.step_callback_user_data)) { - break; - } - } + // For block-based processing + std::vector num_transfer_tokens; + int32_t num_blocks = 1; + int32_t steps_per_block = params.steps; - for (int32_t i = 0; i < max_length; i++) { - batch.token[i] = output_tokens[i]; - batch.pos[i] = i; - batch.n_seq_id[i] = 1; - batch.seq_id[i][0] = 0; - batch.logits[i] = 1; - } + if (params.schedule == BLOCK_BASED) { + GGML_ASSERT(params.max_length % params.block_length == 0); + num_blocks = params.max_length / params.block_length; + GGML_ASSERT(params.steps % num_blocks == 0); + steps_per_block = params.steps / num_blocks; + } - int ret = llama_decode(ctx, batch); - if (ret != 0) { - LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, step, ret); - break; - } + std::vector confidence(params.max_length); - float * raw_logits = llama_get_logits(ctx); - if (!raw_logits) { - LOG_ERR("%s: failed to get logits at step %d\n", __func__, step); - break; + int64_t total_sampling_time = 0; + int64_t total_time = 0; + int64_t time_start = ggml_time_us(); + + for (int block_num = 0; block_num < num_blocks; block_num++) { + int32_t block_start = (params.schedule == BLOCK_BASED) ? n_input + block_num * params.block_length : 0; + int32_t block_end = (params.schedule == BLOCK_BASED) ? + std::min(n_input + (block_num + 1) * params.block_length, params.max_length) : + params.max_length; + + // Count masked tokens in current block for block-based processing + if (params.schedule == BLOCK_BASED) { + int32_t block_mask_count = 0; + for (int i = block_start; i < block_end; i++) { + if (output_tokens[i] == params.mask_token_id) { + block_mask_count++; + } + } + num_transfer_tokens = get_num_transfer_tokens(block_mask_count, steps_per_block); } - auto get_logits_for_pos = [&](int32_t pos) -> const float * { - return pos == 0 ? raw_logits : raw_logits + (pos - 1) * n_vocab; - }; - - int64_t time_start_sampling = ggml_time_us(); + for (int32_t step = 0; step < steps_per_block; step++) { + int32_t global_step = block_num * steps_per_block + step; - mask_positions.clear(); - for (int32_t i = 0; i < max_length; i++) { - if (output_tokens[i] == params.mask_token_id) { - mask_positions.push_back(i); + if (params.step_callback) { + if (!params.step_callback( + global_step, params.steps, output_tokens, params.max_length, params.step_callback_user_data)) { + break; + } } - } - if (mask_positions.empty()) { - break; - } + // Setup batch + for (int32_t i = 0; i < params.max_length; i++) { + batch.token[i] = output_tokens[i]; + batch.pos[i] = i; + batch.n_seq_id[i] = 1; + batch.seq_id[i][0] = 0; + batch.logits[i] = 1; + } - float t = timesteps[step]; - float s = timesteps[step + 1]; + float * logits = nullptr; - if (params.algorithm == DIFFUSION_ALG_ORIGIN) { - float p_transfer = (step < params.steps - 1) ? (1.0f - s / t) : 1.0f; + if (params.cfg_scale > 0.0f) { + int ret = llama_decode(ctx, batch); + if (ret != 0) { + LOG_ERR("Failed to generate conditional"); + break; + } + float * cond_logits_ptr = llama_get_logits(ctx); + std::memcpy(cond_logits_buffer.data(), cond_logits_ptr, logits_size * sizeof(float)); - for (int32_t pos : mask_positions) { - if (std::uniform_real_distribution(0.0f, 1.0f)(rng) < p_transfer) { - const float * pos_logits = get_logits_for_pos(pos); - for (int32_t token_id = 0; token_id < n_vocab; token_id++) { - candidates[token_id].id = token_id; - candidates[token_id].logit = pos_logits[token_id]; - candidates[token_id].p = 0.0f; - } + // Unconditional generation (mask input) + std::copy(output_tokens, output_tokens + params.max_length, un_x_buffer.begin()); + for (int32_t i = 0; i < n_input; i++) { + un_x_buffer[i] = params.mask_token_id; + } - llama_token_data_array cur_p = { - /* .data = */ candidates.data(), - /* .size = */ (size_t) n_vocab, // Reset size to full vocab - /* .selected = */ -1, - /* .sorted = */ false, - }; + for (int32_t i = 0; i < params.max_length; i++) { + batch.token[i] = un_x_buffer[i]; + } + ret = llama_decode(ctx, batch); + if (ret != 0) { + LOG_ERR("Failed to generate unconditional"); + break; + } + float * uncond_logits = llama_get_logits(ctx); - llama_sampler_apply(sampler, &cur_p); - output_tokens[pos] = cur_p.data[cur_p.selected].id; + // Apply CFG + for (int32_t i = 0; i < logits_size; i++) { + cond_logits_buffer[i] = + uncond_logits[i] + (params.cfg_scale + 1.0f) * (cond_logits_buffer[i] - uncond_logits[i]); } - } - } else { - std::vector> confidences; - std::vector sampled_tokens(mask_positions.size()); - - for (size_t i = 0; i < mask_positions.size(); i++) { - int32_t pos = mask_positions[i]; - const float * pos_logits = get_logits_for_pos(pos); - - for (int32_t token_id = 0; token_id < n_vocab; token_id++) { - candidates[token_id].logit = pos_logits[token_id]; - candidates[token_id].p = 0.0f; - candidates[token_id].id = token_id; + logits = cond_logits_buffer.data(); + } else { + int ret = llama_decode(ctx, batch); + if (ret != 0) { + LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, global_step, ret); + break; } + logits = llama_get_logits(ctx); + } - llama_token_data_array cur_p = { - /* .data = */ candidates.data(), - /* .size = */ candidates.size(), - /* .selected = */ -1, - /* .sorted = */ false, - }; + if (!logits) { + LOG_ERR("%s: failed to get logits at step %d\n", __func__, global_step); + break; + } - llama_sampler_apply(sampler, &cur_p); + auto get_logits_for_pos = [&](int32_t pos) -> const float * { + if (params.shift_logits) { + return pos == 0 ? logits : logits + (pos - 1) * n_vocab; + } + return logits + (pos) *n_vocab; + }; - llama_token sampled_token = cur_p.data[cur_p.selected].id; + int64_t time_start_sampling = ggml_time_us(); - float confidence = 0.0f; - if (params.algorithm == DIFFUSION_ALG_ENTROPY) { - const float epsilon = 1e-10f; - for (size_t j = 0; j < cur_p.size; j++) { - float prob = cur_p.data[j].p; - confidence += prob * logf(prob + epsilon); + mask_positions.clear(); + for (int32_t i = 0; i < params.max_length; i++) { + if (output_tokens[i] == params.mask_token_id) { + // For block-based, only consider current block + if (params.schedule != BLOCK_BASED || (i >= block_start && i < block_end)) { + mask_positions.push_back(i); } - } else if (params.algorithm == DIFFUSION_ALG_TOPK_MARGIN) { - confidence = cur_p.data[0].p - cur_p.data[1].p; - } else { - confidence = cur_p.data[cur_p.selected].p; } + } - sampled_tokens[i] = sampled_token; - confidences.emplace_back(confidence, i); + if (mask_positions.empty()) { + break; } - int32_t num_transfer = - (step < params.steps - 1) ? (int32_t) (mask_positions.size() * (1.0f - s / t)) : mask_positions.size(); - - if (num_transfer > 0) { - if (params.alg_temp == 0.0f) { - std::partial_sort(confidences.begin(), confidences.begin() + num_transfer, confidences.end(), - [](const std::pair & a, const std::pair & b) { - if (a.first != b.first) { - return a.first > b.first; - } - return a.second < b.second; - }); - } else { - conf_candidates.clear(); - - for (int32_t pos = 0; pos < max_length; pos++) { - float conf_logit = -std::numeric_limits::infinity(); - - auto it = std::find(mask_positions.begin(), mask_positions.end(), pos); - if (it != mask_positions.end()) { - size_t mask_idx = std::distance(mask_positions.begin(), it); - conf_logit = confidences[mask_idx].first / params.alg_temp; // Apply temperature scaling + if (params.add_gumbel_noise && params.temperature > 0.0f) { + add_gumbel_noise(logits, n_vocab, params.temperature, rng); + } + + if (params.algorithm == ORIGIN) { + int32_t transfer_count = calculate_transfer_count( + step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens); + float p_transfer = (float) transfer_count / mask_positions.size(); + + for (int32_t pos : mask_positions) { + if (std::uniform_real_distribution(0.0f, 1.0f)(rng) < p_transfer) { + const float * pos_logits = get_logits_for_pos(pos); + for (int32_t token_id = 0; token_id < n_vocab; token_id++) { + candidates[token_id].id = token_id; + candidates[token_id].logit = pos_logits[token_id]; + candidates[token_id].p = 0.0f; } - conf_candidates.emplace_back(llama_token_data{ pos, conf_logit, 0.0f }); + llama_token_data_array cur_p = { + candidates.data(), + (size_t) n_vocab, + -1, + false, + }; + + llama_sampler_apply(sampler, &cur_p); + output_tokens[pos] = cur_p.data[cur_p.selected].id; + } + } + } else { + std::vector> confidences; + std::vector sampled_tokens(mask_positions.size()); + + for (size_t i = 0; i < mask_positions.size(); i++) { + int32_t pos = mask_positions[i]; + const float * pos_logits = get_logits_for_pos(pos); + + for (int32_t token_id = 0; token_id < n_vocab; token_id++) { + candidates[token_id].logit = pos_logits[token_id]; + candidates[token_id].p = 0.0f; + candidates[token_id].id = token_id; } - llama_token_data_array conf_array = { - /* .data = */ conf_candidates.data(), - /* .size = */ conf_candidates.size(), - /* .selected = */ -1, - /* .sorted = */ false, + llama_token_data_array cur_p = { + candidates.data(), + candidates.size(), + -1, + false, }; - for (int32_t i = 0; i < num_transfer; i++) { - // Apply distribution sampler to get selected index - llama_sampler_apply(dist_sampler, &conf_array); - int selected_idx = conf_array.selected; - confidences[i].second = conf_candidates[selected_idx].id; + llama_sampler_apply(sampler, &cur_p); + llama_token sampled_token = cur_p.data[cur_p.selected].id; + + float conf = calculate_confidence(cur_p, params.algorithm, rng); - conf_candidates[selected_idx].p = 0.0f; - conf_array.selected = -1; - } + sampled_tokens[i] = sampled_token; + confidences.emplace_back(conf, i); } - if (params.alg_temp == 0.0f) { - // Deterministic - use confidence order - for (int32_t i = 0; i < num_transfer; i++) { - int32_t mask_idx = confidences[i].second; - int32_t pos = mask_positions[mask_idx]; - llama_token token = sampled_tokens[mask_idx]; - output_tokens[pos] = token; - } - } else { - for (int32_t i = 0; i < num_transfer; i++) { - int32_t pos = confidences[i].second; - auto it = std::find(mask_positions.begin(), mask_positions.end(), pos); - if (it != mask_positions.end()) { - int32_t mask_idx = std::distance(mask_positions.begin(), it); + int32_t transfer_count = calculate_transfer_count( + step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens); + + if (transfer_count > 0) { + if (params.alg_temp == 0.0f) { + std::partial_sort(confidences.begin(), + confidences.begin() + std::min(transfer_count, (int32_t) confidences.size()), + confidences.end(), + [](const std::pair & a, const std::pair & b) { + if (a.first != b.first) { + return a.first > b.first; + } + return a.second < b.second; + }); + + for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) { + int32_t mask_idx = confidences[i].second; + int32_t pos = mask_positions[mask_idx]; output_tokens[pos] = sampled_tokens[mask_idx]; } + } else { + conf_candidates.clear(); + for (size_t i = 0; i < confidences.size(); i++) { + float conf_logit = confidences[i].first / params.alg_temp; + conf_candidates.emplace_back(llama_token_data{ (int32_t) i, conf_logit, 0.0f }); + } + + llama_token_data_array conf_array = { + conf_candidates.data(), + conf_candidates.size(), + -1, + false, + }; + + for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) { + llama_sampler_apply(dist_sampler, &conf_array); + int32_t selected_idx = conf_array.selected; + int32_t mask_idx = selected_idx; + int32_t pos = mask_positions[mask_idx]; + output_tokens[pos] = sampled_tokens[mask_idx]; + + conf_candidates[selected_idx].p = 0.0f; + conf_array.selected = -1; + } } } } + + int64_t time_end_sampling = ggml_time_us(); + total_sampling_time += time_end_sampling - time_start_sampling; } - int64_t time_end_sampling = ggml_time_us(); - total_sampling_time += time_end_sampling - time_start_sampling; } + int64_t time_end = ggml_time_us(); total_time += time_end - time_start; LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n", - total_time / 1000.0, total_time / 1000.0 / params.steps, total_sampling_time / 1000.0 / params.steps); - + total_time / 1000.0, + total_time / 1000.0 / params.steps, + total_sampling_time / 1000.0 / params.steps); llama_batch_free(batch); llama_sampler_free(sampler); llama_sampler_free(dist_sampler); - n_generated = max_length; + n_generated = params.max_length; } - - - static std::string format_input_text(const std::string & prompt, bool use_chat_template, llama_model * model) { if (!use_chat_template) { return prompt; @@ -331,66 +529,6 @@ static std::string format_input_text(const std::string & prompt, bool use_chat_t return result.prompt; } -struct callback_data { - const common_params_diffusion * diff_params; - const llama_vocab * vocab; - int32_t n_input; -}; - -static bool diffusion_step_callback(int32_t step, - int32_t total_steps, - const llama_token * tokens, - int32_t n_tokens, - void * user_data) { - (void)user_data; - - callback_data * data = static_cast(user_data); - - auto print_progress_bar = [](int32_t step, int32_t total_steps) { - int progress_percent = (step * 100) / total_steps; - int progress_bars = (step * 50) / total_steps; - LOG_INF("\rdiffusion step: %d/%d [%s%s] %d%%", - step, - total_steps, - std::string(progress_bars, '=').c_str(), - std::string(50 - progress_bars, ' ').c_str(), - progress_percent); - }; - - if (data->diff_params->visual_mode) { - // Visual mode: clear - LOG_INF("\033[2J\033[H"); // Clear screen and move cursor to top-left - - print_progress_bar(step, total_steps); - - LOG_INF("\n"); - - std::string current_text = " "; - - for (int32_t i = data->n_input; i < n_tokens; i++) { - std::string token_str; - if (tokens[i] != llama_vocab_mask(data->vocab)) { - char piece[256]; - int n_chars = llama_token_to_piece(data->vocab, tokens[i], piece, sizeof(piece), 0, false); - if (n_chars > 0) { - piece[n_chars] = '\0'; - token_str = piece; - } - } else { - token_str = " "; - } - - current_text += token_str; - } - - LOG_INF("%s\n", current_text.c_str()); - } else { - print_progress_bar(step, total_steps); - } - - return true; -} - int main(int argc, char ** argv) { ggml_time_init(); @@ -400,11 +538,6 @@ int main(int argc, char ** argv) { return 1; } - const char * alg_names[] = { "ORIGIN", "MASKGIT_PLUS", "TOPK_MARGIN", "ENTROPY" }; - const char * alg_name = (params.diffusion.algorithm >= 0 && params.diffusion.algorithm <= 3) ? - alg_names[params.diffusion.algorithm] : - "UNKNOWN"; - common_init(); llama_backend_init(); @@ -421,6 +554,12 @@ int main(int argc, char ** argv) { return 1; } + if (!llama_model_is_diffusion(model)) { + LOG_ERR("error: unsupported model for diffusion"); + llama_model_free(model); + return 1; + } + llama_context_params ctx_params = llama_context_default_params(); ctx_params.n_ctx = params.n_ctx; ctx_params.n_batch = params.n_batch; @@ -442,10 +581,12 @@ int main(int argc, char ** argv) { const llama_vocab * vocab = llama_model_get_vocab(model); std::string formatted_prompt = format_input_text(params.prompt, params.enable_chat_template, model); - std::vector input_tokens = common_tokenize(vocab, formatted_prompt, + std::vector input_tokens = common_tokenize(vocab, + formatted_prompt, /*add special tokens*/ true, /*parse special*/ true); - int n_input = input_tokens.size(); + + int n_input = input_tokens.size(); if (n_input >= params.n_ctx) { LOG_ERR("error: input too long (%d tokens), max context is %d\n", n_input, params.n_ctx); @@ -454,44 +595,79 @@ int main(int argc, char ** argv) { return 1; } - struct diffusion_params ldiff_params = diffusion_default_params(); - ldiff_params.steps = params.diffusion.steps; - ldiff_params.eps = params.diffusion.eps; - ldiff_params.temperature = params.sampling.temp; - ldiff_params.top_p = params.sampling.top_p; - ldiff_params.top_k = params.sampling.top_k; - ldiff_params.algorithm = static_cast(params.diffusion.algorithm); - ldiff_params.alg_temp = params.diffusion.alg_temp; - ldiff_params.seed = params.sampling.seed; - llama_token mask_token_id = llama_vocab_mask(vocab); GGML_ASSERT(mask_token_id != LLAMA_TOKEN_NULL); - LOG_INF("diffusion_params: - %-25s llama_token = %d\n", "mask_token_id", mask_token_id); - LOG_INF("diffusion_params: - %-25s u32 = %d\n", "steps", params.diffusion.steps); - LOG_INF("diffusion_params: - %-25s f32 = %.6f\n", "eps", params.diffusion.eps); - LOG_INF("diffusion_params: - %-25s u32 = %d (%s)\n", "algorithm", params.diffusion.algorithm, - alg_name); - LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "alg_temp", params.diffusion.alg_temp); + bool visual_mode = params.diffusion.visual_mode; - ldiff_params.mask_token_id = mask_token_id; + int32_t n_generated = 0; + std::vector output_tokens(params.n_ubatch); - callback_data cb_data = { ¶ms.diffusion, vocab, n_input }; + struct diffusion_params diff_params; - ldiff_params.step_callback = diffusion_step_callback; - ldiff_params.step_callback_user_data = &cb_data; + char shift_logits_str[8]; + if (llama_model_meta_val_str(model, "diffusion.shift_logits", shift_logits_str, sizeof(shift_logits_str)) >= 0) { + diff_params.shift_logits = (strcmp(shift_logits_str, "true") == 0); + } else { + diff_params.shift_logits = true; + } - int32_t n_generated = 0; + //Use either eps or block length, but not both + GGML_ASSERT((params.diffusion.eps == 0) ^ (params.diffusion.block_length == 0)); - std::vector output_tokens(params.n_ubatch); - diffusion_generate(ctx, input_tokens.data(), output_tokens.data(), n_input, params.n_ubatch, - ldiff_params, n_generated); + if (params.diffusion.eps) { + diff_params.schedule = TIMESTEP_BASED; + diff_params.eps = params.diffusion.eps; + } else if (params.diffusion.block_length) { + diff_params.schedule = BLOCK_BASED; + diff_params.block_length = params.diffusion.block_length; + } + + diff_params.mask_token_id = mask_token_id; + diff_params.seed = params.sampling.seed; + diff_params.temperature = params.sampling.temp; + diff_params.steps = params.diffusion.steps; + diff_params.algorithm = static_cast(params.diffusion.algorithm); + diff_params.max_length = params.n_ubatch; + diff_params.top_p = params.sampling.top_p; + diff_params.top_k = params.sampling.top_k; + diff_params.visual_mode = params.diffusion.visual_mode; + diff_params.add_gumbel_noise = params.diffusion.add_gumbel_noise; + + diff_params.step_callback = diffusion_step_callback; + callback_data cb_data = { &diff_params, vocab, n_input }; + diff_params.step_callback_user_data = &cb_data; + + const char * alg_names[] = { "ORIGIN", "ENTROPY_BASED", "MARGIN_BASED", "RANDOM", "CONFIDENCE_BASED" }; + const char * sched_names[] = { "TIMESTEP_BASED", "BLOCK_BASED" }; + const char * alg_name = + (diff_params.algorithm >= 0 && diff_params.algorithm <= 4) ? alg_names[diff_params.algorithm] : "UNKNOWN"; + const char * sched_name = + (diff_params.schedule >= 0 && diff_params.schedule <= 1) ? sched_names[diff_params.schedule] : "UNKNOWN"; + + LOG_INF("diffusion_params: - %-25s llama_token = %d\n", "mask_token_id", mask_token_id); + LOG_INF("diffusion_params: - %-25s u32 = %d\n", "steps", diff_params.steps); + LOG_INF("diffusion_params: - %-25s u32 = %d\n", "max_length", diff_params.max_length); + LOG_INF("diffusion_params: - %-25s enum = %d (%s)\n", "algorithm", diff_params.algorithm, alg_name); + LOG_INF("diffusion_params: - %-25s enum = %d (%s)\n", "schedule", diff_params.schedule, sched_name); + LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "temperature", diff_params.temperature); + if (diff_params.schedule == TIMESTEP_BASED) { + LOG_INF("diffusion_params: - %-25s f32 = %.6f\n", "eps", diff_params.eps); + LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "alg_temp", diff_params.alg_temp); + } + if (diff_params.schedule == BLOCK_BASED) { + LOG_INF("diffusion_params: - %-25s u32 = %d\n", "block_length", diff_params.block_length); + LOG_INF("diffusion_params: - %-25s f32 = %.3f\n", "cfg_scale", diff_params.cfg_scale); + } + + diffusion_generate(ctx, input_tokens.data(), output_tokens.data(), n_input, diff_params, n_generated); if (n_generated > 0) { - if (params.diffusion.visual_mode) { + if (visual_mode) { //clear screen and move cursor to top-left LOG_INF("\033[2J\033[H"); } + output_tokens.erase(output_tokens.begin(), output_tokens.begin() + n_input); std::string output_data = common_detokenize(vocab, output_tokens, false); LOG_INF("\n%s\n", output_data.c_str()); diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index c97b61d09c711..ef47ea7359eda 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -279,6 +279,9 @@ class Attention: class Projector: STACK_FACTOR = "clip.audio.projector.stack_factor" + class Diffusion: + SHIFT_LOGITS = "diffusion.shift_logits" + # # recommended mapping of model tensor names for storage in gguf # @@ -377,6 +380,7 @@ class MODEL_ARCH(IntEnum): LFM2 = auto() DREAM = auto() SMALLTHINKER = auto() + LLADA = auto() class VISION_PROJECTOR_TYPE(IntEnum): @@ -697,6 +701,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.LFM2: "lfm2", MODEL_ARCH.DREAM: "dream", MODEL_ARCH.SMALLTHINKER: "smallthinker", + MODEL_ARCH.LLADA: "llada", } VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = { @@ -1318,6 +1323,21 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.LLADA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], MODEL_ARCH.QWEN2VL: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 4f23f9b024619..f4fd64ad822fa 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -1047,6 +1047,11 @@ def add_audio_num_mel_bins(self, value: int) -> None: def add_audio_stack_factor(self, value: int) -> None: self.add_uint32(Keys.ClipAudio.Projector.STACK_FACTOR, value) + # diffusion models + + def add_diffusion_shift_logits(self, value: bool) -> None: + self.add_bool(Keys.Diffusion.SHIFT_LOGITS, value) + def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: pack_prefix = '' if not skip_pack_prefix: diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index bfd4fd37a3f68..15adbfa781845 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -32,6 +32,7 @@ class TensorNameMap: "model.word_embeddings", # bailingmoe "language_model.model.embed_tokens", # llama4 "encoder", # neobert + "model.transformer.wte", # llada ), # Token type embeddings @@ -71,6 +72,7 @@ class TensorNameMap: "head", # rwkv "head.out", # wavtokenizer "lm_head", # llama4 + "model.transformer.ff_out", # llada ), # Output norm @@ -94,6 +96,7 @@ class TensorNameMap: "model.ln_out", # rwkv7 "backbone.final_layer_norm", # wavtokenizer "model.norm", # llama4 + "model.transformer.ln_f", # llada ), # Rope frequencies @@ -139,6 +142,7 @@ class TensorNameMap: "model.layers.{bid}.input_layernorm", # llama4 "transformer_encoder.{bid}.attention_norm", # neobert "model.layers.{bid}.operator_norm", # lfm2 + "model.transformer.blocks.{bid}.attn_norm", # llada ), # Attention norm 2 @@ -183,6 +187,7 @@ class TensorNameMap: "transformer.decoder_layer.{bid}.multi_head_attention.query",# Grok "transformer.h.{bid}.attn.attention.q_proj", # exaone "model.layers.{bid}.self_attn.q_proj", # llama4 + "model.transformer.blocks.{bid}.q_proj", # llada ), # Attention key @@ -199,6 +204,7 @@ class TensorNameMap: "transformer.decoder_layer.{bid}.multi_head_attention.key",# Grok "transformer.h.{bid}.attn.attention.k_proj", # exaone "model.layers.{bid}.self_attn.k_proj", # llama4 + "model.transformer.blocks.{bid}.k_proj", # llada ), # Attention value @@ -214,6 +220,7 @@ class TensorNameMap: "transformer.decoder_layer.{bid}.multi_head_attention.value",# Grok "transformer.h.{bid}.attn.attention.v_proj", # exaone "model.layers.{bid}.self_attn.v_proj", # llama4 + "model.transformer.blocks.{bid}.v_proj", # llada ), # Attention output @@ -246,6 +253,7 @@ class TensorNameMap: "transformer.h.{bid}.attn.attention.out_proj", # exaone "model.layers.{bid}.self_attn.o_proj", # llama4 "transformer_encoder.{bid}.wo", # neobert + "model.transformer.blocks.{bid}.attn_out", # llada ), # Attention output norm @@ -291,6 +299,7 @@ class TensorNameMap: "model.layers.{bid}.post_attention_layernorm", # llama4 "transformer_encoder.{bid}.ffn_norm", # neobert "model.layers.layers.{bid}.pre_mlp_norm", # plamo2 + "model.transformer.blocks.{bid}.ff_norm", # llada ), # Post feed-forward norm @@ -364,6 +373,7 @@ class TensorNameMap: "model.layers.{bid}.feed_forward.up_proj", # llama4 jamba granite-hybrid "transformer_encoder.{bid}.ffn.w12", # neobert "model.layers.{bid}.block_sparse_moe.up", # smallthinker + "model.transformer.blocks.{bid}.up_proj", # llada ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -405,6 +415,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.c_fc_0", # exaone "model.layers.{bid}.feed_forward.gate_proj", # llama4 jamba granite-hybrid "model.layers.{bid}.block_sparse_moe.gate", # smallthinker + "model.transformer.blocks.{bid}.ff_proj", # llada ), MODEL_TENSOR.FFN_GATE_EXP: ( @@ -454,6 +465,7 @@ class TensorNameMap: "model.layers.{bid}.feed_forward.down_proj", # llama4 jamba granite-hybrid "transformer_encoder.{bid}.ffn.w3", # neobert "model.layers.{bid}.block_sparse_moe.down", # smallthinker + "model.transformer.blocks.{bid}.ff_out", # llada ), MODEL_TENSOR.FFN_DOWN_EXP: ( diff --git a/include/llama.h b/include/llama.h index 6f454a508a06c..1a51e74a8d63f 100644 --- a/include/llama.h +++ b/include/llama.h @@ -537,6 +537,9 @@ extern "C" { // Returns true if the model is recurrent (like Mamba, RWKV, etc.) LLAMA_API bool llama_model_is_recurrent(const struct llama_model * model); + // Returns true if the model is diffusion-based (like LLaDA, Dream, etc.) + LLAMA_API bool llama_model_is_diffusion(const struct llama_model * model); + // Returns 0 on success LLAMA_API uint32_t llama_model_quantize( const char * fname_inp, diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index dbf977443ae85..15fb9d0b50809 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -89,6 +89,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_LFM2, "lfm2" }, { LLM_ARCH_DREAM, "dream" }, { LLM_ARCH_SMALLTHINKER, "smallthinker" }, + { LLM_ARCH_LLADA, "llada" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -1972,6 +1973,23 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_LLADA, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -2224,6 +2242,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) { bool llm_arch_is_diffusion(const llm_arch & arch) { switch (arch) { case LLM_ARCH_DREAM: + case LLM_ARCH_LLADA: return true; default: return false; diff --git a/src/llama-arch.h b/src/llama-arch.h index 8267a8d3aa491..8ea80806c9c8d 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -93,6 +93,7 @@ enum llm_arch { LLM_ARCH_LFM2, LLM_ARCH_DREAM, LLM_ARCH_SMALLTHINKER, + LLM_ARCH_LLADA, LLM_ARCH_UNKNOWN, }; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index e3aa9e6f91af9..92a7efed3dab3 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -869,6 +869,21 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.causal_attn = false; } break; + case LLM_ARCH_LLADA: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + // LLaDA-8B has 32 layers, similar to LLaMA but for diffusion + switch (hparams.n_layer) { + case 32: + type = LLM_TYPE_8B; + break; + default: + type = LLM_TYPE_UNKNOWN; + } + // Set non-causal attention for diffusion models + hparams.causal_attn = false; + } + break; case LLM_ARCH_QWEN2MOE: { ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false); @@ -2149,6 +2164,53 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } } } break; + case LLM_ARCH_LLADA: + { + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0); + + // output + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED); + + // if output is NULL, init from the input tok embed + if (output == NULL) { + output = + create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED); + } + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0); + + // Use separate Q, K, V projections without bias, matching LLaDALlamaBlock + layer.wq = + create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, 0); + // No bias for QKV projections as per config: include_bias=false, include_qkv_bias=false + layer.wo = + create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0); + layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0); + + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_rot / 2 }, + TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); + + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0); + + // optional MLP bias + layer.ffn_gate_b = + create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), { n_ff }, TENSOR_NOT_REQUIRED); + layer.ffn_down_b = + create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), { n_embd }, TENSOR_NOT_REQUIRED); + layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), { n_ff }, TENSOR_NOT_REQUIRED); + } + } + break; case LLM_ARCH_LLAMA4: { tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -8042,6 +8104,106 @@ struct llm_build_dream : public llm_graph_context { } }; +struct llm_build_llada : public llm_graph_context { + llm_build_llada(const llama_model & model, const llm_graph_params & params) : + llm_graph_context(params) { + // LLaDA is similar to LLaMA but uses non-causal attention for diffusion + const int64_t n_embd_head = hparams.n_embd_head_v; + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); + + // Non-causal attention for diffusion + auto * inp_attn = build_attn_inp_no_cache(); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // norm + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute separate Q, K, V projections without bias, matching LLaDALlamaBlock + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + + Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, + 1.0f / sqrtf(float(n_embd_head)), il); + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur); + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); + } +}; + struct llm_build_qwen2vl : public llm_graph_context { llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -17201,6 +17363,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, case LLM_ARCH_NEO_BERT: case LLM_ARCH_WAVTOKENIZER_DEC: case LLM_ARCH_DREAM: + case LLM_ARCH_LLADA: { res = nullptr; } break; @@ -17367,6 +17530,11 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { llm = std::make_unique(*this, params); } break; + case LLM_ARCH_LLADA: + { + llm = std::make_unique(*this, params); + } + break; case LLM_ARCH_QWEN2VL: { llm = std::make_unique(*this, params); @@ -17765,6 +17933,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { // use what we call a normal RoPE, operating on pairs of consecutive head values case LLM_ARCH_LLAMA: + case LLM_ARCH_LLADA: case LLM_ARCH_LLAMA4: case LLM_ARCH_DECI: case LLM_ARCH_BAICHUAN: @@ -17943,6 +18112,10 @@ bool llama_model_is_recurrent(const llama_model * model) { return llm_arch_is_recurrent(model->arch); } +bool llama_model_is_diffusion(const llama_model * model) { + return llm_arch_is_diffusion(model->arch); +} + const std::vector> & llama_internal_get_tensor_map(const llama_model * model) { return model->tensors_by_name; }