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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
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12 changed files with 931 additions and 385 deletions
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@ -3438,28 +3438,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
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}
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).set_examples({LLAMA_EXAMPLE_SERVER}));
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// diffusion parameters
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add_opt(common_arg(
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{ "--diffusion-steps" }, "N",
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string_format("number of diffusion steps (default: %d)", params.diffusion.steps),
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[](common_params & params, int value) { params.diffusion.steps = value; }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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add_opt(common_arg(
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{ "--diffusion-eps" }, "F",
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string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps),
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[](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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add_opt(common_arg(
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{ "--diffusion-algorithm" }, "N",
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string_format("diffusion algorithm: 0=ORIGIN, 1=MASKGIT_PLUS, 2=TOPK_MARGIN, 3=ENTROPY (default: %d)",
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params.diffusion.algorithm),
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[](common_params & params, int value) { params.diffusion.algorithm = value; }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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add_opt(common_arg(
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{ "--diffusion-alg-temp" }, "F",
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string_format("algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp),
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[](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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add_opt(common_arg(
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{ "--diffusion-visual" },
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string_format("enable visual diffusion mode (show progressive generation) (default: %s)",
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@ -3467,5 +3450,39 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
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[](common_params & params) { params.diffusion.visual_mode = true; }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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add_opt(common_arg(
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{ "--diffusion-eps" }, "F",
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string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps),
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[](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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add_opt(common_arg(
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{ "--diffusion-algorithm" }, "N",
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string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)",
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params.diffusion.algorithm),
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[](common_params & params, int value) { params.diffusion.algorithm = value; }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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add_opt(common_arg(
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{ "--diffusion-alg-temp" }, "F",
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string_format("dream algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp),
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[](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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add_opt(common_arg(
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{ "--diffusion-block-length" }, "N",
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string_format("llada block length for generation (default: %d)", params.diffusion.block_length),
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[](common_params & params, int value) { params.diffusion.block_length = value; }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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add_opt(common_arg(
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{ "--diffusion-cfg-scale" }, "F",
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string_format("llada classifier-free guidance scale (default: %.3f)", (double) params.diffusion.cfg_scale),
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[](common_params & params, const std::string & value) { params.diffusion.cfg_scale = std::stof(value); }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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add_opt(common_arg(
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{ "--diffusion-add-gumbel-noise" }, "F",
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string_format("add gumbel noise to the logits if temp > 0.0 (default: %s)", params.diffusion.add_gumbel_noise ? "true" : "false"),
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[](common_params & params, const std::string & value) { params.diffusion.add_gumbel_noise = std::stof(value); }
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).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
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return ctx_arg;
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}
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