Upload HDLM model with complete HF integration
Browse files- config.yaml +88 -0
config.yaml
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ngpus: 4
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| 2 |
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gradient_accumulation_steps: 8
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pretrain_autoregressive_path: /home/toolkit/research-diffcodegen/exp_local/openwebtext/mdlm-autoregressive/org-DiTAR-absorb-v2/checkpoints-meta/checkpoint.pth
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tokenizer:
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tokens: 50257
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model: gpt2
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training:
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batch_size: 512
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accum: ${gradient_accumulation_steps}
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n_iters: 1000000
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snapshot_freq: 100
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log_freq: 10
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eval_freq: 100
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snapshot_freq_for_preemption: 3000
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weight: standard
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snapshot_sampling: true
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ema: 0.9999
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warmup_iter: -1
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data:
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train: openwebtext-train
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valid: wikitext103
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cache_dir: /home/toolkit/research-diffcodegen/data
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debug: false
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graph:
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type: QGamma
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gamma: 0.01
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file: /home/toolkit/research-diffcodegen/data
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report_all: false
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expanded_sigma: true
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noise:
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type: loglinear
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sigma_min: 0.0001
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sigma_max: 2.0
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ar_diffusion: false
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expanded_sigma: ${graph.expanded_sigma}
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sampling:
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predictor: analytic
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steps_per_level: 1
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noise_removal: true
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strategy: direct
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strategy_param: 0.9
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annealing:
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type: block
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efficient: false
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width: 1024
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tau: 2048
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eval_tau: 512
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steps_per_level: ${sampling.steps_per_level}
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sampling_method: SAR
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diffusion_loss_weight: 1.0
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ce_loss_weight: 4.0
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sampling_eps: 0.0001
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attention:
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context_type: block_causal
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block_type: full
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match_inference: true
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eval:
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batch_size: 32
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perplexity: true
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perplexity_batch_size: 16
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optim:
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weight_decay: 0.0
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optimizer: AdamW
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lr: 0.0003
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beta1: 0.9
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beta2: 0.999
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eps: 1.0e-08
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warmup: 10000
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grad_clip: 1.0
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scheduler: lambda
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experiment:
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name: QGamma0.01-v2
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wandb_project: debug-QGamma
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model:
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name: gamma_hdlm
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type: ddit
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hidden_size: 768
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cond_dim: 128
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length: 1024
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n_blocks: 12
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n_heads: 12
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scale_by_sigma: false
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dropout: 0.1
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transformer_sigma_conditioning: true
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hybrid_sigma_embedding: true
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post_process_logits: true
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use_timestep_embedding: true
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model_type: gamma_hybrid
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