chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,190 @@
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defaults:
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- hydra: default
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# - deepspeed@ds_cfg: train_hybrid_engine_zero3
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- deepspeed@ds_cfg: train_hybrid_engine_zero1_optim_offload
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- _self_ # see here for more details: https://hydra.cc/docs/tutorials/basic/your_first_app/defaults/#composition-order-of-primary-config
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hydra:
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searchpath:
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- file://conf/
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data_path_prefix: ""
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model_path_prefix: ../pretrained-models/ # /mnt/fangkai_blob/share/models
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output_path_prefix: "" # /mnt/fangkai_blob/reward_modeling/
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train_file: "hf:codeparrot/apps"
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dev_file:
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test_file:
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torch_dtype:
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_target_: general_util.training_utils.return_torch_dtype
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dtype: float16
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tokenizer_init:
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_target_: general_util.tokenization_utils.init_tokenizer
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tokenizer_path: ${model_name_or_path}
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padding_side: left
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model:
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_target_: models.llama.LlamaForCausalLM.from_pretrained
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gradient_checkpointing: True
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attn_implementation: "eager"
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torch_dtype: ${torch_dtype}
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pad_token_id: 100001
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# device_map:
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# _target_: models.utils.return_single_device_map
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chat_prefix: "<｜begin▁of▁sentence｜>You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n### Instruction:\n"
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chat_connect: "\n### Response:\n"
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chat_suffix: "\n<|EOT|>"
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prompt: "{question}\n\nPlease write a program to solve the above problem under the given time constraints and memory limits."
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read_tensor:
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_target_: data.combine_dataset.MultiMappingDataset
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read_fn:
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_target_: data.apps.APPsReader
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split: train
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aligner:
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_target_: data.input_aligner.flat_aligner
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input_index_field: problem_id
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extract_field: [ "solutions" ]
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mode: "multi"
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template:
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_target_: data.input_utils.recompose_template
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units:
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chat_prefix: ${chat_prefix}
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prompt: ${prompt}
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chat_connect: ${chat_connect}
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suffix: "{solutions}"
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compositions:
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prompt: "{chat_prefix}{prompt}{chat_connect}"
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chosen: "{chat_prefix}{prompt}{chat_connect}{suffix}"
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index_field: problem_id
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kv_mapping:
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chosen: chosen
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problem_id: index
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prompt: prompt
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dist_load_data_barrier: False
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extended_vocab:
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# Data collator
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collator:
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_target_: data.general_collator.DPODataSFTCollator
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tokenizer: ${tokenizer_init}
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max_seq_length: 4096
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# Dataloader
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num_workers: 8
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prefetch_factor: 2
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# Wiki path pretrain v8.2
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model_name_or_path: ${model_path_prefix}/deepseek-coder-7b-instruct-v1.5
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pretrain:
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resume:
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dp_size:
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tp_size: 1
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pp_size: 1
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exp_name: deepseek-coder-v1.5-ins.7b.apps.code_gen.A100.w8.v1.0
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exp_notes:
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output_dir: ${output_path_prefix}experiments/${exp_name} # Fix <pad token id>
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do_train: True
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evaluate_during_training: True
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do_eval: True
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eval_sub_path: checkpoint-*
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# Training hyper-parameters
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per_gpu_train_batch_size: 2
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per_gpu_eval_batch_size: 2
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#learning_rate: 1e-4
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#learning_rate: 5e-6
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learning_rate: 2e-5
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gradient_accumulation_steps: 8
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weight_decay: 0.1
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adam_epsilon: 1e-6
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adam_betas: "(0.9, 0.98)"
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#adam_betas: "(0.9, 0.999)"
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#max_grad_norm: 0.0
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total_dataset_len: -1
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max_grad_norm: 1.0
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num_train_epochs: 1
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max_steps: 0
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warmup_proportion: 0.1
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warmup_steps: 0
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# Optimizer
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optimizer:
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use_nvlamb:
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bit_training:
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logging_steps: 5
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save_ds_state: True
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save_steps: 400
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save_best: False
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eval_steps: 400
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ddp_eval: True
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no_cuda: False
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seed: 42
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local_rank: -1
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fp16: True
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fp16_opt_level: O1
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fp16_bfloat16: True
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# Prediction config
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prediction_cfg:
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metric: "acc"
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measure: -1
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best_checkpoint:
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best_result:
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eval_forward_fn:
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_target_: general_util.evaluator.DefaultForwardFn
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post_process:
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_target_: post_processors.dpo.SFTLossOnlyPostProcessor
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ds_cfg:
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train_micro_batch_size_per_gpu: ${per_gpu_train_batch_size}
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gradient_accumulation_steps: ${gradient_accumulation_steps}
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# optimizer:
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# type: AdamW
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# params:
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# lr: ${learning_rate}
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# betas: [ 0.9, 0.95 ]
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# weight_decay: ${weight_decay}
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# bf16:
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# enabled: False
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# fp16:
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# enabled: True
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# auto_cast: False
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# loss_scale": 0
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# initial_scale_power: 16
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# loss_scale_window: 1000
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# hysteresis: 2
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# consecutive_hysteresis: False
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# min_loss_scale: 1
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# zero_optimization:
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# offload_optimizer:
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# device: cpu
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# pin_memory: True
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summary_helper:
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_target_: general_util.tensorboard_helper.WandbWriter
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batch_index_or_keys:
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outputs_index_or_keys:
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# Temporary variables
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n_gpu:
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device:
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train_batch_size:
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eval_batch_size:
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world_size:
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@@ -0,0 +1,190 @@
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defaults:
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- hydra: default
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# - deepspeed@ds_cfg: train_hybrid_engine_zero3
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- deepspeed@ds_cfg: train_hybrid_engine_zero2
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- _self_ # see here for more details: https://hydra.cc/docs/tutorials/basic/your_first_app/defaults/#composition-order-of-primary-config
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hydra:
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searchpath:
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- file://conf/
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data_path_prefix: ""
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model_path_prefix: ../pretrained-models/ # /mnt/fangkai_blob/share/models
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output_path_prefix: "" # /mnt/fangkai_blob/reward_modeling/
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train_file: "hf:codeparrot/apps"
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dev_file:
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test_file:
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torch_dtype:
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_target_: general_util.training_utils.return_torch_dtype
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dtype: float16
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tokenizer_init:
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_target_: general_util.tokenization_utils.init_tokenizer
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tokenizer_path: ${model_name_or_path}
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padding_side: left
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model:
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_target_: models.llama.LlamaForCausalLM.from_pretrained
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gradient_checkpointing: True
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attn_implementation: "eager"
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torch_dtype: ${torch_dtype}
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pad_token_id: 100001
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# device_map:
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# _target_: models.utils.return_single_device_map
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chat_prefix: "<｜begin▁of▁sentence｜>You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n### Instruction:\n"
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chat_connect: "\n### Response:\n"
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chat_suffix: "\n<|EOT|>"
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prompt: "{question}\n\nPlease write a program to solve the above problem under the given time constraints and memory limits."
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read_tensor:
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_target_: data.combine_dataset.MultiMappingDataset
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read_fn:
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_target_: data.apps.APPsReader
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split: train
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aligner:
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_target_: data.input_aligner.flat_aligner
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input_index_field: problem_id
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extract_field: [ "solutions" ]
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mode: "multi"
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template:
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_target_: data.input_utils.recompose_template
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units:
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chat_prefix: ${chat_prefix}
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prompt: ${prompt}
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chat_connect: ${chat_connect}
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suffix: "{solutions}"
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compositions:
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prompt: "{chat_prefix}{prompt}{chat_connect}"
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chosen: "{chat_prefix}{prompt}{chat_connect}{suffix}"
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index_field: problem_id
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kv_mapping:
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chosen: chosen
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problem_id: index
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prompt: prompt
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dist_load_data_barrier: False
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extended_vocab:
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# Data collator
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collator:
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_target_: data.general_collator.DPODataSFTCollator
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tokenizer: ${tokenizer_init}
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max_seq_length: 4096
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# Dataloader
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num_workers: 8
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prefetch_factor: 2
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# Wiki path pretrain v8.2
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model_name_or_path: ${model_path_prefix}/deepseek-coder-7b-instruct-v1.5
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pretrain:
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resume:
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dp_size:
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tp_size: 1
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pp_size: 1
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exp_name: deepseek-coder-v1.5-ins.7b.apps.code_gen.V100.w8.v1.0
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exp_notes:
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output_dir: ${output_path_prefix}experiments/${exp_name} # Fix <pad token id>
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do_train: True
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evaluate_during_training: True
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do_eval: True
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eval_sub_path: checkpoint-*
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# Training hyper-parameters
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per_gpu_train_batch_size: 1
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per_gpu_eval_batch_size: 1
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#learning_rate: 1e-4
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#learning_rate: 5e-6
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learning_rate: 2e-5
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gradient_accumulation_steps: 16
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weight_decay: 0.1
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adam_epsilon: 1e-6
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adam_betas: "(0.9, 0.98)"
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#adam_betas: "(0.9, 0.999)"
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#max_grad_norm: 0.0
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total_dataset_len: -1
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max_grad_norm: 1.0
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num_train_epochs: 1
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max_steps: 0
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warmup_proportion: 0.1
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warmup_steps: 0
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# Optimizer
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optimizer:
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use_nvlamb:
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bit_training:
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logging_steps: 5
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save_ds_state: False
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save_steps: 400
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save_best: False
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eval_steps: 400
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ddp_eval: True
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no_cuda: False
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seed: 42
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local_rank: -1
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fp16: True
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fp16_opt_level: O1
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fp16_bfloat16: False
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# Prediction config
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prediction_cfg:
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metric: "acc"
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measure: -1
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best_checkpoint:
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best_result:
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eval_forward_fn:
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_target_: general_util.evaluator.DefaultForwardFn
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post_process:
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_target_: post_processors.dpo.SFTLossOnlyPostProcessor
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ds_cfg:
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train_micro_batch_size_per_gpu: ${per_gpu_train_batch_size}
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gradient_accumulation_steps: ${gradient_accumulation_steps}
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optimizer:
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type: AdamW
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params:
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lr: ${learning_rate}
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betas: [ 0.9, 0.95 ]
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weight_decay: ${weight_decay}
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bf16:
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enabled: False
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fp16:
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enabled: True
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auto_cast: False
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loss_scale": 0
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initial_scale_power: 16
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loss_scale_window: 1000
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hysteresis: 2
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consecutive_hysteresis: False
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min_loss_scale: 1
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zero_optimization:
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offload_optimizer:
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device: cpu
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pin_memory: True
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summary_helper:
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_target_: general_util.tensorboard_helper.WandbWriter
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batch_index_or_keys:
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outputs_index_or_keys:
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# Temporary variables
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n_gpu:
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device:
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train_batch_size:
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eval_batch_size:
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world_size:
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