Process reward models (#2241)

* adding model_cfg to set num_labels

* using a num_labels field instead

* linting

* WIP stepwise prompt tokenizer

* this should work?

* trainer working?

* pushing to runpod

* fixing saving

* updating conf

* updating config, adding docs

* adding stepwise supervision docpage

* updating tests

* adding test for dataset

* fixing tests

* linting

* addressing some comments

* adding additional cfg fields support

* updating tests, fixing cfg

* fixing tests

* updating loss

* Update test_process_reward_model_smollm2.py

* updating loss values and seed

* dumb pre-commit

examples/gemma2/reward-model.yaml

@@ -1,6 +1,7 @@
 base_model: google/gemma-2-2b
 # optionally might have model_type or tokenizer_type
 model_type: AutoModelForSequenceClassification
+num_labels: 1
 tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name

examples/qwen2/prm.yaml

@@ -0,0 +1,72 @@
+base_model: Qwen/Qwen2.5-3B
+# optionally might have model_type or tokenizer_type
+model_type: AutoModelForTokenClassification
+num_labels: 2
+tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+process_reward_model: true
+chat_template:
+datasets:
+  - path: trl-lib/math_shepherd
+    type: stepwise_supervised
+    step_separator: "\n"
+    max_completion_length:
+    train_on_last_step_only: false
+
+val_set_size: 0.2
+output_dir: ./outputs/out
+remove_unused_columns: false
+
+sequence_len: 2048
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 1
+micro_batch_size: 8
+eval_batch_size: 8
+num_epochs: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32:
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+eval_table_size:
+eval_max_new_tokens: 128
+eval_steps: 100
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

examples/qwen2/reward-model.yaml

@@ -0,0 +1,67 @@
+base_model:  Qwen/Qwen2.5-0.5B
+# optionally might have model_type or tokenizer_type
+model_type: AutoModelForSequenceClassification
+num_labels: 1
+tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+reward_model: true
+chat_template: qwen_25
+datasets:
+  - path: argilla/distilabel-intel-orca-dpo-pairs
+    type: bradley_terry.chat_template
+val_set_size: 0.0
+output_dir: ./outputs/out
+remove_unused_columns: false
+
+sequence_len: 2048
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens: