Revise SFT File

config/llama_sft_hf_ckpt.yaml

@@ -0,0 +1,13 @@
+# Model configuration
+model:
+  type: llama
+  seq_len: 2048
+  hidden_dim: 4096
+  intermediate_dim: 11008
+  num_layers: 32
+  num_heads: 32
+  num_kv_heads: 32
+  use_flash_attention: true
+  flash_attention_block_size: 512
+  use_bias: false
+  use_layer_norm_weight: false

examples/sft/sft.py → src/levanter/main/sft.py

@@ -1,8 +1,8 @@
 import logging
 import os
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from enum import Enum
-from typing import List, Optional
+from typing import List, Optional, Union
 
 import jax.random as jrandom
 import transformers
@@ -15,10 +15,17 @@
 from levanter import callbacks
 from levanter.compat.hf_checkpoints import HFCheckpointConverter, HFCompatConfig, save_hf_checkpoint_callback
 from levanter.data import PermutationDataset
-from levanter.data.text import ChatUrlDataSourceConfig, EpochDataset, mk_chat_sft_dataset, mk_supervised_dataset
-from levanter.main.train_lm import TrainLmConfig
-from levanter.models.lm_model import LmHeadModel, compute_next_token_loss
-from levanter.trainer import Trainer
+from levanter.data.text import (
+    ChatUrlDataSourceConfig,
+    EpochDataset,
+    SupervisedSourceConfig,
+    mk_chat_sft_dataset,
+    mk_supervised_dataset,
+)
+from levanter.models.llama import LlamaConfig
+from levanter.models.lm_model import LmConfig, LmHeadModel, compute_next_token_loss
+from levanter.optim import AdamConfig, OptimizerConfig
+from levanter.trainer import Trainer, TrainerConfig
 
 
 logger = logging.getLogger(__name__)
@@ -38,24 +45,40 @@ class DatasetType(str, Enum):
 
 
 @dataclass
-class SFTConfig(TrainLmConfig):
+class SFTConfig:
     # inherit most of the config from TrainLmConfig
-    max_tune_length: int = 2048
+    trainer: TrainerConfig = field(default_factory=TrainerConfig)
+    model: LmConfig = field(default_factory=LlamaConfig)
+    optimizer: OptimizerConfig = field(default_factory=AdamConfig)
+    supervised_data: Optional[SupervisedSourceConfig | dict[str, SupervisedSourceConfig]] = None
+
+    # config related to continued pretraining
+    initialize_from_hf: Union[bool, str] = False
+    hf_save_path: Optional[str] = None
+    hf_upload: Optional[str] = None
+    hf_save_steps: int = 0
+
+    max_seq_len: int = 2048
     model_name_or_path: str = "meta-llama/Llama-2-7b-hf"
     tokenizer: str = "meta-llama/Llama-2-7b-hf"
 
     # Add dataset type and chat-specific fields
-    dataset_type: DatasetType = DatasetType.HUGGINGFACE
+    dataset_type: DatasetType = DatasetType.CHAT_JSONL
     chat_train_urls: Optional[List[str]] = None
     messages_field: str = "messages"
     input_role: str = "user"
     output_role: str = "assistant"
 
+    data_seed: Optional[int] = None  # if provided, will override the data seed from the trainer
+
+    # if provided, will initialize from this checkpoint, used for llama style data mixture
+    epoch: int = 0
+
 
 def train(config: SFTConfig):
     tokenizer = transformers.AutoTokenizer.from_pretrained(
         config.tokenizer,
-        model_max_length=config.max_tune_length,
+        model_max_length=config.max_seq_len,
         padding_side="right",
         trust_remote_code=True,
     )
@@ -79,7 +102,11 @@ def train(config: SFTConfig):
     elif config.trainer.initialize_from is None:
         raise ValueError("Must specify either --initialize_from_hf or --initialize_from")
     else:
-        converter = None
+        if config.hf_save_steps:
+            converter = HFCheckpointConverter.from_hf(config.model_name_or_path, trust_remote_code=True)
+            converter = converter.replaced(tokenizer=tokenizer)
+        else:
+            converter = None
         model_config = config.model
 
     levanter.initialize(config)
@@ -100,8 +127,16 @@ def train(config: SFTConfig):
     if config.dataset_type == DatasetType.CHAT_JSONL:
         assert config.chat_train_urls is not None
         assert config.supervised_data is not None
+
+        # Get the cache_dir safely
+        cache_dir = (
+            config.supervised_data.cache_dir
+            if not isinstance(config.supervised_data, dict)
+            else next(iter(config.supervised_data.values())).cache_dir
+        )
+
         chat_config = ChatUrlDataSourceConfig(
-            cache_dir=config.supervised_data.cache_dir,
+            cache_dir=cache_dir,
             train_urls=config.chat_train_urls,  # No validation in this config
             messages_field=config.messages_field,
             input_role=config.input_role,
@@ -110,7 +145,13 @@ def train(config: SFTConfig):
         train_dataset = mk_chat_sft_dataset(chat_config, tokenizer, model_config.Pos)
     else:
         assert config.supervised_data is not None
-        train_dataset = mk_supervised_dataset(config.supervised_data, "train", tokenizer, model_config.Pos)
+        if isinstance(config.supervised_data, dict):
+            # TODO: figure out what actually makes sense here
+            # for marin we will just use the url code path
+            config_to_use = next(iter(config.supervised_data.values()))
+        else:
+            config_to_use = config.supervised_data
+        train_dataset = mk_supervised_dataset(config_to_use, "train", tokenizer, model_config.Pos)
     logger.info("Supervised dataset created")
     train_dataset = PermutationDataset(train_dataset, data_key)
 
@@ -161,11 +202,6 @@ def train(config: SFTConfig):
 
         loader = trainer.data_loader(train_dataset, trainer.TrainBatch)
 
-        if int(state.step) != 0:
-            logger.info(f"Resuming training from step {state.step}")
-            for i in range(state.step):
-                next(loader)
-
         if config.hf_save_path is not None:
             # bit gross to reach this far into the config, but it's fine
             if config.trainer.checkpointer.append_run_id_to_base_path: