fix ci

config/llama_7b_tulu.yaml

@@ -0,0 +1,39 @@
+data:
+  train_urls:
+    - "gs://marin-us-central2/documents/instruct/tulu_v2_mix/text/tulu-v2-sft-mixture-000.jsonl.gz"
+    - "gs://marin-us-central2/documents/instruct/tulu_v2_mix/text/tulu-v2-sft-mixture-001.jsonl.gz"
+    - "gs://marin-us-central2/documents/instruct/tulu_v2_mix/text/tulu-v2-sft-mixture-002.jsonl.gz"
+  cache_dir: "gs://marin-us-central2/tokenized/OLMo-1B/tuluv2_sft/"
+  tokenizer: "allenai/OLMo-1B"
+model:  # 7B class model
+  type: llama
+  seq_len: 4096
+  hidden_dim: 4096
+  intermediate_dim: 11008
+  num_layers: 32
+  num_heads: 32
+  num_kv_heads: 32
+  use_flash_attention: True
+  flash_attention_block_size: 1024
+  use_bias: false
+  use_layer_norm_weight: false
+trainer:
+  tracker:
+    type: wandb
+    project: "marin"
+    tags: ["dolma", "olmo", "llama"]
+
+  mp: p=f32,c=bfloat16
+  train_batch_size: 256
+  num_train_steps: 750000  # 3,000,000,000,000 / 4,000,000 = 750,000
+  steps_per_eval: 1000
+  tensor_parallel_axes: ["mlp", "heads"]  
+  fsdp_axis: "embed"
+  batch_axis: "batch"
+optimizer:
+  learning_rate: 4E-4
+  weight_decay: 0.1
+  min_lr_ratio: 0.1
+  warmup: 5000
+
+epoch: False

config/llama_7b_with_olmo_config.yaml

@@ -31,5 +31,5 @@ optimizer:
   weight_decay: 0.1
   min_lr_ratio: 0.1
   warmup: 0.01
- 
- data_shuffle: true
+
+ data_shuffle: true

src/levanter/data/text.py

@@ -93,10 +93,10 @@ async def current_len(self) -> Optional[int]:
     async def get_batch(self, indices: Sequence[int]) -> Sequence[T_co]:
         token_arrays = await self._await_token_cache()
         dataset_len = await self.async_len()
-        
+
         wrapped_indices = [idx % dataset_len for idx in indices]
         offsets = np.array(wrapped_indices) * self.seq_len
-        
+
         with ts.Batch():
             out = []
             for offset in offsets:
@@ -691,9 +691,9 @@ class LMDatasetConfig(LMDatasetSourceConfig, LMTaskConfig):
     cache_dir: Optional[str] = "cache/"
 
     def train_set(
-        self, seq_len: int, monitors: Union[bool, List[MetricsMonitor]] = True, *, key: Optional[PRNGKeyArray] = None, epochs: bool = False 
+        self, seq_len: int, monitors: Union[bool, List[MetricsMonitor]] = True, *, key: Optional[PRNGKeyArray] = None, epochs: bool = False
     ) -> AsyncDataset[np.ndarray]:
-        
+
         if epochs:
             ds = self.token_epoch_dataset("train", seq_len, monitors)
         else:
@@ -750,7 +750,7 @@ def token_seq_dataset(
         if cache is None:
             return None
         return TokenSeqDataset(cache, seq_len)
-    
+
     def token_epoch_dataset(
         self, split: str, seq_len: int, monitors: Union[bool, List[MetricsMonitor]] = True
     ) -> Optional[TokenSeqDataset]:

src/levanter/main/train_lm.py

@@ -119,7 +119,7 @@ def main(config: TrainLmConfig):
         # TODO: fix this
         tagged_eval_datasets: list = config.data.tagged_eval_sets(Pos.size)
         # TokenSeqDataset is config.data.train_set(Pos.size, key=data_key)
-        
+
         train_dataset = CausalLmDataset(
             config.data.train_set(Pos.size, key=data_key, epochs=config.epoch), Pos, KeyPos, ignore_index=config.data.ignore_token_id
         )
@@ -244,7 +244,7 @@ def compute_log_probs(model, example):
 
         ## OK, actually run training!
         trainer.train(state, train_loader)
-        
+
         # checkpointer.on_step(last_step, force=True)