multipack for gemma (#1313)

* multipack for gemma

* chore: lint

* handle cache_position kwarg in updated llama modeling

* add position_ids to rotary embed call for updated llama modeling

examples/gemma/qlora.yml

@@ -1,57 +1,57 @@
 # use google/gemma-7b if you have access
-base_model: mhenrichsen/gemma-7b 
+base_model: mhenrichsen/gemma-7b
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
- 
+
 load_in_8bit: false
 load_in_4bit: true
 strict: false
- 
+
 # huggingface repo
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
 val_set_size: 0.1
 output_dir: ./out
- 
+
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
- 
+
 sequence_len: 4096
 sample_packing: false
 pad_to_sequence_len: false
- 
+
 wandb_project:
 wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
- 
- 
+
+
 gradient_accumulation_steps: 3
 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: auto
 fp16:
 tf32: false
- 
+
 gradient_checkpointing: true
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 logging_steps: 1
 xformers_attention:
 flash_attention: true
- 
+
 warmup_ratio: 0.1
 evals_per_epoch: 4
 eval_table_size:

requirements.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft @ git+https://github.com/huggingface/peft.git
-transformers @ git+https://github.com/huggingface/transformers.git@bebeeee01275c32fccec3fa36d8b148d3813a7dc
+transformers @ git+https://github.com/huggingface/transformers.git@ae49b218c3d718df90d8e4a109016450fb8f0632
 tokenizers==0.15.0
 bitsandbytes>=0.41.1
 accelerate==0.26.1

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -275,7 +275,9 @@ def flashattn_forward_with_s2attn(
     kv_seq_len = key_states.shape[-2]
     if past_key_value is not None:
         kv_seq_len += past_key_value[0].shape[-2]
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    cos, sin = self.rotary_emb(
+        value_states, seq_len=kv_seq_len, position_ids=position_ids
+    )
     query_states, key_states = apply_rotary_pos_emb(
         query_states, key_states, cos, sin, position_ids
     )
@@ -425,7 +427,9 @@ def flashattn_forward(
     if past_key_value is not None:
         kv_seq_len += past_key_value[0].shape[-2]
 
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    cos, sin = self.rotary_emb(
+        value_states, seq_len=kv_seq_len, position_ids=position_ids
+    )
     query_states, key_states = apply_rotary_pos_emb(
         query_states, key_states, cos, sin, position_ids
     )
@@ -688,6 +692,9 @@ def llama_model_forward(
     output_attentions: Optional[bool] = None,
     output_hidden_states: Optional[bool] = None,
     return_dict: Optional[bool] = None,
+    cache_position: Optional[  # pylint: disable=unused-argument
+        torch.LongTensor
+    ] = None,
 ) -> Union[Tuple, BaseModelOutputWithPast]:
     output_attentions = (
         output_attentions

src/axolotl/monkeypatch/multipack.py

@@ -6,7 +6,7 @@
 from axolotl.monkeypatch.mixtral import patch_mixtral_moe_forward_zero3
 from axolotl.monkeypatch.utils import get_unpad_data
 
-SUPPORTED_MULTIPACK_MODEL_TYPES = ["mixtral", "qwen2", "falcon", "phi"]
+SUPPORTED_MULTIPACK_MODEL_TYPES = ["mixtral", "qwen2", "falcon", "phi", "gemma"]
 
 
 def patch_for_multipack(model_type):
@@ -28,3 +28,7 @@ def patch_for_multipack(model_type):
         transformers.models.phi.modeling_phi._get_unpad_data = (  # pylint: disable=protected-access
             get_unpad_data
         )
+    elif model_type == "gemma":
+        transformers.models.gemma.modeling_gemma._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )