perform flakey patched tests in individual runner

.github/workflows/tests-nightly.yml

@@ -44,6 +44,11 @@ jobs:
           python-version: ${{ matrix.python_version }}
           cache: 'pip' # caching pip dependencies
 
+      - name: upgrade pip
+        run: |
+          pip3 install --upgrade pip
+          pip3 install --upgrade packaging setuptools wheel
+
       - name: Install PyTorch
         run: |
           pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu

cicd/cicd.sh

@@ -8,3 +8,8 @@ pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /worksp
 pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/patched/
 pytest -v --durations=10 -n1 --dist loadfile /workspace/axolotl/tests/e2e/integrations/
 pytest -v --durations=10 --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/
+
+tests=$(pytest --collect-only -q tests/e2e/each)
+for t in $tests; do
+    pytest $t
+done

examples/llama-3/fft-8b-tp.yml

@@ -1,58 +0,0 @@
-base_model: NousResearch/Meta-Llama-3.1-8B
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.05
-output_dir: ./outputs/out
-
-sequence_len: 8192
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 8
-micro_batch_size: 1
-num_epochs: 1
-optimizer: paged_adamw_8bit
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-tensor_parallel: 'auto'
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-early_stopping_patience:
-resume_from_checkpoint:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 100
-evals_per_epoch: 2
-eval_table_size:
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: <|end_of_text|>

examples/llama-3/lora-8b-tp.yml

@@ -1,73 +0,0 @@
-base_model: NousResearch/Meta-Llama-3.1-8B
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./outputs/lora-out
-
-sequence_len: 4096
-sample_packing: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-lora_modules_to_save:
-  - embed_tokens
-  - lm_head
-
-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: auto
-fp16:
-tf32: false
-
-tensor_parallel: 'auto'
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-s2_attention:
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-   pad_token: <|end_of_text|>

requirements.txt

@@ -12,7 +12,7 @@ liger-kernel==0.4.2
 
 packaging==23.2
 peft==0.14.0
-transformers>=4.46.3
+transformers==4.47.0
 tokenizers>=0.20.1
 accelerate==1.2.0
 datasets==3.1.0

src/axolotl/core/trainer_builder.py

@@ -1319,10 +1319,6 @@ class TrainerBuilderBase(abc.ABC):
         if hasattr(model, "add_model_tags"):
             model.add_model_tags(["axolotl"])
 
-        if self.cfg.tensor_parallel == "auto" and self.model.supports_tp_plan:
-            os.environ["ACCELERATE_USE_TP"] = "true"
-            # self.model =
-
     @property
     def model_ref(self):
         return self._model_ref

src/axolotl/utils/callbacks/__init__.py

@@ -66,10 +66,7 @@ class EvalFirstStepCallback(
         control: TrainerControl,
         **kwargs,
     ):
-        if (
+        if args.eval_strategy == IntervalStrategy.STEPS and state.global_step == 1:
-            args.evaluation_strategy == IntervalStrategy.STEPS
-            and state.global_step == 1
-        ):
             control.should_evaluate = True
         return control
 

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -393,7 +393,7 @@ class ModelInputConfig(BaseModel):
         default=None, json_schema_extra={"description": "transformers processor class"}
     )
     trust_remote_code: Optional[bool] = None
-    tensor_parallel: Optional[Union[Literal["auto"], bool]] = "auto"
+
     model_kwargs: Optional[Dict[str, Any]] = None
 
     @field_validator("trust_remote_code")

src/axolotl/utils/models.py

@@ -1187,15 +1187,9 @@ class ModelLoader:
             gc.collect()
             torch.cuda.empty_cache()
 
-        self.post_loading_set_env()
-
         # TODO resume_from_checkpoint handling
         return self.model, lora_config
 
-    def post_loading_set_env(self):
-        if self.cfg.tensor_parallel == "auto" and self.model.supports_tp_plan:
-            os.environ["ACCELERATE_USE_TP"] = "true"
-
 
 def load_model(
     cfg: DictDefault,