patches for llama ga

src/axolotl/monkeypatch/trainer_grad_accum.py

@@ -0,0 +1,196 @@
+"""
+fix for FSDP gradient accumulation
+see https://github.com/huggingface/transformers/pull/35128
+"""
+import inspect
+
+from accelerate.logging import get_logger
+from transformers import LlamaForCausalLM
+from transformers.trainer import Trainer
+
+from axolotl.monkeypatch.unsloth_ import detab_code
+
+LOG = get_logger("axolotl.monkeypatch.trainer_grad_accum")
+
+ORIGINAL_CONTEXT_CODE = """
+    with self.compute_loss_context_manager():
+        if self.model_accepts_loss_kwargs:
+            loss = self.compute_loss(model, inputs)
+        else:
+            loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
+"""
+
+PATCHED_CONTEXT_CODE = """
+    with self.compute_loss_context_manager():
+        if self.model_accepts_loss_kwargs:
+            loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
+        else:
+            loss = self.compute_loss(model, inputs)
+"""
+
+ORIGINAL_LLAMA_FCLM_CODE = """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+"""
+
+PATCHED_LLAMA_FCLM_CODE = """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # remove num_items_in_batch otherwise self.model attempts to pass it to flash_attention
+        num_items_in_batch = kwargs.pop("num_items_in_batch")
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, num_items_in_batch=num_items_in_batch, **kwargs)
+"""
+
+
+def get_training_step_code() -> str:
+    training_step = inspect.getsource(
+        Trainer.training_step  # pylint: disable=protected-access
+    )
+    return training_step
+
+
+def check_training_step_is_patchable() -> bool:
+    training_step = get_training_step_code()
+    training_step, _ = detab_code(training_step)
+    return ORIGINAL_CONTEXT_CODE in training_step
+
+
+def patch_training_step_for_ga():
+    """
+    monkeypatch for fixing the training loop for gradient accumulation
+    """
+
+    training_step = get_training_step_code()
+    Trainer._original_training_step = training_step  # pylint: disable=protected-access
+    training_step, _ = detab_code(training_step)
+    assert (
+        ORIGINAL_CONTEXT_CODE in training_step
+    ), "Original training_step code not found"
+
+    training_step = training_step.replace(ORIGINAL_CONTEXT_CODE, PATCHED_CONTEXT_CODE)
+    training_step = training_step.replace(
+        "def training_step(",
+        "def _fixed_training_step(",
+        1,
+    )
+
+    # load imports necessary
+    import transformers.trainer
+
+    items_to_import = []
+    for item in dir(transformers.trainer):
+        if item in training_step:
+            items_to_import.append(item)
+
+    exec(  # pylint: disable=exec-used  # nosec B102
+        "from transformers.trainer import ("
+        + ", ".join(x for x in items_to_import)
+        + ")",
+        globals(),
+    )
+    exec(training_step, globals())  # pylint: disable=exec-used  # nosec B102
+    LOG.info("patching training_step", main_process_only=True)
+    Trainer.training_step = (  # pylint: disable=protected-access
+        _fixed_training_step  # pylint: disable=undefined-variable  # noqa: F821
+    )
+
+
+def get_model_forward_code() -> str:
+    forward = inspect.getsource(
+        LlamaForCausalLM.forward  # pylint: disable=protected-access
+    )
+    return forward
+
+
+def check_forward_is_patchable() -> bool:
+    forward = get_model_forward_code()
+    forward, _ = detab_code(forward)
+    return ORIGINAL_LLAMA_FCLM_CODE in forward
+
+
+def patch_forward_for_ga():
+    """
+    monkeypatch for fixing the training loop for gradient accumulation
+    """
+
+    forward = get_model_forward_code()
+    LlamaForCausalLM._original_forward = forward  # pylint: disable=protected-access
+    forward, _ = detab_code(forward)
+    assert ORIGINAL_LLAMA_FCLM_CODE in forward, "Original forward code not found"
+
+    forward = forward.replace(ORIGINAL_LLAMA_FCLM_CODE, PATCHED_LLAMA_FCLM_CODE)
+    forward = forward.replace(
+        "def forward(",
+        "def _fixed_forward(",
+        1,
+    )
+
+    # load imports necessary
+    import transformers.models.llama.modeling_llama
+
+    items_to_import = []
+    for item in dir(transformers.models.llama.modeling_llama):
+        if item in forward:
+            items_to_import.append(item)
+
+    exec(  # pylint: disable=exec-used  # nosec B102
+        "from transformers.models.llama.modeling_llama import ("
+        + ", ".join(x for x in items_to_import)
+        + ")",
+        globals(),
+    )
+    exec(forward, globals())  # pylint: disable=exec-used  # nosec B102
+    LOG.info("patching forward", main_process_only=True)
+    LlamaForCausalLM.forward = (  # pylint: disable=protected-access
+        _fixed_forward  # pylint: disable=undefined-variable  # noqa: F821
+    )

src/axolotl/utils/models.py

@@ -386,6 +386,15 @@ class ModelLoader:
         if self.cfg.flash_attention:
             self.patch_attention()
 
+        if self.cfg.model_config_type == "llama":
+            from axolotl.monkeypatch.trainer_grad_accum import (
+                patch_forward_for_ga,
+                patch_training_step_for_ga,
+            )
+
+            patch_forward_for_ga()
+            patch_training_step_for_ga()
+
         if self.cfg.sample_packing and self.cfg.s2_attention:
             raise ValueError(
                 "Received `sample_packing=true` and `s2_attention=true`; however, \

tests/patched/test_llama_trainer_ga.py

@@ -0,0 +1,25 @@
+""""Test module for checking whether the Hugging Face Transformers is working as expected."""
+import unittest
+
+from axolotl.monkeypatch.trainer_grad_accum import (
+    check_forward_is_patchable,
+    check_training_step_is_patchable,
+)
+
+
+class TestTrainerGAIntegration(unittest.TestCase):
+    """llama monkeypatch integration tests."""
+
+    def test_train_step_patchable(self):
+        # ensures the current version of transformers has loss code that matches our patching code
+        self.assertTrue(
+            check_training_step_is_patchable(),
+            "HF transformers Trainer.training_step has changed and isn't patchable",
+        )
+
+    def test_model_forward_patchable(self):
+        # ensures the current version of transformers has loss code that matches our patching code
+        self.assertTrue(
+            check_forward_is_patchable(),
+            "HF transformers LlamaForCausalLM.forward has changed and isn't patchable",
+        )