attempt to set start method to spwan to prevent cuda issues for DPO

* support for llama multipack using updated code/patches

* also support unsloth patches

* incorrect arg

* add config validation for unsloth

* add missing return to validation

* add another missing return to validation

.github/workflows/tests.yml

@@ -57,6 +57,10 @@ jobs:
         run: |
           pytest --ignore=tests/e2e/ tests/
 
+      - name: cleanup pip cache
+        run: |
+          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+
   docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
     # this job needs to be run on self-hosted GPU runners...
@@ -99,7 +103,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal jinja2
+          pip install modal==0.63.64 jinja2
       - name: Update env vars
         run: |
           echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV

cicd/cicd.sh

@@ -2,5 +2,5 @@
 set -e
 
 pytest --ignore=tests/e2e/ /workspace/axolotl/tests/
-pytest /workspace/axolotl/tests/e2e/patched/
+pytest -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/
 pytest --ignore=tests/e2e/patched/ /workspace/axolotl/tests/e2e/

src/axolotl/core/trainer_builder.py

@@ -13,6 +13,7 @@ from abc import abstractmethod
 from collections import defaultdict
 from dataclasses import dataclass, field
 from functools import wraps
+from multiprocessing import set_start_method
 from pathlib import Path
 from typing import Dict, List, Literal, Optional, Type, Union
 
@@ -290,6 +291,18 @@ class AxolotlTrainer(Trainer):
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
+    def _wrap_model(self, model, training=True, dataloader=None):
+        if self.args.torch_compile:
+            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
+                256
+            )
+            model = torch.compile(
+                model,
+                backend=self.args.torch_compile_backend,
+                mode=self.args.torch_compile_mode,
+            )
+        return super()._wrap_model(model, training=training, dataloader=dataloader)
+
     def create_optimizer(self):
         if (
             self.args.loraplus_lr_ratio is None
@@ -1758,6 +1771,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         for callback in self.get_post_trainer_create_callbacks(dpo_trainer):
             dpo_trainer.add_callback(callback)
 
+        # prevents multiprocessing issues for datasets on multiple GPUs
+        set_start_method("spawn")
+
         return dpo_trainer
 
 

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -78,6 +78,33 @@ def replace_llama_qkv_with_fused(model):
             set_module_name(model, name, qkv)
 
 
+def patch_llama_cross_entropy():
+    from flash_attn.losses.cross_entropy import CrossEntropyLoss
+
+    LOG.info("patching with flash_attn.losses.cross_entropy")
+    transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
+        CrossEntropyLoss, inplace_backward=True
+    )
+
+
+def patch_llama_rms_norm():
+    try:
+        from flash_attn.ops.rms_norm import RMSNorm
+
+        class LlamaRMSNorm(RMSNorm):
+            """Patched LLamaRMSNorm"""
+
+            def __init__(self, hidden_size, eps=1e-6):
+                super().__init__(hidden_size, eps=eps)
+
+        LOG.info("patching with flash_attn.ops.rms_norm")
+        transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
+    except ImportError:
+        LOG.warning(
+            "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
+        )
+
+
 def replace_llama_attn_with_flash_attn(
     packed: Optional[bool] = False,
     cross_entropy: Optional[bool] = False,
@@ -104,30 +131,11 @@ def replace_llama_attn_with_flash_attn(
 
     # skip only if explicitly disabled
     if cross_entropy:
-        from flash_attn.losses.cross_entropy import CrossEntropyLoss
-
-        LOG.info("patching with flash_attn.losses.cross_entropy")
-        transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
-            CrossEntropyLoss, inplace_backward=True
-        )
+        patch_llama_cross_entropy()
 
     # skip only if explicitly disabled
     if rms_norm:
-        try:
-            from flash_attn.ops.rms_norm import RMSNorm
-
-            class LlamaRMSNorm(RMSNorm):
-                """Patched LLamaRMSNorm"""
-
-                def __init__(self, hidden_size, eps=1e-6):
-                    super().__init__(hidden_size, eps=eps)
-
-            LOG.info("patching with flash_attn.ops.rms_norm")
-            transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
-        except ImportError:
-            LOG.warning(
-                "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
-            )
+        patch_llama_rms_norm()
 
 
 class FusedAttention(LlamaAttention):

src/axolotl/monkeypatch/multipack.py

@@ -10,6 +10,7 @@ from axolotl.monkeypatch.mixtral import patch_mixtral_moe_forward_zero3
 from axolotl.monkeypatch.utils import get_unpad_data
 
 SUPPORTED_MULTIPACK_MODEL_TYPES = [
+    "llama",
     "mixtral",
     "qwen2",
     "qwen2_moe",
@@ -30,6 +31,10 @@ def patch_for_multipack(model_type, model_name=None):
         )
         if is_deepspeed_zero3_enabled():
             patch_mixtral_moe_forward_zero3()
+    elif model_type == "llama":
+        transformers.models.llama.modeling_llama._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
     elif model_type == "qwen2":
         transformers.models.qwen2.modeling_qwen2._get_unpad_data = (  # pylint: disable=protected-access
             get_unpad_data

src/axolotl/train.py

@@ -52,6 +52,13 @@ class TrainDatasetMeta:
 def train(
     *, cfg: DictDefault, cli_args: TrainerCliArgs, dataset_meta: TrainDatasetMeta
 ) -> Tuple[Union[PeftModel, PreTrainedModel], PreTrainedTokenizer]:
+    # enable expandable segments for cuda allocation to improve VRAM usage
+    # torch_version = torch.__version__.split(".")
+    # torch_major, torch_minor = int(torch_version[0]), int(torch_version[1])
+    # if torch_major == 2 and torch_minor >= 2:
+    #     if os.getenv("PYTORCH_CUDA_ALLOC_CONF") is None:
+    #         os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+
     # load the tokenizer first
     LOG.debug(
         f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",

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

@@ -1112,6 +1112,31 @@ class AxolotlInputConfig(
             raise ValueError("either datasets or pretraining_dataset is required")
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_xentropy_patch_conflicts(cls, data):
+        if data.get("flash_attn_cross_entropy") and data.get(
+            "unsloth_cross_entropy_loss"
+        ):
+            raise ValueError(
+                "flash_attn_cross_entropy and unsloth_cross_entropy_loss cannot be both enabled"
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_qlora_unsloth(cls, data):
+        if (
+            data.get("unsloth_lora_mlp")
+            or data.get("unsloth_lora_qkv")
+            or data.get("unsloth_lora_o")
+        ):
+            if data.get("adapter") == "lora" or data.get("load_in_8bit"):
+                raise ValueError(
+                    "unsloth_lora_mlp, unsloth_lora_qkv, and unsloth_lora_o are not compatible with 8-bit LoRA"
+                )
+        return data
+
 
 class AxolotlConfigWCapabilities(AxolotlInputConfig):
     """wrapper to valdiate gpu capabilities with the configured options"""
@@ -1163,3 +1188,18 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
         if data.get("deepspeed") and data.get("fsdp"):
             raise ValueError("deepspeed and fsdp cannot be used together.")
         return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_multigpu_unsloth(cls, data):
+        if (
+            data.get("unsloth_lora_mlp")
+            or data.get("unsloth_lora_qkv")
+            or data.get("unsloth_lora_o")
+        ):
+            capabilities = data.get("capabilities")
+            if capabilities and capabilities.get("num_gpus") > 1:
+                raise ValueError(
+                    "unsloth_lora_mlp, unsloth_lora_qkv, and unsloth_lora_o are not compatible with multi-GPU training."
+                )
+        return data

src/axolotl/utils/models.py

@@ -347,6 +347,27 @@ def load_model(
         and cfg.sample_packing
     ):
         patch_for_multipack(cfg.model_config_type, model_name=cfg.base_model)
+
+        if cfg.is_llama_derived_model:
+            from axolotl.monkeypatch.llama_attn_hijack_flash import (
+                patch_llama_cross_entropy,
+                patch_llama_rms_norm,
+            )
+
+            if cfg.flash_attn_cross_entropy:
+                patch_llama_cross_entropy()
+            if cfg.flash_attn_rms_norm:
+                patch_llama_rms_norm()
+            if cfg.unsloth_cross_entropy_loss:
+                from axolotl.monkeypatch.unsloth_ import (
+                    integrate_cross_entropy_loss_patch,
+                )
+
+                integrate_cross_entropy_loss_patch()
+            if cfg.unsloth_lora_qkv or cfg.unsloth_lora_o:
+                from axolotl.monkeypatch.unsloth_ import patch_self_attn_lora
+
+                patch_self_attn_lora()
     elif cfg.is_llama_derived_model:
         # Modify all llama derived models in one block