swaps to use newer sample packing for mistral

requirements.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.11.1
-transformers==4.42.4
+transformers @ git+https://github.com/huggingface/transformers.git@0fdea8607d7e01eb0e38a1ebeb7feee30a22f0cf
 tokenizers==0.19.1
 bitsandbytes==0.43.1
 accelerate==0.32.0

src/axolotl/monkeypatch/mistral_attn_hijack_flash.py

@@ -2,6 +2,7 @@
 # pylint: disable=duplicate-code
 
 import logging
+from functools import partial
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -45,6 +46,15 @@ def replace_mistral_attn_with_flash_attn(
         )
 
 
+def patch_mistral_cross_entropy():
+    from flash_attn.losses.cross_entropy import CrossEntropyLoss
+
+    LOG.info("patching with flash_attn.losses.cross_entropy")
+    transformers.models.mistral.modeling_mistral.CrossEntropyLoss = partial(
+        CrossEntropyLoss, inplace_backward=True
+    )
+
+
 @torch.jit.script
 def _make_sliding_window_causal_mask(
     bsz: int,

src/axolotl/monkeypatch/multipack.py

@@ -11,6 +11,7 @@
 
 SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "llama",
+    "mistral",
     "mixtral",
     "qwen2",
     "qwen2_moe",
@@ -25,16 +26,35 @@
 
 
 def patch_for_multipack(model_type, model_name=None):
+    if model_type == "gemmoe":
+        patch_remote(model_name, ".configuration_gemmoe", ".modeling_gemmoe")
+    elif model_type == "deepseek_v2":
+        patch_remote(model_name, ".configuration_deepseek", ".modeling_deepseek")
+    elif hasattr(transformers, "modeling_flash_attention_utils"):
+        transformers.modeling_flash_attention_utils._get_unpad_data = (  # pylint: disable=protected-access
+            get_unpad_data
+        )
+        if model_type == "mixtral" and is_deepspeed_zero3_enabled():
+            patch_mixtral_moe_forward_zero3()
+        return
+
+    # retain for legacy
     if model_type == "mixtral":
         transformers.models.mixtral.modeling_mixtral._get_unpad_data = (  # pylint: disable=protected-access
             get_unpad_data
         )
         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
-        )
+        if hasattr(transformers.models.llama.modeling_llama, "_get_unpad_data"):
+            transformers.models.llama.modeling_llama._get_unpad_data = (  # pylint: disable=protected-access
+                get_unpad_data
+            )
+    elif model_type == "mistral":
+        if hasattr(transformers.models.mistral.modeling_mistral, "_get_unpad_data"):
+            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
@@ -63,12 +83,6 @@ def patch_for_multipack(model_type, model_name=None):
         transformers.models.starcoder2.modeling_starcoder2._get_unpad_data = (  # pylint: disable=protected-access
             get_unpad_data
         )
-    elif model_type == "gemmoe":
-        patch_remote(model_name, ".configuration_gemmoe", ".modeling_gemmoe")
-    elif model_type == "jamba":
-        patch_remote(model_name, ".configuration_jamba", ".modeling_jamba")
-    elif model_type == "deepseek_v2":
-        patch_remote(model_name, ".configuration_deepseek", ".modeling_deepseek")
 
 
 def patch_remote(model_name, config_name, modeling_name):

src/axolotl/monkeypatch/unsloth_.py

@@ -99,48 +99,51 @@ def check_self_attn_is_patchable() -> bool:
     return ORIGINAL_QKV_CODE in qkv and ORIGINAL_O_CODE in qkv
 
 
-def integrate_cross_entropy_loss_patch():
-    forward = get_forward_code()
-    LlamaForCausalLM._original_forward = forward  # pylint: disable=protected-access
-    forward, _ = detab_code(forward)
-    assert ORIGINAL_CEL_CODE in forward, "Original forward code not found"
+def integrate_cross_entropy_loss_patch(model_type: str = "llama") -> None:
+    if model_type == "llama":
+        forward = get_forward_code()
+        LlamaForCausalLM._original_forward = forward  # pylint: disable=protected-access
+        forward, _ = detab_code(forward)
+        assert ORIGINAL_CEL_CODE in forward, "Original forward code not found"
+
+        forward = forward.replace(
+            "@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)", ""
+        )
+        forward = forward.replace(
+            "@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)",
+            "",
+        )
+        forward = forward.replace(ORIGINAL_CEL_CODE, PATCHED_CEL_CODE)
+        forward = forward.replace(
+            "def forward(",
+            "def fast_cross_entropy_loss_forward(",
+            1,
+        )
 
-    forward = forward.replace(
-        "@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)", ""
-    )
-    forward = forward.replace(
-        "@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)",
-        "",
-    )
-    forward = forward.replace(ORIGINAL_CEL_CODE, PATCHED_CEL_CODE)
-    forward = forward.replace(
-        "def forward(",
-        "def fast_cross_entropy_loss_forward(",
-        1,
-    )
+        # load imports necessary
+        import transformers.models.llama.modeling_llama
 
-    # 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)
 
-    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 unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss",
-        globals(),
-    )
+        exec(  # pylint: disable=exec-used  # nosec B102
+            "from unsloth.kernels.cross_entropy_loss import fast_cross_entropy_loss",
+            globals(),
+        )
 
-    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 unsloth fast_cross_entropy_loss", main_process_only=True)
-    LlamaForCausalLM.forward = fast_cross_entropy_loss_forward  # pylint: disable=undefined-variable  # noqa: F821
+        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 unsloth fast_cross_entropy_loss", main_process_only=True)
+        LlamaForCausalLM.forward = fast_cross_entropy_loss_forward  # pylint: disable=undefined-variable  # noqa: F821
+    else:
+        raise ValueError("Unsupported model type")
 
 
 def detab_code(code: str) -> Tuple[str, str]:

src/axolotl/utils/models.py

@@ -367,7 +367,7 @@ def load_model(
                     integrate_cross_entropy_loss_patch,
                 )
 
-                integrate_cross_entropy_loss_patch()
+                integrate_cross_entropy_loss_patch(model_type="llama")
             if cfg.unsloth_lora_qkv or cfg.unsloth_lora_o:
                 from axolotl.monkeypatch.unsloth_ import patch_self_attn_lora
 
@@ -424,31 +424,20 @@ def load_model(
         if cfg.unsloth_cross_entropy_loss:
             from axolotl.monkeypatch.unsloth_ import integrate_cross_entropy_loss_patch
 
-            integrate_cross_entropy_loss_patch()
+            integrate_cross_entropy_loss_patch(model_type="llama")
 
         if cfg.unsloth_lora_qkv or cfg.unsloth_lora_o:
             from axolotl.monkeypatch.unsloth_ import patch_self_attn_lora
 
             patch_self_attn_lora()
 
     # Modify mistral derived models
-    if (
-        cfg.model_config_type == "mistral"
-        and cfg.flash_attention
-        and cfg.sample_packing
-    ):
+    if cfg.model_config_type == "mistral" and cfg.flash_attn_cross_entropy_loss:
         from axolotl.monkeypatch.mistral_attn_hijack_flash import (
-            replace_mistral_attn_with_flash_attn,
+            patch_mistral_cross_entropy,
         )
 
-        LOG.info("patching mistral with flash attention")
-        replace_mistral_attn_with_flash_attn(packed=cfg.sample_packing)
-
-    if cfg.is_llama_derived_model and cfg.sample_packing and not inference:
-        from axolotl.monkeypatch.llama_expand_mask import hijack_expand_mask
-
-        LOG.info("patching _expand_mask")
-        hijack_expand_mask()
+        patch_mistral_cross_entropy()
 
     model_kwargs: Dict[str, Any] = {}
 

src/axolotl/utils/trainer.py

@@ -189,9 +189,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
             max_input_len = np.max(get_dataset_lengths(train_dataset))
             LOG.debug(f"max_input_len: {max_input_len}", main_process_only=True)
 
-        if (
-            cfg.is_mistral_derived_model and cfg.flash_attention
-        ) or cfg.model_config_type == "mamba":
+        if cfg.model_config_type == "mamba":
             LOG.info("dropping attention_mask column")
             train_dataset = train_dataset.remove_columns("attention_mask")
             if eval_dataset:

tests/e2e/patched/test_model_patches.py

@@ -4,6 +4,8 @@
 
 import unittest
 
+import transformers
+
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
@@ -87,9 +89,9 @@ def test_mistral_multipack(self, temp_dir):
         normalize_config(cfg)
         cli_args = TrainerCliArgs()
         tokenizer = load_tokenizer(cfg)
-        model, _ = load_model(cfg, tokenizer, inference=cli_args.inference)
+        load_model(cfg, tokenizer, inference=cli_args.inference)
 
         assert (
-            "axolotl.monkeypatch.mistral_attn_hijack_flash"
-            in model.model.layers[0].self_attn.forward.__module__
+            "torch.jit"
+            in transformers.modeling_flash_attention_utils._get_unpad_data.__module__  # pylint: disable=protected-access
         )