c67910fa6f
* bump hf deps * upgrade liger-kernel too * install cce from fork for transformers fix * fix reference to vocab size in gemma3 patch * use padding_idx instead of pad_token_id * remove fixed gemma3 patch * use updated cce fork * fix local mllama cce patches w docstring * add test for multipack with trainer setup and fix trainer for trainer refactor upstream * bump modal version * guard for iterable datasetS * mllama model arch layout changed in latest transformers * fix batch sampler with drop_last * fix: address upstream vlm changes for lora * fix: update references to old lora target path * fix: remove mllama fa2 patch due to upstream fix * fix: lora kernel patch path for multimodal models * fix: removed mllama from quarto * run test for came optim on 2.6.0+ * fix fsdp2 patch and remove deprecated patch * make sure to set sequence_parallel_degree for grpo * Add SP test for GRPO * add sp to grpo config for trainer * use reward_funcs as kwarg to grpo trainer * fix the comprehension for reward funcs * reward funcs already passed in as args * init sp_group right before training * fix check for adding models to SP context * make sure to pass args to super * upgrade deepspeed * use updated trl and add reasoning flags for vllm * patch the worker --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
368 lines
14 KiB
Python
368 lines
14 KiB
Python
"""Patch manager class implementation to complement `axolotl.loaders.ModelLoader`.
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Applies pre- and post-model load patches for various fixes and optimizations.
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"""
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import importlib.util
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from functools import cached_property
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import addict
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import transformers
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from transformers import PretrainedConfig, PreTrainedModel
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from axolotl.integrations.base import PluginManager
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from axolotl.monkeypatch.multipack import (
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SUPPORTED_MULTIPACK_MODEL_TYPES,
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patch_for_multipack,
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)
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.logging import get_logger
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LOG = get_logger(__name__)
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PLUGIN_MANAGER = PluginManager.get_instance()
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class PatchManager:
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"""Manages the application of patches during the model loading process."""
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def __init__(
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self,
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cfg: DictDefault,
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model_config: PretrainedConfig | addict.Dict,
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inference: bool = False,
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):
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"""Initialize the `PatchManager`.
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Args:
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cfg: Configuration dictionary with model and training settings.
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model_config: Configuration object for the model.
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inference: Whether the model is being loaded for inference mode.
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"""
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self.cfg = cfg
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self.model_config = model_config
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self.inference = inference
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@cached_property
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def has_flash_attn(self) -> bool:
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"""Check if flash attention is installed."""
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return importlib.util.find_spec("flash_attn") is not None
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def apply_pre_model_load_patches(self):
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"""Apply pre-model load patches based on config."""
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self._apply_flash_attention_patches()
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self._apply_fsdp_patches()
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self._apply_adapter_patches()
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self._apply_flex_attention_patches()
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self._apply_model_specific_patches()
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self._apply_fp8_patches()
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self._apply_flash_attention_peft_patches()
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self._apply_gradient_checkpointing_patches()
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self._patch_attention()
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self._apply_multipack_patches()
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self._patch_loss_llama()
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self._patch_llama_derived_model()
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self._apply_mistral_cross_entropy_patch()
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self._apply_self_attention_lora_patch()
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def apply_post_model_load_patches(self, model: PreTrainedModel):
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"""Apply patches that require the model instance."""
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self._apply_llama_flash_attn_patches(model)
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self._apply_unsloth_patches(model)
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self._apply_lora_kernel_patch(model)
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def _apply_flash_attention_patches(self):
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"""Apply patches related to Flash Attention."""
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if self.cfg.xformers_attention and self.cfg.sample_packing:
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from axolotl.monkeypatch.attention import patch_xformers_attn_over_fa2
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patch_xformers_attn_over_fa2()
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self.cfg.flash_attention = True
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def _apply_fsdp_patches(self):
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"""Apply patches for FSDP configurations."""
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if self.cfg.fsdp_config and str(self.cfg.fsdp_config.fsdp_version) == "2":
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from axolotl.monkeypatch.accelerate.fsdp2 import patch_accelerate_fsdp2
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patch_accelerate_fsdp2()
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def _apply_adapter_patches(self):
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"""Apply patches for adapter configurations."""
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if self.cfg.adapter and self.cfg.embeddings_skip_upcast:
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from axolotl.monkeypatch.peft.utils import patch_peft_prep_code
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patch_peft_prep_code()
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def _apply_flex_attention_patches(self):
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"""Apply patches for flexible attention."""
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if self.cfg.flex_attention:
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from axolotl.monkeypatch.attention.flex_attn import (
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patch_flex_make_mask,
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patch_flex_wrapper,
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)
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flex_attn_compile_kwargs = self.cfg.flex_attn_compile_kwargs or {}
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patch_flex_wrapper(**flex_attn_compile_kwargs)
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patch_flex_make_mask()
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def _apply_model_specific_patches(self):
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"""Apply patches specific to model architectures."""
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if (
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self.cfg.model_config_type == "llama4"
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and self.cfg.llama4_linearized_experts
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):
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from axolotl.monkeypatch.models.llama4.modeling import (
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patch_llama4_linearized_modeling,
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)
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patch_llama4_linearized_modeling()
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def _apply_fp8_patches(self):
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"""Apply patches for FP8 support."""
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if self.cfg.fp8:
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from axolotl.monkeypatch.trainer_accelerator_args import (
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patch_create_accelerate_code_for_fp8,
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)
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patch_create_accelerate_code_for_fp8()
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def _apply_flash_attention_peft_patches(self):
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"""Apply patches for Flash Attention with PEFT."""
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if self.cfg.adapter:
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from axolotl.monkeypatch.transformers_fa_utils import (
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patch_fa_peft_integration,
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)
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patch_fa_peft_integration()
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def _apply_gradient_checkpointing_patches(self):
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"""Apply patches for gradient checkpointing."""
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if self.cfg.gradient_checkpointing in ["unsloth", "offload"]:
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from axolotl.monkeypatch.gradient_checkpointing import (
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hf_grad_checkpoint_offload_wrapper,
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)
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transformers.modeling_utils.checkpoint = hf_grad_checkpoint_offload_wrapper
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if self.cfg.gradient_checkpointing == "offload_disk":
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from axolotl.monkeypatch.gradient_checkpointing import (
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hf_grad_checkpoint_disk_offload_wrapper,
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)
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transformers.modeling_utils.checkpoint = (
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hf_grad_checkpoint_disk_offload_wrapper
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)
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def _apply_mistral_cross_entropy_patch(self):
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"""Apply Mistral cross entropy patch if configured."""
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if (
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self.cfg.model_config_type == "mistral"
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and self.cfg.flash_attn_cross_entropy_loss
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):
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from axolotl.monkeypatch.mistral_attn_hijack_flash import (
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patch_mistral_cross_entropy,
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)
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patch_mistral_cross_entropy()
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def _apply_self_attention_lora_patch(self):
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"""Apply self-attention LoRA patches if configured."""
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if self.cfg.lora_qkv_kernel or self.cfg.lora_o_kernel:
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from axolotl.monkeypatch.lora_kernels import patch_self_attn_lora
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patch_self_attn_lora(self.cfg)
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def _apply_multipack_patches(self):
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"""Apply multipack patches if necessary."""
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if (
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self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES
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and (self.cfg.flash_attention or self.cfg.flex_attention)
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and self.cfg.sample_packing
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):
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# Get automap config if it exists
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auto_map_config = None
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if isinstance(self.model_config, dict) and "auto_map" in self.model_config:
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auto_map_config = self.model_config["auto_map"]
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elif hasattr(self.model_config, "auto_map"):
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auto_map_config = self.model_config.auto_map
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# Determine if the model has remote code
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if auto_map_config is not None:
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has_remote_code = "AutoModelForCausalLM" in auto_map_config
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else:
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has_remote_code = False
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if has_remote_code and self.cfg.trust_remote_code is False:
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# If explicitly set in YAML, prefer that
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has_remote_code = self.cfg.trust_remote_code
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patch_for_multipack(
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self.cfg.model_config_type,
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model_name=self.cfg.base_model,
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has_remote_code=has_remote_code,
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)
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def _patch_attention(self):
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"""Apply attention-specific patches based on model type."""
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if not (self.cfg.flash_attention and hasattr(self.model_config, "model_type")):
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return
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if self.model_config.model_type == "btlm":
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from axolotl.monkeypatch.btlm_attn_hijack_flash import (
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replace_btlm_attn_with_flash_attn,
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)
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replace_btlm_attn_with_flash_attn(self.cfg.base_model)
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if self.model_config.model_type == "stablelm_epoch" and self.cfg.sample_packing:
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from axolotl.monkeypatch.stablelm_attn_hijack_flash import (
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replace_stablelm_attn_with_flash_attn,
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)
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replace_stablelm_attn_with_flash_attn(self.cfg.base_model)
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def _patch_loss_llama(self):
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"""Patch loss functions and other optimizations for LLaMA models."""
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if not self.cfg.is_llama_derived_model:
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return
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if self.cfg.flash_attn_cross_entropy and self.has_flash_attn:
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from axolotl.monkeypatch.llama_attn_hijack_flash import (
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patch_fa_llama_cross_entropy,
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)
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patch_fa_llama_cross_entropy()
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elif self.cfg.unsloth_cross_entropy_loss:
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from axolotl.monkeypatch.unsloth_ import integrate_cross_entropy_loss_patch
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integrate_cross_entropy_loss_patch(model_type="llama")
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if self.cfg.flash_attn_rms_norm and self.has_flash_attn:
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from axolotl.monkeypatch.llama_attn_hijack_flash import patch_llama_rms_norm
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patch_llama_rms_norm()
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elif self.cfg.unsloth_rms_norm:
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from axolotl.monkeypatch.unsloth_ import patch_unsloth_layernorm
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patch_unsloth_layernorm()
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if self.cfg.unsloth_lora_qkv or self.cfg.unsloth_lora_o:
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from axolotl.monkeypatch.unsloth_ import patch_self_attn_lora
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patch_self_attn_lora()
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def _patch_llama_flash_attention(self, packed=False):
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"""Apply Flash Attention patches for LLaMA models."""
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from axolotl.monkeypatch.llama_attn_hijack_flash import (
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replace_llama_attn_with_flash_attn,
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)
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if packed:
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if self.cfg.device not in ["mps", "cpu"] and not self.inference:
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LOG.info("patching with flash attention for sample packing")
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replace_llama_attn_with_flash_attn(
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packed=True,
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cross_entropy=self.cfg.flash_attn_cross_entropy,
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rms_norm=self.cfg.flash_attn_rms_norm,
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)
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elif self.cfg.s2_attention:
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LOG.info("patching w/ flash-enabled, shifted-sparse attention")
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replace_llama_attn_with_flash_attn(
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packed=False,
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cross_entropy=self.cfg.flash_attn_cross_entropy,
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rms_norm=self.cfg.flash_attn_rms_norm,
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use_shifted_sparse_attn=True,
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)
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elif self.cfg.flash_attn_cross_entropy or self.cfg.flash_attn_rms_norm:
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replace_llama_attn_with_flash_attn(
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packed=False,
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cross_entropy=self.cfg.flash_attn_cross_entropy,
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rms_norm=self.cfg.flash_attn_rms_norm,
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)
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def _patch_llama_xformers_attention(self):
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"""Apply xformers attention patches for LLaMA models."""
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from axolotl.monkeypatch.llama_attn_hijack_xformers import (
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hijack_llama_attention,
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)
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LOG.info("Patching with xformers attention...")
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hijack_llama_attention()
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def _patch_llama_sample_packing(self):
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"""Apply sample packing patches for LLaMA models."""
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from axolotl.monkeypatch.llama_patch_multipack import (
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hijack_llama_prepare_4d_mask,
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)
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LOG.info("Patching llama _prepare_4d_causal_attention_mask*...")
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hijack_llama_prepare_4d_mask()
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def _patch_llama_derived_model(self):
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"""Modify all llama derived models in one block."""
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if self.cfg.is_llama_derived_model and not (
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self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES
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and (self.cfg.flash_attention or self.cfg.flex_attention)
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and self.cfg.sample_packing
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):
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if self.cfg.flash_attention:
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self._patch_llama_flash_attention(packed=self.cfg.sample_packing)
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elif self.cfg.xformers_attention:
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self._patch_llama_xformers_attention()
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elif self.cfg.sample_packing:
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self._patch_llama_sample_packing()
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elif self.cfg.s2_attention:
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raise NotImplementedError(
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"Shifted-sparse attention not currently implemented without flash attention."
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)
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def _apply_llama_flash_attn_patches(self, model):
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"""Apply LLaMA-specific flash attention patches."""
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if (
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self.model_config.model_type in ["llama", "llama4"]
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and not self.cfg.trust_remote_code
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and not self.cfg.gptq
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and self.cfg.flash_attention
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and not self.inference
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):
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# TODO(MengqingCao): split these patches seperately
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from axolotl.monkeypatch.llama_attn_hijack_flash import (
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is_xformers_swiglu_available,
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replace_llama_mlp_with_swiglu,
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replace_llama_qkv_with_fused,
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)
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if self.cfg.flash_attn_fuse_mlp and is_xformers_swiglu_available():
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LOG.info("Patching with SwiGLU...")
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replace_llama_mlp_with_swiglu(model)
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if self.cfg.flash_attn_fuse_qkv:
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LOG.info("Patching with fused QKV...")
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replace_llama_qkv_with_fused(model)
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def _apply_unsloth_patches(self, model):
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"""Apply unsloth optimization patches."""
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if self.cfg.unsloth_lora_mlp:
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from axolotl.monkeypatch.unsloth_ import integrate_lora_mlp_patch
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integrate_lora_mlp_patch(peft_model=model)
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if self.cfg.unsloth_lora_qkv or self.cfg.unsloth_lora_o:
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from axolotl.monkeypatch.unsloth_ import integrate_lora_patch
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integrate_lora_patch(peft_model=model, cfg=self.cfg)
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if self.cfg.unsloth_rope:
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from axolotl.monkeypatch.unsloth_ import integrate_rope_embeddings
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integrate_rope_embeddings()
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def _apply_lora_kernel_patch(self, model):
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"""Apply LoRA kernel patches."""
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if (
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self.cfg.lora_mlp_kernel
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or self.cfg.lora_qkv_kernel
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or self.cfg.lora_o_kernel
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):
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from axolotl.monkeypatch.lora_kernels import apply_lora_kernel_patches
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apply_lora_kernel_patches(model=model, cfg=self.cfg)
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