45adf1bfb9
* get_logger use_environ fix * rethinking * replacing old logger imports * simplify * fix boolean cond
188 lines
8.3 KiB
Python
188 lines
8.3 KiB
Python
# Copyright 2024 Axolotl AI. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Module for the Plugin for LIGER integraton with Axolotl.
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Liger Kernel is the collection of Triton-native kernels for LLM Training.
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It is designed to be performant, correct, and light-weight.
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"""
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import inspect
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import sys
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from axolotl.integrations.base import BasePlugin
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from axolotl.utils.logging import get_logger
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from .args import LigerArgs # pylint: disable=unused-import. # noqa: F401
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from .utils import patch_with_compile_disable
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LOG = get_logger(__name__)
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class LigerPlugin(BasePlugin):
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"""
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Plugin for LIGER integraton with Axolotl.
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"""
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def get_input_args(self):
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return "axolotl.integrations.liger.LigerArgs"
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def pre_model_load(self, cfg):
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if cfg.torch_compile:
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# torch compile will unnecessarily attempt to optimize the triton kernel unless explicitly disabled
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import liger_kernel.ops.fused_linear_cross_entropy
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patch_with_compile_disable(
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liger_kernel.ops.fused_linear_cross_entropy,
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"fused_linear_cross_entropy_forward",
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)
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patch_with_compile_disable(
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liger_kernel.ops.fused_linear_cross_entropy,
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"fused_linear_cross_entropy_backward",
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)
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from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
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from liger_kernel.transformers.functional import liger_cross_entropy
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from liger_kernel.transformers.layer_norm import LigerLayerNorm
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from liger_kernel.transformers.monkey_patch import MODEL_TYPE_TO_APPLY_LIGER_FN
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from liger_kernel.transformers.rms_norm import LigerRMSNorm
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from liger_kernel.transformers.rope import liger_rotary_pos_emb
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from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
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if cfg.liger_cross_entropy and cfg.liger_fused_linear_cross_entropy:
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raise ValueError(
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"Cannot have both `liger_cross_entropy` and `liger_fused_linear_cross_entropy` set."
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)
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if cfg.model_config_type in MODEL_TYPE_TO_APPLY_LIGER_FN:
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apply_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN[cfg.model_config_type]
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liger_fn_sig = inspect.signature(apply_liger_fn)
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kwargs = {}
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if "rope" in liger_fn_sig.parameters:
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kwargs["rope"] = cfg.liger_rope
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if "cross_entropy" in liger_fn_sig.parameters:
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kwargs["cross_entropy"] = cfg.liger_cross_entropy
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if "fused_linear_cross_entropy" in liger_fn_sig.parameters:
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kwargs["fused_linear_cross_entropy"] = (
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cfg.liger_fused_linear_cross_entropy
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)
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if "rms_norm" in liger_fn_sig.parameters:
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kwargs["rms_norm"] = cfg.liger_rms_norm
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if "layer_norm" in liger_fn_sig.parameters:
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kwargs["layer_norm"] = cfg.liger_layer_norm
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if "geglu" in liger_fn_sig.parameters:
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kwargs["geglu"] = cfg.liger_glu_activation
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elif "swiglu" in liger_fn_sig.parameters:
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kwargs["swiglu"] = cfg.liger_glu_activation
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LOG.info(f"Applying LIGER to {cfg.model_config_type} with kwargs: {kwargs}")
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apply_liger_fn(**kwargs)
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elif cfg.model_config_type == "jamba":
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from transformers.models.jamba import modeling_jamba
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from .models.jamba import lce_forward as jamba_lce_forward
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if cfg.liger_rope:
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modeling_jamba.apply_rotary_pos_emb = liger_rotary_pos_emb
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if cfg.liger_rms_norm:
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modeling_jamba.JambaRMSNorm = LigerRMSNorm
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if cfg.liger_glu_activation:
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modeling_jamba.JambaMLP = LigerSwiGLUMLP
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if cfg.liger_layer_norm:
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modeling_jamba.nn.LayerNorm = LigerLayerNorm
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if cfg.liger_cross_entropy:
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from transformers.loss.loss_utils import nn
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nn.functional.cross_entropy = liger_cross_entropy
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if cfg.liger_fused_linear_cross_entropy:
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modeling_jamba.JambaForCausalLM.forward = jamba_lce_forward
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elif cfg.model_config_type == "deepseek_v2":
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from accelerate import init_empty_weights
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from transformers import AutoModelForCausalLM
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with init_empty_weights():
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model = AutoModelForCausalLM.from_pretrained(
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cfg.base_model, trust_remote_code=cfg.trust_remote_code or False
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)
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modeling_mod = sys.modules[model.__class__.__module__]
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from .models.deepseekv2 import lce_forward as deepseekv2_lce_forward
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if cfg.liger_rope:
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# The DeepseekV2 version of RoPE is different than upstream LLaMA.
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# See https://github.com/linkedin/Liger-Kernel/issues/129#issuecomment-2313763528
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LOG.warning("Fused liger_rope is not supported for DeepseekV2.")
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if cfg.liger_glu_activation:
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LOG.warning("liger_glu_activation is not supported for DeepseekV2.")
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if cfg.liger_rms_norm:
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modeling_mod.DeepseekV2RMSNorm = LigerRMSNorm
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if cfg.liger_glu_activation:
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modeling_mod.DeepseekV2MLP.forward = LigerSwiGLUMLP.forward
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if cfg.liger_layer_norm:
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modeling_mod.DeepseekV2MLP.forward = LigerLayerNorm.forward
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if cfg.liger_cross_entropy:
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# We do not patch `nn.functional.cross_entropy` for DeepseekV2 as it still uses
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# nn.CrossEntropyLoss in the forward method.
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modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
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if cfg.liger_fused_linear_cross_entropy:
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modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward
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elif cfg.model_config_type == "llama4":
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from axolotl.integrations.liger.models.llama4 import (
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apply_liger_kernel_to_llama4,
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)
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apply_liger_kernel_to_llama4(
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cross_entropy=cfg.liger_cross_entropy,
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fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
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glu_activation=cfg.liger_glu_activation,
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rms_norm=cfg.liger_rms_norm,
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layer_norm=cfg.liger_layer_norm,
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)
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elif cfg.model_config_type == "qwen3":
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from axolotl.integrations.liger.models.qwen3 import (
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apply_liger_kernel_to_qwen3,
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)
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apply_liger_kernel_to_qwen3(
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cross_entropy=cfg.liger_cross_entropy,
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fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
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glu_activation=cfg.liger_glu_activation,
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rms_norm=cfg.liger_rms_norm,
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layer_norm=cfg.liger_layer_norm,
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)
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elif cfg.model_config_type == "qwen3_moe":
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from axolotl.integrations.liger.models.qwen3_moe import (
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apply_liger_kernel_to_qwen3_moe,
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)
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apply_liger_kernel_to_qwen3_moe(
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cross_entropy=cfg.liger_cross_entropy,
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fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
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glu_activation=cfg.liger_glu_activation,
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rms_norm=cfg.liger_rms_norm,
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layer_norm=cfg.liger_layer_norm,
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)
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elif cfg.model_config_type == "granitemoe":
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from liger_kernel.transformers import apply_liger_kernel_to_granite
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apply_liger_kernel_to_granite(
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rope=cfg.liger_rope,
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cross_entropy=cfg.liger_cross_entropy,
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fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
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rms_norm=cfg.liger_rms_norm,
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swiglu=cfg.liger_glu_activation,
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)
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else:
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LOG.warning(
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f"Unsupported model config type: {cfg.model_config_type}. Liger not applied."
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)
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