fix attribute error

* make sure to download fixtures for kd test

* use same alpaca dataset

cicd/multigpu.sh

@@ -1,13 +1,10 @@
 #!/bin/bash
 set -e
 
-# only run one test at a time so as not to OOM the GPU
-pytest -v  --durations=10 -n2 /workspace/axolotl/tests/e2e/multigpu/ --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/
-pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/solo/
-
 # Only run two tests at a time to avoid OOM on GPU (with coverage collection)
 pytest -v -n2 \
-  --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/
+  --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/ \
+  --ignore=/workspace/axolotl/tests/e2e/multigpu/patched/ \
   /workspace/axolotl/tests/e2e/multigpu/ \
   --cov=axolotl \
   --cov-report=xml:multigpu-coverage.xml
@@ -17,6 +14,11 @@ pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/solo/ \
   --cov-append \
   --cov-report=xml:multigpu-coverage.xml
 
+pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/patched/ \
+  --cov=axolotl \
+  --cov-append \
+  --cov-report=xml:multigpu-coverage.xml
+
 # Upload coverage to Codecov
 if [ -f multigpu-coverage.xml ]; then
   codecov -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION}

docs/config.qmd

@@ -55,20 +55,46 @@ overrides_of_model_config:
 overrides_of_model_kwargs:
   # use_cache: False
 
-# optional overrides to the bnb 4bit quantization configuration
-# https://huggingface.co/docs/transformers/main/main_classes/quantization#transformers.BitsAndBytesConfig
-bnb_config_kwargs:
-  # These are default values
-  llm_int8_has_fp16_weight: false
-  bnb_4bit_quant_type: nf4
-  bnb_4bit_use_double_quant: true
 
 
+# Quantization configuration.
+quantization:
+  backend: bnb | hqq | gptq
+  bits: 8
+  # optional overrides to the bnb 4bit quantization configuration
+  # https://huggingface.co/docs/transformers/main/main_classes/quantization#transformers.BitsAndBytesConfig
+  bnb_config_kwargs:
+    # These are default values
+    llm_int8_has_fp16_weight: false
+    bnb_4bit_quant_type: nf4
+    bnb_4bit_use_double_quant: true
+
+  # If using hqq config, additional config paramters are needed. See: https://huggingface.co/docs/transformers/main/en//quantization/hqq
+  hqq_config:
+    # pick one of the following, depending on if you want to uniformly quantize the whole model or
+    # apply different quantization settings to specific layers in the model:
+
+    # if uniformly quantize the whole model:
+    group_size: 64
+    # if we want to invoke dynamic_config in order to apply specific layers with different quantization settings:
+    - nbits: 4
+      group_size: 64
+      target_modules:
+        - self_attn.k_proj
+        - self_attn.v_proj
+        - self_attn.o_proj
+    - nbits: 3
+      group_size: 32
+      target_modules:
+        - mlp.gate_proj
+        - mlp.up_proj
+        - mlp.down_proj
+
+# (Internal Use Only)
 # Whether you are training a 4-bit GPTQ quantized model
-gptq: true
+gptq:
-
 # This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
-load_in_8bit: true
+load_in_8bit:
 # Use bitsandbytes 4 bit
 load_in_4bit:
 

requirements.txt

@@ -6,7 +6,7 @@ triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
-liger-kernel==0.5.6
+liger-kernel==0.5.8
 # END section
 
 packaging==23.2
@@ -22,6 +22,7 @@ hf_xet==1.0.0
 
 optimum==1.16.2
 hf_transfer
+hqq==0.2.5
 sentencepiece
 gradio==5.23.3
 

src/axolotl/core/trainer_builder.py

@@ -1040,9 +1040,11 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.dataset_processes:
             training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
 
-        if (self.cfg.trl and self.cfg.trl.beta) or self.cfg.rl_beta:
+        if self.cfg.trl and self.cfg.trl.beta is not None:
-            training_args_kwargs["beta"] = self.cfg.trl.beta or self.cfg.rl_beta
+            training_args_kwargs["beta"] = self.cfg.trl.beta
-        if self.cfg.orpo_alpha:
+        elif self.cfg.rl_beta is not None:
+            training_args_kwargs["beta"] = self.cfg.rl_beta
+        elif self.cfg.orpo_alpha is not None:
             # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
             training_args_kwargs["beta"] = self.cfg.orpo_alpha
 

src/axolotl/integrations/liger/README.md

@@ -25,7 +25,7 @@ liger_fused_linear_cross_entropy: true
 - deepseek_v2
 - gemma
 - gemma2
-- gemma3 (partial support, no support for FLCE yet)
+- gemma3
 - granite
 - jamba
 - llama

src/axolotl/integrations/liger/__init__.py

@@ -21,7 +21,6 @@ It is designed to be performant, correct, and light-weight.
 import inspect
 import logging
 import sys
-from functools import partial
 
 from axolotl.integrations.base import BasePlugin
 
@@ -55,7 +54,6 @@ class LigerPlugin(BasePlugin):
             )
         from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
         from liger_kernel.transformers.functional import liger_cross_entropy
-        from liger_kernel.transformers.geglu import LigerGEGLUMLP
         from liger_kernel.transformers.layer_norm import LigerLayerNorm
         from liger_kernel.transformers.monkey_patch import MODEL_TYPE_TO_APPLY_LIGER_FN
         from liger_kernel.transformers.rms_norm import LigerRMSNorm
@@ -141,38 +139,6 @@ class LigerPlugin(BasePlugin):
                 modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward
-        elif cfg.model_config_type in ["gemma3", "gemma3_text"]:
-            from transformers.models.gemma3 import modeling_gemma3
-
-            if cfg.liger_rope:
-                modeling_gemma3.apply_rotary_pos_emb = liger_rotary_pos_emb
-            if cfg.liger_rms_norm:
-
-                def _liger_rms_norm_wrapper(dim, **kwargs):
-                    "Convert 'dim' keyword to 'hidden_size' to pass to LigerRMSNorm"
-                    return LigerRMSNorm(hidden_size=dim, **kwargs)
-
-                modeling_gemma3.Gemma3RMSNorm = partial(
-                    _liger_rms_norm_wrapper,
-                    offset=1.0,
-                    casting_mode="gemma",
-                    init_fn="zeros",
-                    in_place=False,
-                )
-            if cfg.liger_glu_activation:
-                modeling_gemma3.Gemma3MLP = LigerGEGLUMLP
-            if cfg.liger_layer_norm:
-                modeling_gemma3.nn.LayerNorm = LigerLayerNorm
-
-            if cfg.liger_cross_entropy:
-                from transformers.loss.loss_utils import nn
-
-                nn.functional.cross_entropy = liger_cross_entropy
-
-            if cfg.liger_fused_linear_cross_entropy:
-                raise NotImplementedError(
-                    "Fused linear cross entropy is not yet supported for Gemma3."
-                )
         elif cfg.model_config_type == "llama4":
             from axolotl.integrations.liger.models.llama4 import (
                 apply_liger_kernel_to_llama4,

src/axolotl/utils/config/__init__.py

@@ -236,6 +236,18 @@ def normalize_config(cfg):
 
     log_gpu_memory_usage(LOG, "baseline", cfg.device)
 
+    if cfg.quantization:
+        if cfg.quantization.backend in ["bnb"]:
+            if cfg.quantization.bits == 8:
+                cfg.load_in_8bit = True
+            elif cfg.quantization.bits == 4:
+                cfg.load_in_4bit = True
+
+        if cfg.quantization.backend == "gptq":
+            cfg.gptq = True
+        elif cfg.quantization.backend == "hqq":
+            cfg.hqq = True
+
 
 def normalize_cfg_datasets(cfg):
     """

src/axolotl/utils/data/sft.py

@@ -3,6 +3,7 @@
 import functools
 import logging
 import os
+import tempfile
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
@@ -117,9 +118,27 @@ def prepare_dataset(cfg, tokenizer, processor=None, preprocess_iterable=None):
             cfg.pretraining_dataset[0]["type"] or "pretrain",
         )
 
-        iter_ds = load_dataset(
+        # when letting accelerator dispatch batches from the main process, we don't need to load the dataset from
-            path, streaming=True, split=split, name=name, data_files=data_files
+        # other ranks, we just need to present a fake dataset
-        )
+        if (
+            cfg.accelerator_config
+            and cfg.accelerator_config.dispatch_batches
+            and not is_local_main_process()
+        ):
+            with tempfile.NamedTemporaryFile(mode="w+", delete=False) as f:
+                f.write("text\n")
+                f.write("lorem ipsum dolor sit amet\n")
+                # rewind the file pointer to the beginning so we can read it again
+                f.seek(0)
+                iter_ds = load_dataset(
+                    "csv", data_files=f.name, split="train", streaming=True
+                )
+        else:
+            if is_local_main_process():
+                iter_ds = load_dataset(
+                    path, streaming=True, split=split, name=name, data_files=data_files
+                )
+
         if skip:
             LOG.info(f"Skipping {skip} samples from the dataset")
             iter_ds = iter_ds.skip(skip)

src/axolotl/utils/models.py

@@ -36,6 +36,7 @@ from transformers import (
     BitsAndBytesConfig,
     Gemma3ForConditionalGeneration,
     GPTQConfig,
+    HqqConfig,
     Llama4ForConditionalGeneration,
     LlavaForConditionalGeneration,
     Mistral3ForConditionalGeneration,
@@ -833,6 +834,13 @@ class ModelLoader:
             del self.model_kwargs["device_map"]
 
     def set_quantization_config(self) -> None:
+        if (
+            (not self.cfg.quantization)
+            and (not self.cfg.load_in_8bit)
+            and (not self.cfg.load_in_4bit)
+            and not self.cfg.gptq
+        ):
+            return
         self.model_kwargs["load_in_8bit"] = self.cfg.load_in_8bit
         self.model_kwargs["load_in_4bit"] = self.cfg.load_in_4bit
 
@@ -854,21 +862,21 @@ class ModelLoader:
             and hasattr(self.model_config, "quantization_config")
             and self.model_config.quantization_config["quant_method"]
             in ["gptq", "awq", "bitsandbytes"]
+            and not self.cfg.hqq
         ):
-            if self.model_config.quantization_config["quant_method"] == "gptq":
+            quant_config_class_dict = {
-                self.model_kwargs["quantization_config"] = GPTQConfig(
+                "gptq": GPTQConfig,
-                    **self.model_config.quantization_config
+                "awq": AwqConfig,
-                )
+                "bitsandbytes": BitsAndBytesConfig,
-            elif self.model_config.quantization_config["quant_method"] == "awq":
+            }
-                self.model_kwargs["quantization_config"] = AwqConfig(
+
-                    **self.model_config.quantization_config
+            quant_config_class = quant_config_class_dict[
-                )
+                self.model_config.quantization_config["quant_method"]
-            elif (
+            ]
-                self.model_config.quantization_config["quant_method"] == "bitsandbytes"
+            self.model_kwargs["quantization_config"] = quant_config_class(
-            ):
+                **self.model_config.quantization_config
-                self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
+            )
-                    **self.model_config.quantization_config
+
-                )
         elif self.cfg.adapter == "qlora" and self.model_kwargs["load_in_4bit"]:
             bnb_config = {
                 "load_in_4bit": True,
@@ -886,8 +894,8 @@ class ModelLoader:
                 # but deepspeed needs this still in bfloat16
                 bnb_config["bnb_4bit_quant_storage"] = torch.float32
 
-            if self.cfg.bnb_config_kwargs:
+            if self.cfg.quantization and self.cfg.quantization.bnb_config_kwargs:
-                bnb_config.update(self.cfg.bnb_config_kwargs)
+                bnb_config.update(self.cfg.quantization.bnb_config_kwargs)
 
             self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
                 **bnb_config,
@@ -903,6 +911,13 @@ class ModelLoader:
                 **bnb_config,
             )
 
+        if self.cfg.hqq:
+            from axolotl.utils.schemas.quant import get_hqq_quant_config_kwargs
+
+            self.model_kwargs["quantization_config"] = HqqConfig(
+                **get_hqq_quant_config_kwargs(self.cfg)
+            )
+
         # no longer needed per https://github.com/huggingface/transformers/pull/26610
         if "quantization_config" in self.model_kwargs or self.cfg.gptq:
             self.model_kwargs.pop("load_in_8bit", None)
@@ -1036,6 +1051,12 @@ class ModelLoader:
                         config=self.model_config,
                     )
             else:
+                if self.cfg.hqq and torch.cuda.device_count() < 2:
+                    # for some reason on single gpu, we need to set device_map to auto/cuda
+                    # otherwise you run into tensors on two devices error during training
+                    # Doesn't affect multi-gpu tho
+
+                    self.model_kwargs["device_map"] = "auto"
                 self.model = self.auto_model_loader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
@@ -1190,7 +1211,7 @@ class ModelLoader:
         if (
             not skip_prepare_model_for_kbit_training
             and self.cfg.adapter in ["lora", "qlora"]
-            and (self.cfg.load_in_8bit or self.cfg.load_in_4bit)
+            and (self.cfg.load_in_8bit or self.cfg.load_in_4bit or self.cfg.hqq)
         ):
             LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
             self.model = prepare_model_for_kbit_training(
@@ -1460,7 +1481,16 @@ def load_llama_adapter(model, cfg):
 
 
 def find_all_linear_names(model):
-    cls = (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt, torch.nn.Linear)
+    from hqq.core.peft import HQQLinearLoRA
+    from hqq.core.quantize import HQQLinear
+
+    cls = (
+        bnb.nn.Linear4bit,
+        bnb.nn.Linear8bitLt,
+        torch.nn.Linear,
+        HQQLinear,
+        HQQLinearLoRA,
+    )
     lora_module_names = set()
     for name, module in model.named_modules():
         if (

src/axolotl/utils/schedulers.py

@@ -40,7 +40,7 @@ class RexLR(LRScheduler):
         self.max_lr = max_lr
         self.total_steps = total_steps
         self.num_warmup_steps = num_warmup_steps
-        self.last_step = last_step - 1
+        self.last_step = max(last_step - 1, 0)
 
         # Ensure each parameter group has an "initial_lr" key to avoid issues when resuming.
         for group in optimizer.param_groups:

src/axolotl/utils/schemas/config.py

@@ -660,6 +660,7 @@ class AxolotlInputConfig(
             data.get("val_set_size") == 0
             and (data.get("eval_steps") or data.get("eval_strategy"))
             and not data.get("test_datasets")
+            and data.get("eval_strategy") != "no"
         ):
             raise ValueError(
                 "eval_steps and eval_strategy are not supported with val_set_size == 0"

src/axolotl/utils/schemas/peft.py

@@ -1,9 +1,9 @@
 """Pydantic models for PEFT-related configuration"""
 
-from typing import Any
-
 from pydantic import BaseModel, Field, field_validator, model_validator
 
+from axolotl.utils.schemas.quant import QuantizationConfig
+
 
 class LoftQConfig(BaseModel):
     """LoftQ configuration subset"""
@@ -23,8 +23,11 @@ class PeftConfig(BaseModel):
 class LoraConfig(BaseModel):
     """Peft / LoRA configuration subset"""
 
-    load_in_8bit: bool | None = Field(default=False)
+    quantization: QuantizationConfig | None = None
-    load_in_4bit: bool | None = Field(default=False)
+    load_in_4bit: bool | None = None  # for internal use
+    load_in_8bit: bool | None = None  # for internal use
+    hqq: bool | None = None  # for internal use
+    gptq: bool | None = None  # for internal use
 
     adapter: str | None = None
     lora_model_dir: str | None = None
@@ -50,8 +53,6 @@ class LoraConfig(BaseModel):
         },
     )
     lora_on_cpu: bool | None = None
-    gptq: bool | None = None
-    bnb_config_kwargs: dict[str, Any] | None = None
 
     loraplus_lr_ratio: float | None = Field(
         default=None,
@@ -74,11 +75,11 @@ class LoraConfig(BaseModel):
         if (
             not data.get("adapter")
             and not data.get("inference")
-            and (data.get("load_in_8bit") or data.get("load_in_4bit"))
+            and (data.get("quantization"))
         ):
             raise ValueError(
-                "load_in_8bit and load_in_4bit are not supported without setting an adapter for training."
+                "Quantization is not supported without setting an adapter."
-                "If you want to full finetune, please turn off load_in_8bit and load_in_4bit."
+                "If you want to full finetune, please turn off Quantization."
             )
         return data
 
@@ -86,25 +87,26 @@ class LoraConfig(BaseModel):
     def validate_qlora(self):
         if self.adapter == "qlora":
             if self.merge_lora:
-                # can't merge qlora if loaded in 8bit or 4bit
+                if self.quantization.bits == 8 or self.load_in_8bit:
-                if self.load_in_8bit:
                     raise ValueError("Can't merge qlora if loaded in 8bit")
 
-                if self.gptq:
+                if self.quantization.backend == "gptq":
-                    raise ValueError("Can't merge qlora if gptq")
+                    raise ValueError("Can't merge qlora if using gptq")
 
-                if self.load_in_4bit:
+                if self.quantization.bits == 4 or self.load_in_4bit:
                     raise ValueError("Can't merge qlora if loaded in 4bit")
 
             else:
-                if self.load_in_8bit:
+                if self.quantization:
-                    raise ValueError("Can't load qlora in 8bit")
+                    if self.quantization.bits == 8 or self.load_in_8bit:
+                        raise ValueError("Can't load qlora in 8bit")
 
-                if self.gptq:
+                    if self.quantization.backend == "gptq":
-                    raise ValueError("Can't load qlora if gptq")
+                        raise ValueError("Can't load qlora if using gptq")
+
+                    if not self.quantization.bits == 4 or self.load_in_4bit:
+                        raise ValueError("Require quantization.bits <= 4 for qlora")
 
-                if not self.load_in_4bit:
-                    raise ValueError("Require cfg.load_in_4bit to be True for qlora")
         return self
 
     @field_validator("loraplus_lr_embedding")
@@ -121,6 +123,24 @@ class LoraConfig(BaseModel):
             data["lora_dropout"] = 0.0
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def validate_hqq(cls, data):
+        if (
+            data.get("quantization")
+            and data.get("quantization").get("backend") == "hqq"
+        ):
+            if not data.get("quantization").get("hqq_config"):
+                raise ValueError(
+                    "If using HQQ, must set `hqq_config` under `quantization`"
+                )
+
+            if data.get("load_in_4bit") or data.get("load_in_8bit"):
+                raise ValueError(
+                    "If using HQQ quantization, please remove load_in_4bit or load_in_8bit"
+                )
+        return data
+
 
 class ReLoRAConfig(BaseModel):
     """ReLoRA configuration subset"""

src/axolotl/utils/schemas/quant.py

@@ -0,0 +1,93 @@
+""" "
+Takes care of quantization configuration
+"""
+
+from typing import Annotated, Any, Literal
+
+from annotated_types import MinLen
+from pydantic import BaseModel, Field, model_validator
+
+
+class HQQConfig(BaseModel):
+    """HQQ configuration subset"""
+
+    nbits: Literal[8, 4, 3, 2, 1] | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Number of bits for HQQ quantization. 8, 4, 3, 2, or 1."
+        },
+    )
+
+    group_size: int = Field(default=64)
+    target_modules: list[str] | str | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Target modules for HQQ quantization. If not specified, the whole model will be quantized."
+        },
+    )
+
+
+class QuantizationConfig(BaseModel):
+    """Over all Quantization configuration subset"""
+
+    # We will use this class as base future refactoring of all quantization configs
+    backend: Literal["bnb", "hqq", "gptq"] | None = None
+    bits: Literal[8, 4, 3, 2, 1] | None = None
+    bnb_config_kwargs: dict[str, Any] | None = None
+    hqq_config: HQQConfig | Annotated[list[HQQConfig], MinLen(1)] | None = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_hqq_config(cls, data):
+        if data.get("backend") == "hqq" and not data.get("hqq_config"):
+            raise ValueError("If using HQQ, must set `group_size` under `hqq_config`")
+
+        if data.get("hqq_config") and len(data.get("hqq_config")) > 1:
+            for hqq_config in data.get("hqq_config"):
+                if hqq_config.get("target_modules") is None:
+                    raise ValueError(
+                        "For list of hqq configs, `target_modules` must be specified for each"
+                    )
+
+        return data
+
+
+def get_hqq_quant_config_kwargs(cfg):
+
+    # If no target module is specified, then target the whole model
+    if not isinstance(cfg.quantization.hqq_config, list):
+        cfg.quantization.hqq_config = [cfg.quantization.hqq_config]
+
+    if (
+        len(cfg.quantization.hqq_config) == 1
+        and cfg.quantization.hqq_config[0].target_modules is None
+    ):
+
+        nbits = (
+            cfg.quantization.hqq_config[0].nbits
+            if cfg.quantization.hqq_config[0].nbits is not None
+            else cfg.quantization.bits
+        )
+
+        return {
+            "nbits": nbits,
+            "group_size": cfg.quantization.hqq_config[0].group_size,
+        }
+
+    hqq_quant_config_kwargs = {"dynamic_config": {}}
+    for hqq_config in cfg.quantization.hqq_config:
+        nbits = (
+            hqq_config.nbits if hqq_config.nbits is not None else cfg.quantization.bits
+        )
+
+        target_modules = hqq_config.target_modules
+        if not isinstance(target_modules, list):
+            target_modules = [target_modules]
+
+        for module in target_modules:
+            hqq_quant_config_kwargs["dynamic_config"][module] = {
+                "nbits": nbits,
+                "group_size": hqq_config.group_size,
+            }
+
+    return hqq_quant_config_kwargs

tests/conftest.py

@@ -193,6 +193,14 @@ def download_tiny_shakespeare_dataset():
     snapshot_download_w_retry("winglian/tiny-shakespeare", repo_type="dataset")
 
 
+@pytest.fixture(scope="session", autouse=True)
+def download_evolkit_kd_sample_dataset():
+    # download the dataset
+    snapshot_download_w_retry(
+        "axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample", repo_type="dataset"
+    )
+
+
 @pytest.fixture(scope="session", autouse=True)
 def download_deepseek_model_fixture():
     snapshot_download_w_retry("axolotl-ai-co/DeepSeek-V3-11M", repo_type="model")
@@ -208,6 +216,16 @@ def download_huggyllama_model_fixture():
     )
 
 
+@pytest.fixture(scope="session", autouse=True)
+def download_llama33_70b_model_fixture():
+    # download the tokenizer only
+    snapshot_download_w_retry(
+        "axolotl-ai-co/Llama-3.3-70B-Instruct-tokenizer",
+        repo_type="model",
+        allow_patterns=["*token*", "config.json"],
+    )
+
+
 @pytest.fixture(scope="session", autouse=True)
 def download_llama_1b_model_fixture():
     # download the tokenizer only
@@ -315,6 +333,14 @@ def download_llama2_model_fixture():
     )
 
 
+@pytest.fixture(scope="session", autouse=True)
+def download_llama32_1b_model_fixture():
+    snapshot_download_w_retry(
+        "osllmai-community/Llama-3.2-1B",
+        repo_type="model",
+    )
+
+
 @pytest.fixture
 @enable_hf_offline
 def tokenizer_huggyllama(

tests/e2e/multigpu/patched/test_sp.py

@@ -10,7 +10,7 @@ from transformers.testing_utils import get_torch_dist_unique_port
 
 from axolotl.utils.dict import DictDefault
 
-from ..utils import check_tensorboard
+from ...utils import check_tensorboard
 
 os.environ["WANDB_DISABLED"] = "true"
 

tests/e2e/multigpu/solo/__init__.py

@@ -0,0 +1,2 @@
+# Tests under this directory should get run "solo" on their own as they
+# seem to cause issues when run in the same batch as other tests.

tests/e2e/multigpu/solo/test_flex.py

@@ -49,8 +49,9 @@ class TestPackedFlex:
                 },
                 "datasets": [
                     {
-                        "path": "vicgalle/alpaca-gpt4",
+                        "path": "tatsu-lab/alpaca",
                         "type": "alpaca",
+                        "split": "train[:10%]",
                     },
                 ],
                 "num_epochs": 1,

tests/e2e/multigpu/test_eval.py

@@ -30,8 +30,10 @@ class TestMultiGPUEval:
         cfg = DictDefault(
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "load_in_8bit": False,
+                "quantization": {
-                "load_in_4bit": True,
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "strict": False,
                 "sequence_len": 2048,
                 "adapter": "qlora",
@@ -99,8 +101,10 @@ class TestMultiGPUEval:
         cfg = DictDefault(
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "load_in_8bit": False,
+                "quantization": {
-                "load_in_4bit": True,
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "strict": False,
                 "sequence_len": 2048,
                 "adapter": "qlora",

tests/e2e/multigpu/test_llama.py

@@ -171,7 +171,10 @@ class TestMultiGPULlama:
                 "sample_packing": False,
                 "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
-                "load_in_8bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 8,
+                },
                 "adapter": "lora",
                 "lora_r": 8,
                 "lora_alpha": 16,
@@ -249,7 +252,10 @@ class TestMultiGPULlama:
                 "sample_packing": False,
                 "eval_sample_packing": False,
                 "pad_to_sequence_len": True,
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "adapter": "qlora",
                 "lora_r": 8,
                 "lora_alpha": 16,
@@ -548,7 +554,10 @@ class TestMultiGPULlama:
                 "base_model": "axolotl-ai-co/SmolLM2-135M-bnb-nf4-bf16",
                 "adapter": "qlora",
                 "mean_resizing_embeddings": True,
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "lora_r": 8,
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
@@ -648,7 +657,10 @@ class TestMultiGPULlama:
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
             }
         else:
             adapter = {}
@@ -722,7 +734,10 @@ class TestMultiGPULlama:
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
             }
         else:
             adapter = {}
@@ -796,7 +811,10 @@ class TestMultiGPULlama:
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
             }
         else:
             adapter = {}

tests/e2e/multigpu/test_qwen2.py

@@ -28,7 +28,10 @@ class TestMultiGPUQwen2:
         cfg = DictDefault(
             {
                 "base_model": base_model,
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "rl": "dpo",
                 "chat_template": "chatml",
                 "sequence_len": 2048,

tests/e2e/patched/test_falcon_samplepack.py

@@ -32,7 +32,10 @@ class TestFalconPatched(unittest.TestCase):
                 "flash_attention": True,
                 "sample_packing": True,
                 "sequence_len": 2048,
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "adapter": "qlora",
                 "lora_r": 16,
                 "lora_alpha": 32,

tests/e2e/patched/test_lora_llama_multipack.py

@@ -89,6 +89,9 @@ class TestLoraLlama(unittest.TestCase):
                 "sequence_len": 1024,
                 "sample_packing": True,
                 "flash_attention": True,
+                "quantization": {
+                    "backend": "gptq",
+                },
                 "load_in_8bit": True,
                 "adapter": "lora",
                 "gptq": True,

tests/e2e/patched/test_mixtral_samplepack.py

@@ -33,7 +33,10 @@ class TestMixtral(unittest.TestCase):
                 "flash_attention": True,
                 "sample_packing": True,
                 "sequence_len": 2048,
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "adapter": "qlora",
                 "lora_r": 16,
                 "lora_alpha": 32,

tests/e2e/patched/test_resume.py

@@ -46,8 +46,9 @@ class TestResumeLlama:
                 },
                 "datasets": [
                     {
-                        "path": "vicgalle/alpaca-gpt4",
+                        "path": "tatsu-lab/alpaca",
                         "type": "alpaca",
+                        "split": "train[:10%]",
                     },
                 ],
                 "num_epochs": 2,

tests/e2e/solo/test_flex.py

@@ -41,8 +41,9 @@ class TestPackedFlex(unittest.TestCase):
                 },
                 "datasets": [
                     {
-                        "path": "vicgalle/alpaca-gpt4",
+                        "path": "tatsu-lab/alpaca",
                         "type": "alpaca",
+                        "split": "train[:10%]",
                     },
                 ],
                 "num_epochs": 1,

tests/e2e/solo/test_relora_llama.py

@@ -34,7 +34,10 @@ class TestReLoraLlama(unittest.TestCase):
                 "sample_packing": True,
                 "pad_to_sequence_len": True,
                 "flash_attention": True,
-                "load_in_8bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 8,
+                },
                 "adapter": "lora",
                 "lora_r": 8,
                 "lora_alpha": 16,

tests/e2e/test_mixtral.py

@@ -35,7 +35,10 @@ class TestMixtral(unittest.TestCase):
                 "tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
                 "flash_attention": True,
                 "sequence_len": 1024,
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "adapter": "qlora",
                 "lora_r": 4,
                 "lora_alpha": 8,
@@ -91,7 +94,10 @@ class TestMixtral(unittest.TestCase):
                 "tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
                 "flash_attention": False,
                 "sequence_len": 1024,
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "adapter": "qlora",
                 "lora_r": 4,
                 "lora_alpha": 8,

tests/e2e/test_packing_loss.py

@@ -40,8 +40,9 @@ class TestPackedLlama(unittest.TestCase):
                 },
                 "datasets": [
                     {
-                        "path": "vicgalle/alpaca-gpt4",
+                        "path": "tatsu-lab/alpaca",
                         "type": "alpaca",
+                        "split": "train[:10%]",
                     },
                 ],
                 "num_epochs": 1,

tests/e2e/test_quantization.py

@@ -0,0 +1,141 @@
+"""
+E2E tests for training with quantized model
+"""
+
+import logging
+import os
+import unittest
+
+from transformers.utils import is_torch_bf16_gpu_available
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import check_tensorboard, with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestHQQ(unittest.TestCase):
+    """
+    Test cases for training of HQQ-quantized llama models"""
+
+    @with_temp_dir
+    def test_hqq_lora(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "use_hqq": True,
+                "hqq_config": [
+                    {
+                        "nbits": 8,
+                        "group_size": 64,
+                    }
+                ],
+                "adapter": "lora",
+                "lora_r": 16,
+                "lora_alpha": 32,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.0,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "vicgalle/alpaca-gpt4",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "max_steps": 5,
+                "use_tensorboard": True,
+            }
+        )
+        if is_torch_bf16_gpu_available():
+            cfg.bf16 = True
+        else:
+            cfg.fp16 = True
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
+        )
+
+    @with_temp_dir
+    def test_hqq_qlora(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "use_hqq": True,
+                "hqq_config": [
+                    {
+                        "nbits": 4,
+                        "group_size": 64,
+                    }
+                ],
+                "adapter": "qlora",
+                "lora_r": 16,
+                "lora_alpha": 32,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.0,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "vicgalle/alpaca-gpt4",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "max_steps": 5,
+                "use_tensorboard": True,
+            }
+        )
+        if is_torch_bf16_gpu_available():
+            cfg.bf16 = True
+        else:
+            cfg.fp16 = True
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
+        )

tests/patched/test_validation.py

@@ -74,7 +74,11 @@ class TestValidation(BaseValidation):
                 "deepspeed": "deepspeed_configs/zero3_bf16.json",
                 "gradient_checkpointing": True,
                 "gradient_checkpointing_kwargs": {"use_reentrant": False},
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
+                # "load_in_4bit": True
                 "adapter": "qlora",
             }
             | minimal_cfg
@@ -93,7 +97,10 @@ class TestValidation(BaseValidation):
                 "deepspeed": "",
                 "gradient_checkpointing": True,
                 "gradient_checkpointing_kwargs": {"use_reentrant": False},
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "adapter": "qlora",
             }
             | minimal_cfg
@@ -107,7 +114,10 @@ class TestValidation(BaseValidation):
                 "deepspeed": None,
                 "gradient_checkpointing": True,
                 "gradient_checkpointing_kwargs": {"use_reentrant": False},
-                "load_in_4bit": True,
+                "quantization": {
+                    "backend": "bnb",
+                    "bits": 4,
+                },
                 "adapter": "qlora",
             }
             | minimal_cfg
@@ -306,7 +316,10 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "load_in_8bit": True,
+                    "quantization": {
+                        "backend": "bnb",
+                        "bits": 8,
+                    },
                 }
             )
             | base_cfg
@@ -318,7 +331,9 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "gptq": True,
+                    "quantization": {
+                        "backend": "gptq",
+                    },
                 }
             )
             | base_cfg
@@ -330,19 +345,24 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "load_in_4bit": False,
+                    "quantization": {
+                        "bits": None,
+                    },
                 }
             )
             | base_cfg
         )
 
-        with pytest.raises(ValueError, match=r".*4bit.*"):
+        with pytest.raises(ValueError, match=r".*bits <= 4*"):
             validate_config(cfg)
 
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "load_in_4bit": True,
+                    "quantization": {
+                        "backend": "bnb",
+                        "bits": 4,
+                    },
                 }
             )
             | base_cfg
@@ -364,7 +384,10 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "load_in_8bit": True,
+                    "quantization": {
+                        "backend": "bnb",
+                        "bits": 8,
+                    },
                 }
             )
             | base_cfg
@@ -376,7 +399,10 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "gptq": True,
+                    "quantization": {
+                        "backend": "gptq",
+                        "bits": 4,
+                    },
                 }
             )
             | base_cfg
@@ -388,7 +414,9 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(  # pylint: disable=unsupported-binary-operation
                 {
-                    "load_in_4bit": True,
+                    "quantization": {
+                        "bits": 4,
+                    },
                 }
             )
             | base_cfg
@@ -976,7 +1004,9 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(
                 {
-                    "load_in_4bit": True,
+                    "quantization": {
+                        "bits": None,
+                    },
                 }
             )
             | minimal_cfg
@@ -984,29 +1014,16 @@ class TestValidation(BaseValidation):
 
         with pytest.raises(
             ValueError,
-            match=r".*load_in_8bit and load_in_4bit are not supported without setting an adapter.*",
+            match=r"Quantization is not supported without setting an adapter.*",
         ):
             validate_config(cfg)
 
         cfg = (
             DictDefault(
                 {
-                    "load_in_8bit": True,
+                    "quantization": {
-                }
+                        "bits": 4,
-            )
+                    },
-            | minimal_cfg
-        )
-
-        with pytest.raises(
-            ValueError,
-            match=r".*load_in_8bit and load_in_4bit are not supported without setting an adapter.*",
-        ):
-            validate_config(cfg)
-
-        cfg = (
-            DictDefault(
-                {
-                    "load_in_4bit": True,
                     "adapter": "qlora",
                 }
             )
@@ -1018,7 +1035,9 @@ class TestValidation(BaseValidation):
         cfg = (
             DictDefault(
                 {
-                    "load_in_8bit": True,
+                    "quantization": {
+                        "bits": 8,
+                    },
                     "adapter": "lora",
                 }
             )

tests/utils/test_models.py

@@ -21,8 +21,10 @@ class TestModelsUtils:
                 "base_model": "JackFram/llama-68m",
                 "model_type": "LlamaForCausalLM",
                 "tokenizer_type": "LlamaTokenizer",
-                "load_in_8bit": True,
+                "quantization": {
-                "load_in_4bit": False,
+                    "backend": "bnb",
+                    "bits": 8,
+                },
                 "adapter": "lora",
                 "flash_attention": False,
                 "sample_packing": True,