set env vars trainer needs for FSDP

README.md

@@ -375,14 +375,10 @@ dataset_shard_idx:
 sequence_len: 2048
 # max sequence length to concatenate training samples together up to
 # inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
-# FutureWarning: This will soon be DEPRECATED
+# soon to be DEPRECATED
 max_packed_sequence_len: 1024
-# use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
+# use efficient multi-packing with block diagonal attention and per sequence position_ids
 sample_packing:
-# you can set these packing optimizations AFTER starting a training at least once.
-# The trainer will provide recommended values for these values.
-sample_packing_eff_est:
-total_num_tokens:
 
 # if you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
 adapter: lora
@@ -408,12 +404,11 @@ lora_out_dir:
 lora_fan_in_fan_out: false
 
 # wandb configuration if you're using it
-wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
-wandb_project: # your wandb project name
-wandb_entity: # a wandb Team name if using a Team
+wandb_mode:
+wandb_project:
 wandb_watch:
-wandb_run_id: # set the name of your wandb run
-wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
+wandb_run_id:
+wandb_log_model: # 'checkpoint'
 
 # where to save the finished model to
 output_dir: ./completed-model
@@ -428,16 +423,13 @@ learning_rate: 0.00003
 logging_steps:
 save_steps:
 eval_steps:
-save_total_limit:
 
 # save model as safetensors (require safetensors package)
 save_safetensors:
 
 # whether to mask out or include the human's prompt from the training labels
 train_on_inputs: false
-# group similarly sized data to minimize padding
-# may be slower to start, as it must download and sort the entire dataset
-# note that training loss may have an oscillating pattern with this enabled
+# don't use this, leads to wonky training (according to someone on the internet)
 group_by_length: false
 
 # Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
@@ -483,10 +475,6 @@ landmark_attention:
 # xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
 # llama only
 xpos_rope:
-# RoPE Scaling https://github.com/huggingface/transformers/pull/24653
-rope_scaling:
-  type: # linear | dynamic
-  factor: # float
 
 # resume from a specific checkpoint dir
 resume_from_checkpoint:
@@ -518,9 +506,6 @@ torchdistx_path:
 # Set padding for data collator to 'longest'
 collator_pad_to_longest:
 
-# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
-pretraining_dataset:
-
 # Debug mode
 debug:
 
@@ -540,14 +525,7 @@ Run
 accelerate launch scripts/finetune.py configs/your_config.yml
 ```
 
-#### Multi-GPU
-
-You can optionally pre-tokenize dataset with the following before finetuning:
-```bash
-CUDA_VISIBLE_DEVICES="" accelerate ... --prepare_ds_only
-```
-
-##### Config
+#### Multi-GPU Config
 
 - llama FSDP
 ```yaml
@@ -562,18 +540,6 @@ fsdp_config:
 
 - llama Deepspeed: append `ACCELERATE_USE_DEEPSPEED=true` in front of finetune command
 
-##### Weights & Biases Logging
-
-- wandb options
-```yaml
-wandb_mode:
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-```
-
 ### Inference
 
 Pass the appropriate flag to the train command:

examples/cerebras/qlora.yml

@@ -23,7 +23,6 @@ lora_target_modules:
 lora_target_linear:
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -36,7 +35,7 @@ torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0002
 train_on_inputs: false
-group_by_length: false
+group_by_length: true
 bf16: true
 fp16: false
 tf32: true

examples/falcon/config-7b-lora.yml

@@ -24,7 +24,6 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/falcon/config-7b-qlora.yml

@@ -38,7 +38,6 @@ lora_target_linear: true
 lora_fan_in_fan_out:
 
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/falcon/config-7b.yml

@@ -24,7 +24,6 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/gptj/qlora.yml

@@ -20,7 +20,6 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -33,7 +32,7 @@ torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0001
 train_on_inputs: false
-group_by_length: false
+group_by_length: true
 bf16: true
 fp16: false
 tf32: true

examples/gptq-lora-7b/config.yml

@@ -22,7 +22,6 @@ lora_target_modules:
   - v_proj
 lora_fan_in_fan_out: false
 wandb_project: llama-7b-lora-int4
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/jeopardy-bot/config.yml

@@ -18,7 +18,6 @@ lora_dropout:
 lora_target_modules:
 lora_fan_in_fan_out: false
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/llama-2/lora.yml

@@ -26,7 +26,6 @@ lora_target_linear: true
 lora_fan_in_fan_out:
 
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -39,7 +38,7 @@ lr_scheduler: cosine
 learning_rate: 0.0002
 
 train_on_inputs: false
-group_by_length: false
+group_by_length: true
 bf16: true
 fp16: false
 tf32: false

examples/llama-2/qlora.yml

@@ -27,7 +27,6 @@ lora_target_linear: true
 lora_fan_in_fan_out:
 
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -40,7 +39,7 @@ lr_scheduler: cosine
 learning_rate: 0.0002
 
 train_on_inputs: false
-group_by_length: false
+group_by_length: true
 bf16: true
 fp16: false
 tf32: false

examples/mpt-7b/config.yml

@@ -20,7 +20,6 @@ lora_target_modules:
   - v_proj
 lora_fan_in_fan_out: false
 wandb_project: mpt-alpaca-7b
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/openllama-3b/config.yml

@@ -22,7 +22,6 @@ lora_target_modules:
 lora_target_linear:
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/openllama-3b/lora.yml

@@ -28,7 +28,6 @@ lora_target_modules:
   - o_proj
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/openllama-3b/qlora.yml

@@ -22,7 +22,6 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -35,7 +34,7 @@ torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0002
 train_on_inputs: false
-group_by_length: false
+group_by_length: true
 bf16: true
 fp16: false
 tf32: true

examples/pythia-12b/config.yml

@@ -23,7 +23,6 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/pythia/lora.yml

@@ -17,7 +17,6 @@ lora_target_modules:
 lora_target_linear:
 lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/redpajama/config-3b.yml

@@ -21,7 +21,6 @@ lora_target_modules:
   - v_proj
 lora_fan_in_fan_out: false
 wandb_project: redpajama-alpaca-3b
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/replit-3b/config-lora.yml

@@ -20,7 +20,6 @@ lora_target_modules:
   - mlp_down
 lora_fan_in_fan_out:
 wandb_project: lora-replit
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

examples/xgen-7b/xgen-7b-8k-qlora.yml

@@ -37,7 +37,6 @@ lora_target_linear: true
 lora_fan_in_fan_out:
 
 wandb_project:
-wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:

requirements.txt

@@ -1,6 +1,6 @@
 peft @ git+https://github.com/huggingface/peft.git
 transformers @ git+https://github.com/huggingface/transformers.git
-bitsandbytes>=0.41.1
+bitsandbytes>=0.39.0
 accelerate @ git+https://github.com/huggingface/accelerate@2a289f6108e77a77a4efffb3f6316bc98538413b
 addict
 fire
@@ -21,4 +21,3 @@ evaluate==0.4.0
 rouge-score==0.1.2
 scipy
 scikit-learn==1.2.2
-pynvml

scripts/finetune.py

@@ -18,7 +18,6 @@ from optimum.bettertransformer import BetterTransformer
 from transformers import GenerationConfig, TextStreamer
 
 from axolotl.logging_config import configure_logging
-from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.data import load_prepare_datasets, load_pretraining_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import barrier, is_main_process
@@ -269,13 +268,16 @@ def train(
         LOG.info("Finished preparing dataset. Exiting...")
         return
 
-    log_gpu_memory_usage(LOG, "baseline", cfg.device)
-
     # Load the model and tokenizer
-    LOG.info("loading model and (optionally) peft_config...")
-    model, peft_config = load_model(cfg, tokenizer)
-
-    safe_serialization = cfg.save_safetensors is True
+    LOG.info("loading model and peft_config...")
+    model, peft_config = load_model(
+        cfg.base_model,
+        cfg.base_model_config,
+        cfg.model_type,
+        tokenizer,
+        cfg,
+        adapter=cfg.adapter,
+    )
 
     if "merge_lora" in kwargs and cfg.adapter is not None:
         LOG.info("running merge of LoRA with base model")
@@ -284,11 +286,7 @@ def train(
 
         if cfg.local_rank == 0:
             LOG.info("saving merged model")
-            model.save_pretrained(
-                str(Path(cfg.output_dir) / "merged"),
-                safe_serialization=safe_serialization,
-            )
-            tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+            model.save_pretrained(str(Path(cfg.output_dir) / "merged"))
         return
 
     if cfg.inference:
@@ -303,7 +301,7 @@ def train(
         return
 
     if "shard" in kwargs:
-        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+        model.save_pretrained(cfg.output_dir)
         return
 
     trainer = setup_trainer(
@@ -327,7 +325,7 @@ def train(
         def terminate_handler(_, __, model):
             if cfg.flash_optimum:
                 model = BetterTransformer.reverse(model)
-            model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+            model.save_pretrained(cfg.output_dir)
             sys.exit(0)
 
         signal.signal(
@@ -371,13 +369,7 @@ def train(
     elif cfg.local_rank == 0:
         if cfg.flash_optimum:
             model = BetterTransformer.reverse(model)
-
-        if cfg.adapter == "lora" and cfg.relora_steps:
-            model = model.merge_and_unload()
-
-        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
-
-    # trainer.save_model(cfg.output_dir)  # TODO this may be needed for deepspeed to work? need to review another time
+        model.save_pretrained(cfg.output_dir)
 
 
 if __name__ == "__main__":

src/axolotl/datasets.py

@@ -5,7 +5,7 @@ import os
 from typing import List
 
 import torch
-from datasets import Dataset, IterableDataset
+from datasets import IterableDataset
 
 from .prompt_tokenizers import PromptTokenizingStrategy
 
@@ -18,9 +18,9 @@ from .prompt_tokenizers import PromptTokenizingStrategy
 LOG = logging.getLogger("axolotl")
 
 
-class TokenizedPromptDataset(Dataset):
+class TokenizedPromptDataset(IterableDataset):
     """
-    Dataset that returns tokenized prompts from a stream of text files.
+    Iterable dataset that returns tokenized prompts from a stream of text files.
         Args:
             prompt_tokenizer (PromptTokenizingStrategy): The prompt tokenizing method for proccessing the data.
             dataset (dataset.Dataset): Dataset with text files.
@@ -30,18 +30,19 @@ class TokenizedPromptDataset(Dataset):
         self,
         prompt_tokenizer: PromptTokenizingStrategy,
         dataset: IterableDataset,
-        **kwargs,
     ):
         self.prompt_tokenizer = prompt_tokenizer
-        super().__init__(self.process(dataset).data, **kwargs)
+        self.dataset = dataset
 
-    def process(self, dataset):
-        features = dataset.features.keys()
-        num_proc = min(64, os.cpu_count())
-        return dataset.map(
-            self.prompt_tokenizer.tokenize_prompt,
-            num_proc=num_proc,
-            remove_columns=features,
+    def __iter__(self):
+        features = self.dataset.features.keys()
+        num_proc = os.cpu_count()
+        return iter(
+            self.dataset.map(
+                self.prompt_tokenizer.tokenize_prompt,
+                num_proc=num_proc,
+                remove_columns=features,
+            )
         )
 
 

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -7,7 +7,6 @@ from typing import Optional, Tuple
 import torch
 import transformers
 from einops import rearrange
-from flash_attn.bert_padding import pad_input, unpad_input
 
 try:
     from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
@@ -92,8 +91,7 @@ def forward(
             qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
         )
         output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
-    elif position_ids.shape[0] == 1:
-        # special handling using sample packing
+    else:
         qkv = rearrange(qkv, "b s ... -> (b s) ...")
         cu_q_lens, max_s = get_cu_seqlens_from_pos_ids(position_ids)
         cu_q_lens = cu_q_lens.squeeze()
@@ -102,36 +100,6 @@ def forward(
             qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
         )
         output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
-    else:
-        nheads = qkv.shape[-2]
-
-        # pylint: disable=invalid-name
-        x = rearrange(qkv, "b s three h d -> b s (three h d)")
-        x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
-        x_unpad = rearrange(
-            x_unpad,
-            "nnz (three h d) -> nnz three h d",
-            three=3,
-            h=nheads,
-        )
-        output_unpad = flash_attn_varlen_qkvpacked_func(
-            x_unpad,
-            cu_q_lens,
-            max_s,
-            0.0,
-            softmax_scale=None,
-            causal=True,
-        )
-        output = rearrange(
-            pad_input(
-                rearrange(output_unpad, "nnz h d -> nnz (h d)"),
-                indices,
-                bsz,
-                q_len,
-            ),
-            "b s (h d) -> b s h d",
-            h=nheads,
-        )
 
     return (
         self.o_proj(rearrange(output, "b s h d -> b s (h d)")),

src/axolotl/monkeypatch/relora.py

@@ -1,302 +0,0 @@
-# pylint: skip-file
-import glob
-import json
-import logging
-import os.path
-import shutil
-from pathlib import Path
-from typing import Dict, List, Sequence
-
-import bitsandbytes as bnb
-import peft
-import safetensors.torch as st
-import torch
-from torch.optim.lr_scheduler import LRScheduler
-from torch.optim.optimizer import Optimizer
-from transformers import (
-    TrainerCallback,
-    TrainerControl,
-    TrainerState,
-    TrainingArguments,
-)
-from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
-
-from axolotl.utils.dict import DictDefault
-
-LOG = logging.getLogger("axolotl.relora")
-
-
-def reset_optimizer(optimizer: torch.optim.Optimizer):
-    for group in optimizer.param_groups:
-        for param in group["params"]:
-            param_state = optimizer.state[param]
-            for key in param_state:
-                if "qmap" in key:
-                    continue
-                elif key == "step" and isinstance(param_state[key], int):
-                    param_state[key] = 0
-                else:
-                    param_state[key] = torch.zeros_like(param_state[key])
-
-
-class ReLoRACallback(TrainerCallback):
-    def __init__(self, cfg: DictDefault):
-        self.relora_steps = cfg.relora_steps
-        self.cpu_offload = cfg.relora_cpu_offload
-        self.quantised = cfg.load_in_4bit or cfg.load_in_8bit
-        self.last_full_model = cfg.base_model
-
-        assert os.path.exists(
-            self.last_full_model
-        ), "for ReLORA base_model must be a local path"
-
-        self.num_lora_restarts = 0
-        self.need_full_save = False
-
-    def on_step_begin(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        model: peft.LoraModel,
-        optimizer: torch.optim.Optimizer,
-        **_kwargs,
-    ):
-        if state.global_step > 0 and state.global_step % self.relora_steps == 0:
-            checkpoint_folder = os.path.join(
-                args.output_dir,
-                f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
-            )
-
-            with torch.no_grad():
-                merge_and_save(
-                    model,
-                    self.last_full_model,
-                    checkpoint_folder,
-                    reinit=True,
-                    quantized=self.quantised,
-                )
-                reset_optimizer(optimizer)
-
-            if self.quantised:
-                self.last_full_model = checkpoint_folder
-            self.num_lora_restarts += 1
-
-        return control
-
-    def on_save(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        model: peft.LoraModel,
-        **kwargs,
-    ):
-        checkpoint_folder = os.path.join(
-            args.output_dir,
-            f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
-        )
-        if (
-            state.global_step >= self.relora_steps
-            and state.global_step % self.relora_steps != 0
-        ):
-            if self.quantised and self.last_full_model != checkpoint_folder:
-                # ensure the latest full parameter save is in the latest checkpoint
-                # folder, so that automatic pruning of checkpoints does not remove it
-                LOG.info(f"moving last full parameter save to {checkpoint_folder}")
-                chunks = glob.glob(
-                    f"{self.last_full_model}/model*.safetensors"
-                ) + glob.glob(f"{self.last_full_model}/model*.index.json")
-                for path in chunks:
-                    shutil.move(path, checkpoint_folder)
-                self.last_full_model = checkpoint_folder
-            else:
-                model.model.save_pretrained(checkpoint_folder, save_safetensors=True)
-
-        return control
-
-    def on_log(
-        self,
-        _args: TrainingArguments,
-        _state: TrainerState,
-        control: TrainerControl,
-        logs: Dict[str, float],
-        **_kwargs,
-    ):
-        logs["num_lora_restarts"] = self.num_lora_restarts
-        return control
-
-
-class ReLoRAScheduler(LRScheduler):
-    def __init__(
-        self,
-        optimizer: Optimizer,
-        inner_schedule: LRScheduler,
-        relora_steps: int,
-        warmup_steps: int,
-        min_lr_scale: float = 0.001,
-    ) -> None:
-        self.inner_schedule = inner_schedule
-        self.relora_steps = relora_steps
-        self.warmup_steps = warmup_steps
-        self.min_lr_scale = min_lr_scale
-        super().__init__(optimizer, inner_schedule.last_epoch, inner_schedule.verbose)
-
-    def get_lr(self) -> float:
-        self.inner_schedule.last_epoch = self.last_epoch
-
-        original = self.inner_schedule.get_lr()
-        step = self.last_epoch
-        if step < self.relora_steps:
-            scale = 1
-        else:
-            cycle_t = min(1.0, (step % self.relora_steps) / self.warmup_steps)
-            scale = cycle_t * (1 - self.min_lr_scale) + self.min_lr_scale
-        if isinstance(original, Sequence):
-            return [lr * scale for lr in original]
-        else:
-            return original * scale
-
-
-def sharded_paths(path: str, keys: List[str]) -> Dict[str, str]:
-    model_name = "model.safetensors"
-    if not os.path.exists(str(Path(path) / model_name)) and not os.path.exists(
-        str(Path(path) / f"{model_name}.index.json")
-    ):
-        model_name = "pytorch_model.bin"
-
-    index_path = str(Path(path) / f"{model_name}.index.json")
-    if os.path.exists(index_path):
-        data = json.load(open(index_path, "r"))
-        return data["weight_map"]
-    return {key + ".weight": model_name for key in keys}
-
-
-def lora_delta_weight(layer: peft.tuners.lora.LoraLayer) -> torch.Tensor:
-    if isinstance(layer, peft.tuners.lora.Linear8bitLt) or isinstance(
-        layer, peft.tuners.lora.Linear4bit
-    ):
-        adapter = layer.active_adapter
-        return (
-            peft.utils.transpose(
-                layer.lora_B[adapter].weight @ layer.lora_A[adapter].weight,
-                getattr(layer, "fan_in_fan_out", False),
-            )
-            * layer.scaling[adapter]
-        )
-    else:
-        return layer.get_delta_weight()
-
-
-def merge_and_save(
-    model: peft.LoraModel,
-    model_src: str,
-    model_dst: str,
-    reinit: bool = False,
-    quantized: bool = False,
-    cpu_offload: bool = False,
-):
-    key_list = [key for key, _ in model.model.named_modules() if "lora" not in key]
-
-    if not quantized:
-        for key in key_list:
-            try:
-                _parent, target, _target_name = peft.utils._get_submodules(
-                    model.model, key
-                )
-            except AttributeError:
-                continue
-
-            if isinstance(target, peft.tuners.lora.LoraLayer):
-                update = target.get_delta_weight(target.active_adapter).detach()
-                target.weight.data += update
-
-                if reinit:
-                    for adapter_name in target.lora_A:
-                        target.reset_lora_parameters(adapter_name)
-                    for adapter_name in target.lora_embedding_A:
-                        target.reset_lora_parameters(adapter_name)
-        return
-
-    os.makedirs(model_dst, exist_ok=True)
-    shard_paths = sharded_paths(model_src, key_list)
-
-    unique_shards = list(set(shard_paths.values()))
-    for shard_path in unique_shards:
-        out_tensors = {}
-        if shard_path.endswith(".safetensors"):
-            in_tensors = st.load_file(str(Path(model_src) / shard_path))
-        else:
-            in_tensors = torch.load(Path(model_src) / shard_path)
-            if "state_dict" in in_tensors:
-                in_tensors = in_tensors["state_dict"]
-
-        for key in key_list:
-            if (key + ".weight") not in shard_paths or shard_paths[
-                key + ".weight"
-            ] != shard_path:
-                continue
-
-            try:
-                _parent, target, _target_name = peft.utils._get_submodules(
-                    model.model, key
-                )
-            except AttributeError:
-                continue
-
-            if isinstance(target, peft.tuners.lora.LoraLayer):
-                orig_weight = in_tensors[key + ".weight"]
-                old_dev = target.weight.device
-                math_dev = "cpu" if cpu_offload else old_dev
-
-                update = lora_delta_weight(target).detach().to(math_dev)
-                new_weight = orig_weight.to(math_dev) + update
-                out_tensors[key + ".weight"] = new_weight
-
-                if reinit:
-                    for adapter_name in target.lora_A:
-                        target.reset_lora_parameters(adapter_name)
-                    for adapter_name in target.lora_embedding_A:
-                        target.reset_lora_parameters(adapter_name)
-
-                if isinstance(target, peft.tuners.lora.Linear4bit):
-                    target.weight = (
-                        bnb.nn.Params4bit(
-                            new_weight,
-                            requires_grad=False,
-                            compress_statistics=target.weight.compress_statistics,
-                            quant_type=target.weight.quant_type,
-                        )
-                        .cuda(None)
-                        .to(old_dev)
-                    )
-                elif isinstance(target, peft.tuners.lora.Linear8bitLt):
-                    target.weight = (
-                        bnb.nn.Int8Params(new_weight, requires_grad=False)
-                        .cuda(None)
-                        .to(old_dev)
-                    )
-                else:
-                    target.weight.data = new_weight.to(old_dev)
-
-        for key in in_tensors:
-            if key not in out_tensors:
-                out_tensors[key] = in_tensors[key]
-        del in_tensors
-
-        out_shard_name = shard_path
-        if out_shard_name.startswith("pytorch_model"):
-            out_shard_name = (
-                out_shard_name.replace("pytorch_model", "model").rstrip(".bin")
-                + ".safetensors"
-            )
-
-        shard_fn = str(Path(model_dst) / out_shard_name)
-        LOG.info(f"saving tensors to {shard_fn}")
-        st.save_file(out_tensors, shard_fn)
-        del out_tensors
-        torch.cuda.empty_cache()
-
-    if len(unique_shards) > 1:
-        with open(str(Path(model_dst, "model.safetensors.index.json")), "w") as fd:
-            json.dump({"metadata": {}, "weight_map": shard_paths}, fd)

src/axolotl/prompt_strategies/alpaca_w_system.py

@@ -95,9 +95,9 @@ class OpenOrcaSystemDataPrompter(SystemDataPrompter):
             self.turn_format = "### User:\n{instruction}\n\n### Additional Context:\n{input}\n\n### Assistant:\n"
             self.turn_no_input_format = "### User:\n{instruction}\n\n### Assistant:\n"
         if self.prompt_style == PromptStyle.CHAT.value:
-            self.turn_format = "USER: {instruction}\n{input}\nASSISTANT:"
-            self.turn_no_input_format = "USER: {instruction}\nASSISTANT:"
-            self.system_format = "SYSTEM: {system}\n"
+            self.turn_format = "User: {instruction}\n{input}\nAssistant:"
+            self.turn_no_input_format = "User: {instruction}\nAssistant:"
+            self.system_format = "System: {system}\n"
         if self.prompt_style == PromptStyle.CHATML.value:
             self.turn_format = "<|im_start|>user\n{instruction}\n{input}<|im_end|>\n<|im_start|>assistant\n"
             self.turn_no_input_format = (

src/axolotl/prompt_strategies/llama2_chat.py

@@ -29,7 +29,7 @@ from dataclasses import dataclass, field
 from typing import Generator, List, Sequence
 
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
-from axolotl.prompters import IGNORE_TOKEN_ID, SHAREGPT_ASSERTION_FAILED_ROLE
+from axolotl.prompters import IGNORE_TOKEN_ID
 
 
 @dataclass
@@ -190,7 +190,7 @@ class Llama2ChatPrompter:  # pylint: disable=too-few-public-methods
         conv.messages = []  # pylint: disable=R0801
         for j, sentence in enumerate(source):
             role = roles[sentence["from"]]
-            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
+            assert role == conv.roles[j % 2]
             if sentence["value"]:
                 conv.append_message(role, sentence["value"])
         yield conv

src/axolotl/prompters.py

@@ -271,11 +271,6 @@ class Conversation:
         self.messages.append([role, message])
 
 
-SHAREGPT_ASSERTION_FAILED_ROLE = (
-    "Role did not alternate between turns (gpt and human). Please check your data."
-)
-
-
 class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
     """
     A prompter that generates prompts for the ShareGPT
@@ -332,7 +327,7 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
         conv.messages = []
         for j, sentence in enumerate(source):
             role = roles[sentence["from"]]
-            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
+            assert role == conv.roles[j % 2]
             conv.append_message(role, sentence["value"])
 
         for part in conv.get_prompt():

src/axolotl/utils/bench.py

@@ -1,23 +0,0 @@
-"""Benchmarking and measurement utilities"""
-
-import pynvml
-import torch
-
-
-def gpu_memory_usage(device):
-    if isinstance(device, torch.device):
-        device = device.index
-    if isinstance(device, str) and device.startswith("cuda:"):
-        device = int(device[5:])
-
-    # NB torch.cuda.memory_usage returns zero so we use lower level api
-    pynvml.nvmlInit()
-    handle = pynvml.nvmlDeviceGetHandleByIndex(device)
-    info = pynvml.nvmlDeviceGetMemoryInfo(handle)
-    return info.used / 1024.0**3
-
-
-def log_gpu_memory_usage(log, msg, device):
-    log.info(
-        f"GPU memory usage {msg}: {gpu_memory_usage(device):.03f} GB", stacklevel=2
-    )

src/axolotl/utils/callbacks.py

@@ -1,6 +1,5 @@
 """Callbacks for Trainer class"""
 
-import logging
 import os
 
 from optimum.bettertransformer import BetterTransformer
@@ -12,10 +11,6 @@ from transformers import (
 )
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
 
-from axolotl.utils.bench import log_gpu_memory_usage
-
-LOG = logging.getLogger("axolotl.callbacks")
-
 
 class SavePeftModelCallback(TrainerCallback):  # pylint: disable=too-few-public-methods
     """Callback to save the PEFT adapter"""
@@ -33,9 +28,7 @@ class SavePeftModelCallback(TrainerCallback):  # pylint: disable=too-few-public-
         )
 
         peft_model_path = os.path.join(checkpoint_folder, "adapter_model")
-        kwargs["model"].save_pretrained(
-            peft_model_path, save_safetensors=args.save_safetensors
-        )
+        kwargs["model"].save_pretrained(peft_model_path)
 
         return control
 
@@ -74,25 +67,3 @@ class SaveBetterTransformerModelCallback(
             # the trainer will raise an exception since it can't save a BetterTransformer wrapped model
             control.should_save = False
         return control
-
-
-class PrintGPUStatsCallback(
-    TrainerCallback
-):  # pylint: disable=too-few-public-methods disable=unused-argument
-    """Callback to print GPU utilization"""
-
-    def __init__(self, cfg):
-        self.cfg = cfg
-        self.logged = False
-
-    def on_step_end(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        if not self.logged:
-            log_gpu_memory_usage(LOG, "while training", self.cfg.device)
-            self.logged = True
-        return control

src/axolotl/utils/data.py

@@ -1,19 +1,14 @@
 """Module containing data utilities"""
 import functools
 import hashlib
+import itertools
 import logging
 from hashlib import md5
 from pathlib import Path
-from typing import Tuple, Union
+from typing import List, Tuple, Union
 
 import torch
-from datasets import (
-    Dataset,
-    DatasetDict,
-    concatenate_datasets,
-    load_dataset,
-    load_from_disk,
-)
+from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
 from huggingface_hub import hf_hub_download
 from transformers import PreTrainedTokenizerBase
 
@@ -270,12 +265,20 @@ def load_tokenized_prepared_datasets(
                 raise ValueError(
                     f"unhandled prompt tokenization strategy: {d.type} {suffix}"
                 )
-        LOG.info("merging datasets")
-        dataset = concatenate_datasets(datasets)
+        LOG.info("tokenizing, merging, and shuffling master dataset")
 
-        if len(datasets) > 1:
-            LOG.info("shuffle merged datasets")
-            dataset = dataset.shuffle(seed=seed)
+        samples: List[int] = []
+        chunk_size = 1000
+        for d in datasets:
+            d_iter = iter(d)
+            while True:
+                chunk = list(itertools.islice(d_iter, chunk_size))
+                if not chunk:
+                    break
+                samples.extend(chunk)
+
+        LOG.info("shuffle")
+        dataset = Dataset.from_list(samples).shuffle(seed=seed)
         if cfg.local_rank == 0:
             LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
             dataset.save_to_disk(prepared_ds_path)

src/axolotl/utils/dataloader.py

@@ -3,7 +3,9 @@ import hashlib
 import itertools
 import logging
 import math
-from typing import Any, Callable, List, Union
+import queue
+import threading
+from typing import Any, Callable, List, Optional, Union
 
 import numba
 import numpy as np
@@ -78,7 +80,6 @@ def allocate(
     s = 0
     start_index = 0
     result = []
-    result_totseqs = []
 
     while True:
         # binary search [left, right)
@@ -104,10 +105,8 @@ def allocate(
 
         # add local rank
         result.append(batch[rank])
-        # add total seqs for all ranks
-        result_totseqs.append(tot_seqs)
-        # yield batch[rank], tot_seqs, s, len(result) * c * n
-    return result, result_totseqs, s, len(result) * c * n
+
+        yield batch[rank], tot_seqs, s, len(result) * c * n
 
 
 def chunk(iterable, n):
@@ -149,15 +148,14 @@ class MultipackDistributedDataloader:
         packing_efficiency_estimate: float = 1.0,
         sample_packing_seq_len_multiplier: int = 1,
         device_count: int = 1,
+        total_num_tokens: Optional[int] = None,
     ):
         # Dataset
         self.dataset = dataset
-        self.lengths = (
-            dataset.data.column("position_ids")
-            .to_pandas()
-            .apply(lambda x: x[-1] + 1)
-            .values
+        lengths_series = (
+            dataset.data.column("position_ids").to_pandas().apply(lambda x: x[-1] + 1)
         )
+        self.lengths: np.ndarray = lengths_series.values
         assert isinstance(self.lengths, np.ndarray)
         assert batch_size % sample_packing_seq_len_multiplier == 0
         assert batch_size >= sample_packing_seq_len_multiplier
@@ -172,11 +170,17 @@ class MultipackDistributedDataloader:
         self.rank = 0
 
         # statistics
+        self.total_num_tokens = total_num_tokens
         self.eff_total_used = 0
         self.eff_total_slots = 0
         self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
         self.device_count = device_count
 
+        # for non-blocking batch creation
+        self.batch_queue: queue.Queue = queue.Queue(
+            maxsize=10
+        )  # Adjust maxsize as needed
+
     def generate_batches(self, set_stats=False):
         LOG.info("generating packed batches")
         if self.sampler:
@@ -188,65 +192,83 @@ class MultipackDistributedDataloader:
         lengths = self.lengths[indices]
         lengths_cumsum = np.cumsum(lengths)
 
-        batches, totseqs, total_used, total_slots = allocate(
-            lengths=lengths,
-            lengths_cumsum=lengths_cumsum,
-            rank=self.rank,
-            # c=self.batch_max_length,
-            c=self.seq_max_length * self.sample_packing_seq_len_multiplier,
-            n=self.num_replicas,
+        alloc_iter = iter(
+            allocate(
+                lengths=lengths,
+                lengths_cumsum=lengths_cumsum,
+                rank=self.rank,
+                # c=self.batch_max_length,
+                c=self.seq_max_length * self.sample_packing_seq_len_multiplier,
+                n=self.num_replicas,
+            )
         )
 
-        batches = [[indices[b_idx] for b_idx in batch] for batch in batches]
+        for batch, tot_seqs, total_used, total_slots in alloc_iter:
+            self.batch_queue.put([indices[b_idx] for b_idx in batch])
+            # statistics
+            if set_stats:
+                self.eff_total_used = total_used
+                self.eff_total_slots = total_slots
+        self.batch_queue.put(None)  # Signal the end of batch generation
 
-        # statistics
-        if set_stats:
-            self.eff_total_used += total_used
-            self.eff_total_slots += total_slots
-
-        return batches, totseqs
+    def _generate_batches_thread(self):
+        try:
+            self.generate_batches(set_stats=True)
+        except Exception as e:
+            LOG.error(f"Error in batch generation thread: {e}")
+            self.batch_queue.put(
+                None
+            )  # Signal the end of batch generation in case of error
 
     def __iter__(self):
         if hasattr(self.sampler, "set_epoch"):
             new_epoch = self.sampler.epoch + 1
             self.sampler.set_epoch(new_epoch)
             LOG.info(f"calling sampler.set_epoch({new_epoch})")
-        all_batches, _ = self.generate_batches(set_stats=True)
+        # Start the batch generation in a separate thread
+        batch_gen_thread = threading.Thread(target=self._generate_batches_thread)
+        batch_gen_thread.start()
+
         features = self.dataset.features.keys()
         len_remaining = self._len_est()
-        for batches in chunk(
-            all_batches, self.batch_size // self.sample_packing_seq_len_multiplier
-        ):
+        while True:
+            batch = self.batch_queue.get()
+            if batch is None:  # Sentinel value received, stop iteration
+                break
             chunked_data = []
             attn_mask_cum_idx = 0
-            for batch in batches:
-                concatenated = {}
-                batched_data = [self.dataset[batch_idx] for batch_idx in batch]
-                for feature in features:
-                    if feature == "attention_mask":
-                        arrays = [
-                            (attn_mask_cum_idx + idx + 1) * np.array(item[feature])
-                            for idx, item in enumerate(batched_data)
-                            if feature in item
-                        ]
-                        attn_mask_cum_idx += len(batched_data)
-                        concatenated[feature] = np.concatenate(arrays)
-                    else:
-                        arrays = [
-                            np.array(item[feature])
-                            for item in batched_data
-                            if feature in item
-                        ]
-                        concatenated[feature] = np.concatenate(arrays)
-                chunked_data.append(concatenated)
+            concatenated = {}
+            batched_data = [self.dataset[batch_idx] for batch_idx in batch]
+            for feature in features:
+                if feature == "attention_mask":
+                    arrays = [
+                        (attn_mask_cum_idx + idx + 1) * np.array(item[feature])
+                        for idx, item in enumerate(batched_data)
+                        if feature in item
+                    ]
+                    attn_mask_cum_idx += len(batched_data)
+                    concatenated[feature] = np.concatenate(arrays)
+                else:
+                    arrays = [
+                        np.array(item[feature])
+                        for item in batched_data
+                        if feature in item
+                    ]
+                    concatenated[feature] = np.concatenate(arrays)
+            chunked_data.append(concatenated)
+
             yield self.collate_fn(chunked_data)
             len_remaining -= 1
             if not len_remaining:
-                return
+                break
+        # Wait for the batch generation thread to finish
+        batch_gen_thread.join(timeout=5)
+        LOG.info(f"actual packing efficiency: {self.efficiency()}")
 
     def _len_est(self):
-        lengths_sum = np.sum(self.lengths)
-        lengths_sum_per_device = lengths_sum // self.device_count
+        if not self.total_num_tokens:
+            self.total_num_tokens = np.sum(self.lengths)
+        lengths_sum_per_device = self.total_num_tokens // self.device_count
         LOG.info(
             f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
             f"total_num_tokens per device: {lengths_sum_per_device}"

src/axolotl/utils/models.py

@@ -22,7 +22,6 @@ from transformers import (  # noqa: F401
 )
 
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_PAD_TOKEN
-from axolotl.utils.bench import log_gpu_memory_usage
 
 LOG = logging.getLogger("axolotl")
 
@@ -84,22 +83,19 @@ def load_tokenizer(
 
 
 def load_model(
-    cfg, tokenizer
-):  # type: (DictDefault, PreTrainedTokenizerBase) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
+    base_model, base_model_config, model_type, tokenizer, cfg, adapter="lora"
+):
+    # type: (str, str, str, PreTrainedTokenizerBase, DictDefault, Optional[str]) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
     """
-    Load a model for a given configuration and tokenizer.
+    Load a model from a base model and a model type.
     """
-    base_model = cfg.base_model
-    base_model_config = cfg.base_model_config
-    model_type = cfg.model_type
-    adapter = cfg.adapter
 
     # TODO refactor as a kwarg
     load_in_8bit = cfg.load_in_8bit
     cfg.is_llama_derived_model = (
         "llama" in base_model
         or (cfg.model_type and "llama" in cfg.model_type.lower())
-        or cfg.is_llama_derived_model
+        or cfg.is_llama_derived_model is True
     )
 
     if cfg.is_llama_derived_model and cfg.flash_attention:
@@ -235,9 +231,7 @@ def load_model(
         elif cfg.is_llama_derived_model and not cfg.trust_remote_code:
             from transformers import LlamaForCausalLM
 
-            config = LlamaConfig.from_pretrained(
-                base_model_config, rope_scaling=cfg.rope_scaling
-            )
+            config = LlamaConfig.from_pretrained(base_model_config)
             model = LlamaForCausalLM.from_pretrained(
                 base_model,
                 config=config,
@@ -342,9 +336,6 @@ def load_model(
         )
         model.config.max_position_embeddings = cfg.sequence_len
 
-    if model.device.type == "cuda":
-        log_gpu_memory_usage(LOG, "after model load", model.device)
-
     if not cfg.gptq and (
         (cfg.adapter == "lora" and load_in_8bit)
         or (cfg.adapter == "qlora" and cfg.load_in_4bit)
@@ -381,9 +372,6 @@ def load_model(
                 module.scales = module.scales.half()
                 module.bias = module.bias.half()
 
-    if model.device.type == "cuda":
-        log_gpu_memory_usage(LOG, "after adapters", model.device)
-
     if (
         torch.cuda.device_count() > 1
         and int(os.getenv("WORLD_SIZE", "1")) > 1

src/axolotl/utils/trainer.py

@@ -11,7 +11,6 @@ from pathlib import Path
 from typing import Optional, Union
 
 import bitsandbytes as bnb
-import numpy as np
 import torch.cuda
 import transformers
 from datasets import Dataset, set_caching_enabled
@@ -21,9 +20,7 @@ from torch.utils.data import DataLoader, DistributedSampler, RandomSampler
 from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
 from transformers.trainer_pt_utils import get_parameter_names
 
-from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
-    PrintGPUStatsCallback,
     SaveBetterTransformerModelCallback,
     SavePeftModelCallback,
 )
@@ -125,6 +122,10 @@ class AxolotlTrainingArguments(TrainingArguments):
         default=1,
         metadata={"help": "the multiplier for the max len for packed sequences"},
     )
+    train_data_total_num_tokens: Optional[int] = field(
+        default=None,
+        metadata={"help": "the total number of tokens in the train dataset"},
+    )
 
 
 class AxolotlTrainer(Trainer):
@@ -185,6 +186,7 @@ class AxolotlTrainer(Trainer):
                     packing_efficiency_estimate=self.args.sample_packing_efficiency,
                     sample_packing_seq_len_multiplier=self.args.sample_packing_seq_len_multiplier,
                     device_count=int(os.environ.get("WORLD_SIZE", 1)),
+                    total_num_tokens=self.args.train_data_total_num_tokens,
                 )
             )
         return super().get_train_dataloader()
@@ -207,6 +209,7 @@ class AxolotlTrainer(Trainer):
                     packing_efficiency_estimate=self.args.sample_packing_efficiency,
                     sample_packing_seq_len_multiplier=self.args.eval_batch_size,
                     device_count=int(os.environ.get("WORLD_SIZE", 1)),
+                    total_num_tokens=None,
                 )
             )
         return super().get_eval_dataloader(eval_dataset)
@@ -285,16 +288,13 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
     if cfg.sample_packing:
         # we have to drop anything longer then sequence len otherwise
         # flash attention with position ids fails
+        total_num_tokens = (
+            cfg.total_num_tokens
+            if cfg.total_num_tokens
+            else sum(len(s["input_ids"]) for s in train_dataset)
+        )
         if not cfg.total_num_tokens:
-            LOG.info("calculating total_num_tokens")
-            total_num_tokens = np.sum(
-                train_dataset.data.column("input_ids")
-                .to_pandas()
-                .apply(lambda x: len(x))  # pylint: disable=unnecessary-lambda
-                .values
-            )
             LOG.info(f"📝 UPDATE CONFIG WITH: `total_num_tokens: {total_num_tokens}`")
-            cfg.total_num_tokens = total_num_tokens
 
         if cfg.sample_packing_eff_est:
             total_num_steps = (
@@ -302,9 +302,9 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
                 (
                     math.floor(
                         0.99
-                        * cfg.total_num_tokens
+                        * total_num_tokens
                         / cfg.sample_packing_eff_est
-                        / cfg.sequence_len
+                        / 2048
                         // cfg.batch_size
                         // int(os.environ.get("WORLD_SIZE", 1))
                     )
@@ -313,7 +313,7 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
                 * cfg.num_epochs
             )
             LOG.info(
-                f"total_num_tokens: {cfg.total_num_tokens}, total_num_steps: {total_num_steps}"
+                f"total_num_tokens: {total_num_tokens}, total_num_steps: {total_num_steps}"
             )
         else:
             sampler = RandomSampler(train_dataset)
@@ -345,7 +345,6 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
             LOG.info(
                 f"📝 UPDATE CONFIG WITH: `sample_packing_eff_est: {math.ceil(actual_eff * 100.0) / 100.0}`"
             )
-            cfg.sample_packing_eff_est = math.ceil(actual_eff * 100.0) / 100.0
     else:
         total_num_steps = int(
             math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
@@ -484,7 +483,8 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         else "cosine",
         weight_decay=cfg.weight_decay if cfg.weight_decay is not None else 0.0,
         sample_packing=cfg.sample_packing if cfg.sample_packing else False,
-        sample_packing_seq_len_multiplier=cfg.micro_batch_size,
+        sample_packing_seq_len_multiplier=cfg.micro_batch_size or 1,
+        train_data_total_num_tokens=cfg.total_num_tokens,
         **training_arguments_kwargs,
     )
 
@@ -556,19 +556,6 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         trainer_kwargs["optimizers"] = (optimizer, lr_scheduler)
 
     callbacks = []
-    callbacks.append(PrintGPUStatsCallback(cfg))
-
-    if cfg.relora_steps:
-        relora_steps = int(cfg.relora_steps)
-        relora_warmup_steps = int(cfg.relora_warmup_steps)
-        callbacks.append(ReLoRACallback(cfg))
-
-        (optimizer, lr_scheduler) = trainer_kwargs["optimizers"]
-        trainer_kwargs["optimizers"] = (
-            optimizer,
-            ReLoRAScheduler(optimizer, lr_scheduler, relora_steps, relora_warmup_steps),
-        )
-
     # TODO on_save callback to sync checkpoints to GCP/AWS in background
     if cfg.early_stopping_patience:
         early_stop_cb = EarlyStoppingCallback(

src/axolotl/utils/validation.py

@@ -61,9 +61,6 @@ def validate_config(cfg):
     if not cfg.load_in_8bit and cfg.adapter == "lora":
         LOG.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
 
-    if cfg.relora_steps and cfg.adapter not in ("lora", "qlora"):
-        raise ValueError("cfg.adapter must be lora or qlora to use ReLoRA")
-
     if cfg.trust_remote_code:
         LOG.warning(
             "`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model."
@@ -113,13 +110,6 @@ def validate_config(cfg):
             "push_to_hub_model_id is deprecated. Please use hub_model_id instead."
         )
 
-    if cfg.gptq and cfg.model_revision:
-        raise ValueError(
-            "model_revision is not supported for GPTQ models. "
-            + "Please download the model from HuggingFace Hub manually for correct branch, "
-            + "point to its path, and remove model_revision from the config."
-        )
-
     if cfg.sample_packing and cfg.sdp_attention:
         # incompatible due to bug w/ accelerate causing 0.0 loss when using llama2
         raise ValueError(

src/axolotl/utils/wandb.py

@@ -9,8 +9,6 @@ def setup_wandb_env_vars(cfg):
     elif cfg.wandb_project and len(cfg.wandb_project) > 0:
         os.environ["WANDB_PROJECT"] = cfg.wandb_project
         cfg.use_wandb = True
-        if cfg.wandb_entity and len(cfg.wandb_entity) > 0:
-            os.environ["WANDB_ENTITY"] = cfg.wandb_entity
         if cfg.wandb_watch and len(cfg.wandb_watch) > 0:
             os.environ["WANDB_WATCH"] = cfg.wandb_watch
         if cfg.wandb_log_model and len(cfg.wandb_log_model) > 0: