smart resize embeddings

* new default settings

* fix whitespace

* rm max packed sequence length

---------

Co-authored-by: Mads Henrichsen <mads@BrbartiendeMads.lan>

.github/FUNDING.yml

@@ -0,0 +1,13 @@
+# These are supported funding model platforms
+
+github: OpenAccess-AI-Collective # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
+custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

README.md

@@ -136,7 +136,7 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"instruction": "...", "input": "...", "output": "..."}
   ```
-- `sharegpt:chat`: conversations
+- `sharegpt:chat`: conversations where `from` is `human`/`gpt`
   ```json
   {"conversations": [{"from": "...", "value": "..."}]}
   ```
@@ -225,6 +225,10 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"conversations": [{"role": "...", "value": "..."}]}
   ```
+- `sharegpt_simple.load_guanaco`: conversations where `from` is `prompter`/`assistant` instead of default sharegpt
+  ```json
+  {"conversations": [{"from": "...", "value": "..."}]}
+  ```
 - `sharegpt_jokes`: creates a chat where bot is asked to tell a joke, then explain why the joke is funny
   ```json
   {"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
@@ -322,9 +326,9 @@ tokenizer_type: AutoTokenizer
 trust_remote_code:
 # use_fast option for tokenizer loading from_pretrained, default to True
 tokenizer_use_fast:
-# resize the model embeddings when new tokens are added to multiples of 32
-# this is reported to improve training speed on some models
-resize_token_embeddings_to_32x:
+# resize the model embeddings when new tokens are added to multiples of N
+# multiples of 32 are reported to improve training speed on some models
+resize_token_embeddings_multiple:
 
 # whether you are training a 4-bit GPTQ quantized model
 gptq: true
@@ -360,6 +364,9 @@ dataset_prepared_path: data/last_run_prepared
 push_dataset_to_hub: # repo path
 # push checkpoints to hub
 hub_model_id: # repo path to push finetuned model
+# how to push checkpoints to hub
+# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
+hub_strategy:
 # whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
 # required to be true when used in combination with `push_dataset_to_hub`
 hf_use_auth_token: # boolean
@@ -375,10 +382,14 @@ 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
-# soon to be DEPRECATED
+# FutureWarning: This will soon be DEPRECATED
 max_packed_sequence_len: 1024
-# use efficient multi-packing with block diagonal attention and per sequence position_ids
+# use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
 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
@@ -404,11 +415,12 @@ lora_out_dir:
 lora_fan_in_fan_out: false
 
 # wandb configuration if you're using it
-wandb_mode:
-wandb_project:
+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_watch:
-wandb_run_id:
-wandb_log_model: # 'checkpoint'
+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
 
 # where to save the finished model to
 output_dir: ./completed-model
@@ -423,13 +435,17 @@ learning_rate: 0.00003
 logging_steps:
 save_steps:
 eval_steps:
+save_total_limit: # checkpoints saved at a time
+max_steps:
 
 # 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
-# don't use this, leads to wonky training (according to someone on the internet)
+# 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
 group_by_length: false
 
 # Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
@@ -475,6 +491,10 @@ 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:
@@ -506,6 +526,9 @@ 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:
 
@@ -525,7 +548,14 @@ Run
 accelerate launch scripts/finetune.py configs/your_config.yml
 ```
 
-#### Multi-GPU Config
+#### Multi-GPU
+
+You can optionally pre-tokenize dataset with the following before finetuning:
+```bash
+CUDA_VISIBLE_DEVICES="" accelerate ... --prepare_ds_only
+```
+
+##### Config
 
 - llama FSDP
 ```yaml
@@ -540,6 +570,18 @@ 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:

docker/Dockerfile-base

@@ -40,7 +40,7 @@ ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
 
 RUN git clone https://github.com/Dao-AILab/flash-attention.git && \
     cd flash-attention && \
-    git checkout v2.0.1  && \
+    git checkout v2.0.4  && \
     python3 setup.py bdist_wheel && \
     cd csrc/fused_dense_lib && \
     python3 setup.py bdist_wheel && \

examples/cerebras/qlora.yml

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

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

@@ -24,6 +24,7 @@ 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

@@ -38,6 +38,7 @@ 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

@@ -24,6 +24,7 @@ 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

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

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

@@ -22,6 +22,7 @@ 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

@@ -18,6 +18,7 @@ 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

@@ -15,7 +15,7 @@ val_set_size: 0.01
 output_dir: ./lora-out
 
 sequence_len: 4096
-max_packed_sequence_len: 4096
+sample_packing: true
 
 adapter: lora
 lora_model_dir:
@@ -26,6 +26,7 @@ lora_target_linear: true
 lora_fan_in_fan_out:
 
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -38,7 +39,7 @@ lr_scheduler: cosine
 learning_rate: 0.0002
 
 train_on_inputs: false
-group_by_length: true
+group_by_length: false
 bf16: true
 fp16: false
 tf32: false
@@ -48,8 +49,8 @@ early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 logging_steps: 1
-xformers_attention: true
-flash_attention:
+xformers_attention:
+flash_attention: true
 
 warmup_steps: 10
 eval_steps: 20
@@ -63,4 +64,3 @@ special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"
   unk_token: "<unk>"
-  pad_token: "<pad>"

examples/llama-2/qlora.yml

@@ -18,7 +18,8 @@ adapter: qlora
 lora_model_dir:
 
 sequence_len: 4096
-max_packed_sequence_len: 4096
+sample_packing: true
+
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
@@ -27,6 +28,7 @@ lora_target_linear: true
 lora_fan_in_fan_out:
 
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -39,7 +41,7 @@ lr_scheduler: cosine
 learning_rate: 0.0002
 
 train_on_inputs: false
-group_by_length: true
+group_by_length: false
 bf16: true
 fp16: false
 tf32: false
@@ -49,8 +51,8 @@ early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 logging_steps: 1
-xformers_attention: true
-flash_attention:
+xformers_attention:
+flash_attention: true
 
 warmup_steps: 10
 eval_steps: 20
@@ -64,4 +66,3 @@ special_tokens:
   bos_token: "<s>"
   eos_token: "</s>"
   unk_token: "<unk>"
-  pad_token: "<pad>"

examples/mpt-7b/config.yml

@@ -20,6 +20,7 @@ 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

@@ -22,6 +22,7 @@ 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

@@ -28,6 +28,7 @@ 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

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

examples/pythia-12b/config.yml

@@ -23,6 +23,7 @@ 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

@@ -17,6 +17,7 @@ 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

@@ -21,6 +21,7 @@ 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

@@ -20,6 +20,7 @@ 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

@@ -37,6 +37,7 @@ 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>=0.39.0
+bitsandbytes>=0.41.1
 accelerate @ git+https://github.com/huggingface/accelerate@2a289f6108e77a77a4efffb3f6316bc98538413b
 addict
 fire
@@ -21,3 +21,4 @@ evaluate==0.4.0
 rouge-score==0.1.2
 scipy
 scikit-learn==1.2.2
+pynvml

scripts/finetune.py

@@ -18,6 +18,7 @@ from optimum.bettertransformer import BetterTransformer
 from transformers import GenerationConfig, TextStreamer
 
 from axolotl.logging_config import configure_logging
+from axolotl.utils.config import normalize_config, validate_config
 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
@@ -28,7 +29,6 @@ from axolotl.utils.trainer import (
     process_datasets_for_packing,
     setup_trainer,
 )
-from axolotl.utils.validation import validate_config
 from axolotl.utils.wandb import setup_wandb_env_vars
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
@@ -43,27 +43,6 @@ DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
 
-def choose_device(cfg):
-    def get_device():
-        try:
-            if torch.cuda.is_available():
-                return f"cuda:{cfg.local_rank}"
-
-            if torch.backends.mps.is_available():
-                return "mps"
-
-            raise SystemError("No CUDA/mps device found")
-        except Exception:  # pylint: disable=broad-exception-caught
-            return "cpu"
-
-    cfg.device = get_device()
-    if cfg.device_map != "auto":
-        if cfg.device.startswith("cuda"):
-            cfg.device_map = {"": cfg.local_rank}
-        else:
-            cfg.device_map = {"": cfg.device}
-
-
 def get_multi_line_input() -> Optional[str]:
     print("Give me an instruction (Ctrl + D to finish): ")
     instruction = ""
@@ -193,36 +172,13 @@ def train(
 
     validate_config(cfg)
 
-    # setup some derived config / hyperparams
-    cfg.gradient_accumulation_steps = cfg.gradient_accumulation_steps or (
-        cfg.batch_size // cfg.micro_batch_size
-    )
-    cfg.batch_size = (
-        cfg.batch_size or cfg.micro_batch_size * cfg.gradient_accumulation_steps
-    )
-    cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
-    cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
-    choose_device(cfg)
-    cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1
-    if cfg.ddp:
-        cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
-        cfg.batch_size = cfg.batch_size * cfg.world_size
+    normalize_config(cfg)
 
     setup_wandb_env_vars(cfg)
-    if cfg.device == "mps":
-        cfg.load_in_8bit = False
-        cfg.tf32 = False
-        if cfg.bf16:
-            cfg.fp16 = True
-        cfg.bf16 = False
-
-    if cfg.tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
 
     # load the tokenizer first
-    tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
-    LOG.info(f"loading tokenizer... {tokenizer_config}")
-    tokenizer = load_tokenizer(tokenizer_config, cfg.tokenizer_type, cfg)
+    LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
+    tokenizer = load_tokenizer(cfg)
 
     if (
         check_not_in(["shard", "merge_lora"], kwargs) and not cfg.inference
@@ -253,7 +209,13 @@ def train(
                 cfg, train_dataset, eval_dataset
             )
         barrier()
-        total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)
+        if cfg.max_steps:
+            total_num_steps = min(
+                calculate_total_num_steps(cfg, train_dataset, tokenizer), cfg.max_steps
+            )
+            LOG.info(f"Maximum number of steps set at {total_num_steps}")
+        else:
+            total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)
 
     if cfg.debug or "debug" in kwargs:
         LOG.info("check_dataset_labels...")
@@ -269,15 +231,10 @@ def train(
         return
 
     # Load the model and tokenizer
-    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,
-    )
+    LOG.info("loading model and (optionally) peft_config...")
+    model, peft_config = load_model(cfg, tokenizer)
+
+    safe_serialization = cfg.save_safetensors is True
 
     if "merge_lora" in kwargs and cfg.adapter is not None:
         LOG.info("running merge of LoRA with base model")
@@ -286,7 +243,11 @@ def train(
 
         if cfg.local_rank == 0:
             LOG.info("saving merged model")
-            model.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+            model.save_pretrained(
+                str(Path(cfg.output_dir) / "merged"),
+                safe_serialization=safe_serialization,
+            )
+            tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
         return
 
     if cfg.inference:
@@ -301,7 +262,7 @@ def train(
         return
 
     if "shard" in kwargs:
-        model.save_pretrained(cfg.output_dir)
+        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
         return
 
     trainer = setup_trainer(
@@ -325,7 +286,7 @@ def train(
         def terminate_handler(_, __, model):
             if cfg.flash_optimum:
                 model = BetterTransformer.reverse(model)
-            model.save_pretrained(cfg.output_dir)
+            model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
             sys.exit(0)
 
         signal.signal(
@@ -352,6 +313,7 @@ def train(
 
     if not Path(cfg.output_dir).is_dir():
         os.makedirs(cfg.output_dir, exist_ok=True)
+    tokenizer.save_pretrained(cfg.output_dir)
     if cfg.flash_optimum:
         with torch.backends.cuda.sdp_kernel(
             enable_flash=True, enable_math=True, enable_mem_efficient=True
@@ -369,7 +331,7 @@ def train(
     elif cfg.local_rank == 0:
         if cfg.flash_optimum:
             model = BetterTransformer.reverse(model)
-        model.save_pretrained(cfg.output_dir)
+        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
 
 if __name__ == "__main__":

src/axolotl/datasets.py

@@ -5,7 +5,7 @@ import os
 from typing import List
 
 import torch
-from datasets import IterableDataset
+from datasets import Dataset, IterableDataset
 
 from .prompt_tokenizers import PromptTokenizingStrategy
 
@@ -18,9 +18,9 @@ from .prompt_tokenizers import PromptTokenizingStrategy
 LOG = logging.getLogger("axolotl")
 
 
-class TokenizedPromptDataset(IterableDataset):
+class TokenizedPromptDataset(Dataset):
     """
-    Iterable dataset that returns tokenized prompts from a stream of text files.
+    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.
@@ -30,19 +30,18 @@ class TokenizedPromptDataset(IterableDataset):
         self,
         prompt_tokenizer: PromptTokenizingStrategy,
         dataset: IterableDataset,
+        **kwargs,
     ):
         self.prompt_tokenizer = prompt_tokenizer
-        self.dataset = dataset
+        super().__init__(self.process(dataset).data, **kwargs)
 
-    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,
-            )
+    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,
         )
 
 

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -7,6 +7,7 @@ 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
@@ -91,7 +92,8 @@ 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:
+    elif attention_mask.shape[0] == 1:
+        # special handling using sample packing
         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()
@@ -100,6 +102,36 @@ 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/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
+from axolotl.prompters import IGNORE_TOKEN_ID, SHAREGPT_ASSERTION_FAILED_ROLE
 
 
 @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]
+            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
             if sentence["value"]:
                 conv.append_message(role, sentence["value"])
         yield conv

src/axolotl/prompters.py

@@ -271,6 +271,11 @@ 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
@@ -307,7 +312,9 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
         if len(source) < 2:
             # If there isn't a back and forth conversation, ignore it
             # also happens on the data splitting leaving empty conversations
-            raise IndexError
+            raise IndexError(
+                f"A conversation entry has less than 2 messages :\n{source}"
+            )
 
         conv = self._conversation.copy()
         roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
@@ -327,7 +334,7 @@ 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]
+            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
             conv.append_message(role, sentence["value"])
 
         for part in conv.get_prompt():

src/axolotl/utils/bench.py

@@ -0,0 +1,43 @@
+"""Benchmarking and measurement utilities"""
+
+import pynvml
+import torch
+
+
+def gpu_memory_usage(device=0):
+    return torch.cuda.memory_allocated(device) / 1024.0**3
+
+
+def gpu_memory_usage_all(device=0):
+    usage = torch.cuda.memory_allocated(device) / 1024.0**3
+    reserved = torch.cuda.memory_reserved(device) / 1024.0**3
+    smi = gpu_memory_usage_smi(device)
+    return usage, reserved - usage, max(0, smi - reserved)
+
+
+def gpu_memory_usage_smi(device=0):
+    if isinstance(device, torch.device):
+        device = device.index
+    if isinstance(device, str) and device.startswith("cuda:"):
+        device = int(device[5:])
+
+    pynvml.nvmlInit()
+    handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+    info = pynvml.nvmlDeviceGetMemoryInfo(handle)
+    return info.used / 1024.0**3
+
+
+def log_gpu_memory_usage(log, msg, device):
+    if not torch.cuda.is_available():
+        return (0, 0, 0)
+
+    usage, cache, misc = gpu_memory_usage_all(device)
+    extras = []
+    if cache > 0:
+        extras.append(f"+{cache:.03f}GB cache")
+    if misc > 0:
+        extras.append(f"+{misc:.03f}GB misc")
+    log.info(
+        f"GPU memory usage {msg}: {usage:.03f}GB ({', '.join(extras)})", stacklevel=2
+    )
+    return usage, cache, misc

src/axolotl/utils/callbacks.py

@@ -1,5 +1,6 @@
 """Callbacks for Trainer class"""
 
+import logging
 import os
 
 from optimum.bettertransformer import BetterTransformer
@@ -11,6 +12,10 @@ 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"""
@@ -67,3 +72,25 @@ class SaveBetterTransformerModelCallback(
             # the trainer will raise an exception since it can't save a BetterTransformer wrapped model
             control.should_save = False
         return control
+
+
+class GPUStatsCallback(
+    TrainerCallback
+):  # pylint: disable=too-few-public-methods disable=unused-argument
+    """Callback to track 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 and state.global_step > 1:
+            log_gpu_memory_usage(LOG, "while training", self.cfg.device)
+            self.logged = True
+        return control

src/axolotl/utils/config.py

@@ -1,12 +1,70 @@
-"""Module for validating config files"""
+"""Module for working with config dicts"""
 
 import logging
+import os
 
 import torch
 
+from axolotl.utils.bench import log_gpu_memory_usage
+
 LOG = logging.getLogger("axolotl")
 
 
+def choose_device(cfg):
+    def get_device():
+        try:
+            if torch.cuda.is_available():
+                return f"cuda:{cfg.local_rank}"
+
+            if torch.backends.mps.is_available():
+                return "mps"
+
+            raise SystemError("No CUDA/mps device found")
+        except Exception:  # pylint: disable=broad-exception-caught
+            return "cpu"
+
+    cfg.device = get_device()
+    if cfg.device_map != "auto":
+        if cfg.device.startswith("cuda"):
+            cfg.device_map = {"": cfg.local_rank}
+        else:
+            cfg.device_map = {"": cfg.device}
+
+    # in `accelerate launch`, we need to not pass through any device map and let
+    # accelerate figure out which parts of the model to put on which gpu
+    accelerate_vars = [var for var in os.environ if var.startswith("ACCELERATE_USE_")]
+    if accelerate_vars:
+        cfg.device_map = None
+
+
+def normalize_config(cfg):
+    # setup some derived config / hyperparams
+    cfg.gradient_accumulation_steps = cfg.gradient_accumulation_steps or (
+        cfg.batch_size // cfg.micro_batch_size
+    )
+    cfg.batch_size = (
+        cfg.batch_size or cfg.micro_batch_size * cfg.gradient_accumulation_steps
+    )
+    cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
+    cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
+    choose_device(cfg)
+    cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1
+    if cfg.ddp:
+        cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
+        cfg.batch_size = cfg.batch_size * cfg.world_size
+
+    if cfg.device == "mps":
+        cfg.load_in_8bit = False
+        cfg.tf32 = False
+        if cfg.bf16:
+            cfg.fp16 = True
+        cfg.bf16 = False
+    else:
+        torch.backends.cuda.matmul.allow_tf32 = cfg.tf32 or False
+
+    log_gpu_memory_usage(LOG, "baseline", cfg.device)
+
+
 def validate_config(cfg):
     if cfg.max_packed_sequence_len and cfg.sample_packing:
         raise ValueError(
@@ -110,6 +168,13 @@ 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/data.py

@@ -1,14 +1,19 @@
 """Module containing data utilities"""
 import functools
 import hashlib
-import itertools
 import logging
 from hashlib import md5
 from pathlib import Path
-from typing import List, Tuple, Union
+from typing import Tuple, Union
 
 import torch
-from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
+from datasets import (
+    Dataset,
+    DatasetDict,
+    concatenate_datasets,
+    load_dataset,
+    load_from_disk,
+)
 from huggingface_hub import hf_hub_download
 from transformers import PreTrainedTokenizerBase
 
@@ -265,20 +270,12 @@ def load_tokenized_prepared_datasets(
                 raise ValueError(
                     f"unhandled prompt tokenization strategy: {d.type} {suffix}"
                 )
-        LOG.info("tokenizing, merging, and shuffling master dataset")
+        LOG.info("merging datasets")
+        dataset = concatenate_datasets(datasets)
 
-        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 len(datasets) > 1:
+            LOG.info("shuffle merged datasets")
+            dataset = dataset.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

@@ -3,9 +3,7 @@ import hashlib
 import itertools
 import logging
 import math
-import queue
-import threading
-from typing import Any, Callable, List, Optional, Union
+from typing import Any, Callable, List, Union
 
 import numba
 import numpy as np
@@ -80,6 +78,7 @@ def allocate(
     s = 0
     start_index = 0
     result = []
+    result_totseqs = []
 
     while True:
         # binary search [left, right)
@@ -105,8 +104,10 @@ def allocate(
 
         # add local rank
         result.append(batch[rank])
-
-        yield batch[rank], tot_seqs, s, len(result) * c * n
+        # 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
 
 
 def chunk(iterable, n):
@@ -148,14 +149,15 @@ 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
-        lengths_series = (
-            dataset.data.column("position_ids").to_pandas().apply(lambda x: x[-1] + 1)
+        self.lengths = (
+            dataset.data.column("position_ids")
+            .to_pandas()
+            .apply(lambda x: x[-1] + 1)
+            .values
         )
-        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
@@ -170,17 +172,11 @@ 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:
@@ -192,83 +188,65 @@ class MultipackDistributedDataloader:
         lengths = self.lengths[indices]
         lengths_cumsum = np.cumsum(lengths)
 
-        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, 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,
         )
 
-        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
+        batches = [[indices[b_idx] for b_idx in batch] for batch in batches]
 
-    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
+        # statistics
+        if set_stats:
+            self.eff_total_used += total_used
+            self.eff_total_slots += total_slots
+
+        return batches, totseqs
 
     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})")
-        # Start the batch generation in a separate thread
-        batch_gen_thread = threading.Thread(target=self._generate_batches_thread)
-        batch_gen_thread.start()
-
+        all_batches, _ = self.generate_batches(set_stats=True)
         features = self.dataset.features.keys()
         len_remaining = self._len_est()
-        while True:
-            batch = self.batch_queue.get()
-            if batch is None:  # Sentinel value received, stop iteration
-                break
+        for batches in chunk(
+            all_batches, self.batch_size // self.sample_packing_seq_len_multiplier
+        ):
             chunked_data = []
             attn_mask_cum_idx = 0
-            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)
-
+            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)
             yield self.collate_fn(chunked_data)
             len_remaining -= 1
             if not len_remaining:
-                break
-        # Wait for the batch generation thread to finish
-        batch_gen_thread.join(timeout=5)
-        LOG.info(f"actual packing efficiency: {self.efficiency()}")
+                return
 
     def _len_est(self):
-        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
+        lengths_sum = np.sum(self.lengths)
+        lengths_sum_per_device = lengths_sum // 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/dict.py

@@ -10,3 +10,6 @@ class DictDefault(Dict):
 
     def __missing__(self, key):
         return None
+
+    def __or__(self, other):
+        return DictDefault(super().__or__(other))

src/axolotl/utils/models.py

@@ -22,6 +22,7 @@ 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")
 
@@ -31,37 +32,66 @@ if TYPE_CHECKING:
     from axolotl.utils.dict import DictDefault  # noqa: F401
 
 
-def load_tokenizer(
-    tokenizer_config,
-    tokenizer_type,
-    cfg,
+def smart_tokenizer_and_embedding_resize(
+    tokenizer: transformers.PreTrainedTokenizer,
+    model: transformers.PreTrainedModel,
+    resize_token_embeddings_multiple: Optional[int] = None,
 ):
+    """Resize tokenizer and embedding.
+
+    Note: This function resizes the tokenizer to accommodate additional special tokens and the
+    embedding matrix of the model to match the new size of the tokenizer. If any new special tokens
+    have been added, the function computes the average embedding values of the existing embeddings
+    and sets those values for the new special token embeddings. This is done separately for the input
+    embeddings and output embeddings of the model.
+    """
+
+    old_tokens = model.get_input_embeddings().weight.data.shape[0]
+    num_new_tokens = len(tokenizer) - old_tokens
+    embeddings_len = (
+        math.ceil(len(tokenizer) / resize_token_embeddings_multiple)
+        * resize_token_embeddings_multiple
+        if resize_token_embeddings_multiple
+        else len(tokenizer)
+    )
+    model.resize_token_embeddings(embeddings_len)
+
+    if num_new_tokens > 0:
+        input_embeddings = model.get_input_embeddings().weight.data
+        output_embeddings = model.get_output_embeddings().weight.data
+
+        input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
+            dim=0, keepdim=True
+        )
+        output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
+            dim=0, keepdim=True
+        )
+
+        input_embeddings[-num_new_tokens:] = input_embeddings_avg
+        output_embeddings[-num_new_tokens:] = output_embeddings_avg
+
+
+def load_tokenizer(cfg):
     tokenizer_kwargs = {}
     use_fast = True  # this is the default
+
     if cfg.tokenizer_use_fast is not None:
         use_fast = cfg.tokenizer_use_fast
     if cfg.tokenizer_legacy is not None:
         # True is the default w/ https://github.com/huggingface/transformers/pull/25224
         tokenizer_kwargs["legacy"] = cfg.tokenizer_legacy
-    if tokenizer_type:
-        tokenizer = getattr(transformers, tokenizer_type).from_pretrained(
-            tokenizer_config,
-            trust_remote_code=cfg.trust_remote_code or False,
-            use_fast=use_fast,
-            **tokenizer_kwargs,
-        )
-    else:
-        tokenizer = AutoTokenizer.from_pretrained(
-            tokenizer_config,
-            trust_remote_code=cfg.trust_remote_code or False,
-            use_fast=use_fast,
-            **tokenizer_kwargs,
-        )
 
-    LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
-    LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
-    LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
-    LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
+    tokenizer_cls = AutoTokenizer
+    if cfg.tokenizer_type:
+        tokenizer_cls = getattr(transformers, cfg.tokenizer_type)
+
+    tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
+    tokenizer = tokenizer_cls.from_pretrained(
+        tokenizer_config,
+        trust_remote_code=cfg.trust_remote_code or False,
+        use_fast=use_fast,
+        **tokenizer_kwargs,
+    )
 
     if tokenizer.__class__.__name__ in [
         "LlamaTokenizer",
@@ -69,6 +99,11 @@ def load_tokenizer(
     ]:
         tokenizer.pad_token = LLAMA_DEFAULT_PAD_TOKEN
 
+    LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
+    LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
+    LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
+    LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
+
     if tokenizer.__class__.__name__ == "GPTNeoXTokenizerFast":
         tokenizer.add_special_tokens({"pad_token": "[PAD]"})
         os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -83,19 +118,21 @@ def load_tokenizer(
 
 
 def load_model(
-    base_model, base_model_config, model_type, tokenizer, cfg, adapter="lora"
-):
-    # type: (str, str, str, PreTrainedTokenizerBase, DictDefault, Optional[str]) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
+    cfg, tokenizer
+):  # type: (DictDefault, PreTrainedTokenizerBase) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
     """
-    Load a model from a base model and a model type.
+    Load a model for a given configuration and tokenizer.
     """
+    base_model = cfg.base_model
+    base_model_config = cfg.base_model_config
+    model_type = cfg.model_type
 
     # 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 is True
+        or cfg.is_llama_derived_model
     )
 
     if cfg.is_llama_derived_model and cfg.flash_attention:
@@ -231,10 +268,17 @@ 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)
+            config_kwargs = {}
+            if cfg.rope_scaling:
+                config_kwargs["rope_scaling"] = cfg.rope_scaling
+            config = LlamaConfig.from_pretrained(
+                base_model_config,
+                **config_kwargs,
+            )
             model = LlamaForCausalLM.from_pretrained(
                 base_model,
                 config=config,
+                device_map=cfg.device_map,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                 load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                 torch_dtype=torch_dtype,
@@ -269,6 +313,7 @@ def load_model(
         elif model_type and not cfg.trust_remote_code:
             model = getattr(transformers, model_type).from_pretrained(
                 base_model,
+                device_map=cfg.device_map,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                 load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                 torch_dtype=torch_dtype,
@@ -299,6 +344,7 @@ def load_model(
             model = AutoModelForCausalLM.from_pretrained(
                 base_model,
                 config=config,
+                device_map=cfg.device_map,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                 load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                 torch_dtype=torch_dtype,
@@ -312,6 +358,7 @@ def load_model(
         LOG.exception(err)
         model = AutoModelForCausalLM.from_pretrained(
             base_model,
+            device_map=cfg.device_map,
             load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
             load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
             torch_dtype=torch_dtype,
@@ -319,23 +366,25 @@ def load_model(
             **model_kwargs,
         )
 
-    embeddings_len = (
-        math.ceil(len(tokenizer) / 32) * 32
-        if cfg.resize_token_embeddings_to_32x
-        else len(tokenizer)
+    smart_tokenizer_and_embedding_resize(
+        tokenizer,
+        model,
+        resize_token_embeddings_multiple=cfg.resize_token_embeddings_multiple,
     )
-    model.resize_token_embeddings(embeddings_len)
 
     if (
         hasattr(model.config, "max_position_embeddings")
         and model.config.max_position_embeddings
-        and cfg.sequence_len >= model.config.max_position_embeddings
+        and cfg.sequence_len > model.config.max_position_embeddings
     ):
         LOG.warning(
             f"increasing model.config.max_position_embeddings to {cfg.sequence_len}"
         )
         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)
@@ -355,7 +404,7 @@ def load_model(
                     if hasattr(module, "weight"):
                         module.to(torch_dtype)
 
-    model, lora_config = load_adapter(model, cfg, adapter)
+    model, lora_config = load_adapter(model, cfg, cfg.adapter)
 
     if cfg.ddp and not load_in_8bit:
         model.to(f"cuda:{cfg.local_rank}")
@@ -394,6 +443,9 @@ def load_model(
     if cfg.flash_optimum:
         model = BetterTransformer.transform(model)
 
+    if cfg.adapter is not None:
+        log_gpu_memory_usage(LOG, "after adapters", model.device)
+
     # TODO resume_from_checkpoint handling
     return model, lora_config
 

src/axolotl/utils/trainer.py

@@ -11,6 +11,7 @@ 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
@@ -21,6 +22,7 @@ from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
 from transformers.trainer_pt_utils import get_parameter_names
 
 from axolotl.utils.callbacks import (
+    GPUStatsCallback,
     SaveBetterTransformerModelCallback,
     SavePeftModelCallback,
 )
@@ -122,10 +124,6 @@ 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):
@@ -186,7 +184,6 @@ 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()
@@ -209,7 +206,6 @@ 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)
@@ -288,13 +284,16 @@ 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 +301,9 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
                 (
                     math.floor(
                         0.99
-                        * total_num_tokens
+                        * cfg.total_num_tokens
                         / cfg.sample_packing_eff_est
-                        / 2048
+                        / cfg.sequence_len
                         // cfg.batch_size
                         // int(os.environ.get("WORLD_SIZE", 1))
                     )
@@ -313,7 +312,7 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
                 * cfg.num_epochs
             )
             LOG.info(
-                f"total_num_tokens: {total_num_tokens}, total_num_steps: {total_num_steps}"
+                f"total_num_tokens: {cfg.total_num_tokens}, total_num_steps: {total_num_steps}"
             )
         else:
             sampler = RandomSampler(train_dataset)
@@ -345,6 +344,7 @@ 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)
@@ -440,6 +440,9 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         training_arguments_kwargs["push_to_hub"] = True
         training_arguments_kwargs["hub_private_repo"] = True
 
+        if cfg.hub_strategy:
+            training_arguments_kwargs["hub_strategy"] = cfg.hub_strategy
+
     if cfg.save_safetensors:
         training_arguments_kwargs["save_safetensors"] = cfg.save_safetensors
 
@@ -448,8 +451,17 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
             "sample_packing_efficiency"
         ] = cfg.sample_packing_eff_est
 
+    if cfg.val_set_size == 0:
+        evaluation_strategy = "no"
+    elif cfg.eval_steps < 1:
+        # eval every epoch
+        evaluation_strategy = "epoch"
+    else:
+        # eval every eval_steps steps
+        evaluation_strategy = "steps"
+
     training_args = AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
-        # max_steps=total_num_steps,  # this is helpful in case we don't actually know total # of steps
+        max_steps=total_num_steps if cfg.max_steps else -1,
         max_seq_length=cfg.sequence_len,
         per_device_train_batch_size=cfg.micro_batch_size,
         per_device_eval_batch_size=cfg.eval_batch_size
@@ -459,7 +471,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         eval_accumulation_steps=cfg.gradient_accumulation_steps,
         num_train_epochs=cfg.num_epochs,
         learning_rate=cfg.learning_rate,
-        evaluation_strategy="steps" if cfg.val_set_size > 0 else "no",
+        evaluation_strategy=evaluation_strategy,
         save_strategy="steps" if cfg.save_steps else "epoch",
         eval_steps=cfg.eval_steps if cfg.val_set_size > 0 else None,
         save_steps=cfg.save_steps,
@@ -483,8 +495,7 @@ 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 or 1,
-        train_data_total_num_tokens=cfg.total_num_tokens,
+        sample_packing_seq_len_multiplier=cfg.micro_batch_size,
         **training_arguments_kwargs,
     )
 
@@ -556,6 +567,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         trainer_kwargs["optimizers"] = (optimizer, lr_scheduler)
 
     callbacks = []
+    callbacks.append(GPUStatsCallback(cfg))
     # TODO on_save callback to sync checkpoints to GCP/AWS in background
     if cfg.early_stopping_patience:
         early_stop_cb = EarlyStoppingCallback(

src/axolotl/utils/wandb.py

@@ -9,6 +9,8 @@ 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:

tests/test_dict.py

@@ -72,6 +72,13 @@ class DictDefaultTest(unittest.TestCase):
 
         assert cfg.random_key is None, "DictDefault should return None for missing keys"
 
+    def test_dict_or(self):
+        cfg = DictDefault({}) | DictDefault({})
+
+        assert (
+            cfg.random_key is None
+        ), "DictDefault should return None for missing keys after | operation"
+
     def test_dict_nested_missingparentkey(self):
         """
         Due to subclassing Dict, DictDefault will error if we try to access a nested key whose parent key does not exist.

tests/test_tokenizers.py

@@ -13,17 +13,22 @@ class TestTokenizers(unittest.TestCase):
     """
 
     def test_default_use_fast(self):
-        cfg = DictDefault({})
-        tokenizer = load_tokenizer("huggyllama/llama-7b", None, cfg)
+        cfg = DictDefault(
+            {
+                "tokenizer_config": "huggyllama/llama-7b",
+            }
+        )
+        tokenizer = load_tokenizer(cfg)
         assert "Fast" in tokenizer.__class__.__name__
 
     def test_dont_use_fast(self):
         cfg = DictDefault(
             {
+                "tokenizer_config": "huggyllama/llama-7b",
                 "tokenizer_use_fast": False,
             }
         )
-        tokenizer = load_tokenizer("huggyllama/llama-7b", None, cfg)
+        tokenizer = load_tokenizer(cfg)
         assert "Fast" not in tokenizer.__class__.__name__
 
 

tests/test_validation.py

@@ -6,8 +6,8 @@ from typing import Optional
 
 import pytest
 
+from axolotl.utils.config import validate_config
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.validation import validate_config
 
 
 class ValidationTest(unittest.TestCase):