support for batched sharegpt tokenization to skip bad data

* Fix: Higher vram usage for mistral and sample_packing

* chore: update comment

* chore: lint

.github/ISSUE_TEMPLATE/bug-report.yaml

@@ -53,6 +53,13 @@ body:
     validations:
       required: true
 
+  - type: textarea
+    id: config
+    attributes:
+      label: Config yaml
+      description: |
+        Please attach the config yaml!
+
   - type: textarea
     id: possible-solution
     attributes:

.github/workflows/tests.yml

@@ -6,9 +6,11 @@ on:
       - "main"
     paths:
       - '**.py'
+      - 'requirements.txt'
   pull_request:
       paths:
        - '**.py'
+       - 'requirements.txt'
   workflow_dispatch:
 
 jobs:
@@ -44,7 +46,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip3 install -e .
+          pip3 install -U -e .
           pip3 install -r requirements-tests.txt
 
       - name: Run tests
@@ -69,8 +71,8 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip3 install -e .
-          pip3 install flash-attn
+          pip3 uninstall -y transformers accelerate
+          pip3 install -U -e .[flash-attn]
           pip3 install -r requirements-tests.txt
 
       - name: Run e2e tests

.pylintrc

@@ -12,3 +12,4 @@ generated-members=numpy.*, torch.*
 disable=missing-function-docstring, line-too-long, import-error,
     too-many-arguments, too-many-locals, too-many-statements, too-many-branches, too-few-public-methods,
     too-many-instance-attributes, fixme, import-outside-toplevel, logging-fstring-interpolation,
+    too-many-nested-blocks,

README.md

@@ -124,6 +124,11 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
         pip3 install packaging
         pip3 install -e '.[flash-attn,deepspeed]'
         ```
+  4. (Optional) Login to Huggingface to use gated models/datasets.
+        ```bash
+        huggingface-cli login
+        ```
+        Get the token at huggingface.co/settings/tokens
 
 - LambdaLabs
   <details>
@@ -180,7 +185,7 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"instruction": "...", "input": "...", "output": "..."}
   ```
-- `sharegpt:chat`: conversations where `from` is `human`/`gpt`
+- `sharegpt`: conversations where `from` is `human`/`gpt`
   ```json
   {"conversations": [{"from": "...", "value": "..."}]}
   ```
@@ -245,6 +250,10 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"article": "...", "question": "...", "answer": "..."}
   ```
+- `context_qa.load_v2`: in context question answering (alternate)
+  ```json
+  {"context": "...", "question": "...", "answer": "..."}
+  ```
 - `context_qa.load_404`: in context question answering from an article, with default response for no answer from context
   ```json
   {"article": "...", "unanswerable_question": "..."}
@@ -269,11 +278,11 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"prompt": "...", "generation": "..."}
   ```
-- `sharegpt_simple.load_role`: conversations where `role` is used instead of `from`
+- `sharegpt.load_role`: conversations where `role` is used instead of `from`
   ```json
   {"conversations": [{"role": "...", "value": "..."}]}
   ```
-- `sharegpt_simple.load_guanaco`: conversations where `from` is `prompter`/`assistant` instead of default sharegpt
+- `sharegpt.load_guanaco`: conversations where `from` is `prompter`/`assistant` instead of default sharegpt
   ```json
   {"conversations": [{"from": "...", "value": "..."}]}
   ```
@@ -308,7 +317,7 @@ Using file:
 #### How to use your custom pretokenized dataset
 
 - Do not pass a `type:`
-- Dataset must contain `input_ids`, `attention_mask`, `labels` in columns
+- Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`
 
 
 ### Config
@@ -351,6 +360,12 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
     - path: data.jsonl # or json
       ds_type: json # see other options below
       type: alpaca
+
+  # dataset with splits, but no train split
+  dataset:
+    - path: knowrohit07/know_sql
+      type: context_qa.load_v2
+      train_on_split: validation
   ```
 
 - loading
@@ -408,6 +423,11 @@ tokenizer_legacy:
 # this is reported to improve training speed on some models
 resize_token_embeddings_to_32x:
 
+# used to identify which the model is based on
+is_falcon_derived_model:
+is_llama_derived_model:
+is_mistral_derived_model:
+
 # whether you are training a 4-bit GPTQ quantized model
 gptq: true
 gptq_groupsize: 128 # group size
@@ -439,6 +459,7 @@ datasets:
     data_files: # Optional[str] path to source data files
     shards: # Optional[int] number of shards to split data into
     name: # Optional[str] name of dataset configuration to load
+    conversation:  # Optional[str] fastchat conversation type, only used with type: sharegpt
 
   # custom user prompt
   - path: repo
@@ -466,6 +487,9 @@ datasets:
 dataset_prepared_path: data/last_run_prepared
 # push prepared dataset to hub
 push_dataset_to_hub: # repo path
+# The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
+# if not set.
+dataset_processes: # defaults to os.cpu_count() if not set
 # push checkpoints to hub
 hub_model_id: # repo path to push finetuned model
 # how to push checkpoints to hub
@@ -547,7 +571,7 @@ torch_compile_backend:  # Optional[str]
 # training hyperparameters
 gradient_accumulation_steps: 1
 micro_batch_size: 2
-eval_batch_size: 2
+eval_batch_size:
 num_epochs: 3
 warmup_steps: 100
 learning_rate: 0.00003
@@ -631,6 +655,8 @@ flash_optimum:
 xformers_attention:
 # whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
 flash_attention:
+flash_attn_cross_entropy:  # Whether to use flash-attention cross entropy implementation - advanced use only
+flash_attn_rms_norm:  # Whether to use flash-attention rms norm implementation - advanced use only
 # whether to use scaled-dot-product attention
 # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
 sdp_attention:

docker/Dockerfile

@@ -12,17 +12,18 @@ RUN apt-get update && \
 WORKDIR /workspace
 
 RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
+
+WORKDIR /workspace/axolotl
+
 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN cd axolotl && \
-    if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
+RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install -e .[flash-attn,$AXOLOTL_EXTRAS]; \
     else \
         pip install -e .[flash-attn]; \
     fi
 
 # fix so that git fetch/pull from remote works
-RUN cd axolotl && \
-    git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
+RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
     git config --get remote.origin.fetch
 
 # helper for huggingface-login cli

examples/cerebras/qlora.yml

@@ -7,7 +7,7 @@ push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 adapter: qlora
 lora_model_dir:

examples/code-llama/13b/lora.yml

@@ -11,7 +11,7 @@ strict: false
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./lora-out
 

examples/code-llama/13b/qlora.yml

@@ -11,7 +11,7 @@ strict: false
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./qlora-out
 

examples/code-llama/34b/lora.yml

@@ -11,7 +11,7 @@ strict: false
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./lora-out
 

examples/code-llama/34b/qlora.yml

@@ -11,7 +11,7 @@ strict: false
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./qlora-out
 

examples/code-llama/7b/lora.yml

@@ -11,7 +11,7 @@ strict: false
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./lora-out
 

examples/code-llama/7b/qlora.yml

@@ -11,7 +11,7 @@ strict: false
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./qlora-out
 

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

@@ -3,6 +3,7 @@ base_model_config: tiiuae/falcon-7b
 trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+is_falcon_derived_model: true
 load_in_8bit: true
 load_in_4bit: false
 gptq: false
@@ -11,7 +12,7 @@ push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca:chat
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 adapter: lora
 lora_model_dir:

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

@@ -6,6 +6,7 @@ base_model_config: tiiuae/falcon-7b
 trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+is_falcon_derived_model: true
 load_in_8bit: false
 # enable 4bit for QLoRA
 load_in_4bit: true
@@ -17,7 +18,7 @@ datasets:
     data_files:
       - Chain-of-Thought/formatted_cot_data/gsm8k_train.json
     type: "alpaca:chat"
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 # enable QLoRA
 adapter: qlora

examples/falcon/config-7b.yml

@@ -3,6 +3,7 @@ base_model_config: tiiuae/falcon-7b
 trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
+is_falcon_derived_model: true
 load_in_8bit: false
 load_in_4bit: false
 gptq: false
@@ -11,7 +12,7 @@ push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca:chat
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 adapter:
 lora_model_dir:

examples/gptj/qlora.yml

@@ -7,7 +7,7 @@ push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 adapter: qlora
 lora_model_dir:

examples/jeopardy-bot/config.yml

@@ -6,7 +6,7 @@ load_in_8bit: false
 datasets:
   - path: openaccess-ai-collective/jeopardy
     type: jeopardy
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.02
 adapter:
 lora_model_dir:

examples/llama-2/gptq-lora.yml

@@ -15,7 +15,7 @@ hf_use_auth_token: true
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 adapter: lora
 lora_model_dir:

examples/llama-2/lora.yml

@@ -11,7 +11,7 @@ strict: false
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./lora-out
 
@@ -56,7 +56,7 @@ flash_attention: true
 
 warmup_steps: 10
 eval_steps: 20
-eval_table_size: 5
+eval_table_size:
 eval_table_max_new_tokens: 128
 save_steps:
 debug:

examples/llama-2/qlora.yml

@@ -11,7 +11,7 @@ strict: false
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./qlora-out
 
@@ -58,7 +58,7 @@ flash_attention: true
 
 warmup_steps: 10
 eval_steps: 20
-eval_table_size: 5
+eval_table_size:
 save_steps:
 debug:
 deepspeed:

examples/llama-2/relora.yml

@@ -11,7 +11,7 @@ strict: false
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./relora-out
 

examples/llama-2/tiny-llama.yml

@@ -12,7 +12,7 @@ strict: false
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 output_dir: ./lora-out
 
@@ -56,7 +56,7 @@ flash_attention: true
 
 warmup_steps: 10
 eval_steps: 20
-eval_table_size: 5
+eval_table_size:
 save_steps:
 debug:
 deepspeed:

examples/mistral/README.md

@@ -0,0 +1,12 @@
+**Mistral 7B** is a language model with a total of 7.3 billion parameters, showcasing a notable performance across a variety of benchmarks.
+
+Fine Tune:
+```shell
+accelerate launch -m axolotl.cli.train examples/mistral/config.yml
+
+```
+
+If you run into CUDA OOM, use deepspeed with config zero2.json:
+```shell
+accelerate launch -m axolotl.cli.train examples/mistral/config.yml --deepspeed deepspeed/zero2.json
+```

examples/mistral/config.yml

@@ -0,0 +1,62 @@
+base_model: mistralai/Mistral-7B-v0.1
+base_model_config: mistralai/Mistral-7B-v0.1
+model_type: MistralForCausalLM
+tokenizer_type: LlamaTokenizer
+is_mistral_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path:
+val_set_size: 0.01
+output_dir: ./out
+
+sequence_len: 8192
+sample_packing:
+pad_to_sequence_len:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+eval_table_size: 5
+eval_table_max_new_tokens: 128
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/mistral/qlora.yml

@@ -0,0 +1,79 @@
+base_model: mistralai/Mistral-7B-v0.1
+base_model_config: mistralai/Mistral-7B-v0.1
+model_type: MistralForCausalLM
+tokenizer_type: LlamaTokenizer
+is_mistral_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 8192
+sample_packing: True
+pad_to_sequence_len: True
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 4
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+eval_table_size: 5
+eval_table_max_new_tokens: 128
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

examples/mpt-7b/config.yml

@@ -6,7 +6,7 @@ load_in_8bit: false
 datasets:
   - path: vicgalle/alpaca-gpt4
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.02
 adapter:
 lora_model_dir:

examples/openllama-3b/config.yml

@@ -9,7 +9,7 @@ push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.02
 adapter:
 lora_model_dir:

examples/openllama-3b/lora.yml

@@ -9,7 +9,7 @@ push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.02
 adapter: lora
 lora_model_dir:

examples/openllama-3b/qlora.yml

@@ -9,7 +9,7 @@ push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 adapter: qlora
 lora_model_dir:

examples/phi/phi-ft.yml

@@ -13,7 +13,7 @@ datasets:
   - path: garage-bAInd/Open-Platypus
     type: alpaca
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.05
 output_dir: ./phi-sft-out
 

examples/phi/phi-qlora.yml

@@ -13,7 +13,7 @@ datasets:
   - path: garage-bAInd/Open-Platypus
     type: alpaca
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.05
 output_dir: ./phi-sft-out
 

examples/pythia-12b/config.yml

@@ -10,7 +10,7 @@ device_map: auto
 datasets:
   - path: vicgalle/alpaca-gpt4
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.05
 adapter:
 lora_model_dir:

examples/pythia/lora.yml

@@ -4,7 +4,7 @@ load_in_8bit: true
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.05
 adapter: lora
 lora_model_dir:

examples/redpajama/config-3b.yml

@@ -7,7 +7,7 @@ load_in_8bit: false
 datasets:
   - path: vicgalle/alpaca-gpt4
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.02
 adapter:
 lora_model_dir:

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

@@ -5,7 +5,7 @@ load_in_8bit: false
 datasets:
   - path: vicgalle/alpaca-gpt4
     type: alpaca
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.05
 adapter: lora
 lora_model_dir:

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

@@ -16,7 +16,7 @@ datasets:
     data_files:
       - openassistant_best_replies_train.jsonl
     type: "completion"
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path:
 val_set_size: 0.01
 # enable QLoRA
 adapter: qlora

requirements.txt

@@ -4,16 +4,15 @@ torch==2.0.1
 auto-gptq
 packaging
 peft @ git+https://github.com/huggingface/peft.git
-transformers @ git+https://github.com/huggingface/transformers.git
+transformers @ git+https://github.com/huggingface/transformers.git@bd6205919aad4d3a2300a39a98a642f1cc3a5348
 bitsandbytes>=0.41.1
-accelerate @ git+https://github.com/huggingface/accelerate
+accelerate @ git+https://github.com/huggingface/accelerate@80da9cfb09bb3cc9f1b385cb55d6b90d025a5fd9
 deepspeed
 addict
-evaluate
 fire
 PyYAML>=6.0
 datasets
-flash-attn>=2.2.1
+flash-attn>=2.3.0
 sentencepiece
 wandb
 einops
@@ -31,3 +30,4 @@ scipy
 scikit-learn==1.2.2
 pynvml
 art
+fschat==0.2.29

scripts/finetune.py

@@ -7,6 +7,7 @@ import transformers
 
 from axolotl.cli import (
     check_accelerate_default_config,
+    check_user_token,
     do_inference,
     do_merge_lora,
     load_cfg,
@@ -31,6 +32,7 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
     )
     parsed_cfg = load_cfg(config, **kwargs)
     check_accelerate_default_config()
+    check_user_token()
     parser = transformers.HfArgumentParser((TrainerCliArgs))
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
         return_remaining_strings=True

src/axolotl/cli/__init__.py

@@ -14,6 +14,8 @@ import yaml
 # add src to the pythonpath so we don't need to pip install this
 from accelerate.commands.config import config_args
 from art import text2art
+from huggingface_hub import HfApi
+from huggingface_hub.utils import LocalTokenNotFoundError
 from transformers import GenerationConfig, TextStreamer
 
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
@@ -49,7 +51,7 @@ def print_axolotl_text_art(suffix=None):
 
 
 def get_multi_line_input() -> Optional[str]:
-    print("Give me an instruction (Ctrl + D to finish): ")
+    print("Give me an instruction (Ctrl + D to submit): ")
     instruction = ""
     for line in sys.stdin:
         instruction += line  # pylint: disable=consider-using-join
@@ -247,3 +249,16 @@ def check_accelerate_default_config():
         LOG.warning(
             f"accelerate config file found at {config_args.default_yaml_config_file}. This can lead to unexpected errors"
         )
+
+
+def check_user_token():
+    # Verify if token is valid
+    api = HfApi()
+    try:
+        user_info = api.whoami()
+        return bool(user_info)
+    except LocalTokenNotFoundError:
+        LOG.warning(
+            "Error verifying HuggingFace token. Remember to log in using `huggingface-cli login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
+        )
+        return False

src/axolotl/cli/train.py

@@ -8,6 +8,7 @@ import transformers
 
 from axolotl.cli import (
     check_accelerate_default_config,
+    check_user_token,
     load_cfg,
     load_datasets,
     print_axolotl_text_art,
@@ -21,6 +22,7 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
     print_axolotl_text_art()
     parsed_cfg = load_cfg(config, **kwargs)
     check_accelerate_default_config()
+    check_user_token()
     parser = transformers.HfArgumentParser((TrainerCliArgs))
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
         return_remaining_strings=True

src/axolotl/datasets.py

@@ -5,7 +5,7 @@ import os
 from typing import List
 
 import torch
-from datasets import Dataset, IterableDataset
+from datasets import Dataset, IterableDataset, Sequence, Value
 
 from .prompt_tokenizers import PromptTokenizingStrategy
 
@@ -22,7 +22,7 @@ class TokenizedPromptDataset(Dataset):
     """
     Dataset that returns tokenized prompts from a stream of text files.
         Args:
-            prompt_tokenizer (PromptTokenizingStrategy): The prompt tokenizing method for proccessing the data.
+            prompt_tokenizer (PromptTokenizingStrategy): The prompt tokenizing method for processing the data.
             dataset (dataset.Dataset): Dataset with text files.
     """
 
@@ -42,11 +42,15 @@ class TokenizedPromptDataset(Dataset):
         if self.prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
             map_kwargs["batch_size"] = 100
-        return dataset.map(
-            self.prompt_tokenizer.tokenize_prompt,
-            num_proc=num_proc,
-            remove_columns=features,
-            **map_kwargs,
+        return (
+            dataset.map(
+                self.prompt_tokenizer.tokenize_prompt,
+                num_proc=num_proc,
+                remove_columns=features,
+                **map_kwargs,
+            )
+            .cast_column("input_ids", Sequence(feature=Value(dtype="int32", id=None)))
+            .cast_column("labels", Sequence(feature=Value(dtype="int32", id=None)))
         )
 
 
@@ -55,7 +59,7 @@ class ConstantLengthDataset(IterableDataset):
     """
     Iterable dataset that returns constant length chunks of tokens from stream of text files.
         Args:
-            tokenizer (Tokenizer): The processor used for proccessing the data.
+            tokenizer (Tokenizer): The processor used for processing the data.
             dataset (dataset.Dataset): Dataset with text files.
             seq_length (int): Length of token sequences to return.
     """

src/axolotl/models/phi/modeling_mixformer_sequential.py

@@ -711,12 +711,8 @@ class ParallelBlock(nn.Module):
         self.resid_dropout = nn.Dropout(config.resid_pdrop)
         self.block_idx = block_idx
 
-        self.mixer = MHA(config=config, **mixer, layer_idx=block_idx)
-        mlp_cls = mlp.pop("mlp_cls")
-        if mlp_cls == "fused_mlp":
-            self.mlp = FusedMLP(config=config, **mlp)
-        else:
-            self.mlp = MLP(config=config, **mlp)
+        self.mixer = MHA(config, layer_idx=block_idx)
+        self.mlp = MLP(config)
 
     def forward(
         self,

src/axolotl/monkeypatch/btlm_attn_hijack_flash.py

@@ -7,6 +7,7 @@ import logging
 from typing import Optional, Tuple
 
 import torch
+from accelerate import init_empty_weights
 from flash_attn.flash_attn_interface import flash_attn_func
 from transformers import AutoConfig, AutoModelForCausalLM
 
@@ -17,7 +18,8 @@ def replace_btlm_attn_with_flash_attn(model_name="cerebras/btlm-3b-8k-base"):
     # this is a wonky hack to get the remotely loaded module
     model_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
     # we need to load the model here in order for modeling_btlm to be available
-    AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+    with init_empty_weights():
+        AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
     module_name = model_config.__class__.__module__.replace(
         ".configuration_btlm", ".modeling_btlm"
     )

src/axolotl/monkeypatch/falcon_attn_hijack_flash.py

@@ -1,101 +0,0 @@
-"""
-Flash Attention monkey patch for Falcon
-
-copied from https://github.com/pacman100/DHS-LLM-Workshop/blob/main/chat_assistant/training/falcon_flash_attn_monkey_patch.py
-"""
-
-from typing import Optional, Tuple
-
-import torch
-import transformers
-from flash_attn import flash_attn_func
-
-
-def forward(
-    self,
-    hidden_states: torch.Tensor,
-    alibi: Optional[torch.Tensor],
-    attention_mask: torch.Tensor,  # pylint: disable=unused-argument
-    layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    head_mask: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
-    use_cache: bool = False,
-    output_attentions: bool = False,  # pylint: disable=unused-argument
-):
-    fused_qkv = self.query_key_value(
-        hidden_states
-    )  # [batch_size, seq_length, 3 x hidden_size]
-    num_kv_heads = (
-        self.num_heads if self.new_decoder_architecture else self.num_kv_heads
-    )
-    # 3 x [batch_size, seq_length, num_heads, head_dim]
-    (
-        query_layer,
-        key_layer,
-        value_layer,
-    ) = self._split_heads(  # pylint: disable=protected-access
-        fused_qkv
-    )
-
-    batch_size, query_length, _, _ = query_layer.shape
-
-    query_layer = query_layer.transpose(1, 2).reshape(
-        batch_size * self.num_heads, query_length, self.head_dim
-    )
-    key_layer = key_layer.transpose(1, 2).reshape(
-        batch_size * num_kv_heads,
-        query_length,
-        self.head_dim,
-    )
-    value_layer = value_layer.transpose(1, 2).reshape(
-        batch_size * num_kv_heads, query_length, self.head_dim
-    )
-
-    past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
-    query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
-
-    if layer_past is not None:
-        past_key, past_value = layer_past
-        # concatenate along seq_length dimension:
-        #  - key: [batch_size * self.num_heads, kv_length, head_dim]
-        #  - value: [batch_size * self.num_heads, kv_length, head_dim]
-        key_layer = torch.cat((past_key, key_layer), dim=1)
-        value_layer = torch.cat((past_value, value_layer), dim=1)
-
-    # unused
-    # _, kv_length, _ = key_layer.shape
-    if use_cache:
-        present = (key_layer, value_layer)
-    else:
-        present = None
-    # unused
-    # attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype)
-    query_layer_ = (
-        query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
-        .transpose(1, 2)
-        .to(torch.bfloat16)
-    )
-    key_layer_ = (
-        key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
-        .transpose(1, 2)
-        .to(torch.bfloat16)
-    )
-    value_layer_ = (
-        value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
-        .transpose(1, 2)
-        .to(torch.bfloat16)
-    )
-
-    if alibi is not None:
-        raise ValueError("`alibi` is not supported when `use_flash_attn` is True")
-
-    # below output will have shape (batch_size, seqlen, nheads, headdim)
-    attn_output = flash_attn_func(query_layer_, key_layer_, value_layer_, causal=True)
-    attn_output = attn_output.reshape(
-        batch_size, query_length, self.num_heads * self.head_dim
-    )
-    output_tensor = self.dense(attn_output)
-    return output_tensor, present
-
-
-def replace_falcon_attn_with_flash_attn():
-    transformers.models.falcon.modeling_falcon.FalconAttention.forward = forward

src/axolotl/monkeypatch/fastchat_conversation_turns.py

@@ -0,0 +1,174 @@
+"""
+monkeypatch to add a get_turns method
+"""
+
+import logging
+from typing import Generator, Tuple
+
+from fastchat.conversation import SeparatorStyle
+
+LOG = logging.getLogger("axolotl.monkeypatch.fastchat_conversation_turns")
+
+
+def get_prompt(self) -> str:
+    ret = ""
+    for role, msg in self.get_turns():
+        ret += role + msg
+    return ret
+
+
+def get_turns(  # pylint: disable=too-many-return-statements
+    self,
+) -> Generator[Tuple[str, str], None, None]:
+    """Get the prompt for generation."""
+    system_prompt = self.system_template.format(system_message=self.system_message)
+    if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
+        yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", message + self.sep
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.ADD_COLON_TWO:
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt + seps[0]
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield role + ": ", message + seps[i % 2]
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
+        yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", message + self.sep
+            else:
+                yield role + ": ", ""  # must be end with a space
+        return
+    if self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
+        yield "", "" if system_prompt == "" else system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + "\n", message + self.sep
+            else:
+                yield role + "\n", ""
+        return
+    if self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
+        yield "", system_prompt
+        for role, message in self.messages:
+            if message:
+                yield role, message + self.sep
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.NO_COLON_TWO:
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield role, message + seps[i % 2]
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.RWKV:
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                yield role + ": ", message.replace("\r\n", "\n").replace(
+                    "\n\n", "\n"
+                ) + "\n\n"
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.LLAMA2:
+        seps = [self.sep, self.sep2]
+        if self.system_message:
+            yield "", system_prompt
+        else:
+            yield "", "[INST] "
+        for i, (role, message) in enumerate(self.messages[1:]):
+            if message:
+                yield role + " ", message + seps[i % 2]
+            else:
+                yield role, ""
+        return
+    if self.sep_style == SeparatorStyle.CHATGLM:
+        # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
+        # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
+        round_add_n = 1 if self.name == "chatglm2" else 0
+        if system_prompt:
+            yield "", system_prompt + self.sep
+
+        for i, (role, message) in enumerate(self.messages):
+            if i % 2 == 0:
+                yield "", f"[Round {i//2 + round_add_n}]{self.sep}"
+
+            if message:
+                yield f"{role}：", f"{message}{self.sep}"
+            else:
+                yield f"{role}：", ""
+        return
+    if self.sep_style == SeparatorStyle.CHATML:
+        yield "", "" if system_prompt == "" else system_prompt + self.sep + "\n"
+        for role, message in self.messages:
+            if message:
+                yield role + "\n", message + self.sep + "\n"
+            else:
+                yield role + "\n", ""
+        return
+    if self.sep_style == SeparatorStyle.CHATINTERN:
+        # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            prefix = "<s>" if i % 2 == 0 else ""
+            if message:
+                yield prefix + role + ":", message + seps[i % 2] + "\n"
+            else:
+                yield role + ":", ""
+        return
+    if self.sep_style == SeparatorStyle.DOLLY:
+        seps = [self.sep, self.sep2]
+        yield "", system_prompt
+        for i, (role, message) in enumerate(self.messages):
+            if message:
+                suffix = "\n\n" if i % 2 == 1 else ""
+                yield role + ":\n", message + seps[i % 2] + suffix
+            else:
+                yield role + ":\n", ""
+        return
+    if self.sep_style == SeparatorStyle.PHOENIX:
+        yield "", system_prompt
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", "<s>" + message + "</s>"
+            else:
+                yield role + ": " + "<s>", ""
+        return
+    if self.sep_style == SeparatorStyle.ROBIN:
+        yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ":\n", message + self.sep
+            else:
+                yield role + ":\n", ""
+        return
+    if self.sep_style == SeparatorStyle.FALCON_CHAT:
+        if self.system_message:
+            yield "", system_prompt + self.sep
+        for role, message in self.messages:
+            if message:
+                yield role + ": ", message + self.sep
+            else:
+                yield role + ":", ""
+    else:
+        raise ValueError(f"Invalid style: {self.sep_style}")
+
+
+def add_get_turns_to_conversation():
+    import fastchat.conversation
+
+    fastchat.conversation.Conversation.get_turns = get_turns
+    fastchat.conversation.Conversation.get_prompt = get_prompt

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -38,7 +38,11 @@ except ImportError:
 LOG = logging.getLogger("axolotl")
 
 
-def replace_llama_attn_with_flash_attn(packed: Optional[bool] = False):
+def replace_llama_attn_with_flash_attn(
+    packed: Optional[bool] = False,
+    cross_entropy: Optional[bool] = False,
+    rms_norm: Optional[bool] = False,
+):
     transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (  # pylint: disable=protected-access
         _prepare_decoder_attention_mask
     )
@@ -49,33 +53,37 @@ def replace_llama_attn_with_flash_attn(packed: Optional[bool] = False):
             llama_model_forward
         )
 
-    try:
-        from flash_attn.losses.cross_entropy import CrossEntropyLoss
+    # skip only if explicitly disabled
+    if cross_entropy:
+        try:
+            from flash_attn.losses.cross_entropy import CrossEntropyLoss
 
-        LOG.info("patching with flash_attn.losses.cross_entropy")
-        transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
-            CrossEntropyLoss, inplace_backward=True
-        )
-    except ImportError:
-        LOG.info(
-            "optimized flash-attention CrossEntropyLoss not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=xentropy_cuda_lib&subdirectory=csrc/xentropy'`)"
-        )
+            LOG.info("patching with flash_attn.losses.cross_entropy")
+            transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
+                CrossEntropyLoss, inplace_backward=True
+            )
+        except ImportError:
+            LOG.info(
+                "optimized flash-attention CrossEntropyLoss not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=xentropy_cuda_lib&subdirectory=csrc/xentropy'`)"
+            )
 
-    try:
-        from flash_attn.ops.rms_norm import RMSNorm
+    # skip only if explicitly disabled
+    if rms_norm:
+        try:
+            from flash_attn.ops.rms_norm import RMSNorm
 
-        class LlamaRMSNorm(RMSNorm):
-            """Patched LLamaRMSNorm"""
+            class LlamaRMSNorm(RMSNorm):
+                """Patched LLamaRMSNorm"""
 
-            def __init__(self, hidden_size, eps=1e-6):
-                super().__init__(hidden_size, eps=eps)
+                def __init__(self, hidden_size, eps=1e-6):
+                    super().__init__(hidden_size, eps=eps)
 
-        LOG.info("patching with flash_attn.ops.rms_norm")
-        transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
-    except ImportError:
-        LOG.info(
-            "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
-        )
+            LOG.info("patching with flash_attn.ops.rms_norm")
+            transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
+        except ImportError:
+            LOG.info(
+                "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
+            )
 
 
 # Disable the transformation of the attention mask in LlamaModel as the flash attention
@@ -99,6 +107,7 @@ def flashattn_forward(
     past_key_value: Optional[Tuple[torch.Tensor]] = None,
     output_attentions: bool = False,
     use_cache: bool = False,
+    padding_mask: Optional[torch.LongTensor] = None,  # pylint: disable=unused-argument
     cu_seqlens: Optional[torch.Tensor] = None,
     max_seqlen: Optional[torch.Tensor] = None,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
@@ -476,6 +485,13 @@ def llama_model_forward(
             dtype=torch.bool,
             device=inputs_embeds.device,
         )
+        padding_mask = None
+    else:
+        if 0 in attention_mask:
+            padding_mask = attention_mask
+        else:
+            padding_mask = None
+
     attention_mask = (
         self._prepare_decoder_attention_mask(  # pylint: disable=protected-access
             attention_mask,
@@ -510,7 +526,9 @@ def llama_model_forward(
             def create_custom_forward(module):
                 def custom_forward(*inputs):
                     # None for past_key_value
-                    return module(*inputs)
+                    return module(
+                        *inputs,
+                    )
 
                 return custom_forward
 
@@ -519,9 +537,10 @@ def llama_model_forward(
                 hidden_states,
                 attention_mask,
                 position_ids,
-                None,
+                past_key_value,
                 output_attentions,
                 None,
+                padding_mask,
                 cu_seqlens,
                 max_seqlen,
             )
@@ -533,6 +552,7 @@ def llama_model_forward(
                 past_key_value=past_key_value,
                 output_attentions=output_attentions,
                 use_cache=use_cache,
+                padding_mask=padding_mask,
                 cu_seqlens=cu_seqlens,
                 max_seqlen=max_seqlen,
             )
@@ -579,6 +599,7 @@ class LlamaDecoderLayer(OriginalLlamaDecoderLayer):
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
+        padding_mask: Optional[torch.LongTensor] = None,
         cu_seqlens: Optional[torch.Tensor] = None,
         max_seqlen: Optional[torch.Tensor] = None,
     ) -> Tuple[
@@ -611,6 +632,7 @@ class LlamaDecoderLayer(OriginalLlamaDecoderLayer):
             past_key_value=past_key_value,
             output_attentions=output_attentions,
             use_cache=use_cache,
+            padding_mask=padding_mask,
             cu_seqlens=cu_seqlens,
             max_seqlen=max_seqlen,
         )

src/axolotl/monkeypatch/mistral_attn_hijack_flash.py

@@ -0,0 +1,541 @@
+"""Flash attention monkey patch for mistral model"""
+# pylint: disable=duplicate-code
+
+import logging
+from typing import List, Optional, Tuple, Union
+
+import torch
+import transformers
+from einops import rearrange
+from flash_attn.bert_padding import pad_input, unpad_input
+from flash_attn.flash_attn_interface import (  # pylint: disable=ungrouped-imports
+    flash_attn_kvpacked_func,
+    flash_attn_varlen_kvpacked_func,
+    flash_attn_varlen_qkvpacked_func,
+)
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.models.mistral.modeling_mistral import (
+    MistralDecoderLayer as OriginalMistralDecoderLayer,
+)
+from transformers.models.mistral.modeling_mistral import apply_rotary_pos_emb, repeat_kv
+
+from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
+
+LOG = logging.getLogger("axolotl.monkeypatch.mistral")
+
+
+def replace_mistral_attn_with_flash_attn(
+    packed: Optional[bool] = False,
+):
+    transformers.models.mistral.modeling_mistral.MistralModel._prepare_decoder_attention_mask = (  # pylint: disable=protected-access
+        _prepare_decoder_attention_mask
+    )
+    transformers.models.mistral.modeling_mistral.MistralAttention.forward = (
+        flashattn_forward
+    )
+    if packed:
+        transformers.models.mistral.modeling_mistral.MistralDecoderLayer = (
+            MistralDecoderLayer
+        )
+        transformers.models.mistral.modeling_mistral.MistralModel.forward = (
+            mistral_model_forward
+        )
+
+
+# Disable the transformation of the attention mask in LlamaModel as the flash attention
+# requires the attention mask to be the same as the key_padding_mask
+def _prepare_decoder_attention_mask(
+    self,
+    attention_mask,
+    input_shape,
+    inputs_embeds,
+    past_key_values_length,
+    sliding_window,
+):  # pylint: disable=unused-argument
+    # [bsz, seq_len]
+    return attention_mask
+
+
+def flashattn_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    bsz, q_len, _ = hidden_states.size()
+
+    query_states = self.q_proj(hidden_states)
+    key_states = self.k_proj(hidden_states)
+    value_states = self.v_proj(hidden_states)
+
+    query_states = query_states.view(
+        bsz, q_len, self.num_heads, self.head_dim
+    ).transpose(1, 2)
+    key_states = key_states.view(
+        bsz, q_len, self.num_key_value_heads, self.head_dim
+    ).transpose(1, 2)
+    value_states = value_states.view(
+        bsz, q_len, self.num_key_value_heads, self.head_dim
+    ).transpose(1, 2)
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    # repeat k/v heads if n_kv_heads < n_heads
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    if self.training:
+        # during training q,k,v always have same seqlen
+        assert key_states.shape == query_states.shape
+        is_causal = True
+    else:
+        # turn off FA causal mask after first inference autoregressive iteration
+        # only on first autoregressive step q,k,v have same seqlen
+        is_causal = key_states.shape == query_states.shape
+
+    if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
+        # special handling using sample packing
+        qkv = torch.stack(
+            [query_states, key_states, value_states], dim=2
+        )  # [bsz, nh, 3, q_len, hd]
+        qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
+        qkv = rearrange(qkv, "b s ... -> (b s) ...")
+
+        output = flash_attn_varlen_qkvpacked_func(
+            qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=None, causal=True
+        )
+        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
+    elif query_states.shape == key_states.shape:
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        qkv_unpad, cu_seqlens_q, max_seqlen_q, _, output_pad_fn = generate_qkv(
+            query_states,
+            key_states,
+            value_states,
+            qkvpacked=True,
+            # We have disabled _prepare_decoder_attention_mask in LlamaModel
+            # the attention_mask should be the same as the key_padding_mask
+            key_padding_mask=attention_mask,
+            query_padding_mask=attention_mask[:, -query_states.size(1) :]
+            if attention_mask is not None
+            else None,
+        )
+        output_unpad = flash_attn_varlen_qkvpacked_func(
+            qkv_unpad,
+            cu_seqlens_q,
+            max_seqlen_q,
+            0.0,
+            softmax_scale=None,
+            causal=is_causal,
+        )
+        output = output_pad_fn(output_unpad)
+    else:
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        if attention_mask is None or attention_mask.all().item():
+            output = flash_attn_kvpacked_func(
+                query_states,
+                torch.stack([key_states, value_states], 2),
+                causal=is_causal,
+            )
+        else:
+            (  # pylint: disable=unbalanced-tuple-unpacking
+                q_unpad,
+                kv_unpad,
+                cu_seqlens_q,
+                cu_seqlens_k,
+                max_seqlen_q,
+                max_seqlen_k,
+                _,
+                _,
+                output_pad_fn,
+            ) = generate_qkv(
+                query_states,
+                key_states,
+                value_states,
+                kvpacked=True,
+                key_padding_mask=attention_mask,
+                query_padding_mask=attention_mask[:, -query_states.size(1) :]
+                if attention_mask is not None
+                else None,
+            )
+            if q_unpad.dtype != kv_unpad.dtype:
+                kv_unpad = kv_unpad.to(q_unpad.dtype)
+            output_unpad = flash_attn_varlen_kvpacked_func(
+                q_unpad,
+                kv_unpad,
+                cu_seqlens_q,
+                cu_seqlens_k,
+                max_seqlen_q,
+                max_seqlen_k,
+                0.0,
+                softmax_scale=None,
+                causal=is_causal,
+            )
+            output = output_pad_fn(output_unpad)
+
+    attn_output = output
+    if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
+        raise ValueError(
+            f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
+            f" {attn_output.size()}"
+        )
+    attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
+
+    attn_output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return attn_output, attn_weights, past_key_value
+
+
+# based on https://github.com/Dao-AILab/flash-attention/blob/364a5b/tests/test_flash_attn.py#L38
+def generate_qkv(
+    q,
+    k,
+    v,
+    query_padding_mask=None,
+    key_padding_mask=None,
+    kvpacked=False,
+    qkvpacked=False,
+):  # pylint: disable=invalid-name,unnecessary-lambda-assignment
+    """
+    Arguments:
+        q: (batch_size, seqlen_q, nheads, d)
+        k: (batch_size, seqlen_k, nheads_k, d)
+        v: (batch_size, seqlen_k, nheads_k, d)
+        query_padding_mask: (batch_size, seqlen), bool
+        key_padding_mask: (batch_size, seqlen), bool
+    """
+    assert not (kvpacked and qkvpacked)
+    batch_size, seqlen_q, nheads, d = q.shape
+    _, seqlen_k, nheads_k, _ = k.shape
+    assert k.shape == (batch_size, seqlen_k, nheads_k, d)
+    assert v.shape == (batch_size, seqlen_k, nheads_k, d)
+
+    if query_padding_mask is not None:
+        q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
+            q, query_padding_mask
+        )
+
+        output_pad_fn = lambda output_unpad: pad_input(  # noqa: E731
+            output_unpad, indices_q, batch_size, seqlen_q
+        )
+
+    else:
+        q_unpad = rearrange(q, "b s h d -> (b s) h d")
+        cu_seqlens_q = torch.arange(
+            0,
+            (batch_size + 1) * seqlen_q,
+            step=seqlen_q,
+            dtype=torch.int32,
+            device=q_unpad.device,
+        )
+        max_seqlen_q = seqlen_q
+
+        output_pad_fn = lambda output_unpad: rearrange(  # noqa: E731
+            output_unpad, "(b s) h d -> b s h d", b=batch_size
+        )
+
+    if key_padding_mask is not None:
+        k_unpad, _, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
+        v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
+    else:
+        k_unpad = rearrange(k, "b s h d -> (b s) h d")
+        v_unpad = rearrange(v, "b s h d -> (b s) h d")
+        cu_seqlens_k = torch.arange(
+            0,
+            (batch_size + 1) * seqlen_k,
+            step=seqlen_k,
+            dtype=torch.int32,
+            device=k_unpad.device,
+        )
+        max_seqlen_k = seqlen_k
+
+    if qkvpacked:
+        assert nheads == nheads_k
+        qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
+        qkv = torch.stack([q, k, v], dim=2)
+        return (qkv_unpad, cu_seqlens_q, max_seqlen_q, qkv, output_pad_fn)
+
+    if kvpacked:
+        kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
+        kv = torch.stack([k, v], dim=2)
+        return (
+            q_unpad,
+            kv_unpad,
+            cu_seqlens_q,
+            cu_seqlens_k,
+            max_seqlen_q,
+            max_seqlen_k,
+            q,
+            kv,
+            output_pad_fn,
+        )
+
+    return (
+        q_unpad,
+        k_unpad,
+        v_unpad,
+        cu_seqlens_q,
+        cu_seqlens_k,
+        max_seqlen_q,
+        max_seqlen_k,
+        q,
+        k,
+        v,
+        output_pad_fn,
+    )
+
+
+def mistral_model_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, BaseModelOutputWithPast]:
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # retrieve input_ids and inputs_embeds
+    if input_ids is not None and inputs_embeds is not None:
+        raise ValueError(
+            "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+        )
+    if input_ids is not None:
+        batch_size, seq_length = input_ids.shape
+    elif inputs_embeds is not None:
+        batch_size, seq_length, _ = inputs_embeds.shape
+    else:
+        raise ValueError(
+            "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+        )
+
+    seq_length_with_past = seq_length
+    past_key_values_length = 0
+
+    if past_key_values is not None:
+        past_key_values_length = past_key_values[0][0].shape[2]
+        seq_length_with_past = seq_length_with_past + past_key_values_length
+
+    cu_seqlens = None
+    max_seqlen = None
+    if position_ids is None:
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        position_ids = torch.arange(
+            past_key_values_length,
+            seq_length + past_key_values_length,
+            dtype=torch.long,
+            device=device,
+        )
+        position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+    else:
+        position_ids = position_ids.view(-1, seq_length).long()
+        cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
+        cu_seqlens = cu_seqlens.squeeze()
+
+    if inputs_embeds is None:
+        inputs_embeds = self.embed_tokens(input_ids)
+    # embed positions
+    if attention_mask is None:
+        attention_mask = torch.ones(
+            (batch_size, seq_length_with_past),
+            dtype=torch.bool,
+            device=inputs_embeds.device,
+        )
+    attention_mask = (
+        self._prepare_decoder_attention_mask(  # pylint: disable=protected-access
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+            sliding_window=self.config.sliding_window,
+        )
+    )
+
+    hidden_states = inputs_embeds
+
+    if self.gradient_checkpointing and self.training:
+        if use_cache:
+            transformers.logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+    # decoder layers
+    all_hidden_states = () if output_hidden_states else None
+    all_self_attns = () if output_attentions else None
+    next_decoder_cache = () if use_cache else None
+
+    for idx, decoder_layer in enumerate(self.layers):
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+        if self.gradient_checkpointing and self.training:
+
+            def create_custom_forward(module):
+                def custom_forward(*inputs):
+                    # None for past_key_value
+                    return module(*inputs)
+
+                return custom_forward
+
+            layer_outputs = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(decoder_layer),
+                hidden_states,
+                attention_mask,
+                position_ids,
+                past_key_value,
+                output_attentions,
+                None,
+                cu_seqlens,
+                max_seqlen,
+            )
+        else:
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+
+        hidden_states = layer_outputs[0]
+
+        if use_cache:
+            next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+        if output_attentions:
+            all_self_attns += (layer_outputs[1],)
+
+    hidden_states = self.norm(hidden_states)
+
+    # add hidden states from the last decoder layer
+    if output_hidden_states:
+        all_hidden_states += (hidden_states,)
+
+    next_cache = next_decoder_cache if use_cache else None
+    if not return_dict:
+        return tuple(
+            v
+            for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+            if v is not None
+        )
+    return BaseModelOutputWithPast(
+        last_hidden_state=hidden_states,
+        past_key_values=next_cache,
+        hidden_states=all_hidden_states,
+        attentions=all_self_attns,
+    )
+
+
+class MistralDecoderLayer(OriginalMistralDecoderLayer):
+    """
+    patched version of MistralDecoderLayer to pass through the precalculated cu_seqlens
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[torch.Tensor] = None,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cu_seqlens (`torch.Tensor`, *optional*) cumulative sequence len when packing
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs

src/axolotl/monkeypatch/stablelm_attn_hijack_flash.py

@@ -0,0 +1,415 @@
+# coding=utf-8
+# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This code is based off the following work:
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+""" PyTorch StableLM Epoch model. """
+import importlib
+import math
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from accelerate import init_empty_weights
+from einops import rearrange
+from flash_attn.flash_attn_interface import (  # pylint: disable=ungrouped-imports
+    flash_attn_varlen_qkvpacked_func,
+)
+from torch import nn
+from transformers import AutoConfig, AutoModelForCausalLM
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.utils import logging
+
+from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
+
+logger = logging.get_logger(__name__)
+
+
+def replace_stablelm_attn_with_flash_attn(model_name="stabilityai/stablelm-3b-4e1t"):
+    # this is a wonky hack to get the remotely loaded module
+    model_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+    # we need to load the model here in order for modeling_stablelm_epoch to be available
+    with init_empty_weights():
+        AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+    module_name = model_config.__class__.__module__.replace(
+        ".configuration_stablelm_epoch", ".modeling_stablelm_epoch"
+    )
+    modeling_stablelm = importlib.import_module(module_name)
+    modeling_stablelm.Attention.forward = (  # pylint: disable=protected-access
+        flashattn_attn
+    )
+    modeling_stablelm.StableLMEpochModel.forward = (  # pylint: disable=protected-access
+        stablelm_model_forward
+    )
+    modeling_stablelm.DecoderLayer.forward = (  # pylint: disable=protected-access
+        decoder_layer_forward
+    )
+
+
+def rotate_half(x: torch.Tensor):
+    """Rotates half the hidden dims of the input."""
+    # pylint: disable=invalid-name
+    x1, x2 = torch.chunk(x, 2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    # pylint: disable=invalid-name
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def flashattn_attn(
+    self,
+    hidden_states: torch.FloatTensor,
+    attention_mask: torch.FloatTensor,
+    position_ids: torch.LongTensor,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: Optional[bool] = False,  # pylint: disable=unused-argument
+    use_cache: Optional[bool] = False,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    bsz, q_len, _ = hidden_states.size()
+
+    query_states = self.q_proj(hidden_states)
+    key_states = self.k_proj(hidden_states)
+    value_states = self.v_proj(hidden_states)
+
+    query_states = query_states.view(
+        bsz, q_len, self.num_heads, self.head_dim
+    ).transpose(1, 2)
+    key_states = key_states.view(
+        bsz, q_len, self.num_key_value_heads, self.head_dim
+    ).transpose(1, 2)
+    value_states = value_states.view(
+        bsz, q_len, self.num_key_value_heads, self.head_dim
+    ).transpose(1, 2)
+
+    query_rot = query_states[..., : self.rotary_ndims]
+    query_pass = query_states[..., self.rotary_ndims :]
+    key_rot = key_states[..., : self.rotary_ndims]
+    key_pass = key_states[..., self.rotary_ndims :]
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = apply_rotary_pos_emb(
+        query_rot, key_rot, cos, sin, position_ids
+    )
+
+    # [batch_size, num_heads, seq_len, head_dim]
+    query_states = torch.cat((query_states, query_pass), dim=-1)
+    key_states = torch.cat((key_states, key_pass), dim=-1)
+
+    if past_key_value is not None:
+        # Reuse k, v, self_attention
+        key_states = torch.cat((past_key_value[0], key_states), dim=2)
+        value_states = torch.cat((past_key_value[1], value_states), dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    # Repeat k/v heads if n_kv_heads < n_heads
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
+        # special handling using sample packing
+        qkv = torch.stack(
+            [query_states, key_states, value_states], dim=2
+        )  # [bsz, nh, 3, q_len, hd]
+        qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
+        qkv = rearrange(qkv, "b s ... -> (b s) ...")
+        softmax_scale = None
+
+        output = flash_attn_varlen_qkvpacked_func(
+            qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=softmax_scale, causal=True
+        )
+
+        attn_output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
+        attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
+    else:
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # Upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        # Merge heads
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+    # Final linear projection
+    attn_output = self.o_proj(attn_output)
+
+    return attn_output, None, past_key_value
+
+
+def decoder_layer_forward(
+    self,
+    hidden_states: Optional[torch.FloatTensor],
+    attention_mask: Optional[torch.FloatTensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: Optional[bool] = False,
+    use_cache: Optional[bool] = False,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[torch.Tensor] = None,
+) -> Union[
+    Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]
+]:
+    # pylint: disable=duplicate-code
+    residual = hidden_states
+
+    hidden_states = self.input_layernorm(hidden_states)
+
+    # Self Attention
+    hidden_states, self_attn_weights, present_key_value = self.self_attn(
+        hidden_states=hidden_states,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_value=past_key_value,
+        output_attentions=output_attentions,
+        use_cache=use_cache,
+        cu_seqlens=cu_seqlens,
+        max_seqlen=max_seqlen,
+    )
+    hidden_states = residual + hidden_states
+
+    # Fully Connected
+    residual = hidden_states
+    hidden_states = self.post_attention_layernorm(hidden_states)
+    hidden_states = self.mlp(hidden_states)
+    hidden_states = residual + hidden_states
+
+    outputs = (hidden_states,)
+
+    if output_attentions:
+        outputs += (self_attn_weights,)
+
+    if use_cache:
+        outputs += (present_key_value,)
+
+    return outputs
+
+
+def stablelm_model_forward(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.FloatTensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, BaseModelOutputWithPast]:
+    # pylint: disable=duplicate-code
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # Retrieve input_ids and inputs_embeds
+    if input_ids is not None and inputs_embeds is not None:
+        raise ValueError(
+            "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+        )
+    if input_ids is not None:
+        batch_size, seq_length = input_ids.shape
+    elif inputs_embeds is not None:
+        batch_size, seq_length, _ = inputs_embeds.shape
+    else:
+        raise ValueError(
+            "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+        )
+
+    seq_length_with_past = seq_length
+    past_key_values_length = 0
+
+    if past_key_values is not None:
+        past_key_values_length = past_key_values[0][0].shape[2]
+        seq_length_with_past = seq_length_with_past + past_key_values_length
+
+    cu_seqlens = None
+    max_seqlen = None
+    if position_ids is None:
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        position_ids = torch.arange(
+            past_key_values_length,
+            seq_length + past_key_values_length,
+            dtype=torch.long,
+            device=device,
+        )
+        position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+    else:
+        position_ids = position_ids.view(-1, seq_length).long()
+        cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
+        cu_seqlens = cu_seqlens.squeeze()
+
+    if inputs_embeds is None:
+        inputs_embeds = self.embed_tokens(input_ids)
+    # Embed positions
+    if attention_mask is None:
+        attention_mask = torch.ones(
+            (batch_size, seq_length_with_past),
+            dtype=torch.bool,
+            device=inputs_embeds.device,
+        )
+    attention_mask = (
+        self._prepare_decoder_attention_mask(  # pylint: disable=protected-access
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+    )
+
+    hidden_states = inputs_embeds
+
+    if self.gradient_checkpointing and self.training:
+        if use_cache:
+            logger.warning(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+    # Decoder layers
+    all_hidden_states = () if output_hidden_states else None
+    all_self_attns = () if output_attentions else None
+    next_decoder_cache = () if use_cache else None
+
+    for idx, decoder_layer in enumerate(self.layers):
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+        if self.gradient_checkpointing and self.training:
+
+            def create_custom_forward(module):
+                def custom_forward(*inputs):
+                    # None for past_key_value
+                    return module(*inputs)
+
+                return custom_forward
+
+            layer_outputs = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(decoder_layer),
+                hidden_states,
+                attention_mask,
+                position_ids,
+                past_key_value,
+                output_attentions,
+                None,
+                cu_seqlens,
+                max_seqlen,
+            )
+        else:
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+
+        hidden_states = layer_outputs[0]
+
+        if use_cache:
+            next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+        if output_attentions:
+            all_self_attns += (layer_outputs[1],)
+
+    hidden_states = self.norm(hidden_states)
+
+    # Add hidden states from the last decoder layer
+    if output_hidden_states:
+        all_hidden_states += (hidden_states,)
+
+    next_cache = next_decoder_cache if use_cache else None
+    if not return_dict:
+        return tuple(
+            v
+            for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+            if v is not None
+        )
+    return BaseModelOutputWithPast(
+        last_hidden_state=hidden_states,
+        past_key_values=next_cache,
+        hidden_states=all_hidden_states,
+        attentions=all_self_attns,
+    )

src/axolotl/prompt_strategies/context_qa.py

@@ -24,6 +24,15 @@ def load(tokenizer, cfg):
     )
 
 
+def load_v2(tokenizer, cfg):
+    return ContextQaV2PromptTokenizingStrategy(
+        ContextV2Prompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
 class AlpacaContextPrompter(AlpacaPrompter):
     """
     Customized system prompted for concise QA
@@ -50,6 +59,38 @@ class AlpacaContextPromptTokenizingStrategy(InstructionPromptTokenizingStrategy)
         )
 
 
+class ContextQaV2PromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    """
+    Tokenization Strategy to combine in-context article with a question and answer
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
+        return (
+            "Context: "
+            + prompt["context"]
+            + "\nQuestion: "
+            + prompt["question"]
+            + "\n",
+            "",
+            "Answer: " + prompt["answer"],
+        )
+
+
+class ContextV2Prompter(AlpacaPrompter):
+    """
+    Customized system prompted for concise QA
+    """
+
+    system_prompt = ""
+    system_no_input_prompt = ""
+
+    def match_prompt_style(self):
+        # pylint: disable=duplicate-code
+        self.turn_format = "{instruction}\n{input}"
+        self.turn_no_input_format = "{instruction}"
+        self.system_format = "{system}"
+
+
 class AlpacaMissingInfoContextPromptTokenizingStrategy(
     InstructionPromptTokenizingStrategy
 ):

src/axolotl/prompt_strategies/sharegpt.py

@@ -0,0 +1,119 @@
+"""Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
+from typing import Any, Dict, Optional
+
+from fastchat.conversation import Conversation, SeparatorStyle, register_conv_template
+
+from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
+from axolotl.prompters import ShareGPTPrompterV2
+
+register_conv_template(
+    Conversation(
+        name="chatml",
+        system_template="<|im_start|>system\n{system_message}",
+        system_message="You are a helpful assistant.",
+        roles=["<|im_start|>user", "<|im_start|>assistant"],
+        sep_style=SeparatorStyle.CHATML,
+        sep="<|im_end|>\n",
+    )
+)
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    conversation = (
+        ds_cfg["conversation"] if ds_cfg and "conversation" in ds_cfg else None
+    )
+    field_human = ds_cfg["field_human"] if ds_cfg and "field_human" in ds_cfg else None
+    field_model = ds_cfg["field_model"] if ds_cfg and "field_model" in ds_cfg else None
+    strat = ShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompterV2(
+            conversation=conversation,
+            role_key_model=field_model,
+            role_key_human=field_human,
+        ),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+    if ds_cfg and ds_cfg["skip"]:
+        strat.skip_invalid = True
+    return strat
+
+
+def load_role(tokenizer, cfg):
+    return SimpleRoleShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompterV2(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_guanaco(tokenizer, cfg):
+    return GuanacoShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompterV2(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_nous(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    conversation = (
+        ds_cfg["conversation"] if ds_cfg and "conversation" in ds_cfg else None
+    )
+    field_human = ds_cfg["field_human"] if ds_cfg and "field_human" in ds_cfg else None
+    field_model = ds_cfg["field_model"] if ds_cfg and "field_model" in ds_cfg else None
+    return NousShareGPTPromptTokenizingStrategy(
+        ShareGPTPrompterV2(
+            conversation=conversation,
+            role_key_model=field_model,
+            role_key_human=field_human,
+        ),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+class NousShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    basic sharegpt strategy used by nous/ldj for input/output keyed data
+    """
+
+    def get_conversation_thread(self):
+        return "conversation"
+
+    def map_conversation_thread(self, conversation):
+        turns = []
+        for turn in conversation:
+            turns.append({"from": "human", "value": turn["input"]})
+            turns.append({"from": "gpt", "value": turn["output"]})
+        return turns
+
+
+class SimpleRoleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    basic sharegpt strategy to grab conversations from the sample row, but uses role instead of from
+    """
+
+    def map_conversation_thread(self, conversation):
+        # remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
+        turns = [
+            {"from": turn["role"], "value": turn["value"]} for turn in conversation
+        ]
+        return turns
+
+
+class GuanacoShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
+    """
+    sharegpt strategy that remaps oasst data to sharegpt format
+    """
+
+    def map_conversation_thread(self, conversation):
+        # remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
+        role_map = {"prompter": "human", "assistant": "gpt"}
+        turns = [
+            {"from": role_map[turn["role"]], "value": turn["text"]}
+            for turn in conversation
+        ]
+        return turns

src/axolotl/prompt_strategies/sharegpt_jokes.py

@@ -1,11 +1,11 @@
 """Module for Jokes prompts using sharegpt style """
 from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
-from axolotl.prompters import PromptStyle, ShareGPTPrompter
+from axolotl.prompters import ShareGPTPrompterV2
 
 
 def load(tokenizer, cfg):
     return SimpleJokesShareGPTPromptTokenizingStrategy(
-        ShareGPTPrompter(PromptStyle.CHAT.value),
+        ShareGPTPrompterV2(),
         tokenizer,
         cfg.train_on_inputs,
         cfg.sequence_len,

src/axolotl/prompt_strategies/sharegpt_simple.py

@@ -1,67 +0,0 @@
-"""Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
-
-from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
-from axolotl.prompters import PromptStyle, ShareGPTPrompter
-
-
-def load(tokenizer, cfg):
-    return SimpleShareGPTPromptTokenizingStrategy(
-        ShareGPTPrompter(PromptStyle.CHAT.value),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-
-
-def load_role(tokenizer, cfg):
-    return SimpleRoleShareGPTPromptTokenizingStrategy(
-        ShareGPTPrompter(PromptStyle.CHAT.value),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-
-
-def load_guanaco(tokenizer, cfg):
-    return GuanacoShareGPTPromptTokenizingStrategy(
-        ShareGPTPrompter(PromptStyle.CHAT.value),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-
-
-class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
-    """
-    basic sharegpt strategy to grab conversations from the sample row
-    """
-
-    def get_conversation_thread(self, prompt):
-        return prompt["conversations"]
-
-
-class SimpleRoleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
-    """
-    basic sharegpt strategy to grab conversations from the sample row, but uses role instead of from
-    """
-
-    def get_conversation_thread(self, prompt):
-        conversations = prompt["conversations"]
-        # remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
-        turns = [{"from": t["role"], "value": t["value"]} for t in conversations]
-        return turns
-
-
-class GuanacoShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
-    """
-    sharegpt strategy that remaps oasst data to sharegpt format
-    """
-
-    def get_conversation_thread(self, prompt):
-        conversations = prompt["conversations"]
-        # remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
-        role_map = {"prompter": "human", "assistant": "gpt"}
-        turns = [
-            {"from": role_map[t["role"]], "value": t["text"]} for t in conversations
-        ]
-        return turns

src/axolotl/prompt_tokenizers.py

@@ -4,10 +4,15 @@ import abc
 import copy
 import functools
 import logging
+from collections import defaultdict
 from typing import Dict, List, Tuple, Union
 
+from fastchat.conversation import Conversation
 from transformers import BatchEncoding, PreTrainedTokenizer
 
+from axolotl.monkeypatch.fastchat_conversation_turns import (
+    add_get_turns_to_conversation,
+)
 from axolotl.prompters import IGNORE_TOKEN_ID
 
 LOG = logging.getLogger("axolotl")
@@ -18,6 +23,8 @@ LLAMA_DEFAULT_EOS_TOKEN = "</s>"  # nosec
 LLAMA_DEFAULT_BOS_TOKEN = "<s>"  # nosec
 LLAMA_DEFAULT_UNK_TOKEN = "<unk>"  # nosec
 
+add_get_turns_to_conversation()
+
 
 class InvalidDataException(Exception):
     """
@@ -75,7 +82,7 @@ class PromptTokenizingStrategy(abc.ABC):
         self, prompt: str, add_eos_token: bool = True, strip_bos_token: bool = False
     ) -> BatchEncoding:
         result: BatchEncoding
-        if not prompt.strip():
+        if not prompt:
             LOG.warning("Empty text requested for tokenization.")
             result = BatchEncoding(data={"input_ids": [], "attention_mask": []})
         else:
@@ -345,72 +352,109 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
     Tokenizing strategy for ShareGPT prompts.
     """
 
-    def get_conversation_thread(self, prompt):
-        return prompt["conversations"]
+    _skip_invalid = False
+
+    @property
+    def supports_batched(self):
+        return True
+
+    @property
+    def skip_invalid(self):
+        return self._skip_invalid
+
+    @skip_invalid.setter
+    def skip_invalid(self, value):
+        self._skip_invalid = value
+
+    def get_conversation_thread(self):
+        return "conversations"
+
+    def map_conversation_thread(self, conversation):
+        return conversation
 
     def tokenize_prompt(self, prompt):
-        result, current_len = tokenize_prompt_default()
-        user_token = self._get_user_token()
-        assistant_token = self._get_assistant_token()
-        try:
-            for _, part in enumerate(
-                self.prompter.build_prompt(self.get_conversation_thread(prompt))
-            ):
-                if isinstance(part, tuple):
-                    if part[0] == "USER:":
-                        turn = part[0] + part[1] if not user_token else part[1]
-                        # this is still the user query, we should
-                        if not part[1].strip():
-                            LOG.warning(f"user turn has empty text: {prompt}")
-                        res = self._tokenize(
-                            turn.strip(),
-                            add_eos_token=False,
-                            strip_bos_token=True,
-                        )
-                        if user_token:
-                            res["input_ids"] = [user_token, *res["input_ids"]]
-                        # everything from this is masked out from the labels
-                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
-                    elif part[0] == "ASSISTANT:":
-                        # TODO label assistant token/tokens w/ IGNORE_TOKEN_ID
-                        turn = part[0] + part[1] if not assistant_token else part[1]
-                        # this should be the assistant response, should end with an eos token
-                        if not part[1].strip():
-                            LOG.warning(f"assistant turn has empty text: {prompt}")
-                        res = self._tokenize(
-                            turn.strip(),
-                            add_eos_token=True,
-                            strip_bos_token=True,
-                        )
-                        if assistant_token:
-                            res["input_ids"] = [
-                                assistant_token,
-                                *res["input_ids"],
-                            ]
-                        # not masked out from labels
-                        labels = copy.deepcopy(res["input_ids"])
-                    elif part[0] == "SYSTEM:":
-                        part = part[1]  # Ignore the system role from preamble
-                        # this is only ever the first part, should include the bos token and the user query
-                        res = self._tokenize(
-                            part.strip(), add_eos_token=False, strip_bos_token=False
-                        )
-                        # everything from this is masked out from the labels
-                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
-                    else:
-                        LOG.warning(f"unhandled role: {part[0]}")
+        tokenized_res = defaultdict(lambda: [])
+        conv_field = self.get_conversation_thread()
+        for prmpt in prompt[conv_field]:
+            result, current_len = tokenize_prompt_default()
+            user_token = self._get_user_token()
+            assistant_token = self._get_assistant_token()
+            conversation: Conversation = (
+                self.prompter._conversation  # pylint: disable=protected-access
+            )
+            try:
+                for _, part in enumerate(
+                    self.prompter.build_prompt(self.map_conversation_thread(prmpt))
+                ):
+                    if isinstance(part, tuple):
+                        if conversation.roles[0] in part[0]:
+                            turn = part[0] + part[1] if not user_token else part[1]
+                            # this is still the user query, we should
+                            if not part[1].strip():
+                                err_msg = f"user turn has empty text: {prmpt}"
+                                if self.skip_invalid:
+                                    raise ValueError(err_msg)
+                                LOG.warning(err_msg)
+                            res = self._tokenize(
+                                turn,
+                                add_eos_token=False,
+                                strip_bos_token=True,
+                            )
+                            if user_token:
+                                res["input_ids"] = [user_token, *res["input_ids"]]
+                            # everything from this is masked out from the labels
+                            labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
+                        elif conversation.roles[1] in part[0]:
+                            # TODO label assistant token/tokens w/ IGNORE_TOKEN_ID
+                            turn = part[0] + part[1] if not assistant_token else part[1]
+                            # this should be the assistant response, should end with an eos token
+                            if not part[1].strip():
+                                err_msg = f"assistant turn has empty text: {prmpt}"
+                                if self.skip_invalid:
+                                    raise ValueError(err_msg)
+                                LOG.warning(err_msg)
+                            res = self._tokenize(
+                                turn,
+                                add_eos_token=True,
+                                strip_bos_token=True,
+                            )
+                            if assistant_token:
+                                res["input_ids"] = [
+                                    assistant_token,
+                                    *res["input_ids"],
+                                ]
+                            # not masked out from labels
+                            labels = copy.deepcopy(res["input_ids"])
+                        elif part[0] == "":
+                            turn = part[1]
+                            # this is only ever the first part, should include the bos token and the user query
+                            res = self._tokenize(
+                                turn, add_eos_token=False, strip_bos_token=False
+                            )
+                            # everything from this is masked out from the labels
+                            labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
+                        else:
+                            err_msg = f"unhandled role: {part[0]}"
+                            if self.skip_invalid:
+                                raise ValueError(err_msg)
+                            LOG.warning(err_msg)
+                            continue
 
-                # pylint: disable=duplicate-code
-                result, current_len = parse_tokenized_to_result(
-                    result,
-                    current_len,
-                    res,
-                    labels,
-                    pad_token_id=self.tokenizer.pad_token_id,
-                )
-            return result
-        except (KeyError, AssertionError, IndexError) as err:
-            raise InvalidDataException(str(err)) from err
+                    # pylint: disable=duplicate-code
+                    result, current_len = parse_tokenized_to_result(
+                        result,
+                        current_len,
+                        res,
+                        labels,
+                        pad_token_id=self.tokenizer.pad_token_id,
+                    )
+                for key, val in sorted(result.items(), key=lambda x: x[0]):
+                    tokenized_res[key].append(val)
+            except (KeyError, AssertionError, IndexError) as err:
+                raise InvalidDataException(str(err)) from err
+            except ValueError as err:
+                LOG.warning("skipping prompt: %s", str(err))
+        return tokenized_res
 
     def _tokenize(self, prompt, add_eos_token=True, strip_bos_token=False):
         if not prompt.strip():

src/axolotl/prompters.py

@@ -1,9 +1,10 @@
 """Module containing prompters"""
 
-import dataclasses
 import logging
-from enum import Enum, auto
-from typing import Generator, List, Optional, Tuple, Union
+from enum import Enum
+from typing import Generator, Optional, Union
+
+from fastchat.conversation import Conversation, get_conv_template
 
 LOG = logging.getLogger("axolotl")
 IGNORE_TOKEN_ID = -100
@@ -214,53 +215,6 @@ class ReflectAlpacaPrompter:
         yield res
 
 
-class SeparatorStyle(Enum):
-    """Different separator style."""
-
-    SINGLE = auto()
-    TWO = auto()
-    DOLLY = auto()
-
-
-# TODO clean this 💩 up
-@dataclasses.dataclass
-class Conversation:
-    """A class that keeps all conversation history."""
-
-    system: str
-    roles: List[str]
-    messages: List[List[str]]
-    offset: int
-    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
-    sep: str = "###"
-    sep2: Optional[str] = None
-
-    def get_prompt(self) -> Generator[Tuple[str, str], None, None]:
-        # seps = [self.sep, self.sep2]
-        preamble = self.system + self.sep
-        yield ("SYSTEM:", preamble)
-        for _, (role, message) in enumerate(self.messages):
-            if message:
-                yield (role + ":", " " + message)
-            else:
-                LOG.warning(f"role with empty message: {role}")
-                yield (role + ":", "")
-
-    def copy(self):
-        return Conversation(
-            system=self.system,
-            roles=self.roles,
-            messages=[[x, y] for x, y in self.messages],
-            offset=self.offset,
-            sep_style=self.sep_style,
-            sep=self.sep,
-            sep2=self.sep2,
-        )
-
-    def append_message(self, role, message):
-        self.messages.append([role, message])
-
-
 SHAREGPT_ASSERTION_FAILED_ROLE = (
     "Role did not alternate between turns (gpt and human). Please check your data."
 )
@@ -271,28 +225,27 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
     A prompter that generates prompts for the ShareGPT
     """
 
-    def __init__(self, prompt_style=None, system_prompt: Optional[str] = None):
-        if prompt_style != PromptStyle.CHAT.value:
-            raise ValueError(
-                f"unsupported prompt_style for ShareGPTPrompter({prompt_style})"
-            )
-        system: str = (
-            system_prompt
-            if system_prompt
-            else (
-                "A chat between a curious user and an artificial intelligence assistant. "
-                "The assistant gives helpful, detailed, and polite answers to the user's questions."
-            )
-        )
-        self._conversation = Conversation(
-            system=system,
-            roles=["USER", "ASSISTANT"],
-            messages=[],
-            offset=0,
-            sep_style=SeparatorStyle.TWO,
-            sep=" ",
-            sep2=" ",
-        )
+    role_key_human = "human"
+    role_key_model = "gpt"
+
+    def __init__(
+        self,
+        prompt_style=None,  # pylint: disable=unused-argument
+        conversation: Optional[Union[str, Conversation]] = None,
+        role_key_human: Optional[str] = None,
+        role_key_model: Optional[str] = None,
+    ):
+        if conversation:
+            if isinstance(conversation, Conversation):
+                self._conversation = conversation
+            else:
+                self._conversation = get_conv_template(conversation)
+        else:
+            self._conversation = get_conv_template("vicuna_v1.1")
+        if role_key_human:
+            self.role_key_human = role_key_human
+        if role_key_model:
+            self.role_key_model = role_key_model
 
     def build_prompt(self, source) -> Generator[str, None, None]:
         if len(source) < 2:
@@ -306,17 +259,14 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
 
         # Add the conversation system prompt if provided, otherwise use the default one
         if source[0]["from"] == "system":
-            conv.system = source[0]["value"]
+            conv.set_system_message(source[0]["value"])
             source.pop(0)
 
-        roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
+        roles = {self.role_key_human: conv.roles[0], self.role_key_model: conv.roles[1]}
 
         try:
             # Apply prompt templates
-            if (
-                source[0]["from"] not in roles
-                or roles[source[0]["from"]] != conv.roles[0]
-            ):
+            if source[0]["from"] not in roles:
                 # Skip the first one if it is not from human
                 source = source[1:]
         except IndexError as err:
@@ -326,8 +276,29 @@ 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
+            if role != conv.roles[j % 2]:
+                LOG.warning(f"{SHAREGPT_ASSERTION_FAILED_ROLE}: {sentence}")
             conv.append_message(role, sentence["value"])
 
-        for part in conv.get_prompt():
+        for part in conv.get_turns():
+            if part[0] and not part[1]:
+                LOG.warning(f"role with empty message: {part[0]}")
             yield part
+
+
+class ShareGPTPrompterV2(ShareGPTPrompter):
+    """
+    A V2 prompter that generates prompts for the ShareGPT
+    """
+
+    def __init__(
+        self,
+        conversation: Optional[Union[str, Conversation]] = None,
+        role_key_human: Optional[str] = None,
+        role_key_model: Optional[str] = None,
+    ):
+        super().__init__(
+            conversation=conversation,
+            role_key_human=role_key_human,
+            role_key_model=role_key_model,
+        )

src/axolotl/train.py

@@ -58,7 +58,9 @@ def train(
 
     safe_serialization = cfg.save_safetensors is True
 
-    if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
+    if (
+        cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints
+    ) or cfg.resume_from_checkpoint is True:
         possible_checkpoints = [
             str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
         ]
@@ -71,7 +73,9 @@ def train(
             LOG.info(
                 f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
             )
-    resume_from_checkpoint = cfg.resume_from_checkpoint
+    resume_from_checkpoint = (
+        cfg.resume_from_checkpoint if cfg.resume_from_checkpoint is not True else None
+    )
 
     trainer = setup_trainer(
         cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps

src/axolotl/utils/config.py

@@ -49,6 +49,8 @@ def normalize_config(cfg):
     cfg.batch_size = (
         cfg.batch_size or cfg.micro_batch_size * cfg.gradient_accumulation_steps
     )
+    if cfg.eval_batch_size is None:
+        cfg.eval_batch_size = cfg.micro_batch_size
     cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
     cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
     cfg.eval_table_size = cfg.eval_table_size or 0
@@ -75,6 +77,8 @@ def normalize_config(cfg):
     else:
         cfg.torch_dtype = torch.float32
 
+    cfg.dataset_processes = cfg.dataset_processes or os.cpu_count()
+
     model_config = load_model_config(cfg)
     cfg.model_config_type = model_config.model_type
 
@@ -82,10 +86,39 @@ def normalize_config(cfg):
     cfg.is_llama_derived_model = (
         (hasattr(model_config, "model_type") and model_config.model_type == "llama")
         or cfg.is_llama_derived_model
-        or "llama" in cfg.base_model
+        or "llama" in cfg.base_model.lower()
         or (cfg.model_type and "llama" in cfg.model_type.lower())
     )
 
+    # figure out if the model is falcon
+    cfg.is_falcon_derived_model = (
+        (
+            hasattr(model_config, "model_type")
+            and model_config.model_type
+            in [
+                "falcon",
+                "RefinedWebModel",
+                "RefinedWeb",
+            ]
+        )
+        or cfg.is_falcon_derived_model
+        or "falcon" in cfg.base_model.lower()
+        or (cfg.model_type and "rwforcausallm" in cfg.model_type.lower())
+    )
+
+    cfg.is_mistral_derived_model = (
+        (
+            hasattr(model_config, "model_type")
+            and model_config.model_type
+            in [
+                "mistral",
+            ]
+        )
+        or cfg.is_mistral_derived_model
+        or "mistral" in cfg.base_model.lower()
+        or (cfg.model_type and "mistral" in cfg.model_type.lower())
+    )
+
     log_gpu_memory_usage(LOG, "baseline", cfg.device)
 
 
@@ -126,6 +159,11 @@ def validate_config(cfg):
             "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
             "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
         )
+    if cfg.eval_batch_size != cfg.micro_batch_size:
+        LOG.warning(
+            "eval_batch_size != micro_batch_size. This can lead to VRAM instability."
+        )
+
     if cfg.load_4bit:
         raise ValueError("cfg.load_4bit parameter has been deprecated")
 
@@ -262,6 +300,45 @@ def validate_config(cfg):
                     "`model_type: MixFormerSequentialForCausalLM` required for sample_packing"
                 )
 
+    if cfg.datasets:
+        for idx, ds_cfg in enumerate(cfg.datasets):
+            if not ds_cfg.type:
+                continue
+            if ds_cfg.type == "sharegpt:chat":
+                LOG.warning(
+                    PendingDeprecationWarning(
+                        "`type: sharegpt:chat` will soon be deprecated. simply use `type: sharegpt` instead."
+                    )
+                )
+                cfg.datasets[idx].type = "sharegpt"
+            if "sharegpt_simple" in ds_cfg.type:
+                LOG.warning(
+                    PendingDeprecationWarning(
+                        "`type: sharegpt_simple` will soon be deprecated. simply use `type: sharegpt` instead."
+                    )
+                )
+                cfg.datasets[idx].type = cfg.datasets[idx].type.replace(
+                    "sharegpt_simple", "sharegpt"
+                )
+    if cfg.save_strategy and cfg.save_steps and cfg.save_strategy != "steps":
+        raise ValueError(
+            "save_strategy and save_steps mismatch. Please set save_strategy to 'steps' or remove save_steps."
+        )
+
+    if (
+        cfg.evaluation_strategy
+        and cfg.eval_steps
+        and cfg.evaluation_strategy != "steps"
+    ):
+        raise ValueError(
+            "evaluation_strategy and eval_steps mismatch. Please set evaluation_strategy to 'steps' or remove eval_steps."
+        )
+
+    if cfg.val_set_size == 0 and (cfg.eval_steps or cfg.evaluation_strategy):
+        raise ValueError(
+            "eval_steps and evaluation_strategy are not supported with val_set_size == 0"
+        )
+
     # TODO
     # MPT 7b
     # https://github.com/facebookresearch/bitsandbytes/issues/25

src/axolotl/utils/data.py

@@ -25,7 +25,6 @@ from axolotl.prompt_tokenizers import (
     GPTeacherPromptTokenizingStrategy,
     JeopardyPromptTokenizingStrategy,
     OpenAssistantPromptTokenizingStrategy,
-    ShareGPTPromptTokenizingStrategy,
     SummarizeTLDRPromptTokenizingStrategy,
 )
 from axolotl.prompters import (
@@ -35,7 +34,6 @@ from axolotl.prompters import (
     MultipleChoiceConcisePrompter,
     MultipleChoiceExplainPrompter,
     ReflectAlpacaPrompter,
-    ShareGPTPrompter,
     SummarizeTLDRPrompter,
 )
 from axolotl.utils.dict import DictDefault
@@ -76,7 +74,7 @@ def prepare_dataset(cfg, tokenizer):
 
     with zero_first(is_main_process()):
         train_dataset, eval_dataset = process_datasets_for_packing(
-            cfg, train_dataset, eval_dataset
+            cfg, train_dataset, eval_dataset, tokenizer
         )
     if cfg.max_steps:
         total_num_steps = min(
@@ -116,7 +114,7 @@ def load_tokenized_prepared_datasets(
         if cfg.push_dataset_to_hub:
             dataset = load_dataset(
                 f"{cfg.push_dataset_to_hub}/{ds_hash}",
-                use_auth_token=use_auth_token,
+                token=use_auth_token,
             )
             dataset = dataset["train"]
     except Exception:  # pylint: disable=broad-except # nosec
@@ -124,7 +122,7 @@ def load_tokenized_prepared_datasets(
 
     if dataset:
         ...
-    elif any(prepared_ds_path.glob("*")):
+    elif cfg.dataset_prepared_path and any(prepared_ds_path.glob("*")):
         LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
         dataset = load_from_disk(str(prepared_ds_path))
         LOG.info("Prepared dataset loaded from disk...")
@@ -157,24 +155,26 @@ def load_tokenized_prepared_datasets(
                     d.path,
                     name=d.name,
                     streaming=True,
-                    use_auth_token=use_auth_token,
+                    token=use_auth_token,
                 )
                 ds_from_hub = True
-            except FileNotFoundError:
+            except (FileNotFoundError, ValueError):
                 pass
 
             # prefer local dataset, even if hub exists
             local_path = Path(d.path)
             if local_path.exists():
                 if local_path.is_dir():
-                    # TODO dirs with arrow or parquet files could be loaded with `load_from_disk`
-                    ds = load_dataset(
-                        d.path,
-                        name=d.name,
-                        data_files=d.data_files,
-                        streaming=False,
-                        split=None,
-                    )
+                    if not d.type:
+                        ds = load_from_disk(d.path)
+                    else:
+                        ds = load_dataset(
+                            d.path,
+                            name=d.name,
+                            data_files=d.data_files,
+                            streaming=False,
+                            split=None,
+                        )
                 elif local_path.is_file():
                     ds_type = "json"
                     if d.ds_type:
@@ -204,14 +204,29 @@ def load_tokenized_prepared_datasets(
                     name=d.name,
                     streaming=False,
                     data_files=d.data_files,
-                    use_auth_token=use_auth_token,
+                    token=use_auth_token,
                 )
             else:
-                fp = hf_hub_download(
-                    repo_id=d.path,
-                    repo_type="dataset",
-                    filename=d.data_files,
-                )
+                if isinstance(d.data_files, str):
+                    fp = hf_hub_download(
+                        repo_id=d.path,
+                        repo_type="dataset",
+                        filename=d.data_files,
+                    )
+                elif isinstance(d.data_files, list):
+                    fp = []
+                    for file in d.data_files:
+                        fp.append(
+                            hf_hub_download(
+                                repo_id=d.path,
+                                repo_type="dataset",
+                                filename=file,
+                            )
+                        )
+                else:
+                    raise ValueError(
+                        "data_files must be either a string or list of strings"
+                    )
                 ds = load_dataset(
                     "json", name=d.name, data_files=fp, streaming=False, split=None
                 )
@@ -234,6 +249,16 @@ def load_tokenized_prepared_datasets(
                 d_prompt_style = d_type_split[1] if len(d_type_split) > 1 else None
             if "train" in ds:
                 ds = ds["train"]
+            elif (
+                isinstance(ds, DatasetDict)
+                and d.train_on_split
+                and d.train_on_split in ds
+            ):
+                ds = ds[d.train_on_split]
+            elif isinstance(ds, DatasetDict):
+                raise ValueError(
+                    f"no train split found for dataset {d.path}, you may specify a split with 'train_on_split: `"
+                )
             if (
                 "input_ids" in ds.features
                 and "attention_mask" in ds.features
@@ -320,15 +345,6 @@ def load_tokenized_prepared_datasets(
                 )
                 ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
                 datasets.append(ds_wrapper)
-            elif d_base_type == "sharegpt":
-                ds_strategy = ShareGPTPromptTokenizingStrategy(
-                    ShareGPTPrompter(d_prompt_style),
-                    tokenizer,
-                    cfg.train_on_inputs,
-                    cfg.sequence_len,
-                )
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
             else:
                 suffix = ""
                 if ":load_" in d.type:
@@ -343,7 +359,7 @@ def load_tokenized_prepared_datasets(
         if len(datasets) > 1:
             LOG.info("shuffle merged datasets")
             dataset = dataset.shuffle(seed=seed)
-        if cfg.local_rank == 0:
+        if cfg.local_rank == 0 and cfg.dataset_prepared_path:
             LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
             dataset.save_to_disk(prepared_ds_path)
             if cfg.push_dataset_to_hub:
@@ -403,7 +419,7 @@ def load_prepare_datasets(
                 )
                 dataset = load_dataset(
                     f"{cfg.push_dataset_to_hub}/{ds_hash}",
-                    use_auth_token=use_auth_token,
+                    token=use_auth_token,
                 )
                 dataset = dataset["train"]
         except Exception:  # pylint: disable=broad-except # nosec
@@ -411,7 +427,7 @@ def load_prepare_datasets(
 
         if dataset:
             ...
-        elif any(prepared_ds_path.glob("*")):
+        elif cfg.dataset_prepared_path and any(prepared_ds_path.glob("*")):
             LOG.info(
                 f"Loading prepared packed dataset from disk at {prepared_ds_path}..."
             )

src/axolotl/utils/models.py

@@ -1,5 +1,4 @@
 """Module for models and model loading"""
-import importlib
 import logging
 import math
 import os
@@ -12,6 +11,7 @@ from optimum.bettertransformer import BetterTransformer
 from peft import PeftConfig, prepare_model_for_kbit_training
 from peft.tuners.lora import QuantLinear
 from transformers import (  # noqa: F401
+    AddedToken,
     AutoConfig,
     AutoModelForCausalLM,
     AutoTokenizer,
@@ -81,11 +81,22 @@ def load_tokenizer(cfg):
         tokenizer.add_special_tokens({"pad_token": "[PAD]"})
         os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
+    # Mistral's official FA implementation requires left padding
+    if cfg.is_mistral_derived_model and cfg.flash_attention and not cfg.sample_packing:
+        tokenizer.padding_side = "left"
+
     if cfg.special_tokens:
         for k, val in cfg.special_tokens.items():
-            tokenizer.add_special_tokens({k: val})
+            tokenizer.add_special_tokens(
+                {k: AddedToken(val, rstrip=False, lstrip=False, normalized=False)}
+            )
     if cfg.tokens:
-        tokenizer.add_tokens(list(cfg.tokens))
+        tokenizer.add_tokens(
+            [
+                AddedToken(token, rstrip=False, lstrip=False, normalized=False)
+                for token in cfg.tokens
+            ]
+        )
 
     return tokenizer
 
@@ -114,26 +125,29 @@ def load_model(
 
             replace_btlm_attn_with_flash_attn(cfg.base_model)
 
-    if hasattr(model_config, "model_type") and model_config.model_type in [
-        "falcon",
-        "RefinedWebModel",
-        "RefinedWeb",
-    ]:
-        if cfg.flash_attention:
-            from axolotl.monkeypatch.falcon_attn_hijack_flash import (
-                replace_falcon_attn_with_flash_attn,
+    if (
+        hasattr(model_config, "model_type")
+        and model_config.model_type == "stablelm_epoch"
+    ):
+        if cfg.flash_attention and cfg.sample_packing:
+            from axolotl.monkeypatch.stablelm_attn_hijack_flash import (
+                replace_stablelm_attn_with_flash_attn,
             )
 
-            replace_falcon_attn_with_flash_attn()
+            replace_stablelm_attn_with_flash_attn(cfg.base_model)
 
-    if cfg.is_llama_derived_model and cfg.flash_attention:
+    if cfg.is_llama_derived_model and cfg.flash_attention and cfg.sample_packing:
         if cfg.device not in ["mps", "cpu"] and not inference:
             from axolotl.monkeypatch.llama_attn_hijack_flash import (
                 replace_llama_attn_with_flash_attn,
             )
 
-            LOG.info("patching with flash attention")
-            replace_llama_attn_with_flash_attn(packed=cfg.sample_packing)
+            LOG.info("patching with flash attention for sample packing")
+            replace_llama_attn_with_flash_attn(
+                packed=cfg.sample_packing,
+                cross_entropy=cfg.flash_attn_cross_entropy,
+                rms_norm=cfg.flash_attn_rms_norm,
+            )
     elif cfg.is_llama_derived_model and cfg.xformers_attention:
         from axolotl.monkeypatch.llama_attn_hijack_xformers import (
             hijack_llama_attention,
@@ -158,6 +172,14 @@ def load_model(
         # Note: This might overwrite previous additional_special_tokens
         tokenizer.add_special_tokens({"additional_special_tokens": [MEM_TOKEN]})
 
+    if cfg.is_mistral_derived_model and cfg.flash_attention and cfg.sample_packing:
+        from axolotl.monkeypatch.mistral_attn_hijack_flash import (
+            replace_mistral_attn_with_flash_attn,
+        )
+
+        LOG.info("patching with flash attention")
+        replace_mistral_attn_with_flash_attn(packed=cfg.sample_packing)
+
     if cfg.is_llama_derived_model and cfg.xpos_rope:
         from axolotl.monkeypatch.xpos_rope_llama_monkey_patch import (
             replace_llama_rope_with_xpos_rope,
@@ -176,21 +198,11 @@ def load_model(
         LOG.info("patching _expand_mask")
         hijack_expand_mask()
 
-    # special handling b/c remote MixFormers code doesn't have _no_split_modules set
-    if (
-        "MixFormerSequentialConfig" in model_config.__class__.__name__
-        and cfg.model_type == "AutoModelForCausalLM"
-    ):
-        module_name = model_config.__class__.__module__.replace(
-            ".configuration_mixformer_sequential", ".modeling_mixformer_sequential"
-        )
-        modeling_phi = importlib.import_module(module_name)
-        # pylint:disable=protected-access
-        modeling_phi.MixFormerSequentialForCausalLM._no_split_modules = [
-            "ParallelBlock"
-        ]
-
     model_kwargs = {}
+
+    model_kwargs["device_map"] = cfg.device_map
+    model_kwargs["torch_dtype"] = cfg.torch_dtype
+
     if cfg.model_revision:
         model_kwargs["revision"] = cfg.model_revision
     if cfg.gptq:
@@ -213,6 +225,15 @@ def load_model(
             bnb_4bit_use_double_quant=True,
             bnb_4bit_quant_type="nf4",
         )
+    # sample packing uses custom FA2 patch
+    if cfg.flash_attention and not cfg.sample_packing:
+        if (
+            cfg.is_llama_derived_model
+            or cfg.is_falcon_derived_model
+            or cfg.is_mistral_derived_model
+        ):
+            model_kwargs["use_flash_attention_2"] = True
+
     try:
         if cfg.is_llama_derived_model and not cfg.trust_remote_code and not cfg.gptq:
             from transformers import LlamaForCausalLM
@@ -227,10 +248,8 @@ def load_model(
             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=cfg.torch_dtype,
                 **model_kwargs,
             )
         # elif model_type == "GPTNeoXForCausalLM" and cfg.flash_attention:
@@ -264,28 +283,22 @@ def load_model(
 
             model = MixFormerSequentialForCausalLM.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=cfg.torch_dtype,
                 **model_kwargs,
             )
         elif model_type and not cfg.trust_remote_code:
             if cfg.gptq:
                 model = AutoModelForCausalLM.from_pretrained(
                     base_model,
-                    device_map=cfg.device_map,
-                    torch_dtype=cfg.torch_dtype,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
             else:
                 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=cfg.torch_dtype,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
@@ -314,8 +327,6 @@ def load_model(
                 model = AutoModelForCausalLM.from_pretrained(
                     base_model,
                     config=config,
-                    device_map=cfg.device_map,
-                    torch_dtype=cfg.torch_dtype,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
@@ -323,10 +334,8 @@ 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=cfg.torch_dtype,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
@@ -337,10 +346,8 @@ 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=cfg.torch_dtype,
             trust_remote_code=cfg.trust_remote_code or False,
             **model_kwargs,
         )
@@ -375,7 +382,7 @@ def load_model(
         if model_config.model_type == "btlm":
             # don't upcast lm_head for btlm
             continue
-        if "lm_head" in name or "embed_tokens" in name:
+        if any(x in name for x in ["lm_head", "embed_tokens", "wte", "wpe"]):
             if hasattr(module, "weight"):
                 module.to(torch.float32)
 

src/axolotl/utils/tokenization.py

@@ -31,7 +31,9 @@ def check_example_labels(example, tokenizer, text_only=False):
         )
         colored_tokens.append(colored_token)
 
-    LOG.info(" ".join(colored_tokens))
+    delimiter = "" if text_only else " "
+    LOG.info(delimiter.join(colored_tokens))
     LOG.info("\n\n\n")
+    print(" ".join(colored_tokens))
 
     return " ".join(colored_tokens)

src/axolotl/utils/trainer.py

@@ -397,23 +397,36 @@ def disable_datasets_caching():
         set_caching_enabled(True)
 
 
-def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
+def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
     drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len)
     with zero_first(is_main_process()):
-        train_dataset = train_dataset.filter(drop_long, num_proc=os.cpu_count())
+        train_dataset = train_dataset.filter(drop_long, num_proc=cfg.dataset_processes)
         if eval_dataset:
-            eval_dataset = eval_dataset.filter(drop_long, num_proc=os.cpu_count())
+            eval_dataset = eval_dataset.filter(
+                drop_long, num_proc=cfg.dataset_processes
+            )
 
         if cfg.group_by_length:
-            train_dataset = train_dataset.map(add_length, num_proc=os.cpu_count())
+            train_dataset = train_dataset.map(
+                add_length, num_proc=cfg.dataset_processes
+            )
 
         if cfg.sample_packing:
-            train_dataset = train_dataset.map(add_position_ids, num_proc=os.cpu_count())
+            train_dataset = train_dataset.map(
+                add_position_ids, num_proc=cfg.dataset_processes
+            )
             if cfg.eval_sample_packing is not False:
                 if eval_dataset:
                     eval_dataset = eval_dataset.map(
-                        add_position_ids, num_proc=os.cpu_count()
+                        add_position_ids, num_proc=cfg.dataset_processes
                     )
+
+        # Phi doesn't want the attention_mask feature when training
+        if "CodeGenTokenizer" in tokenizer.__class__.__name__:
+            train_dataset = train_dataset.remove_columns("attention_mask")
+            if eval_dataset:
+                eval_dataset = eval_dataset.remove_columns("attention_mask")
+
     return train_dataset, eval_dataset
 
 
@@ -597,26 +610,19 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
             "sample_packing_efficiency"
         ] = cfg.sample_packing_eff_est
 
-    if cfg.eval_steps and cfg.evaluation_strategy:
-        # assume if the user set both, they know what they're doing
-        training_arguments_kwargs["evaluation_strategy"] = cfg.evaluation_strategy
+    if cfg.eval_steps:
+        training_arguments_kwargs["evaluation_strategy"] = "steps"
         training_arguments_kwargs["eval_steps"] = cfg.eval_steps
+    elif cfg.evaluation_strategy:
+        training_arguments_kwargs["evaluation_strategy"] = cfg.evaluation_strategy
     elif cfg.val_set_size == 0:
         # no eval set, so don't eval
         training_arguments_kwargs["evaluation_strategy"] = "no"
-    elif cfg.evaluation_strategy and cfg.evaluation_strategy in ["epoch", "no"]:
-        # if explicitly set for epoch, just set, and eval steps don't matter
-        training_arguments_kwargs["evaluation_strategy"] = cfg.evaluation_strategy
-    elif cfg.eval_steps:
-        # steps isn't used w/ epochs
-        training_arguments_kwargs["evaluation_strategy"] = "steps"
-        training_arguments_kwargs["eval_steps"] = cfg.eval_steps
     else:
         # we have an eval set, but no steps defined, default to use epoch
         training_arguments_kwargs["evaluation_strategy"] = "epoch"
 
     if cfg.save_steps:
-        # save_steps implies save_strategy of steps
         training_arguments_kwargs["save_strategy"] = "steps"
         training_arguments_kwargs["save_steps"] = cfg.save_steps
     elif cfg.save_strategy:
@@ -662,9 +668,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         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
-        if cfg.eval_batch_size is not None
-        else cfg.micro_batch_size,
+        per_device_eval_batch_size=cfg.eval_batch_size,
         gradient_accumulation_steps=cfg.gradient_accumulation_steps,
         eval_accumulation_steps=cfg.gradient_accumulation_steps,
         num_train_epochs=cfg.num_epochs,

tests/e2e/test_mistral.py

@@ -0,0 +1,116 @@
+"""
+E2E tests for lora llama
+"""
+
+import logging
+import os
+import tempfile
+import unittest
+from pathlib import Path
+
+from transformers.utils import is_torch_bf16_gpu_available
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestMistral(unittest.TestCase):
+    """
+    Test case for Llama models using LoRA
+    """
+
+    def test_lora(self):
+        # pylint: disable=duplicate-code
+        output_dir = tempfile.mkdtemp()
+        cfg = DictDefault(
+            {
+                "base_model": "openaccess-ai-collective/tiny-mistral",
+                "base_model_config": "openaccess-ai-collective/tiny-mistral",
+                "flash_attention": True,
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 32,
+                "lora_alpha": 64,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 1,
+                "output_dir": output_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "eval_steps": 10,
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(output_dir) / "adapter_model.bin").exists()
+
+    def test_ft(self):
+        # pylint: disable=duplicate-code
+        output_dir = tempfile.mkdtemp()
+        cfg = DictDefault(
+            {
+                "base_model": "openaccess-ai-collective/tiny-mistral",
+                "base_model_config": "openaccess-ai-collective/tiny-mistral",
+                "flash_attention": True,
+                "sequence_len": 1024,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 1,
+                "output_dir": output_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "eval_steps": 10,
+            }
+        )
+        if is_torch_bf16_gpu_available():
+            cfg.bf16 = True
+        else:
+            cfg.fp16 = True
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(output_dir) / "pytorch_model.bin").exists()

tests/e2e/test_mistral_samplepack.py

@@ -0,0 +1,118 @@
+"""
+E2E tests for lora llama
+"""
+
+import logging
+import os
+import tempfile
+import unittest
+from pathlib import Path
+
+from transformers.utils import is_torch_bf16_gpu_available
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestMistral(unittest.TestCase):
+    """
+    Test case for Llama models using LoRA
+    """
+
+    def test_lora_packing(self):
+        # pylint: disable=duplicate-code
+        output_dir = tempfile.mkdtemp()
+        cfg = DictDefault(
+            {
+                "base_model": "openaccess-ai-collective/tiny-mistral",
+                "base_model_config": "openaccess-ai-collective/tiny-mistral",
+                "flash_attention": True,
+                "sample_packing": True,
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 32,
+                "lora_alpha": 64,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 1,
+                "output_dir": output_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "eval_steps": 10,
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(output_dir) / "adapter_model.bin").exists()
+
+    def test_ft_packing(self):
+        # pylint: disable=duplicate-code
+        output_dir = tempfile.mkdtemp()
+        cfg = DictDefault(
+            {
+                "base_model": "openaccess-ai-collective/tiny-mistral",
+                "base_model_config": "openaccess-ai-collective/tiny-mistral",
+                "flash_attention": True,
+                "sample_packing": True,
+                "sequence_len": 1024,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 1,
+                "output_dir": output_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "eval_steps": 10,
+            }
+        )
+        if is_torch_bf16_gpu_available():
+            cfg.bf16 = True
+        else:
+            cfg.fp16 = True
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(output_dir) / "pytorch_model.bin").exists()

tests/test_prompt_tokenizers.py

@@ -21,7 +21,7 @@ from axolotl.prompt_tokenizers import (
     AlpacaPromptTokenizingStrategy,
     ShareGPTPromptTokenizingStrategy,
 )
-from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompter
+from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompterV2
 
 LOG = logging.getLogger("axolotl")
 
@@ -60,7 +60,7 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
         ) as fin:
             data = fin.read()
             tokenized_conversation = json.loads(data)
-        prompter = ShareGPTPrompter("chat")
+        prompter = ShareGPTPrompterV2()
         strat = ShareGPTPromptTokenizingStrategy(
             prompter,
             self.tokenizer,
@@ -79,7 +79,7 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
         ) as fin:
             data = fin.read()
             conversation = json.loads(data)
-        prompter = ShareGPTPrompter("chat")
+        prompter = ShareGPTPrompterV2()
         strat = ShareGPTPromptTokenizingStrategy(
             prompter,
             self.tokenizer,

tests/test_validation.py

@@ -374,3 +374,194 @@ class ValidationTest(unittest.TestCase):
         )
 
         validate_config(cfg)
+
+    def test_sharegpt_deprecation(self):
+        cfg = DictDefault(
+            {"datasets": [{"path": "lorem/ipsum", "type": "sharegpt:chat"}]}
+        )
+        with self._caplog.at_level(logging.WARNING):
+            validate_config(cfg)
+            assert any(
+                "`type: sharegpt:chat` will soon be deprecated." in record.message
+                for record in self._caplog.records
+            )
+        assert cfg.datasets[0].type == "sharegpt"
+
+        cfg = DictDefault(
+            {"datasets": [{"path": "lorem/ipsum", "type": "sharegpt_simple:load_role"}]}
+        )
+        with self._caplog.at_level(logging.WARNING):
+            validate_config(cfg)
+            assert any(
+                "`type: sharegpt_simple` will soon be deprecated." in record.message
+                for record in self._caplog.records
+            )
+        assert cfg.datasets[0].type == "sharegpt:load_role"
+
+    def test_no_conflict_save_strategy(self):
+        cfg = DictDefault(
+            {
+                "save_strategy": "epoch",
+                "save_steps": 10,
+            }
+        )
+
+        with pytest.raises(
+            ValueError, match=r".*save_strategy and save_steps mismatch.*"
+        ):
+            validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "save_strategy": "no",
+                "save_steps": 10,
+            }
+        )
+
+        with pytest.raises(
+            ValueError, match=r".*save_strategy and save_steps mismatch.*"
+        ):
+            validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "save_strategy": "steps",
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "save_strategy": "steps",
+                "save_steps": 10,
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "save_steps": 10,
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "save_strategy": "no",
+            }
+        )
+
+        validate_config(cfg)
+
+    def test_no_conflict_eval_strategy(self):
+        cfg = DictDefault(
+            {
+                "evaluation_strategy": "epoch",
+                "eval_steps": 10,
+            }
+        )
+
+        with pytest.raises(
+            ValueError, match=r".*evaluation_strategy and eval_steps mismatch.*"
+        ):
+            validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "evaluation_strategy": "no",
+                "eval_steps": 10,
+            }
+        )
+
+        with pytest.raises(
+            ValueError, match=r".*evaluation_strategy and eval_steps mismatch.*"
+        ):
+            validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "evaluation_strategy": "steps",
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "evaluation_strategy": "steps",
+                "eval_steps": 10,
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "eval_steps": 10,
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "evaluation_strategy": "no",
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "evaluation_strategy": "epoch",
+                "val_set_size": 0,
+            }
+        )
+
+        with pytest.raises(
+            ValueError,
+            match=r".*eval_steps and evaluation_strategy are not supported with val_set_size == 0.*",
+        ):
+            validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "eval_steps": 10,
+                "val_set_size": 0,
+            }
+        )
+
+        with pytest.raises(
+            ValueError,
+            match=r".*eval_steps and evaluation_strategy are not supported with val_set_size == 0.*",
+        ):
+            validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "val_set_size": 0,
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "eval_steps": 10,
+                "val_set_size": 0.01,
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "evaluation_strategy": "epoch",
+                "val_set_size": 0.01,
+            }
+        )
+
+        validate_config(cfg)