better handling for tokenizers like flan that don't have a bos token

.github/workflows/base.yml

@@ -26,7 +26,7 @@ jobs:
             pytorch: 2.0.0
             axolotl_extras:
           - cuda: "117"
-            cuda_version: 11.7.1
+            cuda_version: 11.7.0
             python_version: "3.9"
             pytorch: 1.13.1
             axolotl_extras:

.github/workflows/main.yml

@@ -30,7 +30,7 @@ jobs:
             pytorch: 2.0.0
             axolotl_extras: gptq
           - cuda: cu117
-            cuda_version: 11.7.1
+            cuda_version: 11.7.0
             python_version: "3.9"
             pytorch: 1.13.1
             axolotl_extras:
@@ -85,7 +85,7 @@ jobs:
             pytorch: 2.0.0
             axolotl_extras: gptq
           - cuda: cu117
-            cuda_version: 11.7.1
+            cuda_version: 11.7.0
             python_version: "3.9"
             pytorch: 1.13.1
             axolotl_extras:

.pre-commit-config.yaml

@@ -1,5 +1,5 @@
 default_language_version:
-    python: python3
+    python: python3.9
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks

README.md

@@ -195,10 +195,6 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"message_1": "...", "message_2": "..."}
   ```
-- `alpaca_w_system.load_open_orca`: support for open orca datasets with included system prompts, instruct
-  ```json
-  {"system_prompt": "...", "question": "...", "response": "..."}
-  ```
 - `context_qa`: in context question answering from an article
   ```json
   {"article": "...", "question": "...", "answer": "..."}
@@ -237,7 +233,7 @@ Have dataset(s) in one of the following format (JSONL recommended):
 #### How to add custom prompts
 
   1. Add your method to a file in [prompt_strategies](src/axolotl/prompt_strategies). Please see other files as example.
-  2. Use your custom file name as the dataset type `<prompt_strategies_file>.load_<load_fn>`.
+  2. Use your custom file name as the dataset type.
 
 Optionally, download some datasets, see [data/README.md](data/README.md)
 
@@ -255,18 +251,10 @@ See sample configs in [configs](configs) folder or [examples](examples) for quic
 
 - dataset
   ```yaml
-  sequence_len: 2048 # max token length for prompt
-
-  # huggingface repo
   datasets:
-    - path: vicgalle/alpaca-gpt4
-      type: alpaca # format from earlier
-
-  # local
-  datasets:
-    - path: json
-      data_files: data.jsonl # or json
+    - path: vicgalle/alpaca-gpt4 # local or huggingface repo
       type: alpaca # format from earlier
+  sequence_len: 2048 # max token length / prompt
   ```
 
 - loading
@@ -305,8 +293,6 @@ base_model_ignore_patterns:
 # if the base_model repo on hf hub doesn't include configuration .json files,
 # you can set that here, or leave this empty to default to base_model
 base_model_config: ./llama-7b-hf
-# you can specify to choose a specific model revision from huggingface hub
-model_revision:
 # Optional tokenizer configuration override in case you want to use a different tokenizer
 # than the one defined in the base model
 tokenizer_config:
@@ -316,8 +302,6 @@ model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 # Trust remote code for untrusted source
 trust_remote_code:
-# use_fast option for tokenizer loading from_pretrained, default to True
-tokenizer_use_fast:
 
 # whether you are training a 4-bit GPTQ quantized model
 gptq: true
@@ -338,10 +322,10 @@ tf32: true # require >=ampere
 
 # a list of one or more datasets to finetune the model with
 datasets:
-  # hf dataset repo | "json" for local dataset, make sure to fill data_files
+  # this can be either a hf dataset, or relative path
   - path: vicgalle/alpaca-gpt4
   # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
-    type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
+    type: alpaca # format OR format:prompt_style (chat/instruct)
     data_files: # path to source data files
     shards: # number of shards to split data into
 
@@ -350,8 +334,6 @@ datasets:
 dataset_prepared_path: data/last_run_prepared
 # push prepared dataset to hub
 push_dataset_to_hub: # repo path
-# push checkpoints to hub
-hub_model_id: # repo path
 # whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
 # required to be true when used in combination with `push_dataset_to_hub`
 hf_use_auth_token: # boolean
@@ -413,9 +395,6 @@ logging_steps:
 save_steps:
 eval_steps:
 
-# save model as safetensors (require safetensors package)
-save_safetensors:
-
 # whether to mask out or include the human's prompt from the training labels
 train_on_inputs: false
 # don't use this, leads to wonky training (according to someone on the internet)

docker/Dockerfile-base

@@ -77,7 +77,7 @@ FROM base-builder
 RUN python3 -m pip uninstall -y apex
 RUN git clone https://github.com/NVIDIA/apex
 #  `MAX_JOBS=1` disables parallel building to avoid cpu memory OOM when building image on GitHub Action (standard) runners
-RUN cd apex && MAX_JOBS=1 python3 -m pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
+RUN cd apex && MAX_JOBS=1 python3 -m pip install --global-option="--cpp_ext" --global-option="--cuda_ext" --no-cache -v --disable-pip-version-check .
 
 RUN mkdir -p /workspace/builds
 COPY --from=bnb-builder /workspace/bitsandbytes /workspace/builds/bitsandbytes
@@ -97,4 +97,4 @@ RUN cd /workspace/builds/bitsandbytes && python3 setup.py install
 RUN git lfs install --skip-repo
 RUN pip3 install awscli && \
     # The base image ships with `pydantic==1.8.2` which is not working
-    pip3 install -U --no-cache-dir pydantic==1.10.10
+    pip3 install -U --no-cache-dir pydantic

requirements.txt

@@ -1,6 +1,7 @@
 peft @ git+https://github.com/huggingface/peft.git
 transformers @ git+https://github.com/huggingface/transformers.git
 bitsandbytes>=0.39.0
+accelerate
 addict
 fire
 PyYAML==6.0
@@ -17,4 +18,3 @@ evaluate==0.4.0
 rouge-score==0.1.2
 scipy
 scikit-learn==1.2.2
-numba

src/axolotl/datasets.py

@@ -126,7 +126,6 @@ class ConstantLengthDataset(IterableDataset):
                     buffer_len = 0
 
                 if example:
-                    # FIXME
                     # just going to drop data points that are too long
                     if len(example["input_ids"]) <= self.seq_length:
                         input_ids = example["input_ids"]

src/axolotl/prompt_strategies/alpaca_chat.py

@@ -6,7 +6,7 @@ from axolotl.prompt_tokenizers import (
     AlpacaPromptTokenizingStrategy,
     InstructionPromptTokenizingStrategy,
 )
-from axolotl.prompters import AlpacaPrompter, PromptStyle, UnpromptedPrompter
+from axolotl.prompters import AlpacaPrompter, PromptStyle
 
 
 def load(tokenizer, cfg):
@@ -45,10 +45,8 @@ class NoSystemPrompter(AlpacaPrompter):
     Null Prompter with no system prompts
     """
 
-    system_prompt = ""
-    system_no_input_prompt = ""
-    turn_format = "{instruction} {input} "
-    turn_no_input_format = "{instruction} "
+    prompt_input = "{instruction} {input} "
+    prompt_no_input = "{instruction} "
 
     def __init__(self):  # pylint: disable=super-init-not-called
         pass
@@ -105,12 +103,3 @@ def load_camel_ai(tokenizer, cfg):
         cfg.train_on_inputs,
         cfg.sequence_len,
     )
-
-
-def load_no_prompt(tokenizer, cfg):
-    return AlpacaPromptTokenizingStrategy(
-        UnpromptedPrompter(PromptStyle.CHAT.value),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )

src/axolotl/prompt_strategies/alpaca_instruct.py

@@ -1,7 +1,7 @@
 """Module loading the AlpacaInstructPromptTokenizingStrategy class"""
 
 from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
-from axolotl.prompters import AlpacaPrompter, PromptStyle, UnpromptedPrompter
+from axolotl.prompters import AlpacaPrompter, PromptStyle
 
 
 def load(tokenizer, cfg):
@@ -11,12 +11,3 @@ def load(tokenizer, cfg):
         cfg.train_on_inputs,
         cfg.sequence_len,
     )
-
-
-def load_no_prompt(tokenizer, cfg):
-    return AlpacaPromptTokenizingStrategy(
-        UnpromptedPrompter(PromptStyle.INSTRUCT.value),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )

src/axolotl/prompt_strategies/alpaca_w_system.py

@@ -1,120 +0,0 @@
-"""
-Prompt strategies loader for alpaca instruction datasets with system prompts
-"""
-from typing import Generator, Tuple, Union
-
-from axolotl.prompt_tokenizers import PromptTokenizingStrategy
-from axolotl.prompters import AlpacaPrompter, PromptStyle
-
-
-class InstructionWSystemPromptTokenizingStrategy(PromptTokenizingStrategy):
-    """
-    Tokenizing strategy for instruction-based prompts.
-    """
-
-    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str, str]:
-        return (
-            prompt["instruction"],
-            prompt["input"] if "input" in prompt else "",
-            prompt["output"],
-            prompt["system"],
-        )
-
-    def tokenize_prompt(self, prompt):
-        # pylint: disable=duplicate-code
-        (
-            instruction,
-            input,  # pylint: disable=redefined-builtin
-            response,
-            system,
-        ) = self.parse_instruction_fields(prompt)
-        user_prompt = next(
-            iter(
-                self.prompter.build_prompt_w_system(
-                    system,
-                    instruction,
-                    input,
-                )
-            )
-        )
-        tokenized_prompt = self._tokenize(user_prompt, add_eos_token=False)
-        if not self.train_on_inputs:
-            user_prompt_len = len(tokenized_prompt["input_ids"])
-            # TODO this could be sped up using numpy array slicing
-            tokenized_prompt["labels"] = [-100] * user_prompt_len
-        tokenized_res_prompt = self._tokenize(
-            response, strip_bos_token=True, add_eos_token=True
-        )
-        tokenized_prompt["input_ids"] += tokenized_res_prompt["input_ids"]
-        tokenized_prompt["attention_mask"] += tokenized_res_prompt["attention_mask"]
-        tokenized_prompt["labels"] += tokenized_res_prompt["input_ids"]
-
-        return tokenized_prompt
-
-
-class SystemDataPrompter(AlpacaPrompter):
-    """
-    Alpaca Style Prompter that uses system prompts from the dataset
-    """
-
-    def build_prompt_w_system(
-        self,
-        system: str,
-        instruction: str,
-        input: Union[None, str] = None,  # pylint: disable=redefined-builtin
-        output: Union[None, str] = None,
-    ) -> Generator[str, None, None]:
-        # returns the full prompt from instruction and optional input
-        # if a label (=response, =output) is provided, it's also appended.
-        if input:
-            res = system + self.turn_format.format(instruction=instruction, input=input)
-        else:
-            res = system + self.turn_no_input_format.format(instruction=instruction)
-        if output:
-            res = f"{res}{output}"
-        yield res
-
-
-class OpenOrcaPromptTokenizingStrategy(InstructionWSystemPromptTokenizingStrategy):
-    """
-    Tokenizing strategy for OpenOrca datasets
-    """
-
-    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str, str]:
-        return (
-            prompt["question"],
-            "",
-            prompt["response"],
-            prompt["system_prompt"],
-        )
-
-
-def load(tokenizer, cfg):
-    return load_chat(tokenizer, cfg)
-
-
-def load_instruct(tokenizer, cfg):
-    return InstructionWSystemPromptTokenizingStrategy(
-        SystemDataPrompter(PromptStyle.INSTRUCT.value),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-
-
-def load_chat(tokenizer, cfg):
-    return InstructionWSystemPromptTokenizingStrategy(
-        SystemDataPrompter(PromptStyle.CHAT.value),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-
-
-def load_open_orca(tokenizer, cfg):
-    return OpenOrcaPromptTokenizingStrategy(
-        SystemDataPrompter(PromptStyle.INSTRUCT.value),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )

src/axolotl/prompt_tokenizers.py

@@ -73,8 +73,17 @@ class PromptTokenizingStrategy(abc.ABC):
         ):
             result["input_ids"].append(self.tokenizer.eos_token_id)
             result["attention_mask"].append(1)
+        elif (  # some tokenizers automatically add an eos token, let's remove it
+            not add_eos_token and result["input_ids"][-1] == self.tokenizer.eos_token_id
+        ):
+            result["input_ids"] = result["input_ids"][:-1]
+            result["attention_mask"] = result["attention_mask"][:-1]
 
-        if result["input_ids"][0] == self.tokenizer.bos_token_id and strip_bos_token:
+        if (
+            self.tokenizer.bos_token_id
+            and result["input_ids"][0] == self.tokenizer.bos_token_id
+            and strip_bos_token
+        ):
             result["input_ids"] = result["input_ids"][1:]
             result["attention_mask"] = result["attention_mask"][1:]
 
@@ -87,9 +96,7 @@ class InstructionPromptTokenizingStrategy(PromptTokenizingStrategy):
     Tokenizing strategy for instruction-based prompts.
     """
 
-    def parse_instruction_fields(
-        self, prompt
-    ) -> Union[Tuple[str, str, str], Tuple[str, str, str, str]]:
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
         raise NotImplementedError
 
     def tokenize_prompt(self, prompt):
@@ -414,7 +421,11 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
             result["input_ids"].append(self.tokenizer.eos_token_id)
             result["attention_mask"].append(1)
 
-        if result["input_ids"][0] == self.tokenizer.bos_token_id and strip_bos_token:
+        if (
+            self.tokenizer.bos_token_id
+            and result["input_ids"][0] == self.tokenizer.bos_token_id
+            and strip_bos_token
+        ):
             result["input_ids"] = result["input_ids"][1:]
             result["attention_mask"] = result["attention_mask"][1:]
 
@@ -440,7 +451,7 @@ def parse_tokenized_to_result(
     result: Dict[str, List[int]],
     current_len: int,
     res: Dict[str, List[int]],
-    labels: List[int],
+    labels: list[int],
     pad_token_id: Union[int, None] = None,
 ) -> Tuple[Dict[str, List[int]], int]:
     """

src/axolotl/prompters.py

@@ -24,8 +24,6 @@ class AlpacaPrompter:
 
     system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
     system_no_input_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
-    turn_format: str
-    turn_no_input_format: str
     prompt_style: Optional[PromptStyle] = None
 
     def __init__(self, prompt_style=PromptStyle.INSTRUCT.value):
@@ -34,13 +32,23 @@ class AlpacaPrompter:
 
     def match_prompt_style(self):
         if self.prompt_style == PromptStyle.INSTRUCT.value:
-            self.turn_format = "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
-            self.turn_no_input_format = (
-                "### Instruction:\n{instruction}\n\n### Response:\n"
+            self.prompt_input = (
+                self.system_prompt
+                + "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
             )
+            self.prompt_no_input = (
+                self.system_no_input_prompt
+                + "### Instruction:\n{instruction}\n\n### Response:\n"
+            )
+            self.response_split = "### Response:"
         if self.prompt_style == PromptStyle.CHAT.value:
-            self.turn_format = "USER: {instruction}\n{input}\nASSISTANT:"
-            self.turn_no_input_format = "USER: {instruction}\nASSISTANT:"
+            self.prompt_input = (
+                self.system_prompt + "USER: {instruction}\n{input}\nASSISTANT:"
+            )
+            self.prompt_no_input = (
+                self.system_no_input_prompt + "USER: {instruction}\nASSISTANT:"
+            )
+            self.response_split = "ASSISTANT:"
 
     def build_prompt(
         self,
@@ -51,17 +59,16 @@ class AlpacaPrompter:
         # returns the full prompt from instruction and optional input
         # if a label (=response, =output) is provided, it's also appended.
         if input:
-            res = self.system_prompt + self.turn_format.format(
-                instruction=instruction, input=input
-            )
+            res = self.prompt_input.format(instruction=instruction, input=input)
         else:
-            res = self.system_no_input_prompt + self.turn_no_input_format.format(
-                instruction=instruction
-            )
+            res = self.prompt_no_input.format(instruction=instruction)
         if output:
             res = f"{res}{output}"
         yield res
 
+    def get_response(self, output: str) -> str:
+        return output.split(self.response_split)[1].strip()
+
 
 class UnpromptedPrompter(AlpacaPrompter):
     """
@@ -86,10 +93,7 @@ class MultipleChoiceExplainPrompter(AlpacaPrompter):
     """
 
     system_prompt = (
-        "Choose the answer that best answers the question. Explain your reasoning.\n"
-    )
-    system_no_input_prompt = (
-        "Choose the answer that best answers the question. Explain your reasoning.\n"
+        "Choose the answer that best answers the question. Explain your reasoning."
     )
 
 
@@ -98,12 +102,7 @@ class MultipleChoiceConcisePrompter(AlpacaPrompter):
     Prompter for multiple choice concise
     """
 
-    system_prompt = "Choose the answer that best answers the question. Be concise in your response.\n\n"
-    system_no_input_prompt = "Choose the answer that best answers the question. Be concise in your response.\n\n"
-
-    def match_prompt_style(self):
-        self.turn_format = "USER: {instruction}\n{input}\nASSISTANT:"
-        self.turn_no_input_format = "USER: {instruction}\nASSISTANT:"
+    prompt_input = "Choose the answer that best answers the question. Be concise in your response.\n\nUSER: {instruction}\n{input}\nASSISTANT:\n"
 
 
 class SummarizeTLDRPrompter(AlpacaPrompter):
@@ -111,12 +110,9 @@ class SummarizeTLDRPrompter(AlpacaPrompter):
     Prompter for summarize TLDR
     """
 
-    system_prompt = ""
-    system_no_input_prompt = ""
-
-    def match_prompt_style(self):
-        self.turn_format = "USER: Summarize the following article as a TL;DR.\n{instruction}\n{input}\nASSISTANT:"
-        self.turn_no_input_format = "USER: Summarize the following article as a TL;DR.\n{instruction}\nASSISTANT:"
+    prompt_no_input = (
+        "USER: Summarize the following article as a TL;DR.\n{instruction}\nASSISTANT:"
+    )
 
 
 class CompletionPrompter:
@@ -132,6 +128,9 @@ class CompletionPrompter:
     ) -> Generator[str, None, None]:
         yield instruction
 
+    def get_response(self, output: str) -> str:
+        return output.strip()
+
 
 class GPTeacherPrompter(AlpacaPrompter):
     """
@@ -211,6 +210,9 @@ class ReflectAlpacaPrompter:
             res = f"{res}{label}"
         yield res
 
+    def get_response(self, output: str) -> str:
+        return output.split(self.response_split)[1].strip()
+
 
 class SeparatorStyle(Enum):
     """Different separator style."""
@@ -287,6 +289,12 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
             sep2=" ",
         )
 
+    # def match_prompt_style(self):
+    #     if self.prompt_style == PromptStyle.chat.value:
+    #         self.prompt_input = self.system_prompt + "USER: {instruction}\n{input}\nASSISTANT:"
+    #         self.prompt_no_input = self.system_no_input_prompt + "USER: {instruction}\nASSISTANT:"
+    #         self.response_split = "ASSISTANT:"
+
     def build_prompt(self, source) -> Generator[str, None, None]:
         # ignore the system prompt if provided
         if source[0]["from"] == "system":

src/axolotl/utils/data.py

@@ -102,26 +102,13 @@ def load_tokenized_prepared_datasets(
                 pass
 
             # prefer local dataset, even if hub exists
-            local_path = Path(d.path)
-            if local_path.exists():
-                if local_path.is_dir():
-                    ds = load_dataset(
-                        d.path,
-                        data_files=d.data_files,
-                        streaming=False,
-                        split=None,
-                    )
-                elif local_path.is_file():
-                    ds = load_dataset(
-                        "json",
-                        data_files=d.path,
-                        streaming=False,
-                        split=None,
-                    )
-                else:
-                    raise ValueError(
-                        "unhandled dataset load: local path exists, but is neither a directory or a file"
-                    )
+            if Path(d.path).exists():
+                ds = load_dataset(
+                    "json",
+                    data_files=d.path,
+                    streaming=False,
+                    split=None,
+                )
             elif ds_from_hub:
                 if d.data_files:
                     ds = load_dataset(

src/axolotl/utils/models.py

@@ -34,20 +34,15 @@ def load_tokenizer(
     tokenizer_type,
     cfg,
 ):
-    use_fast = True  # this is the default
-    if cfg.tokenizer_use_fast is not None:
-        use_fast = cfg.tokenizer_use_fast
     if tokenizer_type:
         tokenizer = getattr(transformers, tokenizer_type).from_pretrained(
             tokenizer_config,
             trust_remote_code=cfg.trust_remote_code or False,
-            use_fast=use_fast,
         )
     else:
         tokenizer = AutoTokenizer.from_pretrained(
             tokenizer_config,
             trust_remote_code=cfg.trust_remote_code or False,
-            use_fast=use_fast,
         )
 
     logging.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
@@ -154,8 +149,6 @@ def load_model(
         )
 
     model_kwargs = {}
-    if cfg.model_revision:
-        model_kwargs["revision"] = cfg.model_revision
     if cfg.adapter == "qlora" and cfg.load_in_4bit:
         model_kwargs["quantization_config"] = BitsAndBytesConfig(
             load_in_4bit=True,
@@ -204,7 +197,7 @@ def load_model(
                 else True,
             )
             load_in_8bit = False
-        elif cfg.is_llama_derived_model and not cfg.trust_remote_code:
+        elif cfg.is_llama_derived_model:
             from transformers import LlamaForCausalLM
 
             config = LlamaConfig.from_pretrained(base_model_config)
@@ -243,7 +236,7 @@ def load_model(
         #         device=cfg.device,
         #     )
         #     model.train() # sets to train instead of eval mode
-        elif model_type and not cfg.trust_remote_code:
+        elif model_type:
             model = getattr(transformers, model_type).from_pretrained(
                 base_model,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,

src/axolotl/utils/sampler.py

@@ -1,173 +0,0 @@
-# pylint: skip-file
-
-from typing import Any, List, Optional
-
-import numba
-import numpy as np
-import torch.distributed as dist
-from torch.utils.data import Sampler
-
-
-@numba.njit
-def ffd_check(a: np.ndarray, c: int, n: int):
-    # First-fit-decreasing bin packing
-    # Check if a[] could fit in n bins with capacity c
-    # https://en.wikipedia.org/wiki/First-fit-decreasing_bin_packing
-
-    a = np.sort(a)[::-1]
-    bins = np.full((n,), c, dtype=a.dtype)
-    for size in a:
-        not_found = True
-        for idx in range(n):
-            if bins[idx] >= size:
-                bins[idx] -= size
-                not_found = False
-                break
-
-        if not_found:
-            return False
-
-    return True
-
-
-@numba.njit
-def ffd_with_result(a: np.ndarray, c: int, start_index: int):
-    # First-fit-decreasing bin packing (with result return)
-
-    indices = np.argsort(a)[::-1]
-    a = a[indices]
-
-    bins: List[int] = []
-    bins_result: List[Any] = []
-    for a_id, size in enumerate(a):
-        add_new = True
-        for idx in range(len(bins)):
-            if bins[idx] >= size:
-                bins[idx] -= size
-                bins_result[idx].append(indices[a_id] + start_index)
-                add_new = False
-                break
-
-        if add_new:
-            bins.append(c - size)
-            bins_result.append([indices[a_id] + start_index])
-
-    return bins_result
-
-
-@numba.njit
-def allocate(
-    lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
-):
-    # Dynamic batch allocator, similar to Multifit
-    # https://en.wikipedia.org/wiki/Multifit_algorithm
-    # ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
-
-    s = 0
-    start_index = 0
-    result = []
-
-    while True:
-        # binary search [l, r)
-        left = 1
-        right = 1 + np.searchsorted(lengths_cumsum[start_index:], s + c * n, "right")
-
-        while right - left > 1:
-            m = (left + right) // 2
-            if ffd_check(lengths[start_index : start_index + m], c, n):
-                left = m
-            else:
-                right = m
-
-        # use length l
-        batch = ffd_with_result(
-            lengths[start_index : start_index + left], c, start_index
-        )
-        assert len(batch) <= n
-        if len(batch) < n:
-            break
-
-        start_index += left
-        s = lengths_cumsum[start_index - 1]
-
-        # add local rank
-        result.append(batch[rank])
-
-    return result, s, len(result) * c * n
-
-
-class MultipackDistributedBatchSampler(Sampler):
-    """Unpadded length sampling using Multipack.
-    Approximate (at most ~1.22x) the optimal solution of the identical-machines scheduling problem, which is NP-hard.
-    """
-
-    def __init__(
-        self,
-        batch_max_length: int,
-        lengths: List[int],
-        num_replicas: Optional[int] = None,
-        rank: Optional[int] = None,
-        seed: int = 0,
-    ):
-        # Get rank
-        if num_replicas is None:
-            if not dist.is_available():
-                raise RuntimeError("Requires distributed package to be available")
-            num_replicas = dist.get_world_size()
-        if rank is None:
-            if not dist.is_available():
-                raise RuntimeError("Requires distributed package to be available")
-            rank = dist.get_rank()
-
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.seed = seed
-
-        self.batch_max_length = batch_max_length
-        self.lengths = lengths
-        assert isinstance(self.lengths, np.ndarray)
-
-        self.epoch = 0
-
-        # statistics
-        self.eff_total_used = 0
-        self.eff_total_slots = 0
-
-    def set_epoch(self, epoch: int):
-        self.epoch = epoch
-
-    def generate_batches(self, set_stats=False):
-        indices = np.random.default_rng(seed=self.seed + self.epoch).permutation(
-            len(self.lengths)
-        )
-
-        lengths = self.lengths[indices]
-        lengths_cumsum = np.cumsum(lengths)
-
-        batches, total_used, total_slots = allocate(
-            lengths=lengths,
-            lengths_cumsum=lengths_cumsum,
-            rank=self.rank,
-            c=self.batch_max_length,
-            n=self.num_replicas,
-        )
-
-        batches = [indices[batch] for batch in batches]
-
-        # statistics
-        if set_stats:
-            self.eff_total_used += total_used
-            self.eff_total_slots += total_slots
-
-        return batches
-
-    def __iter__(self):
-        batches = self.generate_batches(set_stats=True)
-        return iter(batches)
-
-    def num_batches(self):
-        batches = self.generate_batches()
-        return len(batches)
-
-    def efficiency(self):
-        return self.eff_total_used / self.eff_total_slots

src/axolotl/utils/schedulers.py

@@ -1,9 +1,6 @@
 """Module for custom LRScheduler class"""
-import math
-from functools import partial
 
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import LambdaLR, LRScheduler
+from torch.optim.lr_scheduler import LRScheduler
 
 
 class InterpolatingLogScheduler(LRScheduler):
@@ -45,58 +42,3 @@ class InterpolatingLogScheduler(LRScheduler):
             lrs = [self.max_lr for base_lr in self.base_lrs]
 
         return lrs
-
-
-def _get_cosine_schedule_with_quadratic_warmup_lr_lambda(
-    current_step: int,
-    *,
-    num_warmup_steps: int,
-    num_training_steps: int,
-    num_cycles: float
-):
-    if current_step < num_warmup_steps:
-        return (float(current_step) / float(max(1, num_warmup_steps))) ** 2
-    progress = float(current_step - num_warmup_steps) / float(
-        max(1, num_training_steps - num_warmup_steps)
-    )
-    return max(
-        0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))
-    )
-
-
-def get_cosine_schedule_with_quadratic_warmup(
-    optimizer: Optimizer,
-    num_warmup_steps: int,
-    num_training_steps: int,
-    num_cycles: float = 0.5,
-    last_epoch: int = -1,
-):
-    """
-    Create a schedule with a learning rate that decreases following the values of the cosine function between the
-    initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
-    initial lr set in the optimizer.
-
-    Args:
-        optimizer ([`~torch.optim.Optimizer`]):
-            The optimizer for which to schedule the learning rate.
-        num_warmup_steps (`int`):
-            The number of steps for the warmup phase.
-        num_training_steps (`int`):
-            The total number of training steps.
-        num_cycles (`float`, *optional*, defaults to 0.5):
-            The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
-            following a half-cosine).
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
-
-    Return:
-        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
-    """
-
-    lr_lambda = partial(
-        _get_cosine_schedule_with_quadratic_warmup_lr_lambda,
-        num_warmup_steps=num_warmup_steps,
-        num_training_steps=num_training_steps,
-        num_cycles=num_cycles,
-    )
-    return LambdaLR(optimizer, lr_lambda, last_epoch)

src/axolotl/utils/tokenization.py

@@ -34,5 +34,3 @@ def check_example_labels(example, tokenizer):
 
     logging.info(" ".join(colored_tokens))
     logging.info("\n\n\n")
-
-    return " ".join(colored_tokens)

src/axolotl/utils/trainer.py

@@ -5,185 +5,25 @@ import logging
 import math
 import os
 import sys
-from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Optional
 
 import bitsandbytes as bnb
-import numpy as np
 import torch.cuda
 import transformers
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
-from torch.utils.data import Dataset
-from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
+from transformers import EarlyStoppingCallback, Trainer
 from transformers.trainer_pt_utils import get_parameter_names
 
 from axolotl.utils.callbacks import (
     SaveBetterTransformerModelCallback,
     SavePeftModelCallback,
 )
-from axolotl.utils.sampler import MultipackDistributedBatchSampler
-from axolotl.utils.schedulers import (
-    InterpolatingLogScheduler,
-    get_cosine_schedule_with_quadratic_warmup,
-)
-
-IGNORE_LABEL_ID = -100
+from axolotl.utils.schedulers import InterpolatingLogScheduler
 
 
-def _find_multiple(val1, val2):
-    return (-(val1 // -val2)) * val2
-
-
-def batch_to_tensor(batch, pad_id=0, dtype=torch.long, loss_dtype=torch.bfloat16):
-    # Pad an unused item to reach multiple of 64, for faster GEMM
-    pad_cur_len = sum(list(batch["length"]))
-    pad_len = _find_multiple(pad_cur_len, 64) - pad_cur_len
-
-    if pad_len > 0:
-        assert pad_len < 64
-
-        batch["input_ids"].append([pad_id] * pad_len)
-        batch["labels"].append([pad_id] * pad_len)
-        batch["attention_mask"].append([0] * pad_len)
-        batch["length"].append(pad_len)
-
-    # seqlen
-    batch_lengths = torch.tensor(list(batch["length"]), dtype=torch.int32, device="cpu")
-
-    max_seqlen = torch.max(batch_lengths)
-    cu_seqlens = torch.nn.functional.pad(
-        batch_lengths.cumsum(-1, dtype=torch.int32), (1, 0)
-    )
-
-    # nz elements
-    nz_num = cu_seqlens[-1]
-    nz_input_ids = torch.zeros((nz_num,), dtype=dtype, pin_memory=True, device="cpu")
-    nz_position_ids = torch.zeros((nz_num,), dtype=dtype, pin_memory=True, device="cpu")
-    nz_shifted_label_ids = torch.zeros(
-        (nz_num,), dtype=dtype, pin_memory=True, device="cpu"
-    )
-    nz_shifted_loss_weights = torch.zeros(
-        (nz_num,), dtype=loss_dtype, pin_memory=True, device="cpu"
-    )
-
-    index = 0
-    for token_list, length, labels_list in zip(
-        batch["input_ids"], batch["length"], batch["labels"]
-    ):
-        tokens = torch.tensor(token_list, dtype=dtype, device="cpu")
-        position_ids = torch.arange(length, dtype=dtype, device="cpu")
-
-        # Input IDs & shifted labels
-        # shifted_label_ids = torch.where(masks, tokens, IGNORE_LABEL_ID)
-        shifted_label_ids = labels_list
-        shifted_label_ids = torch.nn.functional.pad(
-            shifted_label_ids[1:], (0, 1), "constant", IGNORE_LABEL_ID
-        )
-
-        nz_input_ids[index : index + length] = tokens
-        nz_position_ids[index : index + length] = position_ids
-        nz_shifted_label_ids[index : index + length] = shifted_label_ids
-
-        # Loss weights
-        mask_count = sum(1 for label in labels_list[1:] if label != IGNORE_LABEL_ID)
-        loss_weight = (
-            1 / mask_count if mask_count > 0 else 0
-        )  # Avoid division by zero for paddings
-
-        nz_shifted_loss_weights[index : index + length] = loss_weight
-
-        index += length
-
-    # inputs
-    return {
-        "max_seqlen": max_seqlen,
-        "cu_seqlens": cu_seqlens,
-        "nz_input_ids": nz_input_ids,
-        "nz_position_ids": nz_position_ids,
-        "nz_shifted_label_ids": nz_shifted_label_ids,
-        "nz_shifted_loss_weights": nz_shifted_loss_weights,
-    }
-
-
-@dataclass
-class AxolotlTrainingArguments(TrainingArguments):
-    """
-    Extend the base TrainingArguments for axolotl helpers
-    """
-
-    lr_quadratic_warmup: bool = field(
-        default=False,
-        metadata={"help": "Use quadratic warmup for cosine scheduling."},
-    )
-    sample_packing: bool = field(
-        default=True,
-        metadata={"help": "Use sample packing for efficient training."},
-    )
-    max_seq_length: int = field(
-        default=2048,
-        metadata={"help": "The maximum sequence length the model can handle"},
-    )
-
-
-class AxolotlTrainer(Trainer):
-    """
-    Extend the base Trainer for axolotl helpers
-    """
-
-    args = None  # type: AxolotlTrainingArguments
-
-    def create_scheduler(
-        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
-    ):
-        """
-        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
-        passed as an argument.
-
-        Args:
-            num_training_steps (int): The number of training steps to do.
-            optimizer (torch.optim.Optimizer): The training optimizer
-        """
-
-        # fmt: off
-        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
-            # fmt: on
-            if (
-                self.args.lr_scheduler_type == "cosine"
-                and self.args.lr_quadratic_warmup is True
-            ):
-                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                )
-            else:
-                return super().create_scheduler(num_training_steps, optimizer)
-        return self.lr_scheduler
-
-    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        lengths = np.array([len(sample["input_ids"]) for sample in self.train_dataset])
-        return MultipackDistributedBatchSampler(
-            batch_max_length=self.args.per_device_train_batch_size
-            * self.args.max_seq_length,
-            lengths=lengths,
-            seed=self.args.seed,
-        )
-
-    def _get_eval_sampler(
-        self, eval_dataset: Dataset
-    ) -> Optional[torch.utils.data.Sampler]:
-        lengths = np.array([len(sample["input_ids"]) for sample in eval_dataset])
-        return MultipackDistributedBatchSampler(
-            batch_max_length=self.args.per_device_eval_batch_size
-            * self.args.max_seq_length,
-            lengths=lengths,
-            seed=self.args.seed,
-        )
-
-
-class OneCycleLRSchedulerTrainer(AxolotlTrainer):
+class OneCycleLRSchedulerTrainer(Trainer):
     """
     Trainer subclass that uses the OneCycleLR scheduler
     """
@@ -263,9 +103,6 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
         if cfg.fsdp_config:
             training_arguments_kwargs["fsdp_config"] = dict(cfg.fsdp_config)
 
-    if cfg.lr_quadratic_warmup is not None:
-        training_arguments_kwargs["lr_quadratic_warmup"] = cfg.lr_quadratic_warmup
-
     # deepspeed
     if (
         os.environ.get("ACCELERATE_USE_DEEPSPEED") == "true"
@@ -287,16 +124,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
     if cfg.max_grad_norm:
         training_arguments_kwargs["max_grad_norm"] = cfg.max_grad_norm
 
-    if cfg.hub_model_id:
-        training_arguments_kwargs["hub_model_id"] = cfg.hub_model_id
-        training_arguments_kwargs["push_to_hub"] = True
-        training_arguments_kwargs["hub_private_repo"] = True
-
-    if cfg.save_safetensors:
-        training_arguments_kwargs["save_safetensors"] = cfg.save_safetensors
-
-    training_args = AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
-        max_steps=total_num_steps * cfg.num_epochs,
+    training_args = transformers.TrainingArguments(
         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
@@ -446,7 +274,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
     trainer_cls = (
         OneCycleLRSchedulerTrainer
         if cfg.lr_scheduler == "one_cycle" and (cfg.fsdp or cfg.adapter == "qlora")
-        else AxolotlTrainer
+        else transformers.Trainer
     )
     trainer = trainer_cls(
         model=model,

src/axolotl/utils/validation.py

@@ -87,16 +87,11 @@ def validate_config(cfg):
             "You probably want to disable group_by_length as it will force a streamed dataset to download completely."
         )
 
-    if any([cfg.adam_beta1, cfg.adam_beta2, cfg.adam_epsilon]) and (
+    if any([cfg.adamw_beta1, cfg.adamw_beta2, cfg.adamw_epsilon]) and (
         not cfg.optimizer or "adamw" not in cfg.optimizer
     ):
         logging.warning("adamw hyperparameters found, but no adamw optimizer set")
 
-    if cfg.push_to_hub_model_id:
-        raise ValueError(
-            "push_to_hub_model_id is deprecated. Please use hub_model_id instead."
-        )
-
     # TODO
     # MPT 7b
     # https://github.com/facebookresearch/bitsandbytes/issues/25

tests/test_prompt_tokenizers.py

@@ -7,15 +7,11 @@ from pathlib import Path
 from transformers import AutoTokenizer
 
 from axolotl.prompt_strategies.alpaca_chat import NoSystemPrompter
-from axolotl.prompt_strategies.alpaca_w_system import (
-    InstructionWSystemPromptTokenizingStrategy,
-    SystemDataPrompter,
-)
 from axolotl.prompt_tokenizers import (
     AlpacaPromptTokenizingStrategy,
     ShareGPTPromptTokenizingStrategy,
 )
-from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompter
+from axolotl.prompters import AlpacaPrompter, ShareGPTPrompter
 
 logging.basicConfig(level="INFO")
 
@@ -100,39 +96,5 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
         assert example["labels"][world_idx - 1] == -100
 
 
-class InstructionWSystemPromptTokenizingStrategyTest(unittest.TestCase):
-    """
-    Test class for prompt tokenization strategies with sys prompt from the dataset
-    """
-
-    def setUp(self) -> None:
-        # pylint: disable=duplicate-code
-        self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
-        self.tokenizer.add_special_tokens(
-            {
-                "bos_token": "<s>",
-                "eos_token": "</s>",
-                "unk_token": "<unk>",
-            }
-        )
-
-    def test_system_alpaca(self):
-        prompter = SystemDataPrompter(PromptStyle.CHAT.value)
-        strat = InstructionWSystemPromptTokenizingStrategy(
-            prompter,
-            self.tokenizer,
-            False,
-            2048,
-        )
-        sample = {
-            "system": "use cot",
-            "instruction": "hello!",
-            "output": "Hi! How can I help?",
-        }
-        example = strat.tokenize_prompt(sample)
-        assert example["input_ids"][0:3] == [1, 671, 20118]  # <s>use cot
-        assert example["input_ids"][3] == 11889  # USER
-
-
 if __name__ == "__main__":
     unittest.main()

tests/test_prompters.py

@@ -2,13 +2,7 @@
 
 import unittest
 
-from axolotl.prompt_strategies.alpaca_w_system import SystemDataPrompter
-from axolotl.prompters import (
-    AlpacaPrompter,
-    MultipleChoiceExplainPrompter,
-    PromptStyle,
-    UnpromptedPrompter,
-)
+from axolotl.prompters import AlpacaPrompter, PromptStyle
 
 
 class AlpacaPrompterTest(unittest.TestCase):
@@ -61,64 +55,3 @@ class AlpacaPrompterTest(unittest.TestCase):
         assert "### Response:" not in res
         assert "USER:" in res
         assert "ASSISTANT:" in res
-
-    def test_system_prompt(self):
-        prompter = SystemDataPrompter(prompt_style=PromptStyle.CHAT.value)
-        res = next(
-            prompter.build_prompt_w_system(
-                "use cot", "tell me a joke about the following", "alpacas"
-            )
-        )
-        assert "use cot" in res
-        assert res.startswith("use cot")
-        assert "### Instruction:" not in res
-        assert "### Input:" not in res
-        assert "alpacas" in res
-        assert "### Response:" not in res
-        assert "USER:" in res
-        assert "ASSISTANT:" in res
-
-
-class UnpromptedPrompterTest(unittest.TestCase):
-    """
-    Test class for UnpromptedPrompter with no system prompts
-    """
-
-    def test_prompt_style_w_none(self):
-        prompter = UnpromptedPrompter(prompt_style=None)
-        res = next(prompter.build_prompt("tell me a joke"))
-        assert "### Instruction:" in res
-        assert "tell me a joke" in res
-        assert res.startswith("###")
-
-    def test_prompt_style_w_instruct(self):
-        prompter = UnpromptedPrompter(prompt_style=PromptStyle.INSTRUCT.value)
-        res = next(
-            prompter.build_prompt("tell me a joke about the following", "alpacas")
-        )
-        assert "### Instruction:" in res
-        assert "tell me a joke" in res
-        assert res.startswith("###")
-
-    def test_prompt_style_w_chat(self):
-        prompter = UnpromptedPrompter(prompt_style=PromptStyle.CHAT.value)
-        res = next(
-            prompter.build_prompt("tell me a joke about the following", "alpacas")
-        )
-        assert "USER:" in res
-        assert "tell me a joke" in res
-        assert res.startswith("USER:")
-
-
-class MultipleChoiceExplainPrompterTest(unittest.TestCase):
-    """
-    Test class for MultipleChoiceExplainPrompter
-    """
-
-    def test_prompt_style_w_chat(self):
-        prompter = MultipleChoiceExplainPrompter(prompt_style=PromptStyle.CHAT.value)
-        res = next(prompter.build_prompt("choose one", "- A\n- B\n- C", "C"))
-        assert "USER:" in res
-        assert "choose one" in res
-        assert "Choose the answer that best answers the question." in res
-        assert "- A\n- B\n- C" in res

tests/test_tokenizers.py

@@ -1,31 +0,0 @@
-"""
-Test cases for the tokenizer loading
-"""
-import unittest
-
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_tokenizer
-
-
-class TestTokenizers(unittest.TestCase):
-    """
-    test class for the load_tokenizer fn
-    """
-
-    def test_default_use_fast(self):
-        cfg = DictDefault({})
-        tokenizer = load_tokenizer("huggyllama/llama-7b", None, cfg)
-        assert "Fast" in tokenizer.__class__.__name__
-
-    def test_dont_use_fast(self):
-        cfg = DictDefault(
-            {
-                "tokenizer_use_fast": False,
-            }
-        )
-        tokenizer = load_tokenizer("huggyllama/llama-7b", None, cfg)
-        assert "Fast" not in tokenizer.__class__.__name__
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_validation.py

@@ -268,7 +268,7 @@ class ValidationTest(unittest.TestCase):
         cfg = DictDefault(
             {
                 "optimizer": None,
-                "adam_epsilon": 0.0001,
+                "adamw_epsilon": 0.0001,
             }
         )
 
@@ -283,7 +283,7 @@ class ValidationTest(unittest.TestCase):
         cfg = DictDefault(
             {
                 "optimizer": "adafactor",
-                "adam_beta1": 0.0001,
+                "adamw_beta1": 0.0001,
             }
         )
 
@@ -298,9 +298,9 @@ class ValidationTest(unittest.TestCase):
         cfg = DictDefault(
             {
                 "optimizer": "adamw_bnb_8bit",
-                "adam_beta1": 0.9,
-                "adam_beta2": 0.99,
-                "adam_epsilon": 0.0001,
+                "adamw_beta1": 0.0001,
+                "adamw_beta2": 0.0001,
+                "adamw_epsilon": 0.0001,
             }
         )