Max Length rework (#741)

* implementation

* c

* implementation

* docs

* c

* dont allow freeze and lora

* implementation

* format

* rm

* c

* c

* c

* fix

* r

* c

* format

* fix

* docs

* readme

* ui test

* fixing unfreeze + lora + dpo

* fixing position_id issue

* c

* c

Author: psinger

Makefile

@@ -99,6 +99,19 @@ test: reports
     -o log_cli=true -o log_level=INFO -o log_file=reports/tests.log \
     tests/* 2>&1 | tee reports/tests.log'
 
+
+.PHONY: test-debug
+test-debug: reports
+	@bash -c 'set -o pipefail; export PYTHONPATH=$(PWD); \
+	$(PIPENV) run pytest -v --junitxml=reports/junit.xml \
+	--import-mode importlib \
+	--html=./reports/pytest.html \
+	-k test_encode \
+	-s \
+    -o log_cli=false -o log_level=WARNING -o log_file=/dev/null \
+    tests/*'
+	
+
 .PHONY: test-ui
 test-ui: reports setup-ui
 	@bash -c 'set -o pipefail; \

README.md

@@ -54,6 +54,7 @@ Using CLI for fine-tuning LLMs:
 ## What's New
 
 - [PR 747](https://github.com/h2oai/h2o-llmstudio/pull/747) Fully removed RLHF in favor of DPO/IPO/KTO optimization.
+- [PR 741](https://github.com/h2oai/h2o-llmstudio/pull/741) Removing separate max length settings for prompt and answer in favor of a single `max_length` settings better resembling `chat_template` functionality from `transformers`.
 - [PR 592](https://github.com/h2oai/h2o-llmstudio/pull/599) Added `KTOPairLoss` for DPO modeling allowing to train models with simple preference data. Data currently needs to be manually prepared by randomly matching positive and negative examples as pairs.
 - [PR 592](https://github.com/h2oai/h2o-llmstudio/pull/592) Starting to deprecate RLHF in favor of DPO/IPO optimization. Training is disabled, but old experiments are still viewable. RLHF will be fully removed in a future release.
 - [PR 530](https://github.com/h2oai/h2o-llmstudio/pull/530) Introduced a new problem type for DPO/IPO optimization. This optimization technique can be used as an alternative to RLHF.

documentation/docs/get-started/llm-studio-performance.md

@@ -137,8 +137,6 @@ problem_type: text_causal_language_modeling
 tokenizer:
     add_prompt_answer_tokens: false
     max_length: 512
-    max_length_answer: 256
-    max_length_prompt: 256
     padding_quantile: 1.0
 training:
     batch_size: 2

documentation/docs/guide/experiments/experiment-settings.md

@@ -19,8 +19,6 @@ import DStextAnswerSeparator from '../../tooltips/experiments/_text-answer-separ
 import DSaddEosTokentoprompt from '../../tooltips/experiments/_add-eos-token-to-prompt.mdx';
 import DSaddEosTokentoanswer from '../../tooltips/experiments/_add-eos-token-to-answer.mdx';
 import DSmaskPromptlabels from '../../tooltips/experiments/_mask-prompt-labels.mdx';
-import TSmaxLengthPrompt from '../../tooltips/experiments/_max-length-prompt.mdx';
-import TSmaxLengthAnswer from '../../tooltips/experiments/_max-length-answer.mdx';
 import TSmaxLength from '../../tooltips/experiments/_max-length.mdx';
 import TSaddpromptanswertokens from '../../tooltips/experiments/_add-prompt-answer-tokens.mdx';
 import TSpaddingQuantile from '../../tooltips/experiments/_padding-quantile.mdx';
@@ -173,14 +171,6 @@ The settings under each category are listed and described below.
 
 ## Tokenizer settings
 
-### Max length prompt
-
-<TSmaxLengthPrompt/>
-
-### Max length answer
-
-<TSmaxLengthAnswer/>
-
 ### Max length 
 
 <TSmaxLength/>

documentation/docs/tooltips/experiments/_answer-column.mdx

@@ -1,3 +1,3 @@
 The column in the dataset containing the expected output.
 
-For classification, this needs to be an integer column containing the class label.
+For classification, this needs to be an integer column starting from zero containing the class label.

documentation/docs/tooltips/experiments/_max-length-answer.mdx

@@ -76,8 +76,6 @@ problem_type: text_causal_language_modeling
 tokenizer:
     add_prompt_answer_tokens: false
     max_length: 512
-    max_length_answer: 256
-    max_length_prompt: 256
     padding_quantile: 1.0
     tokenizer_kwargs: '{"use_fast": true, "add_prefix_space": false}'
 training:

documentation/docs/tooltips/experiments/_max-length-prompt.mdx

@@ -113,7 +113,7 @@ async def answer_chat(q: Q) -> str:
     logger.info(f"Full prompt: {full_prompt}")
 
     inputs = cfg.dataset.dataset_class.encode(
-        tokenizer, full_prompt, cfg.tokenizer.max_length_prompt, "left"
+        tokenizer, full_prompt, cfg.tokenizer.max_length, "left"
     )
     inputs["prompt_input_ids"] = (
         inputs.pop("input_ids").unsqueeze(0).to(cfg.environment._device)

examples/example_oasst2.yaml

@@ -92,14 +92,11 @@ def __post_init__(self):
 
 @dataclass
 class ConfigNLPCausalClassificationTokenizer(ConfigNLPCausalLMTokenizer):
-    max_length_prompt: int = 512
     max_length: int = 512
 
     def __post_init__(self):
         super().__post_init__()
 
-        self._visibility["max_length_answer"] = -1
-
 
 @dataclass
 class ConfigNLPCausalClassificationArchitecture(ConfigNLPCausalLMArchitecture):

llm_studio/app_utils/sections/chat_update.py

@@ -174,8 +174,8 @@ def __post_init__(self):
         )
         self._possible_values["differential_learning_rate_layers"] = (
             possible_values.String(
-                values=("backbone", "embed"),
-                allow_custom=False,
+                values=("backbone", "embed", "head"),
+                allow_custom=True,
                 placeholder="Select optional layers...",
             )
         )
@@ -250,17 +250,13 @@ def __post_init__(self):
 
 @dataclass
 class ConfigNLPCausalLMTokenizer(DefaultConfig):
-    max_length_prompt: int = 256
-    max_length_answer: int = 256
     max_length: int = 512
     add_prompt_answer_tokens: bool = False
     padding_quantile: float = 1.0
     tokenizer_kwargs: str = '{"use_fast": true, "add_prefix_space": false}'
 
     def __post_init__(self):
         super().__post_init__()
-        self._possible_values["max_length_prompt"] = (32, 1024 * 16, 32)
-        self._possible_values["max_length_answer"] = (32, 1024 * 16, 32)
         self._possible_values["max_length"] = (32, 1024 * 16, 32)
         self._possible_values["padding_quantile"] = (0, 1, 0.01)
         self._padding_side = "left"
@@ -353,7 +349,7 @@ def __post_init__(self):
 
         self._possible_values["num_beams"] = (1, 4, 1)
         self._possible_values["temperature"] = (0, 10, 0.05)
-        self._possible_values["repetition_penalty"] = (1, 10, 0.05)
+        self._possible_values["repetition_penalty"] = (1, 10, 0.025)
         self._possible_values["top_k"] = (0, 100, 1)
         self._possible_values["top_p"] = (0.5, 1, 0.05)
         self._possible_values["num_history"] = (1, 50, 1)

llm_studio/python_configs/text_causal_classification_modeling_config.py

@@ -42,6 +42,9 @@ def __getitem__(self, idx: int) -> Dict:
         input_text_dict["prompts"] = [
             self.parse_prompt(self.cfg, prompt) for prompt in input_text_dict["prompts"]
         ]
+        input_text_dict["answers"] = [
+            self.parse_answer(self.cfg, answer) for answer in input_text_dict["answers"]
+        ]
 
         sample = dict()
         system_encoding, prompt_encodings, answer_encodings = self.get_encodings(
@@ -72,7 +75,7 @@ def __getitem__(self, idx: int) -> Dict:
             self.pad_tokens(
                 answer_encodings[-1],
                 attention_mask=torch.ones_like(answer_encodings[-1]),
-                max_length=self.cfg.tokenizer.max_length_answer,
+                max_length=self.cfg.tokenizer.max_length,
                 pad_token_id=self.tokenizer.pad_token_id,
                 direction="right",
                 prefix="answer_",
@@ -99,14 +102,6 @@ def __getitem__(self, idx: int) -> Dict:
             )
         )
 
-        # make sure system encoding is always prepended if max_length exceeded
-        if sample["input_ids"][0] != self.tokenizer.pad_token_id:
-            sample["input_ids"][: len(system_encoding)] = system_encoding
-            if self.cfg.dataset.mask_prompt_labels and "labels" in sample.keys():
-                sample["labels"][: len(system_encoding)] = -100
-        if sample["prompt_input_ids"][0] != self.tokenizer.pad_token_id:
-            sample["prompt_input_ids"][: len(system_encoding)] = system_encoding
-
         return sample
 
     @staticmethod
@@ -122,6 +117,12 @@ def parse_prompt(cfg: Any, prompt: str):
         )
         return prompt
 
+    @staticmethod
+    def parse_answer(cfg: Any, answer: str):
+        if cfg.dataset.add_eos_token_to_answer:
+            answer += cfg._tokenizer_eos_token
+        return answer
+
     @staticmethod
     def parse_system(cfg: Any, system: str):
         # no system tokens if empty
@@ -375,9 +376,6 @@ def get_labels(self, prompt_encodings, answer_encodings):
                 ]
             ).to(torch.bool)
             labels.masked_fill_(prompt_mask, -100)
-        if self.cfg.dataset.add_eos_token_to_answer:
-            # eos_token may be equal to pad_token. Add the label back manually.
-            labels[-1] = self.tokenizer.eos_token_id
         if self.cfg.tokenizer.max_length < len(labels):
             labels = labels[-self.cfg.tokenizer.max_length :]
 
@@ -446,27 +444,16 @@ def augment_data(self, encodings):
     def _get_sample_encoding(self, system: str, prompt: str, answer: str) -> List:
         if len(system) > 0:
             system_encoding = self.encode(
-                self.tokenizer, system, self.cfg.tokenizer.max_length_prompt, "right"
+                self.tokenizer, system, self.cfg.tokenizer.max_length, "right"
             )["input_ids"]
         else:
             system_encoding = torch.empty(0)
         prompt_encoding = self.encode(
-            self.tokenizer, prompt, self.cfg.tokenizer.max_length_prompt, "left"
+            self.tokenizer, prompt, self.cfg.tokenizer.max_length, "left"
         )["input_ids"]
-        max_length_answer = self.cfg.tokenizer.max_length_answer - int(
-            self.cfg.dataset.add_eos_token_to_answer
-        )
         answer_encoding = self.encode(
-            self.tokenizer, answer, max_length_answer, "right"
+            self.tokenizer, answer, self.cfg.tokenizer.max_length, "right"
         )["input_ids"]
-        if self.cfg.dataset.add_eos_token_to_answer:
-            answer_encoding = torch.cat(
-                [
-                    answer_encoding,
-                    torch.Tensor([self.tokenizer.eos_token_id]),
-                ],
-                dim=0,
-            )
 
         return [system_encoding, prompt_encoding, answer_encoding]
 
@@ -482,6 +469,7 @@ def pad_tokens(
         sample = {}
 
         if max_length < len(input_ids):
+            logger.info(f"Input exceeds max_length of {max_length}, truncating sample.")
             input_ids = input_ids[-max_length:]
             attention_mask = attention_mask[-max_length:]
 

llm_studio/python_configs/text_causal_language_modeling_config.py

@@ -5,7 +5,11 @@
 from transformers import AutoModelForCausalLM
 
 from llm_studio.src.utils.data_utils import batch_padding
-from llm_studio.src.utils.modeling_utils import create_nlp_backbone, prepare_lora
+from llm_studio.src.utils.modeling_utils import (
+    create_nlp_backbone,
+    forward,
+    prepare_lora,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -67,7 +71,8 @@ def forward(
                 padding_side=self.cfg.tokenizer._padding_side,
             )
 
-        output = self.backbone(
+        output = forward(
+            self.backbone,
             input_ids=batch["prompt_input_ids"],
             attention_mask=batch["prompt_attention_mask"],
         )

llm_studio/src/datasets/text_causal_language_modeling_ds.py

@@ -8,6 +8,7 @@
 from llm_studio.src.utils.data_utils import batch_padding
 from llm_studio.src.utils.modeling_utils import (
     create_nlp_backbone,
+    forward,
     generate,
     prepare_lora,
 )
@@ -92,10 +93,7 @@ def forward(
                 padding_side=self.cfg.tokenizer._padding_side,
             )
 
-        output = self.backbone(
-            input_ids=batch["input_ids"],
-            attention_mask=batch["attention_mask"],
-        )
+        output = forward(self.backbone, batch["input_ids"], batch["attention_mask"])
 
         if "labels" in batch:
             loss = self.loss_fn(output.logits, batch["labels"])

llm_studio/src/models/text_causal_classification_modeling_model.py

@@ -13,6 +13,7 @@
 from llm_studio.src.utils.data_utils import batch_padding
 from llm_studio.src.utils.modeling_utils import (
     create_nlp_backbone,
+    forward,
     generate,
     prepare_lora,
 )
@@ -83,7 +84,12 @@ def __init__(self, cfg: Any):
 
         if cfg.training.lora:
             self.backbone = prepare_lora(cfg=cfg, backbone=self.backbone)
+
+        if cfg.training.lora and not cfg.training.lora_unfreeze_layers:
+            self.backbone_orig = None
         else:
+            if cfg.environment._local_rank == 0:
+                logger.info("Duplicating backbone for reference model.")
             self.backbone_orig, self.backbone_orig_config = create_nlp_backbone(
                 cfg, model_class=AutoModelForCausalLM
             )
@@ -137,7 +143,8 @@ def forward(
                         f"{answer}_labels",
                     ],
                 )
-            logits = self.backbone(
+            logits = forward(
+                self.backbone,
                 input_ids=batch[f"{answer}_input_ids"],
                 attention_mask=batch[f"{answer}_attention_mask"],
             ).logits
@@ -152,18 +159,21 @@ def forward(
             )
 
             with torch.no_grad():
-                if self.cfg.training.lora:
-                    with self.backbone.disable_adapter():
-                        reference_logits = self.backbone(
+                if self.backbone_orig:
+                    with torch.no_grad():
+                        reference_logits = forward(
+                            self.backbone_orig,
                             input_ids=batch[f"{answer}_input_ids"],
                             attention_mask=batch[f"{answer}_attention_mask"],
                         ).logits
                 else:
-                    with torch.no_grad():
-                        reference_logits = self.backbone_orig(
+                    with self.backbone.disable_adapter():
+                        reference_logits = forward(
+                            self.backbone,
                             input_ids=batch[f"{answer}_input_ids"],
                             attention_mask=batch[f"{answer}_attention_mask"],
                         ).logits
+
                 outputs[f"{answer}_reference_logps"] = get_batch_logps(
                     reference_logits,
                     batch[f"{answer}_labels"],