fix loss masking and padding

fast_llm/data/data/gpt/config.py

@@ -48,14 +48,15 @@ class GPTDataConfig(DataConfig, GPTLegacyConfig):
         desc="Multiprocessing context. Do not touch.",
         hint=FieldHint.expert,
     )
-    truncate_documents: bool = Field(
-        default=True,
+    truncate_documents: bool | None = Field(
+        default=None,
         desc=(
+            "Please use batch.truncate_documents instead "
             "If enabled, documents may be truncated while being packed to fit the sequence length."
             "Otherwise, sequences will be padded such that every document lies entirely within a sample"
             " (and documents exceeding the sequence length will be skipped altogether)."
         ),
-        hint=FieldHint.feature,
+        hint=FieldHint.deprecated,
     )
 
     def _validate(self) -> None:

fast_llm/data/data/gpt/data.py

@@ -127,7 +127,6 @@ def setup(
                     distributed=distributed,
                     dataset_name=dataset_name,
                     tokenizer=self._tokenizer,
-                    truncate_documents=self._config.truncate_documents,
                 )
                 dataset = self._config.datasets[dataset_name].build_and_sample(sampling)
                 self._datasets[dataset_name] = DatasetMonitor(dataset, self._config.data_sample_warn_time_ms)

fast_llm/data/dataset/gpt/config.py

@@ -75,6 +75,7 @@ class GPTSamplingParameters(SamplingParameters):
     use_loss_masking_spans: bool = False
     use_preference_loss_spans: bool = False
     cross_document_attention: bool = True
+    truncate_documents: bool = True
     # How many extra tokens to add to the sequence length.
     # This is used to provide labels even for the last tokens in the sequence.
     extra_tokens: int = 1
@@ -90,7 +91,6 @@ class GPTSamplingData(SamplingData):
     config: GPTSamplingConfig
     parameters: GPTSamplingParameters
     tokenizer: "Tokenizer"
-    truncate_documents: bool = True
 
 
 @config_class(registry=True)

fast_llm/data/dataset/gpt/sampled.py

@@ -89,7 +89,7 @@ def __init__(
         self._indexed_dataset = indexed_dataset
         self._config = sampling.config
         self._parameters = sampling.parameters
-        self._truncate_documents = sampling.truncate_documents
+        self._truncate_documents = sampling.parameters.truncate_documents
         self._device = torch.device("cuda" if self._config.gpu else "cpu")
 
         if sampling.cache_directory is None:
@@ -144,7 +144,7 @@ def _sample(self) -> None:
                 " Please make sure Fast-LLM is installed correctly."
             )
             long_docs_filter = document_sizes > self._parameters.sequence_length + 1
-            ignored_documents = sum(long_docs_filter)
+            ignored_documents = long_docs_filter.sum().item()
             if ignored_documents:
                 log_main_rank(
                     f" > {ignored_documents}/{documents_per_epoch} documents are longer than {self._parameters.sequence_length+1} tokens and will be ignored.",
@@ -201,9 +201,10 @@ def _sample(self) -> None:
 
         if self._yaml_path is not None and self._yaml_path.is_file():
             loaded_yaml_data = yaml.safe_load(self._yaml_path.open("r"))
+            # Hack to make sure unshuffled tokens are loaded
+            if not self._truncate_documents:
+                yaml_data["unshuffled_tokens"] = loaded_yaml_data["unshuffled_tokens"]
             self._load_yaml_data(yaml_data)
-            if not self._truncate_documents and not self._parameters.use_preference_loss_spans:
-                del loaded_yaml_data["unshuffled_tokens"]
 
             if loaded_yaml_data != yaml_data:
                 raise RuntimeError(
@@ -469,7 +470,7 @@ def __getitem__(self, index: int) -> typing.Any:
                         token_count += padding_size
 
             # Determine if the document belongs to the requested sample.
-            if token_count + document_size >= token_start:
+            if token_count + document_size > token_start:
                 # Determine which part of the document belong to the sample, and add it to the list.
                 token_start_index_in_document = max(token_start - token_count, 0)
                 token_end_index_in_document = min(token_end - token_count, document_size)
@@ -487,7 +488,7 @@ def __getitem__(self, index: int) -> typing.Any:
                             0,
                             self._parameters.sequence_length + self._parameters.extra_tokens,
                         )
-                        if span[1] > span[0]:
+                        if span[1] >= span[0]:
                             loss_masking_spans.append(span)
 
             # Go to the next document.
@@ -547,7 +548,7 @@ def __init__(
     ):
         assert isinstance(sampling, GPTSamplingData)
         self._indexed_dataset = indexed_dataset
-        if not sampling.truncate_documents:
+        if not sampling.parameters.truncate_documents:
             raise NotImplementedError(
                 "Legacy sampling only supports document truncation. Please use the latest dataset format."
             )

fast_llm/functional/cross_entropy.py

@@ -145,9 +145,9 @@ def _fused_cross_entropy_forward_backward(
 
     per_sample_loss = sum_exp_logits.log() - predicted_logits
     if loss_mask is not None:
-        per_sample_loss = per_sample_loss * loss_mask
-
-    loss = per_sample_loss.mean()
+        loss = (per_sample_loss * loss_mask).sum() / torch.maximum(loss_mask.sum(), 1)
+    else:
+        loss = per_sample_loss.mean()
     if target_format != TargetFormat.labels and group is not None:
         all_reduce(loss, op=ReduceOp.MEAN, group=group)
 

fast_llm/layers/language_model/config.py

@@ -38,6 +38,7 @@ class LanguageModelKwargs:
     chosen_spans = "chosen_spans"
     rejected_spans = "rejected_spans"
     loss_mask = "loss_mask"
+    mask_inputs = "mask_inputs"
 
 
 @config_class()

fast_llm/layers/language_model/embedding.py

@@ -84,7 +84,7 @@ def __init__(
             )
 
     @torch.compile
-    def _forward(self, input_: torch.Tensor, position_ids: torch.Tensor | None) -> torch.Tensor:
+    def _forward(self, input_: torch.Tensor, position_ids: torch.Tensor | None, mask_inputs: bool) -> torch.Tensor:
         Assert.eq(position_ids is not None, self._use_absolute_position_embeddings)
         group = self._tensor_space.distributed.tensor_group
         if self._parallel_embeddings:
@@ -101,9 +101,17 @@ def _forward(self, input_: torch.Tensor, position_ids: torch.Tensor | None) -> t
                 input_ = split(input_, group=group, dim=0)
                 if self._use_absolute_position_embeddings:
                     position_ids = split(position_ids, group=group, dim=0)
-            embeddings = torch.embedding(self.word_embeddings_weight, input_)
+            # handle masked tokens
+            if mask_inputs:
+                input_mask = input_ >= 0
+                masked_input = input_ * input_mask
+                embeddings = torch.embedding(self.word_embeddings_weight, masked_input)
+            else:
+                embeddings = torch.embedding(self.word_embeddings_weight, input_)
             if self._use_absolute_position_embeddings:
                 embeddings = embeddings + torch.nn.functional.embedding(position_ids, self.position_embeddings_weight)
+            if mask_inputs:
+                embeddings = embeddings * input_mask.unsqueeze(2)
         with set_generator(
             self._tensor_space.distributed.tp_generator
             if self._sequence_parallel
@@ -125,4 +133,6 @@ def forward(
                 tensor_name="Embedding output",
                 dtype=self._residual_dtype,
             )
-        return self._forward(input_, kwargs.get(LanguageModelKwargs.position_ids))
+        return self._forward(
+            input_, kwargs.get(LanguageModelKwargs.position_ids), kwargs.get(LanguageModelKwargs.mask_inputs)
+        )

fast_llm/models/gpt/config.py

@@ -1,4 +1,5 @@
 import functools
+import logging
 import typing
 
 from fast_llm.config import Field, FieldHint, FieldUpdate, check_field, config_class
@@ -17,6 +18,8 @@
     from fast_llm.models.gpt.model import GPTInferenceRunner, GPTModel
     from fast_llm.models.gpt.trainer import GPTTrainer
 
+logger = logging.getLogger(__name__)
+
 
 class GPTHuggingfaceCheckpointFormat(CheckpointFormat):
     support_optimizer: typing.ClassVar[bool] = False
@@ -91,6 +94,15 @@ class GPTBatchConfig(BatchConfig):
         desc="Read loss masking spans from the dataset.",
         hint=FieldHint.feature,
     )
+    truncate_documents: bool | None = Field(
+        default=True,
+        desc=(
+            "If enabled, documents may be truncated while being packed to fit the sequence length."
+            "Otherwise, sequences will be padded such that every document lies entirely within a sample"
+            " (and documents exceeding the sequence length will be skipped altogether)."
+        ),
+        hint=FieldHint.feature,
+    )
 
     def _validate(self) -> None:
         if self.micro_sequence_length is None:
@@ -183,6 +195,12 @@ def _validate(self) -> None:
             self.batch.sequence_length = self.model.base_model.max_position_embeddings
         if self.model.base_model.use_megatron_initialization:
             set_megatron_distributed_seeds(self.model.distributed)
+        if self.data.truncate_documents is not None:
+            logger.warning(
+                "Using deprecated field `data.truncate_documents`, `batch.truncate_documents` will be overridden if specified. "
+                "Use `batch.truncate_documents` instead."
+            )
+            self.batch.truncate_documents = self.data.truncate_documents
         super()._validate()
 
         if self.model.base_model.use_absolute_position_embeddings:

fast_llm/models/gpt/model.py

@@ -169,6 +169,7 @@ def preprocess_meta(
             TransformerKwargs.hidden_dims: hidden_dims,
             TransformerKwargs.sequence_length: sequence_length,
             TransformerKwargs.sequence_q_dim: sequence_q_dim,
+            LanguageModelKwargs.mask_inputs: not batch_meta.truncate_documents,
         }
 
         sequence_k_pasts = range(
@@ -302,7 +303,7 @@ def preprocess(
                 if batch.loss_masking_spans is not None:
                     # avoid changing input tokens
                     labels = labels.clone()
-                    for i, spans in enumerate(batch.loss_masking_spans):
+                    for idx, spans in enumerate(batch.loss_masking_spans):
                         if not spans.numel():
                             continue
                         valid_spans = spans[
@@ -316,9 +317,9 @@ def preprocess(
                             loss_mask = torch.ones_like(labels, dtype=torch.bool)
                             for start, end in valid_spans:
                                 if sequence_first:
-                                    loss_mask[start : end + 1, i] = False
+                                    loss_mask[start : end + 1, idx] = False
                                 else:
-                                    loss_mask[i, start : end + 1] = False
+                                    loss_mask[idx, start : end + 1] = False
                             if self._config.distillation_model is not None:
                                 kwargs[LanguageModelKwargs.loss_mask] = loss_mask
                             labels = torch.where(loss_mask, labels, -100)

fast_llm/models/gpt/trainer.py

@@ -30,6 +30,7 @@ def _get_sampling_parameters(
                 "use_loss_masking_spans": self._config.batch.use_loss_masking_spans,
                 "use_preference_loss_spans": self._config.model.base_model.enable_dpo,
                 "cross_document_attention": self._config.batch.cross_document_attention,
+                "truncate_documents": self._config.batch.truncate_documents,
                 "extra_tokens": self._config.model.base_model.prediction_heads,
             }
         )

tests/data/common.py

@@ -51,12 +51,12 @@ def get_sampling_data(
             num_samples=num_samples,
             sequence_length=sequence_length,
             vocab_size=vocab_size,
+            truncate_documents=truncate_documents,
         ),
         cache_directory=cache_directory,
         distributed=distributed,
         dataset_name=phase.value,
         tokenizer=tokenizer,
-        truncate_documents=truncate_documents,
     )