clean history

Author: jlamypoirier

Dockerfile

@@ -29,16 +29,17 @@ ENV PIP_CONSTRAINT=""
 # There is no pre-build mamba image for pytorch 2.8, we build it before the rest to avoid rebuilds.
 # We need to compile from the repo because of https://github.com/state-spaces/mamba/issues/720 (same for causal-conv1d)
 # We set the number of workers to avoid OOM when compiling on laptop. (TODO: Can we make it configurable?)
+# Using varlen_mamba for variable length sequence support
 RUN MAX_JOBS=2 pip install --no-build-isolation  "causal-conv1d@git+https://github.com/Dao-AILab/causal-conv1d@2a288a1"
-RUN MAX_JOBS=2 pip install --no-build-isolation "mamba_ssm[causal-conv1d]@git+https://github.com/state-spaces/mamba@4a8a2a2"
+RUN MAX_JOBS=2 pip install --no-build-isolation "mamba_ssm[causal-conv1d]@git+https://github.com/jxiw/varlen_mamba@varlen_mamba"
 # Copy dependency files with universal write permissions for all users.
 COPY --chmod=777 setup.py setup.cfg pyproject.toml ./
 COPY --chmod=777 ./fast_llm_external_models/__init__.py fast_llm_external_models/
 COPY --chmod=777 ./fast_llm/__init__.py fast_llm/
 COPY --chmod=777 ./fast_llm/csrc/ fast_llm/csrc/
 
 # Install dependencies within the virtual environment.
-RUN pip install --no-cache-dir --no-build-isolation -e ".[CORE,OPTIONAL,HUGGINGFACE,SSM,GENERATION,DEV]" triton==3.1.0
+RUN pip install --no-cache-dir --no-build-isolation -e ".[CORE,OPTIONAL,HUGGINGFACE,SSM,VISION,GENERATION,DEV]" triton==3.1.0
 
 # Copy the remaining source code with universal write permissions.
 COPY --chmod=777 ./Megatron-LM Megatron-LM

fast_llm/data/data/gpt/data.py

@@ -32,6 +32,8 @@ class GPTBatch:
     token_ids: torch.Tensor
     loss_masking_spans: list[torch.Tensor] | None = None
     sequence_lengths: list[torch.Tensor] | None = None
+    images: list[torch.Tensor] | None = None
+    image_positions: list[torch.Tensor] | None = None
     chosen_spans: list[torch.Tensor] | None = None
     rejected_spans: list[torch.Tensor] | None = None
 
@@ -49,12 +51,28 @@ def gpt_data_collate_fn(batch: list[GPTSample], sampling_parameters: GPTSampling
         stacked_rejected_spans = [torch.from_numpy(sample.rejected_span) for sample in batch]
     if not sampling_parameters.cross_document_attention:
         sequence_lengths = [torch.tensor(sample.sequence_lengths) for sample in batch]
+    has_images = False
+    batch_images = []
+    for sample in batch:
+        if sample.images is not None:
+            batch_images.append([torch.from_numpy(image) for image in sample.images])
+            has_images = True
+        else:
+            batch_images.append([])
+    batch_image_positions = []
+    for sample in batch:
+        if sample.image_positions is not None:
+            batch_image_positions.append(torch.from_numpy(sample.image_positions))
+        else:
+            batch_image_positions.append([])
     return GPTBatch(
         token_ids=torch.from_numpy(stacked_ids),
         loss_masking_spans=stacked_spans,
         sequence_lengths=sequence_lengths,
         chosen_spans=stacked_chosen_spans,
         rejected_spans=stacked_rejected_spans,
+        images=batch_images if has_images else None,
+        image_positions=batch_image_positions if has_images else None,
     )
 
 

fast_llm/data/dataset/gpt/config.py

@@ -72,6 +72,10 @@ class GPTSamplingParameters(SamplingParameters):
     use_preference_loss_spans: bool = False
     cross_document_attention: bool = True
     truncate_documents: bool = True
+    patch_size: int | None = None
+    max_image_size: int | None = None
+    image_break_token: int | None = None
+    image_end_token: int | None = None
     # 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
@@ -138,11 +142,18 @@ class GPTMemmapDatasetConfig(GPTIndexedDatasetConfig):
         desc="Expected number of tokens in the dataset.",
         hint=FieldHint.optional,
     )
+    num_pixels: int | None = Field(
+        default=None,
+        desc="Expected number of pixels in the dataset.",
+        hint=FieldHint.optional,
+    )
 
     def build(self) -> "GPTMemmapDataset":
         from fast_llm.data.dataset.gpt.memmap import GPTMemmapDataset
 
-        return GPTMemmapDataset(str(self.path).replace("/", "__"), self.path, self.num_documents, self.num_tokens)
+        return GPTMemmapDataset(
+            str(self.path).replace("/", "__"), self.path, self.num_documents, self.num_tokens, self.num_pixels
+        )
 
 
 @config_class(dynamic_type={GPTSampledDatasetConfig: "concatenated"})

fast_llm/data/dataset/gpt/fim.py

@@ -158,9 +158,9 @@ def _fim_permute_sequence(
         middle = contents[boundaries[0] : boundaries[1]]
         suffix = contents[boundaries[1] :]
 
-        prefix = np.array([*self._tokenizer.tokenize(prefix, end=False)], dtype=np.int64)
-        middle = np.array([*self._tokenizer.tokenize(middle, begin=False, end=False)], dtype=np.int64)
-        suffix = np.array([*self._tokenizer.tokenize(suffix, begin=False)], dtype=np.int64)
+        prefix = np.array([*self._tokenizer._tokenize(prefix, end=False)], dtype=np.int64)
+        middle = np.array([*self._tokenizer._tokenize(middle, begin=False, end=False)], dtype=np.int64)
+        suffix = np.array([*self._tokenizer._tokenize(suffix, begin=False)], dtype=np.int64)
 
         # here we truncate each given segment to fit the same length as it was before
         # A consequence is that we never reach the end of a file?

fast_llm/data/dataset/gpt/indexed.py

@@ -30,6 +30,14 @@ def sample(self, sampling: GPTSamplingData) -> "GPTSampledIndexedDataset":
 
         return GPTSampledIndexedDataset(self, sampling)
 
+    @property
+    @abc.abstractmethod
+    def has_images(self) -> bool:
+        """
+        Whether the dataset contains images.
+        This is used to determine whether to use image-related fields in the sampled data.
+        """
+
 
 class GPTDatasetSlice[IndexedDatasetType: GPTIndexedDataset](DatasetSlice[IndexedDatasetType], GPTIndexedDataset):
     """
@@ -40,11 +48,16 @@ class GPTDatasetSlice[IndexedDatasetType: GPTIndexedDataset](DatasetSlice[Indexe
 
     def get_document_sizes(self) -> np.ndarray:
         # TODO: This can be really big.
-        return self._dataset.get_document_sizes()[self._begin : self._end]
+        doc_sizes, im_sizes = self._dataset.get_document_sizes()
+        return doc_sizes[self._begin : self._end], im_sizes[self._begin : self._end] if im_sizes else np.array([])
 
     def get_document_size(self, index: int) -> int:
         return self._dataset.get_document_size(self._begin + index)
 
+    @property
+    def has_images(self) -> bool:
+        return self._dataset.has_images
+
 
 class GPTConcatenatedDataset[IndexedDatasetType: GPTIndexedDataset](
     ConcatenatedDataset[IndexedDatasetType], GPTIndexedDataset
@@ -53,8 +66,17 @@ class GPTConcatenatedDataset[IndexedDatasetType: GPTIndexedDataset](
 
     def get_document_sizes(self) -> np.ndarray:
         # TODO: This can be really big.
-        return np.concatenate([dataset.get_document_sizes() for dataset in self._datasets])
+        # return np.concatenate([dataset.get_document_sizes() for dataset in self._datasets])
+        sizes = [dataset.get_document_sizes() for dataset in self._datasets]
+        return (
+            np.concatenate([size[0] for size in sizes]),
+            np.concatenate([size[1] for size in sizes]) if sizes[0][1] is not None else np.array([]),
+        )
 
     def get_document_size(self, index: int) -> int:
         dataset = np.searchsorted(self._dataset_splits[1:], index, side="right")
         return self._datasets[dataset].get_document_size(index - self._dataset_splits[dataset].item())
+
+    @property
+    def has_images(self) -> bool:
+        return any(dataset.has_images for dataset in self._datasets)