phi 4 multimodal training version 1 ( with limitations )

configs/recipes/vision/phi4/sft/README.md

@@ -0,0 +1,11 @@
+# Phi-4-multimodal-instruct
+
+Configs for Phi-4-multimodal-instruct 5.6Β model. See https://huggingface.co/microsoft/Phi-4-multimodal-instruct
+
+This is a multimodal model that combines text, visual, and audio inputs.
+It uses a "Mixture of LoRAs" approach, allowing you to plug in adapters for each
+modality without needing to retrain the base model. For more information consider
+reading the following:
+
+- [Mixture-of-LoRAs](https://arxiv.org/abs/2403.03432)
+- [Phi-4 Multimodal Technical Report](https://arxiv.org/abs/2503.01743)

configs/recipes/vision/phi4/sft/gcp_job.yaml

@@ -0,0 +1,60 @@
+# Phi4 multimodal 5.6B full fine-tune training job config.
+#
+# Requirements:
+#   - Set up SkyPilot GCP: https://oumi.ai/docs/en/latest/user_guides/launch/launch.html#setup
+#   - Log into WandB (`wandb login`) or disable `enable_wandb`
+#
+# Usage:
+#   oumi launch up --config configs/recipes/vision/phi4/sft/gcp_job.yaml --cluster phi4-multimodal
+#
+# See Also:
+#   - Documentation: https://oumi.ai/docs/en/latest/user_guides/launch/launch.html
+#   - Config class: oumi.core.configs.JobConfig
+#   - Config source: https://github.com/oumi-ai/oumi/blob/main/src/oumi/core/configs/job_config.py
+#   - Other job configs: configs/**/*job.yaml
+
+name: phi4-mm-sft-oumi-train
+
+resources:
+  cloud: gcp
+  accelerators: "A100:1" # Feel free to bump up the number of GPUs!
+  use_spot: false
+  disk_size: 1000 # Disk size in GBs
+
+num_nodes: 1 # Set it to N for multi-node training.
+
+working_dir: .
+
+file_mounts:
+  ~/.netrc: ~/.netrc # WandB credentials
+  ~/.cache/huggingface/token: ~/.cache/huggingface/token # HF credentials
+
+envs:
+  WANDB_PROJECT: oumi-train
+  OUMI_RUN_NAME: phi4-vl.fft.oumi
+
+setup: |
+  set -e
+
+  pip install uv && uv pip install oumi[gpu] hf_transfer
+  # Install model from HF Hub. This tool increases download speed compared to
+  # downloading the model during training.
+  HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download microsoft/Phi-4-multimodal-instruct
+
+  # The model requires flash_attention_2! Install it here.
+  pip install -U flash-attn --no-build-isolation
+
+
+run: |
+  set -e  # Exit if any command failed.
+  source ./configs/examples/misc/sky_init.sh
+  set -x
+  oumi distributed torchrun \
+      -m oumi train \
+      -c configs/recipes/vision/phi4/sft/train.yaml \
+      --training.run_name "${OUMI_RUN_NAME}.${SKYPILOT_TASK_ID}" \
+      --training.max_steps 25 \
+      --training.save_steps 0 \
+      --training.save_final_model false
+
+  echo "Node ${SKYPILOT_NODE_RANK} is all done!"

configs/recipes/vision/phi4/sft/train.yaml

@@ -0,0 +1,94 @@
+# Phi-4-multimodal-instruct training config for SFT finetuning.
+#
+# Phi-4-multimodal-instruct is a multimodal model that combines text, visual, and audio # inputs. It uses a "Mixture of LoRAs" approach, allowing you to plug in adapters for
+# each modality without needing to retrain the base model.
+#
+# Important Note: Oumi has currently integrated and fully tested Phi-4 for vision and
+# text modalities only (!).
+#
+# Requirements:
+#   - Log into WandB (`wandb login`) or disable `enable_wandb`
+#   - Run `pip install -U flash-attn --no-build-isolation`
+#
+# Usage:
+#   oumi train -c configs/recipes/vision/phi4/sft/train.yaml
+#
+# See Also:
+#   - Documentation: https://oumi.ai/docs/en/latest/user_guides/train/train.html
+#   - Config class: oumi.core.configs.TrainingConfig
+#   - Config source: https://github.com/oumi-ai/oumi/blob/main/src/oumi/core/configs/training_config.py
+#   - Other training configs: configs/**/pretraining/, configs/**/sft/, configs/**/dpo/
+
+model:
+  model_name: "microsoft/Phi-4-multimodal-instruct"
+  torch_dtype_str: "bfloat16"
+  model_max_length: 4096
+  trust_remote_code: True
+  attn_implementation: "flash_attention_2" # The model requires Flash Attention.
+
+  # The model by default freezes the following audio/image-related modules:
+  #   model.embed_tokens_extend.audio_embed
+  #   model.embed_tokens_extend.image_embed
+
+data:
+  train:
+    collator_name: "vision_language_with_padding"
+    use_torchdata: true
+    datasets:
+      - dataset_name: "merve/vqav2-small"
+        split: "validation"
+        shuffle: True
+        seed: 42
+        trust_remote_code: True
+        transform_num_workers: "auto"
+        dataset_kwargs:
+          processor_name: "microsoft/Phi-4-multimodal-instruct"
+          return_tensors: True
+
+      # Below are examples of other vision SFT datasets
+      # - dataset_name: "HuggingFaceH4/llava-instruct-mix-vsft"
+      #   split: "train"
+      #   shuffle: True
+      #   seed: 42
+      #   trust_remote_code: True
+      #   transform_num_workers: "auto"
+      #   dataset_kwargs:
+      #     processor_name: "microsoft/Phi-4-multimodal-instruct"
+      #     return_tensors: True
+      # - dataset_name: "coco_captions"
+      #   split: "train"
+      #   trust_remote_code: True
+      #   dataset_kwargs:
+      #     processor_name: "microsoft/Phi-4-multimodal-instruct"
+      #     return_tensors: True
+      # - dataset_name: vision_language_jsonl
+      #   dataset_path: "training.jsonl"  # See notebook for example how to generate this file
+      #   dataset_kwargs:
+      #     data_column: "messages"
+      #     processor_name: "microsoft/Phi-4-multimodal-instruct"
+
+training:
+  output_dir: "output/vlm_finetuned"
+  trainer_type: "TRL_SFT"
+  enable_gradient_checkpointing: True
+  per_device_train_batch_size: 1 # Due to processor's handling of variable sized img-features.
+  gradient_accumulation_steps: 8
+  max_steps: 20
+
+  gradient_checkpointing_kwargs:
+    # Reentrant docs: https://pytorch.org/docs/stable/checkpoint.html#torch.utils.checkpoint.checkpoint
+    use_reentrant: False
+  ddp_find_unused_parameters: True
+
+  optimizer: "adamw_torch_fused"
+  learning_rate: 2e-5
+  warmup_ratio: 0.03
+  weight_decay: 0.0
+  lr_scheduler_type: "cosine"
+
+  logging_steps: 10
+  dataloader_main_process_only: True
+  dataloader_num_workers: 4
+  dataloader_prefetch_factor: 8
+  include_performance_metrics: True
+  enable_wandb: True

pyproject.toml

@@ -42,6 +42,7 @@ dependencies = [
     "aiohttp>=3.10,<3.12",          # Used by inference engine
     "aiofiles>=22.1.0,<25",         # Allows to use async file operations
     "aioresponses>=0.7.6,<0.8",     # User by inference engine tests
+    "backoff>=2.2.1,<2.3",
     "datasets>=3.2.0,<3.3",
     "jsonlines",
     "lm_eval[wandb]>=0.4.5,<0.5.0",

src/oumi/builders/collators.py

@@ -161,6 +161,9 @@ def build_collator_from_config(
         collator_kwargs["allow_multi_image_inputs"] = (
             model_config.visual_config.supports_multiple_images
         )
+        collator_kwargs["main_image_feature"] = (
+            model_config.visual_config.main_image_feature
+        )
 
     if collator_name == "vision_language_sft":
         processor_name = collator_kwargs.get(

src/oumi/builders/processors.py

@@ -50,9 +50,11 @@ def build_processor(
 
     # Initialize model-specific params.
     label_ignore_index: Optional[int] = constants.LABEL_IGNORE_INDEX
+    ignore_features: Optional[list[str]] = None
     processor_kwargs = {}
     if model_config is not None:
         label_ignore_index = model_config.label_ignore_index
+        ignore_features = model_config.ignore_features
         processor_kwargs.update(model_config.processor_kwargs)
 
     create_processor_fn = functools.partial(
@@ -70,4 +72,5 @@ def build_processor(
         worker_processor,
         tokenizer,
         label_ignore_index=label_ignore_index,
+        ignore_features=ignore_features,
     )

src/oumi/core/collators/vision_language_collator_with_padding.py

@@ -21,8 +21,6 @@
 from oumi.core.tokenizers.base_tokenizer import BaseTokenizer
 from oumi.utils.torch_utils import pad_to_max_dim_and_stack
 
-_PIXEL_VALUES_KEY = "pixel_values"
-
 
 class VisionLanguageCollatorWithPadding:
     def __init__(
@@ -33,6 +31,7 @@ def __init__(
         truncation: bool = False,
         label_ignore_index: Optional[int] = None,
         allow_multi_image_inputs: bool = True,
+        main_image_feature: str = "pixel_values",
     ):
         """Custom collator for multi-modal vision-language training.
 
@@ -45,8 +44,11 @@ def __init__(
         label_ignore_index:  If set, then label values of tokens that shouldn't
             contribute to the loss computation will be replaced by this special value.
         allow_multi_image_inputs: Whether to allow multi-image inputs.
+        main_image_feature: The key to use for fetching the main image data
+        (e.g., raw pixels, patches, etc.) from the input.
         """
         self._allow_multi_image_inputs = allow_multi_image_inputs
+        self._main_image_feature = main_image_feature
         self._text_collator: TextCollatorWithPadding = TextCollatorWithPadding(
             tokenizer=tokenizer,
             max_length=max_length,
@@ -60,7 +62,7 @@ def __init__(
         )
 
     def __call__(self, batch) -> dict[str, Any]:
-        """Custom collator for multi-modal  vision-language training.
+        """Custom collator for multi-modal vision-language training.
 
         Args:
             batch: List of batch items.
@@ -71,7 +73,7 @@ def __call__(self, batch) -> dict[str, Any]:
         # Collate batch prompts
         collated_batch = self._text_collator(batch)  # type: ignore
         known_input_names: set[str] = set(collated_batch.keys()).union(
-            {_PIXEL_VALUES_KEY}
+            {self._main_image_feature}
         )
         other_input_names: set[str] = set()
 
@@ -80,12 +82,12 @@ def __call__(self, batch) -> dict[str, Any]:
             # TODO Consider relaxing this constraint: a vision/language model
             # can handle text-only inputs e.g., a follow-up to an answer,
             # or image-only inputs e.g., captioning.
-            if _PIXEL_VALUES_KEY not in item:
+            if self._main_image_feature not in item:
                 raise ValueError(
-                    f"Item doesn't contain '{_PIXEL_VALUES_KEY}' key. "
+                    f"Item doesn't contain '{self._main_image_feature}' key. "
                     f"Available keys: {item.keys()}"
                 )
-            images.append(item[_PIXEL_VALUES_KEY])
+            images.append(item[self._main_image_feature])
 
             for key in item:
                 if (
@@ -96,10 +98,10 @@ def __call__(self, batch) -> dict[str, Any]:
                     other_input_names.add(key)
 
         # Collate images.
-        pixel_values = self.collate_images(images)
+        image_input_features = self.collate_images(images)
 
         # Add images to other inputs.
-        collated_batch[_PIXEL_VALUES_KEY] = pixel_values
+        collated_batch[self._main_image_feature] = image_input_features
 
         # For other inputs, let's verify they present in all examples and stack them.
         if len(other_input_names) > 0:

src/oumi/core/configs/internal/internal_model_config.py

@@ -74,6 +74,12 @@ class InternalFeatureSpec(NamedTuple):
 
 @dataclass
 class InternalVisualModelConfig(BaseConfig):
+    main_image_feature: str = "pixel_values"
+    """The key corresponding to the main image feature consumed by the model.
+
+    E.g., raw pixels, transformed image patches, etc. resulting from data
+    preprocessing and consumed by the underlying model."""
+
     variable_shape_image_features: bool = False
     """Whether image features can be of variable shape.
 
@@ -133,5 +139,8 @@ class InternalModelConfig(BaseConfig):
     processor_kwargs: dict[str, Any] = field(default_factory=dict)
     """Extra params to pass to processor constructor."""
 
+    ignore_features: list[str] = field(default_factory=list)
+    """Features from processing the input to ignore in the model's forward method."""
+
     visual_config: Optional[InternalVisualModelConfig] = None
     """Configuration specific to visual models."""

src/oumi/core/configs/internal/supported_models.py

@@ -200,6 +200,51 @@ def _create_phi3_vlm_config() -> InternalModelConfig:
     return config
 
 
+def _create_phi4_vlm_config() -> InternalModelConfig:
+    config = InternalModelConfig()
+    config.chat_template = "phi3-instruct"
+    config.ignore_features = [
+        "audio_attention_mask",  # We won't use audio features.
+        "audio_embed_sizes",
+        "input_audio_embeds",
+    ]
+
+    config.model_input_features.update(
+        {
+            feature_name: InternalFeatureSpec(
+                name=feature_name,
+                required=True,
+                variable_shape=True,
+                image_dependent=True,
+                first_dim_action=InternalFeatureFirstDimAction.DROP_IF_DUMMY,
+            )
+            for feature_name in (
+                "input_image_embeds",
+                "image_attention_mask",
+            )
+        }
+    )
+    config.model_input_features.update(
+        {
+            feature_name: InternalFeatureSpec(
+                name=feature_name,
+                required=True,
+                variable_shape=False,
+                image_dependent=True,
+            )
+            for feature_name in ("image_sizes",)
+        }
+    )
+    visual_config = InternalVisualModelConfig()
+    # FIXME OPE-355 Set to True once multi-image issues are resolved for the model.
+    visual_config.supports_multiple_images = False
+    visual_config.variable_shape_image_features = True
+    visual_config.main_image_feature = "input_image_embeds"
+
+    config.visual_config = visual_config
+    return config
+
+
 def _create_idefics3_vlm_config() -> InternalModelConfig:
     config = _create_default_vlm_config(
         supports_multiple_images=True, pixel_values_variable_shape=True
@@ -321,6 +366,11 @@ def get_all_models_map() -> (
             tested=True,
             config=_create_phi3_vlm_config(),
         ),
+        _ModelTypeInfo(
+            model_type="phi4mm",
+            model_class=transformers.AutoModelForCausalLM,
+            config=_create_phi4_vlm_config(),
+        ),
     ]
 
     # Make it immutable.

src/oumi/core/processors/base_processor.py

@@ -87,6 +87,12 @@ def label_ignore_index(self) -> Optional[int]:
         """Returns a label ignore index."""
         raise NotImplementedError
 
+    @property
+    @abc.abstractmethod
+    def ignore_features(self) -> list[str]:
+        """Returns a list of keys of features to ignore from feeding the model."""
+        raise NotImplementedError
+
     @abc.abstractmethod
     def __call__(
         self,