Feat: save_pretrained for tensor parallel (and other parallelisms) models

src/transformers/integrations/tensor_parallel.py

@@ -61,6 +61,22 @@ def _blocks_to_block_sizes(total_size: int, blocks: Union[int, List[int]]) -> Li
         return [single_size] * blocks
 
 
+def _get_parameter_tp_plan(parameter_name: str, tp_plan: dict[str, str]) -> Optional[str]:
+    """
+    Get the TP style for a parameter from the TP plan.
+
+    The TP plan is a dictionary that maps parameter names to TP styles.
+    The parameter name can be a generic name with wildcards (e.g. "*.weight") or a specific name (e.g. "layer_1.weight").
+    """
+    generic_param_name = re.sub(r"\d+", "*", parameter_name)
+    if generic_param_name in tp_plan:
+        return tp_plan[generic_param_name]
+    elif "." in generic_param_name and generic_param_name.rsplit(".", 1)[0] in tp_plan:
+        return tp_plan[generic_param_name.rsplit(".", 1)[0]]
+    else:
+        return None
+
+
 str_to_torch_dtype = {
     "BOOL": torch.bool,
     "U8": torch.uint8,
@@ -138,6 +154,71 @@ def get_packed_weights(param, empty_param, device_mesh, rank, dim):
     return tensor.to(str_to_torch_dtype[slice_dtype])
 
 
+def repack_weights(
+    packed_parameter: torch.Tensor,
+    sharded_dim: int,  # The dimension index in the global tensor that was sharded
+    world_size: int,
+    num_blocks: int = 2,
+) -> torch.Tensor:
+    """
+    Reorders a tensor that was reconstructed from sharded packed weights into its canonical packed format.
+
+    For example, if a weight was packed (e.g., gate_proj and up_proj) and then sharded,
+    DTensor.full_tensor() might produce an interleaved layout like [G0, U0, G1, U1, ...]
+    along the sharded dimension. This function reorders it to [G0, G1, ..., U0, U1, ...].
+    This is an inverse operation to get_packed_weights.
+
+    Args:
+        reconstructed_tensor: The tensor reconstructed from DTensor (e.g., via .full_tensor().contiguous()).
+        sharded_dim: The dimension index in the reconstructed_tensor that was originally sharded.
+        world_size: The tensor parallel world size.
+        num_packed_projs: The number of projections that were packed together (e.g., 2 for gate_up_proj).
+
+    Returns:
+        The reordered tensor in canonical packed format.
+    """
+
+    if num_blocks != 2:
+        raise ValueError(
+            "Num blocks different from 2 is not supported yet. This is most likely a bug in your implementation as we only pack gate and up projections together."
+        )
+
+    actual_sharded_dim = sharded_dim if sharded_dim >= 0 else sharded_dim + packed_parameter.ndim
+    total_size_on_sharded_dim = packed_parameter.shape[actual_sharded_dim]
+    original_block_size_on_dim = total_size_on_sharded_dim // num_blocks
+    shard_chunk_size = original_block_size_on_dim // world_size
+
+    prefix_shape = packed_parameter.shape[:actual_sharded_dim]
+    suffix_shape = packed_parameter.shape[actual_sharded_dim + 1 :]
+
+    tensor_view = packed_parameter.view(
+        *prefix_shape,
+        world_size,
+        num_blocks,
+        shard_chunk_size,
+        *suffix_shape,
+    )
+
+    # Permute to bring num_packed_projs first, then world_size, then shard_chunk_size
+    # This groups all chunks of G together, then all chunks of U together.
+    # Target order of these middle dimensions: (num_packed_projs, world_size, shard_chunk_size)
+    # Current order of view's middle dimensions: (world_size, num_packed_projs, shard_chunk_size)
+    # Absolute indices of the dimensions to be permuted (world_size, num_packed_projs)
+    axis_ws_abs = len(prefix_shape)
+    axis_npp_abs = len(prefix_shape) + 1
+
+    permute_order = list(range(tensor_view.ndim))
+    permute_order[axis_ws_abs], permute_order[axis_npp_abs] = permute_order[axis_npp_abs], permute_order[axis_ws_abs]
+
+    tensor_permuted = tensor_view.permute(*permute_order)
+
+    # Reshape back to the original tensor's ndim, with the sharded dimension now correctly ordered as [G_all, U_all].
+    # The final shape should be the same as reconstructed_tensor.
+    final_ordered_tensor = tensor_permuted.reshape_as(packed_parameter)
+
+    return final_ordered_tensor
+
+
 def get_tensor_shard(param, empty_param, device_mesh, rank, dim):
     if dim == 0:
         size_ = empty_param.shape[0]
@@ -578,6 +659,49 @@ def translate_to_torch_parallel_style(style: str):
         raise ValueError(f"Unsupported parallel style value: {style}")
 
 
+def convert_local_tensor_to_dtensor(
+    parameter: torch.Tensor, parameter_name: str, device_mesh, tp_plan: dict[str, str]
+) -> DTensor:
+    """
+    Converts a local variant of weights to a DTensor with corresponding placements. Shouldn't be done ever except of before saving the model.
+    """
+    _, param_type = parameter_name.rsplit(".", 1) if "." in parameter_name else parameter_name
+    tp_style = _get_parameter_tp_plan(parameter_name, tp_plan)
+    if not tp_style:
+        return parameter
+
+    if tp_style not in ["local_packed_rowwise", "local_rowwise", "local_colwise"]:
+        return parameter
+    # TODO: this logic should be wrapped in a function, this is copied from corresponding tp classes.
+    if tp_style == "local_packed_rowwise":
+        placements = [Shard(-1)]
+    elif tp_style == "local_rowwise":
+        if param_type == "bias":
+            placements = [Replicate()]
+        else:
+            placements = [Shard(-1)]
+    elif tp_style == "local_colwise":
+        if param_type == "bias":
+            placements = [Shard(-1)]
+        else:
+            placements = [Shard(-2)]
+    return DTensor.from_local(parameter, device_mesh, placements, run_check=False)
+
+
+def replace_state_dict_local_with_dtensor(
+    state_dict: dict[str, torch.Tensor],
+    tp_plan: dict[str, str],
+    device_mesh,
+) -> dict[str, torch.Tensor]:
+    """
+    Replaces all tensors that were sharded with `local_*` strategy with DTensor to make determining their proper size possible.
+    """
+    for key, value in state_dict.items():
+        if isinstance(value, torch.Tensor) and not isinstance(value, DTensor):
+            state_dict[key] = convert_local_tensor_to_dtensor(value, key, device_mesh, tp_plan)
+    return state_dict
+
+
 def add_tensor_parallel_hooks_to_module(model, module, tp_plan, layer_name, current_module_plan, device_mesh):
     """
     Add hooks to the module holding the layer. Meaning:
@@ -632,13 +756,9 @@ def shard_and_distribute_module(
     param_name, param_type = parameter_name.rsplit(".", 1) if "." in parameter_name else parameter_name
     tp_plan = model._tp_plan
     module_to_tp = model.get_submodule(param_name)
-    current_module_plan = None
     rank = int(rank)
-    generic_param_name = re.sub(r"\d+", "*", parameter_name)
-    if generic_param_name in tp_plan:
-        current_module_plan = tp_plan[generic_param_name]
-    elif "." in generic_param_name and generic_param_name.rsplit(".", 1)[0] in tp_plan:
-        current_module_plan = tp_plan[generic_param_name.rsplit(".", 1)[0]]
+
+    current_module_plan = _get_parameter_tp_plan(parameter_name, tp_plan)
 
     # Add hooks to the module if not done yet
     # add_tensor_parallel_hooks_to_module(model, module_to_tp, tp_plan, param_name, current_module_plan, device_mesh)

src/transformers/modeling_utils.py

@@ -63,6 +63,9 @@
 from .integrations.sdpa_attention import sdpa_attention_forward
 from .integrations.tensor_parallel import (
     SUPPORTED_TP_STYLES,
+    _get_parameter_tp_plan,
+    repack_weights,
+    replace_state_dict_local_with_dtensor,
     shard_and_distribute_module,
     verify_tp_plan,
 )
@@ -123,6 +126,7 @@
 from .utils.hub import create_and_tag_model_card, get_checkpoint_shard_files
 from .utils.import_utils import (
     ENV_VARS_TRUE_VALUES,
+    is_huggingface_hub_greater_or_equal,
     is_sagemaker_mp_enabled,
     is_torch_fx_proxy,
     is_torchdynamo_compiling,
@@ -168,6 +172,9 @@
 _is_ds_init_called = False
 _torch_distributed_available = torch.distributed.is_available()
 
+if _torch_distributed_available and is_torch_greater_or_equal("2.5"):
+    from torch.distributed.tensor import DTensor
+
 
 def is_fsdp_enabled():
     return (
@@ -3413,6 +3420,12 @@ def save_pretrained(
         if safe_serialization and not is_safetensors_available():
             raise ImportError("`safe_serialization` requires the `safetensors library: `pip install safetensors`.")
 
+        # we need to check against tp_size, not tp_plan, as tp_plan is substituted to the class one
+        if self._tp_size is not None and not is_huggingface_hub_greater_or_equal("0.31.4"):
+            raise ImportError(
+                "Saving a model with tensor parallelism requires `huggingface_hub` version 0.31.4 or higher."
+            )
+
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
@@ -3540,6 +3553,10 @@ def save_pretrained(
         # Rename state_dict keys before saving to file. Do nothing unless overridden in a particular model.
         # (initially introduced with TimmWrapperModel to remove prefix and make checkpoints compatible with timm)
         state_dict = self._fix_state_dict_keys_on_save(state_dict)
+        # If model was sharded, we cannot properly determine sizes of tensors that `local_*` strategy was used,
+        # therefore we replace them with DTensors that are equivalently sharded
+        if self._tp_size is not None:
+            state_dict = replace_state_dict_local_with_dtensor(state_dict, self._tp_plan, self._device_mesh)
 
         if safe_serialization:
             # Safetensors does not allow tensor aliasing.
@@ -3548,7 +3565,7 @@ def save_pretrained(
             for name, tensor in state_dict.items():
                 # Sometimes in the state_dict we have non-tensor objects.
                 # e.g. in bitsandbytes we have some `str` objects in the state_dict
-                if isinstance(tensor, torch.Tensor):
+                if isinstance(tensor, torch.Tensor) or isinstance(tensor, DTensor):
                     ptrs[id_tensor_storage(tensor)].append(name)
                 else:
                     # In the non-tensor case, fall back to the pointer of the object itself
@@ -3658,7 +3675,14 @@ def save_pretrained(
         for shard_file, tensors in filename_to_tensors:
             shard = {}
             for tensor in tensors:
-                shard[tensor] = state_dict[tensor].contiguous()
+                if isinstance(state_dict[tensor], DTensor):
+                    full_tensor = state_dict[tensor].full_tensor()
+                    # to get the correctly ordered tensor we need to repack if packed
+                    if _get_parameter_tp_plan(tensor, self._tp_plan) in ("local_packed_rowwise",):
+                        full_tensor = repack_weights(full_tensor, -1, self._tp_size, 2)
+                    shard[tensor] = full_tensor.contiguous()  # only do contiguous after it's permuted correctly
+                else:
+                    shard[tensor] = state_dict[tensor].contiguous()
                 # delete reference, see https://github.com/huggingface/transformers/pull/34890
                 del state_dict[tensor]
 
@@ -4606,6 +4630,7 @@ def _assign_original_dtype(module):
 
         # record tp degree the model sharded to
         model._tp_size = tp_size
+        model._device_mesh = device_mesh
 
         # make sure token embedding weights are still tied if needed
         model.tie_weights()

src/transformers/pytorch_utils.py

@@ -296,6 +296,13 @@ def id_tensor_storage(tensor: torch.Tensor) -> tuple[torch.device, int, int]:
     guaranteed to be unique and constant for this tensor's storage during its lifetime. Two tensor storages with
     non-overlapping lifetimes may have the same id.
     """
+    if is_torch_greater_or_equal_than_2_0:
+        from torch.distributed.tensor import DTensor
+
+        if isinstance(tensor, DTensor):
+            local_tensor = tensor.to_local()
+            return tensor.device, local_tensor.storage().data_ptr(), tensor.nbytes
+
     if tensor.device.type == "xla" and is_torch_xla_available():
         # NOTE: xla tensors dont have storage
         # use some other unique id to distinguish.

src/transformers/testing_utils.py

@@ -97,6 +97,7 @@
     is_grokadamw_available,
     is_hadamard_available,
     is_hqq_available,
+    is_huggingface_hub_greater_or_equal,
     is_ipex_available,
     is_jieba_available,
     is_jinja_available,
@@ -542,6 +543,21 @@ def decorator(test_case):
     return decorator
 
 
+def require_huggingface_hub_greater_or_equal(version: str):
+    """
+    Decorator marking a test that requires huggingface_hub version >= `version`.
+
+    These tests are skipped when huggingface_hub version is less than `version`.
+    """
+
+    def decorator(test_case):
+        return unittest.skipUnless(
+            is_huggingface_hub_greater_or_equal(version), f"test requires huggingface_hub version >= {version}"
+        )(test_case)
+
+    return decorator
+
+
 def require_flash_attn(test_case):
     """
     Decorator marking a test that requires Flash Attention.

src/transformers/utils/__init__.py

@@ -167,6 +167,7 @@
     is_habana_gaudi1,
     is_hadamard_available,
     is_hqq_available,
+    is_huggingface_hub_greater_or_equal,
     is_in_notebook,
     is_ipex_available,
     is_jieba_available,

src/transformers/utils/import_utils.py

@@ -1077,6 +1077,19 @@ def is_torch_greater_or_equal(library_version: str, accept_dev: bool = False):
         return version.parse(importlib.metadata.version("torch")) >= version.parse(library_version)
 
 
+@lru_cache()
+def is_huggingface_hub_greater_or_equal(library_version: str, accept_dev: bool = False):
+    if not _is_package_available("huggingface_hub"):
+        return False
+
+    if accept_dev:
+        return version.parse(
+            version.parse(importlib.metadata.version("huggingface_hub")).base_version
+        ) >= version.parse(library_version)
+    else:
+        return version.parse(importlib.metadata.version("huggingface_hub")) >= version.parse(library_version)
+
+
 def is_torchdistx_available():
     return _torchdistx_available