Fix context parallel padding for indivisible sequence lengths (#12568)

src/diffusers/hooks/context_parallel.py

@@ -77,6 +77,26 @@ def _get_parameter_from_args_kwargs(self, identifier: str, args=(), kwargs=None)
         return args[index], False, index
 
 
+def _get_cp_pad_state(parallel_config: ContextParallelConfig) -> dict[int, int]:
+    if not hasattr(parallel_config, "_cp_pad_state"):
+        parallel_config._cp_pad_state = {}
+    return parallel_config._cp_pad_state
+
+
+def _pad_tensor_for_context_parallel(
+    tensor: torch.Tensor, dim: int, world_size: int, pad_value: float | int
+) -> torch.Tensor:
+    seq_len = tensor.size(dim)
+    pad_len = (world_size - seq_len % world_size) % world_size
+    if pad_len == 0:
+        return tensor
+
+    pad_width = [0] * (2 * tensor.dim())
+    pad_idx = tensor.dim() - 1 - dim
+    pad_width[2 * pad_idx + 1] = pad_len
+    return torch.nn.functional.pad(tensor, tuple(pad_width), mode="constant", value=pad_value)
+
+
 def apply_context_parallel(
     module: torch.nn.Module,
     parallel_config: ContextParallelConfig,
@@ -156,7 +176,7 @@ def pre_forward(self, module, *args, **kwargs):
             # The input_val may be a tensor or list/tuple of tensors. In certain cases, user may specify to shard
             # the output instead of input for a particular layer by setting split_output=True
             if isinstance(input_val, torch.Tensor):
-                input_val = self._prepare_cp_input(input_val, cpm)
+                input_val = self._prepare_cp_input(input_val, cpm, name)
             elif isinstance(input_val, (list, tuple)):
                 if len(input_val) != len(cpm):
                     raise ValueError(
@@ -198,23 +218,32 @@ def post_forward(self, module, output):
             if index >= len(output):
                 raise ValueError(f"Index {index} out of bounds for output of length {len(output)}.")
             current_output = output[index]
-            current_output = self._prepare_cp_input(current_output, cpm)
+            current_output = self._prepare_cp_input(current_output, cpm, str(index))
             output[index] = current_output
 
         return output[0] if is_tensor else tuple(output)
 
-    def _prepare_cp_input(self, x: torch.Tensor, cp_input: ContextParallelInput) -> torch.Tensor:
+    def _prepare_cp_input(self, x: torch.Tensor, cp_input: ContextParallelInput, name: str = "") -> torch.Tensor:
         if cp_input.expected_dims is not None and x.dim() != cp_input.expected_dims:
             logger.warning_once(
                 f"Expected input tensor to have {cp_input.expected_dims} dimensions, but got {x.dim()} dimensions, split will not be applied."
             )
             return x
-        else:
-            if self.parallel_config.ulysses_anything or self.parallel_config.ring_anything:
-                return PartitionAnythingSharder.shard_anything(
-                    x, cp_input.split_dim, self.parallel_config._flattened_mesh
-                )
-            return EquipartitionSharder.shard(x, cp_input.split_dim, self.parallel_config._flattened_mesh)
+
+        mesh = self.parallel_config._flattened_mesh
+        if self.parallel_config.ulysses_anything or self.parallel_config.ring_anything:
+            return PartitionAnythingSharder.shard_anything(x, cp_input.split_dim, mesh)
+
+        dim = cp_input.split_dim
+        world_size = mesh.size()
+        seq_len = x.size(dim)
+        if world_size > 1 and seq_len % world_size != 0:
+            pad_value = 0 if "mask" in name.lower() else 0.0
+            x = _pad_tensor_for_context_parallel(x, dim, world_size, pad_value)
+            pad_state = _get_cp_pad_state(self.parallel_config)
+            pad_state.setdefault(dim, seq_len)
+
+        return EquipartitionSharder.shard(x, dim, mesh)
 
 
 class ContextParallelGatherHook(ModelHook):
@@ -240,13 +269,18 @@ def post_forward(self, module, output):
             if cpm is None:
                 continue
             if self.parallel_config.ulysses_anything or self.parallel_config.ring_anything:
-                output[i] = PartitionAnythingSharder.unshard_anything(
+                x = PartitionAnythingSharder.unshard_anything(
                     output[i], cpm.gather_dim, self.parallel_config._flattened_mesh
                 )
             else:
-                output[i] = EquipartitionSharder.unshard(
-                    output[i], cpm.gather_dim, self.parallel_config._flattened_mesh
-                )
+                x = EquipartitionSharder.unshard(output[i], cpm.gather_dim, self.parallel_config._flattened_mesh)
+
+            pad_state = _get_cp_pad_state(self.parallel_config)
+            original_s = pad_state.pop(cpm.gather_dim, None)
+            if original_s is not None:
+                x = x.narrow(cpm.gather_dim, 0, original_s)
+
+            output[i] = x
 
         return output[0] if is_tensor else tuple(output)
 

tests/hooks/test_hooks.py

@@ -13,11 +13,14 @@
 # limitations under the License.
 
 import gc
+import unittest
+from unittest.mock import patch
 
 import pytest
 import torch
 
 from diffusers.hooks import HookRegistry, ModelHook
+from diffusers.hooks.context_parallel import ContextParallelGatherHook, ContextParallelSplitHook
 from diffusers.training_utils import free_memory
 from diffusers.utils.logging import get_logger
 
@@ -372,3 +375,82 @@ def test_invocation_order_stateful_last(self):
             .replace("\n", "")
         )
         assert output == expected_invocation_order_log
+
+
+class _DummyMesh:
+    def __init__(self, size: int):
+        self._size = size
+
+    def size(self):
+        return self._size
+
+
+class _DummyParallelConfig:
+    def __init__(self, mesh_size: int):
+        self._flattened_mesh = _DummyMesh(mesh_size)
+        self.ulysses_anything = False
+        self.ring_anything = False
+
+
+class _DummyCPInput:
+    def __init__(self, split_dim: int, expected_dims: int | None = None, split_output: bool = False):
+        self.split_dim = split_dim
+        self.expected_dims = expected_dims
+        self.split_output = split_output
+
+
+class _DummyCPOutput:
+    def __init__(self, gather_dim: int, expected_dims: int | None = None):
+        self.gather_dim = gather_dim
+        self.expected_dims = expected_dims
+
+
+class ContextParallelHooksTests(unittest.TestCase):
+    def setUp(self):
+        self.parallel_config = _DummyParallelConfig(mesh_size=3)
+        self.hook = ContextParallelSplitHook(metadata={}, parallel_config=self.parallel_config)
+        self.module = DummyModel(in_features=1, hidden_features=1, out_features=1, num_layers=1)
+        self.hook.initialize_hook(self.module)
+
+    def test_prepare_cp_input_pads_hidden_states_and_stores_original(self):
+        x = torch.randn(1, 7, 16)
+        cp_input = _DummyCPInput(split_dim=1, expected_dims=3, split_output=False)
+
+        with patch.object(self.EquipartitionSharder, "shard", side_effect=lambda t, dim, mesh: t):
+            out = self.hook._prepare_cp_input(x, cp_input, name="hidden_states")
+
+        self.assertEqual(out.shape[1], 9)
+        self.assertEqual(self.parallel_config._cp_pad_state[1], 7)
+
+    def test_prepare_cp_input_pads_mask_with_zeros(self):
+        mask = torch.ones(1, 7, dtype=torch.long)
+        cp_input = _DummyCPInput(split_dim=1, expected_dims=2, split_output=False)
+
+        with patch.object(self.EquipartitionSharder, "shard", side_effect=lambda t, dim, mesh: t):
+            out_mask = self.hook._prepare_cp_input(mask, cp_input, name="encoder_hidden_states_mask")
+
+        self.assertEqual(out_mask.shape[1], 9)
+        self.assertTrue(torch.equal(out_mask[:, -2:], torch.zeros(1, 2, dtype=torch.long)))
+
+    def test_prepare_cp_input_no_pad_when_divisible(self):
+        x = torch.randn(1, 6, 16)
+        cp_input = _DummyCPInput(split_dim=1, expected_dims=3, split_output=False)
+
+        with patch.object(self.EquipartitionSharder, "shard", side_effect=lambda t, dim, mesh: t):
+            out = self.hook._prepare_cp_input(x, cp_input, name="hidden_states")
+
+        self.assertEqual(out.shape[1], 6)
+        self.assertNotIn(1, getattr(self.parallel_config, "_cp_pad_state", {}))
+
+    def test_gather_hook_trims_padded_output(self):
+        gather_hook = ContextParallelGatherHook(
+            metadata=[_DummyCPOutput(gather_dim=1, expected_dims=3)], parallel_config=self.parallel_config
+        )
+        self.parallel_config._cp_pad_state = {1: 7}
+
+        padded_output = torch.randn(1, 9, 16)
+        with patch.object(self.EquipartitionSharder, "unshard", side_effect=lambda t, dim, mesh: t):
+            result = gather_hook.post_forward(self.module, padded_output)
+
+        self.assertEqual(result.shape[1], 7)
+        self.assertNotIn(1, self.parallel_config._cp_pad_state)