Merge branch 'main' into cp-fixes-attn-backends

This reverts commit c8abb5d7c0.

.github/workflows/pr_modular_tests.yml

@@ -77,47 +77,63 @@ jobs:
 
   run_fast_tests:
     needs: [check_code_quality, check_repository_consistency]
-    name: Fast PyTorch Modular Pipeline CPU tests
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - name: Fast PyTorch Modular Pipeline CPU tests
+            framework: pytorch_pipelines
+            runner: aws-highmemory-32-plus
+            image: diffusers/diffusers-pytorch-cpu
+            report: torch_cpu_modular_pipelines
+
+    name: ${{ matrix.config.name }}
+
     runs-on:
-      group: aws-highmemory-32-plus
+      group: ${{ matrix.config.runner }}
+
     container:
-      image: diffusers/diffusers-pytorch-cpu
+      image: ${{ matrix.config.image }}
       options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+
     defaults:
       run:
         shell: bash
 
     steps:
-      - name: Checkout diffusers
+    - name: Checkout diffusers
-        uses: actions/checkout@v3
+      uses: actions/checkout@v3
-        with:
+      with:
-          fetch-depth: 2
+        fetch-depth: 2
 
-      - name: Install dependencies
+    - name: Install dependencies
-        run: |
+      run: |
-          uv pip install -e ".[quality]"
+        uv pip install -e ".[quality]"
-          #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
+        #uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
-          uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
+        uv pip uninstall transformers huggingface_hub && uv pip install transformers==4.57.1
-          uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+        uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
 
-      - name: Environment
+    - name: Environment
-        run: |
+      run: |
-          python utils/print_env.py
+        python utils/print_env.py
 
-      - name: Run fast PyTorch Pipeline CPU tests
+    - name: Run fast PyTorch Pipeline CPU tests
-        run: |
+      if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
-          pytest -n 8 --max-worker-restart=0 --dist=loadfile \
+      run: |
-            -s -v \
+        pytest -n 8 --max-worker-restart=0 --dist=loadfile \
-            --make-reports=tests_torch_cpu_modular_pipelines \
+          -k "not Flax and not Onnx" \
-            tests/modular_pipelines
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/modular_pipelines
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
+        path: reports
 
-      - name: Failure short reports
-        if: ${{ failure() }}
-        run: cat reports/tests_torch_cpu_modular_pipelines_failures_short.txt
 
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: pr_pytorch_pipelines_torch_cpu_modular_pipelines_test_reports
-          path: reports

src/diffusers/models/attention_dispatch.py

@@ -235,6 +235,10 @@ class _AttentionBackendRegistry:
     def get_active_backend(cls):
         return cls._active_backend, cls._backends[cls._active_backend]
 
+    @classmethod
+    def set_active_backend(cls, backend: str):
+        cls._active_backend = backend
+
     @classmethod
     def list_backends(cls):
         return list(cls._backends.keys())
@@ -294,12 +298,12 @@ def attention_backend(backend: Union[str, AttentionBackendName] = AttentionBacke
     _maybe_download_kernel_for_backend(backend)
 
     old_backend = _AttentionBackendRegistry._active_backend
-    _AttentionBackendRegistry._active_backend = backend
+    _AttentionBackendRegistry.set_active_backend(backend)
 
     try:
         yield
     finally:
-        _AttentionBackendRegistry._active_backend = old_backend
+        _AttentionBackendRegistry.set_active_backend(old_backend)
 
 
 def dispatch_attention_fn(
@@ -348,6 +352,18 @@ def dispatch_attention_fn(
             check(**kwargs)
 
     kwargs = {k: v for k, v in kwargs.items() if k in _AttentionBackendRegistry._supported_arg_names[backend_name]}
+
+    if "_parallel_config" in kwargs and kwargs["_parallel_config"] is not None:
+        attention_backend = AttentionBackendName(backend_name)
+        if not _AttentionBackendRegistry._is_context_parallel_available(attention_backend):
+            compatible_backends = sorted(_AttentionBackendRegistry._supports_context_parallel)
+            raise ValueError(
+                f"Context parallelism is enabled but backend '{attention_backend.value}' "
+                f"which does not support context parallelism. "
+                f"Please set a compatible attention backend: {compatible_backends} using `model.set_attention_backend()` before "
+                f"calling `model.enable_parallelism()`."
+            )
+
     return backend_fn(**kwargs)
 
 

src/diffusers/models/modeling_utils.py

@@ -602,6 +602,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         from .attention import AttentionModuleMixin
         from .attention_dispatch import (
             AttentionBackendName,
+            _AttentionBackendRegistry,
             _check_attention_backend_requirements,
             _maybe_download_kernel_for_backend,
         )
@@ -629,6 +630,9 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                 continue
             processor._attention_backend = backend
 
+        # Important to set the active backend so that it propagates gracefully throughout.
+        _AttentionBackendRegistry.set_active_backend(backend)
+
     def reset_attention_backend(self) -> None:
         """
         Resets the attention backend for the model. Following calls to `forward` will use the environment default, if
@@ -1541,7 +1545,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                         f"Context parallelism is enabled but the attention processor '{processor.__class__.__name__}' "
                         f"is using backend '{attention_backend.value}' which does not support context parallelism. "
                         f"Please set a compatible attention backend: {compatible_backends} using `model.set_attention_backend()` before "
-                        f"calling `enable_parallelism()`."
+                        f"calling `model.enable_parallelism()`."
                     )
 
                 # All modules use the same attention processor and backend. We don't need to

tests/conftest.py

@@ -32,8 +32,6 @@ warnings.simplefilter(action="ignore", category=FutureWarning)
 
 def pytest_configure(config):
     config.addinivalue_line("markers", "big_accelerator: marks tests as requiring big accelerator resources")
-    config.addinivalue_line("markers", "slow: mark test as slow")
-    config.addinivalue_line("markers", "nightly: mark test as nightly")
 
 
 def pytest_addoption(parser):

tests/modular_pipelines/test_modular_pipelines_custom_blocks.py

@@ -1,272 +0,0 @@
-# Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-import tempfile
-from collections import deque
-from typing import List
-
-import numpy as np
-import torch
-
-from diffusers import FluxTransformer2DModel
-from diffusers.modular_pipelines import (
-    ComponentSpec,
-    InputParam,
-    ModularPipelineBlocks,
-    OutputParam,
-    PipelineState,
-    WanModularPipeline,
-)
-
-from ..testing_utils import nightly, require_torch, slow
-
-
-class DummyCustomBlockSimple(ModularPipelineBlocks):
-    def __init__(self, use_dummy_model_component=False):
-        self.use_dummy_model_component = use_dummy_model_component
-        super().__init__()
-
-    @property
-    def expected_components(self):
-        if self.use_dummy_model_component:
-            return [ComponentSpec("transformer", FluxTransformer2DModel)]
-        else:
-            return []
-
-    @property
-    def inputs(self) -> List[InputParam]:
-        return [InputParam("prompt", type_hint=str, required=True, description="Prompt to use")]
-
-    @property
-    def intermediate_inputs(self) -> List[InputParam]:
-        return []
-
-    @property
-    def intermediate_outputs(self) -> List[OutputParam]:
-        return [
-            OutputParam(
-                "output_prompt",
-                type_hint=str,
-                description="Modified prompt",
-            )
-        ]
-
-    def __call__(self, components, state: PipelineState) -> PipelineState:
-        block_state = self.get_block_state(state)
-
-        old_prompt = block_state.prompt
-        block_state.output_prompt = "Modular diffusers + " + old_prompt
-        self.set_block_state(state, block_state)
-
-        return components, state
-
-
-CODE_STR = """
-from diffusers.modular_pipelines import (
-    ComponentSpec,
-    InputParam,
-    ModularPipelineBlocks,
-    OutputParam,
-    PipelineState,
-    WanModularPipeline,
-)
-from typing import List
-
-class DummyCustomBlockSimple(ModularPipelineBlocks):
-    def __init__(self, use_dummy_model_component=False):
-        self.use_dummy_model_component = use_dummy_model_component
-        super().__init__()
-
-    @property
-    def expected_components(self):
-        if self.use_dummy_model_component:
-            return [ComponentSpec("transformer", FluxTransformer2DModel)]
-        else:
-            return []
-
-    @property
-    def inputs(self) -> List[InputParam]:
-        return [InputParam("prompt", type_hint=str, required=True, description="Prompt to use")]
-
-    @property
-    def intermediate_inputs(self) -> List[InputParam]:
-        return []
-
-    @property
-    def intermediate_outputs(self) -> List[OutputParam]:
-        return [
-            OutputParam(
-                "output_prompt",
-                type_hint=str,
-                description="Modified prompt",
-            )
-        ]
-
-    def __call__(self, components, state: PipelineState) -> PipelineState:
-        block_state = self.get_block_state(state)
-
-        old_prompt = block_state.prompt
-        block_state.output_prompt = "Modular diffusers + " + old_prompt
-        self.set_block_state(state, block_state)
-
-        return components, state
-"""
-
-
-class TestModularCustomBlocks:
-    def _test_block_properties(self, block):
-        assert not block.expected_components
-        assert not block.intermediate_inputs
-
-        actual_inputs = [inp.name for inp in block.inputs]
-        actual_intermediate_outputs = [out.name for out in block.intermediate_outputs]
-        assert actual_inputs == ["prompt"]
-        assert actual_intermediate_outputs == ["output_prompt"]
-
-    def test_custom_block_properties(self):
-        custom_block = DummyCustomBlockSimple()
-        self._test_block_properties(custom_block)
-
-    def test_custom_block_output(self):
-        custom_block = DummyCustomBlockSimple()
-        pipe = custom_block.init_pipeline()
-        prompt = "Diffusers is nice"
-        output = pipe(prompt=prompt)
-
-        actual_inputs = [inp.name for inp in custom_block.inputs]
-        actual_intermediate_outputs = [out.name for out in custom_block.intermediate_outputs]
-        assert sorted(output.values) == sorted(actual_inputs + actual_intermediate_outputs)
-
-        output_prompt = output.values["output_prompt"]
-        assert output_prompt.startswith("Modular diffusers + ")
-
-    def test_custom_block_saving_loading(self):
-        custom_block = DummyCustomBlockSimple()
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            custom_block.save_pretrained(tmpdir)
-            assert any("modular_config.json" in k for k in os.listdir(tmpdir))
-
-            with open(os.path.join(tmpdir, "modular_config.json"), "r") as f:
-                config = json.load(f)
-            auto_map = config["auto_map"]
-            assert auto_map == {"ModularPipelineBlocks": "test_modular_pipelines_custom_blocks.DummyCustomBlockSimple"}
-
-            # For now, the Python script that implements the custom block has to be manually pushed to the Hub.
-            # This is why, we have to separately save the Python script here.
-            code_path = os.path.join(tmpdir, "test_modular_pipelines_custom_blocks.py")
-            with open(code_path, "w") as f:
-                f.write(CODE_STR)
-
-            loaded_custom_block = ModularPipelineBlocks.from_pretrained(tmpdir, trust_remote_code=True)
-
-        pipe = loaded_custom_block.init_pipeline()
-        prompt = "Diffusers is nice"
-        output = pipe(prompt=prompt)
-
-        actual_inputs = [inp.name for inp in loaded_custom_block.inputs]
-        actual_intermediate_outputs = [out.name for out in loaded_custom_block.intermediate_outputs]
-        assert sorted(output.values) == sorted(actual_inputs + actual_intermediate_outputs)
-
-        output_prompt = output.values["output_prompt"]
-        assert output_prompt.startswith("Modular diffusers + ")
-
-    def test_custom_block_supported_components(self):
-        custom_block = DummyCustomBlockSimple(use_dummy_model_component=True)
-        pipe = custom_block.init_pipeline("hf-internal-testing/tiny-flux-kontext-pipe")
-        pipe.load_components()
-
-        assert len(pipe.components) == 1
-        assert pipe.component_names[0] == "transformer"
-
-    def test_custom_block_loads_from_hub(self):
-        repo_id = "hf-internal-testing/tiny-modular-diffusers-block"
-        block = ModularPipelineBlocks.from_pretrained(repo_id, trust_remote_code=True)
-        self._test_block_properties(block)
-
-        pipe = block.init_pipeline()
-
-        prompt = "Diffusers is nice"
-        output = pipe(prompt=prompt)
-        output_prompt = output.values["output_prompt"]
-        assert output_prompt.startswith("Modular diffusers + ")
-
-
-@slow
-@nightly
-@require_torch
-class TestKreaCustomBlocksIntegration:
-    repo_id = "krea/krea-realtime-video"
-
-    def test_loading_from_hub(self):
-        blocks = ModularPipelineBlocks.from_pretrained(self.repo_id, trust_remote_code=True)
-        block_names = sorted(blocks.sub_blocks)
-
-        assert block_names == sorted(["text_encoder", "before_denoise", "denoise", "decode"])
-
-        pipe = WanModularPipeline(blocks, self.repo_id)
-        pipe.load_components(
-            trust_remote_code=True,
-            device_map="cuda",
-            torch_dtype={"default": torch.bfloat16, "vae": torch.float16},
-        )
-        assert len(pipe.components) == 7
-        assert sorted(pipe.components) == sorted(
-            ["text_encoder", "tokenizer", "guider", "scheduler", "vae", "transformer", "video_processor"]
-        )
-
-    def test_forward(self):
-        blocks = ModularPipelineBlocks.from_pretrained(self.repo_id, trust_remote_code=True)
-        pipe = WanModularPipeline(blocks, self.repo_id)
-        pipe.load_components(
-            trust_remote_code=True,
-            device_map="cuda",
-            torch_dtype={"default": torch.bfloat16, "vae": torch.float16},
-        )
-
-        num_frames_per_block = 2
-        num_blocks = 2
-
-        state = PipelineState()
-        state.set("frame_cache_context", deque(maxlen=pipe.config.frame_cache_len))
-
-        prompt = ["a cat sitting on a boat"]
-
-        for block in pipe.transformer.blocks:
-            block.self_attn.fuse_projections()
-
-        for block_idx in range(num_blocks):
-            state = pipe(
-                state,
-                prompt=prompt,
-                num_inference_steps=2,
-                num_blocks=num_blocks,
-                num_frames_per_block=num_frames_per_block,
-                block_idx=block_idx,
-                generator=torch.manual_seed(42),
-            )
-            current_frames = np.array(state.values["videos"][0])
-            current_frames_flat = current_frames.flatten()
-            actual_slices = np.concatenate([current_frames_flat[:4], current_frames_flat[-4:]]).tolist()
-
-            if block_idx == 0:
-                assert current_frames.shape == (5, 480, 832, 3)
-                expected_slices = np.array([211, 229, 238, 208, 195, 180, 188, 193])
-            else:
-                assert current_frames.shape == (8, 480, 832, 3)
-                expected_slices = np.array([179, 203, 214, 176, 194, 181, 187, 191])
-
-            assert np.allclose(actual_slices, expected_slices)