Add support for sharded models when TorchAO quantization is enabled

src/diffusers/models/modeling_utils.py

@@ -802,7 +802,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     revision=revision,
                     subfolder=subfolder or "",
                 )
-                if hf_quantizer is not None:
+                if hf_quantizer is not None and is_bnb_quantization_method:
                     model_file = _merge_sharded_checkpoints(sharded_ckpt_cached_folder, sharded_metadata)
                     logger.info("Merged sharded checkpoints as `hf_quantizer` is not None.")
                     is_sharded = False

src/diffusers/quantizers/torchao/utils.py

@@ -0,0 +1,20 @@
+# Copyright 2024 The HuggingFace Inc. 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.
+
+from ..quantization_config import QuantizationMethod
+
+
+def _check_torchao_status(module) -> bool:
+    is_loaded_in_torchao = getattr(module, "quantization_method", None) == QuantizationMethod.TORCHAO
+    return is_loaded_in_torchao

tests/quantization/torchao/test_torchao.py

@@ -278,13 +278,14 @@ def test_int4wo_quant_bfloat16_conversion(self):
         self.assertEqual(weight.quant_max, 15)
         self.assertTrue(isinstance(weight.layout_type, TensorCoreTiledLayoutType))
 
-    def test_offload(self):
+    def test_device_map(self):
         """
-        Test if the quantized model int4 weight-only is working properly with cpu/disk offload. Also verifies
-        that the device map is correctly set (in the `hf_device_map` attribute of the model).
+        Test if the quantized model int4 weight-only is working properly with "auto" and custom device maps.
+        The custom device map performs cpu/disk offloading as well. Also verifies that the device map is
+        correctly set (in the `hf_device_map` attribute of the model).
         """
 
-        device_map_offload = {
+        custom_device_map_dict = {
             "time_text_embed": torch_device,
             "context_embedder": torch_device,
             "x_embedder": torch_device,
@@ -293,27 +294,50 @@ def test_offload(self):
             "norm_out": torch_device,
             "proj_out": "cpu",
         }
+        device_maps = ["auto", custom_device_map_dict]
 
         inputs = self.get_dummy_tensor_inputs(torch_device)
-
-        with tempfile.TemporaryDirectory() as offload_folder:
-            quantization_config = TorchAoConfig("int4_weight_only", group_size=64)
-            quantized_model = FluxTransformer2DModel.from_pretrained(
-                "hf-internal-testing/tiny-flux-pipe",
-                subfolder="transformer",
-                quantization_config=quantization_config,
-                device_map=device_map_offload,
-                torch_dtype=torch.bfloat16,
-                offload_folder=offload_folder,
-            )
-
-            self.assertTrue(quantized_model.hf_device_map == device_map_offload)
-
-            output = quantized_model(**inputs)[0]
-            output_slice = output.flatten()[-9:].detach().float().cpu().numpy()
-
-            expected_slice = np.array([0.3457, -0.0366, 0.0105, -0.2275, -0.4941, 0.4395, -0.166, -0.6641, 0.4375])
-            self.assertTrue(np.allclose(output_slice, expected_slice, atol=1e-3, rtol=1e-3))
+        expected_slice = np.array([0.3457, -0.0366, 0.0105, -0.2275, -0.4941, 0.4395, -0.166, -0.6641, 0.4375])
+
+        for device_map in device_maps:
+            device_map_to_compare = {"": 0} if device_map == "auto" else device_map
+
+            # Test non-sharded model
+            with tempfile.TemporaryDirectory() as offload_folder:
+                quantization_config = TorchAoConfig("int4_weight_only", group_size=64)
+                quantized_model = FluxTransformer2DModel.from_pretrained(
+                    "hf-internal-testing/tiny-flux-pipe",
+                    subfolder="transformer",
+                    quantization_config=quantization_config,
+                    device_map=device_map,
+                    torch_dtype=torch.bfloat16,
+                    offload_folder=offload_folder,
+                )
+
+                self.assertTrue(quantized_model.hf_device_map == device_map_to_compare)
+
+                output = quantized_model(**inputs)[0]
+                output_slice = output.flatten()[-9:].detach().float().cpu().numpy()
+                self.assertTrue(np.allclose(output_slice, expected_slice, atol=1e-3, rtol=1e-3))
+
+            # Test sharded model
+            with tempfile.TemporaryDirectory() as offload_folder:
+                quantization_config = TorchAoConfig("int4_weight_only", group_size=64)
+                quantized_model = FluxTransformer2DModel.from_pretrained(
+                    "hf-internal-testing/tiny-flux-sharded",
+                    subfolder="transformer",
+                    quantization_config=quantization_config,
+                    device_map=device_map,
+                    torch_dtype=torch.bfloat16,
+                    offload_folder=offload_folder,
+                )
+
+                self.assertTrue(quantized_model.hf_device_map == device_map_to_compare)
+
+                output = quantized_model(**inputs)[0]
+                output_slice = output.flatten()[-9:].detach().float().cpu().numpy()
+
+                self.assertTrue(np.allclose(output_slice, expected_slice, atol=1e-3, rtol=1e-3))
 
     def test_modules_to_not_convert(self):
         quantization_config = TorchAoConfig("int8_weight_only", modules_to_not_convert=["transformer_blocks.0"])