Merge branch 'main' into main

Author: Meatfucker

tests/models/test_modeling_common.py

@@ -1744,6 +1744,10 @@ def test_push_to_hub_library_name(self):
         delete_repo(self.repo_id, token=TOKEN)
 
 
+@require_torch_gpu
+@require_torch_2
+@is_torch_compile
+@slow
 class TorchCompileTesterMixin:
     def setUp(self):
         # clean up the VRAM before each test
@@ -1759,12 +1763,7 @@ def tearDown(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    @require_torch_gpu
-    @require_torch_2
-    @is_torch_compile
-    @slow
     def test_torch_compile_recompilation_and_graph_break(self):
-        torch.compiler.reset()
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
 
         model = self.model_class(**init_dict).to(torch_device)
@@ -1778,6 +1777,31 @@ def test_torch_compile_recompilation_and_graph_break(self):
             _ = model(**inputs_dict)
             _ = model(**inputs_dict)
 
+    def test_compile_with_group_offloading(self):
+        torch._dynamo.config.cache_size_limit = 10000
+
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+
+        if not getattr(model, "_supports_group_offloading", True):
+            return
+
+        model.eval()
+        # TODO: Can test for other group offloading kwargs later if needed.
+        group_offload_kwargs = {
+            "onload_device": "cuda",
+            "offload_device": "cpu",
+            "offload_type": "block_level",
+            "num_blocks_per_group": 1,
+            "use_stream": True,
+            "non_blocking": True,
+        }
+        model.enable_group_offload(**group_offload_kwargs)
+        model.compile()
+        with torch.no_grad():
+            _ = model(**inputs_dict)
+            _ = model(**inputs_dict)
+
 
 @slow
 @require_torch_2

tests/quantization/bnb/test_4bit.py

@@ -476,6 +476,7 @@ def test_generate_quality_dequantize(self):
         r"""
         Test that loading the model and unquantize it produce correct results.
         """
+        torch.use_deterministic_algorithms(True)
         self.pipeline_4bit.transformer.dequantize()
         output = self.pipeline_4bit(
             prompt=self.prompt,

tests/quantization/bnb/test_mixed_int8.py

@@ -478,6 +478,7 @@ def test_generate_quality_dequantize(self):
         r"""
         Test that loading the model and unquantize it produce correct results.
         """
+        torch.use_deterministic_algorithms(True)
         self.pipeline_8bit.transformer.dequantize()
         output = self.pipeline_8bit(
             prompt=self.prompt,