Add test for new model parallelism features (#17401)

src/transformers/modeling_utils.py

@@ -1734,6 +1734,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 same device.
 
                 To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`.
+            max_memory (`Dict`, *optional*):
+                A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
+                GPU and the available CPU RAM if unset.
             offload_folder (`str` or `os.PathLike`, *optional*):
                 If the `device_map` contains any value `"disk"`, the folder where we will offload weights.
             offload_state_dict (`bool`, *optional*, defaults to `False`):
@@ -1822,6 +1825,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         torch_dtype = kwargs.pop("torch_dtype", None)
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", None)
         device_map = kwargs.pop("device_map", None)
+        max_memory = kwargs.pop("max_memory", None)
         offload_folder = kwargs.pop("offload_folder", None)
         offload_state_dict = kwargs.pop("offload_state_dict", False)
 
@@ -2119,7 +2123,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             if model._no_split_modules is None:
                 raise ValueError(f"{model.__class__.__name__} does not support `device_map='auto'` yet.")
             no_split_modules = model._no_split_modules
-            device_map = infer_auto_device_map(model, no_split_module_classes=no_split_modules, dtype=torch_dtype)
+            device_map = infer_auto_device_map(
+                model, no_split_module_classes=no_split_modules, dtype=torch_dtype, max_memory=max_memory
+            )
 
         if from_tf:
             if resolved_archive_file.endswith(".index"):

src/transformers/models/t5/modeling_t5.py

@@ -420,14 +420,12 @@ class T5Attention(nn.Module):
         relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
         return relative_buckets
 
-    def compute_bias(self, query_length, key_length):
+    def compute_bias(self, query_length, key_length, device=None):
         """Compute binned relative position bias"""
-        context_position = torch.arange(
-            query_length, dtype=torch.long, device=self.relative_attention_bias.weight.device
-        )[:, None]
-        memory_position = torch.arange(
-            key_length, dtype=torch.long, device=self.relative_attention_bias.weight.device
-        )[None, :]
+        if device is None:
+            device = self.relative_attention_bias.weight.device
+        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
+        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
         relative_position = memory_position - context_position  # shape (query_length, key_length)
         relative_position_bucket = self._relative_position_bucket(
             relative_position,  # shape (query_length, key_length)
@@ -522,7 +520,7 @@ class T5Attention(nn.Module):
                 if self.gradient_checkpointing and self.training:
                     position_bias.requires_grad = True
             else:
-                position_bias = self.compute_bias(real_seq_length, key_length)
+                position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device)
 
             # if key and values are already calculated
             # we want only the last query position bias

tests/test_modeling_common.py

@@ -51,7 +51,9 @@ from transformers.testing_utils import (
     is_pt_flax_cross_test,
     is_pt_tf_cross_test,
     is_staging_test,
+    require_accelerate,
     require_torch,
+    require_torch_gpu,
     require_torch_multi_gpu,
     require_usr_bin_time,
     slow,
@@ -60,6 +62,7 @@ from transformers.testing_utils import (
 from transformers.utils import (
     WEIGHTS_INDEX_NAME,
     WEIGHTS_NAME,
+    is_accelerate_available,
     is_flax_available,
     is_tf_available,
     is_torch_fx_available,
@@ -72,6 +75,10 @@ sys.path.append(str(Path(__file__).parent.parent / "utils"))
 from test_module.custom_configuration import CustomConfig, NoSuperInitConfig  # noqa E402
 
 
+if is_accelerate_available():
+    from accelerate.utils import compute_module_sizes
+
+
 if is_torch_available():
     import torch
     from torch import nn
@@ -2178,6 +2185,86 @@ class ModelTesterMixin:
             model.parallelize()
             model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2)
 
+    def check_device_map_is_respected(self, model, device_map):
+        for param_name, param in model.named_parameters():
+            # Find device in device_map
+            while len(param_name) > 0 and param_name not in device_map:
+                param_name = ".".join(param_name.split(".")[:-1])
+            if param_name not in device_map:
+                raise ValueError("device map is incomplete, it does not contain any device for `param_name`.")
+
+            param_device = device_map[param_name]
+            if param_device in ["cpu", "disk"]:
+                self.assertEqual(param.device, torch.device("meta"))
+            else:
+                self.assertEqual(param.device, torch.device(param_device))
+
+    @require_accelerate
+    @require_torch_gpu
+    def test_cpu_offload(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        if config.num_hidden_layers < 5:
+            config.num_hidden_layers = 5
+
+        for model_class in self.all_model_classes:
+            if model_class._no_split_modules is None:
+                continue
+
+            inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+            model = model_class(config).eval()
+            model = model.to(torch_device)
+            base_output = model(**inputs_dict)
+
+            model_size = compute_module_sizes(model)[""]
+            # We test several splits of sizes to make sure it works.
+            max_gpu_sizes = [int(p * model_size) for p in [0.5, 0.7, 0.9]]
+            with tempfile.TemporaryDirectory() as tmp_dir:
+                model.cpu().save_pretrained(tmp_dir)
+
+                for max_size in max_gpu_sizes:
+                    max_memory = {0: max_size, "cpu": model_size * 2}
+                    new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
+                    # Making sure part of the model will actually end up offloaded
+                    self.assertSetEqual(set(new_model.hf_device_map.values()), {0, "cpu"})
+
+                    self.check_device_map_is_respected(new_model, new_model.hf_device_map)
+                    new_output = new_model(**inputs_dict)
+
+                    self.assertTrue(torch.allclose(base_output[0], new_output[0]))
+
+    @require_accelerate
+    @require_torch_multi_gpu
+    def test_model_parallelism(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        if config.num_hidden_layers < 5:
+            config.num_hidden_layers = 5
+
+        for model_class in self.all_model_classes:
+            if model_class._no_split_modules is None:
+                continue
+
+            inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+            model = model_class(config).eval()
+            model = model.to(torch_device)
+            base_output = model(**inputs_dict)
+
+            model_size = compute_module_sizes(model)[""]
+            # We test several splits of sizes to make sure it works.
+            max_gpu_sizes = [int(p * model_size) for p in [0.5, 0.7, 0.9]]
+            with tempfile.TemporaryDirectory() as tmp_dir:
+                model.cpu().save_pretrained(tmp_dir)
+
+                for max_size in max_gpu_sizes:
+                    max_memory = {0: max_size, 1: model_size * 2, "cpu": model_size * 2}
+                    new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
+                    # Making sure part of the model will actually end up offloaded
+                    self.assertSetEqual(set(new_model.hf_device_map.values()), {0, 1})
+
+                    self.check_device_map_is_respected(new_model, new_model.hf_device_map)
+                    new_output = new_model(**inputs_dict)
+
+                    self.assertTrue(torch.allclose(base_output[0], new_output[0]))
+
     def test_problem_types(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
@@ -2547,6 +2634,7 @@ class ModelUtilsTest(TestCasePlus):
         for p1, p2 in zip(model.parameters(), ref_model.parameters()):
             self.assertTrue(torch.allclose(p1, p2))
 
+    @require_accelerate
     def test_from_pretrained_low_cpu_mem_usage_functional(self):
         # test that we can use `from_pretrained(..., low_cpu_mem_usage=True)` with normal and
         # sharded models
@@ -2559,6 +2647,7 @@ class ModelUtilsTest(TestCasePlus):
             _ = BertModel.from_pretrained(mname, low_cpu_mem_usage=True)
 
     @require_usr_bin_time
+    @require_accelerate
     def test_from_pretrained_low_cpu_mem_usage_measured(self):
         # test that `from_pretrained(..., low_cpu_mem_usage=True)` uses less cpu memory than default
 
@@ -2597,6 +2686,7 @@ class ModelUtilsTest(TestCasePlus):
         # functionality to load models directly on gpu, this test can be rewritten to use torch's
         # cuda memory tracking and then we should be able to do a much more precise test.
 
+    @require_accelerate
     @require_torch_multi_gpu
     @slow
     def test_model_parallelism_gpt2(self):