[generate] torch.distributed-compatible DynamicCache

src/transformers/cache_utils.py

@@ -3,7 +3,7 @@
 import json
 import os
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
 import torch
 from packaging import version
@@ -358,12 +358,23 @@ class DynamicCache(Cache):
         ```
     """
 
-    def __init__(self) -> None:
+    def __init__(self, _distributed_cache_data: Iterable = None) -> None:
         super().__init__()
         self._seen_tokens = 0  # Used in `generate` to keep tally of how many tokens the cache has seen
         self.key_cache: List[torch.Tensor] = []
         self.value_cache: List[torch.Tensor] = []
 
+        # `_distributed_cache_data` was originally added for compatibility with `torch.distributed` (DDP). See #36121
+        # and #36373 for more information. In a nutshell, it is `map(gather_map, zip(*caches))`, i.e. each item in the
+        # iterable contains the key and value states for a layer gathered across replicas by torch.distributed
+        # (shape=[global batch size, num_heads, seq_len, head_dim]).
+        # WARNING: `_distributed_cache_data` must be the first argument in `__init__`, otherwise we'll break
+        # compatibility. The name of the argument doesn't matter.
+        if _distributed_cache_data is not None:
+            for key_states, value_states in _distributed_cache_data:
+                self.key_cache.append(key_states)
+                self.value_cache.append(value_states)
+
     def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
         """
         Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the

tests/utils/test_cache_utils.py

@@ -20,12 +20,14 @@
 
 from transformers import set_seed
 from transformers.testing_utils import (
+    get_gpu_count,
     is_torch_available,
     require_gptq,
     require_non_xpu,
     require_read_token,
     require_torch,
     require_torch_gpu,
+    require_torch_multi_gpu,
     slow,
     torch_device,
 )
@@ -620,3 +622,35 @@ def test_cache_copy(self):
             'You are a helpful assistant. What is the capital of France?\n\n\n## Response:Paris is the capital of France.\n\n\n\n\n\n## Query:\n\nIn a detailed analysis, compare the economic impacts of the introduction of the'
         ]  # fmt: skip
         self.assertEqual(responses, EXPECTED_DECODED_TEXT)
+
+    @require_torch_multi_gpu
+    def test_data_parallel_dynamic_cache(self):
+        """
+        Tests that the dynamic cache works with nn.DataParallel. Under the hood, `DynamicCache` is rebuilt from
+        multiple `DynamicCache` in the gather step.
+        """
+
+        model_repo = "hf-internal-testing/tiny-random-MistralForCausalLM"
+        model = AutoModelForCausalLM.from_pretrained(model_repo).to(torch_device)
+        tokenizer = AutoTokenizer.from_pretrained(model_repo)
+
+        # w/o DP: batch_size = num_gpu
+        # w DP: batch_size = 1 (with num_gpus replicas)
+        num_gpus = get_gpu_count()
+        model_inputs = tokenizer(["foo bar"] * num_gpus, return_tensors="pt").to(model.device)
+
+        # w/o DP
+        no_parallelism_cache = model(**model_inputs).past_key_values
+        self.assertIsInstance(no_parallelism_cache, DynamicCache)
+
+        # w DP
+        model = torch.nn.DataParallel(model)
+        parallelism_cache = model(**model_inputs).past_key_values
+        self.assertIsInstance(parallelism_cache, DynamicCache)
+
+        # Check that the caches are the same
+        for layer_idx in range(len(no_parallelism_cache)):
+            for kv_idx in range(2):  # 0 = key, 1 = value
+                torch.testing.assert_close(
+                    actual=parallelism_cache[layer_idx][kv_idx], expected=no_parallelism_cache[layer_idx][kv_idx]
+                )