Repository: huggingface/transformers
Pull request: Fix bugs in DynamicCache

# What does this PR do?

When we flatten DynamicCache for export, we never end up flattening the inner tensors of DynamicCache because when we start, there are 0 tensors initialized. As a result, we didn't correctly test the ep.module()(*args, **kwargs) behaviour when we do export when cache is populated. 

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Changed files:
filename: src/transformers/cache_utils.py
patch: @@ -695,7 +695,7 @@ def _flatten_dynamic_cache_for_fx(cache, spec):
         "key_cache": getattr(cache, "key_cache"),
         "value_cache": getattr(cache, "value_cache"),
     }
-    return torch.utils._pytree.tree_flatten(dictionary)[0]
+    return torch.fx._pytree._dict_flatten_spec(dictionary, spec)
 
 
 if is_torch_greater_or_equal("2.3"):

filename: tests/utils/test_cache_utils.py
patch: @@ -626,6 +626,102 @@ def test_dynamic_cache_exportability(self):
         for v1, v2 in zip(res.past_key_values.value_cache, res_eager.past_key_values.value_cache):
             self.assertTrue(torch.allclose(v1, v2))
 
+    def test_dynamic_cache_exportability_multiple_run(self):
+        # When exporting with DynamicCache, you should export two graphs:
+        #   1. A graph without cache
+        #   2. A graph with cache
+        # In the future, we will make improvements to export API to export two graphs
+        # more seamlessly.
+
+        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM")
+        model = model.eval()
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM")
+        prompt = "What is the best way to debug python script?"
+        inputs = tokenizer(prompt, return_tensors="pt")
+        attention_mask = inputs.attention_mask
+        input_ids = inputs.input_ids
+
+        ep = export_with_dynamic_cache(model, input_ids, attention_mask)
+        res = ep.module()(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=DynamicCache(),
+            use_cache=True,
+        )
+        self.assertTrue(len(res.past_key_values.key_cache) == model.config.num_hidden_layers)
+        self.assertEqual(2 * model.config.num_hidden_layers + 1, len(ep.graph_signature.output_specs))
+        self.assertEqual(
+            3,
+            len(
+                [
+                    x
+                    for x in ep.graph_signature.input_specs
+                    if x.kind == torch.export.graph_signature.InputKind.USER_INPUT
+                ]
+            ),
+        )
+
+        res_eager = model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=DynamicCache(),
+            use_cache=True,
+        )
+        past_key_values_eager = res_eager.past_key_values
+        past_key_values = res.past_key_values
+
+        shapes = torch.export.ShapesCollection()
+        dyn = torch.export.Dim("seq", max=512)
+
+        for ix in range(len(past_key_values.key_cache)):
+            shapes[past_key_values.key_cache[ix]] = (None, None, dyn, None)
+            shapes[past_key_values.value_cache[ix]] = (None, None, dyn, None)
+
+        ep_second = torch.export.export(
+            model,
+            (),
+            {
+                "input_ids": input_ids,
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "use_cache": True,
+            },
+            strict=False,
+            dynamic_shapes=shapes,
+        )
+        res_export = ep_second.module()(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+        # It should work with variable len
+        res_export_2 = ep_second.module()(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=res_export.past_key_values,
+            use_cache=True,
+        )
+
+        res_eager = model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values_eager,
+            use_cache=True,
+        )
+        res_eager_2 = model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=res_eager.past_key_values,
+            use_cache=True,
+        )
+
+        for k1, k2 in zip(res_export_2.past_key_values.key_cache, res_eager_2.past_key_values.key_cache):
+            self.assertTrue(torch.allclose(k1, k2))
+
+        for v1, v2 in zip(res_export_2.past_key_values.value_cache, res_eager_2.past_key_values.value_cache):
+            self.assertTrue(torch.allclose(v1, v2))
+
     def test_static_cache_exportability(self):
         """
         Tests that static cache works with `torch.export()`