[llama-mm] Add unit tests for exporting MultiHeadAttention with KVCache (#6801)

* [llama-mm] Add unit tests for exporting MultiHeadAttention with KVCache

Summary: To make sure we can always export MultiHeadAttention.

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* Use assert_close

Summary:

Test Plan:

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Author: larryliu0820

extension/llm/modules/kv_cache.py

@@ -105,9 +105,8 @@ def update(
                 f", but found new key tensors with batch size {k_val.shape[0]}!"
             )
 
-        assert (
-            self.cache_pos[0] + seq_len
-        ) <= self.max_seq_len, f"self.cache_pos[0]: {self.cache_pos[0]} + seq_len: {seq_len} > self.max_seq_len: {self.max_seq_len}"
+        assert (self.cache_pos[0] + seq_len) <= self.max_seq_len
+
         k_out = self.k_cache
         v_out = self.v_cache
 

extension/llm/modules/test/test_attention.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import os
+import tempfile
 import unittest
 
 import torch
@@ -13,6 +15,7 @@
     MultiHeadAttention as ETMultiHeadAttention,
 )
 from executorch.runtime import Runtime
+from torch._inductor.package import load_package, package_aoti
 from torch.testing import assert_close
 from torchtune.models.llama3_1._position_embeddings import Llama3ScaledRoPE
 from torchtune.modules.attention import MultiHeadAttention as TTMultiHeadAttention
@@ -130,34 +133,62 @@ def test_attention_eager(self):
 
     def test_attention_export(self):
         # Self attention.
-        et_mha_ep = torch.export.export(
-            self.et_mha,
-            (self.x, self.x),
-            kwargs={"input_pos": self.input_pos},
-            dynamic_shapes=self.dynamic_shapes,
-        )
+
+        # test with kv cache
+        self.et_mha.setup_cache(1, dtype=torch.float32, max_seq_len=100)
+        self.tt_mha.setup_cache(1, dtype=torch.float32, max_seq_len=100)
+        with torch.no_grad():
+            et_mha_ep = torch.export.export(
+                self.et_mha,
+                (self.x, self.x),
+                kwargs={"input_pos": self.input_pos},
+                dynamic_shapes=self.dynamic_shapes,
+            )
         et_res = et_mha_ep.module()(self.x, self.x, input_pos=self.input_pos)
         tt_res = self.tt_mha(self.x, self.x, input_pos=self.input_pos)
 
         assert_close(et_res, tt_res)
 
-        # TODO: KV cache.
-
     def test_attention_aoti(self):
-        # TODO.
-        pass
+        # Self attention.
+
+        # test with kv cache
+        self.et_mha.setup_cache(1, dtype=torch.float32, max_seq_len=100)
+        self.tt_mha.setup_cache(1, dtype=torch.float32, max_seq_len=100)
+        with torch.no_grad():
+            so = torch._export.aot_compile(
+                self.et_mha,
+                args=(self.x, self.x),
+                kwargs={"input_pos": self.input_pos},
+                options={"aot_inductor.package": True},
+                dynamic_shapes=self.dynamic_shapes,
+            )
+        with tempfile.TemporaryDirectory() as tempdir:
+            path = package_aoti(os.path.join(tempdir, "mha.pt2"), so)
+            mha_aoti = load_package(path)
+
+            aoti_res = mha_aoti(self.x, self.x, input_pos=self.input_pos)
+            tt_res = self.tt_mha(self.x, self.x, input_pos=self.input_pos)
+            assert_close(aoti_res, tt_res)
 
     def test_attention_executorch(self):
         # Self attention.
-        et_mha_ep = torch.export.export(
-            self.et_mha,
-            (self.x, self.x),
-            kwargs={"input_pos": self.input_pos},
-            dynamic_shapes=self.dynamic_shapes,
-        )
+        # TODO: Fix kv cache
+        # self.et_mha.setup_cache(1, dtype=torch.float32, max_seq_len=100)
+        # self.tt_mha.setup_cache(1, dtype=torch.float32, max_seq_len=100)
+
+        with torch.no_grad():
+            et_mha_ep = torch.export.export(
+                self.et_mha,
+                (self.x, self.x),
+                kwargs={"input_pos": self.input_pos},
+                dynamic_shapes=self.dynamic_shapes,
+            )
         et_program = to_edge(
             et_mha_ep,
-            compile_config=EdgeCompileConfig(),
+            compile_config=EdgeCompileConfig(
+                _core_aten_ops_exception_list=[torch.ops.aten._assert_async.msg]
+            ),
         ).to_executorch()
         runtime = Runtime.get()
         program = runtime.load_program(et_program.buffer)
@@ -166,5 +197,3 @@ def test_attention_executorch(self):
         tt_res = self.tt_mha(self.x, self.x, input_pos=self.input_pos)
 
         assert_close(et_res[0], tt_res)
-
-        # TODO: KV cache.