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

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merged 2 commits into from
Nov 13, 2024
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[llama-mm] Add unit tests for exporting MultiHeadAttention with KVCache #6801

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3fd9963
[llama-mm] Add unit tests for exporting MultiHeadAttention with KVCache
larryliu0820 Nov 12, 2024
4af2c24
Use assert_close
larryliu0820 Nov 13, 2024
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5 changes: 2 additions & 3 deletions extension/llm/modules/kv_cache.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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

Expand Down
67 changes: 48 additions & 19 deletions extension/llm/modules/test/test_attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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)
Expand All @@ -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.
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