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[Speculative Decoding] Test refactor #8317

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merged 14 commits into from
Sep 11, 2024
Merged

[Speculative Decoding] Test refactor #8317

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b103055
refactor
LiuXiaoxuanPKU Sep 10, 2024
0179b46
fix
LiuXiaoxuanPKU Sep 10, 2024
8daa366
use llmengine for non distributed tests
LiuXiaoxuanPKU Sep 10, 2024
acc10cf
logprobs
LiuXiaoxuanPKU Sep 10, 2024
e4e0adb
cleanup and tp4 fix
LiuXiaoxuanPKU Sep 10, 2024
ccb0596
minor
LiuXiaoxuanPKU Sep 10, 2024
93ed6c1
fix
LiuXiaoxuanPKU Sep 10, 2024
30f9c90
change interval
LiuXiaoxuanPKU Sep 11, 2024
42d6876
fork process for every test
youkaichao Sep 11, 2024
bb07b94
format
youkaichao Sep 11, 2024
bebb15d
separate tests
youkaichao Sep 11, 2024
fdc0fe8
update dtype
youkaichao Sep 11, 2024
f3d79ad
Merge branch 'main' into sd-test-refactor
youkaichao Sep 11, 2024
ced09b1
disable acceptance rate test
youkaichao Sep 11, 2024
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472 changes: 173 additions & 299 deletions tests/spec_decode/e2e/conftest.py
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97 changes: 49 additions & 48 deletions tests/spec_decode/e2e/test_eagle_correctness.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@

import pytest

from .conftest import run_greedy_equality_correctness_test
from .conftest import run_equality_correctness_test

# main model
MAIN_MODEL = "JackFram/llama-68m"
Expand Down Expand Up @@ -53,7 +53,7 @@
"dtype": PRECISION,

# Main model
"model": MAIN_MODEL,
"model_name": MAIN_MODEL,
}])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
Expand All @@ -68,15 +68,16 @@
])
@pytest.mark.parametrize("batch_size", [1, 32])
@pytest.mark.parametrize("seed", [1])
def test_eagle_e2e_greedy_correctness(baseline_llm_generator,
test_llm_generator, batch_size: int,
output_len: int):
"""Verify greedy equality with different batch size."""
run_greedy_equality_correctness_test(baseline_llm_generator,
test_llm_generator,
batch_size,
max_output_len=output_len,
force_output_len=True)
def test_eagle_e2e_greedy_correctness(vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs, test_llm_kwargs,
batch_size: int, output_len: int,
seed: int):

run_equality_correctness_test(vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs, test_llm_kwargs,
batch_size, output_len, seed)


@pytest.mark.parametrize(
Expand All @@ -94,7 +95,7 @@ def test_eagle_e2e_greedy_correctness(baseline_llm_generator,
"dtype": PRECISION,

# Main model
"model": MAIN_MODEL,
"model_name": MAIN_MODEL,
}])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
Expand All @@ -109,17 +110,16 @@ def test_eagle_e2e_greedy_correctness(baseline_llm_generator,
])
@pytest.mark.parametrize("batch_size", [1, 32])
@pytest.mark.parametrize("seed", [1])
def test_eagle_e2e_greedy_correctness_cuda_graph(baseline_llm_generator,
test_llm_generator,
batch_size: int,
output_len: int):
"""Verify greedy equality with cuda graph enabled and different
def test_eagle_e2e_greedy_correctness_cuda_graph(
vllm_runner, common_llm_kwargs, per_test_common_llm_kwargs,
baseline_llm_kwargs, test_llm_kwargs, batch_size: int, output_len: int,
seed: int):
"""Verify greedy equality with cuda graph enabled and different
batch sizes."""
run_greedy_equality_correctness_test(baseline_llm_generator,
test_llm_generator,
batch_size,
max_output_len=output_len,
force_output_len=True)
run_equality_correctness_test(vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs, test_llm_kwargs,
batch_size, output_len, seed)


@pytest.mark.parametrize(
Expand All @@ -140,7 +140,7 @@ def test_eagle_e2e_greedy_correctness_cuda_graph(baseline_llm_generator,
"dtype": PRECISION,

# Main model
"model": MAIN_MODEL,
"model_name": MAIN_MODEL,
}])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
Expand All @@ -158,18 +158,17 @@ def test_eagle_e2e_greedy_correctness_cuda_graph(baseline_llm_generator,
])
@pytest.mark.parametrize("batch_size", [4])
@pytest.mark.parametrize("seed", [1])
def test_eagle_e2e_greedy_correctness_with_preemption(baseline_llm_generator,
test_llm_generator,
batch_size: int,
output_len: int):
def test_eagle_e2e_greedy_correctness_with_preemption(
vllm_runner, common_llm_kwargs, per_test_common_llm_kwargs,
baseline_llm_kwargs, test_llm_kwargs, batch_size: int, output_len: int,
seed: int):
"""Verify greedy equality, even when some sequences are preempted mid-
generation.
"""
run_greedy_equality_correctness_test(baseline_llm_generator,
test_llm_generator,
batch_size,
max_output_len=output_len,
force_output_len=True)
run_equality_correctness_test(vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs, test_llm_kwargs,
batch_size, output_len, seed)


@pytest.mark.parametrize(
Expand All @@ -185,7 +184,7 @@ def test_eagle_e2e_greedy_correctness_with_preemption(baseline_llm_generator,
"dtype": PRECISION,

# Main model
"model": MAIN_MODEL,
"model_name": MAIN_MODEL,
}])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
Expand All @@ -207,16 +206,17 @@ def test_eagle_e2e_greedy_correctness_with_preemption(baseline_llm_generator,
32,
])
@pytest.mark.parametrize("seed", [1])
def test_eagle_different_k(baseline_llm_generator, test_llm_generator,
batch_size: int, output_len: int):
def test_eagle_different_k(vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs, baseline_llm_kwargs,
test_llm_kwargs, batch_size: int, output_len: int,
seed: int):
"""Verify that eagle speculative decoding produces exact equality
to without spec decode with different values of num_speculative_tokens.
"""
run_greedy_equality_correctness_test(baseline_llm_generator,
test_llm_generator,
batch_size,
max_output_len=output_len,
force_output_len=True)
run_equality_correctness_test(vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs, test_llm_kwargs,
batch_size, output_len, seed)


@pytest.mark.parametrize(
Expand All @@ -232,7 +232,7 @@ def test_eagle_different_k(baseline_llm_generator, test_llm_generator,
"dtype": PRECISION,

# Main model
"model": MAIN_MODEL,
"model_name": MAIN_MODEL,
}])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
Expand All @@ -250,17 +250,18 @@ def test_eagle_different_k(baseline_llm_generator, test_llm_generator,
32,
])
@pytest.mark.parametrize("seed", [1])
def test_eagle_disable_queue(baseline_llm_generator, test_llm_generator,
batch_size: int, output_len: int):
def test_eagle_disable_queue(vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs, baseline_llm_kwargs,
test_llm_kwargs, batch_size: int, output_len: int,
seed: int):
"""Verify that eagle speculative decoding produces exact equality
to without spec decode when speculation is disabled for large
batch sizes.
"""
run_greedy_equality_correctness_test(baseline_llm_generator,
test_llm_generator,
batch_size,
max_output_len=output_len,
force_output_len=True)
run_equality_correctness_test(vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs, test_llm_kwargs,
batch_size, output_len, seed)


if __name__ == "__main__":
Expand Down
52 changes: 32 additions & 20 deletions tests/spec_decode/e2e/test_integration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,9 @@

import pytest

from .conftest import run_greedy_equality_correctness_test
from .conftest import run_equality_correctness_test

MAIN_MODEL = "JackFram/llama-68m"


@pytest.mark.parametrize(
Expand All @@ -15,7 +17,7 @@

# Verify equality when cuda graphs allowed.
"enforce_eager": False,
"model": "JackFram/llama-68m",
"model_name": "JackFram/llama-68m",
}])
@pytest.mark.parametrize(
"per_test_common_llm_kwargs",
Expand All @@ -31,23 +33,27 @@
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("output_len", [32])
@pytest.mark.parametrize("seed", [1])
def test_spec_decode_cuda_graph(baseline_llm_generator, test_llm_generator,
batch_size, output_len):
def test_spec_decode_cuda_graph(vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs, test_llm_kwargs,
batch_size: int, output_len: int, seed: int):
"""Verify spec decode equality when cuda graphs are enabled.
"""
run_greedy_equality_correctness_test(
baseline_llm_generator,
test_llm_generator,
batch_size,
max_output_len=output_len,
force_output_len=True,
)
run_equality_correctness_test(vllm_runner,
common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs,
batch_size,
max_output_len=output_len,
seed=seed,
temperature=0.0)


@pytest.mark.parametrize(
"common_llm_kwargs",
[{
"model": "JackFram/llama-160m",
"model_name": "JackFram/llama-160m",

# Skip cuda graph recording for fast test.
"enforce_eager": True,
Expand Down Expand Up @@ -80,13 +86,19 @@ def test_spec_decode_cuda_graph(baseline_llm_generator, test_llm_generator,
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("batch_size", [2])
@pytest.mark.parametrize("seed", [1])
def test_speculative_model_quantization_config(baseline_llm_generator,
test_llm_generator,
batch_size: int):
def test_speculative_model_quantization_config(vllm_runner, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs,
batch_size: int, seed: int):
"""Verify spec decode works well with draft model quantization configs.
"""
run_greedy_equality_correctness_test(baseline_llm_generator,
test_llm_generator,
batch_size,
max_output_len=32,
force_output_len=True)
run_equality_correctness_test(vllm_runner,
common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs,
batch_size,
max_output_len=32,
seed=seed,
temperature=0.0)
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