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Revert #26113 "[Frontend] CompilationConfig overhaul (#20283): deprecate use_inductor in favor of backend, simplify custom_ops" (#26472)

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Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>
This commit is contained in:
Jiangyun Zhu
2025-10-09 20:43:55 +08:00
committed by GitHub
parent 92be3f3517
commit 5728da11ea
7 changed files with 63 additions and 126 deletions
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31
tests/compile/piecewise/test_toy_llama.py
View File

@@ -258,13 +258,13 @@ def tractable_computation(
@torch.inference_mode
def run_model(
llama_config, use_compile: bool, backend: str, split_attn: bool = False
llama_config, use_compile: bool, use_inductor: bool, split_attn: bool = False
) -> torch.Tensor:
if use_compile:
compilation_config = CompilationConfig(
level=CompilationLevel.PIECEWISE,
use_cudagraph=True,
backend=backend,
use_inductor=use_inductor,
cudagraph_capture_sizes=[1, 2],
)
if split_attn:
@@ -338,8 +338,8 @@ def run_model(
return output.cpu()
@pytest.mark.parametrize("backend", ["inductor", "eager"])
def test_toy_llama(backend: str):
@pytest.mark.parametrize("use_inductor", [True, False])
def test_toy_llama(use_inductor: bool):
# compare output with and without piecewise compilation
llama_config = LlamaConfig(
@@ -358,10 +358,10 @@ def test_toy_llama(backend: str):
num_backend_compilations=0,
num_cudagraph_captured=0,
):
outputs.append(run_model(llama_config, backend="eager", use_compile=False))
run_model(tractable_config, backend="eager", use_compile=False)
outputs.append(run_model(llama_config, use_inductor=False, use_compile=False))
run_model(tractable_config, use_inductor=False, use_compile=False)
if backend == "inductor":
if use_inductor:
kwargs = {"num_inductor_compiles": 1, "num_eager_compiles": 0}
else:
kwargs = {"num_eager_compiles": 1, "num_inductor_compiles": 0}
@@ -377,8 +377,10 @@ def test_toy_llama(backend: str):
num_cudagraph_captured=2,
**kwargs,
):
outputs.append(run_model(llama_config, backend=backend, use_compile=True))
run_model(tractable_config, backend=backend, use_compile=True)
outputs.append(
run_model(llama_config, use_inductor=use_inductor, use_compile=True)
)
run_model(tractable_config, use_inductor=use_inductor, use_compile=True)
with compilation_counter.expect(
num_graphs_seen=1, # one graph for the model
@@ -393,9 +395,16 @@ def test_toy_llama(backend: str):
), # num_cudagraph_sizes * num_piecewise_capturable_graphs_seen
):
outputs.append(
run_model(llama_config, backend=backend, use_compile=True, split_attn=True)
run_model(
llama_config,
use_inductor=use_inductor,
use_compile=True,
split_attn=True,
)
)
run_model(tractable_config, backend=backend, use_compile=True, split_attn=True)
run_model(
tractable_config, use_inductor=use_inductor, use_compile=True, split_attn=True
)
for i in range(1, len(outputs)):
assert torch.allclose(outputs[0], outputs[i])

40
tests/model_executor/test_enabled_custom_ops.py
View File

@@ -37,59 +37,57 @@ class Relu3(ReLUSquaredActivation):
@pytest.mark.parametrize(
"env, torch_level, backend, ops_enabled, default_on",
"env, torch_level, use_inductor, ops_enabled, default_on",
[
# Default values based on compile level
# - All by default (no Inductor compilation)
(None, 0, "eager", [True] * 4, True),
(None, 1, "eager", [True] * 4, True),
(None, 2, "eager", [True] * 4, True),
(None, 3, "eager", [True] * 4, True),
(None, 0, False, [True] * 4, True),
(None, 1, True, [True] * 4, True),
(None, 2, False, [True] * 4, True),
# - None by default (with Inductor)
(None, 0, "inductor", [True] * 4, True),
# - None by default (with Inductor)
(None, 1, "inductor", [False] * 4, False),
(None, 2, "inductor", [False] * 4, False),
(None, 3, "inductor", [False] * 4, False),
(None, 3, True, [False] * 4, False),
(None, 4, True, [False] * 4, False),
# - All by default (without Inductor)
(None, 3, False, [True] * 4, True),
(None, 4, False, [True] * 4, True),
# Explicitly enabling/disabling
#
# Default: all
#
# All but SiluAndMul
("+rms_norm,-silu_and_mul", 0, "inductor", [1, 0, 1, 1], True),
("+rms_norm,-silu_and_mul", 0, True, [1, 0, 1, 1], True),
# Only ReLU3
("none,-rms_norm,+relu3", 1, "eager", [0, 0, 0, 1], False),
("none,-rms_norm,+relu3", 1, False, [0, 0, 0, 1], False),
# All but SiluAndMul
("all,-silu_and_mul", 2, "inductor", [1, 0, 1, 1], True),
("all,-silu_and_mul", 2, True, [1, 0, 1, 1], True),
# All but ReLU3 (even if ReLU2 is on)
("-relu3,+relu2", 3, "eager", [1, 1, 1, 0], True),
("-relu3,+relu2", 3, False, [1, 1, 1, 0], True),
# RMSNorm and SiluAndMul
("none,-relu3,+rms_norm,+silu_and_mul", 3, "eager", [1, 1, 0, 0], False),
("none,-relu3,+rms_norm,+silu_and_mul", 4, False, [1, 1, 0, 0], False),
# All but RMSNorm
("-rms_norm", 3, "eager", [0, 1, 1, 1], True),
("-rms_norm", 3, False, [0, 1, 1, 1], True),
#
# Default: none
#
# Only ReLU3
("none,+relu3", 3, "inductor", [0, 0, 0, 1], False),
("-silu_and_mul,+relu3", 3, True, [0, 0, 0, 1], False),
# All but RMSNorm
("all,-rms_norm", 3, "inductor", [0, 1, 1, 1], True),
("all,-rms_norm", 4, True, [0, 1, 1, 1], True),
],
)
def test_enabled_ops(
env: Optional[str],
torch_level: int,
backend: str,
use_inductor: bool,
ops_enabled: list[int],
default_on: bool,
):
custom_ops = env.split(",") if env else []
vllm_config = VllmConfig(
compilation_config=CompilationConfig(
backend=backend, level=torch_level, custom_ops=custom_ops
use_inductor=bool(use_inductor), level=torch_level, custom_ops=custom_ops
)
)
# breakpoint()
with set_current_vllm_config(vllm_config):
assert CustomOp.default_on() == default_on