[CI][torch.compile] Reduce e2e fusion test time (#33293)

Signed-off-by: Luka Govedič <lgovedic@redhat.com>
Signed-off-by: ProExpertProg <luka.govedic@gmail.com>
Signed-off-by: Luka Govedič <ProExpertProg@users.noreply.github.com>

tests/compile/test_fusion_attn.py

@@ -1,23 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import copy
-import logging
-from typing import Any
 
 import pytest
-import regex as re
 import torch._dynamo
 
 from tests.compile.backend import LazyInitPass, TestBackend
-from tests.compile.fusion_test_utils import (
-    CUSTOM_OPS_FP8,
-    MODELS_FP4,
-    MODELS_FP8,
-    Matches,
-    has_cuda_graph_wrapper_metadata,
-    is_blackwell,
-    run_model,
-)
 from tests.utils import flat_product
 from tests.v1.attention.utils import BatchSpec, create_common_attn_metadata
 from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
@@ -31,7 +19,6 @@ from vllm.config import (
     CacheConfig,
     CompilationConfig,
     CompilationMode,
-    CUDAGraphMode,
     ModelConfig,
     PassConfig,
     SchedulerConfig,
@@ -47,7 +34,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 )
 from vllm.platforms import current_platform
 from vllm.utils.flashinfer import has_flashinfer
-from vllm.utils.torch_utils import is_torch_equal_or_newer
 from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.kv_cache_interface import AttentionSpec
@@ -501,88 +487,3 @@ def test_attention_quant_pattern(
 
     # Check that results are close
     torch.testing.assert_close(result_unfused, result_fused, atol=1e-2, rtol=1e-2)
-
-
-@pytest.mark.parametrize(
-    "model_name, model_kwargs, backend, matches, custom_ops",
-    # Test attention+quant_fp8 fusion with custom and torch impls of QuantFP8
-    list(flat_product(MODELS_FP8, CUSTOM_OPS_FP8))
-    # quant_fp4 only has the custom impl
-    + list(flat_product(MODELS_FP4, [""])),
-)
-@pytest.mark.parametrize(
-    "inductor_graph_partition",
-    [
-        pytest.param(
-            True,
-            marks=pytest.mark.skipif(
-                not has_cuda_graph_wrapper_metadata(),
-                reason="This test requires"
-                "torch._inductor.utils.CUDAGraphWrapperMetadata to run",
-            ),
-        ),
-        False,
-    ],
-)
-def test_attn_quant(
-    model_name: str,
-    model_kwargs: dict[str, Any],
-    backend: AttentionBackendEnum,
-    matches: Matches,
-    custom_ops: str,
-    inductor_graph_partition: bool,
-    caplog_mp_spawn,
-    monkeypatch,
-):
-    if not current_platform.has_device_capability(90):
-        pytest.skip("test_attn_quant requires H100 (SM90) or B200 (SM100) GPU")
-    if backend == AttentionBackendEnum.FLASHINFER and (
-        not is_blackwell() or not has_flashinfer()
-    ):
-        pytest.skip("FlashInfer attn fusion requires Blackwell and flashinfer")
-    if inductor_graph_partition and not is_torch_equal_or_newer("2.9.0.dev"):
-        pytest.skip("Inductor graph partition requires torch>=2.9")
-
-    custom_ops_list = custom_ops.split(",") if custom_ops else []
-
-    if inductor_graph_partition:
-        mode = CUDAGraphMode.FULL_AND_PIECEWISE
-        splitting_ops: list[str] | None = None
-    else:
-        # FIXME: Llama-4-Scout-17B-16E-Instruct-FP8 + FlashInfer + Blackwell end at
-        # CUDAGraphMode.NONE here because it derives an attention backend that
-        # does not support full cudagraphs
-        mode = CUDAGraphMode.FULL_DECODE_ONLY
-        splitting_ops = []
-
-    # Disable, compile cache to make sure custom passes run.
-    # Otherwise, we can't verify fusion happened through the logs.
-    monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")
-
-    # To capture subprocess logs, we need to know whether spawn or fork is used.
-    # Force spawn as it is more general.
-    monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
-    model_kwargs["attention_config"] = {"backend": backend.name}
-
-    compilation_config = CompilationConfig(
-        # Testing properties
-        custom_ops=custom_ops_list,
-        use_inductor_graph_partition=inductor_graph_partition,
-        cudagraph_mode=mode,
-        splitting_ops=splitting_ops,
-        # Common
-        mode=CompilationMode.VLLM_COMPILE,
-        pass_config=PassConfig(fuse_attn_quant=True, eliminate_noops=True),
-        # Inductor caches custom passes by default as well via uuid
-        inductor_compile_config={"force_disable_caches": True},
-    )
-
-    with caplog_mp_spawn(logging.DEBUG) as log_holder:
-        run_model(compilation_config, model_name, **model_kwargs)
-
-    log_matches = re.findall(
-        r"fusion_attn.py:\d+] Fused quant onto (\d+) attention nodes",
-        log_holder.text,
-    )
-    assert len(log_matches) == 1, log_holder.text
-    assert int(log_matches[0]) == matches.attention_fusion