Revert "[Misc] Move pyav and soundfile to common requirements" (#40276)

Co-authored-by: Roger Wang <hey@rogerw.io>
(cherry picked from commit 67eb6083e3)

csrc/moe/topk_softmax_kernels.cu

@@ -126,7 +126,9 @@ __launch_bounds__(TPB) __global__
     {
         const int idx = thread_row_offset + ii;
         const float val = toFloat(input[idx]);
-        const float softmax_val = expf(val - float_max) * normalizing_factor;
+        float softmax_val = expf(val - float_max) * normalizing_factor;
+        // Clamp NaN/Inf to 0 to prevent duplicate expert IDs downstream.
+        if (isnan(softmax_val) || isinf(softmax_val)) softmax_val = 0.f;
         output[idx] = softmax_val;
     }
 }
@@ -147,7 +149,9 @@ __launch_bounds__(TPB) __global__
     {
         const int idx = thread_row_offset + ii;
         const float val = toFloat(input[idx]);
-        const float sigmoid_val = 1.0f / (1.0f + __expf(-val));
+        float sigmoid_val = 1.0f / (1.0f + __expf(-val));
+        // Clamp NaN/Inf to 0 to prevent duplicate expert IDs downstream.
+        if (isnan(sigmoid_val) || isinf(sigmoid_val)) sigmoid_val = 0.f;
         output[idx] = sigmoid_val;
     }
 }
@@ -442,6 +446,19 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
       }
     }
 
+    // Fix: clamp NaN/Inf values to 0 to prevent duplicate expert IDs.
+    // NaN gating (from degenerate hidden states in CUDA graph padding) causes
+    // softmax to produce all-NaN, which makes the argmax loop always pick
+    // expert 0 for every top-k slot, producing duplicate expert IDs that
+    // crash FlashInfer's three-step MoE sort.
+    // With 0s, the argmax uses index tie-breaking to pick [0,1,2,...,k-1].
+#pragma unroll
+    for (int ii = 0; ii < VPT; ++ii) {
+      if (isnan(row_chunk[ii]) || isinf(row_chunk[ii])) {
+        row_chunk[ii] = 0.f;
+      }
+    }
+
     static constexpr int COLS_PER_GROUP_LDG = ELTS_PER_LDG * THREADS_PER_ROW;
 
     // If bias is not null, use biased value for selection

docker/Dockerfile.rocm

@@ -261,6 +261,30 @@ RUN --mount=type=bind,source=.git,target=vllm/.git \
     && echo "Detected vLLM version: ${VLLM_VERSION}" \
     && echo "${VLLM_VERSION}" > /tmp/vllm_version.txt
 
+# Fail if git-based package dependencies are found in requirements files
+# (uv doesn't handle git+ URLs well, and packages should be distributed on PyPI)
+# Extra notes: pip install is able to handle git+ URLs, but uv doesn't.
+RUN echo "Checking for git-based packages in requirements files..." \
+    && echo "Checking common.txt for git-based packages:" \
+    && if grep -q 'git+' ${COMMON_WORKDIR}/vllm/requirements/common.txt; then \
+         echo "ERROR: Git-based packages found in common.txt:"; \
+         grep 'git+' ${COMMON_WORKDIR}/vllm/requirements/common.txt; \
+         echo "Please publish these packages to PyPI instead of using git dependencies."; \
+         exit 1; \
+       else \
+         echo "  ✓ No git-based packages found in common.txt"; \
+       fi \
+    && echo "Checking rocm.txt for git-based packages:" \
+    && if grep -q 'git+' ${COMMON_WORKDIR}/vllm/requirements/rocm.txt; then \
+         echo "ERROR: Git-based packages found in rocm.txt:"; \
+         grep 'git+' ${COMMON_WORKDIR}/vllm/requirements/rocm.txt; \
+         echo "Please publish these packages to PyPI instead of using git dependencies."; \
+         exit 1; \
+       else \
+         echo "  ✓ No git-based packages found in rocm.txt"; \
+       fi \
+    && echo "All requirements files are clean - no git-based packages found"
+
 # Pin vLLM dependencies to exact versions of custom ROCm wheels
 # This ensures 'pip install vllm' automatically installs correct torch/triton/torchvision/amdsmi
 COPY tools/vllm-rocm/pin_rocm_dependencies.py /tmp/pin_rocm_dependencies.py

docs/contributing/profiling.md

@@ -206,8 +206,8 @@ Both the `vllm.utils.profiling.cprofile` and `vllm.utils.profiling.cprofile_cont
 used to profile a section of code.
 
 !!! note
-    The legacy import paths `vllm.utils.cprofile` and `vllm.utils.cprofile_context` are deprecated.
-    Please use `vllm.utils.profiling.cprofile` and `vllm.utils.profiling.cprofile_context` instead.
+    The `vllm.utils.profiling` helpers are deprecated and will be removed in
+    `v0.21`. Please use Python's `cProfile` module directly instead.
 
 ### Example usage - decorator
 

requirements/common.txt

@@ -32,9 +32,7 @@ pyzmq >= 25.0.0
 msgspec
 gguf >= 0.17.0
 mistral_common[image] >= 1.11.0
-av  # required for audio in video IO
 opencv-python-headless >= 4.13.0    # required for video IO
-soundfile  # required for audio IO
 pyyaml
 six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that needs to be the latest version for python 3.12
 setuptools>=77.0.3,<81.0.0; python_version > '3.11' # Setuptools is used by triton, we need to ensure a modern version is installed for 3.12+ so that it does not try to import distutils, which was removed in 3.12

requirements/rocm.txt

@@ -20,6 +20,4 @@ conch-triton-kernels==1.2.1
 timm>=1.0.17
 # amd-quark: required for Quark quantization on ROCm 
 # To be consistent with test_quark.py
-amd-quark>=0.8.99
-# Required for faster safetensors model loading
-fastsafetensors @ git+https://github.com/foundation-model-stack/fastsafetensors.git@0.2.2
+amd-quark>=0.8.99

requirements/test/rocm.txt

@@ -76,9 +76,7 @@ attrs==26.1.0
 audioread==3.0.1
     # via librosa
 av==16.1.0
-    # via
-    #   -r requirements/test/../common.txt
-    #   -r requirements/test/rocm.in
+    # via -r requirements/test/rocm.in
 azure-core==1.39.0
     # via
     #   azure-identity
@@ -278,9 +276,7 @@ fastar==0.10.0
 fastparquet==2026.3.0
     # via genai-perf
 fastsafetensors @ git+https://github.com/foundation-model-stack/fastsafetensors.git@65d80088fca7a8f567fba30415fbcc80f7d2259c
-    # via
-    #   -c requirements/rocm.txt
-    #   -r requirements/test/rocm.in
+    # via -r requirements/test/rocm.in
 filelock==3.25.2
     # via
     #   -c requirements/common.txt
@@ -1333,7 +1329,6 @@ sortedcontainers==2.4.0
     # via hypothesis
 soundfile==0.13.1
     # via
-    #   -r requirements/test/../common.txt
     #   -r requirements/test/rocm.in
     #   genai-perf
     #   librosa

setup.py

@@ -1094,7 +1094,9 @@ setup(
         "instanttensor": ["instanttensor >= 0.1.5"],
         "runai": ["runai-model-streamer[s3,gcs,azure] >= 0.15.7"],
         "audio": [
+            "av",
             "scipy",
+            "soundfile",
             "mistral_common[audio]",
         ],  # Required for audio processing
         "video": [],  # Kept for backwards compatibility

tests/distributed/test_torchrun_example_moe.py

@@ -38,6 +38,8 @@ llm = LLM(
     distributed_executor_backend="external_launcher",
     gpu_memory_utilization=random.uniform(0.7, 0.9),
     seed=0,
+    max_model_len=1024,
+    max_num_seqs=16,
 )
 
 outputs = llm.generate(prompts, sampling_params)

tests/kernels/moe/test_fused_topk.py

@@ -135,3 +135,70 @@ def test_fused_topk_bias(
         topk_weights_ref.to(torch.float32), topk_weights, atol=1e-2, rtol=1e-2
     )
     torch.testing.assert_close(topk_ids_ref.to(torch.int32), topk_ids, atol=0, rtol=0)
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda(), reason="This test is skipped on non-CUDA platform."
+)
+@pytest.mark.parametrize("num_experts", [6, 8, 16])
+@pytest.mark.parametrize("topk", [3, 4])
+@pytest.mark.parametrize("scoring_func", ["softmax", "sigmoid"])
+@pytest.mark.parametrize("bad_value", [float("nan"), float("inf")])
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.half, torch.float32])
+def test_fused_topk_nan_inf_clamp(
+    num_experts: int,
+    topk: int,
+    scoring_func: str,
+    bad_value: float,
+    dtype: torch.dtype,
+):
+    """Regression test for the NaN/Inf clamp in topk_softmax_kernels.cu.
+
+    Degenerate hidden states (e.g., from CUDA graph padding) can produce
+    NaN/Inf gating logits. Without the clamp, softmax/sigmoid outputs are
+    NaN and the argmax loop picks expert 0 for every top-k slot (since
+    "NaN > NaN" is false per IEEE 754), yielding duplicate expert IDs that
+    crash downstream MoE sort kernels. The fix clamps NaN/Inf to 0 before
+    argmax so index tie-breaking selects unique experts [0, 1, ..., k-1].
+    """
+    torch.manual_seed(0)
+    num_tokens = 4
+    hidden_size = 1024
+    hidden_states = torch.randn((num_tokens, hidden_size), dtype=dtype, device="cuda")
+
+    # Row 0: all normal. Rows 1-3: fully poisoned with NaN or Inf.
+    gating_output = torch.randn((num_tokens, num_experts), dtype=dtype, device="cuda")
+    gating_output[1:, :] = bad_value
+
+    topk_weights, topk_ids, _ = fused_topk(
+        hidden_states=hidden_states,
+        gating_output=gating_output,
+        topk=topk,
+        renormalize=False,
+        scoring_func=scoring_func,
+    )
+
+    # Normal row must still match the torch reference.
+    ref_weights, ref_ids = torch_topk(
+        gating_output=gating_output[:1],
+        topk=topk,
+        renormalize=False,
+        scoring_func=scoring_func,
+    )
+    torch.testing.assert_close(
+        ref_weights.to(torch.float32), topk_weights[:1], atol=1e-2, rtol=1e-2
+    )
+    torch.testing.assert_close(ref_ids.to(torch.int32), topk_ids[:1], atol=0, rtol=0)
+
+    # Poisoned rows: IDs must be unique (no duplicates) and weights must be
+    # finite (no NaN/Inf propagation into downstream MoE kernels).
+    for row in range(1, num_tokens):
+        row_ids = topk_ids[row]
+        assert row_ids.unique().numel() == topk, (
+            f"Row {row} has duplicate expert IDs {row_ids.tolist()} "
+            f"(bad_value={bad_value}, scoring_func={scoring_func})"
+        )
+        assert torch.isfinite(topk_weights[row]).all(), (
+            f"Row {row} has non-finite weights {topk_weights[row].tolist()} "
+            f"(bad_value={bad_value}, scoring_func={scoring_func})"
+        )

vllm/utils/profiling.py

@@ -8,7 +8,13 @@ from collections.abc import Callable
 from functools import wraps
 from typing import Any
 
+from typing_extensions import deprecated
 
+
+@deprecated(
+    "vllm.utils.profiling.cprofile_context() is deprecated and will be removed "
+    "in v0.21. Use Python's cProfile module directly instead."
+)
 @contextlib.contextmanager
 def cprofile_context(save_file: str | None = None):
     """Run a cprofile
@@ -32,6 +38,10 @@ def cprofile_context(save_file: str | None = None):
             prof.print_stats(sort="cumtime")
 
 
+@deprecated(
+    "vllm.utils.profiling.cprofile() is deprecated and will be removed in "
+    "v0.21. Use Python's cProfile module directly instead."
+)
 def cprofile(save_file: str | None = None, enabled: bool = True):
     """Decorator to profile a Python method using cProfile.