Sync from v0.13

vllm/v1/worker/gpu/metrics/logits.py

@@ -0,0 +1,42 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+from torch._inductor.runtime.triton_helpers import libdevice
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def _num_nans_kernel(
+    logits_ptr,
+    logits_stride,
+    num_nans_ptr,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    num_nans = 0
+    for i in range(0, vocab_size, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < vocab_size
+        logits = tl.load(
+            logits_ptr + req_idx * logits_stride + block, mask=mask, other=0
+        )
+        logits = logits.to(tl.float32)
+        is_nan = libdevice.isnan(logits).to(tl.int1)
+        num_nans += tl.sum(is_nan).to(tl.int32)
+    tl.store(num_nans_ptr + req_idx, num_nans)
+
+
+def get_num_nans(logits: torch.Tensor) -> torch.Tensor:
+    num_reqs, vocab_size = logits.shape
+    BLOCK_SIZE = 8192
+    num_nans = torch.empty(num_reqs, dtype=torch.int32, device=logits.device)
+    _num_nans_kernel[(num_reqs,)](
+        logits,
+        logits.stride(0),
+        num_nans,
+        vocab_size,
+        BLOCK_SIZE=BLOCK_SIZE,
+    )
+    return num_nans