Upgrade vLLM to v0.10.0 (#1927)

### What this PR does / why we need it?
- Upgrade to v0.10.0
- Drop v0.9.2 version compatibility
- Add patch for
`vllm_ascend/patch/worker/patch_common/patch_sampler_gather_logprobs.py`
as workaround of
f3a683b7c9
for v0.10.0 and also add e2e test `test_models_prompt_logprobs`
- Pin transformers<4.54.0 as workaround of
https://github.com/vllm-project/vllm-ascend/issues/2034

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
- Test locally:
`VLLM_USE_MODELSCOPE=true pytest -sv
tests/e2e/singlecard/test_offline_inference.py::test_models_prompt_logprobs`
- CI passed

- vLLM version: v0.9.2
- vLLM main:
7728dd77bb

---------

Signed-off-by: Yikun Jiang <yikunkero@gmail.com>

vllm_ascend/patch/worker/__init__.py

@@ -18,8 +18,8 @@
 from vllm_ascend.utils import vllm_version_is
 
 # Import specific patches for different versions
-if vllm_version_is("0.9.2"):
-    from vllm_ascend.patch.worker import patch_0_9_2  # noqa: F401
+if vllm_version_is("0.10.0"):
+    from vllm_ascend.patch.worker import patch_0_10_0  # noqa: F401
     from vllm_ascend.patch.worker import patch_common  # noqa: F401
 else:
     from vllm_ascend.patch.worker import patch_common  # noqa: F401

vllm_ascend/patch/worker/patch_0_10_0/__init__.py

@@ -14,3 +14,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+
+import vllm_ascend.patch.worker.patch_0_10_0.patch_sampler_gather_logprobs  # noqa

vllm_ascend/patch/worker/patch_0_10_0/patch_sampler_gather_logprobs.py

@@ -0,0 +1,87 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+# This file is a part of the vllm-ascend project.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import torch
+from vllm.platforms import current_platform
+from vllm.v1.outputs import LogprobsTensors
+from vllm.v1.sample.sampler import Sampler
+
+
+@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
+def batched_count_greater_than(x: torch.Tensor,
+                               values: torch.Tensor) -> torch.Tensor:
+    """
+    Counts elements in each row of x that are greater than the corresponding
+    value in values.  Use torch.compile to generate an optimized kernel for
+    this function. otherwise, it will create additional copies of the input
+    tensors and cause memory issues.
+    Args:
+        x (torch.Tensor): A 2D tensor of shape (batch_size, n_elements).
+        values (torch.Tensor): A 2D tensor of shape (batch_size, 1).
+    Returns:
+        torch.Tensor: A 1D tensor of shape (batch_size,) with the counts.
+    """
+    return (x >= values).sum(-1)
+
+
+def gather_logprobs(
+    self,
+    logprobs: torch.Tensor,
+    num_logprobs: int,
+    token_ids: torch.Tensor,
+) -> LogprobsTensors:
+    """
+    Gather logprobs for topk and sampled/prompt token.
+
+    Args:
+        logprobs: (num tokens) x (vocab) tensor
+        num_logprobs: minimum number of logprobs to
+                    retain per token
+        token_ids: prompt tokens (if prompt logprobs)
+                    or sampled tokens (if sampled
+                    logprobs); 1D token ID tensor
+                    with (num tokens) elements
+                    Must be int64.
+
+    Returns:
+        Top-k int indices tensor, (num tokens) x (num_logprobs + 1)
+        Top-k float logprobs tensor, (num tokens) x (num_logprobs + 1)
+        Sampled token rank tensor, (num tokens)
+    """
+    assert token_ids.dtype == torch.int64
+    # Find the topK values.
+    topk_logprobs, topk_indices = torch.topk(logprobs, num_logprobs, dim=-1)
+
+    # Get with the logprob of the prompt or sampled token.
+    token_ids = token_ids.unsqueeze(-1)
+    token_logprobs = logprobs.gather(-1, token_ids)
+
+    # Compute the ranks of the actual token.
+    token_ranks = batched_count_greater_than(logprobs, token_logprobs)
+
+    # Concatenate together with the topk.
+    indices = torch.cat((token_ids, topk_indices), dim=1)
+    logprobs = torch.cat((token_logprobs, topk_logprobs), dim=1)
+
+    # Use int32 to reduce the tensor size.
+    indices = indices.to(torch.int32)
+
+    return LogprobsTensors(indices, logprobs, token_ranks)
+
+
+Sampler.gather_logprobs = gather_logprobs