[BugFix][0.18.0][310p] fix post-sampling not working in graph mode on 310p (#8077)

### What this PR does / why we need it?

Enabling temperature in post-processing on 310P devices can cause the
service to stall and eventually hang. We first traced the issue to a
timeout where the temperature-related `div` operator was waiting for
results from a sub-stream. After investigating the preceding operators,
we finally identified the root cause as the `q.exponential_()` operator,
which is not well supported on 310P and triggers an internal issue in
the `add` kernel.

### Does this PR introduce _any_ user-facing change?
NA

### How was this patch tested?
This patch was thoroughly tested locally（accuracy-dataset test and
stress test）. It is not easy to design a proper unit test for this case,
and I appreciate your understanding.

Signed-off-by: Tflowers-0129 <2906339855@qq.com>

vllm_ascend/_310p/sample/sampler.py

@@ -0,0 +1,66 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+#
+
+import torch
+from vllm.model_executor.layers.batch_invariant import vllm_is_batch_invariant
+
+from vllm_ascend.sample.sampler import (
+    DEFAULT_LOGPROBS_MODE,
+    AscendSampler,
+    AscendTopKTopPSampler,
+)
+from vllm_ascend.utils import global_stream, npu_stream_switch
+
+
+def _random_sample_310p(
+    probs: torch.Tensor,
+    generators: dict[int, torch.Generator],
+) -> torch.Tensor:
+    """310P-specific random sampling with CPU exponential generation for q."""
+    with npu_stream_switch(global_stream()):
+        q = torch.empty_like(probs)
+        q = q.cpu()
+        if len(generators) != q.shape[0]:
+            q.exponential_()
+        if generators:
+            for i, generator in generators.items():
+                q[i].exponential_(generator=generator)
+        q = q.npu()
+    torch.npu.current_stream().wait_stream(global_stream())
+    return probs.div_(q).argmax(dim=-1).view(-1)
+
+
+class AscendTopKTopPSampler310(AscendTopKTopPSampler):
+    def forward_native(self, logits, generators, k, p):
+        if vllm_is_batch_invariant():
+            return super().forward_native(logits, generators, k, p)
+
+        logits = self.apply_top_k_top_p(logits, k, p)
+        logits_to_return = None
+        if self.logprobs_mode == "processed_logits":
+            logits_to_return = logits
+        elif self.logprobs_mode == "processed_logprobs":
+            logits_to_return = logits.log_softmax(dim=-1, dtype=torch.float32)
+
+        probs = logits.softmax(dim=-1, dtype=torch.float32)
+        return _random_sample_310p(probs, generators), logits_to_return
+
+
+class AscendSampler310(AscendSampler):
+    def __init__(self, logprobs_mode=DEFAULT_LOGPROBS_MODE):
+        super().__init__(logprobs_mode=logprobs_mode)
+        self.topk_topp_sampler = AscendTopKTopPSampler310(logprobs_mode=logprobs_mode)