[model_runner_v2]optimize the performance of the post_update. (#7496)

### What this PR does / why we need it?
- This PR aims to enhance the operator performance in the `post_update`
phase of `model_runner_v2` on NPUs. By optimizing the relevant
operations, it is expected to improve the overall efficiency and speed
of the model running on NPU hardware, which is crucial for scenarios
where high-performance inference is required.
- when bs = 256, time cost reduce from 26us to 11 us; 

### Does this PR introduce _any_ user-facing change?
No, there are no changes to the API, interface, or other high-level
behaviors that would directly affect the user's code or interaction with
the system beyond the performance improvement.

### How was this patch tested?
CI passed with new added/existing tests. In addition to the regular CI
tests, specific benchmark tests were conducted on NPU hardware to
measure the performance improvement of the `post_update` operators.

---------

Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>

tests/e2e/nightly/single_node/ops/singlecard_ops/triton/test_post_update.py

@@ -0,0 +1,97 @@
+from typing import Dict, Any
+
+import torch
+import pytest
+from vllm.v1.worker.gpu.input_batch import post_update as post_update_gpu
+from vllm_ascend.worker.v2.input_batch import post_update as post_update_npu
+
+
+def generate_test_data(num_reqs: int, max_num_reqs: int, vocab_size: int, num_speculative_steps: int, device: str) -> \
+        Dict[str, Any]:
+    """
+    Generate random test data.
+    Return a dictionary containing all input tensors and the additional field 'expected_query_lens' for validation.
+    """
+    num_cols = num_speculative_steps + 1
+
+    if num_reqs > max_num_reqs:
+        raise ValueError("num_reqs cannot be larger than max_num_reqs")
+
+    idx_mapping = torch.arange(num_reqs, dtype=torch.int32, device=device)
+    num_computed_tokens = torch.randint(0, 100, (max_num_reqs,), dtype=torch.int32, device=device)
+    last_sampled_tokens = torch.randint(0, vocab_size, (max_num_reqs,), dtype=torch.int32, device=device)
+    output_bin_counts = torch.randint(0, 10, (max_num_reqs, vocab_size), dtype=torch.int32, device=device)
+    sampled_tokens = torch.randint(0, vocab_size, (num_reqs, num_speculative_steps + 1), dtype=torch.int32,
+                                   device=device)
+    num_sampled = torch.randint(1, num_speculative_steps + 2, (num_reqs,), dtype=torch.int32, device=device)
+    num_rejected = torch.randint(0, num_speculative_steps + 1, (num_reqs,), dtype=torch.int32, device=device)
+    num_rejected = torch.min(num_rejected, num_sampled - 1)
+
+    query_lengths = torch.randint(1, 20, (num_reqs,), dtype=torch.int32, device=device)
+    query_start_loc = torch.cat([
+        torch.tensor([0], dtype=torch.int32, device=device),
+        torch.cumsum(query_lengths, dim=0)
+    ])
+    total_len = torch.randint(50, 200, (max_num_reqs,), dtype=torch.int32, device=device)
+
+    max_model_len = 3000  # 或者可以从total_len的最大值获取
+    all_token_ids = torch.randint(0, vocab_size, (max_num_reqs, max_model_len), dtype=torch.int32, device=device)
+
+    return {
+        "idx_mapping": idx_mapping,
+        "num_computed_tokens": num_computed_tokens,
+        "last_sampled_tokens": last_sampled_tokens,
+        "output_bin_counts": output_bin_counts,
+        "sampled_tokens": sampled_tokens,
+        "num_sampled": num_sampled,
+        "num_rejected": num_rejected,
+        "query_start_loc": query_start_loc,
+        "all_token_ids": all_token_ids,
+        "total_len": total_len
+    }
+
+
+@pytest.mark.parametrize("num_reqs,max_num_reqs,vocab_size,num_speculative_steps", [
+    (36, 36, 200, 2),
+    (48, 48, 32000, 5),
+    (128, 128, 32000, 5),
+])
+def test_post_update(num_reqs: int, max_num_reqs: int, vocab_size: int, num_speculative_steps: int):
+    """Test _topk_log_softmax_kernel for computing log probabilities
+    Args:
+        batch_size: Number of sequences in the batch
+        vocab_size: Size of the vocabulary
+        num_logprobs: Number of tokens to compute log probabilities for
+    """
+    torch.manual_seed(42)
+
+    post_update_params = ["idx_mapping",
+                          "num_computed_tokens",
+                          "last_sampled_tokens",
+                          "output_bin_counts",
+                          "sampled_tokens",
+                          "num_sampled",
+                          "num_rejected",
+                          "query_start_loc",
+                          "all_token_ids",
+                          "total_len"
+                          ]
+
+    data = generate_test_data(num_reqs, max_num_reqs, vocab_size, num_speculative_steps, device="npu")
+    kernel_inputs_gpu = {k: data[k].clone() for k in post_update_params}
+    kernel_inputs_npu = {k: data[k].clone() for k in post_update_params}
+
+    # Invoke Triton kernel
+    post_update_gpu(**kernel_inputs_gpu)
+    torch.npu.synchronize()
+
+    post_update_npu(**kernel_inputs_npu)
+    torch.npu.synchronize()
+
+    # ========== Verify results ==========
+    assert torch.allclose(kernel_inputs_gpu["output_bin_counts"], kernel_inputs_npu["output_bin_counts"], rtol=1e-3,
+                          atol=1e-3), \
+        f"Triton output differs from PyTorch reference.\n" \
+        f"Max diff: {torch.max(torch.abs(kernel_inputs_npu['output_bin_counts'] - kernel_inputs_npu['output_bin_counts']))}\n" \
+        f"Mean diff: {torch.mean(torch.abs(kernel_inputs_npu['output_bin_counts'] - kernel_inputs_npu['output_bin_counts']))}"
+