[Refactor]Refactor sampler (#2050)

Refactor Sampler implementation from patch way to inherit from vLLM
Sampler interface.

Next step: Make the op `TopKTopPSampler` in vLLM support custom ops
register mechanism

- vLLM version: v0.10.0
- vLLM main:
61a6905ab0

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/sample/sampler.py

@@ -0,0 +1,62 @@
+import torch
+import torch_npu
+from vllm.v1.sample.ops.topk_topp_sampler import TopKTopPSampler, random_sample
+from vllm.v1.sample.sampler import Sampler
+
+
+class AscendSampler(Sampler):
+
+    def __init__(self, logprobs_mode="raw_logprobs"):
+        # TODO: support logprobs_mode in vllm-ascend
+        super().__init__(logprobs_mode=logprobs_mode)
+        self.topk_topp_sampler = AscendTopKTopPSampler()
+
+
+class AscendTopKTopPSampler(TopKTopPSampler):
+
+    def _apply_top_k_top_p(
+        self,
+        logits: torch.Tensor,
+        k: torch.Tensor,
+        p: torch.Tensor,
+    ) -> torch.Tensor:
+        if p is not None and k is not None:
+            # npu_top_k_top_p's parameter order is (logits, p, k), not (logits, k, p)
+            return torch_npu.npu_top_k_top_p(logits, p, k)
+
+        if p is None and k is None:
+            return logits
+
+        probs = logits.softmax(dim=-1)
+        probs_sort, _ = probs.sort(dim=-1, descending=False)
+
+        if k is not None:
+            top_k_count = probs_sort.size(1) - k.to(
+                torch.long)  # shape: (batch, )
+            top_k_count = top_k_count.unsqueeze(dim=1)
+            top_k_cutoff = probs_sort.gather(-1, top_k_count)
+
+            # Make sure the no top-k rows are no-op.
+            no_top_k_mask = (k == logits.shape[1]).unsqueeze(dim=1)
+            top_k_cutoff.masked_fill_(no_top_k_mask, -float("inf"))
+
+            elements_to_discard = probs < top_k_cutoff
+            logits.masked_fill_(elements_to_discard, -float("inf"))
+
+        if p is not None:
+            cumprob = torch.cumsum(probs_sort, dim=-1)
+            top_p_mask = cumprob <= 1 - p.unsqueeze(dim=1)
+            top_p_mask[:, -1] = False  # at least one
+
+            top_p_count = top_p_mask.sum(dim=-1).unsqueeze(1)
+            top_p_cutoff = probs_sort.gather(-1, top_p_count)
+            elements_to_discard = probs < top_p_cutoff
+            logits.masked_fill_(elements_to_discard, -float("inf"))
+
+        return logits
+
+    def forward_native(self, logits, generators, k, p):
+        """Override pytorch native implementation to torch_npu"""
+        logits = self._apply_top_k_top_p(logits, k, p)
+        probs = logits.softmax(dim=-1, dtype=torch.float32)
+        return random_sample(probs, generators)