[Feature] Online Chat API Support Return logprobs (#2777)

* online chat support logprobs

* check xpu

* check vl_gpu_model_runner and xpu_model_runner

* get_worker() check platform

fastdeploy/worker/output.py

@@ -15,11 +15,80 @@
 """
 
 from dataclasses import dataclass
-from typing import Optional
+from typing import NamedTuple, Optional
 
 import paddle
 
 
+class LogprobsLists(NamedTuple):
+    """
+    """
+
+    # [num_reqs, max_num_logprobs + 1]
+    logprob_token_ids: list[list[int]]
+    # [num_reqs, max_num_logprobs + 1]
+    logprobs: list[list[float]]
+    # [num_reqs]
+    sampled_token_ranks: list[int]
+
+    def slice(self, start: int, end: int):
+        """slice"""
+        return LogprobsLists(
+            self.logprob_token_ids[start:end],
+            self.logprobs[start:end],
+            self.sampled_token_ranks[start:end],
+        )
+
+
+class LogprobsTensors(NamedTuple):
+    """
+    """
+
+    # [num_reqs, max_num_logprobs + 1]
+    logprob_token_ids: paddle.Tensor
+    # [num_reqs, max_num_logprobs + 1]
+    logprobs: paddle.Tensor
+    # [num_reqs]
+    selected_token_ranks: paddle.Tensor
+
+    def tolists(self):
+        """Convert to lists."""
+        return LogprobsLists(
+            self.logprob_token_ids.tolist(),
+            self.logprobs.tolist(),
+            self.selected_token_ranks.tolist(),
+        )
+
+    @staticmethod
+    def empty_cpu(num_positions: int,
+                  num_tokens_per_position: int) -> "LogprobsTensors":
+        """Create empty LogprobsTensors on CPU."""
+
+        logprob_token_ids = paddle.empty(
+            [num_positions, num_tokens_per_position],
+            dtype=paddle.int64).cpu()
+        logprobs = paddle.empty_like(logprob_token_ids, dtype=paddle.float32)
+        selected_token_ranks = paddle.empty([num_positions],
+                                            dtype=paddle.int64).cpu()
+        return LogprobsTensors(
+            logprob_token_ids=logprob_token_ids,
+            logprobs=logprobs,
+            selected_token_ranks=selected_token_ranks,
+        )
+
+
+@dataclass
+class SamplerOutput:
+    """
+    """
+
+    # [num_reqs, max_num_generated_tokens]
+    # Different requests can have different number of generated tokens.
+    # All requests are padded to max_num_generated_tokens.
+    # PLACEHOLDER_TOKEN_ID (-1 by default) is used for padding.
+    sampled_token_ids: paddle.Tensor
+    logprobs_tensors: Optional[LogprobsTensors]
+
 @dataclass
 class ModelOutputData:
     """