大改

vllm_npu/sample/logits_processor/__init__.py

@@ -0,0 +1,50 @@
+import itertools
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Union
+
+import torch
+from vllm.logger import init_logger
+from vllm.v1.sample import logits_processor
+from vllm.v1.sample.logits_processor.builtin import (LogitBiasLogitsProcessor,
+                                                     MinTokensLogitsProcessor)
+from vllm.v1.sample.logits_processor.interface import LogitsProcessor
+from vllm.v1.sample.logits_processor.state import LogitsProcessors
+
+from vllm_npu.sample.logits_processor.builtin import \
+    AscendMinPLogitsProcessor
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+
+logger = init_logger(__name__)
+
+# Error message when the user tries to initialize vLLM with a pooling model
+# and custom logitsproces
+STR_POOLING_REJECTS_LOGITSPROCS = ("Pooling models do not support custom"
+                                   " logits processors.")
+
+BUILTIN_LOGITS_PROCESSORS: list[type[LogitsProcessor]] = [
+    MinTokensLogitsProcessor,
+    LogitBiasLogitsProcessor,
+    AscendMinPLogitsProcessor,
+]
+
+
+def build_logitsprocs(
+    vllm_config: "VllmConfig",
+    device: torch.device,
+    is_pin_memory: bool,
+    is_pooling_model: bool,
+    custom_logitsprocs: Sequence[Union[str, type[LogitsProcessor]]] = (),
+) -> LogitsProcessors:
+    if is_pooling_model:
+        if custom_logitsprocs:
+            raise ValueError(STR_POOLING_REJECTS_LOGITSPROCS)
+        logger.debug("Skipping logits processor loading because pooling models"
+                     " do not support logits processors.")
+        return LogitsProcessors()
+    custom_logitsprocs_classes = logits_processor._load_custom_logitsprocs(
+        custom_logitsprocs)
+    return LogitsProcessors(
+        ctor(vllm_config, device, is_pin_memory) for ctor in itertools.chain(
+            BUILTIN_LOGITS_PROCESSORS, custom_logitsprocs_classes))

vllm_npu/sample/logits_processor/builtin.py

@@ -0,0 +1,35 @@
+import torch
+from vllm.config import VllmConfig
+from vllm.v1.sample.logits_processor import MinPLogitsProcessor
+
+
+class AscendMinPLogitsProcessor(MinPLogitsProcessor):
+
+    def __init__(self, vllm_config: "VllmConfig", device: torch.device,
+                 is_pin_memory: bool):
+        super().__init__(vllm_config, device, is_pin_memory)
+
+        decode_max_num_seqs = getattr(vllm_config.scheduler_config,
+                                      'decode_max_num_seqs', 0)
+        if decode_max_num_seqs != 0:
+            max_num_reqs = max(vllm_config.scheduler_config.max_num_seqs,
+                               decode_max_num_seqs)
+
+            self.min_p_count: int = 0
+
+            self.min_p_cpu_tensor = torch.zeros((max_num_reqs, ),
+                                                dtype=torch.float32,
+                                                device="cpu",
+                                                pin_memory=is_pin_memory)
+            self.min_p_cpu = self.min_p_cpu_tensor.numpy()
+
+            self.use_double_tensor = torch.device(device).type != "cpu"
+
+            if self.use_double_tensor:
+                # Pre-allocated device tensor
+                self.min_p_device: torch.Tensor = torch.empty(
+                    (max_num_reqs, ), dtype=torch.float32, device=device)
+            else:
+                self.min_p_device = self.min_p_cpu_tensor
+            # Current slice of the device tensor
+            self.min_p: torch.Tensor = self.min_p_device[:0]