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tool_server/.venv/lib/python3.12/site-packages/vllm/v1/sample/logits_processor/__init__.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import importlib
+import itertools
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Optional, Union
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.v1.sample.logits_processor.builtin import (LogitBiasLogitsProcessor,
+                                                     MinPLogitsProcessor,
+                                                     MinTokensLogitsProcessor)
+from vllm.v1.sample.logits_processor.interface import (BatchUpdate,
+                                                       LogitsProcessor,
+                                                       MoveDirectionality)
+from vllm.v1.sample.logits_processor.state import (BatchUpdateBuilder,
+                                                   LogitsProcessors)
+
+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.")
+
+LOGITSPROCS_GROUP = 'vllm.logits_processors'
+
+BUILTIN_LOGITS_PROCESSORS: list[type[LogitsProcessor]] = [
+    MinTokensLogitsProcessor,
+    LogitBiasLogitsProcessor,
+    MinPLogitsProcessor,
+]
+
+
+def _load_logitsprocs_plugins() -> list[type[LogitsProcessor]]:
+    """Load all installed logit processor plugins"""
+
+    import sys
+
+    if sys.version_info < (3, 10):
+        from importlib_metadata import entry_points
+    else:
+        from importlib.metadata import entry_points
+
+    installed_logitsprocs_plugins = entry_points(group=LOGITSPROCS_GROUP)
+    if len(installed_logitsprocs_plugins) == 0:
+        logger.debug("No logitsprocs plugins installed (group %s).",
+                     LOGITSPROCS_GROUP)
+        return []
+
+    # Load logitsprocs plugins
+    logger.debug("Loading installed logitsprocs plugins (group %s):",
+                 LOGITSPROCS_GROUP)
+    classes: list[type[LogitsProcessor]] = []
+    for entrypoint in installed_logitsprocs_plugins:
+        try:
+            logger.debug("- Loading logitproc plugin entrypoint=%s target=%s",
+                         entrypoint.name, entrypoint.value)
+            classes.append(entrypoint.load())
+        except Exception as e:
+            raise RuntimeError(
+                f"Failed to load LogitsProcessor plugin {entrypoint}") from e
+    return classes
+
+
+def _load_logitsprocs_by_fqcns(
+    logits_processors: Optional[Sequence[Union[str, type[LogitsProcessor]]]]
+) -> list[type[LogitsProcessor]]:
+    """Load logit processor types, identifying them by fully-qualified class
+    names (FQCNs).
+
+    Effectively, a mixed list of logitproc types and FQCN strings is converted
+    into a list of entirely logitproc types, by loading from the FQCNs.
+
+    FQCN syntax is <module>:<type> i.e. x.y.z:CustomLogitProc
+
+    Already-loaded logitproc types must be subclasses of LogitsProcessor
+
+    Args:
+      logits_processors: Potentially mixed list of logitsprocs types and FQCN
+                         strings for logitproc types
+
+    Returns:
+      List of logitproc types
+
+    """
+    if not logits_processors:
+        return []
+
+    logger.debug(
+        "%s additional custom logits processors specified, checking whether "
+        "they need to be loaded.", len(logits_processors))
+
+    classes: list[type[LogitsProcessor]] = []
+    for ldx, logitproc in enumerate(logits_processors):
+        if isinstance(logitproc, type):
+            logger.debug(" - Already-loaded logit processor: %s",
+                         logitproc.__name__)
+            if not issubclass(logitproc, LogitsProcessor):
+                raise ValueError(
+                    f"{logitproc.__name__} is not a subclass of LogitsProcessor"
+                )
+            classes.append(logitproc)
+            continue
+
+        logger.debug("- Loading logits processor %s", logitproc)
+        module_path, qualname = logitproc.split(":")
+
+        try:
+            # Load module
+            module = importlib.import_module(module_path)
+        except Exception as e:
+            raise RuntimeError(
+                f"Failed to load {ldx}th LogitsProcessor plugin {logitproc}"
+            ) from e
+
+        # Walk down dotted name to get logitproc class
+        obj = module
+        for attr in qualname.split("."):
+            obj = getattr(obj, attr)
+        if not isinstance(obj, type):
+            raise ValueError("Loaded logit processor must be a type.")
+        if not issubclass(obj, LogitsProcessor):
+            raise ValueError(
+                f"{obj.__name__} must be a subclass of LogitsProcessor")
+        classes.append(obj)
+
+    return classes
+
+
+def _load_custom_logitsprocs(
+    logits_processors: Optional[Sequence[Union[str, type[LogitsProcessor]]]],
+) -> list[type[LogitsProcessor]]:
+    """Load all custom logits processors.
+
+    * First load all installed logitproc plugins
+    * Second load custom logitsprocs pass by the user at initialization time
+
+    Args:
+      logits_processors: potentially mixed list of logitproc types and
+                         logitproc type fully-qualified names (FQCNs)
+                         which need to be loaded
+
+    Returns:
+      A list of all loaded logitproc types
+    """
+    from vllm.platforms import current_platform
+    if current_platform.is_tpu():
+        # No logitsprocs specified by caller
+        # TODO(andy) - vLLM V1 on TPU does not support custom logitsprocs
+        return []
+
+    return (_load_logitsprocs_plugins() +
+            _load_logitsprocs_by_fqcns(logits_processors))
+
+
+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 = _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))
+
+
+__all__ = [
+    "LogitsProcessor", "LogitBiasLogitsProcessor", "MinPLogitsProcessor",
+    "MinTokensLogitsProcessor", "BatchUpdate", "BatchUpdateBuilder",
+    "MoveDirectionality", "LogitsProcessors", "build_logitsprocs",
+    "STR_POOLING_REJECTS_LOGITSPROCS", "LOGITSPROCS_GROUP"
+]

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/sample/logits_processor/builtin.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Optional
+
+import torch
+
+from vllm.v1.sample.logits_processor.interface import (BatchUpdate,
+                                                       LogitsProcessor,
+                                                       MoveDirectionality)
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+
+
+class MinPLogitsProcessor(LogitsProcessor):
+
+    def __init__(self, vllm_config: "VllmConfig", device: torch.device,
+                 is_pin_memory: bool):
+        max_num_reqs = vllm_config.scheduler_config.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]
+
+    def is_argmax_invariant(self) -> bool:
+        """Min-p never impacts greedy sampling"""
+        return True
+
+    def get_min_p_by_index(self, index: int) -> float:
+        return float(self.min_p_cpu[index])
+
+    def update_state(self, batch_update: Optional[BatchUpdate]):
+        if not batch_update:
+            return
+
+        needs_update = False
+        # Process added requests.
+        for index, params, _, _ in batch_update.added:
+            min_p = params.min_p
+            if self.min_p_cpu[index] != min_p:
+                needs_update = True
+                self.min_p_cpu[index] = min_p
+            if min_p:
+                self.min_p_count += 1
+
+        if self.min_p_count:
+            # Process removed requests.
+            needs_update |= bool(batch_update.removed)
+            for index in batch_update.removed:
+                if self.min_p_cpu[index]:
+                    self.min_p_count -= 1
+
+            # Process moved requests, unidirectional (a->b) and swap (a<->b)
+            for adx, bdx, direct in batch_update.moved:
+                change = (min_p_a :=
+                          self.min_p_cpu[adx]) != (min_p_b :=
+                                                   self.min_p_cpu[bdx])
+                needs_update |= change
+                if change:
+                    self.min_p_cpu[bdx] = min_p_a
+                    if direct == MoveDirectionality.SWAP:
+                        self.min_p_cpu[adx] = min_p_b
+
+        # Update tensors if needed.
+        size = batch_update.batch_size
+        if self.min_p_count and (needs_update or self.min_p.shape[0] != size):
+            self.min_p = self.min_p_device[:size]
+            if self.use_double_tensor:
+                self.min_p.copy_(self.min_p_cpu_tensor[:size],
+                                 non_blocking=True)
+            self.min_p.unsqueeze_(1)
+
+    def apply(self, logits: torch.Tensor) -> torch.Tensor:
+        if not self.min_p_count:
+            return logits
+
+        # Convert logits to probability distribution
+        probability_values = torch.nn.functional.softmax(logits, dim=-1)
+        # Calculate maximum probabilities per sequence
+        max_probabilities = torch.amax(probability_values,
+                                       dim=-1,
+                                       keepdim=True)
+        # Adjust min_p
+        adjusted_min_p = max_probabilities.mul_(self.min_p)
+        # Identify valid tokens using threshold comparison
+        invalid_token_mask = probability_values < adjusted_min_p
+        # Apply mask using boolean indexing
+        logits[invalid_token_mask] = -float('inf')
+        return logits
+
+
+class LogitBiasLogitsProcessor(LogitsProcessor):
+
+    def __init__(self, _, device: torch.device, is_pin_memory: bool):
+        self.device = device
+        self.pin_memory = is_pin_memory
+        self.biases: dict[int, dict[int, float]] = {}
+
+        self.bias_tensor: torch.Tensor = torch.tensor(())
+        self.logits_slice = (self._device_tensor([], torch.int32),
+                             self._device_tensor([], torch.int32))
+
+    def is_argmax_invariant(self) -> bool:
+        """Logit bias can rebalance token probabilities and change the
+        outcome of argmax in greedy sampling."""
+        return False
+
+    def update_state(self, batch_update: Optional[BatchUpdate]):
+        if not batch_update:
+            return
+
+        needs_update: bool = False
+        # Process added requests.
+        for index, params, _, _ in batch_update.added:
+            if lb := params.logit_bias:
+                self.biases[index] = lb
+                needs_update = True
+            else:
+                # Drop biases metadata at batch index
+                if self.biases.pop(index, None) is not None:
+                    # If a new request replaces an old request which
+                    # specified biases, we should update processor tensors
+                    needs_update = True
+
+        if self.biases:
+            # Process removed requests.
+            for index in batch_update.removed:
+                if self.biases.pop(index, None):
+                    needs_update = True
+
+            # Process moved requests, unidirectional (a->b) and swap (a<->b)
+            for a_index, b_index, direct in batch_update.moved:
+                if direct == MoveDirectionality.UNIDIRECTIONAL:
+                    if (a_entry := self.biases.pop(a_index, None)) is None:
+                        if self.biases.pop(b_index, None) is not None:
+                            needs_update = True
+                    else:
+                        self.biases[b_index] = a_entry
+                        needs_update = True
+                else:
+                    a_entry = self.biases.pop(a_index, None)
+                    if (b_entry := self.biases.pop(b_index, None)) is not None:
+                        self.biases[a_index] = b_entry
+                        needs_update = True
+                    if a_entry is not None:
+                        self.biases[b_index] = a_entry
+                        needs_update = True
+
+        # Update tensors if needed.
+        if needs_update:
+            reqs, tok_ids, biases = [], [], []
+            for req, lb in self.biases.items():
+                reqs.extend([req] * len(lb))
+                tok_ids.extend(lb.keys())
+                biases.extend(lb.values())
+
+            self.bias_tensor = self._device_tensor(biases, torch.float32)
+            self.logits_slice = (self._device_tensor(reqs, torch.int32),
+                                 self._device_tensor(tok_ids, torch.int32))
+
+    def _device_tensor(self, data: list, dtype: torch.dtype) -> torch.Tensor:
+        return (torch.tensor(data,
+                             device="cpu",
+                             dtype=dtype,
+                             pin_memory=self.pin_memory).to(device=self.device,
+                                                            non_blocking=True))
+
+    def apply(self, logits: torch.Tensor) -> torch.Tensor:
+        if self.biases:
+            logits[self.logits_slice] += self.bias_tensor
+        return logits
+
+
+class MinTokensLogitsProcessor(LogitsProcessor):
+
+    def __init__(self, vllm_config: "VllmConfig", device: torch.device,
+                 is_pin_memory: bool):
+        # index -> (min_toks, output_token_ids, stop_token_ids)
+        self.device = device
+        self.pin_memory = is_pin_memory
+        self.min_toks: dict[int, tuple[int, Sequence[int], set[int]]] = {}
+
+        # (req_idx_tensor,eos_tok_id_tensor)
+        self.logits_slice: tuple[torch.Tensor,
+                                 torch.Tensor] = (self._device_tensor(
+                                     [], torch.int32),
+                                                  self._device_tensor(
+                                                      [], torch.int32))
+
+    def is_argmax_invariant(self) -> bool:
+        """By censoring stop tokens, min-tokens can change the outcome
+        of the argmax operation in greedy sampling."""
+        return False
+
+    def update_state(self, batch_update: Optional[BatchUpdate]):
+        needs_update = False
+
+        if batch_update:
+            # Process added requests.
+            for index, params, _, output_tok_ids in batch_update.added:
+                if ((min_tokens := params.min_tokens)
+                        and len(output_tok_ids) < min_tokens):
+                    # Replace request metadata at batch index
+                    self.min_toks[index] = (min_tokens, output_tok_ids,
+                                            params.all_stop_token_ids)
+                    needs_update = True
+                else:
+                    # Drop min_toks metadata at batch index
+                    if self.min_toks.pop(index, None) is not None:
+                        # If a new request replaces an old request which
+                        # specified min_toks, we should update processor tensors
+                        needs_update = True
+
+            if self.min_toks:
+                # Process removed requests.
+                for index in batch_update.removed:
+                    if self.min_toks.pop(index, None):
+                        needs_update = True
+
+                # Process moved requests, unidirectional (a->b) and
+                # swapped (a<->b)
+                for a_index, b_index, direct in batch_update.moved:
+                    if direct == MoveDirectionality.UNIDIRECTIONAL:
+                        if (a_entry := self.min_toks.pop(a_index,
+                                                         None)) is None:
+                            if self.min_toks.pop(b_index, None) is not None:
+                                needs_update = True
+                        else:
+                            self.min_toks[b_index] = a_entry
+                            needs_update = True
+                    else:
+                        a_entry = self.min_toks.pop(a_index, None)
+                        if (b_entry := self.min_toks.pop(b_index,
+                                                         None)) is not None:
+                            self.min_toks[a_index] = b_entry
+                            needs_update = True
+                        if a_entry is not None:
+                            self.min_toks[b_index] = a_entry
+                            needs_update = True
+
+        if self.min_toks:
+            # Check for any requests that have attained their min tokens.
+            to_remove = tuple(index for index, (min_toks, out_tok_ids,
+                                                _) in self.min_toks.items()
+                              if len(out_tok_ids) >= min_toks)
+            if to_remove:
+                needs_update = True
+                for index in to_remove:
+                    del self.min_toks[index]
+
+        # Update tensors if needed.
+        if needs_update:
+            reqs: list[int] = []
+            tok_ids: list[int] = []
+            for req, (_, _, stop_tok_ids) in self.min_toks.items():
+                reqs.extend([req] * len(stop_tok_ids))
+                tok_ids.extend(stop_tok_ids)
+
+            self.logits_slice = (self._device_tensor(reqs, torch.int32),
+                                 self._device_tensor(tok_ids, torch.int32))
+
+    def _device_tensor(self, data: list, dtype: torch.dtype) -> torch.Tensor:
+        return (torch.tensor(data,
+                             device="cpu",
+                             dtype=dtype,
+                             pin_memory=self.pin_memory).to(device=self.device,
+                                                            non_blocking=True))
+
+    def apply(self, logits: torch.Tensor) -> torch.Tensor:
+        if self.min_toks:
+            # Inhibit EOS token for requests which have not reached min length
+            logits[self.logits_slice] = -float("inf")
+        return logits

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/sample/logits_processor/interface.py

@@ -0,0 +1,86 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from abc import ABC, abstractmethod
+from collections.abc import Sequence
+from dataclasses import dataclass
+from enum import Enum, auto
+from typing import TYPE_CHECKING, Optional
+
+import torch
+
+from vllm import SamplingParams
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+
+
+class MoveDirectionality(Enum):
+    # One-way i1->i2 req move within batch
+    UNIDIRECTIONAL = auto()
+    # Two-way i1<->i2 req swap within batch
+    SWAP = auto()
+
+
+# (index, params, prompt_tok_ids, output_tok_ids) tuples for new
+# requests added to the batch.
+AddedRequest = tuple[int, SamplingParams, list[int], list[int]]
+
+# (index 1, index 2, directionality) tuples representing
+# one-way moves or two-way swaps of requests in batch
+MovedRequest = tuple[int, int, MoveDirectionality]
+
+# Batch indices of any removed requests.
+RemovedRequest = int
+
+
+@dataclass(frozen=True)
+class BatchUpdate:
+    """Persistent batch state change info for logitsprocs"""
+    batch_size: int  # Current num reqs in batch
+
+    # Metadata for requests added to, removed from, and moved
+    # within the persistent batch.
+    #
+    # Key assumption: the `output_tok_ids` list (which is an element of each
+    # tuple in `added`) is a reference to the request's running output tokens
+    # list; via this reference, the logits processors always see the latest
+    # list of generated output tokens
+    removed: Sequence[RemovedRequest]
+    moved: Sequence[MovedRequest]
+    added: Sequence[AddedRequest]
+
+
+class LogitsProcessor(ABC):
+
+    @abstractmethod
+    def __init__(self, vllm_config: "VllmConfig", device: torch.device,
+                 is_pin_memory: bool) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply(self, logits: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError
+
+    @abstractmethod
+    def is_argmax_invariant(self) -> bool:
+        """True if logits processor has no impact on the
+        argmax computation in greedy sampling.
+        NOTE: may or may not have the same value for all
+        instances of a given LogitsProcessor subclass,
+        depending on subclass implementation.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def update_state(
+        self,
+        batch_update: Optional["BatchUpdate"],
+    ) -> None:
+        """Called when there are new output tokens, prior
+        to each forward pass.
+
+        Args:
+            batch_update is non-None iff there have been
+            changes to the batch makeup.
+        """
+        raise NotImplementedError

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/sample/logits_processor/state.py

@@ -0,0 +1,149 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterator
+from itertools import chain
+from typing import TYPE_CHECKING, Optional
+
+from vllm.v1.sample.logits_processor.interface import (AddedRequest,
+                                                       BatchUpdate,
+                                                       MovedRequest,
+                                                       RemovedRequest)
+
+if TYPE_CHECKING:
+    from vllm.v1.sample.logits_processor.interface import LogitsProcessor
+
+
+class BatchUpdateBuilder:
+    """Helps track persistent batch state changes and build
+    a batch update data structure for logitsprocs
+    Assumptions:
+    * All information about requests removed from persistent batch
+      during a step is aggregated in self._removed through calls to
+      self.removed_append() at the beginning of a step. This must happen
+      before the first time that self.removed, self.pop_removed()
+      or self.peek_removed() are invoked in a given step
+    * After the first time that self.removed, self.pop_removed()
+      or self.peek_removed() are read in a step, no new removals
+      are registered using self.removed_append()
+    * Elements of self._removed are never directly modified, added or
+      removed (i.e. modification is only via self.removed_append() and
+      self.pop_removed())
+    Guarantees under above assumptions:
+    * self.removed is always sorted in descending order
+    * self.pop_removed() and self.peek_removed() both return
+      the lowest removed request index in the current step
+    """
+
+    _removed: list[RemovedRequest]
+    _is_removed_sorted: bool
+    moved: list[MovedRequest]
+    added: list[AddedRequest]
+
+    def __init__(
+        self,
+        removed: Optional[list[RemovedRequest]] = None,
+        moved: Optional[list[MovedRequest]] = None,
+        added: Optional[list[AddedRequest]] = None,
+    ) -> None:
+        self._removed = removed or []
+        self.moved = moved or []
+        self.added = added or []
+        self._is_removed_sorted = False
+
+    def _ensure_removed_sorted(self) -> None:
+        """Sort removed request indices in
+        descending order.
+        Idempotent after first call in a
+        given step, until reset.
+        """
+        if not self._is_removed_sorted:
+            self._removed.sort(reverse=True)
+            self._is_removed_sorted = True
+
+    @property
+    def removed(self) -> list[RemovedRequest]:
+        """Removed request indices sorted in
+        descending order"""
+        self._ensure_removed_sorted()
+        return self._removed
+
+    def removed_append(self, index: int) -> None:
+        """Register the removal of a request from the persistent batch.
+
+        Must not be called after the first time self.removed,
+        self.pop_removed() or self.peek_removed() are invoked.
+
+        Args:
+          index: request index
+        """
+        if self._is_removed_sorted:
+            raise RuntimeError("Cannot register new removed request after"
+                               " self.removed has been read.")
+        self._removed.append(index)
+
+    def has_removed(self) -> bool:
+        return bool(self._removed)
+
+    def peek_removed(self) -> Optional[int]:
+        """Return lowest removed request index"""
+        if self.has_removed():
+            self._ensure_removed_sorted()
+            return self._removed[-1]
+        return None
+
+    def pop_removed(self) -> Optional[int]:
+        """Pop lowest removed request index"""
+        if self.has_removed():
+            self._ensure_removed_sorted()
+            return self._removed.pop()
+        return None
+
+    def _is_update(self) -> bool:
+        """True if there is a batch state change"""
+        return any((self._removed, self.moved, self.added))
+
+    def get_and_reset(self, batch_size: int) -> Optional[BatchUpdate]:
+        """Generate a logitsprocs batch update data structure and reset
+        internal batch update builder state.
+        
+        Args:
+          batch_size: current persistent batch size
+
+        Returns:
+          Frozen logitsprocs batch update instance; `None` if no updates
+        """
+        # Reset removal-sorting logic
+        self._is_removed_sorted = False
+        if not self._is_update():
+            # No update; short-circuit
+            return None
+        # Build batch state update
+        batch_update = BatchUpdate(
+            batch_size=batch_size,
+            removed=self._removed,
+            moved=self.moved,
+            added=self.added,
+        )
+        self._removed = []
+        self.moved = []
+        self.added = []
+        return batch_update
+
+
+class LogitsProcessors:
+    """Encapsulates initialized logitsproc objects."""
+
+    def __init__(
+            self,
+            logitsprocs: Optional[Iterator["LogitsProcessor"]] = None) -> None:
+        self.argmax_invariant: list[LogitsProcessor] = []
+        self.non_argmax_invariant: list[LogitsProcessor] = []
+        if logitsprocs:
+            for logitproc in logitsprocs:
+                (self.argmax_invariant if logitproc.is_argmax_invariant() else
+                 self.non_argmax_invariant).append(logitproc)
+
+    @property
+    def all(self) -> Iterator["LogitsProcessor"]:
+        """Iterator over all logits processors."""
+        return chain(self.argmax_invariant, self.non_argmax_invariant)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/sample/ops/bad_words.py

@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+_SMALLEST_LOGIT = float("-inf")
+
+
+def _apply_bad_words_single_batch(
+    logits: torch.Tensor,
+    bad_words_token_ids: list[list[int]],
+    past_tokens_ids: list[int],
+) -> None:
+    for bad_word_ids in bad_words_token_ids:
+        if len(bad_word_ids) > len(past_tokens_ids) + 1:
+            continue
+
+        prefix_length = len(bad_word_ids) - 1
+        last_token_id = bad_word_ids[-1]
+        if prefix_length > 0:
+            actual_prefix = past_tokens_ids[-prefix_length:]
+        else:
+            actual_prefix = []
+        expected_prefix = bad_word_ids[:prefix_length]
+
+        assert len(actual_prefix) == len(expected_prefix)
+
+        if actual_prefix == expected_prefix:
+            logits[last_token_id] = _SMALLEST_LOGIT
+
+
+def apply_bad_words(
+    logits: torch.Tensor,
+    bad_words_token_ids: dict[int, list[list[int]]],
+    past_tokens_ids: list[list[int]],
+) -> None:
+    for i, bad_words_ids in bad_words_token_ids.items():
+        _apply_bad_words_single_batch(logits[i], bad_words_ids,
+                                      past_tokens_ids[i])

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/sample/ops/logprobs.py

@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Some utilities for logprobs, including logits."""
+
+import torch
+
+from vllm.platforms import current_platform
+
+
+@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
+def batched_count_greater_than(x: torch.Tensor,
+                               values: torch.Tensor) -> torch.Tensor:
+    """
+    Counts elements in each row of x that are greater than the corresponding
+    value in values.  Use torch.compile to generate an optimized kernel for
+    this function. otherwise, it will create additional copies of the input
+    tensors and cause memory issues.
+
+    Args:
+        x (torch.Tensor): A 2D tensor of shape (batch_size, n_elements).
+        values (torch.Tensor): A 2D tensor of shape (batch_size, 1).
+
+    Returns:
+        torch.Tensor: A 1D tensor of shape (batch_size,) with the counts.
+    """
+    return (x >= values).sum(-1)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/sample/ops/penalties.py

@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm.model_executor.layers.utils import apply_penalties
+from vllm.utils import is_pin_memory_available, make_tensor_with_pad
+
+
+def apply_all_penalties(
+    logits: torch.Tensor,
+    prompt_token_ids: torch.Tensor,
+    presence_penalties: torch.Tensor,
+    frequency_penalties: torch.Tensor,
+    repetition_penalties: torch.Tensor,
+    output_token_ids: list[list[int]],
+) -> torch.Tensor:
+    """
+    Applies presence, frequency and repetition penalties to the logits.
+    """
+    _, vocab_size = logits.shape
+    output_tokens_t = _convert_to_tensors(output_token_ids, vocab_size,
+                                          logits.device)
+    return apply_penalties(logits, prompt_token_ids, output_tokens_t,
+                           presence_penalties, frequency_penalties,
+                           repetition_penalties)
+
+
+def _convert_to_tensors(output_token_ids: list[list[int]], vocab_size: int,
+                        device: torch.device) -> torch.Tensor:
+    """
+    Convert the different list data structures to tensors.
+    """
+    output_tokens_tensor = make_tensor_with_pad(
+        output_token_ids,
+        # Use the value of vocab_size as a pad since we don't have a
+        # token_id of this value.
+        pad=vocab_size,
+        device="cpu",
+        dtype=torch.int64,
+        pin_memory=is_pin_memory_available(),
+    )
+    return output_tokens_tensor.to(device, non_blocking=True)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/sample/ops/topk_topp_sampler.py

@@ -0,0 +1,298 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from packaging import version
+
+from vllm import envs
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+
+logger = init_logger(__name__)
+
+try:
+    import flashinfer.sampling
+    is_flashinfer_available = True
+except ImportError:
+    is_flashinfer_available = False
+
+
+class TopKTopPSampler(nn.Module):
+    """
+    Module that performs optional top-k and top-p filtering followed by
+    weighted random sampling of logits.
+
+    Implementations may update the logits tensor in-place.
+    """
+
+    def __init__(self):
+        super().__init__()
+        if current_platform.is_cuda():
+            if is_flashinfer_available:
+                flashinfer_version = flashinfer.__version__
+                if version.parse(flashinfer_version) < version.parse("0.2.3"):
+                    logger.warning_once(
+                        "FlashInfer version >= 0.2.3 required. "
+                        "Falling back to default sampling implementation.")
+                    self.forward = self.forward_native
+                elif envs.VLLM_USE_FLASHINFER_SAMPLER is not False:
+                    # NOTE(woosuk): The V0 sampler doesn't use FlashInfer for
+                    # sampling unless VLLM_USE_FLASHINFER_SAMPLER=1 (i.e., by
+                    # default it is unused). For backward compatibility, we set
+                    # `VLLM_USE_FLASHINFER_SAMPLER` as None by default and
+                    # interpret it differently in V0 and V1 samplers: In V0,
+                    # None means False, while in V1, None means True. This is
+                    # why we use the condition
+                    # `envs.VLLM_USE_FLASHINFER_SAMPLER is not False` here.
+                    logger.info_once(
+                        "Using FlashInfer for top-p & top-k sampling.")
+                    self.forward = self.forward_cuda
+                else:
+                    logger.warning_once(
+                        "FlashInfer is available, but it is not enabled. "
+                        "Falling back to the PyTorch-native implementation of "
+                        "top-p & top-k sampling. For the best performance, "
+                        "please set VLLM_USE_FLASHINFER_SAMPLER=1.")
+                    self.forward = self.forward_native
+            else:
+                logger.warning_once(
+                    "FlashInfer is not available. Falling back to the PyTorch-"
+                    "native implementation of top-p & top-k sampling. For the "
+                    "best performance, please install FlashInfer.")
+                self.forward = self.forward_native
+        elif current_platform.is_tpu():
+            self.forward = self.forward_tpu
+        else:
+            self.forward = self.forward_native
+
+    def forward_native(
+        self,
+        logits: torch.Tensor,
+        generators: dict[int, torch.Generator],
+        k: Optional[torch.Tensor],
+        p: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        """
+        PyTorch-native implementation of top-k and top-p sampling.
+
+        The logits tensor may be updated in-place.
+        """
+        logits = apply_top_k_top_p(logits, k, p)
+        probs = logits.softmax(dim=-1, dtype=torch.float32)
+        return random_sample(probs, generators)
+
+    def forward_cuda(
+        self,
+        logits: torch.Tensor,
+        generators: dict[int, torch.Generator],
+        k: Optional[torch.Tensor],
+        p: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        """More optimized implementation for top-k and top-p sampling."""
+        if k is None and p is None:
+            # We prefer `random_sample` over `flashinfer_sample` when sorting is
+            # not needed. This is because `random_sample` does not require
+            # CPU-GPU synchronization while `flashinfer_sample` does.
+            probs = logits.softmax(dim=-1, dtype=torch.float32)
+            return random_sample(probs, generators)
+        if generators:
+            logger.warning_once("FlashInfer 0.2.3+ does not support "
+                                "per-request generators. Falling back to "
+                                "PyTorch-native implementation.")
+            return self.forward_native(logits, generators, k, p)
+        # flashinfer sampling functions expect contiguous logits.
+        # In flex_attn/triton_attn fp32 inference, logits can be non-contiguous
+        # because of slicing operation in logits_processor.
+        return flashinfer_sample(logits.contiguous(), k, p, generators)
+
+    def forward_tpu(
+        self,
+        logits: torch.Tensor,
+        generators: dict[int, torch.Generator],
+        k: Optional[torch.Tensor],
+        p: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        logits = apply_top_k_top_p_tpu(logits, k, p)
+        probs = logits.softmax(dim=-1, dtype=torch.float32)
+        return random_sample(probs, generators)
+
+
+def apply_top_k_top_p_tpu(
+    logits: torch.Tensor,
+    k: torch.Tensor,
+    p: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Apply top-k and top-p optimized for TPU.
+
+    This algorithm avoids using torch.scatter which is extremely slow on TPU.
+    This is achieved by finding a "cut-off" element in the original logit, and
+    after thresholding the logit using this cut-off, the remaining elements
+    shall constitute the top-p set.
+
+    Note: in the case of tie (i.e. multipple cut-off elements present in the
+    logit), all tie elements are included in the top-p set. In other words,
+    this function does not break ties. Instead, these tie tokens have equal
+    chance of being chosen during final sampling, so we can consider the tie
+    being broken then.
+    """
+    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 apply_top_k_top_p(
+    logits: torch.Tensor,
+    k: Optional[torch.Tensor],
+    p: Optional[torch.Tensor],
+) -> torch.Tensor:
+    """Apply top-k and top-p masks to the logits.
+
+    If a top-p is used, this function will sort the logits tensor,
+    which can be slow for large batches.
+
+    The logits tensor may be updated in-place.
+    """
+    if p is None:
+        if k is None:
+            return logits
+
+        # Avoid sorting vocab for top-k only case.
+        return apply_top_k_only(logits, k)
+
+    logits_sort, logits_idx = logits.sort(dim=-1, descending=False)
+
+    if k is not None:
+        # Apply top-k.
+        top_k_mask = logits_sort.size(1) - k.to(torch.long)  # shape: B
+        # Get all the top_k values.
+        top_k_mask = logits_sort.gather(1, top_k_mask.unsqueeze(dim=1))
+        top_k_mask = logits_sort < top_k_mask
+        logits_sort.masked_fill_(top_k_mask, -float("inf"))
+
+    if p is not None:
+        # Apply top-p.
+        probs_sort = logits_sort.softmax(dim=-1)
+        probs_sum = torch.cumsum(probs_sort, dim=-1, out=probs_sort)
+        top_p_mask = probs_sum <= 1 - p.unsqueeze(dim=1)
+        # at least one
+        top_p_mask[:, -1] = False
+        logits_sort.masked_fill_(top_p_mask, -float("inf"))
+
+    # Re-sort the probabilities.
+    logits = logits_sort.scatter(dim=-1, index=logits_idx, src=logits_sort)
+    return logits
+
+
+def apply_top_k_only(
+    logits: torch.Tensor,
+    k: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Apply top-k mask to the logits.
+
+    This implementation doesn't involve sorting the entire vocab.
+
+    The logits tensor may be updated in-place.
+    """
+    no_top_k_mask = k == logits.shape[1]
+    # Set non-top-k rows to 1 so that we can gather.
+    k = k.masked_fill(no_top_k_mask, 1)
+    max_top_k = k.max()
+    # topk.values tensor has shape [batch_size, max_top_k].
+    # Convert top k to 0-based index in range [0, max_top_k).
+    k_index = k.sub_(1).unsqueeze(1)
+    top_k_mask = logits.topk(max_top_k, dim=1).values.gather(1, k_index.long())
+    # Handle non-topk rows.
+    top_k_mask.masked_fill_(no_top_k_mask.unsqueeze(1), -float("inf"))
+    logits.masked_fill_(logits < top_k_mask, -float("inf"))
+    return logits
+
+
+def random_sample(
+    probs: torch.Tensor,
+    generators: dict[int, torch.Generator],
+) -> torch.Tensor:
+    """Randomly sample from the probabilities.
+
+    We use this function instead of torch.multinomial because torch.multinomial
+    causes CPU-GPU synchronization.
+    """
+    q = torch.empty_like(probs)
+    # NOTE(woosuk): To batch-process the requests without their own seeds,
+    # which is the common case, we first assume that every request does
+    # not have its own seed. Then, we overwrite the values for the requests
+    # that have their own seeds.
+    if len(generators) != probs.shape[0]:
+        q.exponential_()
+    if generators:
+        # TODO(woosuk): This can be slow because we handle each request
+        # one by one. Optimize this.
+        for i, generator in generators.items():
+            q[i].exponential_(generator=generator)
+    return probs.div_(q).argmax(dim=-1).view(-1)
+
+
+def flashinfer_sample(
+    logits: torch.Tensor,
+    k: Optional[torch.Tensor],
+    p: Optional[torch.Tensor],
+    generators: dict[int, torch.Generator],
+) -> torch.Tensor:
+    """Sample from the logits using FlashInfer.
+
+    Statistically, this function is equivalent to the `random_sample` function.
+    However, this function is faster because it avoids sorting the logits tensor
+    via rejection sampling.
+
+    NOTE: The outputs of this function do not necessarily match the outputs of
+    the `random_sample` function. It only guarantees that the outputs are
+    statistically equivalent.
+
+    NOTE: This function includes CPU-GPU synchronization, while `random_sample`
+    does not. Call this function at the end of the forward pass to minimize
+    the synchronization overhead.
+    """
+    assert not (k is None and p is None)
+    if k is None:
+        # Top-p only.
+        probs = logits.softmax(dim=-1, dtype=torch.float32)
+        next_token_ids = flashinfer.sampling.top_p_sampling_from_probs(
+            probs, p, deterministic=True)
+    elif p is None:
+        # Top-k only.
+        probs = logits.softmax(dim=-1, dtype=torch.float32)
+        next_token_ids = flashinfer.sampling.top_k_sampling_from_probs(
+            probs, k, deterministic=True)
+    else:
+        # Both top-k and top-p.
+        next_token_ids = flashinfer.sampling.top_k_top_p_sampling_from_logits(
+            logits, k, p, deterministic=True)
+
+    return next_token_ids.view(-1)