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

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+
+from vllm.v1.sample.logits_processor import LogitsProcessors
+
+
+@dataclass
+class SamplingMetadata:
+
+    temperature: Optional[torch.Tensor]
+    all_greedy: bool
+    all_random: bool
+
+    top_p: Optional[torch.Tensor]
+    top_k: Optional[torch.Tensor]
+
+    generators: dict[int, torch.Generator]
+
+    # None means no logprobs, 0 means sampled token logprobs only
+    max_num_logprobs: Optional[int]
+
+    no_penalties: bool
+    prompt_token_ids: Optional[torch.Tensor]
+    frequency_penalties: torch.Tensor
+    presence_penalties: torch.Tensor
+    repetition_penalties: torch.Tensor
+
+    output_token_ids: list[list[int]]
+
+    # `allowed_token_ids_mask` is a 2D bool tensor of shape (max batch size,
+    # vocab size).
+    allowed_token_ids_mask: Optional[torch.Tensor]
+
+    # req_index -> bad_words_token_ids
+    bad_words_token_ids: dict[int, list[list[int]]]
+
+    # Loaded logits processors
+    logitsprocs: LogitsProcessors

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

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+# 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 vllm.logger import init_logger
+from vllm.triton_utils import tl, triton
+from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
+from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
+
+logger = init_logger(__name__)
+
+PLACEHOLDER_TOKEN_ID: tl.constexpr = -1
+GREEDY_TEMPERATURE: tl.constexpr = -1
+# Maximum number of speculative draft tokens allowed per request in a single
+# step. This value is chosen to be large enough to handle typical use cases.
+MAX_SPEC_LEN = 32
+
+
+class RejectionSampler(nn.Module):
+    """
+    The implementation strictly follows the algorithm described in
+        https://arxiv.org/abs/2211.17192.
+    However, we want to clarify the terminology used in the implementation:
+    accepted tokens: tokens that are accepted based on the relationship
+            between the "raw" draft and target probabilities.
+    recovered tokens: tokens that are sampled based on the adjusted probability
+        distribution, which is derived from both the draft and target
+        probabilities.
+    bonus tokens:
+        If all proposed tokens are accepted, the bonus token is added to the
+        end of the sequence. The bonus token is only sampled from the target
+        probabilities. We pass in the bonus tokens instead of sampling them
+        in the rejection sampler to allow for more flexibility in the
+        sampling process. For example, we can use top_p, top_k sampling for
+        bonus tokens, while spec decode does not support these sampling
+        strategies.
+    output tokens:
+        Tokens are finally generated with the rejection sampler.
+        output tokens = accepted tokens + recovered tokens + bonus tokens
+    """
+
+    def forward(
+        self,
+        metadata: SpecDecodeMetadata,
+        # [num_tokens, vocab_size]
+        draft_probs: Optional[torch.Tensor],
+        # [num_tokens, vocab_size]
+        target_logits: torch.Tensor,
+        # [batch_size, 1]
+        bonus_token_ids: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> torch.Tensor:
+        '''
+        Args:
+            metadata:
+                Metadata for spec decoding.
+            draft_probs (Optional[torch.Tensor]):
+                Probability distribution for the draft tokens. Shape is
+                [num_tokens, vocab_size]. Can be None if probabilities are
+                not provided, which is the case for ngram spec decode.
+            target_logits (torch.Tensor):
+                Target model's logits probability distribution.
+                Shape is [num_tokens, vocab_size]. Here, probabilities from
+                different requests are flattened into a single tensor because
+                this is the shape of the output logits.
+                NOTE: `target_logits` can be updated in place to save memory.
+            bonus_token_ids_tensor (torch.Tensor):
+                A tensor containing bonus tokens. Shape is [batch_size, 1].
+                Bonus tokens are added to the end of the sequence if all
+                proposed tokens are accepted. We generate the bonus tokens
+                outside of the rejection sampler with the default sampling
+                strategy. It allows for more flexibility in the sampling
+                process such as top_p, top_k sampling.
+            sampling_metadata (vllm.v1.sample.metadata.SamplingMetadata):
+                Additional metadata needed for sampling, such as temperature,
+                top-k/top-p parameters, or other relevant information.
+        Returns:
+            output_token_ids (torch.Tensor):
+                A tensor containing the final output token IDs.
+        '''
+        assert metadata.max_spec_len <= MAX_SPEC_LEN
+        # [num_tokens, vocab_size]
+        # NOTE(woosuk): `target_logits` can be updated in place inside the
+        # `compute_probs` function.
+        target_probs = compute_probs(
+            target_logits,
+            metadata.cu_num_draft_tokens,
+            sampling_metadata,
+        )
+
+        output_token_ids = rejection_sample(
+            metadata.draft_token_ids,
+            metadata.num_draft_tokens,
+            metadata.max_spec_len,
+            metadata.cu_num_draft_tokens,
+            draft_probs,
+            target_probs,
+            bonus_token_ids,
+            sampling_metadata,
+        )
+        return output_token_ids
+
+    @staticmethod
+    def parse_output(
+        output_token_ids: torch.Tensor,
+        vocab_size: int,
+    ) -> list[list[int]]:
+        """Parse the output of the rejection sampler.
+
+        Args:
+            output_token_ids: The sampled token IDs in shape
+                [batch_size, max_spec_len + 1]. The rejected tokens are
+                replaced with `PLACEHOLDER_TOKEN_ID` by the rejection sampler
+                and will be filtered out in this function.
+            vocab_size: The size of the vocabulary.
+
+        Returns:
+            A list of lists of token IDs.
+        """
+        output_token_ids_np = output_token_ids.cpu().numpy()
+        # Create mask for valid tokens.
+        valid_mask = ((output_token_ids_np != PLACEHOLDER_TOKEN_ID) &
+                      (output_token_ids_np < vocab_size))
+        outputs = [
+            row[valid_mask[i]].tolist()
+            for i, row in enumerate(output_token_ids_np)
+        ]
+        return outputs
+
+
+def rejection_sample(
+    # [num_tokens]
+    draft_token_ids: torch.Tensor,
+    # [batch_size]
+    num_draft_tokens: list[int],
+    max_spec_len: int,
+    # [batch_size]
+    cu_num_draft_tokens: torch.Tensor,
+    # [num_tokens, vocab_size]
+    draft_probs: Optional[torch.Tensor],
+    # [num_tokens, vocab_size]
+    target_probs: torch.Tensor,
+    # [batch_size, 1]
+    bonus_token_ids: torch.Tensor,
+    sampling_metadata: SamplingMetadata,
+) -> torch.Tensor:
+    assert draft_token_ids.ndim == 1
+    assert draft_probs is None or draft_probs.ndim == 2
+    assert cu_num_draft_tokens.ndim == 1
+    assert target_probs.ndim == 2
+
+    batch_size = len(num_draft_tokens)
+    num_tokens = draft_token_ids.shape[0]
+    vocab_size = target_probs.shape[-1]
+    device = target_probs.device
+    assert draft_token_ids.is_contiguous()
+    assert draft_probs is None or draft_probs.is_contiguous()
+    assert target_probs.is_contiguous()
+    assert bonus_token_ids.is_contiguous()
+    assert target_probs.shape == (num_tokens, vocab_size)
+
+    # Create output buffer.
+    output_token_ids = torch.empty(
+        (batch_size, max_spec_len + 1),
+        dtype=torch.int32,  # Consistent with SamplerOutput.sampled_token_ids.
+        device=device,
+    )
+    output_token_ids.fill_(PLACEHOLDER_TOKEN_ID)
+
+    if sampling_metadata.all_greedy:
+        is_greedy = None
+    else:
+        is_greedy = sampling_metadata.temperature == GREEDY_TEMPERATURE
+    if not sampling_metadata.all_random:
+        # Rejection sampling for greedy sampling requests.
+        target_argmax = target_probs.argmax(dim=-1)
+        rejection_greedy_sample_kernel[(batch_size, )](
+            output_token_ids,
+            cu_num_draft_tokens,
+            draft_token_ids,
+            target_argmax,
+            bonus_token_ids,
+            is_greedy,
+            max_spec_len,
+            num_warps=1,
+        )
+        if sampling_metadata.all_greedy:
+            return output_token_ids
+
+    # Generate uniform probabilities for rejection sampling.
+    # [num_tokens]
+    uniform_probs = generate_uniform_probs(
+        num_tokens,
+        num_draft_tokens,
+        sampling_metadata.generators,
+        device,
+    )
+
+    # Sample recovered tokens for each position.
+    # [num_tokens]
+    recovered_token_ids = sample_recovered_tokens(
+        max_spec_len,
+        num_draft_tokens,
+        cu_num_draft_tokens,
+        draft_token_ids,
+        draft_probs,
+        target_probs,
+        sampling_metadata,
+        device,
+    )
+
+    # Rejection sampling for random sampling requests.
+    rejection_random_sample_kernel[(batch_size, )](
+        output_token_ids,
+        cu_num_draft_tokens,
+        draft_token_ids,
+        draft_probs,
+        target_probs,
+        bonus_token_ids,
+        recovered_token_ids,
+        uniform_probs,
+        is_greedy,
+        max_spec_len,
+        vocab_size,
+        NO_DRAFT_PROBS=draft_probs is None,
+        num_warps=1,
+    )
+    return output_token_ids
+
+
+def compute_probs(
+    logits: torch.Tensor,  # [num_tokens, vocab_size]
+    cu_num_draft_tokens: torch.Tensor,  # [batch_size]
+    sampling_metadata: SamplingMetadata,
+) -> torch.Tensor:
+    """Compute probability distribution from logits based on sampling metadata.
+
+    This function applies temperature scaling to the logits and converts
+    them to probabilities using softmax. For greedy decoding, it returns
+    the original logits.
+
+    Args:
+        logits: Input logits tensor to be converted to probabilities.
+        cu_num_draft_tokens: Cumulative number of draft tokens.
+        sampling_metadata: Metadata containing sampling parameters such as
+            temperature and whether greedy sampling is used.
+
+    Returns:
+        torch.Tensor: Probability distribution (softmax of scaled logits)
+            if non-greedy sampling is used, otherwise returns the
+            original logits.
+    """
+    assert logits.ndim == 2
+    assert cu_num_draft_tokens.ndim == 1
+    if sampling_metadata.all_greedy:
+        return logits
+
+    num_tokens = logits.shape[0]
+    temperature = expand_batch_to_tokens(
+        sampling_metadata.temperature,
+        cu_num_draft_tokens,
+        num_tokens,
+        replace_from=GREEDY_TEMPERATURE,
+        replace_to=1,
+    )
+    # NOTE(woosuk): Update `logits` in place to avoid allocating a new tensor.
+    logits.div_(temperature.unsqueeze(-1))
+
+    # Get expanded top_k and top_p tensors.
+    top_k = None
+    if sampling_metadata.top_k is not None:
+        top_k = expand_batch_to_tokens(
+            sampling_metadata.top_k,
+            cu_num_draft_tokens,
+            num_tokens,
+        )
+    top_p = None
+    if sampling_metadata.top_p is not None:
+        top_p = expand_batch_to_tokens(
+            sampling_metadata.top_p,
+            cu_num_draft_tokens,
+            num_tokens,
+        )
+
+    # NOTE(woosuk): `apply_top_k_top_p` uses sorting to calculate the mask,
+    # which is slow for large vocab sizes. This may cause performance issues.
+    logits = apply_top_k_top_p(logits, top_k, top_p)
+    output_prob = logits.softmax(dim=-1, dtype=torch.float32)
+    return output_prob
+
+
+def expand_batch_to_tokens(
+    x: torch.Tensor,  # [batch_size]
+    cu_num_tokens: torch.Tensor,  # [batch_size]
+    num_tokens: int,
+    replace_from: int = 0,
+    replace_to: int = 0,
+) -> torch.Tensor:
+    """Expand [batch_size] tensor to [num_tokens] tensor based on the number of
+    tokens per batch in cu_num_tokens.
+
+    For example, if x = [a, b, c] and cu_num_tokens = [2, 5, 6], then
+    num_tokens = 6, and expanded_x = [a, a, b, b, b, c].
+
+    Args:
+        x: [batch_size] tensor to expand.
+        cu_num_tokens: [batch_size] tensor containing the cumulative number of
+            tokens per batch. Each element represents the total number of
+            tokens up to and including that batch.
+        num_tokens: Total number of tokens.
+        replace_from: int = 0
+            Value to be replaced if it is found in x.
+        replace_to: int = 0
+            Value to replace with when replace_from is found.
+    Returns:
+        expanded_x: [num_tokens] tensor.
+    """
+    batch_size = x.shape[0]
+    assert cu_num_tokens.shape[0] == batch_size
+    expanded_x = x.new_empty(num_tokens)
+    expand_kernel[(batch_size, )](
+        expanded_x,
+        x,
+        cu_num_tokens,
+        replace_from,
+        replace_to,
+        MAX_NUM_TOKENS=MAX_SPEC_LEN,  # To avoid recompilation.
+        num_warps=1,
+    )
+    return expanded_x
+
+
+def generate_uniform_probs(
+    num_tokens: int,
+    num_draft_tokens: list[int],
+    generators: dict[int, torch.Generator],
+    device: torch.device,
+) -> torch.Tensor:
+    """
+    Generates a batch of uniform random samples, with optional seeding
+    if available.
+
+    This method creates a tensor of shape `(num_tokens, )` filled
+    with uniform random values in the range [0, 1). If `generators` is provided,
+    the requests with their own seeds will use the provided `torch.Generator`
+    for reproducibility. The samples for the other requests will be generated
+    without a seed.
+
+    Args:
+        num_tokens : int
+            Total number of tokens.
+        num_draft_tokens : List[List[int]]
+            Number of draft tokens per request.
+        generators : Optional[Dict[int, torch.Generator]]
+            A dictionary mapping indices in the batch to
+            `torch.Generator` objects.
+        device : torch.device
+            The device on which to allocate the tensor.
+    Returns:
+        uniform_rand : torch.Tensor
+            A tensor of shape `(num_tokens, )` containing uniform
+            random values in the range [0, 1).
+    """
+    uniform_probs = torch.rand(
+        (num_tokens, ),
+        dtype=torch.float32,
+        device=device,
+    )
+    start_idx = 0
+    for req_idx, n in enumerate(num_draft_tokens):
+        # Do not generate random numbers for requests with no draft tokens.
+        # This can be important for reproducibility.
+        if n == 0:
+            continue
+        end_idx = start_idx + n
+        generator = generators.get(req_idx)
+        if generator is not None:
+            uniform_probs[start_idx:end_idx].uniform_(generator=generator)
+        start_idx = end_idx
+    return uniform_probs
+
+
+def sample_recovered_tokens(
+    max_spec_len: int,
+    num_draft_tokens: list[int],
+    # [batch_size]
+    cu_num_draft_tokens: torch.Tensor,
+    # [num_tokens]
+    draft_token_ids: torch.Tensor,
+    # [num_tokens, vocab_size]
+    draft_probs: Optional[torch.Tensor],
+    # [num_tokens, vocab_size]
+    target_probs: torch.Tensor,
+    sampling_metadata: SamplingMetadata,
+    device: torch.device,
+) -> torch.Tensor:
+    # NOTE(woosuk): Create only one distribution for each request.
+    batch_size = len(num_draft_tokens)
+    vocab_size = target_probs.shape[-1]
+    q = torch.empty(
+        (batch_size, vocab_size),
+        dtype=torch.float32,
+        device=device,
+    )
+    q.exponential_()
+    for i, generator in sampling_metadata.generators.items():
+        # Do not generate random numbers for requests with no draft tokens.
+        # This can be important for reproducibility.
+        if num_draft_tokens[i] > 0:
+            q[i].exponential_(generator=generator)
+
+    recovered_token_ids = torch.empty_like(draft_token_ids)
+    sample_recovered_tokens_kernel[(batch_size, max_spec_len)](
+        recovered_token_ids,
+        cu_num_draft_tokens,
+        draft_token_ids,
+        draft_probs,
+        target_probs,
+        q,
+        vocab_size,
+        triton.next_power_of_2(vocab_size),
+        NO_DRAFT_PROBS=draft_probs is None,
+    )
+    return recovered_token_ids
+
+
+# NOTE(woosuk): Avoid specialization to prevent unnecessary recompilation.
+@triton.jit(do_not_specialize=["max_spec_len"])
+def rejection_greedy_sample_kernel(
+    output_token_ids_ptr,  # [batch_size, max_spec_len + 1]
+    cu_num_draft_tokens_ptr,  # [batch_size]
+    draft_token_ids_ptr,  # [num_tokens]
+    target_argmax_ptr,  # [num_tokens]
+    bonus_token_ids_ptr,  # [batch_size]
+    is_greedy_ptr,  # [batch_size] or None
+    max_spec_len,
+):
+    req_idx = tl.program_id(0)
+    # FIXME(woosuk): Because is_greedy_ptr is not None at profiling run,
+    # re-compilation may happen during runtime when is_greedy_ptr is None.
+    if is_greedy_ptr is None:
+        is_greedy = True
+    else:
+        is_greedy = tl.load(is_greedy_ptr + req_idx)
+    if not is_greedy:
+        # Early exit for non-greedy sampling requests.
+        return
+
+    if req_idx == 0:
+        start_idx = 0
+    else:
+        start_idx = tl.load(cu_num_draft_tokens_ptr + req_idx - 1)
+    end_idx = tl.load(cu_num_draft_tokens_ptr + req_idx)
+    num_draft_tokens = end_idx - start_idx
+
+    rejected = False
+    for pos in range(num_draft_tokens):
+        if not rejected:
+            draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
+            target_argmax_id = tl.load(target_argmax_ptr + start_idx + pos)
+            tl.store(output_token_ids_ptr + req_idx * (max_spec_len + 1) + pos,
+                     target_argmax_id)
+            if draft_token_id != target_argmax_id:
+                # Reject.
+                rejected = True
+
+    if not rejected:
+        # If all tokens are accepted, append the bonus token.
+        bonus_token_id = tl.load(bonus_token_ids_ptr + req_idx)
+        tl.store(
+            output_token_ids_ptr + req_idx * (max_spec_len + 1) +
+            num_draft_tokens, bonus_token_id)
+
+
+# NOTE(woosuk): Avoid specialization to prevent unnecessary recompilation.
+@triton.jit(do_not_specialize=["max_spec_len"])
+def rejection_random_sample_kernel(
+    output_token_ids_ptr,  # [batch_size, max_spec_len + 1]
+    cu_num_draft_tokens_ptr,  # [batch_size]
+    draft_token_ids_ptr,  # [num_tokens]
+    draft_probs_ptr,  # [num_tokens, vocab_size] or None
+    target_probs_ptr,  # [num_tokens, vocab_size]
+    bonus_token_ids_ptr,  # [batch_size]
+    recovered_token_ids_ptr,  # [num_tokens]
+    uniform_probs_ptr,  # [num_tokens]
+    is_greedy_ptr,  # [batch_size]
+    max_spec_len,
+    vocab_size,
+    NO_DRAFT_PROBS: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    is_greedy = tl.load(is_greedy_ptr + req_idx)
+    if is_greedy:
+        # Early exit for greedy sampling requests.
+        return
+
+    if req_idx == 0:
+        start_idx = 0
+    else:
+        start_idx = tl.load(cu_num_draft_tokens_ptr + req_idx - 1)
+    end_idx = tl.load(cu_num_draft_tokens_ptr + req_idx)
+    num_draft_tokens = end_idx - start_idx
+
+    rejected = False
+    for pos in range(num_draft_tokens):
+        if not rejected:
+            draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
+            if NO_DRAFT_PROBS:
+                draft_prob = 1
+            else:
+                draft_prob = tl.load(draft_probs_ptr +
+                                     (start_idx + pos) * vocab_size +
+                                     draft_token_id)
+            target_prob = tl.load(target_probs_ptr +
+                                  (start_idx + pos) * vocab_size +
+                                  draft_token_id)
+            uniform_prob = tl.load(uniform_probs_ptr + start_idx + pos)
+            # NOTE(woosuk): While the draft probability should never be 0,
+            # we check it to avoid NaNs. If it happens to be 0, we reject.
+            if draft_prob > 0 and target_prob / draft_prob >= uniform_prob:
+                # Accept.
+                token_id = draft_token_id
+            else:
+                # Reject. Use recovered token.
+                rejected = True
+                token_id = tl.load(recovered_token_ids_ptr + start_idx + pos)
+            tl.store(output_token_ids_ptr + req_idx * (max_spec_len + 1) + pos,
+                     token_id)
+
+    if not rejected:
+        # If all tokens are accepted, append the bonus token.
+        bonus_token_id = tl.load(bonus_token_ids_ptr + req_idx)
+        tl.store(
+            output_token_ids_ptr + req_idx * (max_spec_len + 1) +
+            num_draft_tokens, bonus_token_id)
+
+
+# NOTE(woosuk): Avoid specialization to prevent unnecessary recompilation.
+@triton.jit(do_not_specialize=["replace_from", "replace_to"])
+def expand_kernel(
+    output_ptr,  # [num_tokens]
+    input_ptr,  # [batch_size]
+    cu_num_tokens_ptr,  # [batch_size]
+    replace_from,
+    replace_to,
+    MAX_NUM_TOKENS: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    if req_idx == 0:  # noqa: SIM108
+        start_idx = 0
+    else:
+        start_idx = tl.load(cu_num_tokens_ptr + req_idx - 1)
+    end_idx = tl.load(cu_num_tokens_ptr + req_idx)
+    num_tokens = end_idx - start_idx
+
+    src_val = tl.load(input_ptr + req_idx)
+    src_val = tl.where(src_val == replace_from, replace_to, src_val)
+    offset = tl.arange(0, MAX_NUM_TOKENS)
+    tl.store(output_ptr + start_idx + offset,
+             src_val,
+             mask=offset < num_tokens)
+
+
+@triton.jit
+def sample_recovered_tokens_kernel(
+    output_token_ids_ptr,  # [num_tokens]
+    cu_num_draft_tokens_ptr,  # [batch_size]
+    draft_token_ids_ptr,  # [num_tokens]
+    draft_probs_ptr,  # [num_tokens, vocab_size] or None
+    target_probs_ptr,  # [num_tokens, vocab_size]
+    q_ptr,  # [batch_size, vocab_size]
+    vocab_size,
+    PADDED_VOCAB_SIZE: tl.constexpr,
+    NO_DRAFT_PROBS: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    if req_idx == 0:
+        start_idx = 0
+    else:
+        start_idx = tl.load(cu_num_draft_tokens_ptr + req_idx - 1)
+    end_idx = tl.load(cu_num_draft_tokens_ptr + req_idx)
+    num_draft_tokens = end_idx - start_idx
+
+    # Early exit for out-of-range positions.
+    pos = tl.program_id(1)
+    if pos >= num_draft_tokens:
+        return
+
+    vocab_offset = tl.arange(0, PADDED_VOCAB_SIZE)
+    if NO_DRAFT_PROBS:
+        draft_token_id = tl.load(draft_token_ids_ptr + start_idx + pos)
+        orig_prob = tl.load(target_probs_ptr + (start_idx + pos) * vocab_size +
+                            draft_token_id)
+        # Temporarily zero out the probability of the draft token.
+        # This is essentially the same as target_prob - draft_prob, except that
+        # n-gram does not have draft_prob. We regard it as 1.
+        tl.store(
+            target_probs_ptr + (start_idx + pos) * vocab_size + draft_token_id,
+            0)
+        prob = tl.load(target_probs_ptr + (start_idx + pos) * vocab_size +
+                       vocab_offset,
+                       mask=vocab_offset < vocab_size,
+                       other=0)
+    else:
+        draft_prob = tl.load(draft_probs_ptr + (start_idx + pos) * vocab_size +
+                             vocab_offset,
+                             mask=vocab_offset < vocab_size,
+                             other=0)
+        target_prob = tl.load(target_probs_ptr +
+                              (start_idx + pos) * vocab_size + vocab_offset,
+                              mask=vocab_offset < vocab_size,
+                              other=0)
+        prob = tl.maximum(target_prob - draft_prob, 0)
+        # NOTE(woosuk): We don't need `prob = prob / tl.sum(prob)` here because
+        # `tl.argmax` will select the maximum value.
+
+    q = tl.load(q_ptr + req_idx * vocab_size + vocab_offset,
+                mask=vocab_offset < vocab_size,
+                other=float("-inf"))
+    recovered_id = tl.argmax(prob / q, axis=-1)
+    tl.store(output_token_ids_ptr + start_idx + pos, recovered_id)
+
+    if NO_DRAFT_PROBS:
+        # Restore the original probability.
+        tl.store(
+            target_probs_ptr + (start_idx + pos) * vocab_size + draft_token_id,
+            orig_prob)

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

@@ -0,0 +1,240 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""A layer that samples the next tokens from the model's outputs."""
+
+import torch
+import torch.nn as nn
+
+from vllm.config import LogprobsMode
+from vllm.utils import is_pin_memory_available
+from vllm.v1.outputs import LogprobsTensors, SamplerOutput
+from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.sample.ops.bad_words import apply_bad_words
+from vllm.v1.sample.ops.logprobs import batched_count_greater_than
+from vllm.v1.sample.ops.penalties import apply_all_penalties
+from vllm.v1.sample.ops.topk_topp_sampler import TopKTopPSampler
+
+_SAMPLING_EPS = 1e-5
+
+
+class Sampler(nn.Module):
+
+    def __init__(self, logprobs_mode: LogprobsMode = "raw_logprobs"):
+        super().__init__()
+        self.topk_topp_sampler = TopKTopPSampler()
+        self.pin_memory = is_pin_memory_available()
+        self.logprobs_mode = logprobs_mode
+
+    def forward(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> SamplerOutput:
+        # NOTE(woosuk): Use the original logits (before any penalties or
+        # temperature scaling) for the top-k logprobs.
+        # This is different from the V0 sampler, which uses the logits that
+        # is used for sampling (after penalties and temperature scaling).
+        # TODO(rob): provide option for logprobs post sampling.
+        # See https://vllm-dev.slack.com/archives/C07UUL8E61Z/p1735907856007919 # noqa: E501
+        num_logprobs = sampling_metadata.max_num_logprobs
+        if num_logprobs is not None:
+            if self.logprobs_mode == "raw_logprobs":
+                raw_logprobs = self.compute_logprobs(logits)
+            elif self.logprobs_mode == "raw_logits":
+                raw_logprobs = logits.clone()
+
+        # Use float32 for the logits.
+        logits = logits.to(torch.float32)
+        # Apply allowed token ids.
+        logits = self.apply_allowed_token_ids(logits, sampling_metadata)
+        # Apply bad words exclusion.
+        logits = self.apply_bad_words(logits, sampling_metadata)
+
+        # Apply logits processors which can impact greedy sampling
+        for processor in (sampling_metadata.logitsprocs.non_argmax_invariant):
+            logits = processor.apply(logits)
+
+        # Apply penalties (e.g., min_tokens, freq_penalties).
+        logits = self.apply_penalties(logits, sampling_metadata)
+
+        # Get the process logprobs or logits.
+        if num_logprobs is not None:
+            if self.logprobs_mode == "processed_logprobs":
+                raw_logprobs = self.compute_logprobs(logits)
+            elif self.logprobs_mode == "processed_logits":
+                raw_logprobs = logits.clone()
+
+        # Sample the next token.
+        sampled = self.sample(logits, sampling_metadata)
+        # Convert sampled token ids to int64 (long) type to ensure compatibility
+        # with subsequent operations that may use these values as indices.
+        # This conversion is necessary because FlashInfer sampling operations
+        # return int32 (while PyTorch argmax and topk return int64).
+        sampled = sampled.long()
+
+        # Gather the logprobs of the topk and sampled token (if requested).
+        # Get logprobs and rank tensors (if requested)
+        logprobs_tensors = None if num_logprobs is None else \
+            self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=sampled)
+
+        # Use int32 to reduce the tensor size.
+        sampled = sampled.to(torch.int32)
+
+        # These are GPU tensors.
+        sampler_output = SamplerOutput(
+            # The sampled tokens are expanded to 2D tensor with shape
+            # [num_requests, 1], where each row represents one generated
+            # token per request.
+            sampled_token_ids=sampled.unsqueeze(-1),
+            logprobs_tensors=logprobs_tensors,
+        )
+        return sampler_output
+
+    def apply_temperature(
+        self,
+        logits: torch.Tensor,
+        temp: torch.Tensor,
+    ) -> torch.Tensor:
+        # Use in-place division to avoid creating a new tensor.
+        return logits.div_(temp.unsqueeze(dim=1))
+
+    def greedy_sample(self, logits: torch.Tensor) -> torch.Tensor:
+        return logits.argmax(dim=-1).view(-1)
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> torch.Tensor:
+        """Sample logits based on sampling metadata.
+
+        The various logits processing functions called in this method
+        may update the logits tensor in-place.
+        """
+
+        assert not (sampling_metadata.all_greedy
+                    and sampling_metadata.all_random)
+        if sampling_metadata.all_random:
+            greedy_sampled = None
+        else:
+            greedy_sampled = self.greedy_sample(logits)
+            if sampling_metadata.all_greedy:
+                return greedy_sampled
+
+        assert sampling_metadata.temperature is not None
+
+        # Apply temperature.
+        logits = self.apply_temperature(logits, sampling_metadata.temperature)
+
+        # Apply logits processors that only apply to random sampling
+        # (argmax invariant)
+        for processor in sampling_metadata.logitsprocs.argmax_invariant:
+            logits = processor.apply(logits)
+
+        # Apply top_k and/or top_p.
+        random_sampled = self.topk_topp_sampler(
+            logits,
+            sampling_metadata.generators,
+            sampling_metadata.top_k,
+            sampling_metadata.top_p,
+        )
+
+        if greedy_sampled is None:
+            return random_sampled
+
+        sampled = torch.where(
+            sampling_metadata.temperature < _SAMPLING_EPS,
+            greedy_sampled,
+            random_sampled,
+            out=greedy_sampled,  # Reuse tensor
+        )
+        return sampled
+
+    def compute_logprobs(self, logits: torch.Tensor) -> torch.Tensor:
+        return logits.log_softmax(dim=-1, dtype=torch.float32)
+
+    def gather_logprobs(
+        self,
+        logprobs: torch.Tensor,
+        num_logprobs: int,
+        token_ids: torch.Tensor,
+    ) -> LogprobsTensors:
+        """
+        Gather logprobs for topk and sampled/prompt token.
+
+        Args:
+          logprobs: (num tokens) x (vocab) tensor
+          num_logprobs: minimum number of logprobs to
+                        retain per token
+          token_ids: prompt tokens (if prompt logprobs)
+                     or sampled tokens (if sampled
+                     logprobs); 1D token ID tensor
+                     with (num tokens) elements
+                     Must be int64.
+
+        Returns:
+          Top-k int indices tensor, (num tokens) x (num_logprobs + 1)
+          Top-k float logprobs tensor, (num tokens) x (num_logprobs + 1)
+          Sampled token rank tensor, (num tokens)
+        """
+        assert token_ids.dtype == torch.int64
+        # Find the topK values.
+        topk_logprobs, topk_indices = torch.topk(logprobs,
+                                                 num_logprobs,
+                                                 dim=-1)
+
+        # Get with the logprob of the prompt or sampled token.
+        token_ids = token_ids.unsqueeze(-1)
+        token_logprobs = logprobs.gather(-1, token_ids)
+
+        # Compute the ranks of the actual token.
+        token_ranks = batched_count_greater_than(logprobs, token_logprobs)
+
+        # Concatenate together with the topk.
+        indices = torch.cat((token_ids, topk_indices), dim=1)
+        logprobs = torch.cat((token_logprobs, topk_logprobs), dim=1)
+
+        # Use int32 to reduce the tensor size.
+        indices = indices.to(torch.int32)
+
+        return LogprobsTensors(indices, logprobs, token_ranks)
+
+    def apply_penalties(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> torch.Tensor:
+        if not sampling_metadata.no_penalties:
+            assert sampling_metadata.prompt_token_ids is not None
+            logits = apply_all_penalties(
+                logits,
+                sampling_metadata.prompt_token_ids,
+                sampling_metadata.presence_penalties,
+                sampling_metadata.frequency_penalties,
+                sampling_metadata.repetition_penalties,
+                sampling_metadata.output_token_ids,
+            )
+        return logits
+
+    def apply_allowed_token_ids(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> torch.Tensor:
+        if sampling_metadata.allowed_token_ids_mask is not None:
+            logits.masked_fill_(sampling_metadata.allowed_token_ids_mask,
+                                float("-inf"))
+        return logits
+
+    def apply_bad_words(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> torch.Tensor:
+        if sampling_metadata.bad_words_token_ids:
+            apply_bad_words(
+                logits,
+                sampling_metadata.bad_words_token_ids,
+                sampling_metadata.output_token_ids,
+            )
+        return logits

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/spec_decode/eagle.py

@@ -0,0 +1,722 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import ast
+from dataclasses import replace
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from vllm.attention.layer import Attention
+from vllm.config import (CompilationLevel, VllmConfig,
+                         get_layers_from_vllm_config)
+from vllm.distributed.parallel_state import get_pp_group
+from vllm.forward_context import set_forward_context
+from vllm.logger import init_logger
+from vllm.model_executor.model_loader import get_model
+from vllm.model_executor.models import supports_multimodal
+from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
+from vllm.platforms import current_platform
+from vllm.utils import is_pin_memory_available
+from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
+from vllm.v1.attention.backends.rocm_aiter_fa import (
+    AiterFlashAttentionMetadata)
+from vllm.v1.attention.backends.tree_attn import (TreeAttentionMetadata,
+                                                  TreeAttentionMetadataBuilder)
+from vllm.v1.attention.backends.triton_attn import TritonAttentionMetadata
+from vllm.v1.attention.backends.utils import CommonAttentionMetadata
+from vllm.v1.kv_cache_interface import KVCacheConfig
+from vllm.v1.sample.metadata import SamplingMetadata
+
+logger = init_logger(__name__)
+
+PADDING_SLOT_ID = -1
+
+
+class EagleProposer:
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        device: torch.device,
+        runner=None,
+    ):
+        self.vllm_config = vllm_config
+        self.speculative_config = vllm_config.speculative_config
+        self.draft_model_config = self.speculative_config.draft_model_config
+        self.method = self.speculative_config.method
+
+        self.runner = runner
+        self.dtype = vllm_config.model_config.dtype
+        self.max_model_len = vllm_config.model_config.max_model_len
+        self.block_size = vllm_config.cache_config.block_size
+        self.num_speculative_tokens = (
+            self.speculative_config.num_speculative_tokens)
+        self.max_num_tokens = (
+            vllm_config.scheduler_config.max_num_batched_tokens)
+        self.token_arange_np = np.arange(self.max_num_tokens)
+        # We need to get the hidden size from the draft model config because
+        # the draft model's hidden size can be different from the target model's
+        # hidden size (e.g., Llama 3.3 70B).
+        self.hidden_size = self.draft_model_config.get_hidden_size()
+
+        self.is_multimodal_model = vllm_config.model_config \
+            .is_multimodal_model
+
+        self.use_cuda_graph = (self.vllm_config.compilation_config.level
+                               == CompilationLevel.PIECEWISE and
+                               not self.vllm_config.model_config.enforce_eager)
+        self.cudagraph_batch_sizes = list(
+            reversed(
+                self.vllm_config.compilation_config.cudagraph_capture_sizes))
+
+        # persistent buffers for cuda graph
+        self.input_ids = torch.zeros(self.max_num_tokens,
+                                     dtype=torch.int32,
+                                     device=device)
+        self.positions = torch.zeros(self.max_num_tokens,
+                                     dtype=torch.int64,
+                                     device=device)
+        self.hidden_states = torch.zeros(
+            (self.max_num_tokens, self.hidden_size),
+            dtype=self.dtype,
+            device=device)
+
+        max_batch_size = vllm_config.scheduler_config.max_num_seqs
+        self.arange = torch.arange(
+            # We need +1 here because the arange is used to set query_start_loc,
+            # which has one more element than batch_size.
+            max_batch_size + 1,
+            device=device,
+            dtype=torch.int32,
+        )
+
+        self.inputs_embeds = torch.zeros(
+            (self.max_num_tokens, self.hidden_size),
+            dtype=self.dtype,
+            device=device)
+
+        # Parse the speculative token tree.
+        spec_token_tree = self.speculative_config.speculative_token_tree
+        self.tree_choices: list[tuple[int,
+                                      ...]] = ast.literal_eval(spec_token_tree)
+        tree_depth = len(self.tree_choices[-1])
+        # Precompute per-level properties of the tree.
+        num_drafts_per_level = [0] * tree_depth
+        for node in self.tree_choices:
+            num_drafts_per_level[len(node) - 1] += 1
+        self.cu_drafts_per_level = [num_drafts_per_level[0]]
+        self.child_drafts_per_level = [num_drafts_per_level[0]]
+        for level in range(1, tree_depth):
+            self.cu_drafts_per_level.append(self.cu_drafts_per_level[-1] +
+                                            num_drafts_per_level[level])
+            self.child_drafts_per_level.append(num_drafts_per_level[level] //
+                                               num_drafts_per_level[level - 1])
+        # Precompute draft position offsets in flattened tree.
+        self.tree_draft_pos_offsets = torch.arange(
+            1,
+            len(self.tree_choices) + 1,
+            device=device,
+            dtype=torch.int32,
+        ).repeat(max_batch_size, 1)
+
+    def propose(
+        self,
+        # [num_tokens]
+        target_token_ids: torch.Tensor,
+        # [num_tokens]
+        target_positions: torch.Tensor,
+        # [num_tokens, hidden_size]
+        target_hidden_states: torch.Tensor,
+        # [batch_size]
+        next_token_ids: torch.Tensor,
+        common_attn_metadata: CommonAttentionMetadata,
+        sampling_metadata: SamplingMetadata,
+        mm_embeds: Optional[list[torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        num_tokens = target_token_ids.shape[0]
+        batch_size = next_token_ids.shape[0]
+        last_token_indices = common_attn_metadata.query_start_loc[1:] - 1
+
+        if self.method == "eagle3":
+            assert isinstance(self.model, Eagle3LlamaForCausalLM)
+            target_hidden_states = self.model.combine_hidden_states(
+                target_hidden_states)
+            assert target_hidden_states.shape[-1] == self.hidden_size
+
+        # Shift the input ids by one token.
+        # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
+        self.input_ids[:num_tokens - 1] = target_token_ids[1:]
+        # Replace the last token with the next token.
+        # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
+        self.input_ids[last_token_indices] = next_token_ids
+
+        assert self.runner is not None
+
+        # FIXME: need to consider multiple kv_cache_groups
+        attn_metadata = self.runner.attn_groups[0][0].metadata_builder\
+            .build_for_drafting(common_attn_metadata=common_attn_metadata,
+                                draft_index=0)
+
+        # At this moment, we assume all eagle layers belong to the same KV
+        # cache group, thus using the same attention metadata.
+        per_layer_attn_metadata = {}
+        for layer_name in self.attn_layer_names:
+            per_layer_attn_metadata[layer_name] = attn_metadata
+        if self.use_cuda_graph and \
+            num_tokens <= self.cudagraph_batch_sizes[-1]:
+            num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
+        else:
+            num_input_tokens = num_tokens
+        # copy inputs to buffer for cudagraph
+        self.positions[:num_tokens] = target_positions
+        self.hidden_states[:num_tokens] = target_hidden_states
+        if self.is_multimodal_model:
+            input_ids = self.input_ids[:num_tokens]
+            inputs_embeds = self.model.get_input_embeddings(
+                input_ids,
+                multimodal_embeddings=mm_embeds or None,
+            )
+            self.inputs_embeds[:num_tokens] = inputs_embeds
+            inputs_embeds = self.inputs_embeds[:num_input_tokens]
+            input_ids = None
+        else:
+            inputs_embeds = None
+            input_ids = self.input_ids[:num_input_tokens]
+
+        with set_forward_context(per_layer_attn_metadata,
+                                 self.vllm_config,
+                                 num_tokens=num_input_tokens):
+            ret_hidden_states = self.model(
+                input_ids=input_ids,
+                positions=self.positions[:num_input_tokens],
+                hidden_states=self.hidden_states[:num_input_tokens],
+                inputs_embeds=inputs_embeds,
+            )
+            if self.method == "deepseek_mtp":
+                last_hidden_states = ret_hidden_states
+            else:
+                last_hidden_states, hidden_states = ret_hidden_states
+        sample_hidden_states = last_hidden_states[last_token_indices]
+        logits = self.model.compute_logits(sample_hidden_states, None)
+        positions = target_positions[last_token_indices]
+        hidden_states = hidden_states[last_token_indices]
+
+        if isinstance(attn_metadata, TreeAttentionMetadata):
+            # Draft using tree attention.
+            draft_token_ids_list = self.propose_tree(
+                batch_size=batch_size,
+                logits=logits,
+                positions=positions,
+                hidden_states=hidden_states,
+                common_attn_metadata=common_attn_metadata,
+            )
+            # [batch_size, num_tree_tokens]
+            return torch.cat(draft_token_ids_list, dim=1)
+
+        draft_token_ids = logits.argmax(dim=-1)
+
+        # Early exit if there is only one draft token to be generated.
+        if self.num_speculative_tokens == 1:
+            # [batch_size, 1]
+            return draft_token_ids.view(-1, 1)
+
+        # TODO: Currently, MTP module released by deepseek only has
+        # one layer. Adapt this code to support multiple layers once
+        # there's a multi-layer MTP module.
+
+        # On ROCm, both AiterFlashAttention and TritonAttention
+        # support multi-token eagle spec decode.
+        if current_platform.is_rocm():
+            assert isinstance(
+                attn_metadata,
+                (TritonAttentionMetadata, AiterFlashAttentionMetadata,
+                 FlashAttentionMetadata))
+        else:
+            # Currently, only FlashAttention supports multi-token eagle spec
+            # decode. This is because the code below makes assumptions about
+            # attn_metadata attributes available.
+            assert isinstance(attn_metadata, FlashAttentionMetadata)
+
+        # Generate the remaining draft tokens.
+        draft_token_ids_list = [draft_token_ids]
+
+        if self.use_cuda_graph and \
+            batch_size <= self.cudagraph_batch_sizes[-1]:
+            input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
+        else:
+            input_batch_size = batch_size
+        attn_metadata.num_actual_tokens = batch_size
+        attn_metadata.max_query_len = 1
+        attn_metadata.query_start_loc = self.arange[:batch_size + 1]
+        for _ in range(self.num_speculative_tokens - 1):
+            # Update the inputs.
+            # cast to int32 is crucial when eagle model is compiled.
+            # tensor.argmax() returns int64 by default.
+            input_ids = draft_token_ids_list[-1].int()
+            positions += 1
+
+            # NOTE(woosuk): We should handle the case where the draft model
+            # generates tokens beyond the max model length. Since it is complex
+            # to remove such requests from the batch, we keep them in the batch
+            # but adjust the position ids and slot mappings to avoid the
+            # out-of-range access during the model execution. The draft tokens
+            # generated with this adjustment should be ignored.
+            exceeds_max_model_len = positions >= self.max_model_len
+            # Mask out the position ids that exceed the max model length.
+            # Otherwise, we may get out-of-range error in RoPE.
+            clamped_positions = torch.where(exceeds_max_model_len, 0,
+                                            positions)
+
+            # Increment the sequence lengths.
+            attn_metadata.max_seq_len += 1
+            attn_metadata.seq_lens += 1
+            # Consider max model length.
+            attn_metadata.max_seq_len = min(attn_metadata.max_seq_len,
+                                            self.max_model_len)
+            # For the requests that exceed the max model length, we set the
+            # sequence length to 1 to minimize their overheads in attention.
+            attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len, 1)
+
+            # Compute the slot mapping.
+            block_numbers = clamped_positions // self.block_size
+            block_ids = attn_metadata.block_table.gather(
+                dim=1, index=block_numbers.view(-1, 1))
+            block_ids = block_ids.view(-1)
+            attn_metadata.slot_mapping = (block_ids * self.block_size +
+                                          clamped_positions % self.block_size)
+            # Mask out the slot mappings that exceed the max model length.
+            # Otherwise, the KV cache will be inadvertently updated with the
+            # padding tokens.
+            attn_metadata.slot_mapping.masked_fill_(exceeds_max_model_len,
+                                                    PADDING_SLOT_ID)
+
+            # copy inputs to buffer for cudagraph
+            self.input_ids[:batch_size] = input_ids
+            self.positions[:batch_size] = clamped_positions
+            self.hidden_states[:batch_size] = hidden_states
+            if self.is_multimodal_model:
+                inputs_embeds = self.model.get_input_embeddings(input_ids)
+                self.inputs_embeds[:batch_size] = inputs_embeds
+                inputs_embeds = self.inputs_embeds[:input_batch_size]
+                input_ids = None
+            else:
+                inputs_embeds = None
+                input_ids = self.input_ids[:input_batch_size]
+
+            # Run the model.
+            with set_forward_context(per_layer_attn_metadata,
+                                     self.vllm_config,
+                                     num_tokens=input_batch_size):
+                last_hidden_states, hidden_states = self.model(
+                    input_ids=input_ids,
+                    positions=self.positions[:input_batch_size],
+                    hidden_states=self.hidden_states[:input_batch_size],
+                    inputs_embeds=inputs_embeds,
+                )
+            hidden_states = hidden_states[:batch_size]
+            logits = self.model.compute_logits(last_hidden_states[:batch_size],
+                                               None)
+            draft_token_ids = logits.argmax(dim=-1)
+            draft_token_ids_list.append(draft_token_ids)
+
+        # [batch_size, num_speculative_tokens]
+        draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
+        return draft_token_ids
+
+    def propose_tree(
+        self,
+        batch_size: int,
+        # [num_tokens, vocab_size]
+        logits: torch.Tensor,
+        # [num_tokens]
+        positions: torch.Tensor,
+        # [num_tokens, hidden_size]
+        hidden_states: torch.Tensor,
+        common_attn_metadata: CommonAttentionMetadata,
+    ) -> list[torch.Tensor]:
+        tree_attn_metadata_builder = \
+            self.runner.attn_groups[0][0].metadata_builder
+        assert isinstance(tree_attn_metadata_builder,
+                          TreeAttentionMetadataBuilder)
+
+        total_num_drafts = self.cu_drafts_per_level[0]
+        level_num_drafts = total_num_drafts
+        # Sample a draft token for each child at the tree root level.
+        num_children = self.child_drafts_per_level[0]
+        if num_children == 1:
+            draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
+        else:
+            draft_token_ids = torch.topk(logits, num_children,
+                                         dim=-1).indices.view(batch_size, -1)
+        draft_token_ids_list = [draft_token_ids]
+        draft_hidden_states = hidden_states.view(batch_size, 1, -1)
+
+        # Initialize empty tensors for concatenation with the level outputs.
+        tree_input_ids = torch.empty(0,
+                                     device=self.input_ids.device,
+                                     dtype=self.input_ids.dtype)
+        tree_positions = torch.empty(0,
+                                     device=self.positions.device,
+                                     dtype=self.positions.dtype)
+        tree_hidden_states = torch.empty(0,
+                                         device=self.hidden_states.device,
+                                         dtype=self.hidden_states.dtype)
+        # Precompute the draft token positions.
+        flattened_draft_positions = (
+            positions.view(batch_size, -1) +
+            self.tree_draft_pos_offsets[:batch_size, :])
+        tree_depth = len(self.cu_drafts_per_level)
+        for level in range(tree_depth - 1):
+            # Get draft positions for RoPE.
+            draft_positions = positions + (level + 1)
+            exceeds_max_model_len = (positions +
+                                     total_num_drafts) >= self.max_model_len
+            # Mask out the position ids that exceed the max model length.
+            # Otherwise, we may get out-of-range error in RoPE.
+            draft_positions = torch.where(
+                exceeds_max_model_len,
+                0,
+                draft_positions,
+            ).view(batch_size, -1)
+
+            if level_num_drafts > 1:
+                # Repeat the positions for each draft at this level.
+                draft_positions = draft_positions.repeat_interleave(
+                    level_num_drafts, dim=1)
+
+            if num_children > 1:
+                # Repeat draft hidden states for each child.
+                draft_hidden_states = draft_hidden_states.repeat_interleave(
+                    num_children, dim=1)
+
+            # Concatenate the draft tokens, positions, and hidden states.
+            tree_input_ids = torch.cat([tree_input_ids, draft_token_ids],
+                                       dim=1)
+            tree_positions = torch.cat([tree_positions, draft_positions],
+                                       dim=1)
+            tree_hidden_states = torch.cat(
+                [tree_hidden_states, draft_hidden_states], dim=1)
+
+            # Build new attention metadata for the next level of drafts.
+            # This is necessary to support tree attention.
+            query_len = total_num_drafts
+            common_attn_metadata = replace(
+                common_attn_metadata,
+                query_start_loc=query_len * self.arange[:batch_size + 1],
+                seq_lens=common_attn_metadata.seq_lens + level_num_drafts,
+                num_actual_tokens=batch_size * query_len,
+                max_query_len=query_len,
+            )
+            attn_metadata = tree_attn_metadata_builder.build_for_drafting(
+                common_attn_metadata=common_attn_metadata,
+                draft_index=level + 1,
+            )
+
+            # Apply new attention metadata to all layers.
+            per_layer_attn_metadata = {}
+            for layer_name in self.attn_layer_names:
+                per_layer_attn_metadata[layer_name] = attn_metadata
+
+            # Consider max model length.
+            attn_metadata.max_seq_len = min(attn_metadata.max_seq_len,
+                                            self.max_model_len)
+            # For the requests that exceed the max model length, we set the
+            # sequence length to 1 to minimize their overheads in attention.
+            attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len, 1)
+
+            # Compute the slot mapping.
+            query_positions = flattened_draft_positions[:, level:level +
+                                                        query_len]
+            block_numbers = query_positions // self.block_size
+            block_ids = attn_metadata.block_table.gather(dim=1,
+                                                         index=block_numbers)
+            slot_mapping = (block_ids * self.block_size +
+                            query_positions % self.block_size)
+            # Mask out the slot mappings that exceed the max model length.
+            # Otherwise, the KV cache will be inadvertently updated with the
+            # padding tokens.
+            slot_mapping[exceeds_max_model_len] = PADDING_SLOT_ID
+            attn_metadata.slot_mapping = slot_mapping.view(-1)
+
+            # Copy inputs to buffer for cudagraph.
+            num_tokens = attn_metadata.num_actual_tokens
+            input_ids = tree_input_ids.view(-1)
+            self.input_ids[:num_tokens] = input_ids
+            self.positions[:num_tokens] = tree_positions.view(-1)
+            self.hidden_states[:num_tokens] = tree_hidden_states.view(
+                num_tokens, -1)
+
+            if self.use_cuda_graph and \
+                num_tokens <= self.cudagraph_batch_sizes[-1]:
+                num_input_tokens = self.vllm_config.pad_for_cudagraph(
+                    num_tokens)
+            else:
+                num_input_tokens = num_tokens
+            # Run the model.
+            with set_forward_context(per_layer_attn_metadata,
+                                     self.vllm_config,
+                                     num_tokens=num_input_tokens):
+                last_hidden_states, hidden_states = self.model(
+                    input_ids=self.input_ids[:num_input_tokens],
+                    positions=self.positions[:num_input_tokens],
+                    hidden_states=self.hidden_states[:num_input_tokens],
+                    inputs_embeds=None,
+                )
+
+            # Get the output hidden states for the draft tokens.
+            draft_hidden_states = hidden_states[:num_tokens].view(
+                batch_size, query_len, -1)[:, -level_num_drafts:]
+            draft_last_hidden_states = last_hidden_states[:num_tokens].view(
+                batch_size, query_len, -1)[:, -level_num_drafts:]
+
+            # Get the output logits for the draft tokens.
+            logits = self.model.compute_logits(
+                draft_last_hidden_states.reshape(batch_size * level_num_drafts,
+                                                 -1),
+                None,
+            )
+
+            # Sample a draft token for each child at the next tree level.
+            num_children = self.child_drafts_per_level[level + 1]
+            if num_children == 1:
+                draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
+            else:
+                draft_token_ids = torch.topk(logits, num_children,
+                                             dim=-1).indices.view(
+                                                 batch_size, -1)
+            draft_token_ids_list.append(draft_token_ids)
+
+            # Update the # drafts counters for the next tree level.
+            level_num_drafts = self.cu_drafts_per_level[level +
+                                                        1] - total_num_drafts
+            total_num_drafts = self.cu_drafts_per_level[level + 1]
+        return draft_token_ids_list
+
+    def prepare_inputs(
+        self,
+        common_attn_metadata: CommonAttentionMetadata,
+        # [batch_size]
+        num_rejected_tokens: torch.Tensor
+    ) -> tuple[CommonAttentionMetadata, torch.Tensor]:
+        """
+        This function is used to prepare the inputs for the spec decode.
+        It updates to the common_attn_metadata to account for the rejected
+        tokens (and newly sampled tokens). It also returns the token indices
+        of the tokens that should be fed to the speculator.
+        """
+        # E.g.
+        #  common_attn_metadata.query_start_loc{_cpu}:
+        #         [0, q1, q1 + q2, q1 + q2 + q3]
+        #  common_attn_metadata.seq_lens{_cpu}: [s1, s2, s3]
+        #  num_rejected_tokens: [n1, n2, n3]
+        # This function computes the intermediate values:
+        #  num_tokens_per_req: [q1 - n1, q2 - n2, q3 - n3]
+        # And returns:
+        #  common_attn_metadata.query_start_loc{_cpu}:
+        #         [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
+        #  common_attn_metadata.seq_lens{_cpu}:
+        #         [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
+        #  token_indices: [0, 1, ..., q1 - n1 - 1,
+        #                  q1, q1 + 1, ..., q1 + q2 - n2 - 1,
+        #                  q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
+
+        device = common_attn_metadata.query_start_loc.device
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+        new_seq_lens_cpu = common_attn_metadata.seq_lens_cpu \
+            - num_rejected_tokens
+
+        # [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
+        new_query_len_per_req = (query_start_loc_cpu[1:] -
+                                 query_start_loc_cpu[:-1])
+        # [q1, q2, q3] -> [q1 - n1, q2 - n2, q3 - n3]
+        new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens
+        new_num_tokens_per_req_np = new_num_tokens_per_req.numpy()
+
+        # [q1 - n1, q2 - n2, q3 - n3] ->
+        # [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
+        new_query_start_loc_cpu = torch.zeros(
+            query_start_loc_cpu.shape,
+            dtype=torch.int32,
+            pin_memory=is_pin_memory_available())
+        new_query_start_loc_np = new_query_start_loc_cpu.numpy()
+        np.cumsum(new_num_tokens_per_req_np, out=new_query_start_loc_np[1:])
+
+        total_num_tokens = new_query_start_loc_np[-1]
+        # Example assuming num_tokens_per_req_np = [2, 4, 3]
+        # this implies that `new_query_start_locs` is:
+        # [0, 2, 6, 9] ->
+        # [0, 0, 2, 2, 2, 2, 6, 6, 6]
+        #  _r1_  ____r2____  ___r3__
+        new_query_start_locs_expanded = np.repeat(new_query_start_loc_np[:-1],
+                                                  new_num_tokens_per_req_np)
+        # [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
+        # [0, 1, 0, 1, 2, 3, 0, 1, 2]
+        #  _r1_  ____r2____  ___r3__
+        token_offests = self.token_arange_np[:total_num_tokens] \
+            - new_query_start_locs_expanded
+
+        # Expand starting positions to match token pattern
+        # [0, q1, q1 + q2] ->
+        # [0, 0, q1, q1, q1, q1, q1 + q2, q1 + q2, q1 + q2]
+        #  _r1_  _____r2_______  ___________r3____________
+        old_query_start_locs_expanded = np.repeat(
+            query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np)
+        # Final token indices are:
+        # [0, 1,                                   // req 1
+        #  q1 + 0, q1 + 1, q1 + 2, q1 + 3,         // req 2
+        #  q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2]  // req 3
+        token_indices_np = token_offests + old_query_start_locs_expanded
+        token_indices = torch.from_numpy(token_indices_np).to(
+            device, non_blocking=True)
+
+        spec_common_attn_metadata = CommonAttentionMetadata(
+            query_start_loc=new_query_start_loc_cpu.to(device,
+                                                       non_blocking=True),
+            seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
+            query_start_loc_cpu=new_query_start_loc_cpu,
+            seq_lens_cpu=new_seq_lens_cpu,
+            num_computed_tokens_cpu=common_attn_metadata.
+            num_computed_tokens_cpu,
+            num_reqs=common_attn_metadata.num_reqs,
+            num_actual_tokens=total_num_tokens,
+            max_query_len=new_query_len_per_req.max().item(),
+            block_table_tensor=common_attn_metadata.block_table_tensor,
+            slot_mapping=common_attn_metadata.slot_mapping[token_indices],
+            causal=True,
+        )
+
+        return spec_common_attn_metadata, token_indices
+
+    def load_model(self, target_model: nn.Module) -> None:
+        draft_model_config = \
+            self.vllm_config.speculative_config.draft_model_config
+        target_attn_layer_names = set(
+            get_layers_from_vllm_config(self.vllm_config, Attention).keys())
+
+        from vllm.compilation.backends import set_model_tag
+        with set_model_tag("eagle_head"):
+            self.model = get_model(vllm_config=self.vllm_config,
+                                   model_config=draft_model_config)
+
+        draft_attn_layer_names = (
+            get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
+            target_attn_layer_names)
+
+        self.attn_layer_names = list(draft_attn_layer_names)
+
+        if supports_multimodal(target_model):
+            # handle multimodality
+            self.model.config.image_token_index = (
+                target_model.config.image_token_index)
+            target_language_model = target_model.get_language_model()
+        else:
+            target_language_model = target_model
+        # share embed_tokens with the target model if needed
+        if get_pp_group().world_size == 1 \
+            and self.model.model.embed_tokens.weight.shape \
+                == target_language_model.model.embed_tokens.weight.shape:
+            logger.info(
+                "Assuming the EAGLE head shares the same vocab embedding" \
+                " with the target model."
+            )
+            del self.model.model.embed_tokens
+            self.model.model.embed_tokens = (
+                target_language_model.model.embed_tokens)
+        else:
+            logger.info(
+                "The EAGLE head's vocab embedding will be loaded separately" \
+                " from the target model."
+            )
+
+        # share lm_head with the target model if needed
+        # some model definition do not define lm_head explicitly
+        # and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
+        if self.vllm_config.speculative_config.method != "eagle3" and \
+                hasattr(target_language_model, "lm_head"):
+            logger.info("Loading EAGLE LM head weights from the target model.")
+            self.model.lm_head = target_language_model.lm_head
+
+    @torch.inference_mode()
+    def dummy_run(
+        self,
+        num_tokens: int,
+    ) -> None:
+        with set_forward_context(None, self.vllm_config,
+                                 num_tokens=num_tokens):
+            if self.is_multimodal_model:
+                input_ids = None
+                inputs_embeds = self.inputs_embeds[:num_tokens]
+            else:
+                input_ids = self.input_ids[:num_tokens]
+                inputs_embeds = None
+
+            self.model(
+                input_ids=input_ids,
+                positions=self.positions[:num_tokens],
+                hidden_states=self.hidden_states[:num_tokens],
+                inputs_embeds=inputs_embeds,
+            )
+
+    def validate_same_kv_cache_group(self,
+                                     kv_cache_config: KVCacheConfig) -> None:
+        """
+        Validate that all eagle layers belong to the same KVCacheGroup.
+        Need this assumption to ensure all eagle layers can use the
+        same AttentionMetadata.
+        May extend to multiple AttentionMetadata in the future.
+        """
+        kv_cache_groups: dict[str, int] = {}
+        for id, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
+            for layer_name in kv_cache_group.layer_names:
+                kv_cache_groups[layer_name] = id
+        assert len(
+            set([
+                kv_cache_groups[layer_name]
+                for layer_name in self.attn_layer_names
+            ])
+        ) == 1, "All eagle layers should belong to the same kv cache group"
+
+
+# NOTE(woosuk): Currently, the below code is not used and we always use argmax
+# to sample the draft tokens. We will use this after we find a way to manage
+# the draft prob tensor.
+# Refer to https://github.com/vllm-project/vllm/pull/16899 for the details.
+# FIXME(woosuk): The logic here is duplicated with the main sampling code.
+# We should refactor this to reuse the same sampling implementation.
+def compute_probs_and_sample_next_token(
+    logits: torch.Tensor,
+    sampling_metadata: SamplingMetadata,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    if sampling_metadata.all_greedy:
+        # For greedy requests, draft_probs is not used in rejection sampling.
+        # Therefore, we can just return the logits.
+        probs = logits
+        next_token_ids = logits.argmax(dim=-1)
+        return next_token_ids, probs
+
+    is_greedy = sampling_metadata.temperature == -1
+    temperature = torch.where(is_greedy, 1.0, sampling_metadata.temperature)
+    logits.div_(temperature.view(-1, 1))
+    probs = logits.softmax(dim=-1, dtype=torch.float32)
+
+    # NOTE(woosuk): Currently, we ignore most of the sampling parameters in
+    # generating the draft tokens. We only use the temperature. While this
+    # could degrade the acceptance rate, it does not affect the distribution
+    # of the generated tokens after rejection sampling.
+
+    # TODO(woosuk): Consider seeds.
+    q = torch.empty_like(probs)
+    q.exponential_()
+    # NOTE(woosuk): We shouldn't use `probs.div_(q)` because the draft_probs
+    # will be used later for rejection sampling.
+    next_token_ids = probs.div(q).argmax(dim=-1).view(-1)
+    if not sampling_metadata.all_random:
+        greedy_token_ids = probs.argmax(dim=-1)
+        next_token_ids = torch.where(
+            is_greedy,
+            greedy_token_ids,
+            next_token_ids,
+        )
+    return next_token_ids, probs

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/spec_decode/medusa.py

@@ -0,0 +1,64 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+import torch.nn as nn
+
+from vllm.config import VllmConfig
+from vllm.forward_context import set_forward_context
+from vllm.logger import init_logger
+from vllm.model_executor.model_loader import get_model
+from vllm.v1.sample.metadata import SamplingMetadata
+
+# Initialize logger
+logger = init_logger(__name__)
+
+
+class MedusaProposer:
+    """
+    Medusa proposer class for generating token sequences
+    """
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        device: torch.device,
+    ):
+        # Save config parameters
+        self.vllm_config = vllm_config
+        self.device = device
+        self.max_num_tokens = (
+            vllm_config.scheduler_config.max_num_batched_tokens)
+        self.hidden_size = vllm_config.speculative_config.\
+            draft_model_config.get_hidden_size(
+        )
+        self.dtype = vllm_config.model_config.dtype
+
+    def propose(
+        self,
+        target_hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> torch.Tensor:
+        # Generate blocks and compute logits
+        blocks = self.model(target_hidden_states)
+        logits = self.model.compute_logits(blocks, None)
+
+        # Get draft tokens and transpose the result
+        draft_tokens = [logit.argmax(dim=-1).tolist() for logit in logits]
+        return [list(row) for row in zip(*draft_tokens)]
+
+    def load_model(self, target_model: nn.Module) -> None:
+        from vllm.compilation.backends import set_model_tag
+        with set_model_tag("medusa_head"):
+            self.model = get_model(vllm_config=self.vllm_config,
+                                   model_config=self.vllm_config.
+                                   speculative_config.draft_model_config)
+
+    @torch.inference_mode()
+    def dummy_run(self, num_tokens: int) -> None:
+        hidden_states = torch.zeros((self.max_num_tokens, self.hidden_size),
+                                    dtype=self.dtype,
+                                    device=self.device)
+        with set_forward_context(None, self.vllm_config,
+                                 num_tokens=num_tokens):
+            self.model(hidden_states)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/spec_decode/metadata.py

@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+
+
+@dataclass
+class SpecDecodeMetadata:
+
+    # [num_tokens]
+    draft_token_ids: torch.Tensor
+    # [batch_size]
+    num_draft_tokens: list[int]
+    # [batch_size]
+    cu_num_draft_tokens: torch.Tensor
+    # [num_tokens]
+    target_logits_indices: torch.Tensor
+    # [batch_size]
+    bonus_logits_indices: torch.Tensor
+    # [num_tokens + batch_size]
+    logits_indices: torch.Tensor
+
+    def __post_init__(self):
+        self.max_spec_len = max(self.num_draft_tokens)
+
+    @classmethod
+    def make_dummy(
+        cls,
+        draft_token_ids: list[list[int]],
+        device: torch.device,
+    ) -> "SpecDecodeMetadata":
+        batch_size = len(draft_token_ids)
+        num_draft_tokens = [len(ids) for ids in draft_token_ids]
+        flattened_draft_token_ids = sum(draft_token_ids, [])
+        num_tokens = len(flattened_draft_token_ids)
+
+        draft_token_ids_tensor = torch.tensor(flattened_draft_token_ids,
+                                              dtype=torch.int32,
+                                              device=device)
+        cu_num_draft_tokens = np.cumsum(num_draft_tokens, dtype=np.int32)
+        cu_num_draft_tokens_tensor = torch.from_numpy(cu_num_draft_tokens).to(
+            device)
+
+        target_logits_indices = torch.zeros(num_tokens,
+                                            dtype=torch.int32,
+                                            device=device)
+        bonus_logits_indices = torch.zeros(batch_size,
+                                           dtype=torch.int32,
+                                           device=device)
+        logits_indices = torch.zeros(num_tokens + batch_size,
+                                     dtype=torch.int32,
+                                     device=device)
+        return cls(
+            draft_token_ids=draft_token_ids_tensor,
+            num_draft_tokens=num_draft_tokens,
+            cu_num_draft_tokens=cu_num_draft_tokens_tensor,
+            target_logits_indices=target_logits_indices,
+            bonus_logits_indices=bonus_logits_indices,
+            logits_indices=logits_indices,
+        )

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/spec_decode/metrics.py

@@ -0,0 +1,178 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass, field
+from typing import Optional
+
+import numpy as np
+import prometheus_client
+
+from vllm.config import SpeculativeConfig
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+@dataclass
+class SpecDecodingStats:
+    """Per-step iteration decoding stats from scheduler.
+
+    Each scheduler step, statistics on spec decoding performance are
+    aggregated across requests by the scheduler and returned to the
+    frontend in EngineCoreOutputs->SchedulerStats.
+    """
+
+    num_spec_tokens: int
+    num_drafts: int = 0
+    num_draft_tokens: int = 0
+    num_accepted_tokens: int = 0
+    num_accepted_tokens_per_pos: list[int] = field(default_factory=list)
+
+    @classmethod
+    def new(cls, num_spec_tokens: int) -> "SpecDecodingStats":
+        return cls(num_spec_tokens=num_spec_tokens,
+                   num_accepted_tokens_per_pos=[0] * num_spec_tokens)
+
+    def observe_draft(self, num_draft_tokens: int, num_accepted_tokens: int):
+        self.num_drafts += 1
+        self.num_draft_tokens += num_draft_tokens
+        self.num_accepted_tokens += num_accepted_tokens
+        assert num_accepted_tokens <= self.num_spec_tokens
+        for i in range(num_accepted_tokens):
+            self.num_accepted_tokens_per_pos[i] += 1
+
+
+class SpecDecodingLogging:
+    """Aggregate and log spec decoding metrics.
+
+    LoggingStatLogger aggregates per-iteration metrics over a set
+    time interval using observe() and then logs them using log()
+    before resetting to zero.
+    """
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.num_drafts: list[int] = []
+        self.num_draft_tokens: list[int] = []
+        self.num_accepted_tokens: list[int] = []
+        self.accepted_tokens_per_pos_lists: list[list[int]] = []
+
+    def observe(self, spec_decoding_stats: SpecDecodingStats):
+        self.num_drafts.append(spec_decoding_stats.num_drafts)
+        self.num_draft_tokens.append(spec_decoding_stats.num_draft_tokens)
+        self.num_accepted_tokens.append(
+            spec_decoding_stats.num_accepted_tokens)
+        self.accepted_tokens_per_pos_lists.append(
+            spec_decoding_stats.num_accepted_tokens_per_pos)
+
+    def log(self, log_fn=logger.info):
+        if not self.num_drafts:
+            return
+        num_drafts = np.sum(self.num_drafts)
+        num_draft_tokens = np.sum(self.num_draft_tokens)
+        num_accepted_tokens = np.sum(self.num_accepted_tokens)
+
+        draft_acceptance_rate = (num_accepted_tokens / num_draft_tokens *
+                                 100 if num_draft_tokens > 0 else float("nan"))
+
+        # Conventionally, mean acceptance length includes the bonus token
+        mean_acceptance_length = 1 + (num_accepted_tokens / num_drafts)
+
+        pos_matrix = np.array(self.accepted_tokens_per_pos_lists)
+        acceptance_rates = np.sum(pos_matrix, axis=0) / num_drafts
+        rates_str = ", ".join(f"{p:.3f}" for p in acceptance_rates)
+
+        log_fn(
+            "SpecDecoding metrics: "
+            "Draft acceptance rate: %.1f%%, "
+            "Mean acceptance length: %.2f, "
+            "Accepted: %d tokens, "
+            "Drafted: %d tokens, "
+            "Per-position acceptance rate: %s",
+            draft_acceptance_rate,
+            mean_acceptance_length,
+            num_accepted_tokens,
+            num_draft_tokens,
+            rates_str,
+        )
+        self.reset()
+
+
+class SpecDecodingProm:
+    """Record spec decoding metrics in Prometheus.
+
+    The acceptance rate can be calculated using a PromQL query:
+
+      rate(vllm:spec_decode_num_accepted_tokens_total[$interval]) /
+      rate(vllm:spec_decode_num_draft_tokens_total[$interval])
+
+    The mean acceptance length (conventionally including bonus tokens)
+    can be calculated using:
+
+      1 + (
+      rate(vllm:spec_decode_num_accepted_tokens_total[$interval]) /
+      rate(vllm:spec_decode_num_drafts[$interval]))
+
+    A per-position acceptance rate vector can be computed using
+
+      vllm:spec_decode_num_accepted_tokens_per_pos[$interval] /
+      vllm:spec_decode_num_drafts[$interval]
+    """
+
+    _counter_cls = prometheus_client.Counter
+
+    def __init__(
+        self,
+        speculative_config: Optional[SpeculativeConfig],
+        labelnames: list[str],
+        labelvalues: list[str],
+    ):
+        self.spec_decoding_enabled = speculative_config is not None
+        if not self.spec_decoding_enabled:
+            return
+
+        self.counter_spec_decode_num_drafts = \
+            self._counter_cls(
+                name="vllm:spec_decode_num_drafts",
+                documentation="Number of spec decoding drafts.",
+                labelnames=labelnames).labels(*labelvalues)
+        self.counter_spec_decode_num_draft_tokens = \
+            self._counter_cls(
+                name="vllm:spec_decode_num_draft_tokens",
+                documentation="Number of draft tokens.",
+                labelnames=labelnames,).labels(*labelvalues)
+        self.counter_spec_decode_num_accepted_tokens = \
+            self._counter_cls(
+                name="vllm:spec_decode_num_accepted_tokens",
+                documentation="Number of accepted tokens.",
+                labelnames=labelnames).labels(*labelvalues)
+
+        assert speculative_config is not None
+        num_spec_tokens = (speculative_config.num_speculative_tokens
+                           if self.spec_decoding_enabled else 0)
+        pos_labelnames = labelnames + ["position"]
+        base_counter = self._counter_cls(
+            name="vllm:spec_decode_num_accepted_tokens_per_pos",
+            documentation="Accepted tokens per draft position.",
+            labelnames=pos_labelnames,
+        )
+        self.counter_spec_decode_num_accepted_tokens_per_pos: list[
+            prometheus_client.Counter] = []
+        for pos in range(num_spec_tokens):
+            pos_labelvalues = labelvalues + [str(pos)]
+            self.counter_spec_decode_num_accepted_tokens_per_pos.append(
+                base_counter.labels(*pos_labelvalues))
+
+    def observe(self, spec_decoding_stats: SpecDecodingStats):
+        if not self.spec_decoding_enabled:
+            return
+        self.counter_spec_decode_num_drafts.inc(spec_decoding_stats.num_drafts)
+        self.counter_spec_decode_num_draft_tokens.inc(
+            spec_decoding_stats.num_draft_tokens)
+        self.counter_spec_decode_num_accepted_tokens.inc(
+            spec_decoding_stats.num_accepted_tokens)
+        for pos, counter in enumerate(
+                self.counter_spec_decode_num_accepted_tokens_per_pos):
+            counter.inc(spec_decoding_stats.num_accepted_tokens_per_pos[pos])

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/spec_decode/ngram_proposer.py

@@ -0,0 +1,157 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Optional
+
+import numpy as np
+from numba import jit
+
+from vllm.config import VllmConfig
+
+
+class NgramProposer:
+
+    def __init__(self, vllm_config: VllmConfig):
+        assert vllm_config.speculative_config is not None
+        assert vllm_config.speculative_config.prompt_lookup_min is not None
+        assert vllm_config.speculative_config.prompt_lookup_max is not None
+
+        # Minimum length of the n-gram to match.
+        self.min_n = vllm_config.speculative_config.prompt_lookup_min
+        # Maximum length of the n-gram to match.
+        self.max_n = vllm_config.speculative_config.prompt_lookup_max
+        # Number of tokens follow the match. If there are less than k
+        # tokens follow the match, we will return the maximum amount of
+        # tokens until the end.
+        self.k = vllm_config.speculative_config.num_speculative_tokens
+        # Maximum length of the model.
+        self.max_model_len = vllm_config.model_config.max_model_len
+
+        # Trigger Numba JIT compilation for N-gram proposer.
+        # This usually takes less than 1 second.
+        self.propose(np.zeros(1024, dtype=np.int32))
+
+    def propose(
+        self,
+        context_token_ids: np.ndarray,
+    ) -> Optional[np.ndarray]:
+        """Proposes the next sequence of tokens based on n-gram pattern 
+        matching in the context. The function finds matches of the last n 
+        tokens in the previous context, and returns k tokens that followed 
+        that match.
+        
+        Args:
+            context_token_ids: Numpy array of token IDs representing the 
+                               context sequence.
+
+        Returns:
+            np.ndarray: The sequence of tokens that followed 
+                        the matched n-gram in the context.
+            None: If no matching n-gram pattern is found.
+
+        Example:
+            If context_token_ids = [1,2,3,4,2,3], min_n = 2, max_n = 3, and
+            k = 4:
+            - The last 3 (= max_n) tokens [4,2,3] cannot find a match.
+            - The last 2 tokens [2,3] will be matched against the previous 
+              4 tokens [1,2,3,4].
+            - Finding a match of [2,3] would return the tokens that 
+              followed that pattern. Here we will return [4,2,3] because 
+              we only have three tokens after the match.
+        """
+        # TODO(woosuk): Optimize this.
+        return _find_longest_matched_ngram_and_propose_tokens(
+            origin_tokens=context_token_ids,
+            min_ngram=self.min_n,
+            max_ngram=self.max_n,
+            max_model_len=self.max_model_len,
+            k=self.k)
+
+    def load_model(self, *args, **kwargs):
+        # No model to load.
+        pass
+
+
+@jit(nopython=True)
+def _find_longest_matched_ngram_and_propose_tokens(
+        origin_tokens: np.ndarray, min_ngram: int, max_ngram: int,
+        max_model_len: int, k: int) -> Optional[np.ndarray]:
+    """
+    Find the longest n-gram which matches the suffix of the given tokens
+    whose length is within [min_ngram, max_ngram] (inclusive).
+
+    If found, we will extract k right after the matched ngram.
+    """
+    # Do not generate draft tokens is context is shorter than minimum n-gram
+    total_token = origin_tokens.shape[0]
+    if total_token < min_ngram:
+        return None
+
+    # Do not generate draft tokens beyond the max model length.
+    k = min(k, max_model_len - total_token)
+    if k <= 0:
+        return None
+
+    # Flip tokens, and the goal become to find longest ngram
+    # on the rightmost position which matches the prefix with
+    # length [min_n, max_n] (inclusive).
+    tokens = origin_tokens[::-1]
+
+    # Longest prefix (not including itself) which is a suffix of
+    # the current position.
+    #   lps[i] = max{v, where tokens[0:v] == tokens[i+1-v:i+1]}
+    #
+    # As ngram is capped by max_ngram to save memory, we only need to
+    # store lps for the first max_ngram prefix.
+    lps = np.zeros(max_ngram, dtype=np.int32)
+
+    longest_ngram = 0
+    position = 0
+
+    # lps[0] always equal to 0, we starts with index 1
+    prev_lps = 0
+    i = 1
+    while i < total_token:
+        # tokens[:prev_lps] is the longest prefix as a suffix of tokens[:i]
+        if tokens[prev_lps] == tokens[i]:
+            # Token match: tokens[:prev_lps+1] is the longest prefix as
+            # a suffix of tokens[:i+1]
+            prev_lps += 1
+            # Check if we found a longer valid ngram.
+            #
+            # Update position when longest_ngram matched prev_lps,
+            # as we want to get the target n-gram of the earliest position
+            # in the original tokens (i.e.
+            # latest position in the reversed tokens)
+            if prev_lps >= longest_ngram:
+                longest_ngram = prev_lps
+                position = i
+            if i < max_ngram:
+                # Store LPS for the first max_ngram prefix
+                lps[i] = prev_lps
+            if prev_lps == max_ngram:
+                # When prev_lps reached max_ngram, update prev_lps
+                # to lps[max_ngram-1] to avoid matching ngram
+                # longer than max_ngram
+                prev_lps = lps[max_ngram - 1]
+            i += 1
+        elif prev_lps != 0:
+            # Token mismatch: try the second longest prefix
+            # among all suffix of tokens[:i],
+            # which is the longest prefix of tokens[:prev_lps]
+            prev_lps = lps[prev_lps - 1]
+        else:
+            # Token mismatch, and no more prefix (except empty string)
+            # as a suffix of tokens[:i]
+            i += 1
+
+    if longest_ngram < min_ngram:
+        # No valid ngram is found
+        return None
+
+    # Flip the position back, so in origin_tokens,
+    # origin_tokens[total_token-1-position:total_token-1-position+longest_ngram]
+    # is the matched ngram, so we should start drafting tokens from
+    # total_token-1-position+longest_ngram
+    start_position = total_token - 1 - position + longest_ngram
+    k = min(k, total_token - start_position)
+    return origin_tokens[start_position:start_position + k]

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/spec_decode/utils.py

@@ -0,0 +1,14 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from vllm.sampling_params import SamplingParams
+
+_SAMPLING_EPS = 1e-5
+
+
+def is_spec_decode_unsupported(sampling_params: SamplingParams) -> bool:
+    """True if request is incompatible with speculative decoding"""
+    return (sampling_params.frequency_penalty != 0.0
+            or sampling_params.presence_penalty != 0.0
+            or sampling_params.repetition_penalty != 1.0
+            or sampling_params.min_p > _SAMPLING_EPS
+            or sampling_params.logprobs is not None)

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

@@ -0,0 +1,289 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
+import multiprocessing
+from concurrent.futures import Future, ThreadPoolExecutor
+from typing import TYPE_CHECKING, Optional
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.reasoning import ReasoningParserManager
+from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
+from vllm.utils import LazyLoader
+from vllm.v1.structured_output.backend_guidance import GuidanceBackend
+from vllm.v1.structured_output.backend_types import (StructuredOutputBackend,
+                                                     StructuredOutputGrammar)
+from vllm.v1.structured_output.backend_xgrammar import XgrammarBackend
+
+if TYPE_CHECKING:
+    import numpy as np
+    import numpy.typing as npt
+    import torch
+
+    from vllm.reasoning import ReasoningParser
+    from vllm.v1.request import Request
+else:
+    torch = LazyLoader("torch", globals(), "torch")
+
+logger = init_logger(__name__)
+
+
+class StructuredOutputManager:
+    """Engine-level manager for structured output requests."""
+
+    def __init__(self, vllm_config: VllmConfig):
+        self.backend: Optional[StructuredOutputBackend] = None
+        self.reasoner: Optional[ReasoningParser] = None
+        self.vllm_config = vllm_config
+
+        self._grammar_bitmask: Optional[torch.Tensor] = None
+        self._full_mask = torch.tensor(-1, dtype=torch.int32)
+
+        max_batch_size = self.vllm_config.scheduler_config.max_num_seqs
+        self.fill_bitmask_parallel_threshold = 128
+        if self.fill_bitmask_parallel_threshold < max_batch_size:
+            self.fill_bitmask_parallel_batch_size = 16
+            # Use:
+            # - at least 1 CPU
+            # - at most half the number of CPUs or 8, whichever is less
+            max_workers = max(1, min(multiprocessing.cpu_count() // 2, 8))
+            self.executor_for_fillmask = ThreadPoolExecutor(
+                max_workers=max_workers)
+
+        if not self.vllm_config.model_config.skip_tokenizer_init:
+            # The default max_workers if not specified is the number of
+            # CPUs * 5, which is way too high since these tasks are CPU-bound,
+            # not I/O bound. We also know we would never dominate CPU usage
+            # with just grammar compilation, so we set it to half the number
+            # of CPUs.
+            max_workers = max(1, (multiprocessing.cpu_count() + 1) // 2)
+            self.executor = ThreadPoolExecutor(max_workers=max_workers)
+            self.tokenizer = init_tokenizer_from_configs(
+                model_config=self.vllm_config.model_config,
+                scheduler_config=self.vllm_config.scheduler_config,
+                lora_config=self.vllm_config.lora_config,
+            ).get_lora_tokenizer(None)
+            reasoning_backend = \
+                    self.vllm_config.decoding_config.reasoning_backend
+            if reasoning_backend:
+                reasoner_cls = ReasoningParserManager.get_reasoning_parser(
+                    reasoning_backend)
+                self.reasoner = reasoner_cls(tokenizer=self.tokenizer)
+
+    def grammar_init(self, request: Request) -> None:
+        if request.structured_output_request is None:
+            return
+
+        if TYPE_CHECKING:
+            assert request.sampling_params is not None and \
+                request.sampling_params.guided_decoding is not None
+
+        # Initialize the backend the first time it is needed.
+        #
+        # NOTE: We only support a single backend. We do NOT support different
+        # backends on a per-request basis in V1 (for now, anyway...).
+        if self.backend is None:
+            assert request.sampling_params is not None
+            backend = request.sampling_params.guided_decoding.backend
+            vocab_size = self.vllm_config.model_config.get_vocab_size()
+            if backend == "xgrammar":
+                self.backend = XgrammarBackend(
+                    self.vllm_config,
+                    tokenizer=self.tokenizer,
+                    vocab_size=vocab_size,
+                )
+            elif backend == "guidance":
+                self.backend = GuidanceBackend(
+                    self.vllm_config,
+                    tokenizer=self.tokenizer,
+                    vocab_size=vocab_size,
+                )
+            elif backend == "outlines":
+                from vllm.v1.structured_output.backend_outlines import (
+                    OutlinesBackend)
+
+                self.backend = OutlinesBackend(
+                    self.vllm_config,
+                    tokenizer=self.tokenizer,
+                    vocab_size=vocab_size,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported structured output backend: {backend}")
+
+        grammar = self.executor.submit(self._async_create_grammar, request)
+        request.structured_output_request.grammar = grammar  # type: ignore[assignment]
+
+    def _async_create_grammar(
+        self,
+        request: Request,
+    ) -> StructuredOutputGrammar:
+        key = request.structured_output_request.structured_output_key  # type: ignore[union-attr]
+
+        # Note that the request was validated in the engine core client,
+        # so at this point we know it is a supported type of request.
+        #
+        # TODO: we still need to handle xgrammar compilation failures,
+        # though it should be unlikely as we test that up front as well.
+        request_type, grammar_spec = key
+
+        assert self.backend is not None
+        return self.backend.compile_grammar(request_type, grammar_spec)
+
+    def _fill_bitmasks(
+        self,
+        batch: list[tuple[StructuredOutputGrammar, int, bool]],
+    ) -> None:
+        assert self._grammar_bitmask is not None
+        for grammar, index, apply_bitmask in batch:
+            if apply_bitmask and not grammar.is_terminated():
+                grammar.fill_bitmask(self._grammar_bitmask, index)
+            else:
+                # Note that for thinking support, we will need to
+                # reset the relevant part of the bitmask for consequent
+                # requests here.
+                self._grammar_bitmask[index].fill_(self._full_mask)
+
+    def _async_submit_fill_bitmask(
+        self,
+        batch: list[tuple[StructuredOutputGrammar, int, bool]],
+    ) -> Future:
+        return self.executor_for_fillmask.submit(self._fill_bitmasks, batch)
+
+    def grammar_bitmask(
+        self,
+        requests: dict[str, Request],
+        structured_output_request_ids: dict[str, int],
+        scheduled_spec_decode_tokens: dict[str, list[int]],
+    ) -> Optional[npt.NDArray[np.int32]]:
+        # Prepare the structured output bitmask for this batch.
+        if not structured_output_request_ids:
+            return None
+
+        max_num_spec_tokens = 0
+        if self.vllm_config.speculative_config is not None:
+            max_num_spec_tokens = \
+                self.vllm_config.speculative_config.num_speculative_tokens
+
+        if self._grammar_bitmask is None:
+            assert self.backend is not None
+            max_batch_size = self.vllm_config.scheduler_config.max_num_seqs
+
+            # Allocate a bitmask for each token needing to be checked:
+            # one for each speculative position, and one more for the
+            # bonus token / non-speculative token.
+            self._grammar_bitmask = \
+                self.backend.allocate_token_bitmask(
+                    max_batch_size * (1 + max_num_spec_tokens))
+
+        # Generate a batched bitmask for all structured output requests.
+        # When speculative decoding is enabled, we need to include multiple
+        # masks for each request, one for each possible bonus token position.
+        # These are stored inline in the tensor and unpacked by the gpu runner.
+        cumulative_index = 0
+        ordered_seq = sorted(structured_output_request_ids.items(),
+                             key=lambda x: x[1])
+
+        # Optimized parallel filling of bitmasks for
+        # non-spec, large-batch-size cases
+        if len(ordered_seq) > self.fill_bitmask_parallel_threshold and \
+                max_num_spec_tokens == 0:
+            promises = []
+            batch = []
+            for req_id, _ in ordered_seq:
+                request = requests[req_id]
+                structured_output_request = request.structured_output_request
+                if TYPE_CHECKING:
+                    assert structured_output_request is not None
+                    assert structured_output_request.grammar is not None
+
+                apply_bitmask = self.should_fill_bitmask(request)
+                batch.append((structured_output_request.grammar,
+                              cumulative_index, apply_bitmask))
+                if len(batch) == self.fill_bitmask_parallel_batch_size:
+                    promises.append(self._async_submit_fill_bitmask(batch))
+                    batch = []
+
+                cumulative_index += 1
+            if batch:
+                promises.append(self._async_submit_fill_bitmask(batch))
+
+            # Wait for all bitmask filling tasks to complete.
+            for promise in promises:
+                promise.result()
+        else:
+            # Fallback to serial filling of bitmasks for small-batch-size cases
+            for req_id, _ in ordered_seq:
+                request = requests[req_id]
+                structured_output_request = request.structured_output_request
+
+                if TYPE_CHECKING:
+                    assert structured_output_request is not None
+                    assert structured_output_request.grammar is not None
+                apply_bitmask = self.should_fill_bitmask(request)
+
+                state_advancements = 0
+                req_tokens = scheduled_spec_decode_tokens.get(req_id, [])
+                for i, token in enumerate(req_tokens + [None]):
+                    self._fill_bitmasks([(structured_output_request.grammar,
+                                          cumulative_index, apply_bitmask)])
+
+                    if apply_bitmask and token is not None and \
+                        not structured_output_request.grammar.is_terminated():
+                        assert structured_output_request.grammar.accept_tokens(
+                            req_id, [token])
+                        state_advancements += 1
+                    cumulative_index += 1
+                if state_advancements > 0:
+                    structured_output_request.grammar.rollback(
+                        state_advancements)
+
+        bitmask_tensor = self._grammar_bitmask
+        if cumulative_index < bitmask_tensor.shape[0]:
+            bitmask_tensor = bitmask_tensor[:cumulative_index]
+
+        # After finishing with the xgrammar operations, we convert to
+        # np.ndarray, because that is much more efficient for serialization
+        # and deserialization when sending this to the GPU workers.
+        return bitmask_tensor.numpy()
+
+    def should_fill_bitmask(self, request: Request) -> bool:
+        if self.reasoner is not None:
+            assert request.structured_output_request is not None
+            if request.structured_output_request.reasoning_ended is None:
+                request.structured_output_request.reasoning_ended = \
+                    self.reasoner.is_reasoning_end(request.prompt_token_ids)
+            return request.structured_output_request.reasoning_ended
+        return True
+
+    def should_advance(self, request: Request) -> bool:
+        if not request.use_structured_output:
+            return False
+
+        # To determine whether we can advance the FSM.
+        # Supports thinking usage where we skip the reasoning components.
+        if TYPE_CHECKING:
+            assert request.structured_output_request is not None
+            assert request.structured_output_request.grammar is not None
+        # by default, we should always advance
+        # for cases that doesn't uses thinking mode.
+        if self.reasoner is not None:
+            structured_req = request.structured_output_request
+
+            if structured_req.reasoning_ended:
+                return True
+
+            # Check if reasoning ends in *this* step
+            if self.reasoner.is_reasoning_end(request.all_token_ids):
+                # Reasoning just ended, so we shouldn't advanced til
+                # next pass
+                structured_req.reasoning_ended = True
+
+            return False
+        else:
+            return True
+
+    def clear_backend(self) -> None:
+        if self.backend is not None:
+            self.backend.destroy()

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/structured_output/backend_guidance.py

@@ -0,0 +1,245 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import copy
+import json
+import os
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.sampling_params import SamplingParams
+from vllm.utils import LazyLoader
+from vllm.v1.structured_output.backend_types import (StructuredOutputBackend,
+                                                     StructuredOutputGrammar,
+                                                     StructuredOutputOptions)
+from vllm.v1.structured_output.request import get_structured_output_key
+
+if TYPE_CHECKING:
+    import llguidance
+    import llguidance.hf as llguidance_hf
+    import llguidance.torch as llguidance_torch
+else:
+    llguidance = LazyLoader("llguidance", globals(), "llguidance")
+    llguidance_hf = LazyLoader("llguidance.hf", globals(), "llguidance.hf")
+    llguidance_torch = LazyLoader("llguidance.torch", globals(),
+                                  "llguidance.torch")
+
+logger = init_logger(__name__)
+
+
+def _walk_json_for_additional_properties(data: object):
+    if isinstance(data, dict):
+        for value in data.values():
+            _walk_json_for_additional_properties(value)
+        if 'additionalProperties' not in data and \
+            ('properties' in data or 'patternProperties' in data):
+            data['additionalProperties'] = False
+    elif isinstance(data, list):
+        for item in data:
+            _walk_json_for_additional_properties(item)
+
+
+def process_for_additional_properties(
+        guide_json: Union[str, dict[str, Any]]) -> dict[str, Any]:
+    if isinstance(guide_json, str):
+        guide_json_obj = json.loads(guide_json)
+    else:
+        # copy for modifications
+        guide_json_obj = copy.deepcopy(guide_json)
+    _walk_json_for_additional_properties(guide_json_obj)
+    return guide_json_obj
+
+
+@dataclass
+class GuidanceBackend(StructuredOutputBackend):
+
+    def __post_init__(self):
+        self.disable_any_whitespace = \
+            self.vllm_config.decoding_config.disable_any_whitespace
+        self.disable_additional_properties = \
+            self.vllm_config.decoding_config.disable_additional_properties
+
+        self.ll_tokenizer = llguidance_hf.from_tokenizer(
+            self.tokenizer, self.vocab_size)
+
+    def compile_grammar(self, request_type: StructuredOutputOptions,
+                        grammar_spec: str) -> StructuredOutputGrammar:
+        self.serialized_grammar = serialize_guidance_grammar(
+            request_type, grammar_spec, self.disable_any_whitespace,
+            self.disable_additional_properties)
+
+        ll_matcher = llguidance.LLMatcher(
+            self.ll_tokenizer,
+            self.serialized_grammar,
+            log_level=int(os.environ.get("LLGUIDANCE_LOG_LEVEL", "1")),
+        )
+
+        r = GuidanceGrammar(
+            ll_matcher=ll_matcher,
+            ll_tokenizer=self.ll_tokenizer,
+            vocab_size=self.vocab_size,
+        )
+
+        r.check_error()
+        return r
+
+    def allocate_token_bitmask(self, max_num_seqs: int):
+        return llguidance_torch.allocate_token_bitmask(
+            max_num_seqs, self.ll_tokenizer.vocab_size)
+
+    def destroy(self):
+        pass
+
+
+@dataclass
+class GuidanceGrammar(StructuredOutputGrammar):
+    ll_matcher: llguidance.LLMatcher
+    ll_tokenizer: llguidance.LLTokenizer
+    vocab_size: int
+    printed_error: bool = False
+    terminated: bool = False
+
+    def check_error(self):
+        if not self.printed_error:
+            err = self.ll_matcher.get_error()
+            if err:
+                self.printed_error = True
+                logger.warning("LLMatcher error: %s", err)
+
+    def accept_tokens(self, request_id: str, tokens: list[int]) -> bool:
+        """Accepts a list of tokens and advances the parser.
+
+        Returns True if the parser was advanced successfully.
+        Returns False if the parser failed to advance.
+        """
+
+        if self.ll_tokenizer.eos_token in tokens:
+            self.terminated = True
+
+        if self.ll_matcher.is_stopped():
+            return True
+
+        # TODO - Add jump decoding support in the future:
+        # self.ll_matcher.compute_ff_bytes() - this should always work
+        # self.ll_matcher.compute_ff_tokens() - this only works for
+        #   "canonical" tokenizers
+        # For conversion between the two, see
+        # https://github.com/guidance-ai/llguidance/blob/main/docs/fast_forward.md
+
+        r = self.ll_matcher.consume_tokens(tokens)
+
+        self.check_error()
+
+        return r
+
+    def validate_tokens(self, tokens: list[int]) -> list[int]:
+        """Checks if the list of tokens are accepted by the parser in sequence.
+        Will not advance the parser.
+
+        Returns the prefix list of tokens that are accepted by the parser.
+        """
+        if len(tokens) == 0:
+            return []
+        if self.ll_matcher.is_stopped():
+            return []
+
+        num_tokens = self.ll_matcher.validate_tokens(tokens)
+
+        self.check_error()
+
+        return tokens[:num_tokens]
+
+    def rollback(self, num_tokens: int) -> None:
+        self.ll_matcher.rollback(num_tokens)
+        self.check_error()
+
+    def fill_bitmask(self, bitmask: torch.Tensor, idx: int) -> None:
+        # this will automatically return [EOS] mask if the matcher is stopped
+        # or otherwise in an error state
+        llguidance_torch.fill_next_token_bitmask(self.ll_matcher, bitmask, idx)
+        self.check_error()
+
+    def is_terminated(self) -> bool:
+        return self.terminated
+
+    def reset(self):
+        # This method may be not needed anymore? TODO
+        self.ll_matcher.reset()
+
+
+def serialize_guidance_grammar(
+    request_type: StructuredOutputOptions,
+    grammar_spec: Union[str, dict[str, Any]],
+    disable_any_whitespace: bool = False,
+    disable_additional_properties: bool = False,
+) -> str:
+
+    def _process_schema(grammar_spec: Union[str, dict[str, Any]], ) -> str:
+        if disable_additional_properties:
+            grammar_spec = process_for_additional_properties(grammar_spec)
+        return llguidance.LLMatcher.grammar_from_json_schema(
+            grammar_spec,
+            defaults={
+                "whitespace_flexible": not disable_any_whitespace,
+            })
+
+    if request_type == StructuredOutputOptions.JSON:
+        return _process_schema(grammar_spec)
+    elif request_type == StructuredOutputOptions.JSON_OBJECT:
+        return llguidance.LLMatcher.grammar_from_json_schema(
+            '{"type": "object"}',
+            defaults={
+                "whitespace_flexible": not disable_any_whitespace,
+            })
+    else:
+        if request_type == StructuredOutputOptions.REGEX:
+            tp = "regex"
+        elif request_type == StructuredOutputOptions.GRAMMAR:
+            tp = "grammar"
+        elif request_type == StructuredOutputOptions.CHOICE:
+            tp = "choice"
+        elif request_type == StructuredOutputOptions.STRUCTURAL_TAG:
+            if isinstance(grammar_spec, str):
+                s_tag = json.loads(grammar_spec)
+            else:
+                s_tag = grammar_spec
+            triggers: list[str] = s_tag["triggers"]
+            tags: list[llguidance.StructTag] = []
+            for s in s_tag["structures"]:
+                begin: str = s["begin"]
+                trig = next((t for t in triggers if begin.startswith(t)), None)
+                if trig is None:
+                    raise ValueError(
+                        f"Trigger {begin} not found in triggers {triggers}")
+                tags.append(
+                    llguidance.StructTag(
+                        trigger=trig,
+                        begin=s["begin"],
+                        grammar=_process_schema(s["schema"]),
+                        end=s["end"],
+                    ))
+            if not tags:
+                raise ValueError(
+                    "No structural tags found in the grammar spec.")
+            return llguidance.StructTag.to_grammar(tags)
+        else:
+            logger.error("Validation should have already occurred. "
+                         "Please file an issue.")
+            raise ValueError("grammar is not of valid supported types. "
+                             f"({request_type!s})")
+        return llguidance.grammar_from(tp, grammar_spec)
+
+
+def validate_guidance_grammar(
+        sampling_params: SamplingParams,
+        tokenizer: Optional[llguidance.LLTokenizer] = None) -> None:
+    tp, grm = get_structured_output_key(sampling_params)
+    guidance_grm = serialize_guidance_grammar(tp, grm)
+    err = llguidance.LLMatcher.validate_grammar(guidance_grm, tokenizer)
+    if err:
+        raise ValueError(f"Grammar error: {err}")

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/structured_output/backend_outlines.py

@@ -0,0 +1,320 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright 2025-present the Outlines developers
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
+import ast
+import importlib
+import json
+import sys
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING
+
+import torch
+from regex import escape as regex_escape
+
+from vllm.sampling_params import SamplingParams
+from vllm.utils import LazyLoader
+from vllm.v1.structured_output.backend_types import (StructuredOutputBackend,
+                                                     StructuredOutputGrammar,
+                                                     StructuredOutputOptions)
+from vllm.v1.structured_output.utils import (OutlinesVocabulary,
+                                             get_outlines_cache,
+                                             get_outlines_vocabulary)
+
+if TYPE_CHECKING:
+    import outlines_core as oc
+    import outlines_core.json_schema as json_schema
+else:
+    oc = LazyLoader("oc", globals(), "outlines_core")
+    json_schema = LazyLoader("json_schema", globals(),
+                             "outlines_core.json_schema")
+
+# Python 3.11+ sre_parse and sre_constants
+# are deprecated, so we must import them from re
+if sys.version_info >= (3, 11):
+    # Hack to get around pre-commit regex module rule
+    # because going through re is the only way to get sre_parse
+    # and sre_constants in Python 3.11+
+    _re = importlib.import_module("re")
+    sre_parse = _re._parser
+    sre_constants = _re._constants
+else:
+    import sre_constants
+    import sre_parse
+
+
+@dataclass
+class OutlinesBackend(StructuredOutputBackend):
+
+    def __post_init__(self):
+        self.vocabulary = get_outlines_vocabulary(self.tokenizer)
+        self.cache = get_outlines_cache()
+
+    def _compile_index(self, regex_string: str,
+                       vocabulary: OutlinesVocabulary) -> oc.Index:
+        cache_key = f"{vocabulary._hash}_{regex_string}"
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+
+        index = oc.Index(regex_string, vocabulary.inner)
+        self.cache[cache_key] = index
+
+        return index
+
+    def compile_grammar(self, request_type: StructuredOutputOptions,
+                        grammar_spec: str) -> StructuredOutputGrammar:
+        if request_type == StructuredOutputOptions.JSON:
+            regex = json_schema.build_regex_from_schema(grammar_spec)
+        elif request_type == StructuredOutputOptions.REGEX:
+            regex = grammar_spec
+        elif request_type == StructuredOutputOptions.CHOICE:
+            choices = ast.literal_eval(grammar_spec)
+            choices = [regex_escape(c) for c in choices]
+            regex = "(" + "|".join(choices) + ")"
+        else:
+            raise ValueError(
+                f"Invalid request type for Outlines backend ({request_type!s})"
+            )
+        index = self._compile_index(regex, self.vocabulary)
+        max_rollback_tokens = (
+            self.vllm_config.speculative_config.num_speculative_tokens
+            if self.vllm_config.speculative_config is not None else 0)
+        return OutlinesGrammar(vocab_size=self.vocab_size,
+                               guide=oc.Guide(
+                                   index, max_rollback=max_rollback_tokens))
+
+    def allocate_token_bitmask(self, max_num_seqs: int) -> torch.Tensor:
+        return torch.full(
+            (max_num_seqs, (self.vocab_size + 31) // 32),
+            -1,
+            dtype=torch.int32,
+            pin_memory=torch.cuda.is_available(),
+        )
+
+    def destroy(self):
+        pass
+
+
+@dataclass
+class OutlinesGrammar(StructuredOutputGrammar):
+
+    vocab_size: int
+    guide: oc.Guide = field(hash=False)
+    num_processed_tokens: int = field(default_factory=lambda: 0,
+                                      repr=False,
+                                      hash=False,
+                                      init=False)
+
+    # outlines_core signals done on DFA accept; vLLM expects done after EOS.
+    # We delay the finished flag by one step so EOS can still be emitted.
+    _prev_finished: bool = field(default=False,
+                                 init=False,
+                                 repr=False,
+                                 hash=False)
+
+    def accept_tokens(self, request_id: str, tokens: list[int]) -> bool:
+        """Accepts a list of tokens and advances the FSM.
+
+        Returns True if the FSM was advanced successfully.
+        Returns False if the FSM failed to advance.
+        """
+        if self.guide.accepts_tokens(tokens):
+            # Advance cannot fail because we checked Guide.accepts_tokens()
+            for t in tokens:
+                self.guide.advance(t)
+                self.num_processed_tokens += 1
+            return True
+        return False
+
+    def rollback(self, num_tokens: int) -> None:
+        self.guide.rollback_state(num_tokens)
+        self.num_processed_tokens -= num_tokens
+
+    def validate_tokens(self, tokens: list[int]) -> list[int]:
+        accepted: list[int] = []
+        for tok in tokens:
+            accepted.append(tok)
+            if not self.guide.accepts_tokens(accepted):
+                accepted.pop()
+                break
+        return accepted
+
+    def fill_bitmask(self, bitmask: torch.Tensor, idx: int) -> None:
+        mask = bitmask[idx]
+        self.guide.write_mask_into(mask.data_ptr(), mask.numel(),
+                                   mask.element_size())
+
+    def is_terminated(self) -> bool:
+        curr = self.guide.is_finished()
+        prev = self._prev_finished
+        self._prev_finished = curr
+        return prev
+
+    def reset(self):
+        self.num_processed_tokens = 0
+        self._prev_finished = False
+        self.guide.reset()
+
+
+def validate_structured_output_request_outlines(params: SamplingParams):
+    if params.guided_decoding is None:
+        return
+
+    gd_params = params.guided_decoding
+
+    if gd_params.regex:
+        validate_regex_is_buildable(gd_params.regex)
+    elif gd_params.json:
+        if isinstance(gd_params.json, str):
+            try:
+                # make sure schema is valid json
+                json.loads(gd_params.json)
+                schema = gd_params.json
+            except json.JSONDecodeError as e:
+                raise ValueError("Invalid JSON grammar specification.") from e
+        else:
+            try:
+                schema = json.dumps(gd_params.json)
+            except Exception as e:
+                raise ValueError(
+                    f"Error serializing guided decoding jsonschema: {e}"
+                ) from e
+        pattern = json_schema.build_regex_from_schema(schema)
+        validate_regex_is_buildable(pattern)
+    elif gd_params.choice:
+        choices = [regex_escape(str(choice)) for choice in gd_params.choice]
+        regex = "(" + "|".join(choices) + ")"
+        validate_regex_is_buildable(regex)
+    elif gd_params.grammar:
+        raise ValueError("Outlines guided decoding backend "
+                         "does not support grammar specifications")
+
+
+def _prefix_needs_context(parsed) -> bool:
+    """Return True if there's a look-around/anchor before any consumer."""
+
+    def subpattern_consumes(parsed) -> bool:
+        """Return True if subpattern can consume at least one character."""
+        tokens = parsed.data if hasattr(parsed, 'data') else parsed
+        for ttype, tval in tokens:
+            # literal, character class, or dot always consumes
+            if ttype in (sre_parse.LITERAL, sre_parse.IN, sre_parse.ANY):
+                return True
+            # quantified subpattern: check inner pattern
+            elif ttype == sre_parse.MAX_REPEAT:
+                _, mx, sub = tval
+                if mx != 0 and subpattern_consumes(sub):
+                    return True
+            # alternation: if any branch consumes, the whole does
+            elif ttype == sre_parse.BRANCH:
+                _, branches = tval
+                if any(subpattern_consumes(br) for br in branches):
+                    return True
+            # grouped subpattern: recurse into its contents
+            elif ttype == sre_parse.SUBPATTERN and subpattern_consumes(
+                    tval[3]):
+                return True
+        # No consumers, return False
+        return False
+
+    tokens = parsed.data if hasattr(parsed, 'data') else parsed
+    for ttype, tval in tokens:
+        # Direct anchors or look-around
+        if ttype == sre_parse.AT or ttype in (sre_constants.ASSERT,
+                                              sre_constants.ASSERT_NOT):
+            return True
+
+        # Nested subpattern: check
+        if ttype == sre_parse.SUBPATTERN:
+            # tval: (group, add_flags, del_flags, subpattern)
+            if _prefix_needs_context(tval[3]):
+                return True
+            if subpattern_consumes(tval[3]):
+                return False
+
+        # if any branch has a prefix anchor => True,
+        # else if at least one branch consumes => prefix ends => False
+        elif ttype == sre_parse.BRANCH:
+            saw_consumer = False
+            for br in tval[1]:
+                if _prefix_needs_context(br):
+                    return True
+                if subpattern_consumes(br):
+                    saw_consumer = True
+            if saw_consumer:
+                return False
+
+        # Immediate consumer tokens
+        elif ttype in (sre_parse.LITERAL, sre_parse.IN, sre_parse.ANY):
+            return False
+
+        # if subpattern has anchor => True, if it can consume => stop
+        elif ttype == sre_parse.MAX_REPEAT:
+            if _prefix_needs_context(tval[2]):
+                return True
+            if subpattern_consumes(tval[2]):
+                return False
+
+    return False
+
+
+def _check_unsupported(parsed) -> None:
+    """Check for regex features unsupported by regex-automata"""
+    tokens = parsed.data if hasattr(parsed, 'data') else parsed
+    for ttype, tval in tokens:
+
+        # backreference
+        if ttype in (sre_parse.GROUPREF, sre_parse.GROUPREF_EXISTS):
+            raise ValueError("Backreferences are unsupported.")
+
+        # look-around assertion
+        elif ttype in (sre_constants.ASSERT, sre_constants.ASSERT_NOT):
+            raise ValueError("Look-Around assertion are unsupported.")
+
+        # unicode word boundaries
+        elif ttype == sre_parse.AT:
+            if tval in (sre_constants.AT_BOUNDARY,
+                        sre_constants.AT_NON_BOUNDARY):
+                raise ValueError("Unicode word boundaries are unsupported.")
+
+        elif ttype == sre_parse.BRANCH:
+            # tval is (None, branches)
+            for branch in tval[1]:
+                _check_unsupported(branch)
+
+        # tval is (min, max, subpattern)
+        elif ttype == sre_parse.MAX_REPEAT:
+            _check_unsupported(tval[2])
+
+
+def validate_regex_is_buildable(pattern: str) -> None:
+    """
+    Validates that the input regex is not using unsupported features
+    of the `regex-automata` crate (outlines_core regex engine) and has a
+    universal start state.
+    definition of universal start state used can be found at:
+    https://docs.rs/regex-automata/latest/regex_automata/dfa/trait.Automaton.html#method.universal_start_state
+    """
+    try:
+        parsed = sre_parse.parse(pattern)
+
+    except sre_constants.error as e:
+        raise ValueError(f"Error parsing regex: {e}") from e
+
+    try:
+        _check_unsupported(parsed)
+    except ValueError as e:
+        raise ValueError(
+            f"Regex uses unsupported feature for guided decoding: {e}. "
+            "Only basic matching constructs are supported—lookarounds, "
+            "backreferences, and unicode boundaries are not.") from e
+
+    if _prefix_needs_context(parsed):
+        raise ValueError(
+            "Regex does not have a anchored universal start state"
+            "This means that the Regex uses anchors (^) or look-arounds "
+            "in a way which requires context before any token is matched."
+            "Guided decoding needs regexes that can match without needing "
+            "that context. Try rewriting the pattern without using these "
+            f"constructs. Pattern:\n{pattern}")

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/structured_output/backend_types.py

@@ -0,0 +1,134 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import enum
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import torch
+
+    from vllm.config import VllmConfig
+    from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+
+class StructuredOutputOptions(enum.Enum):
+    JSON = enum.auto()
+    JSON_OBJECT = enum.auto()
+    REGEX = enum.auto()
+    GRAMMAR = enum.auto()
+    CHOICE = enum.auto()
+    STRUCTURAL_TAG = enum.auto()
+
+
+StructuredOutputKey = tuple[StructuredOutputOptions, str]
+
+
+class StructuredOutputGrammar(ABC):
+    """Request-level backend for structured output requests."""
+
+    @abstractmethod
+    def accept_tokens(self, request_id: str, tokens: list[int]) -> bool:
+        """
+        Determines whether the provided tokens are accepted for the
+        given request.
+
+        Args:
+            request_id (str): The unique identifier for the request.
+            tokens (list[int]): A list of token IDs to evaluate.
+
+        Returns:
+            bool: True if the tokens are accepted, False otherwise.
+        """
+
+    @abstractmethod
+    def validate_tokens(self, tokens: list[int]) -> list[int]:
+        """
+        Validates the provided tokens against the grammar.
+        Will not advance the FSM.
+
+        Args:
+            tokens (list[int]): A list of token IDs to validate.
+
+        Returns:
+            list[int]: A list of accepted token IDs. Will be a prefix
+                of the input tokens, and empty if none are accepted.
+        """
+
+    @abstractmethod
+    def rollback(self, num_tokens: int) -> None:
+        """
+        Rolls back the state of the grammar by a specified number of tokens.
+        Will also revert counters for the number of processed tokens.
+
+        Args:
+            num_tokens (int): The number of tokens to roll back.
+        """
+
+    @abstractmethod
+    def fill_bitmask(self, bitmask: torch.Tensor, batch_index: int) -> None:
+        """
+        Fills the bitmask for a specific batch index.
+
+        Args:
+            bitmask (torch.Tensor): The bitmask to fill
+            batch_index (int): The index in the bitmask to fill
+        """
+
+    @abstractmethod
+    def is_terminated(self) -> bool:
+        """
+        Checks whether the structured output process has terminated.
+
+        Returns:
+            bool: True if the process is terminated, False otherwise.
+        """
+
+    @abstractmethod
+    def reset(self):
+        """
+        Resets the state of the structured output grammar.
+        """
+
+
+@dataclass
+class StructuredOutputBackend(ABC):
+    """Engine-level backend for structured output requests."""
+
+    vllm_config: VllmConfig
+    tokenizer: AnyTokenizer
+    vocab_size: int
+
+    @abstractmethod
+    def compile_grammar(self, request_type: StructuredOutputOptions,
+                        grammar_spec: str) -> StructuredOutputGrammar:
+        """
+        Compiles a grammar specification into a structured output grammar.
+
+        Args:
+            request_type (StructuredOutputOptions): The type of structured
+              output request.
+            grammar_spec (str): The grammar specification to compile.
+
+        Returns:
+            StructuredOutputGrammar: The compiled structured output grammar.
+        """
+
+    @abstractmethod
+    def allocate_token_bitmask(self, max_num_seqs: int) -> torch.Tensor:
+        """
+        Allocates a token bitmask for the specified maximum number of sequences.
+
+        Args:
+            max_num_seqs (int): The maximum number of sequences for which
+              to allocate the bitmask.
+        """
+
+    @abstractmethod
+    def destroy(self):
+        """
+        Backend-specific cleanup.
+        """

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/structured_output/backend_xgrammar.py

@@ -0,0 +1,323 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+import torch
+
+import vllm.envs
+from vllm.logger import init_logger
+from vllm.sampling_params import SamplingParams
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
+from vllm.utils import LazyLoader
+from vllm.v1.structured_output.backend_types import (StructuredOutputBackend,
+                                                     StructuredOutputGrammar,
+                                                     StructuredOutputOptions)
+from vllm.v1.structured_output.utils import (choice_as_grammar,
+                                             convert_lark_to_ebnf,
+                                             grammar_is_likely_lark)
+
+if TYPE_CHECKING:
+    import xgrammar as xgr
+else:
+    xgr = LazyLoader("xgr", globals(), "xgrammar")
+
+logger = init_logger(__name__)
+
+
+@dataclass
+class XgrammarBackend(StructuredOutputBackend):
+
+    def __post_init__(self):
+        self.disable_any_whitespace = \
+            self.vllm_config.decoding_config.disable_any_whitespace
+
+        if isinstance(self.tokenizer, MistralTokenizer):
+            # NOTE: ideally, xgrammar should handle this accordingly.
+            # refer to https://github.com/mlc-ai/xgrammar/blob/d77c0a0173ef14779c918e3be7966ba852f7910f/python/xgrammar/tokenizer_info.py#L98
+            try:
+                if self.tokenizer.is_tekken:
+                    encoded_vocab = self.tokenizer._vocab
+                else:
+                    encoded_vocab = [
+                        token for token, _ in sorted(
+                            self.tokenizer.get_vocab().items(),
+                            key=lambda x: x[1],
+                        )
+                    ]
+                stop_token_ids = None
+                if (hasattr(
+                        self.tokenizer,
+                        "eos_token_id",
+                ) and self.tokenizer.eos_token_id is not None):
+                    stop_token_ids = [self.tokenizer.eos_token_id]
+            except AttributeError as e:
+                raise ValueError(
+                    f"Cannot get the vocabulary of the tokenizer "
+                    f"{type(self.tokenizer)}. The tokenizer should have a "
+                    "get_vocab method.") from e
+            tokenizer_info = xgr.TokenizerInfo(  # type: ignore
+                encoded_vocab=encoded_vocab,
+                # NOTE: https://github.com/mlc-ai/xgrammar/blob/5e141f6ff1ca02bc31f9e512e68b61f2a8ae88e5/tests/python/test_tokenizer_info.py#L43 # noqa: E501
+                vocab_type=xgr.VocabType.RAW
+                if self.tokenizer.is_tekken else xgr.VocabType.BYTE_FALLBACK,
+                vocab_size=self.vocab_size,
+                stop_token_ids=stop_token_ids,
+                add_prefix_space=True,
+            )
+        else:
+            tokenizer_info = xgr.TokenizerInfo.from_huggingface(
+                self.tokenizer,
+                vocab_size=self.vocab_size,
+            )
+        self.compiler = xgr.GrammarCompiler(
+            tokenizer_info,
+            max_threads=8,
+            cache_enabled=True,
+            cache_limit_bytes=vllm.envs.VLLM_XGRAMMAR_CACHE_MB * 1024 * 1024,
+        )
+
+        self.num_speculative_tokens = 0
+        if self.vllm_config.speculative_config is not None:
+            self.num_speculative_tokens = \
+                self.vllm_config.speculative_config.num_speculative_tokens
+
+    def compile_grammar(self, request_type: StructuredOutputOptions,
+                        grammar_spec: str) -> StructuredOutputGrammar:
+        if request_type == StructuredOutputOptions.JSON:
+            ctx = self.compiler.compile_json_schema(
+                grammar_spec, any_whitespace=not self.disable_any_whitespace)
+        elif request_type == StructuredOutputOptions.JSON_OBJECT:
+            ctx = self.compiler.compile_json_schema(
+                '{"type": "object"}',
+                any_whitespace=not self.disable_any_whitespace)
+        elif request_type == StructuredOutputOptions.GRAMMAR:
+            ctx = self.compiler.compile_grammar(grammar_spec)
+        elif request_type == StructuredOutputOptions.REGEX:
+            ctx = self.compiler.compile_regex(grammar_spec)
+        elif request_type == StructuredOutputOptions.STRUCTURAL_TAG:
+            s_tag = json.loads(grammar_spec)
+            tags = [
+                xgr.StructuralTagItem(
+                    begin=s["begin"],
+                    schema=json.dumps(s["schema"]),
+                    end=s["end"],
+                ) for s in s_tag["structures"]
+            ]
+            ctx = self.compiler.compile_structural_tag(tags, s_tag["triggers"])
+        else:
+            logger.error(
+                "Validation should have already occurred. Please file an issue."
+            )
+            raise ValueError(
+                f"grammar is not of valid supported types. ({request_type!s})")
+
+        return XgrammarGrammar(
+            matcher=xgr.GrammarMatcher(
+                ctx,
+                max_rollback_tokens=self.num_speculative_tokens,
+            ),
+            vocab_size=self.vocab_size,
+            ctx=ctx,
+        )
+
+    def allocate_token_bitmask(self, max_num_seqs: int):
+        return xgr.allocate_token_bitmask(max_num_seqs, self.vocab_size)
+
+    def destroy(self):
+        del self.compiler
+
+
+@dataclass
+class XgrammarGrammar(StructuredOutputGrammar):
+    # NOTE: This would be a generic-enough class for
+    # supporting different backends, in the future.
+    # For now, just xgrammar.
+    #
+    # https://xgrammar.mlc.ai/docs/api/python/index.html#xgrammar.GrammarMatcher.find_jump_forward_string
+    # for jump-forward decoding
+
+    vocab_size: int
+    matcher: xgr.GrammarMatcher = field(hash=False)
+    ctx: xgr.CompiledGrammar = field(hash=False)
+    num_processed_tokens: int = field(default_factory=lambda: 0,
+                                      repr=False,
+                                      hash=False,
+                                      init=False)
+    _is_terminated: bool = field(default=False, repr=False, hash=False)
+
+    def accept_tokens(self, request_id: str, tokens: list[int]) -> bool:
+        """Accepts a list of tokens and advances the FSM.
+
+        Returns True if the FSM was advanced successfully.
+        Returns False if the FSM failed to advance.
+        """
+        if self._is_terminated:
+            return False
+        for token in tokens:
+            if not self.matcher.accept_token(token):
+                logger.error(
+                    "Failed to advance FSM for request %s "
+                    "for tokens %s. Please file an issue.", request_id, token)
+                return False
+            self.num_processed_tokens += 1
+        self._is_terminated = self.matcher.is_terminated()
+        return True
+
+    def validate_tokens(self, tokens: list[int]) -> list[int]:
+        """Checks if the list of tokens are accepted by the FSM in sequence.
+        Will not advance the FSM.
+
+        Returns the prefix list of tokens that are accepted by the FSM.
+        """
+        accepted_tokens = []
+        for token in tokens:
+            if self.matcher.accept_token(token):
+                accepted_tokens.append(token)
+            else:
+                break
+        if len(accepted_tokens) > 0:
+            # Rollback the FSM to the initial state
+            self.matcher.rollback(len(accepted_tokens))
+        return accepted_tokens
+
+    def rollback(self, num_tokens: int) -> None:
+        self.matcher.rollback(num_tokens)
+        self.num_processed_tokens -= num_tokens
+        self._is_terminated = self.matcher.is_terminated()
+
+    def fill_bitmask(self, bitmask: torch.Tensor, idx: int) -> None:
+        self.matcher.fill_next_token_bitmask(bitmask, idx)
+
+    def is_terminated(self) -> bool:
+        return self._is_terminated
+
+    def reset(self):
+        self.num_processed_tokens = 0
+        self.matcher.reset()
+
+
+def has_xgrammar_unsupported_json_features(schema: dict[str, Any]) -> bool:
+    """Check if JSON schema contains features unsupported by xgrammar."""
+
+    def check_object(obj: dict[str, Any]) -> bool:
+        if not isinstance(obj, dict):
+            return False
+
+        # Check for numeric ranges
+        if obj.get("type") in ("integer", "number") and ("multipleOf" in obj):
+            return True
+
+        # Check for array unsupported keywords
+        if obj.get("type") == "array" and any(
+                key in obj for key in ("uniqueItems", "contains",
+                                       "minContains", "maxContains")):
+            return True
+
+        # Unsupported keywords for strings
+        if obj.get("type") == "string" and "format" in obj:
+            return True
+
+        # Unsupported keywords for objects
+        if obj.get("type") == "object" and any(
+                key in obj for key in ("minProperties", "maxProperties",
+                                       "propertyNames", "patternProperties")):
+            return True
+
+        # Recursively check all nested objects and arrays
+        for value in obj.values():
+            if isinstance(value, dict):
+                if check_object(value):
+                    return True
+            elif isinstance(value, list):
+                for item in value:
+                    if isinstance(item, dict) and check_object(item):
+                        return True
+
+        return False
+
+    return check_object(schema)
+
+
+def validate_xgrammar_grammar(sampling_params: SamplingParams) -> None:
+    """Validate that the request is supported by structured output.
+
+    Raises ValueError if the request is not supported.
+    """
+    if sampling_params.guided_decoding is None:
+        return
+
+    gd_params = sampling_params.guided_decoding
+
+    if gd_params.regex:
+        try:
+            xgr.Grammar.from_regex(gd_params.regex)
+        except Exception as err:
+            raise ValueError("Failed to transform regex into a grammar: "
+                             f"{err}") from err
+
+    if gd_params.choice:
+        choice_grammar = choice_as_grammar(gd_params.choice)
+        try:
+            xgr.Grammar.from_ebnf(choice_grammar)
+        except Exception as err:
+            raise ValueError("Failed to transform choices into a grammar: "
+                             "{err}") from err
+        gd_params.choice = None
+        gd_params.grammar = choice_grammar
+        return
+
+    if gd_params.json:
+        if isinstance(gd_params.json, str):
+            try:
+                schema = json.loads(gd_params.json)
+            except json.JSONDecodeError as e:
+                raise ValueError("Invalid JSON grammar specification.") from e
+        else:
+            schema = gd_params.json
+
+        try:
+            xgr.Grammar.from_json_schema(schema)
+        except Exception as err:
+            raise ValueError("Failed to transform json schema into a grammar: "
+                             f"{err}") from err
+
+        if has_xgrammar_unsupported_json_features(schema):
+            raise ValueError("The provided JSON schema contains features not "
+                             "supported by xgrammar.")
+        return
+
+    if gd_params.grammar:
+        if grammar_is_likely_lark(gd_params.grammar):
+            # xgrammar supports EBNF grammars only
+            try:
+                gd_params.grammar = convert_lark_to_ebnf(gd_params.grammar)
+            except ValueError as e:
+                raise ValueError(
+                    "Failed to convert the grammar from Lark to EBNF. ") from e
+
+        # Test parsing EBNF grammar, possibly already converted from Lark
+        try:
+            # parse the grammar, but we aren't compiling it.
+            xgr.Grammar.from_ebnf(gd_params.grammar)
+        except Exception as e:
+            raise ValueError("Invalid grammar specification.") from e
+        return
+
+    if gd_params.structural_tag:
+        try:
+            s_tag = json.loads(gd_params.structural_tag)
+            tags = [
+                xgr.StructuralTagItem(
+                    begin=s["begin"],
+                    schema=json.dumps(s["schema"]),
+                    end=s["end"],
+                ) for s in s_tag["structures"]
+            ]
+            xgr.Grammar.from_structural_tag(tags, s_tag["triggers"])
+        except Exception as e:
+            raise ValueError("Invalid structural tag specification.") from e

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/structured_output/request.py

@@ -0,0 +1,86 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
+import dataclasses
+import functools
+import json
+from concurrent.futures import Future
+from concurrent.futures._base import TimeoutError
+from typing import Optional, Union, cast
+
+from vllm.sampling_params import SamplingParams
+from vllm.v1.structured_output.backend_types import (StructuredOutputGrammar,
+                                                     StructuredOutputKey,
+                                                     StructuredOutputOptions)
+
+
+@dataclasses.dataclass
+class StructuredOutputRequest:
+
+    sampling_params: SamplingParams
+    _grammar: Optional[Union[Future[StructuredOutputGrammar],
+                             StructuredOutputGrammar]] = None
+    reasoning_ended: Optional[bool] = None
+
+    def _check_grammar_completion(self) -> bool:
+        # NOTE: We have to lazy import to gate circular imports
+        from vllm.v1.request import RequestStatus
+
+        if isinstance(self._grammar, Future):
+            try:
+                # We will check whether the future is ready within 100 us
+                self._grammar = self._grammar.result(timeout=0.0001)
+                self.status = RequestStatus.WAITING
+            except TimeoutError:
+                return False
+        return True
+
+    @property
+    def is_grammar_ready(self) -> bool:
+        return self._check_grammar_completion()
+
+    @property
+    def grammar(self) -> Optional[StructuredOutputGrammar]:
+        completed = self._check_grammar_completion()
+        return cast(Optional[StructuredOutputGrammar],
+                    self._grammar) if completed else None
+
+    @grammar.setter
+    def grammar(
+        self, grammar: Union[StructuredOutputGrammar,
+                             Future[StructuredOutputGrammar]]
+    ) -> None:
+        self._grammar = grammar
+
+    @functools.cached_property
+    def structured_output_key(self) -> StructuredOutputKey:
+        return get_structured_output_key(self.sampling_params)
+
+
+def get_structured_output_key(
+        sampling_params: SamplingParams) -> StructuredOutputKey:
+    params = sampling_params.guided_decoding
+    assert params is not None, "params can't be None."
+    if params.json is not None:
+        if not isinstance(params.json, str):
+            json_str = json.dumps(params.json)
+        else:
+            json_str = params.json
+        return (StructuredOutputOptions.JSON, json_str)
+    elif params.json_object:
+        return (StructuredOutputOptions.JSON_OBJECT, "")
+    elif params.regex is not None:
+        return (StructuredOutputOptions.REGEX, params.regex)
+    elif params.choice is not None:
+        if not isinstance(params.choice, str):
+            json_str = json.dumps(params.choice)
+        else:
+            json_str = params.choice
+        return (StructuredOutputOptions.CHOICE, json_str)
+    elif params.grammar is not None:
+        return (StructuredOutputOptions.GRAMMAR, params.grammar)
+    elif params.structural_tag is not None:
+        return (StructuredOutputOptions.STRUCTURAL_TAG, params.structural_tag)
+    else:
+        raise ValueError("No valid structured output parameter found")

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/structured_output/utils.py

@@ -0,0 +1,373 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import hashlib
+import importlib.metadata
+import os
+from typing import TYPE_CHECKING
+
+import regex as re
+from cachetools import LRUCache
+from diskcache import Cache
+
+import vllm.envs as envs
+from vllm.logger import init_logger
+from vllm.utils import LazyLoader
+
+if TYPE_CHECKING:
+    import outlines_core as oc
+    import transformers.file_utils as file_utils
+    import transformers.models.gpt2.tokenization_gpt2 as tokenization_gpt2
+
+    from vllm.transformers_utils.tokenizer import AnyTokenizer
+else:
+    oc = LazyLoader("oc", globals(), "outlines_core")
+    file_utils = LazyLoader("file_utils", globals(), "transformers.file_utils")
+    tokenization_gpt2 = LazyLoader(
+        "tokenization_gpt2",
+        globals(),
+        "transformers.models.gpt2.tokenization_gpt2",
+    )
+
+logger = init_logger(__name__)
+
+CACHE = None
+
+
+class OutlinesVocabulary:
+    """
+    Wrapper class for `outlines_core.Vocabulary`,
+    which allows us to store a hash with the vocabulary
+    """
+
+    def __init__(self, vocabulary: oc.Vocabulary) -> None:
+        # Actual vocabulary object
+        self.inner = vocabulary
+        # Have to do abs(hash()) because python hashes can
+        # be negative, and we are using hash as a cache key.
+        hex_str = hashlib.sha256(
+            vocabulary.__repr__().encode('utf-8')).hexdigest()
+        hash_int = int(hex_str, 16)
+        self._hash = hash_int
+
+
+def get_outlines_cache_path() -> str:
+    """Get the context object that contains previously-computed return values"""
+    outlines_cache_dir = os.getenv("OUTLINES_CACHE_DIR")
+    xdg_cache_home = os.getenv("XDG_CACHE_HOME")
+    home_dir = os.path.expanduser("~")
+
+    if outlines_cache_dir:
+        # OUTLINES_CACHE_DIR takes precedence
+        return outlines_cache_dir
+    elif xdg_cache_home:
+        return os.path.join(xdg_cache_home, ".cache", "outlines")
+    # If homedir is "/", we may be inside a container, and thus writing to
+    # root would be problematic, so we fallback to using a tempfile.
+    # Also validate the path exists, since os.path.expanduser does
+    # not garuntee existence.
+    elif os.path.isdir(home_dir) and home_dir != "/":
+        # Default Unix fallback: ~/.cache/outlines
+        return os.path.join(home_dir, ".cache", "outlines")
+    else:
+        import tempfile
+
+        # home_dir may be / inside a docker container without existing user
+        tempdir = tempfile.gettempdir()
+        return os.path.join(tempdir, ".cache", "outlines")
+
+
+def get_outlines_cache():
+    """Get the Cache instance to be used for index caching"""
+
+    cache_dir = get_outlines_cache_path()
+    if envs.VLLM_V1_USE_OUTLINES_CACHE:
+        logger.warning("Enabling outlines cache. This is an unbounded on-disk "
+                       "cache. It may consume a lot of disk space and should "
+                       "not be used with untrusted clients.")
+        cache = Cache(cache_dir, eviction_policy="none", cull_limit=0)
+        outlines_version = importlib.metadata.version("outlines_core")
+
+        cached_version = cache.get('__version__', None)
+        if cached_version != outlines_version:
+            cache.clear()
+        cache.set('__version__', outlines_version)
+        return cache
+    else:
+        return LRUCache(maxsize=128)
+
+
+re_llama_byte_token = re.compile(r"^<0x[0-9A-F]{2}>$")
+re_replacement_seq = re.compile(r"^.{0,6}�+.{0,6}$")
+
+
+def _reduced_vocabulary(
+    tokenizer: AnyTokenizer,
+    eos_token_id: int,
+) -> dict[bytes, list[int]]:
+    """Create a map from vocabulary tokens to lists of equivalent token ids.
+
+    Returns:
+        A Dict of token string -> equivalent token ids
+    """
+
+    unicode_to_bytes = {
+        v: k
+        for k, v in tokenization_gpt2.bytes_to_unicode().items()
+    }
+
+    def convert_token_to_string(token: str) -> str:
+
+        string = tokenizer.convert_tokens_to_string([token])
+
+        # A hack to handle missing spaces to HF's Llama tokenizers
+        if (type(token) is str
+                and token.startswith(file_utils.SPIECE_UNDERLINE)
+                or token == "<0x20>"):
+            return " " + string
+
+        return string
+
+    vocabulary: dict[bytes, list[int]] = {}
+    empty_token_ids: list[int] = []
+    for token, token_idx in tokenizer.get_vocab().items():
+        if token in tokenizer.all_special_tokens:  # type: ignore
+            continue
+
+        token_str = convert_token_to_string(token)
+        if token_str:
+            if isinstance(token, (bytes, bytearray)):
+                # For BPE tokenizers where tokens are stored as bytes.
+
+                # safe to ignore since token_str is of type (bytearray, bytes)
+                # by this point.
+                token_bytes = bytes(token_str)  # type: ignore[arg-type]
+
+            elif "\ufffd" in token_str and not re_replacement_seq.match(
+                    token_str):
+                # Handle tokens with invalid UTF-8 sequences.
+                if re_llama_byte_token.match(token):
+                    # Llama-like tokenizers use <0xXX> for incomplete sequences.
+                    token_bytes = bytes([int(token[3:5], 16)])
+                else:
+                    # GPT2 tokenizers: map each byte back using unicode_to_bytes
+                    byte_vals = [unicode_to_bytes.get(c) for c in token]
+                    if None in byte_vals:
+                        raise RuntimeError(
+                            f"Cannot convert token `{token}`"
+                            f" ({token_idx}) to bytes: {token_str}")
+                    # safe to ignore, since if None in byte_vals,
+                    # an error is thrown.
+                    token_bytes = bytes(byte_vals)  # type: ignore[arg-type]
+            else:
+                token_bytes = token_str.encode('utf-8')
+
+            if token_idx != eos_token_id:
+                vocabulary.setdefault(token_bytes, []).append(token_idx)
+        else:
+            empty_token_ids.append(token_idx)
+
+    return vocabulary
+
+
+def get_outlines_vocabulary(tokenizer: AnyTokenizer) -> oc.Vocabulary:
+    """Get the `Vocabulary` object for a given tokenizer.
+    """
+    if hasattr(tokenizer, "_outlines_vocabulary"):
+        return tokenizer._outlines_vocabulary  # type: ignore
+
+    try:
+        if hasattr(
+                tokenizer,
+                "eos_token_id",
+        ) and tokenizer.eos_token_id is not None:
+            eos_token_id = tokenizer.eos_token_id
+        else:
+            raise ValueError(
+                f"Error during structured outputs setup for outlines: Tokenizer ({type(tokenizer)}) has no `eos_token_id` property, but `eos_token_id` is required for structured outputs to work properly."  # noqa: E501
+            )
+
+        reduced_vocab = _reduced_vocabulary(
+            tokenizer,
+            eos_token_id  #type: ignore
+        )
+        vocabulary = OutlinesVocabulary(
+            oc.Vocabulary(eos_token_id, reduced_vocab))
+        tokenizer._outlines_vocabulary = vocabulary  # type: ignore
+
+        return vocabulary
+    except AttributeError as e:
+        raise ValueError(f"Cannot get the vocabulary of the tokenizer "
+                         f"({type(tokenizer)}). The tokenizer should have a "
+                         "get_vocab method.") from e
+
+
+def grammar_is_likely_lark(grammar_str: str) -> bool:
+    """
+    Check if grammar appears to use Lark syntax.
+
+    Args:
+        grammar_str: Input grammar string
+
+    Returns:
+        bool: True if grammar appears to be in Lark format, False otherwise
+
+    Examples:
+        >>> grammar_is_likely_lark("rule: 'abc'")
+        True
+        >>> grammar_is_likely_lark("rule ::= 'abc'")
+        False
+    """
+    if not grammar_str or not isinstance(grammar_str, str):
+        return False
+
+    for line in grammar_str.split('\n'):
+        # Remove both comment styles
+        line = re.sub(r'(#|//).*$', '', line).strip()
+        if not line:
+            continue
+
+        # Look for EBNF rule definition
+        if '::=' in line:
+            return False
+
+    return True
+
+
+def convert_lark_to_ebnf(grammar_str: str) -> str:
+    """
+    Convert a Lark grammar string to EBNF format.
+
+    EBNF reference:
+    https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md
+    Lark grammar reference:
+    https://lark-parser.readthedocs.io/en/latest/grammar.html
+
+    Args:
+        grammar_str: Input grammar in Lark format
+
+    Returns:
+        str: Converted grammar in EBNF format
+
+    Examples:
+        >>> print(convert_lark_to_ebnf("rule: 'hello'"))
+        root ::= rule
+        rule ::= "hello"
+    """
+    if not isinstance(grammar_str, str):
+        raise ValueError(f"Grammar must be a string, got {type(grammar_str)}")
+    if not grammar_str.strip():
+        raise ValueError("Grammar string cannot be empty")
+
+    defined_rules = set()
+    referenced_rules = set()
+    output_lines = []
+
+    def clean_line(line: str) -> str:
+        """Remove comments and whitespace from line."""
+        return re.sub(r'(#|//).*$', '', line).strip()
+
+    def check_quotes(text: str, rule_name: str, line_num: int) -> None:
+        """Validate quote matching in text."""
+        if text.count("'") % 2 != 0 or text.count('"') % 2 != 0:
+            raise ValueError(
+                f"Mismatched quotes in {rule_name} on line {line_num}")
+
+    def extract_references(text: str) -> set[str]:
+        """Extract rule references from text."""
+        # Remove quoted strings and special characters
+        text = re.sub(r'"[^"]*"', '', text)
+        text = re.sub(r'[+*?()|\[\]{}]', ' ', text)
+        return set(re.findall(r'\b[a-zA-Z_][a-zA-Z0-9_]*\b', text))
+
+    # First pass: Find root rule and validate rule definitions
+    lines = [clean_line(line) for line in grammar_str.split('\n')]
+    first_rule = None
+
+    for line_num, line in enumerate(lines, 1):
+        if not line or line.startswith('|'):
+            continue
+
+        if ':' in line:
+            try:
+                name = line.split(':', 1)[0].strip().strip('?')
+                defined_rules.add(name)
+                if first_rule is None:
+                    first_rule = name
+                if name == 'start':
+                    first_rule = 'start'
+            except IndexError as e:
+                raise ValueError(f"Invalid rule format on line {line_num}. "
+                                 "Expected 'rule_name: definition'") from e
+
+    if not defined_rules:
+        raise ValueError("No valid rules found in grammar")
+
+    # Add root rule
+    output_lines.append(f"root ::= {first_rule}")
+
+    # Second pass: Process rule definitions and alternatives
+    current_rule = None
+    current_definition = []
+
+    for line_num, line in enumerate(lines, 1):
+        if not line:
+            continue
+
+        try:
+            if ':' in line and not line.startswith('|'):
+                # Save previous rule if exists
+                if current_rule:
+                    output_lines.append(
+                        f"{current_rule} ::= {' | '.join(current_definition)}")
+
+                # Process new rule
+                name, definition = line.split(':', 1)
+                current_rule = name.strip().strip('?')
+
+                check_quotes(definition, f"rule '{current_rule}'", line_num)
+                definition = re.sub(r"'([^']*)'", r'"\1"', definition)
+                referenced_rules.update(extract_references(definition))
+                current_definition = [definition.strip()]
+
+            elif line.startswith('|'):
+                if not current_rule:
+                    raise ValueError(f"Alternative '|' on line {line_num} "
+                                     "without a preceding rule definition")
+
+                alt_def = line[1:].strip()
+                check_quotes(alt_def, f"alternative for rule '{current_rule}'",
+                             line_num)
+                alt_def = re.sub(r"'([^']*)'", r'"\1"', alt_def)
+                referenced_rules.update(extract_references(alt_def))
+                current_definition.append(alt_def)
+
+        except ValueError as e:
+            raise ValueError(f"Error on line {line_num}: {str(e)}") from e
+
+    # Add final rule if exists
+    if current_rule:
+        output_lines.append(
+            f"{current_rule} ::= {' | '.join(current_definition)}")
+
+    # Validate all rules are defined
+    undefined_rules = referenced_rules - defined_rules - {'root'}
+    if undefined_rules:
+        raise ValueError("Referenced rules are not defined: "
+                         f"{', '.join(sorted(undefined_rules))}")
+
+    return '\n'.join(output_lines)
+
+
+def choice_as_grammar(choice: list[str]) -> str:
+
+    def escape_ebnf_string(s: str) -> str:
+        """Escape special characters in a EBNF string."""
+        # Escape double quotes and backslashes
+        return re.sub(r'(["\\])', r'\\\1', s)
+
+    escaped_choices = (escape_ebnf_string(c) for c in choice)
+    grammar = ('root ::= ' + ' | '.join(f'"{c}"' for c in escaped_choices))
+    return grammar

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/block_table.py

@@ -0,0 +1,174 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import numpy as np
+import torch
+
+from vllm.logger import init_logger
+from vllm.utils import cdiv
+
+logger = init_logger(__name__)
+
+
+class BlockTable:
+
+    def __init__(
+        self,
+        block_size: int,
+        max_num_reqs: int,
+        max_num_blocks_per_req: int,
+        max_num_batched_tokens: int,
+        pin_memory: bool,
+        device: torch.device,
+    ):
+        self.block_size = block_size
+        self.max_num_reqs = max_num_reqs
+        self.max_num_blocks_per_req = max_num_blocks_per_req
+        self.max_num_batched_tokens = max_num_batched_tokens
+        self.pin_memory = pin_memory
+        self.device = device
+
+        self.block_table = torch.zeros(
+            (max_num_reqs, max_num_blocks_per_req),
+            device=self.device,
+            dtype=torch.int32,
+        )
+        self.block_table_cpu = torch.zeros(
+            (max_num_reqs, max_num_blocks_per_req),
+            device="cpu",
+            dtype=torch.int32,
+            pin_memory=pin_memory,
+        )
+        self.block_table_np = self.block_table_cpu.numpy()
+        self.num_blocks_per_row = np.zeros(max_num_reqs, dtype=np.int32)
+
+        self.slot_mapping_cpu = torch.zeros(self.max_num_batched_tokens,
+                                            dtype=torch.int64,
+                                            device="cpu",
+                                            pin_memory=self.pin_memory)
+        self.slot_mapping_np = self.slot_mapping_cpu.numpy()
+        self.slot_mapping = torch.zeros(self.max_num_batched_tokens,
+                                        dtype=torch.int64,
+                                        device=self.device)
+
+    def append_row(
+        self,
+        block_ids: list[int],
+        row_idx: int,
+    ) -> None:
+        if not block_ids:
+            return
+        num_blocks = len(block_ids)
+        start = self.num_blocks_per_row[row_idx]
+        self.num_blocks_per_row[row_idx] += num_blocks
+        self.block_table_np[row_idx, start:start + num_blocks] = block_ids
+
+    def add_row(self, block_ids: list[int], row_idx: int) -> None:
+        self.num_blocks_per_row[row_idx] = 0
+        self.append_row(block_ids, row_idx)
+
+    def move_row(self, src: int, tgt: int) -> None:
+        num_blocks = self.num_blocks_per_row[src]
+        self.block_table_np[tgt, :num_blocks] = self.block_table_np[
+            src, :num_blocks]
+        self.num_blocks_per_row[tgt] = num_blocks
+
+    def swap_row(self, src: int, tgt: int) -> None:
+        num_blocks_src = self.num_blocks_per_row[src]
+        num_blocks_tgt = self.num_blocks_per_row[tgt]
+        self.num_blocks_per_row[src] = num_blocks_tgt
+        self.num_blocks_per_row[tgt] = num_blocks_src
+
+        self.block_table_np[[src, tgt]] = self.block_table_np[[tgt, src]]
+
+    def compute_slot_mapping(self, req_indices: np.ndarray,
+                             positions: np.ndarray) -> None:
+        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
+        # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
+        # where K is the max_num_blocks_per_req and the block size is 2.
+        # NOTE(woosuk): We can't simply use `token_indices // block_size`
+        # here because M (max_model_len) is not necessarily divisible by
+        # block_size.
+        block_table_indices = (req_indices * self.max_num_blocks_per_req +
+                               positions // self.block_size)
+        block_numbers = self.block_table_np.ravel()[block_table_indices]
+        block_offsets = positions % self.block_size
+        np.add(block_numbers * self.block_size,
+               block_offsets,
+               out=self.slot_mapping_np[:req_indices.shape[0]])
+
+    def commit_block_table(self, num_reqs: int) -> None:
+        self.block_table[:num_reqs].copy_(self.block_table_cpu[:num_reqs],
+                                          non_blocking=True)
+
+    def commit_slot_mapping(self, num_tokens: int) -> None:
+        self.slot_mapping[:num_tokens].copy_(
+            self.slot_mapping_cpu[:num_tokens], non_blocking=True)
+
+    def clear(self) -> None:
+        self.block_table.fill_(0)
+        self.block_table_cpu.fill_(0)
+
+    def get_device_tensor(self) -> torch.Tensor:
+        """Ruturns the device tensor of the block table."""
+        return self.block_table
+
+    def get_cpu_tensor(self) -> torch.Tensor:
+        """Returns the CPU tensor of the block table."""
+        return self.block_table_cpu
+
+    def get_numpy_array(self) -> np.ndarray:
+        """Returns the numpy array of the block table."""
+        return self.block_table_np
+
+
+class MultiGroupBlockTable:
+    """The BlockTables for each KV cache group."""
+
+    def __init__(self, max_num_reqs: int, max_model_len: int,
+                 max_num_batched_tokens: int, pin_memory: bool,
+                 device: torch.device, block_sizes: list[int]) -> None:
+        self.block_tables = [
+            BlockTable(block_size, max_num_reqs, cdiv(max_model_len,
+                                                      block_size),
+                       max_num_batched_tokens, pin_memory, device)
+            for block_size in block_sizes
+        ]
+
+    def append_row(self, block_ids: tuple[list[int], ...],
+                   row_idx: int) -> None:
+        for i, block_table in enumerate(self.block_tables):
+            block_table.append_row(block_ids[i], row_idx)
+
+    def add_row(self, block_ids: tuple[list[int], ...], row_idx: int) -> None:
+        for i, block_table in enumerate(self.block_tables):
+            block_table.add_row(block_ids[i], row_idx)
+
+    def move_row(self, src: int, tgt: int) -> None:
+        for block_table in self.block_tables:
+            block_table.move_row(src, tgt)
+
+    def swap_row(self, src: int, tgt: int) -> None:
+        for block_table in self.block_tables:
+            block_table.swap_row(src, tgt)
+
+    def compute_slot_mapping(self, req_indices: np.ndarray,
+                             positions: np.ndarray) -> None:
+        for block_table in self.block_tables:
+            block_table.compute_slot_mapping(req_indices, positions)
+
+    def commit_block_table(self, num_reqs: int) -> None:
+        for block_table in self.block_tables:
+            block_table.commit_block_table(num_reqs)
+
+    def commit_slot_mapping(self, num_tokens: int) -> None:
+        for block_table in self.block_tables:
+            block_table.commit_slot_mapping(num_tokens)
+
+    def clear(self) -> None:
+        for block_table in self.block_tables:
+            block_table.clear()
+
+    def __getitem__(self, idx: int) -> "BlockTable":
+        """Returns the BlockTable for the i-th KV cache group."""
+        return self.block_tables[idx]

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/cpu_model_runner.py

@@ -0,0 +1,124 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from contextlib import contextmanager
+from typing import TYPE_CHECKING, Any
+
+import torch
+import torch.nn as nn
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.model_loader import get_model
+from vllm.v1.attention.backends.cpu_attn import TorchSDPAMetadataBuilderV1
+from vllm.v1.worker.gpu_model_runner import GPUModelRunner
+
+if TYPE_CHECKING:
+    from vllm.v1.core.sched.output import SchedulerOutput
+
+logger = init_logger(__name__)
+
+
+class CPUModelRunner(GPUModelRunner):
+
+    def __init__(self, vllm_config: VllmConfig, device: torch.device):
+        super().__init__(vllm_config, device)
+
+        assert device == torch.device("cpu")
+        assert self.speculative_config is None, "spec decode is not supported."
+
+        self.use_cuda_graph = False
+        self.cascade_attn_enabled = False
+
+        self._postprocess_tensors()
+
+    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
+        """
+        Update the order of requests in the batch based on the attention
+        backend's needs. For example, some attention backends (namely MLA) may
+        want to separate requests based on if the attention computation will be
+        compute-bound or memory-bound.
+
+        Args:
+            scheduler_output: The scheduler output.
+        """
+        # Attention free models have zero kv_cache_goups, however models
+        # like Mamba are also attention free but use the kv_cache for
+        # keeping its internal state. This is why we check the number
+        # of kv_cache groups instead of solely checking
+        # for self.model_config.is_attention_free.
+        if len(self.kv_cache_config.kv_cache_groups) == 0:
+            return
+
+        if len(self.kv_cache_config.kv_cache_groups) > 1:
+            raise ValueError("Multiple KVCacheGroups is not"
+                             "currently supported with CPU model runner.")
+
+        assert type(self.attn_groups[0]
+                    [0].metadata_builder) is TorchSDPAMetadataBuilderV1
+
+        self.attn_groups[0][0].metadata_builder.reorder_batch(
+            self.input_batch, scheduler_output)
+
+    def _postprocess_tensors(self) -> None:
+        # Note: replace device tensors with cpu tensors
+        def replace_tensor(obj: Any, cpu_attr_name: str,
+                           device_attr_name) -> None:
+            cpu_tensor = getattr(obj, cpu_attr_name, None)
+            device_tensor = getattr(obj, device_attr_name, None)
+            if cpu_tensor is not None and device_tensor is not None:
+                assert isinstance(cpu_tensor, torch.Tensor)
+                assert isinstance(device_tensor, torch.Tensor)
+                setattr(obj, device_attr_name, cpu_tensor)
+
+        for k, v in vars(self).items():
+            if k.endswith("_cpu") and isinstance(v, torch.Tensor):
+                replace_tensor(self, k, k[:-4])
+
+        for k, v in vars(self.input_batch).items():
+            if k.endswith("_cpu_tensor") and isinstance(v, torch.Tensor):
+                replace_tensor(self.input_batch, k, k[:-11])
+
+        for block_table in self.input_batch.block_table.block_tables:
+            for k, v in vars(block_table).items():
+                if k.endswith("_cpu") and isinstance(v, torch.Tensor):
+                    replace_tensor(block_table, k, k[:-4])
+
+    def load_model(self, eep_scale_up: bool = False) -> None:
+        logger.info("Starting to load model %s...", self.model_config.model)
+        self.model = get_model(vllm_config=self.vllm_config)
+
+        if self.lora_config:
+            self.model = self.load_lora_model(self.model, self.model_config,
+                                              self.scheduler_config,
+                                              self.lora_config, self.device)
+
+    def get_model(self) -> nn.Module:
+        return self.model
+
+    def warming_up_model(self) -> None:
+        logger.info("Warming up model for the compilation...")
+        # Only generate graph for the generic shape
+        with _set_global_compilation_settings(self.vllm_config):
+            self._dummy_run(max(16, self.max_num_reqs))
+        logger.info("Warming up done.")
+
+    def _init_device_properties(self) -> None:
+        pass
+
+    def _sync_device(self) -> None:
+        pass
+
+
+@contextmanager
+def _set_global_compilation_settings(config: VllmConfig):
+    import torch._inductor.config
+
+    inductor_config = config.compilation_config.inductor_compile_config
+    try:
+        # Note: The MKLDNN and CPPGEMM backend requires freezing parameters.
+        freezing_value = torch._inductor.config.freezing
+        if inductor_config.get("max_autotune", False):
+            torch._inductor.config.freezing = True
+        yield
+    finally:
+        torch._inductor.config.freezing = freezing_value