Skip to content

vllm.v1.worker.gpu.structured_outputs

StructuredOutputsWorker

Source code in vllm/v1/worker/gpu/structured_outputs.py 
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
class StructuredOutputsWorker:
    def __init__(
        self,
        max_num_logits: int,
        vocab_size: int,
    ):
        # NOTE(woosuk): Here, we use UvaBufferPool instead of UvaBackedTensor
        # to save a unnecessary CPU-to-CPU copy.
        self.logits_indices = UvaBufferPool(max_num_logits, torch.int32)
        self.grammar_bitmask = UvaBufferPool(
            (max_num_logits, cdiv(vocab_size, 32)), torch.int32
        )

    def apply_grammar_bitmask(
        self,
        logits: torch.Tensor,
        input_batch: InputBatch,
        grammar_req_ids: list[str],
        grammar_bitmask: np.ndarray,
    ) -> None:
        if not grammar_req_ids:
            return

        # Construct bitmask -> logits mapping
        mapping: list[int] = []
        req_ids = input_batch.req_ids
        cu_num_logits = input_batch.cu_num_logits_np.tolist()
        req_id_to_idx = {req_id: i for i, req_id in enumerate(req_ids)}
        for grammar_req_id in grammar_req_ids:
            req_idx = req_id_to_idx[grammar_req_id]
            logits_start_idx = cu_num_logits[req_idx]
            logits_end_idx = cu_num_logits[req_idx + 1]
            mapping.extend(range(logits_start_idx, logits_end_idx))
        # Copy the mapping.
        mapping_np = np.array(mapping, dtype=np.int32)
        logits_indices = self.logits_indices.copy_to_uva(mapping_np)

        # Copy the bitmask.
        bitmask = self.grammar_bitmask.copy_to_uva(grammar_bitmask)

        num_masks = bitmask.shape[0]
        assert num_masks == len(mapping)
        vocab_size = logits.shape[-1]
        BLOCK_SIZE = 8192
        grid = (num_masks, triton.cdiv(vocab_size, BLOCK_SIZE))
        _apply_grammar_bitmask_kernel[grid](
            logits,
            logits.stride(0),
            logits_indices,
            bitmask,
            bitmask.stride(0),
            vocab_size,
            BLOCK_SIZE=BLOCK_SIZE,
        )

grammar_bitmask instance-attribute 

grammar_bitmask = UvaBufferPool(
    (max_num_logits, cdiv(vocab_size, 32)), int32
)

logits_indices instance-attribute 

logits_indices = UvaBufferPool(max_num_logits, int32)

__init__ 

__init__(max_num_logits: int, vocab_size: int)
Source code in vllm/v1/worker/gpu/structured_outputs.py 
13
14
15
16
17
18
19
20
21
22
23
def __init__(
    self,
    max_num_logits: int,
    vocab_size: int,
):
    # NOTE(woosuk): Here, we use UvaBufferPool instead of UvaBackedTensor
    # to save a unnecessary CPU-to-CPU copy.
    self.logits_indices = UvaBufferPool(max_num_logits, torch.int32)
    self.grammar_bitmask = UvaBufferPool(
        (max_num_logits, cdiv(vocab_size, 32)), torch.int32
    )

apply_grammar_bitmask 

apply_grammar_bitmask(
    logits: Tensor,
    input_batch: InputBatch,
    grammar_req_ids: list[str],
    grammar_bitmask: ndarray,
) -> None
Source code in vllm/v1/worker/gpu/structured_outputs.py 
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
def apply_grammar_bitmask(
    self,
    logits: torch.Tensor,
    input_batch: InputBatch,
    grammar_req_ids: list[str],
    grammar_bitmask: np.ndarray,
) -> None:
    if not grammar_req_ids:
        return

    # Construct bitmask -> logits mapping
    mapping: list[int] = []
    req_ids = input_batch.req_ids
    cu_num_logits = input_batch.cu_num_logits_np.tolist()
    req_id_to_idx = {req_id: i for i, req_id in enumerate(req_ids)}
    for grammar_req_id in grammar_req_ids:
        req_idx = req_id_to_idx[grammar_req_id]
        logits_start_idx = cu_num_logits[req_idx]
        logits_end_idx = cu_num_logits[req_idx + 1]
        mapping.extend(range(logits_start_idx, logits_end_idx))
    # Copy the mapping.
    mapping_np = np.array(mapping, dtype=np.int32)
    logits_indices = self.logits_indices.copy_to_uva(mapping_np)

    # Copy the bitmask.
    bitmask = self.grammar_bitmask.copy_to_uva(grammar_bitmask)

    num_masks = bitmask.shape[0]
    assert num_masks == len(mapping)
    vocab_size = logits.shape[-1]
    BLOCK_SIZE = 8192
    grid = (num_masks, triton.cdiv(vocab_size, BLOCK_SIZE))
    _apply_grammar_bitmask_kernel[grid](
        logits,
        logits.stride(0),
        logits_indices,
        bitmask,
        bitmask.stride(0),
        vocab_size,
        BLOCK_SIZE=BLOCK_SIZE,
    )

_apply_grammar_bitmask_kernel 

_apply_grammar_bitmask_kernel(
    logits_ptr,
    logits_stride,
    logits_indices_ptr,
    bitmask_ptr,
    bitmask_stride,
    vocab_size,
    BLOCK_SIZE: constexpr,
)
Source code in vllm/v1/worker/gpu/structured_outputs.py 
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
@triton.jit
def _apply_grammar_bitmask_kernel(
    logits_ptr,
    logits_stride,
    logits_indices_ptr,
    bitmask_ptr,
    bitmask_stride,
    vocab_size,
    BLOCK_SIZE: tl.constexpr,
):
    bitmask_idx = tl.program_id(0)
    logits_idx = tl.load(logits_indices_ptr + bitmask_idx)

    # Load the bitmask.
    block_id = tl.program_id(1)
    bitmask_offset = (block_id * BLOCK_SIZE) // 32 + tl.arange(0, BLOCK_SIZE // 32)
    packed_bitmask = tl.load(
        bitmask_ptr + bitmask_idx * bitmask_stride + bitmask_offset,
        mask=bitmask_offset < bitmask_stride,
    )
    # Unpack the bitmask.
    bitmask = ((packed_bitmask[:, None] >> (tl.arange(0, 32)[None, :])) & 1) == 0
    bitmask = bitmask.reshape(BLOCK_SIZE)

    # Apply the bitmask to the logits.
    block_offset = block_id * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    tl.store(
        logits_ptr + logits_idx * logits_stride + block_offset,
        -float("inf"),
        mask=bitmask & (block_offset < vocab_size),
    )