890 lines
33 KiB
Python
890 lines
33 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Attention layer with FlexAttention."""
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from dataclasses import dataclass
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import torch
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import torch._dynamo.decorators
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import torch.nn.functional as F
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from torch.nn.attention.flex_attention import (
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BlockMask,
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_mask_mod_signature,
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_score_mod_signature,
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and_masks,
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create_block_mask,
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flex_attention,
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)
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from vllm.attention.backends.abstract import (
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AttentionBackend,
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AttentionImpl,
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AttentionMetadata,
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AttentionType,
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is_quantized_kv_cache,
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)
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from vllm.config import VllmConfig
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from vllm.logger import init_logger
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from vllm.model_executor.layers.batch_invariant import (
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vllm_is_batch_invariant,
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)
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from vllm.utils import cdiv, is_torch_equal_or_newer
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from vllm.v1.attention.backends.utils import (
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AttentionMetadataBuilder,
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CommonAttentionMetadata,
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)
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from vllm.v1.kv_cache_interface import AttentionSpec
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logger = init_logger(__name__)
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create_block_mask_compiled = torch.compile(
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create_block_mask, fullgraph=True, mode="reduce-overhead"
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)
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flex_attention_compiled = torch.compile(flex_attention, fullgraph=True)
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def _offsets_to_doc_ids_tensor(offsets: torch.Tensor) -> torch.Tensor:
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device = offsets.device
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counts = offsets[1:] - offsets[:-1]
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return torch.repeat_interleave(
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torch.arange(len(counts), device=device, dtype=torch.int32), counts
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)
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def pad_to_multiple(x: torch.Tensor, multiple: int, dim: int):
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difference = (multiple - (x.shape[dim] % multiple)) % multiple
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if difference == 0:
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return x
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dim = dim if dim >= 0 else x.ndim + dim
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pad_list = []
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for i in range(x.ndim - 1, dim - 1, -1):
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if i == dim:
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pad_list.extend([0, difference])
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else:
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pad_list.extend([0, 0])
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return F.pad(x, pad_list, mode="constant", value=0)
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class FlexAttentionBackend(AttentionBackend):
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accept_output_buffer: bool = True
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@classmethod
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def get_supported_dtypes(cls) -> list[torch.dtype]:
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return [torch.float16, torch.bfloat16, torch.float32]
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@classmethod
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def validate_head_size(cls, head_size: int) -> None:
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return # FlexAttention supports any head size
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@staticmethod
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def get_name() -> str:
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return "FLEX_ATTENTION"
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@staticmethod
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def get_impl_cls() -> type["FlexAttentionImpl"]:
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return FlexAttentionImpl
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@staticmethod
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def get_metadata_cls() -> type["AttentionMetadata"]:
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return FlexAttentionMetadata
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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cache_dtype_str: str = "auto",
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) -> tuple[int, ...]:
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return (2, num_blocks, block_size, num_kv_heads, head_size)
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@staticmethod
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def get_builder_cls() -> type["FlexAttentionMetadataBuilder"]:
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return FlexAttentionMetadataBuilder
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@staticmethod
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def use_cascade_attention(*args, **kwargs) -> bool:
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return False
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# @torch.compile(fullgraph=True, mode="reduce-overhead")
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def physical_to_logical_mapping(
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block_table: torch.Tensor,
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seq_lens: torch.Tensor,
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block_size: int,
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total_blocks: int,
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) -> torch.Tensor:
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"""
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Creates an inverse mapping from physical block locations to logical indices.
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The original block_table maps from logical blocks to physical locations:
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Logical to Physical (Original block_table):
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┌───────────────────────────────────────────┐
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│ Request 0: │
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│ │
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│ Logical Blocks: 0 1 2 3 4 5 6 7 │
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│ │ │ │ │ │ │ │ │ │
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│ v v v v v v v v │
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│ Physical Blocks: 3 5 1 7 4 2 0 6 │
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└───────────────────────────────────────────┘
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This function creates the inverse mapping:
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Physical to Logical (Inverse mapping):
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┌───────────────────────────────────────────┐
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│ Request 0: │
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│ │
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│ Physical Blocks: 0 1 2 3 4 5 6 7 │
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│ │ │ │ │ │ │ │ │ │
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│ v v v v v v v v │
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│ Logical Blocks: 6 2 5 0 4 1 7 3 │
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└───────────────────────────────────────────┘
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If multiple logical blocks map to the same physical block,
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this function returns the first (minimum) logical block index.
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If a physical block is not mapped to by any logical block,
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its value in the result will be -1.
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IMPORTANT: Garbage Value Protection
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────────────────────────────────────
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The block_table tensor may contain garbage values in unused positions
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(beyond the actual sequence length). For example, if a sequence only
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needs 3 blocks but the table has space for 8:
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block_table[0] = [10, 25, 7, 999, 1234, 888, ...]
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^^^^^^^^^^^^^^^^^^^^
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garbage values
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These garbage values can cause issues because:
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1. They may map to valid physical blocks by coincidence
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2. The scatter_ operation will assign them logical indices
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3. Later attention computations may incorrectly access these blocks
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To prevent this, we use seq_lens and block_size to mask out unused
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entries, ensuring only valid block references are processed.
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Args:
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block_table: Tensor of shape [max_reqs, max_num_blocks]
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mapping logical blocks to physical locations. May contain
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garbage values in unused positions.
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seq_lens: Tensor of sequence lengths for each request. Used to
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determine how many blocks are actually needed per sequence.
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block_size: Size of each block in tokens. Used with seq_lens to
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compute the number of valid blocks per sequence.
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total_blocks: Total number of physical blocks available
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Returns:
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A tensor of shape [max_reqs, total_blocks] where each entry
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physical_to_logical[req_id, physical_block] contains the logical
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block index for that physical block, or -1 if unused.
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"""
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max_reqs, max_num_blocks = block_table.shape
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device = block_table.device
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physical_to_logical = torch.full(
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(max_reqs, total_blocks), -1, dtype=torch.long, device=device
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)
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# Only process valid blocks to avoid garbage values
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num_blocks_per_seq = cdiv(seq_lens, block_size)
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mask = (
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torch.arange(max_num_blocks, device=device)[None, :]
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< num_blocks_per_seq[:, None]
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)
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valid_block_table = torch.where(mask, block_table, 0)
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valid_logical_indices = torch.where(
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mask, torch.arange(max_num_blocks, device=device)[None, :], 0
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)
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physical_to_logical.scatter_(
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-1, valid_block_table.to(torch.int64), valid_logical_indices
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)
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# NB - Seems like block 0 is always empty so we reset it manually
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physical_to_logical[:, 0] = -1
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return physical_to_logical
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def unique_static_unsorted(
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x: torch.Tensor,
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*,
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M: int, # maximum positive value (0 is “skip me”)
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dim: int = -1, # axis along which to deduplicate
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ignored_val: int = 0, # value to ignore
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pad_val: int = -1, # sentinel for unused slots
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) -> torch.Tensor:
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"""
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- Keeps the first occurrence of each non-zero value while preserving order,
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then left-packs those uniques and fills the rest with `pad_val`.
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- Returns (packed, keep_mask) with the *same shape* as `x`.
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- Requires that all values be in the range [0, M]
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- Skips ignored_val
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Works on CPU or GPU, no Python loops, O(B·N) time / O(B·M) memory.
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Example:
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x =[3, 1, 0, 1, 2], M=3, ignored_val=0 => [3, 1, 2, -1, -1]
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"""
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if not (-1 <= pad_val <= M):
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raise ValueError("`pad_val` must lie in [-1, M]")
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# ── move `dim` to the end so we can treat tensor as [B, N] ──────────
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dim = dim % x.ndim
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x_perm = x.movedim(dim, -1) # shape [..., N]
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B, N = x_perm.numel() // x_perm.shape[-1], x_perm.shape[-1]
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x_flat = x_perm.reshape(B, N) # [B, N]
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device = x.device
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idx = torch.arange(N, device=device).expand(B, N) # per-row indices
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# ── build first-occurrence table for every v ∈ [0, M] ───────────────
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first_idx = torch.full((B, M + 1), N, device=device) # “∞”
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# scatter_reduce_: first_idx[b, v] = min(first_idx[b, v], i) for each i
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first_idx.scatter_reduce_(1, x_flat, idx, reduce="amin")
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# ── keep mask: first occurrence *and* value ≠ 0 ─────────────────────
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keep = (x_flat != ignored_val) & (idx == first_idx.gather(1, x_flat)) # [B, N]
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# ── left-pack uniques into a fresh tensor ───────────────────────────
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dest_pos = torch.cumsum(keep.to(torch.long), dim=1) - 1 # where to go
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packed_flat = torch.full_like(x_flat, pad_val)
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rows, src_cols = torch.nonzero(keep, as_tuple=True)
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packed_flat[rows, dest_pos[rows, src_cols]] = x_flat[rows, src_cols]
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# ── restore original layout ─────────────────────────────────────────
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packed = packed_flat.reshape(x_perm.shape).movedim(-1, dim)
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return packed
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def causal_mask_mod(
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b: torch.Tensor, h: torch.Tensor, q_idx: torch.Tensor, kv_idx: torch.Tensor
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):
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return q_idx >= kv_idx
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@dataclass
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class FlexAttentionMetadata:
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causal: bool
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num_actual_tokens: int # Number of tokens excluding padding.
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max_query_len: int
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query_start_loc: torch.Tensor
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max_seq_len: int
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seq_lens: torch.Tensor
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block_table: torch.Tensor
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slot_mapping: torch.Tensor
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use_cascade: bool
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common_prefix_len: int
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cu_prefix_query_lens: torch.Tensor | None
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prefix_kv_lens: torch.Tensor | None
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suffix_kv_lens: torch.Tensor | None
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# Block info
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total_cache_tokens: int
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block_size: int
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max_possible_sequence_length: int
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num_reqs: int
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physical_to_logical: torch.Tensor
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decode_offset: torch.Tensor
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num_blocks_per_seq: torch.Tensor
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# For logging.
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num_input_tokens: int = 0 # Number of tokens including padding.
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# Flex Metadata
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num_blocks = 0
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block_mask: BlockMask | None = None
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score_mod: _score_mod_signature | None = None
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logical_mask_mod: _mask_mod_signature = causal_mask_mod
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doc_ids: torch.Tensor | None = None
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direct_build: bool = True
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q_block_size: int = 16
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kv_block_size: int = 16
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transformed_score_mod: _score_mod_signature | None = None
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sliding_window: int | None = None
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def _convert_physical_to_logical(
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self,
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request_lookup: torch.Tensor,
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q_idx: torch.Tensor,
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physical_kv_idx: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Convert physical indices to logical indices for both query and kv.
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NB is_within_lower_bound: do sequences start on block_boundaries?
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Returns:
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tuple of (is_valid, logical_q_idx, logical_kv_idx)
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"""
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# Map query indices to corresponding request indices
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q_req = request_lookup[q_idx]
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# Convert physical KV indices to logical indices
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physical_kv_block = physical_kv_idx // self.block_size
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physical_kv_offset = physical_kv_idx % self.block_size
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logical_block_idx = self.physical_to_logical[q_req, physical_kv_block]
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logical_kv_idx = logical_block_idx * self.block_size + physical_kv_offset
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# Determine valid kv indices
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live_block = logical_block_idx >= 0
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within_upper_bound = logical_kv_idx < self.seq_lens[q_req]
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within_lower_bound = logical_kv_idx >= 0
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is_valid = live_block & within_upper_bound & within_lower_bound
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# Convert physical query indices to logical indices
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local_q_idx = q_idx - self.query_start_loc[q_req]
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logical_q_idx = local_q_idx + self.decode_offset[q_req]
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return is_valid, logical_q_idx, logical_kv_idx
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def get_causal_mask_mod(self) -> _mask_mod_signature:
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"""Creates the mask_mod function for FlexAttention.
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This function creates the combined mask mod function that handles:
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1. The paged attention block mapping
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2. The mapping from packed query sequences to logical query entries
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It also by defaults adds the decoding offset to the query indices.
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With this info we create the "logical" indices that are passed to
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mask_mod functions. This allows mask mod functions to be agnostic to
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layout of the query and key/value tensors.
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"""
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assert self.doc_ids is not None
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def final_mask_mod(
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b: torch.Tensor,
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h: torch.Tensor,
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q_idx: torch.Tensor,
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physical_kv_idx: torch.Tensor,
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) -> torch.Tensor:
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(is_valid, logical_q_idx, logical_kv_idx) = (
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self._convert_physical_to_logical(self.doc_ids, q_idx, physical_kv_idx)
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)
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# Apply mask modification only for valid indices
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return torch.where(
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is_valid,
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self.logical_mask_mod(b, h, logical_q_idx, logical_kv_idx),
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False,
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)
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return final_mask_mod
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def get_bidirectional_mask_mod(self) -> _mask_mod_signature:
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"""Creates the encoder mask_mod function for FlexAttention.
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Since the encoder bidirectional attention doesn't run with
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KV cache, this function creates a mask based on the
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packed query sequences.
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"""
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# Create a lookup mapping from query indices -> request number
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request_lookup = _offsets_to_doc_ids_tensor(self.query_start_loc)
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def final_mask_mod(
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b: torch.Tensor,
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h: torch.Tensor,
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q_idx: torch.Tensor,
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kv_idx: torch.Tensor,
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) -> torch.Tensor:
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return request_lookup[q_idx] == request_lookup[kv_idx]
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return final_mask_mod
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def get_sliding_window_mask_mod(self) -> _mask_mod_signature:
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"""Creates the sliding window mask_mod function for FlexAttention.
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Note that the sliding window mask here is bidirectional, we need
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to mask it with the bidirectional/causal mask for encoder/decoder.
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"""
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if self.sliding_window is None:
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raise ValueError("sliding_window must be set for sliding window attention")
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def sliding_window_mask_mod(
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b: torch.Tensor, h: torch.Tensor, q_idx: torch.Tensor, kv_idx: torch.Tensor
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):
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return torch.abs(q_idx - kv_idx) < self.sliding_window
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def final_mask_mod(
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b: torch.Tensor,
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h: torch.Tensor,
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q_idx: torch.Tensor,
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physical_kv_idx: torch.Tensor,
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) -> torch.Tensor:
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(is_valid, logical_q_idx, logical_kv_idx) = (
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self._convert_physical_to_logical(self.doc_ids, q_idx, physical_kv_idx)
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)
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return torch.where(
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is_valid,
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sliding_window_mask_mod(b, h, logical_q_idx, logical_kv_idx),
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False,
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)
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return final_mask_mod if self.causal else sliding_window_mask_mod
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def get_mask_mod(self):
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# Stage-1: initialize the base mask_mod
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# (causal mask for decoder or bidirectional mask for encoder)
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if self.causal:
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mask_mod = self.get_causal_mask_mod()
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else:
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mask_mod = self.get_bidirectional_mask_mod()
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# stage-2: add external mask_mod for special attention during
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# forwarding runtime to create the combined mask_mod.
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if self.sliding_window is not None:
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# Add sliding window mask for sliding window attention
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sliding_window_mask_mod = self.get_sliding_window_mask_mod()
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mask_mod = and_masks(mask_mod, sliding_window_mask_mod)
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return mask_mod
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def get_transformed_score_mod(self) -> _score_mod_signature | None:
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"""Creates the transformed score_mod function for FlexAttention.
|
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This function wraps the user's score_mod to handle physical-to-logical
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index conversion, similar to how get_mask_mod works for mask functions.
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||
"""
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||
if self.score_mod is None:
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return None
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# Create a lookup mapping from query indices -> request number
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||
request_lookup = _offsets_to_doc_ids_tensor(self.query_start_loc)
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user_score_mod = self.score_mod
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def transformed_score_mod(
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score: torch.Tensor,
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b: torch.Tensor,
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h: torch.Tensor,
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q_idx: torch.Tensor,
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physical_kv_idx: torch.Tensor,
|
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) -> torch.Tensor:
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(is_valid, logical_q_idx, logical_kv_idx) = (
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self._convert_physical_to_logical(
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request_lookup, q_idx, physical_kv_idx
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||
)
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||
)
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return torch.where(
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is_valid,
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user_score_mod(
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score, b, h, logical_q_idx, logical_kv_idx, physical_q=q_idx
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),
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-float("inf"),
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||
)
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return transformed_score_mod
|
||
|
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def _build_block_mask_direct(self) -> BlockMask:
|
||
"""Direct block mask construction for standard causal attention.
|
||
|
||
This method constructs the block mask directly using
|
||
BlockMask.from_kv_blocks which is much more efficient than the
|
||
generic create_block_mask approach.
|
||
|
||
The direct path works as follows:
|
||
1. For each query token, fetch blocks from block_table using max_seq_len
|
||
(this fetches more blocks than needed for shorter sequences)
|
||
2. Group query tokens into chunks of q_block_size
|
||
3. For each group, deduplicate the blocks using unique_static_unsorted
|
||
4. Create BlockMask using the deduplicated block indices
|
||
|
||
Over-estimation occurs when a group of q_block_size tokens contains
|
||
multiple sequence IDs (doc_ids). In this case, we fetch ALL blocks for
|
||
each sequence represented in the group, even though individual query
|
||
tokens may only need a subset of those blocks based on causal masking
|
||
and their position.
|
||
|
||
"""
|
||
page_to_block_ratio = self.kv_block_size // self.block_size
|
||
if page_to_block_ratio != 1:
|
||
raise ValueError(
|
||
f"FlexAttention currently requires the cache block size "
|
||
f"({self.block_size}) to be equal to the kv_block_size "
|
||
f"({self.kv_block_size}). Please check your model's "
|
||
f"configuration."
|
||
)
|
||
|
||
used_pages = self.block_table[
|
||
self.doc_ids, : cdiv(self.max_seq_len, self.block_size)
|
||
]
|
||
used_pages_padded = pad_to_multiple(
|
||
used_pages, multiple=self.q_block_size, dim=0
|
||
)
|
||
used_pages_padded = used_pages_padded.reshape(
|
||
used_pages_padded.shape[0] // self.q_block_size, -1
|
||
)
|
||
used_pages_padded = used_pages_padded // page_to_block_ratio
|
||
kv_indices = unique_static_unsorted(
|
||
(used_pages_padded.long()), M=self.num_blocks
|
||
).to(torch.int32)
|
||
|
||
kv_num_blocks = (kv_indices >= 0).sum(dim=-1).to(torch.int32)
|
||
block_mask_kwargs = {
|
||
"seq_lengths": (self.num_actual_tokens, self.total_cache_tokens),
|
||
"kv_num_blocks": kv_num_blocks[None, None],
|
||
"kv_indices": kv_indices[None, None],
|
||
"full_kv_num_blocks": None,
|
||
"full_kv_indices": None,
|
||
"BLOCK_SIZE": (self.q_block_size, self.kv_block_size),
|
||
"mask_mod": self.mask_mod,
|
||
}
|
||
|
||
# compute_q_blocks parameter is available in PyTorch 2.9+
|
||
if is_torch_equal_or_newer("2.9.0.dev0"):
|
||
block_mask_kwargs["compute_q_blocks"] = False
|
||
return BlockMask.from_kv_blocks(**block_mask_kwargs)
|
||
|
||
def build_block_mask(self) -> BlockMask:
|
||
mask_mod = self.get_mask_mod()
|
||
kv_len = self.total_cache_tokens if self.causal else self.num_actual_tokens
|
||
return create_block_mask_compiled(
|
||
mask_mod,
|
||
None,
|
||
None,
|
||
self.num_actual_tokens,
|
||
kv_len,
|
||
device=self.block_table.device,
|
||
BLOCK_SIZE=(self.q_block_size, self.kv_block_size),
|
||
)
|
||
|
||
def __post_init__(self):
|
||
assert self.use_cascade is False, "Not implemented yet."
|
||
assert self.common_prefix_len == 0, "Not implemented yet."
|
||
assert self.cu_prefix_query_lens is None, "Not implemented yet."
|
||
assert self.prefix_kv_lens is None, "Not implemented yet."
|
||
assert self.suffix_kv_lens is None, "Not implemented yet."
|
||
# Create a lookup mapping from query indices -> request number
|
||
self.doc_ids = _offsets_to_doc_ids_tensor(self.query_start_loc)
|
||
self.num_blocks = self.total_cache_tokens // self.block_size
|
||
|
||
self.mask_mod = self.get_mask_mod()
|
||
self.transformed_score_mod = self.get_transformed_score_mod()
|
||
|
||
if self.direct_build and self.causal:
|
||
self.block_mask = self._build_block_mask_direct()
|
||
else:
|
||
self.block_mask = self.build_block_mask()
|
||
|
||
|
||
class FlexAttentionMetadataBuilder(AttentionMetadataBuilder[FlexAttentionMetadata]):
|
||
def __init__(
|
||
self,
|
||
kv_cache_spec: AttentionSpec,
|
||
layer_names: list[str],
|
||
vllm_config: VllmConfig,
|
||
device: torch.device,
|
||
):
|
||
super().__init__(kv_cache_spec, layer_names, vllm_config, device)
|
||
|
||
self.model_config = vllm_config.model_config
|
||
self.parallel_config = vllm_config.parallel_config
|
||
self.cache_config = vllm_config.cache_config
|
||
|
||
self.num_heads_q = self.model_config.get_num_attention_heads(
|
||
self.parallel_config
|
||
)
|
||
self.num_heads_kv = self.model_config.get_num_kv_heads(self.parallel_config)
|
||
self.headdim = self.model_config.get_head_size()
|
||
self.block_size = kv_cache_spec.block_size
|
||
self.kv_cache_spec = kv_cache_spec
|
||
self.direct_build: bool = is_torch_equal_or_newer("2.9.0.dev0")
|
||
self.q_block_size: int = 16 if is_torch_equal_or_newer("2.9.0.dev0") else 128
|
||
self.kv_block_size: int = 16 if is_torch_equal_or_newer("2.9.0.dev0") else 128
|
||
|
||
def build(
|
||
self,
|
||
common_prefix_len: int,
|
||
common_attn_metadata: CommonAttentionMetadata,
|
||
fast_build: bool = False,
|
||
) -> FlexAttentionMetadata:
|
||
num_reqs = common_attn_metadata.num_reqs
|
||
num_actual_tokens = common_attn_metadata.num_actual_tokens
|
||
max_query_len = common_attn_metadata.max_query_len
|
||
|
||
max_seq_len = common_attn_metadata.max_seq_len
|
||
query_start_loc = common_attn_metadata.query_start_loc
|
||
seq_lens = common_attn_metadata.seq_lens
|
||
block_table_tensor = common_attn_metadata.block_table_tensor
|
||
slot_mapping = common_attn_metadata.slot_mapping
|
||
num_blocks_per_seq = cdiv(seq_lens, self.block_size)
|
||
|
||
use_cascade = common_prefix_len > 0
|
||
cu_prefix_query_lens = None
|
||
prefix_kv_lens = None
|
||
suffix_kv_lens = None
|
||
if use_cascade:
|
||
raise NotImplementedError("Not yet my friend")
|
||
|
||
block_size = self.kv_cache_spec.block_size
|
||
max_possible_seq_len = self.model_config.max_model_len
|
||
num_gpu_blocks = self.cache_config.num_gpu_blocks
|
||
|
||
assert num_gpu_blocks is not None, (
|
||
"FlexAttention requires num_gpu_blocks to be set"
|
||
)
|
||
total_cache_tokens = num_gpu_blocks * block_size
|
||
|
||
inverse_block_table = physical_to_logical_mapping(
|
||
block_table_tensor, seq_lens, block_size, num_gpu_blocks
|
||
)
|
||
|
||
offset_tensor = common_attn_metadata.num_computed_tokens_cpu.to(
|
||
self.device, non_blocking=True
|
||
)
|
||
|
||
out = FlexAttentionMetadata(
|
||
causal=common_attn_metadata.causal,
|
||
num_actual_tokens=num_actual_tokens,
|
||
max_query_len=max_query_len,
|
||
query_start_loc=query_start_loc,
|
||
max_seq_len=max_seq_len,
|
||
seq_lens=seq_lens,
|
||
block_table=block_table_tensor,
|
||
slot_mapping=slot_mapping,
|
||
use_cascade=use_cascade,
|
||
common_prefix_len=common_prefix_len,
|
||
cu_prefix_query_lens=cu_prefix_query_lens,
|
||
prefix_kv_lens=prefix_kv_lens,
|
||
suffix_kv_lens=suffix_kv_lens,
|
||
block_size=block_size,
|
||
max_possible_sequence_length=max_possible_seq_len,
|
||
num_reqs=num_reqs,
|
||
physical_to_logical=inverse_block_table,
|
||
total_cache_tokens=total_cache_tokens,
|
||
decode_offset=offset_tensor,
|
||
num_blocks_per_seq=num_blocks_per_seq,
|
||
direct_build=self.direct_build,
|
||
q_block_size=self.q_block_size,
|
||
kv_block_size=self.kv_block_size,
|
||
)
|
||
return out
|
||
|
||
def use_cascade_attention(self, *args, **kwargs) -> bool:
|
||
return False
|
||
|
||
|
||
class FlexAttentionImpl(AttentionImpl):
|
||
sliding_window: int | None
|
||
alibi_slopes: torch.Tensor | None
|
||
logits_soft_cap: float | None
|
||
|
||
def __init__(
|
||
self,
|
||
num_heads: int,
|
||
head_size: int,
|
||
scale: float,
|
||
num_kv_heads: int,
|
||
alibi_slopes: list[float] | None,
|
||
sliding_window: int | None,
|
||
kv_cache_dtype: str,
|
||
logits_soft_cap: float | None = None,
|
||
attn_type: AttentionType = AttentionType.DECODER,
|
||
kv_sharing_target_layer_name: str | None = None,
|
||
**kwargs,
|
||
) -> None:
|
||
self.num_heads = num_heads
|
||
self.head_size = head_size
|
||
self.scale = float(scale)
|
||
self.num_kv_heads = num_kv_heads
|
||
self.attn_type = attn_type
|
||
|
||
if attn_type not in (AttentionType.ENCODER_ONLY, AttentionType.DECODER):
|
||
raise NotImplementedError(
|
||
f"FlexAttention does not support {attn_type} attention"
|
||
)
|
||
|
||
if alibi_slopes is not None:
|
||
raise NotImplementedError(
|
||
"FlexAttention does not support alibi slopes yet."
|
||
)
|
||
else:
|
||
self.alibi_slopes = None
|
||
|
||
self.sliding_window = sliding_window
|
||
|
||
self.kv_cache_dtype = kv_cache_dtype
|
||
self.logits_soft_cap = logits_soft_cap
|
||
if self.logits_soft_cap is not None:
|
||
raise NotImplementedError(
|
||
"FlexAttention does not support logits soft cap yet."
|
||
)
|
||
|
||
assert self.num_heads % self.num_kv_heads == 0
|
||
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
|
||
|
||
if kv_sharing_target_layer_name is not None:
|
||
raise NotImplementedError("FlexAttention does not support kv sharing yet.")
|
||
|
||
FlexAttentionBackend.validate_head_size(head_size)
|
||
if is_quantized_kv_cache(self.kv_cache_dtype):
|
||
raise NotImplementedError(
|
||
"FlexAttention does not support quantized kv-cache. Yet"
|
||
)
|
||
|
||
@staticmethod
|
||
def view_as_4d(tensor: torch.Tensor) -> torch.Tensor:
|
||
"""View a 3d tensor as 4D."""
|
||
if tensor.ndim == 4:
|
||
return tensor
|
||
assert tensor.ndim == 3
|
||
return tensor[None, :, :, :]
|
||
|
||
def forward(
|
||
self,
|
||
layer: torch.nn.Module,
|
||
query: torch.Tensor,
|
||
key: torch.Tensor,
|
||
value: torch.Tensor,
|
||
kv_cache: torch.Tensor,
|
||
attn_metadata: FlexAttentionMetadata,
|
||
output: torch.Tensor | None = None,
|
||
output_scale: torch.Tensor | None = None,
|
||
output_block_scale: torch.Tensor | None = None,
|
||
) -> torch.Tensor:
|
||
"""Forward pass with FLexAttention.
|
||
|
||
Args:
|
||
query: shape = [num_tokens, num_heads, head_size]
|
||
key: shape = [num_tokens, num_kv_heads, head_size]
|
||
value: shape = [num_tokens, num_kv_heads, head_size]
|
||
kv_cache: shape =
|
||
[2, num_blocks, block_size, num_kv_heads, head_size]
|
||
attn_metadata: Metadata for attention.
|
||
Returns:
|
||
shape = [num_tokens, num_heads * head_size]
|
||
"""
|
||
assert output is not None, "Output tensor must be provided."
|
||
if output_scale is not None or output_block_scale is not None:
|
||
raise NotImplementedError(
|
||
"fused output quantization is not yet supported for FlexAttentionImpl"
|
||
)
|
||
|
||
enable_gqa = self.num_kv_heads != self.num_heads
|
||
|
||
if attn_metadata is None:
|
||
# Profiling run.
|
||
return output.fill_(0)
|
||
# query = self.view_as_4d(query).permute(0, 2, 1, 3)
|
||
# return torch.empty_like(query)
|
||
|
||
num_actual_tokens = attn_metadata.num_actual_tokens
|
||
|
||
if attn_metadata.sliding_window != self.sliding_window:
|
||
attn_metadata.sliding_window = self.sliding_window
|
||
if attn_metadata.direct_build:
|
||
# TODO: Support skipping the computation of sliding window
|
||
# in direct block mask building code path.
|
||
logger.warning_once(
|
||
"Using direct block mask building with sliding window, "
|
||
"which is suboptimal now. Performance may be degraded."
|
||
)
|
||
# update mask mod in attention metadata
|
||
attn_metadata.mask_mod = attn_metadata.get_mask_mod()
|
||
attn_metadata.block_mask = attn_metadata._build_block_mask_direct()
|
||
else:
|
||
attn_metadata.block_mask = attn_metadata.build_block_mask()
|
||
|
||
if not attn_metadata.causal:
|
||
assert self.attn_type == AttentionType.ENCODER_ONLY
|
||
|
||
query, key_tensor, value_tensor = map(
|
||
lambda x: self.view_as_4d(x).permute(0, 2, 1, 3),
|
||
(query, key, value),
|
||
)
|
||
|
||
query = query[:, :, :num_actual_tokens, :]
|
||
if (key_tensor.size(-2) > num_actual_tokens) or (
|
||
value_tensor.size(-2) > num_actual_tokens
|
||
):
|
||
# In the encoder-only model with torch.compile,
|
||
# qkv might be padded, which might cause exception.
|
||
# see: https://github.com/vllm-project/vllm/pull/24872#discussion_r2353252290
|
||
key_tensor = key_tensor[:, :, :num_actual_tokens, :]
|
||
value_tensor = value_tensor[:, :, :num_actual_tokens, :]
|
||
|
||
else:
|
||
assert self.attn_type == AttentionType.DECODER
|
||
key_cache, value_cache = kv_cache.unbind(0)
|
||
|
||
torch.ops._C_cache_ops.reshape_and_cache_flash(
|
||
key,
|
||
value,
|
||
key_cache,
|
||
value_cache,
|
||
attn_metadata.slot_mapping,
|
||
self.kv_cache_dtype,
|
||
layer._k_scale,
|
||
layer._v_scale,
|
||
)
|
||
|
||
# View out the block_size dim
|
||
key_cache = key_cache.view(-1, self.num_kv_heads, self.head_size)
|
||
value_cache = value_cache.view(-1, self.num_kv_heads, self.head_size)
|
||
query, key_tensor, value_tensor = map(
|
||
lambda x: self.view_as_4d(x).permute(0, 2, 1, 3),
|
||
(query, key_cache, value_cache),
|
||
)
|
||
|
||
query = query[:, :, :num_actual_tokens, :]
|
||
|
||
# Doesn't work for now -> constraint violation
|
||
# torch._dynamo.try_mark_dynamic(query, 2)
|
||
|
||
assert attn_metadata.block_mask is not None
|
||
block_m, block_n = attn_metadata.block_mask.BLOCK_SIZE
|
||
|
||
kernel_options = get_kernel_options(
|
||
query, block_m, block_n, attn_metadata.direct_build
|
||
)
|
||
out = flex_attention_compiled(
|
||
query,
|
||
key_tensor,
|
||
value_tensor,
|
||
attn_metadata.transformed_score_mod,
|
||
attn_metadata.block_mask,
|
||
self.scale,
|
||
enable_gqa=enable_gqa,
|
||
kernel_options=kernel_options,
|
||
)
|
||
|
||
# Flex doesn't have an out variant today, rely on epilogue fusion
|
||
out = out.permute(0, 2, 1, 3).squeeze(0)
|
||
output[:num_actual_tokens, :, :].copy_(out)
|
||
return output
|
||
|
||
|
||
def get_kernel_options(
|
||
query, block_m, block_n, use_direct_build: bool
|
||
) -> dict[str, int | bool]:
|
||
kernel_options: dict[str, int | bool] = {
|
||
"FORCE_USE_FLEX_ATTENTION": True,
|
||
}
|
||
if vllm_is_batch_invariant():
|
||
kernel_options["BLOCK_M"] = 16
|
||
kernel_options["BLOCK_N"] = 16
|
||
kernel_options["IS_DIVISIBLE"] = False
|
||
return kernel_options
|
||
if use_direct_build:
|
||
kernel_options["BLOCK_M"] = block_m
|
||
kernel_options["BLOCK_N"] = block_n
|
||
return kernel_options
|
||
else:
|
||
kernel_options["BLOCK_M"] = 64
|
||
kernel_options["BLOCK_N"] = 64
|
||
if query.dtype == torch.float32:
|
||
kernel_options["BLOCK_M"] = 32
|
||
kernel_options["BLOCK_N"] = 32
|
||
# if current_platform.is_cuda():
|
||
if torch.cuda.is_available():
|
||
device_props = torch.cuda.get_device_properties()
|
||
max_shared_memory = device_props.shared_memory_per_block_optin
|
||
if max_shared_memory < 144 * 1024:
|
||
kernel_options["BLOCK_M"] = kernel_options["BLOCK_M"] // 2
|
||
kernel_options["BLOCK_N"] = kernel_options["BLOCK_N"] // 2
|
||
|
||
return kernel_options
|