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from cudnn.datatypes import _convert_to_cutlass_data_type
from cudnn.api_base import APIBase, TupleDict
import cutlass
import cutlass.cute as cute
from cutlass.cute.runtime import make_fake_stream
from cuda.bindings import driver as cuda
import torch
from typing import Tuple, Optional
import math
class SelectionAttention(APIBase):
def __init__(
self,
sample_q: torch.Tensor,
sample_k: torch.Tensor,
sample_v: torch.Tensor,
sample_o: torch.Tensor,
sample_l: torch.Tensor,
sample_m: torch.Tensor,
sample_block_indices: torch.Tensor,
sample_block_counts: torch.Tensor,
sample_cum_seqlen_q: Optional[torch.Tensor] = None,
sample_cum_seqlen_k: Optional[torch.Tensor] = None,
max_s_q: Optional[int] = 1024,
max_s_k: Optional[int] = 1024,
acc_dtype: torch.dtype = torch.float32,
block_size: int = 64,
scale_softmax: Optional[float] = None,
):
super().__init__()
self._kernel = HopperSelectAttentionFwd
self._logger.warning("SelectionAttention is an experimental API")
self._logger.debug("Entering __init__")
self.q_desc = self._make_tensor_desc(sample_q, name="sample_q")
self.k_desc = self._make_tensor_desc(sample_k, name="sample_k")
self.v_desc = self._make_tensor_desc(sample_v, name="sample_v")
self.o_desc = self._make_tensor_desc(sample_o, name="sample_o")
self.l_desc = self._make_tensor_desc(sample_l, name="sample_l")
self.m_desc = self._make_tensor_desc(sample_m, name="sample_m")
self.block_indices_desc = self._make_tensor_desc(sample_block_indices, name="sample_block_indices")
self.block_counts_desc = self._make_tensor_desc(sample_block_counts, name="sample_block_counts")
self.cum_seqlen_q_desc = self._make_tensor_desc(sample_cum_seqlen_q, name="sample_cum_seqlen_q")
self.cum_seqlen_k_desc = self._make_tensor_desc(sample_cum_seqlen_k, name="sample_cum_seqlen_k")
if sample_cum_seqlen_q is not None and sample_cum_seqlen_k is not None:
if not torch.equal(sample_cum_seqlen_q, sample_cum_seqlen_k):
raise NotImplementedError("sample_cum_seqlen_k is not yet supported. Must be None or identical to sample_cum_seqlen_q")
self.max_s_q = max_s_q
self.max_s_k = max_s_k
# Types and kernel configuration
self.acc_dtype = acc_dtype
self.block_size = block_size
# Derived attributes (populated in check_support)
self.input_layout = None
self.dtype = None
self.h_q = None
self.h_kv = None
self.gqa_group_size = None
self.head_dim = None
self.value_dim = None
self.scale_softmax = scale_softmax
self._logger.debug(
f"__init__ completed with args: sample_q {self.q_desc.shape}, sample_k {self.k_desc.shape}, sample_v {self.v_desc.shape}, sample_o {self.o_desc.shape}, sample_l {self.l_desc.shape}, sample_m {self.m_desc.shape}, sample_block_indices {self.block_indices_desc.shape}, sample_block_counts {self.block_counts_desc.shape}, sample_cum_seqlen_q {self.cum_seqlen_q_desc.shape if self.cum_seqlen_q_desc is not None else 'None'}, sample_cum_seqlen_k {self.cum_seqlen_k_desc.shape if self.cum_seqlen_k_desc is not None else 'None'}, acc_dtype {acc_dtype}, max_s_q {max_s_q}, max_s_k {max_s_k}, block_size {block_size}, scale_softmax {scale_softmax}"
)
def check_support(self) -> bool:
self._logger.debug("Entering check_support")
# Shape normalization and validation
self._logger.debug("Checking shape normalization and validation")
if self.q_desc.ndim == 4:
# B, H_q, S, D format
self.input_layout = "B,H,S,D"
raise NotImplementedError("B, H_q, S, D format not implemented")
elif self.q_desc.ndim == 3:
# T, H_q, D format
self.input_layout = "T,H,D"
t, h_q, d_qk = self.q_desc.shape
t, h_kv, d_qk = self.k_desc.shape
t, h_kv, d_v = self.v_desc.shape
t, h_q, d_v = self.o_desc.shape
self._check_tensor_shape(self.q_desc, (t, h_q, d_qk), name="Q")
self._check_tensor_shape(self.k_desc, (t, h_kv, d_qk), name="K")
self._check_tensor_shape(self.v_desc, (t, h_kv, d_v), name="V")
self._check_tensor_shape(self.o_desc, (t, h_q, d_v), name="O")
self.l_desc = self._unpad_tensor_to_ndim(self.l_desc, 2, "sample_l")
self._check_tensor_shape(self.l_desc, (t, h_q), name="L")
self.m_desc = self._unpad_tensor_to_ndim(self.m_desc, 2, "sample_m")
self._check_tensor_shape(self.m_desc, (t, h_q), name="M")
if self.cum_seqlen_q_desc is None:
raise ValueError(f"cum_seqlen_q must be provided for T,H,D format, got {self.cum_seqlen_q_desc}")
if self.max_s_q is None:
raise ValueError(f"max_s_q must be provided for T,H,D format, got {self.max_s_q}")
if self.max_s_k is not None and self.max_s_q != self.max_s_k:
raise NotImplementedError(f"SelectionAttention requires max_s_q and max_s_k to be identical, but got {self.max_s_q} and {self.max_s_k}")
self.batch_size = self.cum_seqlen_q_desc.shape[0] - 1
if self.batch_size <= 0:
raise ValueError(f"batch_size (len(cum_seqlen_q) - 1) must be greater than 0, got {self.batch_size}")
if self.cum_seqlen_q_desc.dtype not in (torch.int32, torch.int64):
raise ValueError(f"cum_seqlen_q must be int32 or int64, got {self.cum_seqlen_q_desc.dtype}")
if self.block_indices_desc.shape[:2] != (t, h_kv) and self.block_indices_desc.ndim != 3:
raise ValueError(f"block_indices shape mismatch: expected {(t, h_kv, 'K')}, got {tuple(self.block_indices_desc.shape)}")
if self.block_counts_desc.shape != (t, h_kv):
raise ValueError(f"block_counts shape mismatch: expected {(t, h_kv)}, got {tuple(self.block_counts_desc.shape)}")
if self.block_indices_desc.dtype != torch.int32 or self.block_counts_desc.dtype != torch.int32:
raise ValueError(f"block_indices and block_counts must be int32, got {self.block_indices_desc.dtype} and {self.block_counts_desc.dtype}")
else:
raise ValueError(f"q must be rank-3 (T,H,D) or rank-4 (B,H,S,D), got {self.q_desc.ndim}")
# Shared derived attributes
if h_q % h_kv != 0:
raise ValueError("H_q must be a multiple of H_kv (GQA/MQA constraint)")
self.h_q = h_q
self.h_kv = h_kv
self.gqa_group_size = h_q // h_kv
self.head_dim = d_qk
self.value_dim = d_v
# Validate dtypes and config
self._logger.debug("Checking dtypes and config")
self.dtype = self._check_dtype(self.q_desc, dtype=[torch.float16, torch.bfloat16], name="Q")
_ = self._check_dtype(self.k_desc, dtype=self.dtype, name="K", extra_error_msg="K must have the same dtype as Q")
_ = self._check_dtype(self.v_desc, dtype=self.dtype, name="V", extra_error_msg="V must have the same dtype as Q")
_ = self._check_dtype(self.o_desc, dtype=self.dtype, name="O", extra_error_msg="O must have the same dtype as Q")
_ = self._check_dtype(self.acc_dtype, dtype=torch.float32, name="Acc", extra_error_msg="acc_dtype must be Float32")
if self.block_size not in {16, 32, 64}:
raise ValueError("block_size must be 16, 32, or 64")
# Compute default scale_softmax if needed
if self.scale_softmax is None:
self.scale_softmax = 1.0 / math.sqrt(self.head_dim)
if not torch.cuda.is_available():
self._logger.error("CUDA is not available")
raise RuntimeError("CUDA is not available")
self._logger.debug("Checking environment")
device = torch.cuda.current_device()
major, minor = torch.cuda.get_device_capability(device)
compute_capability = major * 10 + minor
if compute_capability < 90:
self._logger.error(f"Requires SM90+ compute capability, but found SM{compute_capability} on device {device}")
raise RuntimeError(f"Requires SM90+ compute capability, but found SM{compute_capability} on device {device}")
if compute_capability == 103:
raise RuntimeError("cuteDSL SelectionAttention is not supported on SM103")
self._is_supported = True
self._logger.debug("check_support completed successfully")
return True
def compile(self) -> None:
self._logger.debug("Entering compile")
self._ensure_support_checked()
if self._compiled_kernel is not None:
self._logger.debug("Kernel already compiled; skipping recompilation")
return
selection_attention = self._kernel(
head_dim=self.head_dim,
value_dim=self.value_dim,
GQA_group_size=self.gqa_group_size,
block_size=self.block_size,
dtype=_convert_to_cutlass_data_type(self.dtype),
acc_dtype=_convert_to_cutlass_data_type(self.acc_dtype),
)
if self.input_layout == "T,H,D":
_q_desc = self.q_desc.unsqueeze(0)
_k_desc = self.k_desc.unsqueeze(0)
_v_desc = self.v_desc.unsqueeze(0)
_o_desc = self.o_desc.unsqueeze(0)
_l_desc = self.l_desc.unsqueeze(0)
_m_desc = self.m_desc.unsqueeze(0)
else:
raise NotImplementedError(f"Invalid input layout: {self.input_layout}")
fake_stream = make_fake_stream(use_tvm_ffi_env_stream=False)
self._logger.debug("Compiling selection_attention")
_compiled_kernel = cute.compile(
selection_attention,
Q=self._make_fake_cute_tensor_from_desc(_q_desc, assumed_align=128),
K=self._make_fake_cute_tensor_from_desc(_k_desc, assumed_align=128),
V=self._make_fake_cute_tensor_from_desc(_v_desc, assumed_align=128),
O=self._make_fake_cute_tensor_from_desc(_o_desc, assumed_align=128),
L=self._make_fake_cute_tensor_from_desc(_l_desc),
M=self._make_fake_cute_tensor_from_desc(_m_desc),
block_indices=self._make_fake_cute_tensor_from_desc(self.block_indices_desc),
block_counts=self._make_fake_cute_tensor_from_desc(self.block_counts_desc),
max_length=self.max_s_q,
seq_offsets=self._make_fake_cute_tensor_from_desc(self.cum_seqlen_q_desc),
softmax_scale=self.scale_softmax,
stream=fake_stream,
options="--enable-tvm-ffi",
)
def tensor_api(
q_tensor,
k_tensor,
v_tensor,
o_tensor,
l_tensor,
m_tensor,
block_indices_tensor,
block_counts_tensor,
cum_seqlen_q_tensor,
softmax_scale,
stream,
):
# assumed T,H,D format
q_tensor = q_tensor.unsqueeze(0)
k_tensor = k_tensor.unsqueeze(0)
v_tensor = v_tensor.unsqueeze(0)
o_tensor = o_tensor.unsqueeze(0)
l_tensor = self._unpad_tensor_to_ndim(l_tensor, 2, "l_tensor").unsqueeze(0)
m_tensor = self._unpad_tensor_to_ndim(m_tensor, 2, "m_tensor").unsqueeze(0)
return _compiled_kernel(
q_tensor,
k_tensor,
v_tensor,
o_tensor,
l_tensor,
m_tensor,
block_indices_tensor,
block_counts_tensor,
self.max_s_q,
cum_seqlen_q_tensor,
softmax_scale,
stream,
)
self._compiled_kernel = tensor_api
self._logger.debug("Kernel compiled successfully")
def execute(
self,
q_tensor: torch.Tensor,
k_tensor: torch.Tensor,
v_tensor: torch.Tensor,
o_tensor: torch.Tensor,
l_tensor: torch.Tensor,
m_tensor: torch.Tensor,
block_indices_tensor: torch.Tensor,
block_counts_tensor: torch.Tensor,
cum_seqlen_q_tensor: Optional[torch.Tensor] = None,
cum_seqlen_k_tensor: Optional[torch.Tensor] = None,
scale_softmax: Optional[float] = None,
current_stream: Optional[cuda.CUstream] = None,
):
self._logger.debug("Entering execute")
current_stream = self._get_default_stream(current_stream)
scale_softmax = self.scale_softmax if scale_softmax is None else scale_softmax
if self._compiled_kernel is None:
raise RuntimeError("SelectionAttention kernel not compiled")
self._logger.debug("Executing with compiled kernel")
self._compiled_kernel(
q_tensor=q_tensor,
k_tensor=k_tensor,
v_tensor=v_tensor,
o_tensor=o_tensor,
l_tensor=l_tensor,
m_tensor=m_tensor,
block_indices_tensor=block_indices_tensor,
block_counts_tensor=block_counts_tensor,
cum_seqlen_q_tensor=cum_seqlen_q_tensor,
softmax_scale=scale_softmax,
stream=current_stream,
)
self._logger.debug("Executed with compiled kernel successfully")
import logging
_logger = logging.getLogger(__name__)
_cache_of_SelectionAttentionObjects = {}
def selection_attention_wrapper(
q_tensor: torch.Tensor,
k_tensor: torch.Tensor,
v_tensor: torch.Tensor,
block_indices_tensor: torch.Tensor,
block_counts_tensor: torch.Tensor,
cum_seqlen_q_tensor: Optional[torch.Tensor] = None,
cum_seqlen_k_tensor: Optional[torch.Tensor] = None,
block_size: int = 64,
scale_softmax: Optional[float] = None,
o_dtype: Optional[torch.dtype] = None,
acc_dtype: torch.dtype = torch.float32,
max_s_q: Optional[int] = None,
max_s_k: Optional[int] = None,
stream: Optional[cuda.CUstream] = None,
) -> TupleDict:
"""
Selection Attention Wrapper that returns output tensors.
Returns:
TupleDict: (o_tensor, l_tensor, m_tensor) - Output, logsumexp, and max tensors
"""
_logger.debug("selection_attention_wrapper: Creating empty output tensors o, l, and m")
max_s_q = max(cum_seqlen_q_tensor[1:] - cum_seqlen_q_tensor[:-1]).item() if max_s_q is None else max_s_q
max_s_k = max(cum_seqlen_k_tensor[1:] - cum_seqlen_k_tensor[:-1]).item() if max_s_k is None else max_s_k
t, h_q, d = q_tensor.shape
_, h_kv, d_v = v_tensor.shape
o_dtype = o_dtype if o_dtype is not None else q_tensor.dtype
o_tensor = torch.empty((t, h_q, d_v), dtype=o_dtype, device=q_tensor.device)
l_tensor = torch.empty((t, h_q, 1), dtype=torch.float32, device=q_tensor.device)
m_tensor = torch.empty((t, h_q, 1), dtype=torch.float32, device=q_tensor.device)
cache_key = (
q_tensor.shape,
k_tensor.shape,
v_tensor.shape,
block_indices_tensor.shape,
block_counts_tensor.shape,
cum_seqlen_q_tensor.shape,
cum_seqlen_k_tensor.shape,
q_tensor.dtype,
k_tensor.dtype,
v_tensor.dtype,
q_tensor.stride(),
k_tensor.stride(),
v_tensor.stride(),
block_indices_tensor.stride(),
block_counts_tensor.stride(),
cum_seqlen_q_tensor.stride(),
cum_seqlen_k_tensor.stride(),
block_size,
scale_softmax,
acc_dtype,
max_s_q,
max_s_k,
)
if cache_key in _cache_of_SelectionAttentionObjects:
_logger.debug("selection_attention_wrapper: Using previously cached SelectionAttention object")
selection_attention = _cache_of_SelectionAttentionObjects[cache_key]
selection_attention.execute(
q_tensor=q_tensor,
k_tensor=k_tensor,
v_tensor=v_tensor,
o_tensor=o_tensor,
l_tensor=l_tensor,
m_tensor=m_tensor,
block_indices_tensor=block_indices_tensor,
block_counts_tensor=block_counts_tensor,
cum_seqlen_q_tensor=cum_seqlen_q_tensor,
cum_seqlen_k_tensor=cum_seqlen_k_tensor,
scale_softmax=scale_softmax,
current_stream=stream,
)
else:
_logger.debug("selection_attention_wrapper: No previously cached SelectionAttention object found, creating new SelectionAttention object")
selection_attention = SelectionAttention(
sample_q=q_tensor,
sample_k=k_tensor,
sample_v=v_tensor,
sample_o=o_tensor,
sample_l=l_tensor,
sample_m=m_tensor,
sample_block_indices=block_indices_tensor,
sample_block_counts=block_counts_tensor,
sample_cum_seqlen_q=cum_seqlen_q_tensor,
sample_cum_seqlen_k=cum_seqlen_k_tensor,
acc_dtype=acc_dtype,
max_s_q=max_s_q,
max_s_k=max_s_k,
block_size=block_size,
scale_softmax=scale_softmax,
)
assert selection_attention.check_support()
selection_attention.compile()
selection_attention.execute(
q_tensor=q_tensor,
k_tensor=k_tensor,
v_tensor=v_tensor,
o_tensor=o_tensor,
l_tensor=l_tensor,
m_tensor=m_tensor,
block_indices_tensor=block_indices_tensor,
block_counts_tensor=block_counts_tensor,
cum_seqlen_q_tensor=cum_seqlen_q_tensor,
cum_seqlen_k_tensor=cum_seqlen_k_tensor,
scale_softmax=scale_softmax,
current_stream=stream,
)
_cache_of_SelectionAttentionObjects[cache_key] = selection_attention
return TupleDict(
o_tensor=o_tensor,
l_tensor=l_tensor,
m_tensor=m_tensor,
)
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