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import torch
from sgl_kernel import FusedSetKVBufferArg, apply_rope_with_cos_sin_cache_inplace
# vLLM torch native
def _apply_rotary_emb(
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
is_neox_style: bool,
) -> torch.Tensor:
"""
Args:
x: [num_tokens, num_heads, head_size]
cos: [num_tokens, head_size // 2]
sin: [num_tokens, head_size // 2]
is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
positional embeddings.
"""
cos = cos.unsqueeze(-2).to(x.dtype)
sin = sin.unsqueeze(-2).to(x.dtype)
if is_neox_style:
x1, x2 = torch.chunk(x, 2, dim=-1)
else:
x1 = x[..., ::2]
x2 = x[..., 1::2]
o1 = x1 * cos - x2 * sin
o2 = x2 * cos + x1 * sin
if is_neox_style:
return torch.cat((o1, o2), dim=-1)
else:
return torch.stack((o1, o2), dim=-1).flatten(-2)
class RotaryEmbedding(torch.nn.Module):
# Reference: https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/rotary_embedding.py
def __init__(
self,
head_size: int,
rotary_dim: int,
max_position_embeddings: int,
base: int,
is_neox_style: bool,
dtype: torch.dtype,
) -> None:
super().__init__()
self.head_size = head_size
self.rotary_dim = rotary_dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.is_neox_style = is_neox_style
self.dtype = dtype
cache = self._compute_cos_sin_cache()
self.cos_sin_cache: torch.Tensor
self.register_buffer("cos_sin_cache", cache, persistent=False)
def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
inv_freq = 1.0 / (
base
** (
torch.arange(0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim
)
)
return inv_freq
def _compute_cos_sin_cache(self) -> torch.Tensor:
"""Compute the cos and sin cache."""
inv_freq = self._compute_inv_freq(self.base)
t = torch.arange(self.max_position_embeddings, dtype=torch.float)
freqs = torch.einsum("i,j -> ij", t, inv_freq)
cos = freqs.cos()
sin = freqs.sin()
cache = torch.cat((cos, sin), dim=-1)
return cache
def forward_native(
self,
positions: torch.Tensor,
query: torch.Tensor,
key: torch.Tensor,
offsets: Optional[torch.Tensor] = None,
fused_set_kv_buffer_arg: Optional[FusedSetKVBufferArg] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""A PyTorch-native implementation of forward()."""
assert (
fused_set_kv_buffer_arg is None
), "fused_set_kv_buffer_arg is not supported for native implementation"
if offsets is not None:
positions = positions + offsets
positions = positions.flatten()
num_tokens = positions.shape[0]
cos_sin = self.cos_sin_cache.index_select(0, positions)
cos, sin = cos_sin.chunk(2, dim=-1)
query_shape = query.shape
query = query.view(num_tokens, -1, self.head_size)
query_rot = query[..., : self.rotary_dim]
query_pass = query[..., self.rotary_dim :]
query_rot = _apply_rotary_emb(query_rot, cos, sin, self.is_neox_style)
query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
key_shape = key.shape
key = key.view(num_tokens, -1, self.head_size)
key_rot = key[..., : self.rotary_dim]
key_pass = key[..., self.rotary_dim :]
key_rot = _apply_rotary_emb(key_rot, cos, sin, self.is_neox_style)
key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
# Modification: convert to the correct dtype
query = query.to(self.dtype)
key = key.to(self.dtype)
return query, key
class FlashInferRotaryEmbedding(RotaryEmbedding):
def forward_cuda(
self,
positions: torch.Tensor,
query: torch.Tensor,
key: torch.Tensor,
offsets: Optional[torch.Tensor] = None,
fused_set_kv_buffer_arg: Optional[FusedSetKVBufferArg] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
apply_rope_with_cos_sin_cache_inplace(
positions=positions,
query=query,
key=key,
fused_set_kv_buffer_arg=fused_set_kv_buffer_arg,
head_size=self.head_size,
cos_sin_cache=self.cos_sin_cache,
is_neox=self.is_neox_style,
)
return query, key
class SglKernelRotaryEmbedding(RotaryEmbedding):
def forward_cuda(
self,
positions: torch.Tensor,
query: torch.Tensor,
key: torch.Tensor,
offsets: Optional[torch.Tensor] = None,
fused_set_kv_buffer_arg: Optional[FusedSetKVBufferArg] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
assert (
fused_set_kv_buffer_arg is None
), "fused_set_kv_buffer_arg is not supported for sgl-kernel implementation"
if self.cos_sin_cache.dtype != query.dtype:
self.cos_sin_cache = self.cos_sin_cache.to(query.dtype)
torch.ops.sgl_kernel.rotary_embedding(
positions,
query,
key,
self.head_size,
self.cos_sin_cache,
self.is_neox_style,
)
return query, key
class MHATokenToKVPool:
KV_POOL_SIZE = 16384
def __init__(
self,
head_num: int,
head_dim: int,
):
self.head_num = head_num
self.head_dim = head_dim
self.size = MHATokenToKVPool.KV_POOL_SIZE
self.page_size = 1
self.store_dtype = torch.bfloat16
self.device = "cuda"
self.layer_num = 1
self.start_layer = 0
self._create_buffers()
def _create_buffers(self):
self.k_buffer = [
torch.zeros(
(self.size + self.page_size, self.head_num, self.head_dim),
dtype=self.store_dtype,
device=self.device,
)
for _ in range(self.layer_num)
]
self.v_buffer = [
torch.zeros(
(self.size + self.page_size, self.head_num, self.head_dim),
dtype=self.store_dtype,
device=self.device,
)
for _ in range(self.layer_num)
]
def set_kv_buffer(
self,
loc: torch.Tensor,
cache_k: torch.Tensor,
cache_v: torch.Tensor,
):
layer_id = 0
self.k_buffer[layer_id - self.start_layer][loc] = cache_k
self.v_buffer[layer_id - self.start_layer][loc] = cache_v
def create_inputs(
head_size: int,
batch_size: int,
seq_len: int,
device,
dtype: torch.dtype,
num_q_heads: int,
num_kv_heads: int,
):
pos_ids = torch.arange(seq_len, device=device).repeat(batch_size)
query = torch.randn(
batch_size * seq_len, num_q_heads * head_size, dtype=dtype, device=device
)
key = torch.randn(
batch_size * seq_len, num_kv_heads * head_size, dtype=dtype, device=device
)
value = torch.randn(
batch_size * seq_len, num_kv_heads * head_size, dtype=dtype, device=device
)
out_cache_loc = torch.randperm(
MHATokenToKVPool.KV_POOL_SIZE, dtype=torch.int64, device=device
)[: batch_size * seq_len].clone()
return dict(
pos_ids=pos_ids, query=query, key=key, value=value, out_cache_loc=out_cache_loc
)
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