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"""
Initial MLA Decode submission — optimised baseline with Triton softmax and RoPE kernels.
"""
import os
import math
from typing import Tuple
import torch
import torch.nn.functional as F
import triton
import triton.language as tl
from reference import KVCache, Config
@triton.jit
def rope_swap_halves_kernel(
x_ptr,
cos_ptr, sin_ptr,
B: tl.constexpr,
T: tl.constexpr,
D: tl.constexpr,
stride_xb, stride_xt, stride_xd,
stride_cos_t, stride_cos_d,
stride_sin_t, stride_sin_d,
BLOCK_HALF: tl.constexpr,
):
pid = tl.program_id(0)
bt = pid
b = bt // T
t = bt - b * T
half = D // 2
off = tl.arange(0, BLOCK_HALF)
mask = off < half
x_base = x_ptr + b * stride_xb + t * stride_xt
x0_ptr = x_base + off * stride_xd
x1_ptr = x_base + (half + off) * stride_xd
cos_base = cos_ptr + t * stride_cos_t
sin_base = sin_ptr + t * stride_sin_t
c_ptr = cos_base + off * stride_cos_d
s_ptr = sin_base + off * stride_sin_d
x0 = tl.load(x0_ptr, mask=mask, other=0.0).to(tl.float32)
x1 = tl.load(x1_ptr, mask=mask, other=0.0).to(tl.float32)
c = tl.load(c_ptr, mask=mask, other=0.0).to(tl.float32)
s = tl.load(s_ptr, mask=mask, other=0.0).to(tl.float32)
out0 = x0 * c - x1 * s
out1 = x1 * c + x0 * s
tl.store(x0_ptr, out0.to(tl.bfloat16), mask=mask)
tl.store(x1_ptr, out1.to(tl.bfloat16), mask=mask)
def rope_inplace_query(q_rope: torch.Tensor, cos_q: torch.Tensor, sin_q: torch.Tensor):
assert q_rope.is_cuda
assert q_rope.shape[-1] % 2 == 0
bs, nh, d_rope = q_rope.shape
half = d_rope // 2
BLOCK_HALF = 1 << (half - 1).bit_length()
grid = (bs * nh,)
rope_swap_halves_kernel[grid](
q_rope,
cos_q, sin_q,
B=bs, T=nh, D=d_rope,
stride_xb=q_rope.stride(0),
stride_xt=q_rope.stride(1),
stride_xd=q_rope.stride(2),
stride_cos_t=0, stride_cos_d=cos_q.stride(0),
stride_sin_t=0, stride_sin_d=sin_q.stride(0),
BLOCK_HALF=BLOCK_HALF,
num_warps=4,
)
_rope_cache = {}
def _rotate_half(x: torch.Tensor) -> torch.Tensor:
half = x.shape[-1] // 2
return torch.cat((-x[..., half:], x[..., :half]), dim=-1)
def _get_rope_tables(dim: int, max_seq_len: int, device: torch.device):
key = (dim, max_seq_len, device)
if key not in _rope_cache:
half = dim // 2
theta = (10000.0 ** (-torch.arange(half, dtype=torch.float32, device=device) / half)).to(
torch.bfloat16
)
pos = torch.arange(max_seq_len, dtype=torch.int64, device=device).unsqueeze_(1)
idx = pos * theta[None, :]
idx = torch.cat([idx, idx], dim=-1)
_rope_cache[key] = (idx.cos().to(torch.bfloat16), idx.sin().to(torch.bfloat16))
return _rope_cache[key]
@triton.jit
def _softmax_kernel(
out_ptr, in_ptr,
stride_out, stride_in,
n_cols,
BLOCK_SIZE: tl.constexpr,
NUM_STAGES: tl.constexpr,
):
row = tl.program_id(0)
row_off_in = row * stride_in
row_off_out = row * stride_out
max_val = tl.full([BLOCK_SIZE], -float("inf"), tl.float32)
col = tl.arange(0, BLOCK_SIZE)
for start in range(0, n_cols, BLOCK_SIZE):
cur = start + col
mask = cur < n_cols
val = tl.load(in_ptr + row_off_in + cur, mask=mask, other=-float('inf'))
max_val = tl.maximum(max_val, tl.cast(val, tl.float32))
row_max = tl.max(max_val)
sum_val = tl.full([BLOCK_SIZE], 0.0, tl.float32)
for start in range(0, n_cols, BLOCK_SIZE):
cur = start + col
mask = cur < n_cols
val = tl.load(in_ptr + row_off_in + cur, mask=mask, other=-float('inf'))
exp_val = tl.exp(tl.cast(val, tl.float32) - row_max)
tl.store(out_ptr + row_off_out + cur, tl.cast(exp_val, tl.bfloat16), mask=mask)
sum_val += exp_val
row_sum = tl.sum(sum_val)
for start in range(0, n_cols, BLOCK_SIZE):
cur = start + col
mask = cur < n_cols
val = tl.load(out_ptr + row_off_out + cur, mask=mask, other=0.0)
norm = tl.cast(val, tl.float32) / row_sum
tl.store(out_ptr + row_off_out + cur, tl.cast(norm, tl.bfloat16), mask=mask)
def _triton_softmax(x: torch.Tensor) -> torch.Tensor:
assert x.is_cuda and x.dtype == torch.bfloat16
n_rows, n_cols = x.shape
if n_cols <= 32:
BLOCK_SIZE = 32
elif n_cols <= 64:
BLOCK_SIZE = 64
elif n_cols <= 128:
BLOCK_SIZE = 128
else:
BLOCK_SIZE = 1 << (n_cols - 1).bit_length()
BLOCK_SIZE = min(BLOCK_SIZE, 1024)
out = torch.empty_like(x)
grid = (n_rows,)
_softmax_kernel[grid](
out, x,
out.stride(0), x.stride(0),
n_cols,
BLOCK_SIZE=BLOCK_SIZE,
NUM_STAGES=2,
num_warps=4,
)
return out
def custom_kernel(data: Tuple[Config, torch.Tensor, KVCache]) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Optimised forward step of the Multi-head Latent Attention (MLA) module.
"""
config, x, kv_cache = data
bs = config.batch_size
sl = config.seq_len
nh = config.n_heads
dq = config.q_lora_rank
dkv = config.kv_lora_rank
d_nope = config.qk_nope_head_dim
d_rope = config.qk_rope_head_dim
dv = config.v_head_dim
msl = config.max_seq_len
wDQ = config.Q_proj_down_weight
wDKV = config.KV_proj_down_weight
wUQ = config.Q_proj_up_weight
wUKV = config.KV_proj_up_weight
wO = config.wo_weight
q_lora = F.linear(x, wDQ)
kv_lora_input = F.linear(x, wDKV)
kv_lora, kv_len = kv_cache(kv_lora_input)
query_pos = kv_len - 1
q_up = F.linear(q_lora.squeeze(1), wUQ)
q_up = q_up.view(bs, nh, d_nope + d_rope)
q_nope = q_up[..., :d_nope]
q_rope = q_up[..., d_nope:]
kv_nope_input = kv_lora[..., :dkv]
k_rope_input = kv_lora[..., dkv:]
cos_table, sin_table = _get_rope_tables(d_rope, msl, x.device)
cos_q = cos_table[query_pos].view(d_rope).contiguous()
sin_q = sin_table[query_pos].view(d_rope).contiguous()
rope_inplace_query(q_rope, cos_q, sin_q)
cos_k = cos_table[:kv_len]
sin_k = sin_table[:kv_len]
k_rope = k_rope_input * cos_k + _rotate_half(k_rope_input) * sin_k
wUKV_view = wUKV.view(nh, d_nope + dv, dkv)
wK = wUKV_view[:, :d_nope, :]
q_nope_latent = torch.einsum('bhd,hdk->bhk', q_nope, wK)
kv_nope_T = kv_nope_input.transpose(1, 2)
scores_nope = torch.matmul(q_nope_latent, kv_nope_T)
scores_rope = torch.matmul(q_rope, k_rope.transpose(-2, -1))
scale = 1.0 / math.sqrt(d_nope + d_rope)
scores = (scores_nope + scores_rope) * scale
scores_flat = scores.reshape(bs * nh, kv_len)
attn_flat = _triton_softmax(scores_flat)
attn = attn_flat.view(bs, nh, kv_len)
M = torch.matmul(attn, kv_nope_input)
wV = wUKV_view[:, d_nope:, :]
wV_T = wV.permute(0, 2, 1)
y_head = torch.einsum('bhd,hdk->bhk', M, wV_T)
y = y_head.reshape(bs, nh * dv)
y = y.unsqueeze(1)
output = F.linear(y, wO)
return output, kv_cache.data
# EVOLVE-BLOCK-END
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