File size: 8,630 Bytes
b88c26d | 1 2 3 4 5 6 7 8 9 10 11 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 66 67 68 69 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 | """
BURT-IMMA Kernel Functions with CPU Fallbacks
License: BSL-1.1
Contact: jessica@collectivekitty.com
For each kernel function: try CUDA version first, fall back to pure PyTorch.
"""
import torch
import torch.nn.functional as F
from typing import Optional
try:
import _burt_imma_cuda
_HAS_CUDA = True
except ImportError:
_HAS_CUDA = False
def constrained_softmax(
logits: torch.Tensor,
max_entropy: float = 0.20,
temperature: float = 1.0,
) -> torch.Tensor:
"""Softmax with spectral norm constraint (entropy bounded).
Applies softmax along the last dimension, iteratively sharpening
(reducing temperature) until entropy <= max_entropy.
Args:
logits: Input logits of any shape (..., N)
max_entropy: Maximum allowed entropy of output distribution
temperature: Initial temperature for scaling
Returns:
Probability distribution with entropy <= max_entropy
"""
if _HAS_CUDA and logits.is_cuda:
return _burt_imma_cuda.constrained_softmax(logits, max_entropy, temperature)
# Pure PyTorch fallback
scaled = logits / temperature
probs = F.softmax(scaled, dim=-1)
# Check entropy and sharpen if needed
ent = -(probs * (probs + 1e-10).log()).sum(dim=-1)
violations = ent > max_entropy
if violations.any():
temp = temperature
for _ in range(50):
temp *= 0.8
p = F.softmax(logits / temp, dim=-1)
h = -(p * (p + 1e-10).log()).sum(dim=-1)
if (h <= max_entropy).all():
return p
# Update only violating entries
probs = torch.where(
violations.unsqueeze(-1).expand_as(probs),
p, probs
)
ent = -(probs * (probs + 1e-10).log()).sum(dim=-1)
violations = ent > max_entropy
if not violations.any():
break
return probs
def cifg_update(
C_old: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
forget_bias: float = 0.0,
) -> torch.Tensor:
"""Coupled Input-Forget Gate memory update.
Implements: C_new = f * C_old + (1-f) * candidate
where candidate = normalized outer product of key and value,
and f = sigmoid(||key|| + forget_bias).
Args:
C_old: (batch, d_mem, d_mem) - current memory matrix
key: (batch, d_mem) - write key
value: (batch, d_mem) - write value
forget_bias: bias term for forget gate
Returns:
C_new: (batch, d_mem, d_mem) - updated memory matrix
"""
if _HAS_CUDA and C_old.is_cuda:
return _burt_imma_cuda.cifg_update(C_old, key, value, forget_bias)
# Pure PyTorch fallback
# Forget gate from key norm
f = torch.sigmoid(key.norm(dim=-1, keepdim=True) + forget_bias) # (batch, 1)
# Outer product candidate
candidate = torch.bmm(
key.unsqueeze(-1), # (batch, d_mem, 1)
value.unsqueeze(-2) # (batch, 1, d_mem)
) # (batch, d_mem, d_mem)
# Normalize candidate
cand_norm = candidate.flatten(1).norm(dim=1, keepdim=True).unsqueeze(-1) + 1e-8
candidate = candidate / cand_norm
# CIFG update
f_exp = f.unsqueeze(-1) # (batch, 1, 1)
C_new = f_exp * C_old + (1.0 - f_exp) * candidate
return C_new
def batched_cifg_update(
C_old: torch.Tensor,
keys: torch.Tensor,
values: torch.Tensor,
forget_biases: torch.Tensor,
) -> torch.Tensor:
"""Batched CIFG update for multiple memory slots.
Args:
C_old: (batch, num_slots, d_mem, d_mem)
keys: (batch, num_slots, d_mem)
values: (batch, num_slots, d_mem)
forget_biases: (num_slots,)
Returns:
C_new: (batch, num_slots, d_mem, d_mem)
"""
if _HAS_CUDA and C_old.is_cuda:
return _burt_imma_cuda.batched_cifg_update(C_old, keys, values, forget_biases)
# Pure PyTorch fallback
batch, num_slots, d_mem, _ = C_old.shape
C_new = torch.zeros_like(C_old)
for s in range(num_slots):
C_slot = C_old[:, s] # (batch, d_mem, d_mem)
k_slot = keys[:, s] # (batch, d_mem)
v_slot = values[:, s] # (batch, d_mem)
bias = forget_biases[s].item()
C_new[:, s] = cifg_update(C_slot, k_slot, v_slot, bias)
return C_new
def sparse_moe_dispatch(
x: torch.Tensor,
gate_weights: torch.Tensor,
expert_weights: torch.Tensor,
top_k: int = 1,
) -> torch.Tensor:
"""Sparse Mixture-of-Experts dispatch with top-k routing.
Routes each token to its top-k experts based on gating scores.
Args:
x: (batch, seq_len, d_model) - input tokens
gate_weights: (d_model, num_experts) - gating projection
expert_weights: (num_experts, d_model, d_model) - expert matrices
top_k: number of experts per token
Returns:
output: (batch, seq_len, d_model) - routed output
"""
if _HAS_CUDA and x.is_cuda:
return _burt_imma_cuda.sparse_moe_dispatch(x, gate_weights, expert_weights, top_k)
# Pure PyTorch fallback
batch, seq_len, d_model = x.shape
num_experts = gate_weights.shape[1]
# Flatten
x_flat = x.reshape(batch * seq_len, d_model)
# Gating scores
scores = torch.mm(x_flat, gate_weights) # (B*S, num_experts)
probs = F.softmax(scores, dim=-1)
# Top-k
top_vals, top_idx = torch.topk(probs, top_k, dim=-1)
top_vals = top_vals / (top_vals.sum(dim=-1, keepdim=True) + 1e-8)
# Dispatch
output = torch.zeros_like(x_flat)
for k in range(top_k):
for e in range(num_experts):
mask = (top_idx[:, k] == e)
if mask.any():
x_masked = x_flat[mask]
W_e = expert_weights[e]
out_e = torch.mm(x_masked, W_e)
output[mask] = output[mask] + top_vals[mask, k:k+1] * out_e
return output.reshape(batch, seq_len, d_model)
def biencoder_attention(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
W_Q: torch.Tensor,
W_K: torch.Tensor,
W_V: torch.Tensor,
) -> torch.Tensor:
"""Bi-encoder cross-attention between two sequences.
Args:
query: (batch, q_len, d_model) - query sequence
key: (batch, kv_len, d_model) - key sequence
value: (batch, kv_len, d_model) - value sequence
W_Q: (d_model, d_model) - query projection
W_K: (d_model, d_model) - key projection
W_V: (d_model, d_model) - value projection
Returns:
output: (batch, q_len, d_model)
"""
if _HAS_CUDA and query.is_cuda:
return _burt_imma_cuda.biencoder_attention(query, key, value, W_Q, W_K, W_V)
# Pure PyTorch fallback
batch, q_len, d_model = query.shape
kv_len = key.shape[1]
scale = d_model ** 0.5
# Project
Q = torch.matmul(query, W_Q) # (batch, q_len, d_model)
K = torch.matmul(key, W_K) # (batch, kv_len, d_model)
V = torch.matmul(value, W_V) # (batch, kv_len, d_model)
# Attention scores
scores = torch.bmm(Q, K.transpose(-2, -1)) / scale # (batch, q_len, kv_len)
attn = F.softmax(scores, dim=-1)
output = torch.bmm(attn, V) # (batch, q_len, d_model)
return output
def attention_softmax(
Q: torch.Tensor,
K: torch.Tensor,
V: torch.Tensor,
causal: bool = True,
) -> torch.Tensor:
"""Fused attention + softmax with optional causal mask.
Computes scaled dot-product attention efficiently.
Args:
Q: (batch, heads, seq_len, d_head)
K: (batch, heads, seq_len, d_head)
V: (batch, heads, seq_len, d_head)
causal: whether to apply causal (autoregressive) mask
Returns:
output: (batch, heads, seq_len, d_head)
"""
if _HAS_CUDA and Q.is_cuda:
return _burt_imma_cuda.attention_softmax(Q, K, V, causal)
# Pure PyTorch fallback
d_head = Q.shape[-1]
seq_len = Q.shape[-2]
scale = d_head ** 0.5
# Compute scores
scores = torch.matmul(Q, K.transpose(-2, -1)) / scale
# Apply causal mask
if causal:
mask = torch.triu(
torch.full((seq_len, seq_len), float("-inf"), device=Q.device, dtype=Q.dtype),
diagonal=1
)
scores = scores + mask
# Softmax + weighted sum
attn = F.softmax(scores, dim=-1)
return torch.matmul(attn, V)
|