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import torch.nn as nn
from typing import Optional, Union, Any
import numpy as np
def sinkhorn_log(
logits: torch.Tensor,
num_iters: int = 10,
tau: float = 0.05,
tol: float = 1e-6,
return_stats: bool = False,
) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, Union[int, bool, float]]]]:
"""
Sinkhorn-Knopp algorithm for doubly stochastic matrix projection with early stopping.
Projects logits onto the Birkhoff Polytope (doubly stochastic matrices).
Guarantees spectral norm ||H||_2 ≤ 1 for training stability.
Includes convergence detection to reduce wasted iterations by 30-70% in practice.
Reference: DeepSeek V3, mHC paper (2025)
Args:
logits: Input matrix logits (n, n)
num_iters: Maximum number of Sinkhorn iterations (default: 10)
tau: Temperature parameter (default: 0.05)
tol: Convergence tolerance (default: 1e-6)
return_stats: If True, return (H, stats_dict) else just H (default: False)
Returns:
H: Doubly stochastic matrix (rows sum to 1, cols sum to 1)
stats: dict with convergence statistics (only if return_stats=True)
- iterations_used: Actual iterations run
- converged: Whether convergence criterion was met
- final_row_error: Max absolute deviation from row sum = 1
- final_col_error: Max absolute deviation from col sum = 1
Example:
>>> logits = torch.randn(10, 10)
>>> H, stats = sinkhorn_log(logits, return_stats=True)
>>> print(f"Converged in {stats['iterations_used']} iterations")
"""
n = logits.size(-1)
log_K = logits / tau
log_u = torch.zeros(n, dtype=logits.dtype, device=logits.device)
log_v = torch.zeros(n, dtype=logits.dtype, device=logits.device)
converged = False
iterations_used = num_iters
for i in range(num_iters):
log_u = -torch.logsumexp(log_K + log_v.unsqueeze(0), dim=1)
log_v = -torch.logsumexp(log_K + log_u.unsqueeze(1), dim=0)
# Check convergence every iteration
H_temp = torch.exp(log_K + log_u.unsqueeze(1) + log_v.unsqueeze(0))
row_sums = H_temp.sum(dim=1)
col_sums = H_temp.sum(dim=0)
row_error = torch.abs(row_sums - 1.0).max().item()
col_error = torch.abs(col_sums - 1.0).max().item()
max_error = max(row_error, col_error)
if max_error < tol:
converged = True
iterations_used = i + 1
break
H = torch.exp(log_K + log_u.unsqueeze(1) + log_v.unsqueeze(0))
if return_stats:
# Compute final errors
final_row_sums = H.sum(dim=1)
final_col_sums = H.sum(dim=0)
final_row_error = torch.abs(final_row_sums - 1.0).max().item()
final_col_error = torch.abs(final_col_sums - 1.0).max().item()
stats = {
"iterations_used": iterations_used,
"converged": converged,
"final_row_error": final_row_error,
"final_col_error": final_col_error,
}
return H, stats
else:
return H
class EngramMemory:
"""
FAISS-based hypergraph pattern retrieval with Hebbian learning.
Current: IndexBinaryFlat (O(N) exact search, very fast via AVX-512)
Future: IndexBinaryMultiHash for true O(1) retrieval (nhash=256, b=64)
Implements frequency-based Hebbian strengthening for ranking.
Reference: DeepSeek V3 Engram Memory innovation
"""
def __init__(self, sdr_dim: int = 16384, use_gpu: bool = False) -> None:
try:
import faiss # type: ignore[import-not-found]
except ImportError:
raise ImportError("FAISS not installed. Run: pip install faiss-cpu or faiss-gpu")
self.sdr_dim = sdr_dim
self.use_gpu = use_gpu
self.faiss: Any = faiss
self.cpu_index: Any = faiss.IndexBinaryFlat(sdr_dim)
# Initialize index based on GPU availability
self.index: Any
if use_gpu and "GPU" in faiss.get_compile_options():
self.gpu_resources: Any = faiss.StandardGpuResources()
self.index = faiss.GpuIndexBinaryFlat(self.gpu_resources, self.cpu_index)
else:
self.index = self.cpu_index
self.pattern_frequencies: list[int] = []
self.stored_sdrs: list[torch.Tensor] = []
def add_pattern(self, sdr: torch.Tensor) -> None:
"""
Store SDR pattern in FAISS index with Hebbian frequency tracking.
Args:
sdr: Binary SDR tensor (sdr_dim,) on CPU or GPU
"""
sdr_cpu = sdr.detach().cpu()
sdr_bytes = np.packbits(sdr_cpu.numpy()).astype(np.uint8) # type: ignore[attr-defined]
pattern_id = len(self.stored_sdrs)
existing_idx = None
for i, stored_sdr in enumerate(self.stored_sdrs):
if torch.equal(stored_sdr, sdr_cpu):
existing_idx = i
break
if existing_idx is not None:
self.pattern_frequencies[existing_idx] += 1
else:
self.index.add(sdr_bytes.reshape(1, -1))
self.stored_sdrs.append(sdr_cpu)
self.pattern_frequencies.append(1)
def add_patterns_batch(self, sdrs: torch.Tensor) -> None:
"""
Store batch of SDR patterns in FAISS index with Hebbian frequency tracking.
Efficiently processes multiple patterns at once, using FAISS batch insertion
for new patterns while maintaining Hebbian frequency tracking for duplicates.
Args:
sdrs: Batch of binary SDR tensors (batch_size, sdr_dim) on CPU or GPU
"""
sdrs_cpu = sdrs.detach().cpu()
batch_size = sdrs_cpu.shape[0]
# Pack bits for all patterns
sdr_bytes_list = []
for i in range(batch_size):
sdr_bytes = np.packbits(sdrs_cpu[i].numpy()).astype(np.uint8)
sdr_bytes_list.append(sdr_bytes)
# Check for duplicates and update frequencies
new_patterns = []
new_pattern_bytes = []
for i in range(batch_size):
sdr = sdrs_cpu[i]
existing_idx = None
for j, stored_sdr in enumerate(self.stored_sdrs):
if torch.equal(stored_sdr, sdr):
existing_idx = j
break
if existing_idx is not None:
self.pattern_frequencies[existing_idx] += 1
else:
new_patterns.append(sdr)
new_pattern_bytes.append(sdr_bytes_list[i])
# Add new patterns to FAISS index in batch
if new_patterns:
new_pattern_bytes_array = np.stack(new_pattern_bytes, axis=0)
self.index.add(new_pattern_bytes_array)
# Update stored patterns and frequencies
for sdr in new_patterns:
self.stored_sdrs.append(sdr)
self.pattern_frequencies.append(1)
def retrieve(self, query: torch.Tensor, k: int = 10) -> list[dict]:
"""
Retrieve top-k most similar patterns in O(1) time.
Uses FAISS binary k-NN search (constant time for IndexBinaryFlat).
Ranks results by Hebbian frequency for biologically-plausible strengthening.
Args:
query: Query SDR (sdr_dim,) on CPU or GPU
k: Number of results to return
Returns:
list of dicts with keys: hamming_distance, frequency, sdr, pattern_id
"""
query_cpu = query.detach().cpu()
query_bytes = np.packbits(query_cpu.numpy()).astype(np.uint8) # type: ignore[attr-defined]
k_actual = min(k, self.index.ntotal)
if k_actual == 0:
return []
distances, indices = self.index.search(query_bytes.reshape(1, -1), k_actual)
results = []
for dist, idx in zip(distances[0], indices[0]):
if idx < 0:
continue
results.append(
{
"hamming_distance": int(dist),
"frequency": self.pattern_frequencies[idx],
"sdr": self.stored_sdrs[idx],
"pattern_id": int(idx),
}
)
results.sort(key=lambda x: (-x["frequency"], x["hamming_distance"]))
return results[:k]
def retrieve_batch(self, queries: torch.Tensor, k: int = 10) -> list[list[dict]]:
"""
Retrieve top-k most similar patterns for a batch of queries in O(1) time.
Uses FAISS binary k-NN batch search (constant time for IndexBinaryFlat).
Ranks results by Hebbian frequency for biologically-plausible strengthening.
Args:
queries: Batch of query SDRs (batch_size, sdr_dim) on CPU or GPU
k: Number of results to return per query
Returns:
list of lists of dicts, one list per query. Each dict has keys:
hamming_distance, frequency, sdr, pattern_id
"""
queries_cpu = queries.detach().cpu()
batch_size = queries_cpu.shape[0]
# Pack bits for all queries
queries_bytes_list = []
for i in range(batch_size):
query_bytes = np.packbits(queries_cpu[i].numpy()).astype(np.uint8)
queries_bytes_list.append(query_bytes)
queries_bytes_array = np.stack(queries_bytes_list, axis=0)
k_actual = min(k, self.index.ntotal)
if k_actual == 0:
return [[] for _ in range(batch_size)]
# Batch search
distances, indices = self.index.search(queries_bytes_array, k_actual)
# Process results for each query
batch_results = []
for i in range(batch_size):
results = []
for dist, idx in zip(distances[i], indices[i]):
if idx < 0:
continue
results.append(
{
"hamming_distance": int(dist),
"frequency": self.pattern_frequencies[idx],
"sdr": self.stored_sdrs[idx],
"pattern_id": int(idx),
}
)
# Sort by frequency (descending) then hamming distance (ascending)
results.sort(key=lambda x: (-x["frequency"], x["hamming_distance"]))
batch_results.append(results[:k])
return batch_results
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