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fix(cache): compute codebooks on CPU at fp64 for MPS compatibility #5

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fix(cache): compute codebooks on CPU at fp64 for MPS compatibility #5

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39 changes: 31 additions & 8 deletions src/cache.py
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Original file line number Diff line number Diff line change
Expand Up @@ -280,8 +280,15 @@ def compute_lloyd_max_codebook(
lo = max(-1.0, -6.0 * sigma)
hi = min(1.0, 6.0 * sigma)

# Lloyd-Max optimization requires float64 precision for stable convergence.
# Some accelerator backends (e.g., MPS) don't support float64, so the
# computation runs on CPU; the resulting Codebook tensors are moved to
# the caller's target device before return. Codebook.quantize/dequantize
# handle further device migration at usage time.
cpu_dev = torch.device("cpu")

grid_size = 16385
grid = torch.linspace(lo, hi, grid_size, device=device, dtype=torch.float64)
grid = torch.linspace(lo, hi, grid_size, device=cpu_dev, dtype=torch.float64)

if d >= 64:
pdf = torch.exp(-0.5 * d * grid ** 2) * math.sqrt(d / (2.0 * math.pi))
Expand All @@ -295,7 +302,11 @@ def compute_lloyd_max_codebook(
pdf = pdf / mass

centroids, boundaries = _solve_lloyd_max(pdf, grid, K, max_iter, tol)
return Codebook(centroids=centroids, boundaries=boundaries, d=d, b=b, K=K)
return Codebook(
centroids=centroids.to(device),
boundaries=boundaries.to(device),
d=d, b=b, K=K,
)


def compute_online_codebook(
Expand All @@ -316,24 +327,36 @@ def compute_online_codebook(
max_iter: k-means iterations
"""
K = 2 ** b

# 1D k-means requires float64 precision for stable convergence. Some
# accelerator backends (e.g., MPS) don't support float64, so the
# computation runs on CPU; the resulting Codebook tensors are moved to
# the caller's target device before return. Codebook.quantize/dequantize
# handle further device migration at usage time.
cpu_dev = torch.device("cpu")

# Flatten all coordinates into a single 1D distribution
flat = data.reshape(-1).float().to(device)
flat = data.reshape(-1).float().to(cpu_dev)

# Build empirical PDF via histogram
n_bins = 16385
lo = flat.min().item() - 1e-6
hi = flat.max().item() + 1e-6
grid = torch.linspace(lo, hi, n_bins, device=device, dtype=torch.float64)
hist = torch.histogram(flat.cpu().double(), bins=n_bins, range=(lo, hi))
pdf = hist.hist.to(device).double()
grid = torch.linspace(lo, hi, n_bins, device=cpu_dev, dtype=torch.float64)
hist = torch.histogram(flat.double(), bins=n_bins, range=(lo, hi))
pdf = hist.hist.double()
pdf = pdf / (pdf.sum() * (hi - lo) / n_bins)

# Grid for PDF (bin centers)
bin_edges = hist.bin_edges.to(device).double()
bin_edges = hist.bin_edges.double()
grid = 0.5 * (bin_edges[:-1] + bin_edges[1:])

centroids, boundaries = _solve_lloyd_max(pdf, grid, K, max_iter)
return Codebook(centroids=centroids, boundaries=boundaries, d=data.shape[-1], b=b, K=K)
return Codebook(
centroids=centroids.to(device),
boundaries=boundaries.to(device),
d=data.shape[-1], b=b, K=K,
)


# ---------------------------------------------------------------------------
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