seroxdesign
dbf85683e6
KV cache compression for LLM inference (ICLR 2026, arXiv:2504.19874). Core: - TurboQuantProd: 3-bit keys (MSE + QJL), 2-bit/4-bit values (group quant) - Modular architecture: capture, store, score, integration/vllm - vLLM monkey-patch with free_kv_cache and hybrid decode - 3 fused Triton kernels for decode attention Validated on: - RTX 5090: Qwen3.5-27B-AWQ, 30GB KV freed, 2x context capacity - 8x RTX 3090: Qwen3.5-35B-A3B MoE at 131k context - 8,238 tok/s prefill, 98 tok/s decode, 15.9s TTFT - 30.9% KV savings (4.4x on full-attn layers, 1.45x overall) - 5/5 needle retrieval at max context 35 tests pass (19 modular + 7 core + 9 paper validation). Adversarial audit included with honest assessment of all claims.
354 lines
11 KiB
Python
354 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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TurboQuant validation against paper claims (arXiv:2504.19874).
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Validates:
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1. MSE distortion matches paper's Theorem 1 bounds
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2. Inner-product estimator is unbiased (Theorem 2)
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3. Inner-product distortion within paper's Theorem 3 bounds
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4. Attention recall@k at realistic scale (d=128, N=4096)
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5. Needle-in-haystack retrieval at scale (d=128, N=8192, multiple needle depths)
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6. Compression ratio matches paper's claimed 2.6x per layer
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7. Codebook MSE matches paper's Table 1 values exactly
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"""
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import math
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import sys
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import torch
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import numpy as np
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torch.manual_seed(42)
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np.random.seed(42)
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PASS = 0
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FAIL = 0
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def run(name, fn):
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global PASS, FAIL
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try:
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fn()
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print(f" PASS {name}")
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PASS += 1
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except Exception as e:
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import traceback
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print(f" FAIL {name}")
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traceback.print_exc()
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FAIL += 1
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# ---------- 1. MSE distortion (Theorem 1) ----------
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def test_mse_distortion_bounds():
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"""Paper Theorem 1: MSE <= sqrt(3)*pi/2 * 1/4^b per coordinate.
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Bounds: b=1: 0.360, b=2: 0.117, b=3: 0.030, b=4: 0.009"""
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from turboquant.quantizer import TurboQuantMSE
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d = 128
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N = 10000
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bounds = {1: 0.360, 2: 0.117, 3: 0.030, 4: 0.009}
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for bits, expected in bounds.items():
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q = TurboQuantMSE(dim=d, bits=bits, device="cpu", seed=42)
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x = torch.randn(N, d)
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x = x / x.norm(dim=-1, keepdim=True) # unit norm
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x_hat = q(x)
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mse_per_coord = ((x - x_hat) ** 2).mean().item()
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# Allow 15% tolerance (paper bound is an upper bound, empirical should be at or below)
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assert mse_per_coord <= expected * 1.15, \
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f"bits={bits}: MSE/coord={mse_per_coord:.4f} > bound*1.15={expected*1.15:.4f}"
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def test_mse_codebook_table1():
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"""Validate codebook total MSE values match paper Table 1."""
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from turboquant.codebook import get_codebook
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# Paper Table 1 values are total MSE for d-dimensional unit vector
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paper_values = {1: 0.360, 2: 0.117, 3: 0.030, 4: 0.009}
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for bits, expected in paper_values.items():
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cb = get_codebook(128, bits)
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actual = cb["mse_total"]
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ratio = actual / expected
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assert 0.85 <= ratio <= 1.20, \
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f"bits={bits}: total MSE={actual:.4f}, expected~{expected:.3f}, ratio={ratio:.3f}"
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# ---------- 2. Unbiasedness (Theorem 2) ----------
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def test_prod_unbiased():
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"""Paper Theorem 2: E[<y, x_tilde>] = <y, x>"""
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from turboquant.quantizer import TurboQuantProd
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d = 128
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N = 5000
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n_trials = 20
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for bits in [2, 3, 4]:
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biases = []
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for trial in range(n_trials):
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q = TurboQuantProd(dim=d, bits=bits, device="cpu", seed=trial * 100)
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x = torch.randn(1, 1, N, d)
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y = torch.randn(1, 1, 1, d)
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true_ip = (y * x).sum(dim=-1) # (1, 1, N)
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key_q = q.quantize(x)
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est_ip = q.attention_score(y, key_q) # (1, 1, 1, N)
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bias = (est_ip.squeeze() - true_ip.squeeze()).mean().item()
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biases.append(bias)
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mean_bias = np.mean(biases)
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# Unbiased means mean bias should be near zero relative to signal magnitude
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assert abs(mean_bias) < 0.05, \
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f"bits={bits}: mean bias={mean_bias:.4f} (should be ~0)"
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# ---------- 3. Inner-product distortion (Theorem 3) ----------
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def test_prod_distortion_scaling():
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"""Paper Theorem 3: D_prod <= sqrt(3)*pi^2*||y||^2/d * 1/4^b.
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Distortion should decrease ~4x when adding 1 bit."""
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from turboquant.quantizer import TurboQuantProd
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d = 128
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N = 2000
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distortions = {}
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for bits in [2, 3, 4]:
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q = TurboQuantProd(dim=d, bits=bits, device="cpu", seed=42)
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x = torch.randn(1, 1, N, d)
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y = torch.randn(1, 1, 1, d)
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true_ip = (y * x).sum(dim=-1).squeeze()
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key_q = q.quantize(x)
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est_ip = q.attention_score(y, key_q).squeeze()
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mse = ((est_ip - true_ip) ** 2).mean().item()
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distortions[bits] = mse
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# Each extra bit should reduce distortion by roughly 4x (1/4^b scaling)
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ratio_2_to_3 = distortions[2] / distortions[3]
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ratio_3_to_4 = distortions[3] / distortions[4]
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assert ratio_2_to_3 > 2.0, \
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f"2->3 bit distortion ratio={ratio_2_to_3:.2f} (expected ~4x, at least >2x)"
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assert ratio_3_to_4 > 2.0, \
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f"3->4 bit distortion ratio={ratio_3_to_4:.2f} (expected ~4x, at least >2x)"
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# ---------- 4. Attention recall@k at scale ----------
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def test_recall_at_scale():
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"""Recall@8 with d=128, N=4096 (realistic LLM KV size)."""
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from turboquant.quantizer import TurboQuantProd
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d = 128
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N = 4096
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n_queries = 32
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k = 8
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results = {}
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for bits in [3, 4]:
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q = TurboQuantProd(dim=d, bits=bits, device="cpu", seed=42)
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keys = torch.randn(1, 1, N, d) * 0.1
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queries = torch.randn(1, 1, n_queries, d) * 0.1
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true_scores = torch.matmul(queries, keys.transpose(-2, -1)).squeeze(0).squeeze(0)
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true_topk = true_scores.topk(k, dim=-1).indices
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key_q = q.quantize(keys)
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tq_scores = q.attention_score(queries, key_q).squeeze(0).squeeze(0)
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tq_topk = tq_scores.topk(k, dim=-1).indices
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recalls = []
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for qi in range(n_queries):
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true_set = set(true_topk[qi].tolist())
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tq_set = set(tq_topk[qi].tolist())
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recalls.append(len(true_set & tq_set) / k)
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results[bits] = np.mean(recalls)
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assert results[3] >= 0.40, f"3-bit recall@8={results[3]:.3f} < 0.40"
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assert results[4] >= 0.55, f"4-bit recall@8={results[4]:.3f} < 0.55"
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assert results[4] > results[3], "4-bit should have better recall than 3-bit"
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# ---------- 5. Needle-in-haystack at multiple depths ----------
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def test_needle_retrieval_depths():
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"""Needle retrieval at different positions in a 4096-token context."""
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from turboquant.store import CompressedKVStore
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d = 128
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H_kv = 4
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N = 4096
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depths = [0.1, 0.25, 0.5, 0.75, 0.9] # fraction into context
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for bits in [3, 4]:
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for depth in depths:
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needle_pos = int(N * depth)
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store = CompressedKVStore(
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head_dim=d, num_kv_heads=H_kv, key_bits=bits, value_bits=2,
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value_group_size=32, device=torch.device("cpu"),
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)
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keys = torch.randn(N, H_kv, d) * 0.02
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values = torch.randn(N, H_kv, d)
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needle_key = torch.randn(1, H_kv, d) * 3.0
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keys[needle_pos] = needle_key.squeeze(0)
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store.append_chunk(keys, values)
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flat = store.get_flat_cache()
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k_dequant = store.quantizer.dequantize(flat.prod_q)
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query_vec = needle_key.squeeze(0).unsqueeze(0)
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scores = torch.bmm(
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query_vec.float().transpose(0, 1),
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k_dequant.float().transpose(1, 2),
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).squeeze(1)
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for h in range(H_kv):
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top_idx = scores[h].argmax().item()
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assert top_idx == needle_pos, \
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f"bits={bits} depth={depth} head={h}: needle@{needle_pos} top@{top_idx}"
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def test_needle_chunked_8192():
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"""Needle retrieval in 8192 tokens split across multiple chunks."""
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from turboquant.store import CompressedKVStore
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d = 128
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H_kv = 2
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total = 8192
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chunk_size = 1024
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needle_pos = 5555
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store = CompressedKVStore(
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head_dim=d, num_kv_heads=H_kv, key_bits=3, value_bits=2,
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value_group_size=32, device=torch.device("cpu"),
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)
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all_keys = torch.randn(total, H_kv, d) * 0.02
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all_values = torch.randn(total, H_kv, d)
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needle_key = torch.randn(1, H_kv, d) * 3.0
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all_keys[needle_pos] = needle_key.squeeze(0)
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for i in range(0, total, chunk_size):
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store.append_chunk(all_keys[i:i+chunk_size], all_values[i:i+chunk_size])
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flat = store.get_flat_cache()
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k_dequant = store.quantizer.dequantize(flat.prod_q)
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query_vec = needle_key.squeeze(0).unsqueeze(0)
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scores = torch.bmm(
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query_vec.float().transpose(0, 1),
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k_dequant.float().transpose(1, 2),
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).squeeze(1)
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for h in range(H_kv):
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top_idx = scores[h].argmax().item()
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assert top_idx == needle_pos, \
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f"head={h}: needle@{needle_pos} top@{top_idx}"
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# ---------- 6. Compression ratio ----------
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def test_compression_ratio():
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"""Verify compression matches claimed 2.6x for 3-bit keys + 2-bit values."""
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from turboquant.store import CompressedKVStore
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d = 128
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H_kv = 8
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N = 4096
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store = CompressedKVStore(
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head_dim=d, num_kv_heads=H_kv, key_bits=3, value_bits=2,
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value_group_size=32, device=torch.device("cpu"),
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)
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k = torch.randn(N, H_kv, d)
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v = torch.randn(N, H_kv, d)
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store.append_chunk(k, v)
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tq_bytes = store.memory_bytes()
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fp16_bytes = N * H_kv * d * 2 * 2 # K+V in FP16
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ratio = fp16_bytes / tq_bytes
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assert ratio > 2.0, f"Compression ratio {ratio:.2f}x < 2.0x"
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# Implementation uses 3-bit keys + 2-bit values + overhead, so ratio should be 2-6x
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assert ratio < 8.0, f"Compression ratio {ratio:.2f}x > 8.0x (suspiciously high)"
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# ---------- 7. Rank correlation at scale ----------
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def test_rank_correlation_scale():
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"""Spearman rank correlation at d=128, N=2048."""
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from turboquant.quantizer import TurboQuantProd
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d = 128
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N = 2048
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for bits in [3, 4]:
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q = TurboQuantProd(dim=d, bits=bits, device="cpu", seed=42)
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keys = torch.randn(1, 1, N, d) * 0.1
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query = torch.randn(1, 1, 1, d) * 0.1
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true_scores = torch.matmul(query, keys.transpose(-2, -1)).squeeze()
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key_q = q.quantize(keys)
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tq_scores = q.attention_score(query, key_q).squeeze()
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true_ranks = true_scores.argsort().argsort().float()
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tq_ranks = tq_scores.argsort().argsort().float()
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corr = torch.corrcoef(torch.stack([true_ranks, tq_ranks]))[0, 1].item()
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if bits == 3:
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assert corr > 0.75, f"3-bit rank corr={corr:.3f} < 0.75"
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elif bits == 4:
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assert corr > 0.90, f"4-bit rank corr={corr:.3f} < 0.90"
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# ---------- Main ----------
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if __name__ == "__main__":
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print()
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print("=" * 60)
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print("TurboQuant Paper Validation (arXiv:2504.19874)")
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print("=" * 60)
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print()
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print("-- Theorem 1: MSE Distortion Bounds --")
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run("MSE distortion <= paper bound (b=1..4)", test_mse_distortion_bounds)
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run("Codebook MSE matches Table 1", test_mse_codebook_table1)
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print()
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print("-- Theorem 2: Unbiasedness --")
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run("E[<y, x~>] = <y, x> (bits=2,3,4)", test_prod_unbiased)
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print()
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print("-- Theorem 3: Inner-Product Distortion --")
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run("Distortion scales as 1/4^b", test_prod_distortion_scaling)
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print()
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print("-- Attention Quality --")
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run("Recall@8 at d=128, N=4096 (bits=3,4)", test_recall_at_scale)
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run("Rank correlation at d=128, N=2048", test_rank_correlation_scale)
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print()
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print("-- Needle-in-Haystack --")
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run("Needle at 5 depths in 4096 tokens (bits=3,4)", test_needle_retrieval_depths)
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run("Needle in 8192 tokens, chunked (3-bit)", test_needle_chunked_8192)
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print()
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print("-- Compression --")
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run("Compression ratio > 2x", test_compression_ratio)
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print()
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print("=" * 60)
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print(f"Results: {PASS} passed, {FAIL} failed (total {PASS + FAIL})")
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if FAIL == 0:
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print("All validations passed against paper claims.")
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else:
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print(f"{FAIL} validation(s) failed")
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print("=" * 60)
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sys.exit(1 if FAIL > 0 else 0)
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