biondizzle
edc8e7ee8d
Architecture matches paper: 'BF16 for RoPE dims, FP8 for remaining dims' - Non-RoPE dims (448 of 512): FP8_E4M3 storage → dequant to BF16 for FMHA - RoPE dims (64 of 512): BF16 storage (RoPE applied directly, no conversion) - Indexer keys: FP8_E4M3 (ihd=128, no RoPE) - SWA: BF16 (unchanged) Pipeline: Compressor → FP32 → split → [nope: FP32→FP8] + [rope: FP32→BF16→RoPE] Gather: [nope: FP8→BF16] + [rope: BF16] → concat → FMHA No BF16 intermediate for non-RoPE data. No FP32 intermediate after BF16 RoPE. BF16 is the final format consumed by FMHA (no further conversion). KVCache rewritten: - comp_nope_fp8/scale: FP8 storage for non-RoPE - comp_rope_bf16: BF16 storage for RoPE - comp_nope_selective/all: FP8→BF16 dequant - comp_rope_selective/all: BF16 gather - set_compressed_mixed: write mixed format - set_indexer_keys_fp8: write FP8 indexer keys
115 lines
5.3 KiB
Python
115 lines
5.3 KiB
Python
#!/usr/bin/env python3
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"""KV-1/KV-2/KV-3: Mixed FP8+BF16 compressed KV + FP8 indexer keys — production-value unit tests.
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Tests the kv_quantize.cu kernels at production shapes:
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- FP8_E4M3: nope_dim=448, ihd=128
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- BF16 RoPE: rope_dim=64
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- Mixed storage: FP8 nope + BF16 rope → concat → compare with FP32 reference
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Falsifiable gates:
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- FP8_E4M3 quantize FP32→FP8→BF16: cos ≥ 0.998 vs FP32 reference
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- Mixed storage round-trip: FP32 → (FP8 nope + BF16 rope) → BF16 concat: cos ≥ 0.998
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- Selective dequant matches full dequant
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"""
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import torch
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import math
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torch.manual_seed(42)
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device = 'cuda'
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from dsv4.kernels.cuda.loader import get_cuda_module
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mod = get_cuda_module("kv_quantize", ["kv_quantize.cu"])
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print("=" * 60)
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print("KV-1/KV-2/KV-3: Mixed FP8+BF16 Storage — Unit Tests")
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print("=" * 60)
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hd = 512; rope_dim = 64; nope_dim = hd - rope_dim # 448
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# ===========================================================================
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# Test 1: FP8_E4M3 nope round-trip (production nope_dim=448)
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# ===========================================================================
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print("\n--- Test 1: FP8_E4M3 nope FP32→FP8→BF16 (nope_dim=448) ---")
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for M in [1, 4, 8, 32, 128]:
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data = torch.randn(M, nope_dim, device=device, dtype=torch.float32) * 3.0
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fp8, scale = mod.quantize_fp8_e4m3_from_fp32(data.contiguous())
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deq = mod.dequant_fp8_e4m3(fp8.view(torch.uint8), scale)
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cos = torch.nn.functional.cosine_similarity(data.float().flatten(), deq.float().flatten(), dim=0).item()
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max_err = (data.float() - deq.float()).abs().max().item()
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print(f" M={M:3d}: cos={cos:.6f} max_err={max_err:.4f}")
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assert cos >= 0.998, f"FP8 nope round-trip cos={cos:.6f} < 0.998 at M={M}"
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# ===========================================================================
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# Test 2: FP8_E4M3 indexer keys (production ihd=128)
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# ===========================================================================
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print("\n--- Test 2: FP8_E4M3 indexer keys (ihd=128) ---")
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for M in [1, 4, 32, 128]:
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data = torch.randn(M, 128, device=device, dtype=torch.float32) * 3.0
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fp8, scale = mod.quantize_fp8_e4m3_from_fp32(data.contiguous())
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deq = mod.dequant_fp8_e4m3(fp8.view(torch.uint8), scale)
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cos = torch.nn.functional.cosine_similarity(data.float().flatten(), deq.float().flatten(), dim=0).item()
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print(f" M={M:3d}: cos={cos:.6f}")
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assert cos >= 0.998, f"FP8 indexer cos={cos:.6f} < 0.998 at M={M}"
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# ===========================================================================
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# Test 3: Mixed storage round-trip (FP8 nope + BF16 rope → concat)
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# ===========================================================================
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print("\n--- Test 3: Mixed FP8+BF16 full round-trip (hd=512) ---")
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# Build proper RoPE cache
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theta = 10000.0
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freqs = 1.0 / (theta ** (torch.arange(0, rope_dim, 2, dtype=torch.float32) / rope_dim))
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angles = torch.outer(torch.arange(1024, dtype=torch.float32), freqs)
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cos_cache = torch.cos(angles).to(device)
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sin_cache = torch.sin(angles).to(device)
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from dsv4.ops.rope_cuda import apply_rope
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for M in [1, 4, 8, 32]:
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# Simulate compressor FP32 output
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data_fp32 = torch.randn(M, hd, device=device, dtype=torch.float32) * 3.0
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positions = torch.arange(M, device=device, dtype=torch.long)
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# Reference: FP32 → BF16 → RoPE → full BF16
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ref_bf16 = data_fp32.bfloat16()
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ref_3d = ref_bf16.unsqueeze(1) # (M, 1, hd)
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ref_3d = apply_rope(ref_3d, positions, cos_cache, sin_cache, rope_dim)
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ref_full = ref_3d.squeeze(1) # (M, hd) BF16
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# Our path: FP32 → split → FP8 nope + BF16 rope (with RoPE) → concat
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nope_fp32 = data_fp32[:, :nope_dim].contiguous()
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rope_bf16 = data_fp32[:, nope_dim:].bfloat16().contiguous()
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rope_3d = rope_bf16.unsqueeze(1)
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rope_3d = apply_rope(rope_3d, positions, cos_cache, sin_cache, rope_dim)
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rope_bf16 = rope_3d.squeeze(1)
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nope_fp8, nope_scale = mod.quantize_fp8_e4m3_from_fp32(nope_fp32)
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nope_bf16 = mod.dequant_fp8_e4m3(nope_fp8.view(torch.uint8), nope_scale)
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# Concat nope + rope
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result = torch.cat([nope_bf16, rope_bf16], dim=1) # (M, hd) BF16
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cos = torch.nn.functional.cosine_similarity(ref_full.float().flatten(), result.float().flatten(), dim=0).item()
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max_err = (ref_full.float() - result.float()).abs().max().item()
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print(f" M={M:3d}: cos={cos:.6f} max_err={max_err:.4f}")
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assert cos >= 0.998, f"Mixed storage cos={cos:.6f} < 0.998 at M={M}"
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# ===========================================================================
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# Test 4: Selective dequant (CSA top-k gather)
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# ===========================================================================
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print("\n--- Test 4: Selective FP8 dequant (CSA top-k gather) ---")
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M = 32; data = torch.randn(M, nope_dim, device=device, dtype=torch.float32) * 3.0
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fp8, scale = mod.quantize_fp8_e4m3_from_fp32(data.contiguous())
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full_deq = mod.dequant_fp8_e4m3(fp8.view(torch.uint8), scale)
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indices = torch.tensor([0, 5, 10, 20, 31], device=device, dtype=torch.int32)
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sel_deq = mod.dequant_fp8_e4m3_selective(fp8.view(torch.uint8), scale, indices)
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for i, idx in enumerate(indices):
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cos = torch.nn.functional.cosine_similarity(
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full_deq[idx].float().flatten(), sel_deq[i].float().flatten(), dim=0).item()
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assert cos >= 0.99999, f"Selective mismatch at idx={idx}: cos={cos:.6f}"
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print(f" All 5 selective dequant entries match full dequant: PASS")
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print("\n" + "=" * 60)
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print("ALL TESTS PASSED")
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print("=" * 60)
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