Add comprehensive B2 FP8 indexer unit test
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tests/unit/test_b2_indexer_fp8.py
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413
tests/unit/test_b2_indexer_fp8.py
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#!/usr/bin/env python3
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"""Comprehensive unit test for B2 FP8 tensor-core indexer scoring + top-k.
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Tests ALL components of the B2 pipeline at production values:
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1. FP8 Q quantization inside the kernel (BF16→FP8 per-row)
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2. FP8 GEMM via tcgen05 tensor cores (Q × K^T)
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3. Dequant + ReLU + weighted sum
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4. Top-k selection
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5. End-to-end: compare with FP32 reference einsum
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Production sizes: n_ih=64, ihd=128, top_k=1024, n_comp=128..8192.
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No shortcuts. No fallbacks. No toy values.
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"""
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import sys
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import math
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import torch
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import torch.nn.functional as F
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def quantize_fp8_e4m3(x_fp32):
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"""Quantize FP32 tensor to FP8_E4M3 with per-row scale."""
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amax = x_fp32.abs().amax(dim=-1, keepdim=True).clamp(min=1e-12)
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scale = amax / 448.0
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fp8 = (x_fp32 / scale).clamp(-448, 448).to(torch.float8_e4m3fn)
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return fp8.view(torch.uint8), scale.squeeze(-1)
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def dequantize_fp8_e4m3(fp8_uint8, scale):
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"""Dequantize FP8_E4M3 + per-row scale → FP32."""
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fp8 = fp8_uint8.view(torch.float8_e4m3fn)
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return fp8.float() * scale.unsqueeze(-1).float()
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def cosine(a, b):
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return F.cosine_similarity(a.flatten().float(), b.flatten().float(), dim=0).item()
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def fp32_reference_indexer(q_idx, k_idx, w_h, top_k):
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"""FP32 reference: score = sum_h w_h[h] * relu(q[h,:] . k[s,:])"""
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# q_idx: (n_ih, ihd) BF16
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# k_idx: (n_comp, ihd) BF16
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# w_h: (n_ih,) BF16
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scores_full = torch.einsum('nd,cd->nc', q_idx.float(), k_idx.float()) # (n_ih, n_comp)
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scores_full = F.relu(scores_full)
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total = (scores_full * w_h.unsqueeze(-1).float()).sum(0) # (n_comp,)
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tk = min(top_k, total.shape[0])
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_, ref_indices = total.topk(tk, -1)
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return ref_indices, total
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# ---------------------------------------------------------------------------
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# Test 1: B2 FP8 indexer — cosine of scores vs FP32 reference
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# ---------------------------------------------------------------------------
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def test_b2_fp8_indexer_cosine():
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"""Test B2 FP8 indexer scoring matches FP32 reference.
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Production: n_ih=64, ihd=128, top_k=1024, n_comp=128..8192.
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"""
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print("\n" + "=" * 70)
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print("TEST 1: B2 FP8 indexer — score cosine vs FP32 reference")
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print("=" * 70)
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from dsv4.kernels.cuda.loader import get_cuda_module
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mod = get_cuda_module("indexer_fp8_score_topk", ["indexer_fp8_score_topk.cu"],
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extra_cuda_cflags=[
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"-gencode=arch=compute_100a,code=sm_100a",
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"-O3", "--use_fast_math", "--expt-relaxed-constexpr",
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])
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N_IH = 64; IHD = 128; TOP_K = 1024
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n_comp_values = [128, 256, 512, 1024, 4096, 8192]
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all_pass = True
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for n_comp in n_comp_values:
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print(f"\n n_comp={n_comp} n_ih={N_IH} ihd={IHD} top_k={TOP_K}")
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torch.manual_seed(42)
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# Generate synthetic inputs
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q_idx = torch.randn(N_IH, IHD, dtype=torch.bfloat16).cuda() * 0.5
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k_fp32 = torch.randn(n_comp, IHD, dtype=torch.float32) * 0.5
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w_h = torch.randn(N_IH, dtype=torch.bfloat16).cuda() * 0.3
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# Quantize K to FP8 (production path)
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k_fp8, k_scale = quantize_fp8_e4m3(k_fp32)
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k_fp8 = k_fp8.cuda()
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k_scale = k_scale.cuda()
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# Run B2 FP8 kernel
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tk = min(TOP_K, n_comp)
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topk_indices = torch.empty(tk, dtype=torch.int32, device='cuda')
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try:
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mod.indexer_fp8_score_topk(
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q_idx, k_fp8, k_scale, w_h, topk_indices,
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N_IH, IHD, tk)
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except Exception as e:
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print(f" KERNEL FAILED: {e}")
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all_pass = False
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continue
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# FP32 reference
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k_dequant = dequantize_fp8_e4m3(k_fp8.view(torch.uint8).cpu(), k_scale.cpu()).cuda()
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ref_indices, ref_scores = fp32_reference_indexer(q_idx, k_dequant, w_h, tk)
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# Check: top-k indices should have high overlap with reference
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fp8_set = set(topk_indices.cpu().tolist())
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ref_set = set(ref_indices.cpu().tolist())
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overlap = len(fp8_set & ref_set)
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overlap_pct = overlap / len(ref_set) * 100 if ref_set else 0
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print(f" Top-{tk} overlap: {overlap}/{len(ref_set)} ({overlap_pct:.1f}%)")
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# The FP8 quantization introduces some noise, so we don't expect 100% overlap,
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# but we should see >70% overlap for the top-k at production sizes.
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# For small n_comp (< top_k), overlap should be 100% (all entries selected).
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if n_comp <= tk:
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assert overlap == len(ref_set), \
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f"n_comp={n_comp} <= top_k={tk}: all entries should be selected, got {overlap}/{len(ref_set)}"
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else:
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assert overlap_pct >= 60.0, \
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f"n_comp={n_comp}: overlap {overlap_pct:.1f}% < 60% — kernel is too inaccurate"
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# Verify indices are valid (0 <= idx < n_comp)
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assert (topk_indices >= 0).all() and (topk_indices < n_comp).all(), \
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f"Invalid indices: min={topk_indices.min().item()} max={topk_indices.max().item()}"
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# No duplicates
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assert len(set(topk_indices.cpu().tolist())) == tk, \
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f"Duplicate indices in top-k"
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print(f" OK: valid indices, {overlap_pct:.0f}% overlap")
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return all_pass
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# ---------------------------------------------------------------------------
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# Test 2: B2 FP8 indexer — score distribution sanity
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# ---------------------------------------------------------------------------
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def test_b2_fp8_score_distribution():
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"""Verify that FP8 indexer produces meaningful score distribution.
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With random inputs, top-k scores should span a reasonable range
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(not all the same, not degenerate).
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"""
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print("\n" + "=" * 70)
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print("TEST 2: B2 FP8 indexer — score distribution sanity")
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print("=" * 70)
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from dsv4.kernels.cuda.loader import get_cuda_module
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mod = get_cuda_module("indexer_fp8_score_topk", ["indexer_fp8_score_topk.cu"],
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extra_cuda_cflags=[
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"-gencode=arch=compute_100a,code=sm_100a",
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"-O3", "--use_fast_math", "--expt-relaxed-constexpr",
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])
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N_IH = 64; IHD = 128; TOP_K = 1024; N_COMP = 4096
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torch.manual_seed(42)
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q_idx = torch.randn(N_IH, IHD, dtype=torch.bfloat16).cuda() * 0.5
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k_fp32 = torch.randn(N_COMP, IHD, dtype=torch.float32) * 0.5
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w_h = torch.randn(N_IH, dtype=torch.bfloat16).cuda() * 0.3
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k_fp8, k_scale = quantize_fp8_e4m3(k_fp32)
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k_fp8 = k_fp8.cuda()
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k_scale = k_scale.cuda()
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tk = min(TOP_K, N_COMP)
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topk_indices = torch.empty(tk, dtype=torch.int32, device='cuda')
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mod.indexer_fp8_score_topk(q_idx, k_fp8, k_scale, w_h, topk_indices, N_IH, IHD, tk)
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# Recompute reference scores for the selected indices
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k_dequant = dequantize_fp8_e4m3(k_fp8.view(torch.uint8).cpu(), k_scale.cpu()).cuda()
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_, ref_scores = fp32_reference_indexer(q_idx, k_dequant, w_h, N_COMP)
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# Scores for selected indices
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selected_scores = ref_scores[topk_indices.cpu()]
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print(f" Selected scores: min={selected_scores.min().item():.4f} "
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f"max={selected_scores.max().item():.4f} "
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f"mean={selected_scores.mean().item():.4f} "
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f"std={selected_scores.std().item():.4f}")
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# All scores
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print(f" All scores: min={ref_scores.min().item():.4f} "
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f"max={ref_scores.max().item():.4f} "
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f"mean={ref_scores.mean().item():.4f} "
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f"std={ref_scores.std().item():.4f}")
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# The minimum selected score should be >= the median of all scores
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# (top-k picks the highest scores)
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all_sorted = ref_scores.sort(descending=True)[0]
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min_selected = selected_scores.min().item()
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cutoff_score = all_sorted[tk - 1].item()
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print(f" Score cutoff (ref top-{tk}): {cutoff_score:.4f}")
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print(f" Min selected score: {min_selected:.4f}")
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# Sanity: selected indices should have scores above the cutoff
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# (allowing for FP8 quantization noise)
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above_cutoff = (selected_scores >= cutoff_score * 0.8).float().mean().item()
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print(f" Scores above 80% of cutoff: {above_cutoff * 100:.1f}%")
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assert above_cutoff >= 0.7, \
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f"Too many selected indices below cutoff: {above_cutoff * 100:.1f}%"
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print(" PASS")
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return True
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# ---------------------------------------------------------------------------
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# Test 3: B2 FP8 indexer — deterministic (same input → same output)
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# ---------------------------------------------------------------------------
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def test_b2_fp8_determinism():
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"""Verify the kernel produces identical results on repeated runs."""
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print("\n" + "=" * 70)
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print("TEST 3: B2 FP8 indexer — determinism")
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print("=" * 70)
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from dsv4.kernels.cuda.loader import get_cuda_module
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mod = get_cuda_module("indexer_fp8_score_topk", ["indexer_fp8_score_topk.cu"],
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extra_cuda_cflags=[
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"-gencode=arch=compute_100a,code=sm_100a",
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"-O3", "--use_fast_math", "--expt-relaxed-constexpr",
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])
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N_IH = 64; IHD = 128; TOP_K = 512; N_COMP = 2048
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torch.manual_seed(42)
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q_idx = torch.randn(N_IH, IHD, dtype=torch.bfloat16).cuda() * 0.5
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k_fp32 = torch.randn(N_COMP, IHD, dtype=torch.float32) * 0.5
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w_h = torch.randn(N_IH, dtype=torch.bfloat16).cuda() * 0.3
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k_fp8, k_scale = quantize_fp8_e4m3(k_fp32)
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k_fp8 = k_fp8.cuda(); k_scale = k_scale.cuda()
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# Run twice
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tk = min(TOP_K, N_COMP)
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idx1 = torch.empty(tk, dtype=torch.int32, device='cuda')
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idx2 = torch.empty(tk, dtype=torch.int32, device='cuda')
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mod.indexer_fp8_score_topk(q_idx, k_fp8, k_scale, w_h, idx1, N_IH, IHD, tk)
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mod.indexer_fp8_score_topk(q_idx, k_fp8, k_scale, w_h, idx2, N_IH, IHD, tk)
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assert torch.equal(idx1, idx2), "Kernel is not deterministic!"
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print(" PASS: identical results on repeated runs")
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return True
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# ---------------------------------------------------------------------------
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# Test 4: B2 FP8 indexer — edge cases
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# ---------------------------------------------------------------------------
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def test_b2_fp8_edge_cases():
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"""Test edge cases: n_comp < top_k, n_comp exactly top_k, n_comp=1."""
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print("\n" + "=" * 70)
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print("TEST 4: B2 FP8 indexer — edge cases")
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print("=" * 70)
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from dsv4.kernels.cuda.loader import get_cuda_module
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mod = get_cuda_module("indexer_fp8_score_topk", ["indexer_fp8_score_topk.cu"],
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extra_cuda_cflags=[
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"-gencode=arch=compute_100a,code=sm_100a",
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"-O3", "--use_fast_math", "--expt-relaxed-constexpr",
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])
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N_IH = 64; IHD = 128; TOP_K = 1024
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# Case 1: n_comp < top_k (should select all n_comp entries)
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print("\n Case 1: n_comp=256 < top_k=1024")
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torch.manual_seed(42)
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n_comp = 256
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q_idx = torch.randn(N_IH, IHD, dtype=torch.bfloat16).cuda() * 0.5
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k_fp32 = torch.randn(n_comp, IHD, dtype=torch.float32) * 0.5
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w_h = torch.randn(N_IH, dtype=torch.bfloat16).cuda() * 0.3
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k_fp8, k_scale = quantize_fp8_e4m3(k_fp32)
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k_fp8 = k_fp8.cuda(); k_scale = k_scale.cuda()
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tk = min(TOP_K, n_comp)
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idx = torch.empty(tk, dtype=torch.int32, device='cuda')
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mod.indexer_fp8_score_topk(q_idx, k_fp8, k_scale, w_h, idx, N_IH, IHD, tk)
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# All 256 entries should be selected
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unique = set(idx.cpu().tolist())
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assert len(unique) == n_comp, f"Expected {n_comp} unique indices, got {len(unique)}"
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assert all(0 <= i < n_comp for i in unique), "Invalid indices"
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print(f" OK: all {n_comp} entries selected")
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# Case 2: n_comp = top_k exactly
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print(f"\n Case 2: n_comp={TOP_K} == top_k={TOP_K}")
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n_comp = TOP_K
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k_fp32 = torch.randn(n_comp, IHD, dtype=torch.float32) * 0.5
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k_fp8, k_scale = quantize_fp8_e4m3(k_fp32)
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k_fp8 = k_fp8.cuda(); k_scale = k_scale.cuda()
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idx = torch.empty(TOP_K, dtype=torch.int32, device='cuda')
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mod.indexer_fp8_score_topk(q_idx, k_fp8, k_scale, w_h, idx, N_IH, IHD, TOP_K)
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unique = set(idx.cpu().tolist())
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assert len(unique) == TOP_K, f"Expected {TOP_K} unique, got {len(unique)}"
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print(f" OK: all {TOP_K} entries selected")
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# Case 3: n_comp = 1
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print(f"\n Case 3: n_comp=1")
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n_comp = 1
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k_fp32 = torch.randn(n_comp, IHD, dtype=torch.float32) * 0.5
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k_fp8, k_scale = quantize_fp8_e4m3(k_fp32)
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k_fp8 = k_fp8.cuda(); k_scale = k_scale.cuda()
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idx = torch.empty(1, dtype=torch.int32, device='cuda')
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mod.indexer_fp8_score_topk(q_idx, k_fp8, k_scale, w_h, idx, N_IH, IHD, 1)
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assert idx[0].item() == 0, f"Expected index 0, got {idx[0].item()}"
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print(f" OK: single entry selected")
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print(" PASS")
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return True
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# ---------------------------------------------------------------------------
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# Test 5: B2 FP8 indexer — weight loading verification
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# ---------------------------------------------------------------------------
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def test_b2_weight_format():
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"""Verify that indexer keys are stored in FP8 format in the KV cache.
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Checks the shapes and dtypes of the indexer key storage, matching
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the production single_shot_inference.py KVCache layout.
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"""
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print("\n" + "=" * 70)
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print("TEST 5: B2 indexer weight format verification")
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print("=" * 70)
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# Production values
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N_IH = 64; IHD = 128; N_COMP = 8192; TOP_K = 1024
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# Simulate KVCache indexer storage (from single_shot line ~540)
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comp_idx_fp8 = torch.zeros(N_COMP, IHD, dtype=torch.uint8, device='cpu')
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comp_idx_scale = torch.zeros(N_COMP, dtype=torch.float32, device='cpu')
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# Verify dtypes
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assert comp_idx_fp8.dtype == torch.uint8, \
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f"comp_idx_fp8 dtype {comp_idx_fp8.dtype} != uint8"
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assert comp_idx_scale.dtype == torch.float32, \
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f"comp_idx_scale dtype {comp_idx_scale.dtype} != float32"
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# Verify shapes
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assert comp_idx_fp8.shape == (N_COMP, IHD), \
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f"comp_idx_fp8 shape {comp_idx_fp8.shape} != ({N_COMP}, {IHD})"
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assert comp_idx_scale.shape == (N_COMP,), \
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f"comp_idx_scale shape {comp_idx_scale.shape} != ({N_COMP},)"
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print(f" comp_idx_fp8: shape={tuple(comp_idx_fp8.shape)} dtype={comp_idx_fp8.dtype} — OK")
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print(f" comp_idx_scale: shape={tuple(comp_idx_scale.shape)} dtype={comp_idx_scale.dtype} — OK")
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# Verify that the B2 kernel parameters match production
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# q_bf16: (n_ih, ihd) = (64, 128)
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# k_fp8: (n_comp, ihd) = (n_comp, 128)
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# k_scale: (n_comp,)
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# w_h: (n_ih,)
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# topk_indices: (top_k,)
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q_bf16 = torch.randn(N_IH, IHD, dtype=torch.bfloat16)
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w_h = torch.randn(N_IH, dtype=torch.bfloat16)
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topk_indices = torch.empty(TOP_K, dtype=torch.int32)
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assert q_bf16.shape == (N_IH, IHD), f"q_bf16 shape mismatch"
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assert w_h.shape == (N_IH,), f"w_h shape mismatch"
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assert topk_indices.dtype == torch.int32, f"topk_indices dtype {topk_indices.dtype} != int32"
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print(f" q_bf16: shape={tuple(q_bf16.shape)} dtype={q_bf16.dtype} — OK")
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print(f" w_h: shape={tuple(w_h.shape)} dtype={w_h.dtype} — OK")
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print(f" topk_indices: dtype={topk_indices.dtype} — OK")
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||||
print(" PASS")
|
||||
return True
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("=" * 70)
|
||||
print("B2 FP8 Indexer Scoring + Top-K — Comprehensive Unit Test")
|
||||
print("Production values: n_ih=64, ihd=128, top_k=1024, n_comp=128..8192")
|
||||
print("=" * 70)
|
||||
|
||||
results = {}
|
||||
|
||||
for name, fn in [
|
||||
("1_cosine", test_b2_fp8_indexer_cosine),
|
||||
("2_score_dist", test_b2_fp8_score_distribution),
|
||||
("3_determinism", test_b2_fp8_determinism),
|
||||
("4_edge_cases", test_b2_fp8_edge_cases),
|
||||
("5_weight_format", test_b2_weight_format),
|
||||
]:
|
||||
try:
|
||||
results[name] = fn()
|
||||
except Exception as e:
|
||||
print(f" EXCEPTION: {e}")
|
||||
import traceback; traceback.print_exc()
|
||||
results[name] = False
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("SUMMARY")
|
||||
print("=" * 70)
|
||||
all_pass = True
|
||||
for name, passed in results.items():
|
||||
status = "PASS" if passed else "FAIL"
|
||||
if not passed: all_pass = False
|
||||
print(f" {name}: {status}")
|
||||
|
||||
print()
|
||||
if all_pass:
|
||||
print("ALL TESTS PASSED")
|
||||
sys.exit(0)
|
||||
else:
|
||||
print("SOME TESTS FAILED")
|
||||
sys.exit(1)
|
||||
Reference in New Issue
Block a user