From efdedab399b78e6a9f5d2ca872f550eae19bf5b7 Mon Sep 17 00:00:00 2001 From: biondizzle Date: Mon, 25 May 2026 16:54:56 +0000 Subject: [PATCH] fix tests: use 3D tensors (M, hd, 1) matching kernel local_tile expectations --- tests/unit/test_d2_headpacked.py | 136 +++++++++++++------------------ 1 file changed, 55 insertions(+), 81 deletions(-) diff --git a/tests/unit/test_d2_headpacked.py b/tests/unit/test_d2_headpacked.py index 8fd09109..17fd0ee3 100644 --- a/tests/unit/test_d2_headpacked.py +++ b/tests/unit/test_d2_headpacked.py @@ -1,21 +1,16 @@ """ FMHA D2: Head-packed multi-head attention. -Strategy A: Fold the head dimension into M. Each CTA processes -all heads' queries for its M tile. At decode T=1, n_h=128, M=128 -fills exactly one MMA tile. The kernel doesn't need to know about -heads — it just processes M rows with per-row softmax. - -Q is reshaped from (n_h, T, hd) to (n_h * T, hd) in Python. -K/V are shared (MQA) with shape (s_k, hd). +Strategy A: Fold the head dimension into M. Q is reshaped from (n_h, T, hd) +to (n_h*T, hd, 1). K/V are (s_k, hd, 1) — shared MQA. +At decode T=1, n_h=128: M=128, exactly one MMA tile. +The kernel treats each row as independent attention (per-row softmax). Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d2_headpacked.py """ import torch import math -import cutlass import cutlass.cute as cute -from cutlass import Float32, BFloat16 import cuda.bindings.driver as cuda import cutlass.torch as ct @@ -33,107 +28,93 @@ def reference_fmha(q, k, v, scale): return o.to(torch.bfloat16) +def _run_fmha(fmha, q_3d, k_3d, v_3d, o_3d): + """Run FmhaKernel with CuTe tensors.""" + stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + q_c = ct.from_dlpack(q_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_3d)) + k_c = ct.from_dlpack(k_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_3d)) + v_c = ct.from_dlpack(v_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_3d)) + o_c = ct.from_dlpack(o_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o_3d)) + fmha(q_c, k_c, v_c, o_c, stream) + + def test_d2_headpacked_n1(): - """Regression: n_h=1 (same as single-head, backward compatible).""" + """Regression: n_h=1 (same as single-head).""" print("\n=== Test 1: n_h=1 regression (hd=64) ===") torch.manual_seed(42) - T, s_k, hd = 1, 128, 64 + M, s_k, hd = 128, 128, 64 scale = 1.0 / math.sqrt(hd) - q = torch.randn(T, hd, dtype=torch.bfloat16, device='cuda') - k = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda') - v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda') + q = torch.randn(M, hd, 1, dtype=torch.bfloat16, device='cuda') + k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda') + v = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda') + o = torch.zeros(M, hd, 1, dtype=torch.bfloat16, device='cuda') fmha = FmhaKernel(head_dim=hd, s_k=s_k, normalize=True) - stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + _run_fmha(fmha, q, k, v, o) - # Use the existing test pattern: pad Q to 128 rows - q_128 = torch.randn(128, hd, dtype=torch.bfloat16, device='cuda') - q_128[0] = q[0] # Put real data in row 0 - o_128 = torch.zeros(128, hd, dtype=torch.bfloat16, device='cuda') - - q_c = ct.from_dlpack(q_128).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_128)) - k_c = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k)) - v_c = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v)) - o_c = ct.from_dlpack(o_128).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o_128)) - fmha(q_c, k_c, v_c, o_c, stream) - - o = o_128[:T] # Take only the real rows - ref = reference_fmha(q, k, v, scale) + ref = reference_fmha(q[:,:,0], k[:,:,0], v[:,:,0], scale) cos = torch.nn.functional.cosine_similarity( - o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0) + o[:,:,0].flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0) ).item() print(f" cos = {cos:.6f}") assert cos >= 0.99, f"cosine too low: {cos}" print(" ✅ PASS") -def test_d2_headpacked_basic(): - """n_h=128, T=1 (Pro decode): M=128, exactly one M tile.""" +def test_d2_headpacked_128(): + """n_h=128, T=1 (Pro decode): M=128, one M tile, all heads packed.""" print("\n=== Test 2: n_h=128, T=1 (Pro decode, hd=64) ===") torch.manual_seed(42) n_h, T, s_k, hd = 128, 1, 128, 64 scale = 1.0 / math.sqrt(hd) - # Q: (n_h, T, hd) → (n_h*T, hd) = (128, 64) q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda') - q = q_heads.reshape(n_h * T, hd) - k = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda') - v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda') + # Pack heads into M: (n_h*T, hd) → (128, 64, 1) + q = q_heads.reshape(n_h * T, hd).unsqueeze(-1) + k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda') + v = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda') + o = torch.zeros(n_h * T, hd, 1, dtype=torch.bfloat16, device='cuda') fmha = FmhaKernel(head_dim=hd, s_k=s_k, normalize=True, num_query_heads=n_h) - o = torch.zeros(n_h * T, hd, dtype=torch.bfloat16, device='cuda') - stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) - - q_c = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q)) - k_c = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k)) - v_c = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v)) - o_c = ct.from_dlpack(o).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o)) - fmha(q_c, k_c, v_c, o_c, stream) + _run_fmha(fmha, q, k, v, o) # Reference: per-head attention o_ref = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda') for h in range(n_h): - o_ref[h, 0] = reference_fmha(q_heads[h], k, v, scale)[0] + o_ref[h, 0] = reference_fmha(q_heads[h], k[:,:,0], v[:,:,0], scale)[0] o_ref_flat = o_ref.reshape(n_h * T, hd) cos = torch.nn.functional.cosine_similarity( - o.flatten().float().unsqueeze(0), o_ref_flat.flatten().float().unsqueeze(0) + o[:,:,0].flatten().float().unsqueeze(0), o_ref_flat.flatten().float().unsqueeze(0) ).item() print(f" cos = {cos:.6f}") assert cos >= 0.99, f"cosine too low: {cos}" print(" ✅ PASS") -def test_d2_headpacked_flash(): - """n_h=64, T=1 (Flash decode): M=64, underutilized (1 CTA, 64 rows).""" +def test_d2_headpacked_64(): + """n_h=64, T=1 (Flash decode): M=64, pad to 128.""" print("\n=== Test 3: n_h=64, T=1 (Flash decode, hd=64) ===") torch.manual_seed(42) n_h, T, s_k, hd = 64, 1, 128, 64 scale = 1.0 / math.sqrt(hd) q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda') - q = q_heads.reshape(n_h * T, hd) - # Pad to 128 rows (M tile size) — kernel expects M >= 128 - q_padded = torch.nn.functional.pad(q, (0, 0, 0, 128 - n_h * T)) - k = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda') - v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda') + q_flat = q_heads.reshape(n_h * T, hd) # (64, 64) + # Pad to 128 rows + q = torch.nn.functional.pad(q_flat, (0, 0, 0, 128 - n_h * T)).unsqueeze(-1) # (128, 64, 1) + k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda') + v = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda') + o_padded = torch.zeros(128, hd, 1, dtype=torch.bfloat16, device='cuda') fmha = FmhaKernel(head_dim=hd, s_k=s_k, normalize=True, num_query_heads=n_h) - o_padded = torch.zeros(128, hd, dtype=torch.bfloat16, device='cuda') - stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) - - q_c = ct.from_dlpack(q_padded).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_padded)) - k_c = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k)) - v_c = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v)) - o_c = ct.from_dlpack(o_padded).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o_padded)) - fmha(q_c, k_c, v_c, o_c, stream) - - o = o_padded[:n_h * T] # Trim padding + _run_fmha(fmha, q, k, v, o_padded) + o = o_padded[:n_h * T, :, 0] # Trim padding o_ref = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda') for h in range(n_h): - o_ref[h, 0] = reference_fmha(q_heads[h], k, v, scale)[0] + o_ref[h, 0] = reference_fmha(q_heads[h], k[:,:,0], v[:,:,0], scale)[0] o_ref_flat = o_ref.reshape(n_h * T, hd) cos = torch.nn.functional.cosine_similarity( @@ -145,33 +126,26 @@ def test_d2_headpacked_flash(): def test_d2_headpacked_hd128(): - """n_h=8, T=1, hd=128 (multi-head with larger head dim).""" + """n_h=8, T=1, hd=128: pad to 128 rows, larger head dim.""" print("\n=== Test 4: n_h=8, T=1, hd=128 ===") torch.manual_seed(42) n_h, T, s_k, hd = 8, 1, 128, 128 scale = 1.0 / math.sqrt(hd) q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda') - q = q_heads.reshape(n_h * T, hd) # (8, 128) - # Pad to 128 rows (M tile) - q_padded = torch.nn.functional.pad(q, (0, 0, 0, 128 - n_h * T)) - k = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda') - v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda') + q_flat = q_heads.reshape(n_h * T, hd) # (8, 128) + q = torch.nn.functional.pad(q_flat, (0, 0, 0, 128 - n_h * T)).unsqueeze(-1) # (128, 128, 1) + k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda') + v = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda') + o_padded = torch.zeros(128, hd, 1, dtype=torch.bfloat16, device='cuda') fmha = FmhaKernel(head_dim=hd, s_k=s_k, normalize=True, num_query_heads=n_h) - o_padded = torch.zeros(128, hd, dtype=torch.bfloat16, device='cuda') - stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream) + _run_fmha(fmha, q, k, v, o_padded) - q_c = ct.from_dlpack(q_padded).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_padded)) - k_c = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k)) - v_c = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v)) - o_c = ct.from_dlpack(o_padded).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o_padded)) - fmha(q_c, k_c, v_c, o_c, stream) - - o = o_padded[:n_h * T] # Trim padding + o = o_padded[:n_h * T, :, 0] o_ref = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda') for h in range(n_h): - o_ref[h, 0] = reference_fmha(q_heads[h], k, v, scale)[0] + o_ref[h, 0] = reference_fmha(q_heads[h], k[:,:,0], v[:,:,0], scale)[0] o_ref_flat = o_ref.reshape(n_h * T, hd) cos = torch.nn.functional.cosine_similarity( @@ -185,8 +159,8 @@ def test_d2_headpacked_hd128(): def test(): print("=== D2: Head-Packed Multi-Head FMHA ===") test_d2_headpacked_n1() - test_d2_headpacked_basic() - test_d2_headpacked_flash() + test_d2_headpacked_128() + test_d2_headpacked_64() test_d2_headpacked_hd128() print("\n=== ALL TESTS PASSED ===")