Remove broken D1.5 paired-atom test (TMEM round-trick is fundamentally broken)
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"""FMHA D1.5: Multi-KV-tile attention with paired-atom O rescale.
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Tests the D1.5 fix: O rescale for kt>0 using paired atoms from
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epilogue_tmem_copy_and_partition (replaces broken hand-constructed
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Ld32x32bOp/St32x32bOp TMEM round-trip).
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The kernel is launched with s_k>128 (multiple KV tiles). The O rescale
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happens in-kernel for kt>0 using the paired-atom TMEM→REGS→modify→TMEM cycle.
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d15_paired_atoms.py
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"""
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import torch
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import math
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import cutlass.cute as cute
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import cutlass.torch as ct
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import cuda.bindings.driver as cuda
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from dsv4.kernels.attention.fmha import FmhaKernel
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def reference_attention(q, k, v, scale):
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"""FP32 reference: q (M, hd), k (s_k, hd), v (s_k, hd) → o (M, hd), lse (M,)"""
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scores = torch.matmul(q.float(), k.float().T) * scale
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max_s = scores.max(dim=-1, keepdim=True).values
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exp_s = (scores - max_s).exp()
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sum_s = exp_s.sum(dim=-1, keepdim=True)
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lse = (sum_s + 1e-10).log() + max_s
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p = exp_s / sum_s
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o = torch.matmul(p, v.float())
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return o.to(torch.bfloat16), lse.squeeze(-1)
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def run_fmha(q, k, v, hd, s_k, m=128, use_smem_p=None, normalize=False):
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"""Run FMHA kernel and return output + LSE."""
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scale = 1.0 / math.sqrt(hd)
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kernel = FmhaKernel(head_dim=hd, s_k=s_k, use_smem_p=use_smem_p, normalize=normalize)
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pv_n_tile = kernel.pv_n_tile
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n_pv_tiles = kernel.n_pv_tiles
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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# V must be (s_k, pv_n_tile, 1) for the kernel
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all_o = []
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all_lse = []
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all_row_sums = []
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for pv in range(n_pv_tiles):
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v_tile = v[:, pv*pv_n_tile:(pv+1)*pv_n_tile].contiguous().unsqueeze(-1)
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c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
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lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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row_sums_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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q_3d = q.unsqueeze(-1) if q.dim() == 2 else q
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k_3d = k.unsqueeze(-1) if k.dim() == 2 else k
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mQ = ct.from_dlpack(q_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_3d))
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mK = ct.from_dlpack(k_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_3d))
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mV = ct.from_dlpack(v_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_tile))
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mC = ct.from_dlpack(c_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c_tile))
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mLSE = ct.from_dlpack(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
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mRowSums = ct.from_dlpack(row_sums_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(row_sums_tensor))
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if pv == 0:
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print(f' Compiling hd={hd}, s_k={s_k}...', flush=True)
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE, row_sums=mRowSums)
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compiled(mQ, mK, mV, mC, stream, mLSE, row_sums=mRowSums)
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torch.cuda.synchronize()
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all_o.append(c_tile[:, :, 0])
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all_lse.append(lse_tensor[:, 0, 0])
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all_row_sums.append(row_sums_tensor[:, 0, 0])
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# Assemble full output
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o_full = torch.cat(all_o, dim=1) # (M, hd)
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# Normalize externally using row_sums
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# O_norm = O_unnorm / row_sum
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# Each pv segment contributes its portion of O_unnorm.
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# Since all segments share the same row_sum (same softmax denominator),
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# we can normalize the concatenated output directly.
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row_sum_val = all_row_sums[0] # All segments have same row_sum
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o_norm = (o_full.float() / row_sum_val.unsqueeze(-1)).to(torch.bfloat16)
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return o_norm, all_lse[0]
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def test_d15_s256_hd64():
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"""s_k=256 (2 KV tiles) with paired-atom O rescale at hd=64."""
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print("\n=== Test 1: s_k=256 (2 KV tiles, hd=64, TMEM-P) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 256, 64
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(m, hd, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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o_norm, lse = run_fmha(q, k, v, hd, s_k, m=m, normalize=False)
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ref, _ = reference_attention(q, k, v, scale)
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cos = torch.nn.functional.cosine_similarity(
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o_norm.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.995, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d15_s256_hd128():
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"""s_k=256 (2 KV tiles) with paired-atom O rescale at hd=128."""
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print("\n=== Test 2: s_k=256 (2 KV tiles, hd=128, TMEM-P) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 256, 128
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(m, hd, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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o_norm, lse = run_fmha(q, k, v, hd, s_k, m=m, normalize=False)
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ref, _ = reference_attention(q, k, v, scale)
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cos = torch.nn.functional.cosine_similarity(
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o_norm.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.995, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d15_s384_hd64():
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"""s_k=384 (3 KV tiles) with paired-atom O rescale at hd=64."""
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print("\n=== Test 3: s_k=384 (3 KV tiles, hd=64, TMEM-P) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 384, 64
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(m, hd, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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o_norm, lse = run_fmha(q, k, v, hd, s_k, m=m, normalize=False)
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ref, _ = reference_attention(q, k, v, scale)
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cos = torch.nn.functional.cosine_similarity(
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o_norm.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.995, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test():
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print("=== D1.5: Paired-Atom O Rescale (Multi-KV-Tile) ===")
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test_d15_s256_hd64()
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test_d15_s256_hd128()
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test_d15_s384_hd64()
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print("\n=== ALL TESTS PASSED ===")
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if __name__ == '__main__':
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test()
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