D5b: Per-row LSE output + Python KV merge test
- Fix LSE output: all 128 rows now write (mLSE[sfw_idx, 0, 0]) instead of only row 0 (mLSE[0]) - Each softmax thread (sfw_idx 0..127) independently writes its LSE - This enables accurate Python-side KV merge for multi-KV-tile - New test: test_d5b_perrow_lse.py with LSE verification + KV merge
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@@ -542,14 +542,17 @@ class FmhaKernel:
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# Compute LSE: lse = ln(row_sum) + row_max * ln(2)
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# Only when emitting un-normalized output (D5a path).
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# When normalize=True, LSE is not needed (in-kernel normalization).
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#
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# Per-row LSE: each softmax thread (sfw_idx 0..127) handles one row.
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# sfw_idx maps directly to the row index in the attention matrix.
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# All 128 threads write independently to mLSE[sfw_idx] — no sync needed.
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if const_expr(not self.normalize):
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_row_max_safe = row_max
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if row_max == -cutlass.Float32.inf:
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_row_max_safe = Float32(0.0)
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if sfw_idx == 0:
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_ln2 = Float32(0.6931471805599453) # ln(2)
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lse_val = cute.math.log(row_sum, fastmath=True) + _row_max_safe * _ln2
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mLSE[0] = lse_val
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_ln2 = Float32(0.6931471805599453) # ln(2)
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lse_val = cute.math.log(row_sum, fastmath=True) + _row_max_safe * _ln2
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mLSE[sfw_idx, Int32(0), Int32(0)] = lse_val
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tmem.relinquish_alloc_permit()
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tmem.free(tmem_ptr)
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195
tests/unit/test_d5b_perrow_lse.py
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195
tests/unit/test_d5b_perrow_lse.py
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@@ -0,0 +1,195 @@
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"""
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FMHA D5b: Per-row LSE output + Python KV merge.
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Tests that all 128 rows have correct LSE output, enabling accurate
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Python-side KV merge for multi-KV-tile scenarios.
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The merge formula (for un-normalized O):
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O = (O_unnorm_sparse + exp(attn_sink) * O_unnorm_swa)
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/ (exp(lse_sparse) + exp(attn_sink) * exp(lse_swa))
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With per-row LSE, each row can be correctly normalized and merged.
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d5b_perrow_lse.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_with_lse(q, k, v, scale):
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"""FP32 reference attention returning O and per-row LSE."""
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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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p = exp_s / sum_s
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o = torch.matmul(p, v.float())
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# LSE = ln(sum_s) + max_s (natural log domain)
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lse = torch.log(sum_s.squeeze(-1)) + max_s.squeeze(-1)
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return o.to(torch.bfloat16), lse
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def _run_fmha_with_lse(q_3d, k_3d, v, m, s_k, hd, use_smem_p=False):
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"""Run FMHA and return (o_norm, lse) with per-row 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=False)
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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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o_unnorm = torch.zeros(m, hd, dtype=torch.float32, device='cuda')
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lse_all = torch.zeros(m, dtype=torch.float32, device='cuda')
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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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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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if pv == 0:
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
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compiled(mQ, mK, mV, mC, stream, mLSE)
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o_unnorm[:, pv * pv_n_tile:(pv + 1) * pv_n_tile] = c_tile[:, :, 0].float()
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lse_all = lse_tensor[:, 0, 0] # Per-row LSE
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# Normalize using per-row LSE
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# O_norm = O_unnorm / exp(lse) ... wait, O_unnorm = O_norm * exp(lse)
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# So O_norm = O_unnorm / exp(lse).unsqueeze(-1)
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o_norm = (o_unnorm / lse_all.exp().unsqueeze(-1)).to(torch.bfloat16)
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return o_norm, lse_all
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def test_lse_per_row_hd64():
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"""Per-row LSE at hd=64: all 128 rows should have correct LSE."""
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print("\n=== Test 1: Per-row LSE (hd=64) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 128, 64
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 1, 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, lse = _run_fmha_with_lse(q, k, v, m, s_k, hd)
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ref_o, ref_lse = reference_attention_with_lse(q[:, :, 0], k[:, :, 0], v, scale)
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# Check per-row LSE accuracy
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lse_err = (lse - ref_lse).abs().max().item()
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print(f" LSE max error: {lse_err:.6f}")
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assert lse_err < 0.01, f"LSE error too high: {lse_err}"
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# Check per-row LSE: all rows should be non-zero
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zero_rows = (lse == 0).sum().item()
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print(f" Zero LSE rows: {zero_rows}")
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assert zero_rows == 0, f"Expected 0 zero LSE rows, got {zero_rows}"
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# Check output cosine
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref_o.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
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print(" ✅ PASS")
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def test_lse_per_row_hd128():
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"""Per-row LSE at hd=128 (SMEM-P path)."""
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print("\n=== Test 2: Per-row LSE (hd=128) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 128, 128
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 1, 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, lse = _run_fmha_with_lse(q, k, v, m, s_k, hd, use_smem_p=True)
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ref_o, ref_lse = reference_attention_with_lse(q[:, :, 0], k[:, :, 0], v, scale)
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lse_err = (lse - ref_lse).abs().max().item()
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print(f" LSE max error: {lse_err:.6f}")
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assert lse_err < 0.01, f"LSE error too high: {lse_err}"
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zero_rows = (lse == 0).sum().item()
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print(f" Zero LSE rows: {zero_rows}")
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assert zero_rows == 0, f"Expected 0 zero LSE rows, got {zero_rows}"
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref_o.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
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print(" ✅ PASS")
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def test_lse_kv_merge():
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"""Python KV merge using per-row LSE (s_k=256, 2 KV tiles)."""
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print("\n=== Test 3: KV merge with per-row LSE (s_k=256) ===")
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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, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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# Reference: full attention with s_k=256
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ref_o, _ = reference_attention_with_lse(q[:, :, 0], k[:, :, 0], v, scale)
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# Kernel: two segments of 128, merge with per-row LSE
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seg_size = 128
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o_merged = torch.zeros(m, hd, dtype=torch.float32, device='cuda')
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lse_max = None
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weighted_sum = None
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for seg in range(s_k // seg_size):
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k_seg = k[seg * seg_size:(seg + 1) * seg_size]
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v_seg = v[seg * seg_size:(seg + 1) * seg_size]
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k_seg_3d = k_seg.unsqueeze(-1)
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o_seg, lse_seg = _run_fmha_with_lse(q, k_seg_3d, v_seg, m, seg_size, hd)
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o_seg_f = o_seg.float()
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lse_seg_f = lse_seg.float()
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if lse_max is None:
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lse_max = lse_seg_f
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weighted_sum = lse_seg_f.exp().unsqueeze(-1) * o_seg_f
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else:
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# Online merge: O = (exp(lse0)*O0 + exp(lse1)*O1) / (exp(lse0) + exp(lse1))
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new_lse_max = torch.max(lse_max, lse_seg_f)
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# Rescale existing
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scale0 = (lse_max - new_lse_max).exp()
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scale1 = (lse_seg_f - new_lse_max).exp()
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weighted_sum = scale0.unsqueeze(-1) * weighted_sum + scale1.unsqueeze(-1) * lse_seg_f.exp().unsqueeze(-1) * o_seg_f
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lse_max = new_lse_max
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o_merged = (weighted_sum / lse_max.exp().unsqueeze(-1)).to(torch.bfloat16)
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cos = torch.nn.functional.cosine_similarity(
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o_merged.flatten().float().unsqueeze(0), ref_o.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.99, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test():
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print("=== D5b: Per-Row LSE Output ===")
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test_lse_per_row_hd64()
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test_lse_per_row_hd128()
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test_lse_kv_merge()
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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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