D3: SWA mask with BF16 min pre-masking approach (K[invalid]=BF16_MIN → scores≈-inf)
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@@ -103,7 +103,7 @@ class FmhaKernel:
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cute.size_in_bytes(self.q_dtype, v_s)) * cta
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@cute.jit
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def __call__(self, q, k, v, c, stream, lse=None):
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def __call__(self, q, k, v, c, stream, lse=None, swa_lens=None):
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self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
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self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
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self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
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@@ -133,10 +133,10 @@ class FmhaKernel:
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lse = cute.make_tensor(c.iterator, cute.make_layout((1,), stride=(0,)))
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# Grid: (M_tiles, 1, batch) where M = n_h * T packed into M dimension
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# For single-head (n_h=1): grid=(1,1,1) — backward compatible
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self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.p_smem_s,self.c_smem_s,self.epi_tile,lse).launch(grid=(1,1,self.batch_size),block=[self.threads_per_cta,1,1],stream=stream)
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self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.p_smem_s,self.c_smem_s,self.epi_tile,lse,swa_lens).launch(grid=(1,1,self.batch_size),block=[self.threads_per_cta,1,1],stream=stream)
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@cute.kernel
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def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, p_smem_s, c_smem_s, epi_tile, mLSE):
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def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, p_smem_s, c_smem_s, epi_tile, mLSE, swa_lens):
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warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
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tidx,_,_ = cute.arch.thread_idx()
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if warp_idx == self.tma_warp_id:
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@@ -1,13 +1,10 @@
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"""
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FMHA D3: SWA sequence length mask (Python pre-masking approach).
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FMHA D3: SWA sequence length mask (large-negative pre-masking approach).
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For the SWA branch, K/V rows at positions >= swa_lens are zeroed out
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before passing to the kernel. This gives QK score ≈ 0 for invalid
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positions, which produces exp(0) = 1 contribution to the softmax
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denominator (not exactly correct -inf masking, but close enough for
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SWA with small windows).
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The proper in-kernel masking (set logits to -inf) is deferred.
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K/V rows at positions >= swa_lens are set to BF16 min (-65504) before
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passing to the kernel. This gives very large negative QK scores for
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invalid positions, producing exp(score) ≈ 0 contribution to the softmax.
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Effectively equivalent to -inf masking for practical purposes.
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d3_swa_mask.py
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"""
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@@ -18,6 +15,8 @@ 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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BF16_MIN = torch.tensor(-65504.0, dtype=torch.bfloat16)
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def reference_swa_attention(q, k, v, swa_lens, scale):
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"""FP32 reference with proper -inf masking."""
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@@ -34,23 +33,6 @@ def reference_swa_attention(q, k, v, swa_lens, scale):
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return o.to(torch.bfloat16), sum_s
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def reference_swa_zero_mask(q, k, v, swa_lens, scale):
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"""FP32 reference with zero-masking (matches kernel behavior)."""
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# Zero out K rows at positions >= swa_lens
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k_masked = k.clone()
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for i in range(q.shape[0]):
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sl = swa_lens[i].item()
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if sl < k.shape[0]:
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k_masked[sl:] = 0
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scores = torch.matmul(q.float(), k_masked.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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return o.to(torch.bfloat16), sum_s
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def _run_fmha(q_3d, k_3d, v, m, s_k, hd, use_smem_p=False):
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"""Run FMHA and return normalized output."""
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scale = 1.0 / math.sqrt(hd)
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@@ -76,7 +58,7 @@ def _run_fmha(q_3d, k_3d, v, m, s_k, hd, use_smem_p=False):
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c_tile.zero_(); lse_tensor.zero_()
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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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mLSE = ct.from_dlset(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
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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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@@ -91,7 +73,7 @@ def _run_fmha(q_3d, k_3d, v, m, s_k, hd, use_smem_p=False):
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def test_d3_full_window():
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"""Full SWA window (swa_lens=128): no masking needed."""
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print("\n=== Test 1: Full SWA window (swa_lens=128, hd=64) ===")
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print("\n=== Test 1: Full SWA window (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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@@ -101,7 +83,6 @@ def test_d3_full_window():
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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o = _run_fmha(q, k, v, m, s_k, hd)
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ref, _ = reference_swa_attention(q[:,:,0], k[:,:,0], v, torch.full((m,), s_k, dtype=torch.int32, device='cuda'), scale)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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@@ -111,9 +92,9 @@ def test_d3_full_window():
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print(" ✅ PASS")
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def test_d3_partial_window():
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"""Partial SWA window (swa_lens=64): zero-mask K rows >= 64."""
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print("\n=== Test 2: Partial SWA window (swa_lens=64, hd=64) ===")
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def test_d3_swa64():
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"""SWA with swa_lens=64: mask K rows >= 64 with BF16 min."""
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print("\n=== Test 2: SWA swa_lens=64 (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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@@ -123,33 +104,55 @@ def test_d3_partial_window():
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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swa_lens = torch.full((m,), 64, dtype=torch.int32, device='cuda')
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# Zero-mask K rows at positions >= swa_lens[0]
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# Mask K rows >= 64 with BF16 min
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k_masked = k.clone()
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k_masked[64:, :, :] = 0
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k_masked[64:] = BF16_MIN.to(k.device)
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# Also mask V (otherwise invalid positions contribute to output)
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v_masked = v.clone()
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v_masked[64:] = 0
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o = _run_fmha(q, k_masked, v, m, s_k, hd)
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# Compare with zero-mask reference (not -inf reference)
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ref_zero, _ = reference_swa_zero_mask(q[:,:,0], k[:,:,0], v, swa_lens, scale)
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o = _run_fmha(q, k_masked, v_masked, m, s_k, hd)
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ref, _ = reference_swa_attention(q[:,:,0], k[:,:,0], v, swa_lens, scale)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref_zero.flatten().float().unsqueeze(0)
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o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos (zero-mask) = {cos:.6f}")
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assert cos >= 0.995
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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_d3_swa32():
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"""SWA with swa_lens=32: only 32 valid tokens."""
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print("\n=== Test 3: SWA swa_lens=32 (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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# Also compare with proper -inf reference
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ref_inf, _ = reference_swa_attention(q[:,:,0], k[:,:,0], v, swa_lens, scale)
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cos_inf = torch.nn.functional.cosine_similarity(
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ref_zero.flatten().float().unsqueeze(0), ref_inf.flatten().float().unsqueeze(0)
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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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swa_lens = torch.full((m,), 32, dtype=torch.int32, device='cuda')
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k_masked = k.clone()
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k_masked[32:] = BF16_MIN.to(k.device)
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v_masked = v.clone()
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v_masked[32:] = 0
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o = _run_fmha(q, k_masked, v_masked, m, s_k, hd)
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ref, _ = reference_swa_attention(q[:,:,0], k[:,:,0], v, swa_lens, scale)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos (zero-mask vs -inf reference) = {cos_inf:.6f} (precision loss from zero-masking)")
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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("=== D3: SWA Sequence Length Mask ===")
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test_d3_full_window()
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test_d3_partial_window()
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test_d3_swa64()
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test_d3_swa32()
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print("\n=== ALL TESTS PASSED ===")
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