D4: Causal mask on SWA branch
- Add is_causal flag to FmhaKernel constructor - Mask positions where k_coord > m_coord to -inf (causal attention) - Combined with D3 SWA mask: both conditions use OR logic - Same tTMEM_LOADcS coordinate mapping as D3 - const_expr guarded: zero overhead when is_causal=False - New test: test_d4_causal_mask.py with causal + combined masking
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@@ -32,6 +32,7 @@ class FmhaKernel:
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self.batch_size = batch_size
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self.normalize = normalize # D5a: False = emit un-normalized O + lse
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self.apply_swa_mask = apply_swa_mask # D3: mask logits at positions >= swa_lens
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self.is_causal = False # D4: causal mask (k_coord > m_coord) on SWA branch
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self.acc_dtype = Float32; self.qk_acc_dtype = Float32
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self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
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self.use_2cta_instrs = False; self.epilog_sync_bar_id = 1
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@@ -410,23 +411,33 @@ class FmhaKernel:
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cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
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cute.arch.fence_view_async_tmem_load()
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# D3: In-kernel SWA sequence length masking.
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# After loading S from TMEM, mask positions >= swa_lens[batch_idx] to -inf.
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# D3/D4: In-kernel logit masking.
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# After loading S from TMEM, mask invalid positions to -inf.
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# Uses tTMEM_LOADcS coordinate tensor to map register indices to (row, col).
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# col = position in KV sequence. For kt > 0, actual pos = kt*128 + col.
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# This is the PROPER approach: post-QK masking in the softmax,
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# not pre-masking K with BF16 min (which can't produce true -inf).
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if const_expr(self.apply_swa_mask):
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# D3: SWA mask — positions >= swa_lens[batch_idx] → -inf
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# D4: Causal mask — positions where k_coord > m_coord → -inf
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# Both use the same coordinate mapping from tTMEM_LOADcS.
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# For kt > 0, absolute KV pos = kt*128 + k_coord.
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if const_expr(self.apply_swa_mask or self.is_causal):
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_bidx, _bidy, _bidz = cute.arch.block_idx()
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swa_len = swa_lens[_bidz]
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if const_expr(self.apply_swa_mask):
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swa_len = swa_lens[_bidz]
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kt_offset = Int32(kt * 128) # KV position offset for this tile
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# Iterate using same coordinate indexing as SMEM-P path
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for j0 in range(32):
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for j1 in range(4):
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coord = tTMEM_LOADcS[(j0, 0), j1, 0, 0]
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m_coord = coord[0] # query row position
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k_coord = coord[1] # position within this KV tile
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kv_pos = kt_offset + k_coord # absolute KV position
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if kv_pos >= swa_len:
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should_mask = Boolean(0)
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if const_expr(self.apply_swa_mask):
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if kv_pos >= swa_len:
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should_mask = Boolean(1)
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if const_expr(self.is_causal):
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if k_coord > m_coord:
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should_mask = Boolean(1)
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if should_mask:
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tTMEM_LOADrS[(j0, 0), j1, 0, 0] = -Float32.inf
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old_row_max = row_max
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211
tests/unit/test_d4_causal_mask.py
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211
tests/unit/test_d4_causal_mask.py
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@@ -0,0 +1,211 @@
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"""
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FMHA D4: Causal mask on SWA branch.
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In-kernel causal masking: for each query row m, mask KV positions where
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k_coord > m_coord to -inf. This is the proper causal attention mask.
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Combined with D3 SWA length masking: both conditions can be active
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simultaneously (OR logic).
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d4_causal_mask.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_causal_attention(q, k, v, scale, swa_lens=None):
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"""FP32 reference with causal mask (and optional SWA length mask).
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Args:
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q: (M, hd) BF16
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k: (s_k, hd) BF16
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v: (s_k, hd) BF16
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scale: float
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swa_lens: (M,) int32 or None — per-row valid KV count
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Returns:
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o: (M, hd) BF16
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"""
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M, hd = q.shape
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s_k = k.shape[0]
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scores = torch.matmul(q.float(), k.float().T) * scale
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# Causal mask: row i can only attend to positions 0..i
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for i in range(M):
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scores[i, i + 1:] = float('-inf')
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# SWA length mask: positions >= swa_lens
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if swa_lens is not None:
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for i in range(M):
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sl = swa_lens[i].item()
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if sl < s_k:
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scores[i, sl:] = float('-inf')
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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)
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def _run_fmha(q_3d, k_3d, v, m, s_k, hd, use_smem_p=False,
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apply_swa_mask=False, is_causal=False, swa_lens_tensor=None):
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"""Run FMHA with masking and return normalized output."""
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scale = 1.0 / math.sqrt(hd)
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kernel = FmhaKernel(
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head_dim=hd, s_k=s_k, use_smem_p=use_smem_p,
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apply_swa_mask=apply_swa_mask, is_causal=is_causal,
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normalize=False,
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)
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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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# swa_lens as CuTe tensor
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if swa_lens_tensor is not None:
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mSwaLens = ct.from_dlpack(swa_lens_tensor).mark_layout_dynamic(
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leading_dim=ct.get_leading_dim(swa_lens_tensor)
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)
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else:
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mSwaLens = None
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o_unnorm = torch.zeros(m, hd, 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, mSwaLens)
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compiled(mQ, mK, mV, mC, stream, mLSE, mSwaLens)
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o_unnorm[:, pv * pv_n_tile:(pv + 1) * pv_n_tile] = c_tile[:, :, 0].float()
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# External normalization
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q_flat = q_3d[:, :, 0]
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k_flat = k_3d[:, :, 0]
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swa_lens_for_ref = swa_lens_tensor.cpu().expand(m) if swa_lens_tensor is not None else None
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ref = reference_causal_attention(q_flat, k_flat, v, scale, swa_lens=swa_lens_for_ref)
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# Use reference attn_sum for normalization (same pattern as other tests)
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scores = torch.matmul(q_flat.float(), k_flat.float().T) * scale
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for i in range(m):
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scores[i, i + 1:] = float('-inf')
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if swa_lens_tensor is not None:
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sl = swa_lens_tensor[0].item()
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if sl < s_k:
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scores[:, sl:] = float('-inf')
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max_s = scores.max(dim=-1, keepdim=True).values
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attn_sum = (scores - max_s).exp().sum(dim=-1, keepdim=True)
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o_norm = (o_unnorm / attn_sum).to(torch.bfloat16)
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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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return cos
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def test_d4_causal_hd64():
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"""Causal mask only (hd=64)."""
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print("\n=== Test 1: Causal mask only (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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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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cos = _run_fmha(q, k, v, m, s_k, hd, is_causal=True)
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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_d4_causal_swa64():
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"""Causal + SWA mask combined (swa_lens=64, hd=64)."""
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print("\n=== Test 2: Causal + 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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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.tensor([64], dtype=torch.int32, device='cuda')
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cos = _run_fmha(q, k, v, m, s_k, hd, apply_swa_mask=True, is_causal=True, swa_lens_tensor=swa_lens)
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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_d4_causal_hd128():
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"""Causal mask at hd=128 (SMEM-P path)."""
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print("\n=== Test 3: Causal mask only (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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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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cos = _run_fmha(q, k, v, m, s_k, hd, use_smem_p=True, is_causal=True)
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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_d4_causal_swa32():
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"""Causal + SWA with very short window (swa_lens=32, hd=64)."""
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print("\n=== Test 4: Causal + 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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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.tensor([32], dtype=torch.int32, device='cuda')
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cos = _run_fmha(q, k, v, m, s_k, hd, apply_swa_mask=True, is_causal=True, swa_lens_tensor=swa_lens)
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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_d4_no_mask_regression():
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"""No masking (regression — should match D1 results)."""
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print("\n=== Test 5: No mask regression (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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cos = _run_fmha(q, k, v, m, s_k, hd)
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print(f" cos = {cos:.6f}")
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assert cos >= 0.995, f"Regression: cosine too low: {cos}"
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print(" ✅ PASS")
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def test():
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print("=== D4: Causal Mask on SWA Branch ===")
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test_d4_no_mask_regression()
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test_d4_causal_hd64()
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test_d4_causal_swa64()
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test_d4_causal_hd128()
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test_d4_causal_swa32()
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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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