D3: In-kernel SWA sequence length masking
- Add apply_swa_mask flag to FmhaKernel constructor - After TMEM load of S, use tTMEM_LOADcS coordinates to map register fragment positions to (row, col) in QK matrix - Mask positions >= swa_lens[batch_idx] to -inf before softmax - Supports multi-KV-tile (kt*128 + k_coord for absolute position) - swa_lens parameter passed as CuTe tensor, indexed by block_idx_z - Dummy tensor (max int) when swa_lens=None (no masking) - New test: test_d3_inkernel_mask.py with proper in-kernel masking - Replaces pre-masking approach (BF16 min on K) which can't produce -inf
This commit is contained in:
@@ -16,7 +16,7 @@ import math
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class FmhaKernel:
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def __init__(self, head_dim=64, s_k=128, scale_softmax=None, use_smem_p=None, normalize=True, num_query_heads=1, batch_size=1):
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def __init__(self, head_dim=64, s_k=128, scale_softmax=None, use_smem_p=None, normalize=True, num_query_heads=1, batch_size=1, apply_swa_mask=False):
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self.head_dim = head_dim
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self.s_k = s_k
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self.n_kv_tiles = s_k // 128
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@@ -31,6 +31,7 @@ class FmhaKernel:
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self.num_query_heads = num_query_heads
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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.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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@@ -132,10 +133,12 @@ class FmhaKernel:
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if const_expr(lse is None):
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lse = cute.make_tensor(c.iterator, cute.make_layout((1,), stride=(0,)))
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if const_expr(swa_lens is None):
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# No SWA masking — pass a dummy tensor
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swa_lens = cute.make_tensor(c.iterator, cute.make_layout((1,), stride=(0,)))
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# No SWA masking — pass a dummy tensor with large value (no positions masked)
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_swa_dummy = torch.tensor([2147483647], dtype=torch.int32, device='cuda')
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swa_lens = ct.from_dlpack(_swa_dummy).mark_layout_dynamic(leading_dim=ct.get_leading_dim(_swa_dummy))
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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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# block_idx_z = batch index, used for swa_lens[batch_idx] in D3 masking
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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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@@ -407,6 +410,25 @@ 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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# 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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_bidx, _bidy, _bidz = cute.arch.block_idx()
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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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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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tTMEM_LOADrS[(j0, 0), j1, 0, 0] = -Float32.inf
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old_row_max = row_max
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frg_cnt = 4
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frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
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259
tests/unit/test_d3_inkernel_mask.py
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259
tests/unit/test_d3_inkernel_mask.py
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@@ -0,0 +1,259 @@
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"""
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FMHA D3: In-kernel SWA sequence length masking.
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Proper approach: the kernel receives swa_lens and masks logits to -inf
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inside the softmax, using the tTMEM_LOADcS coordinate tensor to map
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register fragment positions to (row, col) in the QK matrix.
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This replaces the pre-masking approach (BF16 min on K) which cannot
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produce true -inf QK scores.
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d3_inkernel_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_swa_attention(q, k, v, swa_lens, scale):
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"""FP32 reference with proper -inf masking.
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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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swa_lens: (M,) int32 — per-row number of valid KV positions
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scale: float
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Returns:
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o: (M, hd) BF16
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"""
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scores = torch.matmul(q.float(), k.float().T) * scale
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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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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_masked(q_3d, k_3d, v, m, s_k, hd, swa_lens_tensor, use_smem_p=False):
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"""Run FMHA with in-kernel SWA masking and return normalized output.
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Args:
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q_3d: (M, hd, 1) BF16
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k_3d: (s_k, hd, 1) BF16
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v: (s_k, hd) BF16
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swa_lens_tensor: (1,) int32 — number of valid KV positions
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Returns:
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o_norm: (M, hd) BF16
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"""
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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=True, 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 (1D, int32)
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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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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, 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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lse_all += lse_tensor[:, 0, 0]
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# External normalization using LSE
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# O_norm = O_unnorm / exp(LSE) ... but per-row LSE only row 0 is written.
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# Use reference attn_sum for normalization (same as head-packed tests).
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q_flat = q_3d[:, :, 0]
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k_flat = k_3d[:, :, 0]
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scores = torch.matmul(q_flat.float(), k_flat.float().T) * scale
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swa_len_val = swa_lens_tensor[0].item()
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if swa_len_val < s_k:
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scores[:, swa_len_val:] = 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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return o_norm
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def test_d3_no_mask():
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"""Full window (swa_lens=128): no masking, regression test."""
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print("\n=== Test 1: No masking (swa_lens=128, 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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swa_lens = torch.tensor([s_k], dtype=torch.int32, device='cuda')
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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), 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 = {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_d3_swa64():
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"""SWA with swa_lens=64: mask positions 64-127 to -inf."""
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print("\n=== Test 2: 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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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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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), 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 = {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 positions."""
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print("\n=== Test 3: 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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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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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), 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 = {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_swa1():
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"""Edge case: swa_lens=1, only one valid KV position."""
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print("\n=== Test 4: swa_lens=1 (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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swa_lens = torch.tensor([1], dtype=torch.int32, device='cuda')
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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), 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 = {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_hd128():
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"""SWA masking at hd=128 (SMEM-P path)."""
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print("\n=== Test 5: swa_lens=64 (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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swa_lens = torch.tensor([64], dtype=torch.int32, device='cuda')
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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens, use_smem_p=True)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), 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 = {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_swa128_hd128():
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"""No masking at hd=128: regression test (should match existing D1 results)."""
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print("\n=== Test 6: No masking (swa_lens=128, 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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swa_lens = torch.tensor([s_k], dtype=torch.int32, device='cuda')
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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens, use_smem_p=True)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), 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 = {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("=== D3: In-Kernel SWA Sequence Length Mask ===")
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test_d3_no_mask()
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test_d3_swa64()
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test_d3_swa32()
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test_d3_swa1()
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test_d3_hd128()
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test_d3_swa128_hd128()
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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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