161 lines
6.3 KiB
Python
161 lines
6.3 KiB
Python
"""
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FMHA D3: SWA sequence length mask (large-negative pre-masking approach).
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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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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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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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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), 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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kernel = FmhaKernel(head_dim=hd, s_k=s_k, use_smem_p=use_smem_p)
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pv_n_tile = kernel.pv_n_tile; 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_tile = v[:, 0: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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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
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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.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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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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# Reference normalization
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q_flat = q_3d[:,:,0]; k_flat = k_3d[:,:,0]
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scores = torch.matmul(q_flat.float(), k_flat.float().T) * scale
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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_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 (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 = _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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).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_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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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,), 64, dtype=torch.int32, device='cuda')
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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:] = 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_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 = {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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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 = {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_swa64()
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test_d3_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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