181 lines
6.6 KiB
Python
181 lines
6.6 KiB
Python
"""
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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. Combined with D3 SWA length masking.
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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_attention(q, k, v, scale, is_causal=False, swa_len=None):
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"""FP32 reference attention with optional causal and SWA masking."""
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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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if is_causal:
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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_len is not None and swa_len < s_k:
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scores[:, swa_len:] = 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_len_val=None):
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"""Run FMHA with masking and return cosine similarity vs reference."""
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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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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, swa_len_val)
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compiled(mQ, mK, mV, mC, stream, mLSE, swa_len_val)
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o_unnorm[:, pv * pv_n_tile:(pv + 1) * pv_n_tile] = c_tile[:, :, 0].float()
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# Reference
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q_flat = q_3d[:, :, 0]
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k_flat = k_3d[:, :, 0]
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ref = reference_attention(q_flat, k_flat, v, scale, is_causal=is_causal, swa_len=swa_len_val)
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# Normalize using the same mask as the reference
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scores = torch.matmul(q_flat.float(), k_flat.float().T) * scale
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if is_causal:
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for i in range(m):
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scores[i, i + 1:] = float('-inf')
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if apply_swa_mask and swa_len_val is not None and 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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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_len=64, hd=64)."""
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print("\n=== Test 2: Causal + SWA swa_len=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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cos = _run_fmha(q, k, v, m, s_k, hd, apply_swa_mask=True, is_causal=True, swa_len_val=64)
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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 short window (swa_len=32, hd=64)."""
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print("\n=== Test 4: Causal + SWA swa_len=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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cos = _run_fmha(q, k, v, m, s_k, hd, apply_swa_mask=True, is_causal=True, swa_len_val=32)
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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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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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