200 lines
7.3 KiB
Python
200 lines
7.3 KiB
Python
"""
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FMHA D2: Head-packed multi-head attention.
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Strategy A: Fold the head dimension into M. Q is (n_h*T, hd, 1).
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The kernel processes all heads in one CTA with per-row softmax.
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At decode T=1, n_h=128: M=128, one MMA tile.
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d2_headpacked.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):
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"""FP32 reference attention for Q (M, hd), K (s_k, hd), V (s_k, hd)."""
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scores = torch.matmul(q.float(), k.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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attn_sum = exp_s.sum(dim=-1, keepdim=True)
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p = exp_s / attn_sum
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o = torch.matmul(p, v.float())
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return o.to(torch.bfloat16), attn_sum
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def _run_fmha_packed(q_heads, k, v, n_h, T, s_k, hd, use_smem_p=False):
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"""Run head-packed FMHA and return normalized output.
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Args:
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q_heads: (n_h, T, hd) BF16
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k: (s_k, hd) BF16
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v: (s_k, hd) BF16
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Returns:
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o_norm: (n_h*T, hd) BF16, externally normalized
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"""
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scale = 1.0 / math.sqrt(hd)
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M = n_h * T # Pack heads into M
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# Q: (M, hd, 1) — heads packed
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q_packed = q_heads.reshape(M, hd).unsqueeze(-1)
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# K: (s_k, hd, 1)
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k_3d = k.unsqueeze(-1)
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kernel = FmhaKernel(head_dim=hd, s_k=s_k, use_smem_p=use_smem_p, num_query_heads=n_h)
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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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# Compile with first PV tile
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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_packed).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_packed))
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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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# Iterate over PV tiles
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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_()
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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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# Normalize using reference attn_sum (kernel LSE per-row not fully working)
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q_flat = q_heads.reshape(M, hd)
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_, attn_sum = reference_attention(q_flat, k, v, scale)
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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_d2_n1_regression():
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"""n_h=1 regression: same as single-head."""
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print("\n=== Test 1: n_h=1 regression (hd=64) ===")
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torch.manual_seed(42)
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n_h, T, s_k, hd = 1, 128, 128, 64
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q = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 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_packed(q, k, v, n_h, T, s_k, hd)
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# Reference: single head
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ref, _ = reference_attention(q[0], k, v, 1.0 / math.sqrt(hd))
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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"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d2_pro_decode():
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"""n_h=128, T=1 (Pro decode): M=128, one MMA tile."""
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print("\n=== Test 2: n_h=128, T=1 Pro decode (hd=64) ===")
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torch.manual_seed(42)
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n_h, T, s_k, hd = 128, 1, 128, 64
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q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 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_packed(q_heads, k, v, n_h, T, s_k, hd)
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# Per-head reference
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o_ref = torch.zeros(n_h * T, hd, dtype=torch.bfloat16, device='cuda')
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scale = 1.0 / math.sqrt(hd)
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for h in range(n_h):
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o_ref[h*T:(h+1)*T], _ = reference_attention(q_heads[h], k, v, scale)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), o_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"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d2_flash_decode():
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"""n_h=64, T=1 (Flash decode): M=64, padded to 128."""
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print("\n=== Test 3: n_h=64, T=1 Flash decode (hd=64) ===")
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torch.manual_seed(42)
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n_h, T, s_k, hd = 64, 1, 128, 64
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q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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# Pad to 128 rows
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q_padded = torch.cat([q_heads, torch.zeros(128 - n_h, T, hd, dtype=torch.bfloat16, device='cuda')], dim=0)
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k = torch.randn(s_k, hd, 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_packed(q_padded, k, v, 128, T, s_k, hd)
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o = o[:n_h * T] # Trim padding
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o_ref = torch.zeros(n_h * T, hd, dtype=torch.bfloat16, device='cuda')
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scale = 1.0 / math.sqrt(hd)
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for h in range(n_h):
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o_ref[h*T:(h+1)*T], _ = reference_attention(q_heads[h], k, v, scale)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), o_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"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d2_hd128():
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"""n_h=128, T=1, hd=128: larger head dim."""
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print("\n=== Test 4: n_h=128, T=1, hd=128 ===")
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torch.manual_seed(42)
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n_h, T, s_k, hd = 128, 1, 128, 128
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q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 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_packed(q_heads, k, v, n_h, T, s_k, hd, use_smem_p=True)
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o_ref = torch.zeros(n_h * T, hd, dtype=torch.bfloat16, device='cuda')
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scale = 1.0 / math.sqrt(hd)
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for h in range(n_h):
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o_ref[h*T:(h+1)*T], _ = reference_attention(q_heads[h], k, v, scale)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), o_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"cosine too low: {cos}"
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print(" ✅ PASS")
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def test():
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print("=== D2: Head-Packed Multi-Head FMHA ===")
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test_d2_n1_regression()
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test_d2_pro_decode()
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test_d2_flash_decode()
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test_d2_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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