170 lines
6.5 KiB
Python
170 lines
6.5 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 reshaped from (n_h, T, hd)
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to (n_h*T, hd, 1). K/V are (s_k, hd, 1) — shared MQA.
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At decode T=1, n_h=128: M=128, exactly one MMA tile.
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The kernel treats each row as independent attention (per-row softmax).
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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 cuda.bindings.driver as cuda
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import cutlass.torch as ct
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from dsv4.kernels.attention.fmha import FmhaKernel
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def reference_fmha(q, k, v, scale):
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"""FP32 reference: q (M, hd), k (s_k, hd), v (s_k, hd) → o (M, 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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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(fmha, q_3d, k_3d, v_3d, o_3d):
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"""Run FmhaKernel with CuTe tensors."""
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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q_c = ct.from_dlpack(q_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_3d))
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k_c = ct.from_dlpack(k_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_3d))
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v_c = ct.from_dlpack(v_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_3d))
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o_c = ct.from_dlpack(o_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o_3d))
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fmha(q_c, k_c, v_c, o_c, stream)
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def test_d2_headpacked_n1():
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"""Regression: n_h=1 (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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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, 1, dtype=torch.bfloat16, device='cuda')
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o = torch.zeros(M, hd, 1, dtype=torch.bfloat16, device='cuda')
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fmha = FmhaKernel(head_dim=hd, s_k=s_k, normalize=True)
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_run_fmha(fmha, q, k, v, o)
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ref = reference_fmha(q[:,:,0], k[:,:,0], v[:,:,0], scale)
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cos = torch.nn.functional.cosine_similarity(
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o[:,:,0].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_d2_headpacked_128():
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"""n_h=128, T=1 (Pro decode): M=128, one M tile, all heads packed."""
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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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scale = 1.0 / math.sqrt(hd)
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q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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# Pack heads into M: (n_h*T, hd) → (128, 64, 1)
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q = q_heads.reshape(n_h * T, hd).unsqueeze(-1)
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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, 1, dtype=torch.bfloat16, device='cuda')
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o = torch.zeros(n_h * T, hd, 1, dtype=torch.bfloat16, device='cuda')
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fmha = FmhaKernel(head_dim=hd, s_k=s_k, normalize=True, num_query_heads=n_h)
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_run_fmha(fmha, q, k, v, o)
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# Reference: per-head attention
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o_ref = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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for h in range(n_h):
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o_ref[h, 0] = reference_fmha(q_heads[h], k[:,:,0], v[:,:,0], scale)[0]
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o_ref_flat = o_ref.reshape(n_h * T, hd)
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cos = torch.nn.functional.cosine_similarity(
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o[:,:,0].flatten().float().unsqueeze(0), o_ref_flat.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_d2_headpacked_64():
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"""n_h=64, T=1 (Flash decode): M=64, pad 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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scale = 1.0 / math.sqrt(hd)
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q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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q_flat = q_heads.reshape(n_h * T, hd) # (64, 64)
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# Pad to 128 rows
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q = torch.nn.functional.pad(q_flat, (0, 0, 0, 128 - n_h * T)).unsqueeze(-1) # (128, 64, 1)
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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, 1, dtype=torch.bfloat16, device='cuda')
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o_padded = torch.zeros(128, hd, 1, dtype=torch.bfloat16, device='cuda')
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fmha = FmhaKernel(head_dim=hd, s_k=s_k, normalize=True, num_query_heads=n_h)
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_run_fmha(fmha, q, k, v, o_padded)
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o = o_padded[:n_h * T, :, 0] # Trim padding
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o_ref = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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for h in range(n_h):
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o_ref[h, 0] = reference_fmha(q_heads[h], k[:,:,0], v[:,:,0], scale)[0]
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o_ref_flat = o_ref.reshape(n_h * T, hd)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), o_ref_flat.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_d2_headpacked_hd128():
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"""n_h=8, T=1, hd=128: pad to 128 rows, larger head dim."""
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print("\n=== Test 4: n_h=8, T=1, hd=128 ===")
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torch.manual_seed(42)
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n_h, T, s_k, hd = 8, 1, 128, 128
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scale = 1.0 / math.sqrt(hd)
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q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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q_flat = q_heads.reshape(n_h * T, hd) # (8, 128)
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q = torch.nn.functional.pad(q_flat, (0, 0, 0, 128 - n_h * T)).unsqueeze(-1) # (128, 128, 1)
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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, 1, dtype=torch.bfloat16, device='cuda')
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o_padded = torch.zeros(128, hd, 1, dtype=torch.bfloat16, device='cuda')
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fmha = FmhaKernel(head_dim=hd, s_k=s_k, normalize=True, num_query_heads=n_h)
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_run_fmha(fmha, q, k, v, o_padded)
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o = o_padded[:n_h * T, :, 0]
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o_ref = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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for h in range(n_h):
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o_ref[h, 0] = reference_fmha(q_heads[h], k[:,:,0], v[:,:,0], scale)[0]
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o_ref_flat = o_ref.reshape(n_h * T, hd)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), o_ref_flat.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("=== D2: Head-Packed Multi-Head FMHA ===")
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test_d2_headpacked_n1()
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test_d2_headpacked_128()
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test_d2_headpacked_64()
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test_d2_headpacked_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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