78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
"""
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D2: Multi-CTA grid test.
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Tests the multi-CTA FMHA kernel with Q shape (n_h, T, hd, 1).
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Each CTA (indexed by block_idx_y) handles one query head.
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K/V are shared (MQA) — all CTAs load the same K/V.
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"""
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import torch, 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 test_multi_cta(hd=64, n_h=2, s_k=128):
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T = 128
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torch.manual_seed(42)
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# Q: (n_h, T, hd, 1) — head dimension outermost
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q = torch.randn(n_h, T, hd, 1, dtype=torch.bfloat16, device='cuda')
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# K/V: (s_k, hd, 1) — shared KV (no head dim)
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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: (n_h, T, hd, 1)
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o = torch.zeros(n_h, T, hd, 1, dtype=torch.bfloat16, device='cuda')
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# FP32 reference (un-normalized)
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qf = q[:, :, :, 0].float() # (n_h, T, hd)
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kf = k[:, :, 0].float() # (s_k, hd)
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vf = v.float() # (s_k, hd)
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scale = 1.0 / math.sqrt(hd)
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ref_unnorm = torch.zeros(n_h, T, hd, dtype=torch.float32, device='cuda')
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for h in range(n_h):
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attn = qf[h] @ kf.T * scale
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attn_max = attn.max(dim=-1, keepdim=True)[0]
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attn_exp = torch.exp(attn - attn_max)
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ref_unnorm[h] = attn_exp @ vf
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lse_tensor = torch.zeros(T, 1, 1, dtype=torch.float32, device='cuda')
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kernel = FmhaKernel(head_dim=hd, s_k=s_k, use_smem_p=False, normalize=False,
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num_query_heads=n_h, batch_size=1)
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pv_n_tile = kernel.pv_n_tile
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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# Compile with Q having head dimension
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v_tile = v[:, 0:pv_n_tile].contiguous()
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v_kernel = v_tile.unsqueeze(-1)
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c_tile = torch.zeros(T, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
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mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
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mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
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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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print(f' Compiling (hd={hd}, n_h={n_h})...', flush=True)
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
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compiled(mQ, mK, mV, mC, stream, mLSE)
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torch.cuda.synchronize()
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# Check output
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out = o[:, :, :, 0].float() # (n_h, T, hd)
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cos = torch.nn.functional.cosine_similarity(
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out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)
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).item()
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print(f' hd={hd}, n_h={n_h}: cos {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
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def test():
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print("=== D2: Multi-CTA Grid ===\n")
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test_multi_cta(64, 2)
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if __name__ == '__main__':
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test()
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