107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
"""
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D2: Scale test — more heads, larger hd.
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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_multihead(hd=64, n_h=1, batch=1, T=128, s_k=128):
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torch.manual_seed(42)
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q = torch.randn(batch, n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(batch, s_k, hd, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(batch, s_k, hd, dtype=torch.bfloat16, device='cuda')
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# FP32 reference (un-normalized)
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qf = q.float()
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kf = k.float()
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vf = v.float()
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scale = 1.0 / math.sqrt(hd)
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ref_unnorm = torch.zeros(batch, n_h, T, hd, dtype=torch.float32, device='cuda')
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for b in range(batch):
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for h in range(n_h):
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attn = qf[b, h] @ kf[b].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[b, h] = attn_exp @ vf[b]
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kernel = FmhaKernel(head_dim=hd, s_k=s_k, use_smem_p=False, normalize=False)
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pv_n_tile = kernel.pv_n_tile
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n_pv_tiles = hd // pv_n_tile
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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# Compile once
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q0 = q[0, 0].unsqueeze(-1)
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k0 = k[0].unsqueeze(-1)
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v0_tile = v[0, :, 0:pv_n_tile].contiguous()
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v0_k = v0_tile.unsqueeze(-1)
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c0 = torch.zeros(T, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
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lse0 = torch.zeros(T, 1, 1, dtype=torch.float32, device='cuda')
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mQ = ct.from_dlpack(q0).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q0))
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mK = ct.from_dlpack(k0).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k0))
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mV = ct.from_dlpack(v0_k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v0_k))
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mC = ct.from_dlpack(c0).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c0))
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mLSE = ct.from_dlpack(lse0).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse0))
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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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o = torch.zeros(batch, n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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for b in range(batch):
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for h in range(n_h):
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q_h = q[b, h].unsqueeze(-1)
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k_b = k[b].unsqueeze(-1)
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v_b = v[b]
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c_h = torch.zeros(T, hd, dtype=torch.bfloat16, device='cuda')
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for nt in range(n_pv_tiles):
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v_start = nt * pv_n_tile
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v_end = v_start + pv_n_tile
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v_tile = v_b[:, v_start:v_end].contiguous()
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v_k = 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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lse0.zero_()
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mQ = ct.from_dlpack(q_h).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_h))
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mK = ct.from_dlpack(k_b).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_b))
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mV = ct.from_dlpack(v_k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_k))
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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(lse0).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse0))
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compiled(mQ, mK, mV, mC, stream, mLSE)
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c_h[:, v_start:v_end] = c_tile[:, :, 0]
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o[b, h] = c_h
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torch.cuda.synchronize()
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)
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).item()
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print(f' hd={hd}, n_h={n_h}, batch={batch}: cos {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
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def test():
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print("=== D2: Scale Test ===\n")
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# hd=64
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test_multihead(64, 16, 1, 128, 128)
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test_multihead(64, 64, 1, 128, 128) # Flash decode config (hd=64 test)
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# hd=128
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test_multihead(128, 2, 1, 128, 128)
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test_multihead(128, 8, 1, 128, 128)
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# hd=256
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test_multihead(256, 2, 1, 128, 128)
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if __name__ == '__main__':
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test()
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