62 lines
2.6 KiB
Python
62 lines
2.6 KiB
Python
"""D1: Debug hd=128 — check if the pipeline works with TMEM-P."""
|
|
import torch, math
|
|
import cutlass.cute as cute
|
|
import cutlass.torch as ct
|
|
import cuda.bindings.driver as cuda
|
|
from dsv4.kernels.attention.fmha import FmhaKernel
|
|
|
|
|
|
def test_hd(hd, n_kv=128, use_smem_p=False):
|
|
m = 128
|
|
torch.manual_seed(42)
|
|
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
|
k = torch.randn(n_kv, hd, 1, dtype=torch.bfloat16, device='cuda')
|
|
v = torch.randn(n_kv, hd, dtype=torch.bfloat16, device='cuda')
|
|
|
|
qf = q[:, :, 0].float()
|
|
kf = k[:, :, 0].float()
|
|
scale = 1.0 / math.sqrt(hd)
|
|
attn = qf @ kf.T * scale
|
|
attn_max = attn.max(dim=-1, keepdim=True)[0]
|
|
attn_exp = torch.exp(attn - attn_max)
|
|
attn_sum = attn_exp.sum(dim=-1, keepdim=True)
|
|
ref_unnorm = attn_exp @ v.float()
|
|
ref_lse = (torch.log(attn_sum.squeeze(-1)) + attn_max.squeeze(-1))[0].item()
|
|
|
|
lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
|
|
kernel = FmhaKernel(head_dim=hd, s_k=n_kv, use_smem_p=use_smem_p)
|
|
pv_n_tile = kernel.pv_n_tile
|
|
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
|
|
|
v_tile = v[:, 0:pv_n_tile].contiguous().unsqueeze(-1)
|
|
c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
|
|
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
|
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
|
mV = ct.from_dlpack(v_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_tile))
|
|
mC = ct.from_dlpack(c_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c_tile))
|
|
mLSE = ct.from_dlpack(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
|
|
|
|
mode = "SMEM-P" if use_smem_p else "TMEM-P"
|
|
print(f'hd={hd} {mode}: Compiling...', flush=True)
|
|
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
|
|
compiled(mQ, mK, mV, mC, stream, mLSE)
|
|
torch.cuda.synchronize()
|
|
|
|
out = c_tile[:, :, 0].float()
|
|
kernel_lse = lse_tensor[0, 0, 0].item()
|
|
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)).item()
|
|
lse_err = abs(kernel_lse - ref_lse)
|
|
|
|
print(f'hd={hd} {mode}: cos={cos:.6f} lse_err={lse_err:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
|
if cos < 0.99:
|
|
print(f' out[0,:4]={out[0,:4].tolist()}')
|
|
print(f' ref[0,:4]={ref_unnorm[0,:4].tolist()}')
|
|
return cos
|
|
|
|
|
|
if __name__ == '__main__':
|
|
print("=== D1 Debug: TMEM-P at various hd ===\n")
|
|
test_hd(64, use_smem_p=False)
|
|
test_hd(128, use_smem_p=False)
|
|
test_hd(256, use_smem_p=False)
|