116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
"""
|
|
D1: Debug O rescale at s_k=256 with diagnostic prints.
|
|
"""
|
|
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_multi_kv_debug(hd=64, s_k=256):
|
|
m = 128
|
|
n_kv_tiles = s_k // 128
|
|
torch.manual_seed(42)
|
|
|
|
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
|
k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
|
|
v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
|
|
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
|
|
|
|
# FP32 reference
|
|
qf = q[:, :, 0].float()
|
|
kf = k[:, :, 0].float()
|
|
scale = 1.0 / math.sqrt(hd)
|
|
attn_max = (qf @ kf.T * scale).max(dim=-1, keepdim=True)[0]
|
|
attn_exp = torch.exp(qf @ kf.T * scale - attn_max)
|
|
attn_sum = attn_exp.sum(dim=-1, keepdim=True)
|
|
ref_unnorm = attn_exp @ v.float()
|
|
ref_norm = (attn_exp / attn_sum) @ v.float()
|
|
|
|
# Also compute per-tile references
|
|
for kt in range(n_kv_tiles):
|
|
k_start = kt * 128
|
|
k_end = k_start + 128
|
|
kf_t = k[k_start:k_end, :, 0].float()
|
|
vf_t = v[k_start:k_end].float()
|
|
attn_t = qf @ kf_t.T * scale
|
|
print(f" kt={kt}: K[{k_start}:{k_end}] attn range [{attn_t.min().item():.4f}, {attn_t.max().item():.4f}]")
|
|
|
|
lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
|
|
|
|
kernel = FmhaKernel(head_dim=hd, s_k=s_k, use_smem_p=False, normalize=False)
|
|
pv_n_tile = kernel.pv_n_tile
|
|
n_pv_tiles = kernel.n_pv_tiles
|
|
print(f" n_kv_tiles={kernel.n_kv_tiles}, pv_n_tile={pv_n_tile}, n_pv_tiles={n_pv_tiles}")
|
|
# tmem_o0_offset is set in _setup, not __init__
|
|
|
|
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
|
|
|
# Compile with first PV tile
|
|
v_tile = v[:, 0:pv_n_tile].contiguous()
|
|
v_kernel = v_tile.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_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
|
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))
|
|
|
|
print(f' Compiling...', flush=True)
|
|
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
|
|
|
|
lse_val = None
|
|
for nt in range(n_pv_tiles):
|
|
v_start = nt * pv_n_tile
|
|
v_end = v_start + pv_n_tile
|
|
v_tile = v[:, v_start:v_end].contiguous()
|
|
v_kernel = v_tile.unsqueeze(-1)
|
|
c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
|
|
lse_tensor.zero_()
|
|
|
|
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_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
|
|
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))
|
|
|
|
compiled(mQ, mK, mV, mC, stream, mLSE)
|
|
torch.cuda.synchronize()
|
|
|
|
c[:, v_start:v_end, :] = c_tile
|
|
if nt == 0:
|
|
lse_val = lse_tensor[0, 0, 0].item()
|
|
|
|
out_unnorm = c[:, :, 0].float()
|
|
out_norm = out_unnorm / attn_sum
|
|
|
|
# Compare per-row
|
|
row_cos = []
|
|
for i in range(min(8, m)):
|
|
rc = torch.nn.functional.cosine_similarity(
|
|
out_unnorm[i].unsqueeze(0), ref_unnorm[i].unsqueeze(0)
|
|
).item()
|
|
row_cos.append(rc)
|
|
|
|
cos_unnorm = torch.nn.functional.cosine_similarity(
|
|
out_unnorm.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)
|
|
).item()
|
|
|
|
print(f" cos_unnorm={cos_unnorm:.6f}")
|
|
print(f" row 0 cos={row_cos[0]:.6f} row 1 cos={row_cos[1]:.6f}")
|
|
print(f" out[0,:8]={out_unnorm[0,:8].tolist()}")
|
|
print(f" ref[0,:8]={ref_unnorm[0,:8].tolist()}")
|
|
print(f" lse_val={lse_val}, ref_lse={(torch.log(attn_sum[0,0]) + attn_max[0,0]).item()}")
|
|
return cos_unnorm
|
|
|
|
|
|
def test():
|
|
print("=== D1: Multi-KV Debug ===\n")
|
|
test_multi_kv_debug(64, 256)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test()
|