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D1: Add diagnostic test (TMEM-P vs SMEM-P at various hd)

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biondizzle
2026-05-24 03:22:23 +00:00
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"""
Quick D1 diagnostic: test TMEM-P path (use_smem_p=False) at various head dims.
The SMEM-P path (use_smem_p=True, hd>64) has coordinate mapping issues.
This test forces TMEM-P to verify the core pipeline works.
"""
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_tmem_p(hd, n_kv=128):
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')
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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_norm = (attn_exp / attn_sum) @ v.float()
ref_unnorm = attn_exp @ v.float()
lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
# Force TMEM-P
kernel = FmhaKernel(head_dim=hd, s_k=n_kv, use_smem_p=False)
pv_n_tile = kernel.pv_n_tile
n_pv_tiles = kernel.n_pv_tiles
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))
print(f'hd={hd} TMEM-P: Compiling...', flush=True)
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
lse_val = None
for nt in range(n_pv_tiles):
vs, ve = nt * pv_n_tile, (nt + 1) * pv_n_tile
v_t = v[:, vs:ve].contiguous().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_t).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_t))
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[:, vs:ve, :] = c_tile
if nt == 0:
lse_val = lse_tensor[0, 0, 0].item()
out = c[:, :, 0].float()
out_norm = out / attn_sum
cos_unnorm = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)).item()
cos_norm = torch.nn.functional.cosine_similarity(out_norm.flatten().unsqueeze(0), ref_norm.flatten().unsqueeze(0)).item()
status = "PASS" if cos_unnorm >= 0.99 else "FAIL"
print(f'hd={hd} TMEM-P: cos_unnorm {cos_unnorm:.6f} cos_norm {cos_norm:.6f} lse {lse_val:.6f} {status}')
return cos_unnorm
def test_smem_p(hd, n_kv=128):
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')
c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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()
lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
kernel = FmhaKernel(head_dim=hd, s_k=n_kv, use_smem_p=True)
pv_n_tile = kernel.pv_n_tile
n_pv_tiles = kernel.n_pv_tiles
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))
print(f'hd={hd} SMEM-P: Compiling...', flush=True)
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
lse_val = None
for nt in range(n_pv_tiles):
vs, ve = nt * pv_n_tile, (nt + 1) * pv_n_tile
v_t = v[:, vs:ve].contiguous().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_t).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_t))
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[:, vs:ve, :] = c_tile
if nt == 0:
lse_val = lse_tensor[0, 0, 0].item()
out = c[:, :, 0].float()
cos_unnorm = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)).item()
status = "PASS" if cos_unnorm >= 0.99 else "FAIL"
print(f'hd={hd} SMEM-P: cos_unnorm {cos_unnorm:.6f} lse {lse_val:.6f} {status}')
if cos_unnorm < 0.97:
print(f' out[0,:4]={out[0,:4].tolist()}')
print(f' ref[0,:4]={ref_unnorm[0,:4].tolist()}')
return cos_unnorm
if __name__ == '__main__':
print("=== D1 Diagnostic ===\n")
# TMEM-P path (proven at hd=64)
print("--- TMEM-P (force use_smem_p=False) ---")
test_tmem_p(64)
test_tmem_p(128)
test_tmem_p(256)
# SMEM-P path (for hd>64)
print("\n--- SMEM-P (use_smem_p=True) ---")
test_smem_p(128)
test_smem_p(256)