biondizzle/nvfp4-megamoe-kernel
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

D1: Fix SMEM-P (coordinate store), LSE (FP32), add TMEM-P-only test

Browse Source
This commit is contained in:
biondizzle
2026-05-24 03:27:14 +00:00
parent 52570d94cb 70c9d93d28
commit 0f52c3453c
2 changed files with 72 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
dsv4/kernels/attention/fmha.py
View File

@@ -366,10 +366,7 @@ class FmhaKernel:
cute.copy(tiled_tmem_store, rP_words, tTMEM_STOREtP)
cute.arch.fence_view_async_tmem_store()
else:
# SMEM-P: write P to sP using TiledCopy derived from QK MMA.
# SMEM-P: write P to sP using coordinate-indexed store.
# Uses tTMEM_LOADcS identity tensor to get (m, k) coordinates
# and maps them to sP's swizzled layout.
for j0 in range(32):
for j1 in range(4):
coord = tTMEM_LOADcS[(j0, 0), j1, 0, 0]
@@ -443,7 +440,7 @@ class FmhaKernel:
if sfw_idx == 0:
_ln2 = Float32(0.6931471805599453) # ln(2)
lse_val = cute.math.log(row_sum, fastmath=True) + _row_max_safe * _ln2
mLSE[0] = lse_val.to(self.q_dtype)
mLSE[0] = lse_val
tmem.relinquish_alloc_permit()
tmem.free(tmem_ptr)

71
tests/unit/test_d1_tmem_only.py Normal file
View File

@@ -0,0 +1,71 @@
"""D1: Test TMEM-P at all head dims (force use_smem_p=False)."""
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_unnorm = attn_exp @ v.float()
ref_lse = torch.log(attn_sum.squeeze(-1)) + attn_max.squeeze(-1)
lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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()
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)).item()
ref_lse_val = ref_lse[0].item()
lse_err = abs(lse_val - ref_lse_val) if lse_val is not None else float('inf')
print(f'hd={hd} TMEM-P: cos {cos:.6f} lse_err {lse_err:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
return cos
if __name__ == '__main__':
print("=== TMEM-P at various head dims ===\n")
for hd in [64, 128, 256]:
test_tmem_p(hd)