biondizzle
b6b581777a
- Add apply_swa_mask flag to FmhaKernel constructor - After TMEM load of S, use tTMEM_LOADcS coordinates to map register fragment positions to (row, col) in QK matrix - Mask positions >= swa_lens[batch_idx] to -inf before softmax - Supports multi-KV-tile (kt*128 + k_coord for absolute position) - swa_lens parameter passed as CuTe tensor, indexed by block_idx_z - Dummy tensor (max int) when swa_lens=None (no masking) - New test: test_d3_inkernel_mask.py with proper in-kernel masking - Replaces pre-masking approach (BF16 min on K) which can't produce -inf
259 lines
9.5 KiB
Python
259 lines
9.5 KiB
Python
"""
|
|
FMHA D3: In-kernel SWA sequence length masking.
|
|
|
|
Proper approach: the kernel receives swa_lens and masks logits to -inf
|
|
inside the softmax, using the tTMEM_LOADcS coordinate tensor to map
|
|
register fragment positions to (row, col) in the QK matrix.
|
|
|
|
This replaces the pre-masking approach (BF16 min on K) which cannot
|
|
produce true -inf QK scores.
|
|
|
|
Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d3_inkernel_mask.py
|
|
"""
|
|
import torch
|
|
import 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 reference_swa_attention(q, k, v, swa_lens, scale):
|
|
"""FP32 reference with proper -inf masking.
|
|
|
|
Args:
|
|
q: (M, hd) BF16
|
|
k: (s_k, hd) BF16
|
|
v: (s_k, hd) BF16
|
|
swa_lens: (M,) int32 — per-row number of valid KV positions
|
|
scale: float
|
|
|
|
Returns:
|
|
o: (M, hd) BF16
|
|
"""
|
|
scores = torch.matmul(q.float(), k.float().T) * scale
|
|
for i in range(q.shape[0]):
|
|
sl = swa_lens[i].item()
|
|
if sl < k.shape[0]:
|
|
scores[i, sl:] = float('-inf')
|
|
max_s = scores.max(dim=-1, keepdim=True).values
|
|
exp_s = (scores - max_s).exp()
|
|
sum_s = exp_s.sum(dim=-1, keepdim=True)
|
|
p = exp_s / sum_s
|
|
o = torch.matmul(p, v.float())
|
|
return o.to(torch.bfloat16)
|
|
|
|
|
|
def _run_fmha_masked(q_3d, k_3d, v, m, s_k, hd, swa_lens_tensor, use_smem_p=False):
|
|
"""Run FMHA with in-kernel SWA masking and return normalized output.
|
|
|
|
Args:
|
|
q_3d: (M, hd, 1) BF16
|
|
k_3d: (s_k, hd, 1) BF16
|
|
v: (s_k, hd) BF16
|
|
swa_lens_tensor: (1,) int32 — number of valid KV positions
|
|
|
|
Returns:
|
|
o_norm: (M, hd) BF16
|
|
"""
|
|
scale = 1.0 / math.sqrt(hd)
|
|
kernel = FmhaKernel(
|
|
head_dim=hd, s_k=s_k, use_smem_p=use_smem_p,
|
|
apply_swa_mask=True, normalize=False,
|
|
)
|
|
pv_n_tile = kernel.pv_n_tile
|
|
n_pv_tiles = kernel.n_pv_tiles
|
|
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
|
|
|
# swa_lens as CuTe tensor (1D, int32)
|
|
mSwaLens = ct.from_dlpack(swa_lens_tensor).mark_layout_dynamic(
|
|
leading_dim=ct.get_leading_dim(swa_lens_tensor)
|
|
)
|
|
|
|
o_unnorm = torch.zeros(m, hd, dtype=torch.float32, device='cuda')
|
|
lse_all = torch.zeros(m, dtype=torch.float32, device='cuda')
|
|
|
|
for pv in range(n_pv_tiles):
|
|
v_tile = v[:, pv * pv_n_tile:(pv + 1) * pv_n_tile].contiguous().unsqueeze(-1)
|
|
c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
|
|
lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
|
|
|
|
mQ = ct.from_dlpack(q_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_3d))
|
|
mK = ct.from_dlpack(k_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_3d))
|
|
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))
|
|
|
|
if pv == 0:
|
|
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE, mSwaLens)
|
|
|
|
compiled(mQ, mK, mV, mC, stream, mLSE, mSwaLens)
|
|
o_unnorm[:, pv * pv_n_tile:(pv + 1) * pv_n_tile] = c_tile[:, :, 0].float()
|
|
lse_all += lse_tensor[:, 0, 0]
|
|
|
|
# External normalization using LSE
|
|
# O_norm = O_unnorm / exp(LSE) ... but per-row LSE only row 0 is written.
|
|
# Use reference attn_sum for normalization (same as head-packed tests).
|
|
q_flat = q_3d[:, :, 0]
|
|
k_flat = k_3d[:, :, 0]
|
|
scores = torch.matmul(q_flat.float(), k_flat.float().T) * scale
|
|
swa_len_val = swa_lens_tensor[0].item()
|
|
if swa_len_val < s_k:
|
|
scores[:, swa_len_val:] = float('-inf')
|
|
max_s = scores.max(dim=-1, keepdim=True).values
|
|
attn_sum = (scores - max_s).exp().sum(dim=-1, keepdim=True)
|
|
o_norm = (o_unnorm / attn_sum).to(torch.bfloat16)
|
|
return o_norm
|
|
|
|
|
|
def test_d3_no_mask():
|
|
"""Full window (swa_lens=128): no masking, regression test."""
|
|
print("\n=== Test 1: No masking (swa_lens=128, hd=64) ===")
|
|
torch.manual_seed(42)
|
|
m, s_k, hd = 128, 128, 64
|
|
scale = 1.0 / math.sqrt(hd)
|
|
|
|
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')
|
|
swa_lens = torch.tensor([s_k], dtype=torch.int32, device='cuda')
|
|
|
|
o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens)
|
|
ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), scale)
|
|
|
|
cos = torch.nn.functional.cosine_similarity(
|
|
o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
|
|
).item()
|
|
print(f" cos = {cos:.6f}")
|
|
assert cos >= 0.995, f"Regression: cosine too low: {cos}"
|
|
print(" ✅ PASS")
|
|
|
|
|
|
def test_d3_swa64():
|
|
"""SWA with swa_lens=64: mask positions 64-127 to -inf."""
|
|
print("\n=== Test 2: swa_lens=64 (hd=64) ===")
|
|
torch.manual_seed(42)
|
|
m, s_k, hd = 128, 128, 64
|
|
scale = 1.0 / math.sqrt(hd)
|
|
|
|
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')
|
|
swa_lens = torch.tensor([64], dtype=torch.int32, device='cuda')
|
|
|
|
o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens)
|
|
ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), scale)
|
|
|
|
cos = torch.nn.functional.cosine_similarity(
|
|
o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
|
|
).item()
|
|
print(f" cos = {cos:.6f}")
|
|
assert cos >= 0.99, f"cosine too low: {cos}"
|
|
print(" ✅ PASS")
|
|
|
|
|
|
def test_d3_swa32():
|
|
"""SWA with swa_lens=32: only 32 valid positions."""
|
|
print("\n=== Test 3: swa_lens=32 (hd=64) ===")
|
|
torch.manual_seed(42)
|
|
m, s_k, hd = 128, 128, 64
|
|
scale = 1.0 / math.sqrt(hd)
|
|
|
|
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')
|
|
swa_lens = torch.tensor([32], dtype=torch.int32, device='cuda')
|
|
|
|
o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens)
|
|
ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), scale)
|
|
|
|
cos = torch.nn.functional.cosine_similarity(
|
|
o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
|
|
).item()
|
|
print(f" cos = {cos:.6f}")
|
|
assert cos >= 0.99, f"cosine too low: {cos}"
|
|
print(" ✅ PASS")
|
|
|
|
|
|
def test_d3_swa1():
|
|
"""Edge case: swa_lens=1, only one valid KV position."""
|
|
print("\n=== Test 4: swa_lens=1 (hd=64) ===")
|
|
torch.manual_seed(42)
|
|
m, s_k, hd = 128, 128, 64
|
|
scale = 1.0 / math.sqrt(hd)
|
|
|
|
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')
|
|
swa_lens = torch.tensor([1], dtype=torch.int32, device='cuda')
|
|
|
|
o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens)
|
|
ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), scale)
|
|
|
|
cos = torch.nn.functional.cosine_similarity(
|
|
o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
|
|
).item()
|
|
print(f" cos = {cos:.6f}")
|
|
assert cos >= 0.99, f"cosine too low: {cos}"
|
|
print(" ✅ PASS")
|
|
|
|
|
|
def test_d3_hd128():
|
|
"""SWA masking at hd=128 (SMEM-P path)."""
|
|
print("\n=== Test 5: swa_lens=64 (hd=128) ===")
|
|
torch.manual_seed(42)
|
|
m, s_k, hd = 128, 128, 128
|
|
scale = 1.0 / math.sqrt(hd)
|
|
|
|
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')
|
|
swa_lens = torch.tensor([64], dtype=torch.int32, device='cuda')
|
|
|
|
o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens, use_smem_p=True)
|
|
ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), scale)
|
|
|
|
cos = torch.nn.functional.cosine_similarity(
|
|
o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
|
|
).item()
|
|
print(f" cos = {cos:.6f}")
|
|
assert cos >= 0.99, f"cosine too low: {cos}"
|
|
print(" ✅ PASS")
|
|
|
|
|
|
def test_d3_swa128_hd128():
|
|
"""No masking at hd=128: regression test (should match existing D1 results)."""
|
|
print("\n=== Test 6: No masking (swa_lens=128, hd=128) ===")
|
|
torch.manual_seed(42)
|
|
m, s_k, hd = 128, 128, 128
|
|
scale = 1.0 / math.sqrt(hd)
|
|
|
|
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')
|
|
swa_lens = torch.tensor([s_k], dtype=torch.int32, device='cuda')
|
|
|
|
o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_lens, use_smem_p=True)
|
|
ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, swa_lens.cpu().expand(m), scale)
|
|
|
|
cos = torch.nn.functional.cosine_similarity(
|
|
o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
|
|
).item()
|
|
print(f" cos = {cos:.6f}")
|
|
assert cos >= 0.995, f"Regression: cosine too low: {cos}"
|
|
print(" ✅ PASS")
|
|
|
|
|
|
def test():
|
|
print("=== D3: In-Kernel SWA Sequence Length Mask ===")
|
|
test_d3_no_mask()
|
|
test_d3_swa64()
|
|
test_d3_swa32()
|
|
test_d3_swa1()
|
|
test_d3_hd128()
|
|
test_d3_swa128_hd128()
|
|
print("\n=== ALL TESTS PASSED ===")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test() |