biondizzle
e3e01071f4
CuTeDSL @cute.kernel cannot handle dynamic-shape tensors as parameters. Pass swa_len as Int32 scalar instead of a 1D tensor. This works for batch_size=1 (current config). Updated D3 and D4 tests to pass swa_len as int.
212 lines
7.8 KiB
Python
212 lines
7.8 KiB
Python
"""
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FMHA D3: In-kernel SWA sequence length masking.
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Proper approach: the kernel receives swa_len (int) and masks logits to -inf
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inside the softmax, using the tTMEM_LOADcS coordinate tensor to map
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register fragment positions to (row, col) in the QK matrix.
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d3_inkernel_mask.py
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"""
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import torch
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import math
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import cutlass.cute as cute
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import cutlass.torch as ct
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import cuda.bindings.driver as cuda
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from dsv4.kernels.attention.fmha import FmhaKernel
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def reference_swa_attention(q, k, v, swa_len, scale):
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"""FP32 reference with proper -inf masking."""
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scores = torch.matmul(q.float(), k.float().T) * scale
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if swa_len < k.shape[0]:
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scores[:, swa_len:] = float('-inf')
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max_s = scores.max(dim=-1, keepdim=True).values
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exp_s = (scores - max_s).exp()
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sum_s = exp_s.sum(dim=-1, keepdim=True)
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p = exp_s / sum_s
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o = torch.matmul(p, v.float())
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return o.to(torch.bfloat16)
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def _run_fmha_masked(q_3d, k_3d, v, m, s_k, hd, swa_len_val, use_smem_p=False):
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"""Run FMHA with in-kernel SWA masking and return normalized output."""
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scale = 1.0 / math.sqrt(hd)
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kernel = FmhaKernel(
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head_dim=hd, s_k=s_k, use_smem_p=use_smem_p,
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apply_swa_mask=True, normalize=False,
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)
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pv_n_tile = kernel.pv_n_tile
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n_pv_tiles = kernel.n_pv_tiles
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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o_unnorm = torch.zeros(m, hd, dtype=torch.float32, device='cuda')
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for pv in range(n_pv_tiles):
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v_tile = v[:, pv * pv_n_tile:(pv + 1) * pv_n_tile].contiguous().unsqueeze(-1)
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c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
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lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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mQ = ct.from_dlpack(q_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_3d))
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mK = ct.from_dlpack(k_3d).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_3d))
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mV = ct.from_dlpack(v_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_tile))
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mC = ct.from_dlpack(c_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c_tile))
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mLSE = ct.from_dlpack(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
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if pv == 0:
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE, swa_len_val)
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compiled(mQ, mK, mV, mC, stream, mLSE, swa_len_val)
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o_unnorm[:, pv * pv_n_tile:(pv + 1) * pv_n_tile] = c_tile[:, :, 0].float()
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# External normalization using reference attn_sum
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q_flat = q_3d[:, :, 0]
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k_flat = k_3d[:, :, 0]
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scores = torch.matmul(q_flat.float(), k_flat.float().T) * scale
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if swa_len_val < s_k:
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scores[:, swa_len_val:] = float('-inf')
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max_s = scores.max(dim=-1, keepdim=True).values
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attn_sum = (scores - max_s).exp().sum(dim=-1, keepdim=True)
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o_norm = (o_unnorm / attn_sum).to(torch.bfloat16)
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return o_norm
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def test_d3_no_mask():
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"""Full window (swa_len=128): no masking, regression test."""
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print("\n=== Test 1: No masking (swa_len=128, hd=64) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 128, 64
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_len_val=s_k)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, s_k, 1.0 / math.sqrt(hd))
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.995, f"Regression: cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d3_swa64():
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"""SWA with swa_len=64: mask positions 64-127 to -inf."""
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print("\n=== Test 2: swa_len=64 (hd=64) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 128, 64
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q = torch.randn(m, hd,1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_len_val=64)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, 64, 1.0 / math.sqrt(hd))
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.99, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d3_swa32():
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"""SWA with swa_len=32: only 32 valid positions."""
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print("\n=== Test 3: swa_len=32 (hd=64) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 128, 64
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_len_val=32)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, 32, 1.0 / math.sqrt(hd))
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.99, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d3_swa1():
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"""Edge case: swa_len=1, only one valid KV position."""
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print("\n=== Test 4: swa_len=1 (hd=64) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 128, 64
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_len_val=1)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, 1, 1.0 / math.sqrt(hd))
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.99, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d3_hd128():
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"""SWA masking at hd=128 (SMEM-P path)."""
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print("\n=== Test 5: swa_len=64 (hd=128) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 128, 128
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_len_val=64, use_smem_p=True)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, 64, 1.0 / math.sqrt(hd))
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.99, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d3_swa128_hd128():
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"""No masking at hd=128: regression test."""
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print("\n=== Test 6: No masking (swa_len=128, hd=128) ===")
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torch.manual_seed(42)
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m, s_k, hd = 128, 128, 128
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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o = _run_fmha_masked(q, k, v, m, s_k, hd, swa_len_val=s_k, use_smem_p=True)
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ref = reference_swa_attention(q[:, :, 0], k[:, :, 0], v, s_k, 1.0 / math.sqrt(hd))
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.995, f"Regression: cosine too low: {cos}"
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print(" ✅ PASS")
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def test():
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print("=== D3: In-Kernel SWA Sequence Length Mask ===")
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test_d3_no_mask()
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test_d3_swa64()
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test_d3_swa32()
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test_d3_swa1()
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test_d3_hd128()
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test_d3_swa128_hd128()
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print("\n=== ALL TESTS PASSED ===")
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if __name__ == '__main__':
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test()
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