biondizzle
e64392f1ac
For all-SWA segments (n_comp=0), sink bias still needs to be applied to all positions. The apply_sink_bias flag controls compilation of the sink bias code path, independent of n_comp offset.
305 lines
11 KiB
Python
305 lines
11 KiB
Python
"""
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FMHA D5c: Fused sparse + SWA attention via combined KV + sink bias.
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Mathematical insight: the sink merge is equivalent to a single attention
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pass over the concatenated KV with a logit bias (attn_sink) applied to
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the SWA portion. No two passes needed, no merge epilogue.
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S = [S_comp, S_swa + attn_sink]
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O = softmax(S) @ [V_comp; V_swa]
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This is identical to:
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O = (exp(lse_sparse)*O_sparse + exp(sink)*exp(lse_swa)*O_swa)
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/ (exp(lse_sparse) + exp(sink)*exp(lse_swa))
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The kernel changes are minimal:
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1. K = [compressed_K; swa_K], V = [compressed_V; swa_V]
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2. n_comp = length of compressed KV (sink bias applies to positions >= n_comp)
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3. attn_sink = per-head logit bias for SWA positions
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4. D3/D4 masking applies to SWA region (positions >= n_comp)
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d5c_fused.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_combined_attention(q, k_comp, v_comp, k_swa, v_swa,
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attn_sink, scale, swa_len, is_causal=False):
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"""FP32 reference: single softmax over combined KV with sink bias on SWA."""
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m, hd = q.shape
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n_comp = k_comp.shape[0]
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n_swa = k_swa.shape[0]
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# Concatenate KV
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k_combined = torch.cat([k_comp, k_swa], dim=0) # (n_comp + n_swa, hd)
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v_combined = torch.cat([v_comp, v_swa], dim=0)
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# Compute combined logits
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scores = torch.matmul(q.float(), k_combined.float().T) * scale # (m, n_comp + n_swa)
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# Add sink bias to SWA positions
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scores[:, n_comp:] += attn_sink
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# D3: SWA length mask (only SWA region)
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if swa_len < n_swa:
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scores[:, n_comp + swa_len:] = float('-inf')
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# D4: causal mask (only SWA region)
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if is_causal:
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# Within SWA region: mask k_coord > m_coord
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for i in range(m):
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for j in range(n_swa):
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if j > i: # k_coord > m_coord
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scores[i, n_comp + j] = float('-inf')
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# Softmax + PV
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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).clamp(min=1e-30)
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p = exp_s / sum_s
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o = torch.matmul(p, v_combined.float())
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return o.to(torch.bfloat16)
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def reference_sink_merge(q, k_comp, v_comp, k_swa, v_swa,
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attn_sink, scale, swa_len, is_causal=False):
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"""FP32 reference: separate attention + sink merge (original D5b formula)."""
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m, hd = q.shape
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n_comp = k_comp.shape[0]
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n_swa = k_swa.shape[0]
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# Compressed KV attention (no mask)
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attn_comp = torch.matmul(q.float(), k_comp.float().T) * scale
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o_norm_comp = torch.softmax(attn_comp, dim=-1) @ v_comp.float()
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lse_comp = torch.logsumexp(attn_comp, dim=-1, keepdim=True) # (m, 1)
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# SWA KV attention (with swa_len mask)
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attn_swa = torch.matmul(q.float(), k_swa.float().T) * scale
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if swa_len < n_swa:
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attn_swa[:, swa_len:] = float('-inf')
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if is_causal:
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for i in range(m):
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for j in range(n_swa):
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if j > i:
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attn_swa[i, j] = float('-inf')
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o_norm_swa = torch.softmax(attn_swa, dim=-1) @ v_swa.float()
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lse_swa = torch.logsumexp(attn_swa, dim=-1, keepdim=True)
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# Sink merge (normalized formula)
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exp_sink = math.exp(attn_sink)
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numerator = lse_comp.exp() * o_norm_comp + exp_sink * lse_swa.exp() * o_norm_swa
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denominator = (lse_comp.exp() + exp_sink * lse_swa.exp()).clamp(min=1e-30)
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o = numerator / denominator
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return o.to(torch.bfloat16)
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def test_d5c_combined():
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print("=== Stage D5c: Fused Sparse+SWA via Combined KV + Sink Bias ===\n")
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hd = 64
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m = 128 # query rows
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n_comp = 64 # compressed KV length (fit in single 128-wide KV tile)
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n_swa = 64 # SWA window length
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n_total = n_comp + n_swa # combined KV length = 128 (1 KV tile)
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swa_len = 40 # actual SWA fill
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scale = 1.0 / math.sqrt(hd)
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torch.manual_seed(42)
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k_comp = torch.randn(n_comp, hd, 1, dtype=torch.bfloat16, device='cuda')
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v_comp = torch.randn(n_comp, hd, dtype=torch.bfloat16, device='cuda')
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k_swa = torch.randn(n_swa, hd, 1, dtype=torch.bfloat16, device='cuda')
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v_swa = torch.randn(n_swa, hd, dtype=torch.bfloat16, device='cuda')
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# Sink weight (in log domain, per-head). n_h=1 so it's a scalar.
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attn_sink_val = 0.5
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attn_sink = torch.tensor([attn_sink_val], dtype=torch.float32, device='cuda')
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# === FP32 References ===
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qf = q[:, :, 0]
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# Reference 1: Combined softmax with sink bias (our kernel's approach)
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ref_combined = reference_combined_attention(
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qf, k_comp[:, :, 0], v_comp, k_swa[:, :, 0], v_swa,
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attn_sink_val, scale, swa_len
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)
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# Reference 2: Separate attention + sink merge (original D5b formula)
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ref_merge = reference_sink_merge(
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qf, k_comp[:, :, 0], v_comp, k_swa[:, :, 0], v_swa,
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attn_sink_val, scale, swa_len
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)
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# Verify the two references agree
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cos_ref = torch.nn.functional.cosine_similarity(
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ref_combined.flatten().unsqueeze(0).float(),
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ref_merge.flatten().unsqueeze(0).float()
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).item()
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print(f"Reference: combined softmax vs sink merge cos = {cos_ref:.6f}")
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assert cos_ref > 0.999, f"References don't match: cos={cos_ref}"
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# === Kernel ===
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# Concatenate KV for the kernel
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k_combined = torch.cat([k_comp, k_swa], dim=0) # (n_total, hd, 1)
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v_combined = torch.cat([v_comp, v_swa], dim=0) # (n_total, hd)
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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kernel = FmhaKernel(
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head_dim=hd,
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s_k=n_total, # combined KV length
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normalize=False, # D5a: emit un-normalized O + LSE
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apply_swa_mask=True, # D3: mask SWA positions
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is_causal=False, # D4: no causal mask for this test
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n_comp=n_comp, # D5c: compressed KV length (sink bias starts here)
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apply_sink_bias=True, # D5c: enable sink bias logit modification
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)
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# Allocate output
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c_out = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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lse_out = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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row_sum_out = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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# Prepare CuTe tensors
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def to_cute(t):
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return ct.from_dlpack(t).mark_layout_dynamic(leading_dim=ct.get_leading_dim(t))
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mQ = to_cute(q)
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mK = to_cute(k_combined)
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mV = to_cute(v_combined.unsqueeze(-1).contiguous())
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mC = to_cute(c_out)
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mLSE = to_cute(lse_out)
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mRowSums = to_cute(row_sum_out)
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# Compile
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print('Compiling D5c kernel (combined KV + sink bias)...', flush=True)
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mSinkBias = to_cute(attn_sink)
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compiled = cute.compile(
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kernel, mQ, mK, mV, mC, stream, mLSE,
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swa_len=swa_len, sink_bias=mSinkBias, row_sums=mRowSums,
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)
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# Run
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print('Running D5c kernel...', flush=True)
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compiled(
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mQ, mK, mV, mC, stream, mLSE,
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swa_len=swa_len, sink_bias=mSinkBias, row_sums=mRowSums,
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)
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torch.cuda.synchronize()
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# Check results
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# Kernel outputs UN-NORMALIZED O (normalize=False). Normalize using per-row row_sum.
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# O_norm[i] = O_unnorm[i] / row_sum[i]
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o_kernel_unnorm = c_out[:, :, 0].float() # (m, hd)
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row_sums = row_sum_out[:, 0, 0].float() # (m,)
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# Normalize each row by its row_sum
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o_kernel = o_kernel_unnorm / row_sums.unsqueeze(1).clamp(min=1e-30)
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cos = torch.nn.functional.cosine_similarity(
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o_kernel.flatten().unsqueeze(0),
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ref_combined.flatten().unsqueeze(0).float()
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).item()
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max_abs = (o_kernel - ref_combined.float()).abs().max().item()
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status = "PASS" if cos >= 0.99 else "FAIL"
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print(f'\nD5c result: cos {cos:.6f} max_abs {max_abs:.4f} {status}')
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if cos < 0.99:
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print(f' kernel[0,:4]={o_kernel[0,:4].tolist()}')
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print(f' ref[0,:4]={ref_combined[0,:4].tolist()}')
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print(f' row_sum range: {row_sums.min().item():.4f} to {row_sums.max().item():.4f}')
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print(f' LSE range: {lse_out[:,0,0].min().item():.4f} to {lse_out[:,0,0].max().item():.4f}')
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def test_d5c_with_causal():
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"""D5c with causal mask on SWA branch."""
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print("\n=== Stage D5c: Fused Sparse+SWA with Causal Mask ===\n")
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hd = 64
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m = 128
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n_comp = 64
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n_swa = 64
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n_total = n_comp + n_swa # 128, single KV tile
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swa_len = 48 # partially filled SWA
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scale = 1.0 / math.sqrt(hd)
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torch.manual_seed(123)
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k_comp = torch.randn(n_comp, hd, 1, dtype=torch.bfloat16, device='cuda')
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v_comp = torch.randn(n_comp, hd, dtype=torch.bfloat16, device='cuda')
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k_swa = torch.randn(n_swa, hd, 1, dtype=torch.bfloat16, device='cuda')
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v_swa = torch.randn(n_swa, hd, dtype=torch.bfloat16, device='cuda')
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attn_sink_val = 0.3
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attn_sink = torch.tensor([attn_sink_val], dtype=torch.float32, device='cuda')
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qf = q[:, :, 0]
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ref = reference_combined_attention(
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qf, k_comp[:, :, 0], v_comp, k_swa[:, :, 0], v_swa,
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attn_sink_val, scale, swa_len, is_causal=True
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)
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# Concatenate KV
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k_combined = torch.cat([k_comp, k_swa], dim=0)
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v_combined = torch.cat([v_comp, v_swa], dim=0)
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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kernel = FmhaKernel(
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head_dim=hd,
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s_k=n_total,
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normalize=False,
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apply_swa_mask=True,
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is_causal=True,
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n_comp=n_comp,
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apply_sink_bias=True,
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)
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c_out = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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lse_out = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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row_sum_out = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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def to_cute(t):
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return ct.from_dlpack(t).mark_layout_dynamic(leading_dim=ct.get_leading_dim(t))
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mQ = to_cute(q)
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mK = to_cute(k_combined)
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mV = to_cute(v_combined.unsqueeze(-1).contiguous())
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mC = to_cute(c_out)
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mLSE = to_cute(lse_out)
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mRowSums = to_cute(row_sum_out)
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mSinkBias = to_cute(attn_sink)
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compiled = cute.compile(
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kernel, mQ, mK, mV, mC, stream, mLSE,
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swa_len=swa_len, sink_bias=mSinkBias, row_sums=mRowSums,
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)
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compiled(
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mQ, mK, mV, mC, stream, mLSE,
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swa_len=swa_len, sink_bias=mSinkBias, row_sums=mRowSums,
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)
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torch.cuda.synchronize()
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o_kernel_unnorm = c_out[:, :, 0].float()
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row_sums = row_sum_out[:, 0, 0].float()
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o_kernel = o_kernel_unnorm / row_sums.unsqueeze(1).clamp(min=1e-30)
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cos = torch.nn.functional.cosine_similarity(
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o_kernel.flatten().unsqueeze(0),
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ref.flatten().unsqueeze(0).float()
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).item()
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max_abs = (o_kernel - ref.float()).abs().max().item()
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status = "PASS" if cos >= 0.99 else "FAIL"
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print(f'D5c causal result: cos {cos:.6f} max_abs {max_abs:.4f} {status}')
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if __name__ == '__main__':
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test_d5c_combined()
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test_d5c_with_causal()
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