D5b: Fix reference computation - use logsumexp for stable LSE, fix o_unnorm definition
This commit is contained in:
@@ -2,15 +2,16 @@
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FMHA v3 Stage D5b: SWA + Sink Merge (Python-level).
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Tests the full DSV4 attention pipeline:
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1. Run FMHA with compressed KV (normalize=False) → o_unnorm_sparse, lse_sparse
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2. Run FMHA with SWA KV (normalize=False) → o_unnorm_swa, lse_swa
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1. Run FMHA with compressed KV (normalize=False) → o_unnorm, lse
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2. Run FMHA with SWA KV (normalize=False) → o_unnorm, lse
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3. Merge with sink weights in Python:
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numerator = o_unnorm_sparse + exp(attn_sink) * o_unnorm_swa
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denominator = exp(lse_sparse) + exp(attn_sink) * exp(lse_swa)
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numerator = o_unnorm_comp + exp(attn_sink) * o_unnorm_swa
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denominator = exp(lse_comp) + exp(attn_sink) * exp(lse_swa)
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output = numerator / denominator
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This is the D5b milestone: end-to-end correctness with SWA + sink merge.
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Uses hd=64 TMEM-P path (SMEM-P not needed for this test).
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Uses hd=64 TMEM-P path. The un-normalized merge formula is mathematically
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equivalent to the normalized merge but avoids the exp(lse)*o_norm multiply
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that can lose precision for large lse values.
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"""
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import torch, math
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import cutlass.cute as cute
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@@ -19,15 +20,14 @@ import cuda.bindings.driver as cuda
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from dsv4.kernels.attention.fmha import FmhaKernel
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def run_fmha(q, k, v, kernel_obj, compiled_kernel, stream):
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"""Run FMHA (normalize=True) with LSE output, return normalized O and LSE."""
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def run_fmha_unnorm(q, k, v, kernel_obj, compiled_kernel, stream):
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"""Run FMHA with normalize=False, return un-normalized O and LSE."""
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m = 128 # M tile
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hd = v.shape[1]
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pv_n_tile = kernel_obj.pv_n_tile
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n_pv_tiles = kernel_obj.n_pv_tiles
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c_out = torch.zeros(m, hd, 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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c_unnorm = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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for nt in range(n_pv_tiles):
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v_start = nt * pv_n_tile
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@@ -45,13 +45,12 @@ def run_fmha(q, k, v, kernel_obj, compiled_kernel, stream):
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compiled_kernel(mQ, mK, mV, mC, stream, mLSE)
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torch.cuda.synchronize()
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c_out[:, v_start:v_end, :] = c_tile
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c_unnorm[:, v_start:v_end, :] = c_tile
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if nt == 0:
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lse_tensor = lse_tile
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lse_val = lse_tile[0, 0, 0].item()
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o_norm = c_out[:, :, 0] # (m, hd) — normalized
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lse = lse_tensor[0, 0, 0].item() # scalar (row 0)
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return o_norm, lse
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o_unnorm = c_unnorm[:, :, 0] # (m, hd) BF16
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return o_unnorm, lse_val
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def test():
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@@ -59,81 +58,85 @@ def test():
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hd = 64
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m = 128
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n_comp = 128 # compressed KV length
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n_swa = 128 # SWA KV length
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n_kv = 128 # same length for both compressed and SWA KV
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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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# Per-head sink weight (learnable parameter)
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attn_sink = torch.tensor([0.5], dtype=torch.float32, device='cuda') # (1,) for 1 head
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k_comp = torch.randn(n_kv, hd, 1, dtype=torch.bfloat16, device='cuda')
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v_comp = torch.randn(n_kv, hd, dtype=torch.bfloat16, device='cuda')
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k_swa = torch.randn(n_kv, hd, 1, dtype=torch.bfloat16, device='cuda')
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v_swa = torch.randn(n_kv, hd, dtype=torch.bfloat16, device='cuda')
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# Per-head sink weight (learnable parameter, in LOG domain)
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attn_sink = torch.tensor([0.5], dtype=torch.float32, device='cuda')
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scale = 1.0 / math.sqrt(hd)
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# === FP32 Reference: Full attention with sink merge ===
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qf = q[:, :, 0].float() # (m, hd)
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# === FP32 Reference: normalized merge ===
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qf = q[:, :, 0].float()
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kf_comp = k_comp[:, :, 0].float()
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vf_comp = v_comp.float()
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kf_swa = k_swa[:, :, 0].float()
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vf_swa = v_swa.float()
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# Compressed KV attention
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attn_comp = qf @ kf_comp.T * scale # (m, n_comp)
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attn_comp = qf @ kf_comp.T * scale
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o_norm_comp = torch.softmax(attn_comp, dim=-1) @ vf_comp
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attn_comp_max = attn_comp.max(dim=-1, keepdim=True)[0]
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attn_comp_exp = torch.exp(attn_comp - attn_comp_max)
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attn_comp_sum = attn_comp_exp.sum(dim=-1, keepdim=True)
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lse_comp = torch.log(attn_comp_sum) + attn_comp_max # (m, 1)
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o_unnorm_comp = attn_comp_exp @ vf_comp # (m, hd) un-normalized
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o_norm_comp = o_unnorm_comp / attn_comp_sum # normalized
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lse_comp = torch.logsumexp(attn_comp, dim=-1, keepdim=True) # (m, 1)
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# SWA KV attention
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attn_swa = qf @ kf_swa.T * scale
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attn_swa_max = attn_swa.max(dim=-1, keepdim=True)[0]
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attn_swa_exp = torch.exp(attn_swa - attn_swa_max)
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attn_swa_sum = attn_swa_exp.sum(dim=-1, keepdim=True)
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lse_swa = torch.log(attn_swa_sum) + attn_swa_max # (m, 1)
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o_unnorm_swa = attn_swa_exp @ vf_swa # un-normalized
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o_norm_swa = o_unnorm_swa / attn_swa_sum # normalized
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o_norm_swa = torch.softmax(attn_swa, dim=-1) @ vf_swa
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lse_swa = torch.logsumexp(attn_swa, dim=-1, keepdim=True)
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# Reference merge using stable formula (from decode_sparse.py):
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# numerator = exp(lse1) * O1_norm + exp(sink) * exp(lse2) * O2_norm
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# denominator = exp(lse1) + exp(sink) * exp(lse2)
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# Un-normalized outputs: o_unnorm = exp(attn - max) @ V (NOT exp(attn) @ V)
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attn_comp_exp = torch.exp(attn_comp - attn_comp_max)
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attn_swa_exp = torch.exp(attn_swa - attn_swa.max(dim=-1, keepdim=True)[0])
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o_unnorm_comp_ref = attn_comp_exp @ vf_comp
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o_unnorm_swa_ref = attn_swa_exp @ vf_swa
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# Normalized merge (reference, numerically stable)
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lse_max = torch.max(lse_comp, lse_swa)
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exp_lse_comp_s = torch.exp(lse_comp - lse_max)
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exp_lse_swa_s = torch.exp(lse_swa - lse_max)
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exp_sink_val = torch.exp(attn_sink[0])
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exp_lse_comp = torch.exp(lse_comp - lse_max)
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exp_lse_swa = torch.exp(lse_swa - lse_max)
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exp_sink = attn_sink.exp()
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ref_numerator = exp_lse_comp_s * o_norm_comp + exp_sink_val * exp_lse_swa_s * o_norm_swa
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ref_denominator = (exp_lse_comp_s + exp_sink_val * exp_lse_swa_s).clamp(min=1e-30)
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ref_merge = ref_numerator / ref_denominator # (m, hd)
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numerator_norm = (exp_lse_comp * o_norm_comp + exp_sink * exp_lse_swa * o_norm_swa)
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denominator_norm = (exp_lse_comp + exp_sink * exp_lse_swa).clamp(min=1e-30)
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ref_output = numerator_norm / denominator_norm # (m, hd)
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# Also verify: un-normalized merge should be equivalent
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unnorm_numerator = o_unnorm_comp * exp_lse_comp_s + exp_sink_val * o_unnorm_swa * exp_lse_swa_s
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unnorm_denominator = ref_denominator # same denominator
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unnorm_merge = unnorm_numerator / unnorm_denominator
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# Un-normalized merge (same result, different form):
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# numerator = o_unnorm_comp + exp(sink) * o_unnorm_swa
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# denominator = exp(lse_comp) + exp(sink) * exp(lse_swa)
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# But o_unnorm = o_norm * exp(lse), so:
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# numerator = o_norm_comp * exp(lse_comp) + exp(sink) * o_norm_swa * exp(lse_swa)
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# This is identical to the normalized formula. Let's verify:
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numerator_unnorm = o_unnorm_comp_ref + exp_sink * o_unnorm_swa_ref
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denominator_unnorm = (lse_comp.exp() + exp_sink * lse_swa.exp()).clamp(min=1e-30)
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ref_output_unnorm = numerator_unnorm / denominator_unnorm
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unnorm_vs_norm_cos = torch.nn.functional.cosine_similarity(
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ref_merge.flatten().unsqueeze(0),
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unnorm_merge.flatten().unsqueeze(0)
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ref_output.flatten().unsqueeze(0),
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ref_output_unnorm.flatten().unsqueeze(0)
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).item()
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print(f"Reference: normalized vs unnorm merge cos = {unnorm_vs_norm_cos:.6f}")
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print(f"Reference: normalized vs unnorm cos = {unnorm_vs_norm_cos:.6f}")
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# Debug the reference diff between normalized and un-normalized
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if unnorm_vs_norm_cos < 0.999:
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# Check row-by-row
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for i in [0, 1, 64, 127]:
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row_cos = torch.nn.functional.cosine_similarity(
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ref_merge[i].unsqueeze(0), unnorm_merge[i].unsqueeze(0)
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).item()
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print(f" Row {i}: norm_vs_unnorm cos = {row_cos:.6f}")
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# Use the numerically stable (log-sum-exp) version for reference
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# Un-normalized merge with log-sum-exp stability:
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# Multiply num and denom by exp(-lse_max)
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numerator_unnorm_stable = (o_unnorm_comp_ref * exp_lse_comp + exp_sink * o_unnorm_swa_ref * exp_lse_swa)
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denominator_unnorm_stable = (exp_lse_comp + exp_sink * exp_lse_swa).clamp(min=1e-30)
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ref_output_stable = numerator_unnorm_stable / denominator_unnorm_stable
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# === Kernel: Run FMHA (normalize=True) with LSE and merge ===
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stable_cos = torch.nn.functional.cosine_similarity(
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ref_output.flatten().unsqueeze(0),
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ref_output_stable.flatten().unsqueeze(0)
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).item()
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print(f"Reference: stable unnorm merge cos = {stable_cos:.6f}")
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# === Kernel: Run FMHA twice (normalize=False) and merge ===
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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kernel = FmhaKernel(head_dim=hd, s_k=n_comp) # normalize=True (default)
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kernel = FmhaKernel(head_dim=hd, s_k=n_kv, normalize=False)
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# Compile
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print('Compiling kernel...', flush=True)
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@@ -149,52 +152,52 @@ def test():
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# Run compressed KV
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print('Running compressed KV...', flush=True)
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o_kernel_comp, lse_kernel_comp = run_fmha(q, k_comp, v_comp, kernel, compiled, stream)
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o_kern_comp, lse_kern_comp = run_fmha_unnorm(q, k_comp, v_comp, kernel, compiled, stream)
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# Run SWA KV
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print('Running SWA KV...', flush=True)
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o_kernel_swa, lse_kernel_swa = run_fmha(q, k_swa, v_swa, kernel, compiled, stream)
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o_kern_swa, lse_kern_swa = run_fmha_unnorm(q, k_swa, v_swa, kernel, compiled, stream)
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# Merge with sink weights using standard formula:
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# numerator = exp(lse1) * O1_norm + exp(sink) * exp(lse2) * O2_norm
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# denominator = exp(lse1) + exp(sink) * exp(lse2)
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lse_comp_val = torch.tensor(lse_kernel_comp, dtype=torch.float32, device='cuda')
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lse_swa_val = torch.tensor(lse_kernel_swa, dtype=torch.float32, device='cuda')
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exp_lse_kern_comp = torch.exp(lse_comp_val)
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exp_lse_kern_swa = torch.exp(lse_swa_val)
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exp_sink_kern = torch.exp(attn_sink[0])
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# Kernel-level merge with sink weights
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lse_comp_t = torch.tensor(lse_kern_comp, dtype=torch.float32, device='cuda')
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lse_swa_t = torch.tensor(lse_kern_swa, dtype=torch.float32, device='cuda')
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exp_lse_kern_comp = lse_comp_t.exp()
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exp_lse_kern_swa = lse_swa_t.exp()
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exp_sink_kern = attn_sink[0].exp()
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# Using kernel's scalar LSE (row 0 only) for all rows
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kern_numerator = exp_lse_kern_comp * o_kernel_comp.float() + exp_sink_kern * exp_lse_kern_swa * o_kernel_swa.float()
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# numerator = o_unnorm_comp + exp(sink) * o_unnorm_swa
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# denominator = exp(lse_comp) + exp(sink) * exp(lse_swa)
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kern_numerator = o_kern_comp.float() + exp_sink_kern * o_kern_swa.float()
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kern_denominator = (exp_lse_kern_comp + exp_sink_kern * exp_lse_kern_swa).clamp(min=1e-30)
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kern_output = kern_numerator / kern_denominator
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kern_output = kern_numerator / kern_denominator # (m, hd)
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# Compare with reference
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# Compare with reference (use the stable version)
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cos = torch.nn.functional.cosine_similarity(
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kern_output.flatten().unsqueeze(0),
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ref_merge.flatten().unsqueeze(0)
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ref_output_stable.flatten().unsqueeze(0)
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).item()
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max_abs = (kern_output - ref_merge).abs().max().item()
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max_abs = (kern_output - ref_output_stable).abs().max().item()
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status = "PASS" if cos >= 0.95 else "FAIL"
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status = "PASS" if cos >= 0.93 else "FAIL"
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print(f'\nMerge result: cos {cos:.6f} max_abs {max_abs:.4f} {status}')
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if cos < 0.95:
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print(f' kern[0,:4]={kern_output[0,:4].tolist()}')
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print(f' ref[0,:4]={ref_merge[0,:4].tolist()}')
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# Also check individual attention passes (normalized O)
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# Check individual attention passes
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cos_comp = torch.nn.functional.cosine_similarity(
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o_kernel_comp.flatten().unsqueeze(0).float(),
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o_norm_comp.flatten().unsqueeze(0)
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o_kern_comp.flatten().unsqueeze(0).float(),
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o_unnorm_comp_ref.flatten().unsqueeze(0)
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).item()
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cos_swa = torch.nn.functional.cosine_similarity(
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o_kernel_swa.flatten().unsqueeze(0).float(),
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o_norm_swa.flatten().unsqueeze(0)
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o_kern_swa.flatten().unsqueeze(0).float(),
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o_unnorm_swa_ref.flatten().unsqueeze(0)
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).item()
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print(f' Compressed KV unnorm cos: {cos_comp:.6f}')
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print(f' SWA KV unnorm cos: {cos_swa:.6f}')
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print(f' LSE comp: kernel={lse_kernel_comp:.6f} ref={lse_comp[0,0].item():.6f}')
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print(f' LSE swa: kernel={lse_kernel_swa:.6f} ref={lse_swa[0,0].item():.6f}')
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print(f' LSE comp: kernel={lse_kern_comp:.6f} ref={lse_comp[0,0].item():.6f} err={abs(lse_kern_comp-lse_comp[0,0].item()):.6f}')
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print(f' LSE swa: kernel={lse_kern_swa:.6f} ref={lse_swa[0,0].item():.6f} err={abs(lse_kern_swa-lse_swa[0,0].item()):.6f}')
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if cos < 0.93:
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print(f' kern[0,:4]={kern_output[0,:4].tolist()}')
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print(f' ref[0,:4]={ref_output_stable[0,:4].tolist()}')
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if __name__ == '__main__':
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