P3: clean up test, remove debug files, final integration test
- test_p3_fast_decode.py: clean kernel test + full API test - Removed debug tests (sanity, v_debug, v_ref_debug) - Double normalization fix verified: kernel output matches reference at cos >= 0.999990 across all MHA/MQA/GQA configs
This commit is contained in:
@@ -1,9 +1,10 @@
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"""
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P3 Integration Test: Verify 6-warp multi-head decode fast path
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produces identical results to a PyTorch reference.
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P3 Integration Test: 6-warp multi-head decode fast path.
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Tests MHA, MQA, GQA at HD = 64, 128, 256.
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Cosine similarity >= 0.999998 between kernel output and reference.
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Verifies the kernel produces identical results to a PyTorch reference
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for MHA, MQA, and GQA at HD = 64, 128, 256.
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Gate: worst-case cosine >= 0.999990 per configuration.
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"""
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import torch
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import math
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@@ -21,12 +22,34 @@ def cosine_sim(a, b):
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return (a @ b) / (a.norm() * b.norm() + 1e-30)
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def test_fast_path():
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def reference_attention(q_4d, k_4d, v_4d, scale):
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"""PyTorch reference matching kernel tensor layout.
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Q: (1, n_h, 1, hd), K: (1, n_h, N, hd), V: (1, n_h, hd, N)
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V is in kernel layout (hd, N) — transpose to (N, hd) for reference.
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"""
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n_h = q_4d.shape[1]
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q = q_4d[0] # (n_h, 1, hd)
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k = k_4d[0] # (n_h, N, hd)
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v = v_4d[0].transpose(-1, -2) # (n_h, N, hd)
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output = torch.zeros(n_h, 1, q_4d.shape[3], dtype=torch.bfloat16, device='cuda')
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for h in range(n_h):
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q_h = q[h] # (1, hd)
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k_h = k[h] # (N, hd)
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v_h = v[h] # (N, hd)
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s = torch.matmul(q_h.float(), k_h.float().T) * scale
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s = torch.softmax(s, dim=-1)
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o = torch.matmul(s, v_h.float())
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output[h] = o.bfloat16()
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return output
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def test_kernel_correctness():
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"""Test kernel vs PyTorch reference for MHA, MQA, GQA at various HD."""
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torch.manual_seed(42)
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configs = [
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# (n_q, n_kv, N, hd, desc)
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(4, 4, 64, 64, "MHA hd=64"),
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(4, 4, 128, 64, "MHA hd=64 N=128"),
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(4, 4, 64, 128, "MHA hd=128"),
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@@ -44,49 +67,20 @@ def test_fast_path():
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all_pass = True
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for n_q, n_kv, N, hd, desc in configs:
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scale = 1.0 / math.sqrt(hd)
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q_per_kv = n_q // n_kv
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try:
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# ---- Create data in KERNEL layout ----
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# Q: (1, n_q, 1, hd) — each head has 1 row of hd elements
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q_4d = torch.randn(1, n_q, 1, hd, dtype=torch.bfloat16, device='cuda').contiguous()
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# K: (1, n_kv, N, hd)
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k_4d = torch.randn(1, n_kv, N, hd, dtype=torch.bfloat16, device='cuda').contiguous()
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# V: (1, n_kv, hd, N) — the KERNEL expects V transposed (hd, N) per head
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v_4d = torch.randn(1, n_kv, hd, N, dtype=torch.bfloat16, device='cuda').contiguous()
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# ---- Kernel output ----
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sb = torch.zeros(1, n_q, dtype=torch.float32, device='cuda')
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o_4d, lse_4d = fmha_multihead_decode_raw(
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q_4d, k_4d, v_4d, scale, 0, 0, False, sb
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)
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o_kernel = o_4d # (1, n_q, 1, hd)
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o_4d, _ = fmha_multihead_decode_raw(q_4d, k_4d, v_4d, scale, 0, 0, False, sb)
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# ---- PyTorch reference using the SAME data ----
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# Q: (n_q, 1, hd), K: (n_kv, N, hd), V: (n_kv, N, hd) — V is TRANSPOSED from kernel layout
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q_ref = q_4d[0] # (n_q, 1, hd)
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k_ref = k_4d[0] # (n_kv, N, hd)
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v_ref = v_4d[0].transpose(-1, -2) # (n_kv, N, hd) — transpose (hd,N) -> (N,hd)
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o_ref = reference_attention(q_4d, k_4d, v_4d, scale)
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o_ref = torch.zeros(n_q, 1, hd, dtype=torch.bfloat16, device='cuda')
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for kv_idx in range(n_kv):
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k_h = k_ref[kv_idx] # (N, hd)
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v_h = v_ref[kv_idx] # (N, hd)
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for qi in range(q_per_kv):
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q_idx = kv_idx * q_per_kv + qi
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q_h = q_ref[q_idx] # (1, hd)
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s = torch.matmul(q_h.float(), k_h.float().T) * scale # (1, N)
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s = torch.softmax(s, dim=-1)
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o = torch.matmul(s, v_h.float()) # (1, hd)
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o_ref[q_idx] = o.bfloat16()
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# ---- Compare per-head ----
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worst_cos = 1.0
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for h in range(n_q):
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cos = torch.nn.functional.cosine_similarity(
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o_kernel[0, h].float().flatten().unsqueeze(0),
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o_4d[0, h].float().flatten().unsqueeze(0),
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o_ref[h].float().flatten().unsqueeze(0),
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).item()
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worst_cos = min(worst_cos, cos)
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@@ -106,7 +100,7 @@ def test_fast_path():
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def test_full_api():
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"""Test the full dsv4_attention API (goes through fast path for T=1, N<=128)."""
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"""Test the full dsv4_attention API (fast path for T=1, N<=128)."""
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from dsv4.kernels.attention.production import dsv4_attention
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torch.manual_seed(99)
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@@ -131,74 +125,44 @@ def test_full_api():
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k = torch.randn(n_kv, N, hd, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(n_kv, N, hd, dtype=torch.bfloat16, device='cuda')
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# Full API call (should use fast path)
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o_fast = dsv4_attention(q, k, v, scale=scale)
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# Also test the raw kernel directly with the SAME data
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# but constructing the tensors exactly as the working test does
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q_4d_direct = q.unsqueeze(0).contiguous() # (1, n_q, 1, hd)
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# Reference using same data
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if n_kv == 1:
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k_4d_direct = k.unsqueeze(0).unsqueeze(0).contiguous() # (1,1,N,hd)
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# V: (N,hd) -> transpose to (hd,N) -> (1,1,hd,N)
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# BUT we need the SAME V data as production uses
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# production v is (N, hd), transpose to (hd, N)
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v_4d_direct = v.unsqueeze(0).unsqueeze(0).transpose(-1, -2).contiguous()
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else:
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k_4d_direct = k.unsqueeze(0).contiguous() # (1,n_kv,N,hd)
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v_4d_direct = v.unsqueeze(0).transpose(-1, -2).contiguous() # (1,n_kv,hd,N)
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k = k.unsqueeze(0)
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v = v.unsqueeze(0)
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q_per_kv = n_q // n_kv
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o_ref = torch.zeros(n_q, 1, hd, dtype=torch.bfloat16, device='cuda')
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for kv_idx in range(n_kv):
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k_h = k[kv_idx]
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v_h = v[kv_idx]
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for qi in range(q_per_kv):
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q_idx = kv_idx * q_per_kv + qi
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q_h = q[q_idx]
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s = torch.matmul(q_h.float(), k_h.float().T) * scale
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s = torch.softmax(s, dim=-1)
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o = torch.matmul(s, v_h.float())
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o_ref[q_idx] = o.bfloat16()
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from dsv4.kernels.attention.fmha_multihead_op import fmha_multihead_decode_raw
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sb = torch.zeros(1, n_q, dtype=torch.float32, device='cuda')
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o_4d_direct, _ = fmha_multihead_decode_raw(
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q_4d_direct, k_4d_direct, v_4d_direct, scale, 0, 0, False, sb
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)
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o_direct = o_4d_direct.squeeze(0) # (n_q, 1, hd)
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# Compare direct kernel call vs full API
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cos_api_vs_direct = cosine_sim(o_fast, o_direct).item()
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# Reference
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o_ref = reference_attention_api(q, k, v, scale, n_q, n_kv, N, hd)
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cos_direct_vs_ref = cosine_sim(o_ref, o_direct).item()
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print(f" {desc}: api_vs_direct={cos_api_vs_direct:.6f} direct_vs_ref={cos_direct_vs_ref:.6f}")
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if cos_direct_vs_ref < 0.999990:
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cos = cosine_sim(o_ref, o_fast).item()
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status = "PASS" if cos >= 0.999990 else "FAIL"
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if status == "FAIL":
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all_pass = False
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print(f" {status} [API] {desc}: cos={cos:.6f}")
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except Exception as e:
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import traceback
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print(f" FAIL {desc}: {e}")
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print(f" FAIL [API] {desc}: {e}")
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traceback.print_exc()
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all_pass = False
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return all_pass
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def reference_attention_api(q, k, v, scale, n_q, n_kv, N, hd):
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"""Reference that matches dsv4_attention input format."""
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q_per_kv = n_q // n_kv
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if k.dim() == 2:
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k = k.unsqueeze(0)
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if v.dim() == 2:
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v = v.unsqueeze(0)
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output = torch.zeros(n_q, 1, hd, dtype=torch.bfloat16, device='cuda')
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for kv_idx in range(n_kv):
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k_h = k[kv_idx] # (N, hd)
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v_h = v[kv_idx] # (N, hd)
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for qi in range(q_per_kv):
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q_idx = kv_idx * q_per_kv + qi
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q_h = q[q_idx] # (1, hd)
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s = torch.matmul(q_h.float(), k_h.float().T) * scale
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s = torch.softmax(s, dim=-1)
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o = torch.matmul(s, v_h.float())
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output[q_idx] = o.bfloat16()
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return output
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if __name__ == "__main__":
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print("P3 Integration Test: 6-warp decode fast path")
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print("=" * 60)
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ok1 = test_fast_path()
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ok1 = test_kernel_correctness()
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print()
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ok2 = test_full_api()
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print("=" * 60)
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@@ -1,67 +0,0 @@
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"""
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Absolute simplest test: single head, small N, verify kernel == reference.
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"""
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import torch
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import math
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from dsv4.kernels.attention.fmha_multihead_op import fmha_multihead_decode_raw
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def test_single_head_sanity():
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"""Single head, N=128, hd=64. Known values, no randomness."""
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hd = 64
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N = 128
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scale = 1.0 / math.sqrt(hd)
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# Q: (1, 1, 1, hd) — single head, single query token
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q = torch.ones(1, 1, 1, hd, dtype=torch.bfloat16, device='cuda')
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# K: (1, 1, N, hd) — single KV head, N positions
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k = torch.ones(1, 1, N, hd, dtype=torch.bfloat16, device='cuda')
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# V: (1, 1, hd, N) — in kernel layout
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# Let's make V[d, r] = d + r*0.01 (simple pattern)
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v_data = torch.arange(hd, dtype=torch.float32, device='cuda').unsqueeze(1) + \
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torch.arange(N, dtype=torch.float32, device='cuda').unsqueeze(0) * 0.01
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v_4d = v_data.bfloat16().unsqueeze(0).unsqueeze(0) # (1, 1, hd, N)
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sb = torch.zeros(1, 1, dtype=torch.float32, device='cuda')
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o_4d, lse = fmha_multihead_decode_raw(q, k, v_4d, scale, 0, 0, False, sb)
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# Reference: Q is all-ones, K is all-ones, so QK^T gives all-equal scores
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# softmax of uniform = 1/N. So O = (1/N) * sum(V[r, d] for r in 0..N-1)
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v_ref = v_data.T # (N, hd) — reference uses (N, hd) layout
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# Each V[r, d] = d + r*0.01
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# sum over r: sum(d + r*0.01) = N*d + 0.01*sum(r) = N*d + 0.01*N*(N-1)/2
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# O[d] = (1/N) * (N*d + 0.01*N*(N-1)/2) = d + 0.01*(N-1)/2
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o_expected = torch.arange(hd, dtype=torch.float32, device='cuda') + 0.01 * (N - 1) / 2
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cos = torch.nn.functional.cosine_similarity(
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o_4d[0, 0].float().flatten().unsqueeze(0),
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o_expected.flatten().unsqueeze(0),
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).item()
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# Also compute via direct matmul for sanity
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q_f = q.float().squeeze() # (hd,) all ones
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k_f = k.float().squeeze() # (N, hd) all ones
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v_f = v_ref # (N, hd)
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scores = torch.matmul(q_f.unsqueeze(0), k_f.T) * scale # (1, N)
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probs = torch.softmax(scores, dim=-1) # (1, N)
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o_matmul = torch.matmul(probs, v_f) # (1, hd)
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cos_matmul = torch.nn.functional.cosine_similarity(
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o_4d[0, 0].float().flatten().unsqueeze(0),
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o_matmul.flatten().unsqueeze(0),
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).item()
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print(f"Kernel vs expected: cos={cos:.6f}")
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print(f"Kernel vs matmul: cos={cos_matmul:.6f}")
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print(f"Kernel output[0:5]: {o_4d[0, 0, 0, 0:5].float()}")
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print(f"Expected[0:5]: {o_expected[0:5]}")
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print(f"Matmul[0:5]: {o_matmul[0, 0:5]}")
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if __name__ == "__main__":
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test_single_head_sanity()
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@@ -1,103 +0,0 @@
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"""
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Debug test: call fmha_multihead_decode_raw directly with production-style V.
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Isolates whether the issue is in the V transpose or the production.py plumbing.
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"""
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import torch
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import math
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from dsv4.kernels.attention.fmha_multihead_op import fmha_multihead_decode_raw
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def cosine_sim(a, b):
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a = a.flatten().float()
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b = b.flatten().float()
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return (a @ b) / (a.norm() * b.norm() + 1e-30)
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def test_production_v_layout():
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"""Test with V created as (N, hd) then transposed (production path)."""
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torch.manual_seed(42)
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hd = 64
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n_h = 4
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N = 128
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scale = 1.0 / math.sqrt(hd)
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# Create Q, K in the same way as both the working test and production
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q_4d = torch.randn(1, n_h, 1, hd, dtype=torch.bfloat16, device='cuda').contiguous()
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k_4d = torch.randn(1, n_h, N, hd, dtype=torch.bfloat16, device='cuda').contiguous()
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# V: production path creates (n_kv, N, hd) then transposes to (1, n_kv, hd, N)
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v_orig = torch.randn(n_h, N, hd, dtype=torch.bfloat16, device='cuda')
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v_4d = v_orig.unsqueeze(0).transpose(-1, -2).contiguous()
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print(f"V orig shape: {v_orig.shape}")
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print(f"V 4d shape: {v_4d.shape}, strides: {v_4d.stride()}")
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sb = torch.zeros(1, n_h, dtype=torch.float32, device='cuda')
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o_4d, lse_4d = fmha_multihead_decode_raw(q_4d, k_4d, v_4d, scale, 0, 0, False, sb)
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# Reference: use v_orig (N, hd) per head
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q_ref = q_4d[0] # (n_h, 1, hd)
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k_ref = k_4d[0] # (n_h, N, hd)
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for h in range(n_h):
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q_h = q_ref[h] # (1, hd)
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k_h = k_ref[h] # (N, hd)
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v_h = v_orig[h] # (N, hd)
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s = torch.matmul(q_h.float(), k_h.float().T) * scale
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s = torch.softmax(s, dim=-1)
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o_ref = torch.matmul(s, v_h.float())
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cos = torch.nn.functional.cosine_similarity(
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o_4d[0, h].float().flatten().unsqueeze(0),
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o_ref.flatten().unsqueeze(0),
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).item()
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print(f" Head {h}: cos={cos:.6f}")
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def test_native_v_layout():
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"""Test with V created as (hd, N) natively (working test style)."""
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torch.manual_seed(42)
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hd = 64
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n_h = 4
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N = 128
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scale = 1.0 / math.sqrt(hd)
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q_4d = torch.randn(1, n_h, 1, hd, dtype=torch.bfloat16, device='cuda').contiguous()
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k_4d = torch.randn(1, n_h, N, hd, dtype=torch.bfloat16, device='cuda').contiguous()
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v_4d = torch.randn(1, n_h, hd, N, dtype=torch.bfloat16, device='cuda').contiguous()
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sb = torch.zeros(1, n_h, dtype=torch.float32, device='cuda')
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o_4d, lse_4d = fmha_multihead_decode_raw(q_4d, k_4d, v_4d, scale, 0, 0, False, sb)
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# Reference: V is (hd, N) per head, transpose to (N, hd) for reference
|
||||
v_ref = v_4d[0].transpose(-1, -2) # (n_h, N, hd)
|
||||
q_ref = q_4d[0]
|
||||
k_ref = k_4d[0]
|
||||
|
||||
for h in range(n_h):
|
||||
q_h = q_ref[h]
|
||||
k_h = k_ref[h]
|
||||
v_h = v_ref[h] # (N, hd)
|
||||
s = torch.matmul(q_h.float(), k_h.float().T) * scale
|
||||
s = torch.softmax(s, dim=-1)
|
||||
o_ref = torch.matmul(s, v_h.float())
|
||||
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
o_4d[0, h].float().flatten().unsqueeze(0),
|
||||
o_ref.flatten().unsqueeze(0),
|
||||
).item()
|
||||
print(f" Head {h}: cos={cos:.6f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("=== Test 1: V created as (N,hd) then transposed (production path) ===")
|
||||
test_production_v_layout()
|
||||
print()
|
||||
print("=== Test 2: V created natively as (hd,N) (working test style) ===")
|
||||
test_native_v_layout()
|
||||
@@ -1,91 +0,0 @@
|
||||
"""
|
||||
Debug: why does the full API test give cos=0.83?
|
||||
Test 1: V in kernel layout (hd, N), reference transposes -> (N, hd)
|
||||
Test 2: V in standard layout (N, hd), reference uses directly
|
||||
Both should give same result if math is correct.
|
||||
"""
|
||||
import torch
|
||||
import math
|
||||
import sys
|
||||
import os
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from dsv4.kernels.attention.fmha_multihead_op import fmha_multihead_decode_raw
|
||||
|
||||
|
||||
def test_v_layout_comparison():
|
||||
"""Direct comparison: same Q and K, V in two different layouts."""
|
||||
torch.manual_seed(42)
|
||||
|
||||
hd = 64
|
||||
n_h = 4
|
||||
N = 128
|
||||
scale = 1.0 / math.sqrt(hd)
|
||||
|
||||
# Create Q and K once
|
||||
q_4d = torch.randn(1, n_h, 1, hd, dtype=torch.bfloat16, device='cuda').contiguous()
|
||||
k_4d = torch.randn(1, n_h, N, hd, dtype=torch.bfloat16, device='cuda').contiguous()
|
||||
|
||||
# Create V as (n_h, hd, N) natively
|
||||
v_native = torch.randn(1, n_h, hd, N, dtype=torch.bfloat16, device='cuda').contiguous()
|
||||
|
||||
# Also create V as (n_h, N, hd) then transpose
|
||||
v_orig = torch.randn(n_h, N, hd, dtype=torch.bfloat16, device='cuda')
|
||||
v_transposed = v_orig.unsqueeze(0).transpose(-1, -2).contiguous() # (1, n_h, hd, N)
|
||||
|
||||
# Run kernel with native V
|
||||
sb = torch.zeros(1, n_h, dtype=torch.float32, device='cuda')
|
||||
o_native, _ = fmha_multihead_decode_raw(q_4d, k_4d, v_native, scale, 0, 0, False, sb)
|
||||
|
||||
# Run kernel with transposed-from-standard V
|
||||
o_transposed, _ = fmha_multihead_decode_raw(q_4d, k_4d, v_transposed, scale, 0, 0, False, sb)
|
||||
|
||||
# Reference with native V (hd, N) -> transpose to (N, hd)
|
||||
q_ref = q_4d[0] # (n_h, 1, hd)
|
||||
k_ref = k_4d[0] # (n_h, N, hd)
|
||||
v_ref_native = v_native[0].transpose(-1, -2) # (n_h, N, hd) — transposed from (hd, N)
|
||||
v_ref_orig = v_orig # (n_h, N, hd) — already in (N, hd) layout
|
||||
|
||||
# Reference 1: using native V data
|
||||
o_ref1 = torch.zeros(n_h, 1, hd, dtype=torch.bfloat16, device='cuda')
|
||||
for h in range(n_h):
|
||||
q_h = q_ref[h] # (1, hd)
|
||||
k_h = k_ref[h] # (N, hd)
|
||||
v_h = v_ref_native[h] # (N, hd)
|
||||
s = torch.matmul(q_h.float(), k_h.float().T) * scale
|
||||
s = torch.softmax(s, dim=-1)
|
||||
o = torch.matmul(s, v_h.float())
|
||||
o_ref1[h] = o.bfloat16()
|
||||
|
||||
# Reference 2: using original V data
|
||||
o_ref2 = torch.zeros(n_h, 1, hd, dtype=torch.bfloat16, device='cuda')
|
||||
for h in range(n_h):
|
||||
q_h = q_ref[h]
|
||||
k_h = k_ref[h]
|
||||
v_h = v_ref_orig[h] # (N, hd) — same data, different source
|
||||
s = torch.matmul(q_h.float(), k_h.float().T) * scale
|
||||
s = torch.softmax(s, dim=-1)
|
||||
o = torch.matmul(s, v_h.float())
|
||||
o_ref2[h] = o.bfloat16()
|
||||
|
||||
# Compare kernel vs ref1 (native V)
|
||||
for h in range(n_h):
|
||||
cos1 = torch.nn.functional.cosine_similarity(
|
||||
o_native[0, h].float().flatten().unsqueeze(0),
|
||||
o_ref1[h].float().flatten().unsqueeze(0),
|
||||
).item()
|
||||
cos2 = torch.nn.functional.cosine_similarity(
|
||||
o_transposed[0, h].float().flatten().unsqueeze(0),
|
||||
o_ref2[h].float().flatten().unsqueeze(0),
|
||||
).item()
|
||||
# Also compare the two kernel outputs (should differ since different V data)
|
||||
cos_kk = torch.nn.functional.cosine_similarity(
|
||||
o_native[0, h].float().flatten().unsqueeze(0),
|
||||
o_transposed[0, h].float().flatten().unsqueeze(0),
|
||||
).item()
|
||||
print(f" Head {h}: native_vs_ref1={cos1:.6f} transposed_vs_ref2={cos2:.6f} native_vs_transposed={cos_kk:.6f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_v_layout_comparison()
|
||||
Reference in New Issue
Block a user