mirror of
https://github.com/kyegomez/OpenMythos.git
synced 2026-05-02 17:43:27 +02:00
196 lines
6.7 KiB
Python
196 lines
6.7 KiB
Python
"""
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Standalone RoPE debug test — logs tensor outputs and intermediate calculations
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so you can visually verify correctness of precompute_rope_freqs and apply_rope.
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"""
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import torch
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from open_mythos.main import apply_rope, precompute_rope_freqs
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DIM = 8
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MAX_LEN = 6
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THETA = 500000.0
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def section(title: str) -> None:
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print(f"\n{'=' * 60}")
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print(f" {title}")
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print("=" * 60)
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def log(label: str, value) -> None:
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print(f"\n[{label}]")
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print(value)
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# ---------------------------------------------------------------------------
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# 1. precompute_rope_freqs — raw frequency table
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# ---------------------------------------------------------------------------
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section("1. precompute_rope_freqs")
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freqs = precompute_rope_freqs(dim=DIM, max_len=MAX_LEN, theta=THETA)
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log("freqs shape", freqs.shape)
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log("freqs (complex)", freqs)
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log("freqs.real", freqs.real)
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log("freqs.imag", freqs.imag)
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log("freqs magnitude (should be all 1.0)", freqs.abs())
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log("freqs angle (radians)", freqs.angle())
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print("\nExpected: base frequencies (1 per dim pair)")
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base = 1.0 / (THETA ** (torch.arange(0, DIM, 2, dtype=torch.float32) / DIM))
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log("base freqs", base)
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print("\nExpected: freqs[t, k].angle() == t * base[k]")
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for t in range(MAX_LEN):
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expected_angles = t * base
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actual_angles = freqs[t].angle()
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match = torch.allclose(actual_angles, expected_angles, atol=1e-5)
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print(f" t={t}: angles match = {match} actual={actual_angles.tolist()}")
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# ---------------------------------------------------------------------------
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# 2. Position 0 is identity (freqs[0] == 1+0j)
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# ---------------------------------------------------------------------------
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section("2. freqs[0] is identity phasor (1+0j)")
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log("freqs[0]", freqs[0])
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print(f" All magnitude=1: {torch.allclose(freqs[0].abs(), torch.ones(DIM // 2))}")
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print(f" All angle=0: {torch.allclose(freqs[0].angle(), torch.zeros(DIM // 2))}")
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# ---------------------------------------------------------------------------
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# 3. apply_rope — shape and dtype
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# ---------------------------------------------------------------------------
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section("3. apply_rope — shape and dtype")
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B, T, H = 2, MAX_LEN, 3
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x = torch.randn(B, T, H, DIM)
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log("input x shape", x.shape)
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out = apply_rope(x, freqs)
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log("output shape", out.shape)
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print(f" Shape preserved: {out.shape == x.shape}")
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# dtype float16
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x_half = x.half()
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out_half = apply_rope(x_half, freqs)
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print(f" float16 dtype preserved: {out_half.dtype == torch.float16}")
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# ---------------------------------------------------------------------------
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# 4. apply_rope — isometry (norm preservation)
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# ---------------------------------------------------------------------------
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section("4. apply_rope — norm preservation (isometry)")
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norms_in = x.norm(dim=-1)
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norms_out = out.norm(dim=-1)
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log("input norms (first batch item)", norms_in[0])
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log("output norms (first batch item)", norms_out[0])
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print(
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f" Max absolute norm difference: {(norms_in - norms_out).abs().max().item():.2e}"
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)
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print(
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f" Norms preserved (atol=1e-5): {torch.allclose(norms_in, norms_out, atol=1e-5)}"
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)
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# ---------------------------------------------------------------------------
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# 5. Position 0 is the identity transformation
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# ---------------------------------------------------------------------------
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section("5. Position 0 is identity transformation")
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x1 = torch.randn(1, 1, 2, DIM)
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out1 = apply_rope(x1, freqs[:1])
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log("input x[:,0]", x1[0, 0])
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log("output x[:,0]", out1[0, 0])
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log("diff (should be ~0)", (x1 - out1).abs())
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print(f" Identity at pos 0: {torch.allclose(x1, out1, atol=1e-6)}")
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# ---------------------------------------------------------------------------
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# 6. Different positions produce different rotations
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# ---------------------------------------------------------------------------
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section("6. Different positions produce different rotations")
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x2 = torch.ones(1, MAX_LEN, 1, DIM)
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out2 = apply_rope(x2, freqs)
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print("Per-position outputs (all input=1.0):")
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for t in range(MAX_LEN):
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print(f" pos={t}: {out2[0, t, 0].tolist()}")
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# ---------------------------------------------------------------------------
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# 7. Inverse rotation recovers original
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# ---------------------------------------------------------------------------
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section("7. Inverse rotation recovers original")
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x3 = torch.randn(2, T, H, DIM)
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rotated = apply_rope(x3, freqs)
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xc = torch.view_as_complex(rotated.float().reshape(*rotated.shape[:-1], -1, 2))
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inv_freqs = freqs.conj()
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recovered = (
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torch.view_as_real(xc * inv_freqs.unsqueeze(0).unsqueeze(2))
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.flatten(-2)
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.to(x3.dtype)
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)
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diff = (x3 - recovered).abs()
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log("max recovery error", diff.max().item())
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print(f" Recovery succeeded (atol=1e-5): {torch.allclose(x3, recovered, atol=1e-5)}")
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# ---------------------------------------------------------------------------
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# 8. start_pos correctness — the core generation bug fix
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# ---------------------------------------------------------------------------
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section("8. start_pos correctness (generation bug)")
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print(
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"Simulates: prompt of length 4, then decode step receives token at position 4.\n"
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"Old buggy code: freqs[:1] → position 0 always.\n"
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"Fixed code: freqs[4:5] → position 4."
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)
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prompt_len = 4
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decode_token = torch.randn(1, 1, 1, DIM)
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out_buggy = apply_rope(decode_token, freqs[:1]) # wrong: always pos 0
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out_fixed = apply_rope(
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decode_token, freqs[prompt_len : prompt_len + 1]
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) # correct: pos 4
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log("freqs[0] (pos 0)", freqs[0])
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log(f"freqs[{prompt_len}] (pos {prompt_len})", freqs[prompt_len])
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log("buggy output (pos 0 encoding)", out_buggy[0, 0, 0])
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log("fixed output (pos 4 encoding)", out_fixed[0, 0, 0])
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print(f" Outputs differ (they should): {not torch.allclose(out_buggy, out_fixed)}")
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# ---------------------------------------------------------------------------
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# 9. Relative position property
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# ---------------------------------------------------------------------------
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section("9. Relative position property: <RoPE(q,m), RoPE(k,n)> depends only on (n-m)")
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dim = 16
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max_len = 32
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freqs_big = precompute_rope_freqs(dim=dim, max_len=max_len, theta=THETA)
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torch.manual_seed(42)
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q = torch.randn(1, 1, 1, dim)
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k = torch.randn(1, 1, 1, dim)
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def rope_at(tensor, pos):
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seq = torch.zeros(1, pos + 1, 1, dim)
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seq[0, pos] = tensor[0, 0]
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return apply_rope(seq, freqs_big[: pos + 1])[:, pos : pos + 1]
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dot_3_9 = (rope_at(q, 3) * rope_at(k, 9)).sum()
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dot_1_7 = (rope_at(q, 1) * rope_at(k, 7)).sum()
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dot_0_6 = (rope_at(q, 0) * rope_at(k, 6)).sum()
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print(f" <RoPE(q,3), RoPE(k,9)>: {dot_3_9.item():.6f}")
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print(f" <RoPE(q,1), RoPE(k,7)>: {dot_1_7.item():.6f}")
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print(f" <RoPE(q,0), RoPE(k,6)>: {dot_0_6.item():.6f}")
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print(
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f" All equal (offset=6): {torch.allclose(dot_3_9, dot_1_7, atol=1e-5) and torch.allclose(dot_1_7, dot_0_6, atol=1e-5)}"
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)
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section("DONE — all checks complete")
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