mirror of
https://github.com/kyegomez/OpenMythos.git
synced 2026-05-02 17:43:27 +02:00
[fix][rope Every decode token was stuck at position 0, so <q_decoded, k_cached> lost the (n - m) term entirely]
This commit is contained in:
Kye Gomez
parent
537b116b3e
commit
18cca894dd
@ -147,14 +147,19 @@ def apply_rope(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
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Args:
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x -- tensor of shape (B, T, H, head_dim); head_dim must be even
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freqs_cis -- precomputed complex frequencies of shape (max_len, head_dim//2)
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freqs_cis -- precomputed complex frequencies of shape (T, head_dim//2),
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already sliced to exactly the positions being processed
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(caller is responsible for correct start_pos offset)
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Returns:
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Rotated tensor of the same shape and dtype as x
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"""
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xc = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
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freqs_cis = freqs_cis[: x.shape[1]].unsqueeze(0).unsqueeze(2)
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return torch.view_as_real(xc * freqs_cis).flatten(-2).to(x.dtype)
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return (
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torch.view_as_real(xc * freqs_cis.unsqueeze(0).unsqueeze(2))
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.flatten(-2)
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.to(x.dtype)
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)
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# ---------------------------------------------------------------------------
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@ -936,6 +941,7 @@ class OpenMythos(nn.Module):
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input_ids: torch.Tensor,
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n_loops: Optional[int] = None,
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kv_cache: Optional[dict] = None,
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start_pos: int = 0,
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) -> torch.Tensor:
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"""
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Forward pass through Prelude → Recurrent Block → Coda.
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@ -946,17 +952,21 @@ class OpenMythos(nn.Module):
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Increase at inference to extrapolate to harder problems.
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kv_cache -- dict mutated in-place for autoregressive KV caching;
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pass an empty dict {} and reuse across decode steps
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start_pos -- index of the first token in input_ids within the full
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sequence; used to select the correct RoPE frequencies
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during incremental decoding (0 for prefill, prompt_len
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for each subsequent decode step)
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Returns:
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Logits of shape (B, T, vocab_size)
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"""
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B, T = input_ids.shape
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T = input_ids.shape[1]
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device = input_ids.device
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x = self.embed(input_ids)
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freqs_cis = (
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self.freqs_cis_mla if self.cfg.attn_type == "mla" else self.freqs_cis
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)[:T]
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)[start_pos : start_pos + T]
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mask = self._causal_mask(T, device) if T > 1 else None
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for i, layer in enumerate(self.prelude):
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@ -1001,9 +1011,17 @@ class OpenMythos(nn.Module):
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Token indices of shape (B, T + max_new_tokens)
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"""
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kv_cache: dict = {}
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prompt_len = input_ids.shape[1]
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for step in range(max_new_tokens):
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cur_ids = input_ids if step == 0 else input_ids[:, -1:]
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logits = self.forward(cur_ids, n_loops=n_loops, kv_cache=kv_cache)
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if step == 0:
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cur_ids = input_ids
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start_pos = 0
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else:
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cur_ids = input_ids[:, -1:]
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start_pos = prompt_len + step - 1
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logits = self.forward(
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cur_ids, n_loops=n_loops, kv_cache=kv_cache, start_pos=start_pos
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)
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logits = logits[:, -1, :] / temperature
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if top_k > 0:
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v, _ = logits.topk(top_k)
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24
test_main.py
24
test_main.py
@ -96,20 +96,20 @@ class TestRoPE:
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def test_apply_rope_shape(self):
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freqs = precompute_rope_freqs(dim=16, max_len=32)
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x = torch.randn(B, T, 4, 16)
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out = apply_rope(x, freqs)
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out = apply_rope(x, freqs[:T])
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assert out.shape == x.shape
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def test_apply_rope_preserves_norm(self):
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# rotation is an isometry — norms must be unchanged
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freqs = precompute_rope_freqs(dim=16, max_len=32)
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x = torch.randn(B, T, 4, 16)
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out = apply_rope(x, freqs)
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out = apply_rope(x, freqs[:T])
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assert torch.allclose(x.norm(dim=-1), out.norm(dim=-1), atol=1e-5)
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def test_different_positions_differ(self):
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freqs = precompute_rope_freqs(dim=16, max_len=32)
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x = torch.ones(1, 2, 1, 16)
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out = apply_rope(x, freqs)
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out = apply_rope(x, freqs[:2])
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# position 0 and position 1 should produce different rotations
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assert not torch.allclose(out[0, 0], out[0, 1])
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@ -168,27 +168,27 @@ class TestRoPEExtended:
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"""T=1 input uses only freqs[0] = 1+0j, so output must equal input."""
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freqs = precompute_rope_freqs(dim=16, max_len=8)
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x = torch.randn(2, 1, 4, 16)
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out = apply_rope(x, freqs)
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out = apply_rope(x, freqs[:1])
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assert torch.allclose(x, out, atol=1e-6)
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def test_dtype_float32_preserved(self):
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freqs = precompute_rope_freqs(dim=16, max_len=16)
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x = torch.randn(1, 4, 2, 16).float()
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assert apply_rope(x, freqs).dtype == torch.float32
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assert apply_rope(x, freqs[:4]).dtype == torch.float32
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def test_dtype_float16_preserved(self):
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freqs = precompute_rope_freqs(dim=16, max_len=16)
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x = torch.randn(1, 4, 2, 16).half()
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assert apply_rope(x, freqs).dtype == torch.float16
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assert apply_rope(x, freqs[:4]).dtype == torch.float16
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def test_inverse_rotation_recovers_input(self):
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"""Rotating by freqs then by conj(freqs) (inverse) must recover the original."""
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dim = 16
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freqs = precompute_rope_freqs(dim=dim, max_len=8)
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x = torch.randn(2, 4, 3, dim)
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rotated = apply_rope(x, freqs)
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rotated = apply_rope(x, freqs[:4])
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xc = torch.view_as_complex(rotated.float().reshape(*rotated.shape[:-1], -1, 2))
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inv = freqs.conj()[: rotated.shape[1]].unsqueeze(0).unsqueeze(2)
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inv = freqs.conj()[:4].unsqueeze(0).unsqueeze(2)
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recovered = torch.view_as_real(xc * inv).flatten(-2).to(x.dtype)
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assert torch.allclose(x, recovered, atol=1e-5)
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@ -199,8 +199,8 @@ class TestRoPEExtended:
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torch.manual_seed(7)
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x_a = torch.randn(1, 4, 2, dim)
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x_b = torch.randn(1, 4, 2, dim)
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solo = apply_rope(x_a, freqs)
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batched = apply_rope(torch.cat([x_a, x_b], dim=0), freqs)[:1]
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solo = apply_rope(x_a, freqs[:4])
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batched = apply_rope(torch.cat([x_a, x_b], dim=0), freqs[:4])[:1]
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assert torch.allclose(solo, batched, atol=1e-6)
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def test_head_independence(self):
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@ -208,7 +208,7 @@ class TestRoPEExtended:
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dim = 16
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freqs = precompute_rope_freqs(dim=dim, max_len=8)
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x = torch.randn(1, 4, 1, dim).expand(1, 4, 3, dim).contiguous()
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out = apply_rope(x, freqs)
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out = apply_rope(x, freqs[:4])
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assert torch.allclose(out[:, :, 0], out[:, :, 1], atol=1e-6)
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assert torch.allclose(out[:, :, 1], out[:, :, 2], atol=1e-6)
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@ -227,7 +227,7 @@ class TestRoPEExtended:
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"""Rotate tensor at a specific position by embedding it in a zero sequence."""
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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)[:, pos : pos + 1]
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return apply_rope(seq, freqs[: pos + 1])[:, pos : pos + 1]
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# Both pairs have relative offset n - m = 6
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dot_3_9 = (rope_at(q, 3) * rope_at(k, 9)).sum()
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195
tests/test_rope_debug.py
Normal file
195
tests/test_rope_debug.py
Normal file
@ -0,0 +1,195 @@
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"""
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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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@ -113,7 +113,9 @@ def main():
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master = rank == 0
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if master:
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print(f"GPUs: {torch.cuda.device_count()} | World size: {world_size} | Device: {device}")
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print(
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f"GPUs: {torch.cuda.device_count()} | World size: {world_size} | Device: {device}"
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)
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# ------------------------------------------------------------------
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# Tokenizer
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