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# SPDX-License-Identifier: Apache-2.0
"""
Tests for the RotarySetup1D module (1D mesh topology: N150, N300, T3K).
This test suite verifies:
1. Unit tests for config dataclass and transformation matrix utility
2. RotarySetup1D init + API methods (get_both_trans_mats, forward)
3. Numerical correctness vs pure-torch HF reference
4. from_model_args backward compatibility (vs TTTv1)
"""
import math
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Tuple
import pytest
import torch
from loguru import logger
import ttnn
from models.common.auto_compose import to_torch_auto_compose
from models.common.modules.lazy_weight import LazyWeight
from models.common.modules.rope.rope_1d import Rope1DConfig, RotarySetup1D, prepare_rot_idxs
from models.common.tensor_utils import get_rot_transformation_mat
from models.common.utility_functions import comp_pcc
# 1D module suites target the T3K; skip when the host system is a Galaxy.
pytestmark = pytest.mark.usefixtures("skip_on_galaxy_system")
# ============================================================================
# Pure-torch RoPE reference (no TTTv1 dependency)
# ============================================================================
_slow = pytest.mark.slow
@dataclass
class Llama3Scaling:
"""Llama-3.x frequency scaling parameters."""
factor: float = 8.0
original_max_position_embeddings: int = 8192
low_freq_factor: float = 1.0
high_freq_factor: float = 4.0
def _apply_llama3_scaling(freqs: torch.Tensor, s: Llama3Scaling) -> torch.Tensor:
"""Apply Llama-3.x frequency scaling (pure torch, mirrors HF implementation)."""
low_freq_wavelen = s.original_max_position_embeddings / s.low_freq_factor
high_freq_wavelen = s.original_max_position_embeddings / s.high_freq_factor
new_freqs = []
for freq in freqs:
wavelen = 2 * math.pi / freq
if wavelen < high_freq_wavelen:
new_freqs.append(freq)
elif wavelen > low_freq_wavelen:
new_freqs.append(freq / s.factor)
else:
smooth = (s.original_max_position_embeddings / wavelen - s.low_freq_factor) / (
s.high_freq_factor - s.low_freq_factor
)
new_freqs.append((1 - smooth) * freq / s.factor + smooth * freq)
return torch.tensor(new_freqs, dtype=freqs.dtype, device=freqs.device)
def _rope_cos_sin(
head_dim: int,
max_seq_len: int,
theta: float,
scaling: Optional[Llama3Scaling] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Compute RoPE cos/sin tables in Meta interleaved format (pure torch).
This is the HF reference implementation for RoPE, independent of TTTv1.
Returns:
cos, sin: [1, 1, max_seq_len, head_dim] in Meta interleaved format
where adjacent pairs repeat: [c0, c0, c1, c1, ...]
"""
inv_freq = 1.0 / (theta ** (torch.arange(0, head_dim, 2).float() / head_dim))
if scaling is not None:
# Scaled: apply frequency adjustment, then gather
inv_freq = _apply_llama3_scaling(inv_freq, scaling)
t = torch.arange(max_seq_len * 2.0)
freqs = torch.outer(t, inv_freq).float()
cos = freqs.cos()
sin = freqs.sin()
# Gather sequential positions and interleave
positions = torch.arange(max_seq_len)
pos_expanded = positions.unsqueeze(1).expand(-1, cos.shape[-1])
cos = cos.gather(0, pos_expanded)
sin = sin.gather(0, pos_expanded)
else:
# Unscaled: standard RoPE
t = torch.arange(max_seq_len, dtype=inv_freq.dtype)
freqs = torch.outer(t, inv_freq)
cos = freqs.cos()
sin = freqs.sin()
# Convert to Meta interleaved format: [c0, c0, c1, c1, ...]
cos = torch.stack([cos, cos], dim=-1).flatten(-2).unsqueeze(0).unsqueeze(0)
sin = torch.stack([sin, sin], dim=-1).flatten(-2).unsqueeze(0).unsqueeze(0)
return cos, sin
# ============================================================================
# Unit Tests - No device required
# ============================================================================
def test_rope_1d_config_creation():
"""Test that Rope1DConfig can be created with required fields."""
cos_source = torch.randn(1, 1, 8192, 128)
sin_source = torch.randn(1, 1, 8192, 128)
cos_lw = LazyWeight(source=cos_source)
sin_lw = LazyWeight(source=sin_source)
config = Rope1DConfig(
cos_matrix=cos_lw,
sin_matrix=sin_lw,
max_batch_size=32,
)
assert config.max_batch_size == 32
assert config.head_dim is None # derived later
assert config.use_qk_fused is False
assert config.datatype == ttnn.bfloat16
def test_rope_1d_config_with_options():
"""Test config with all optional fields set."""
from unittest.mock import MagicMock
cos_source = torch.randn(1, 1, 8192, 64)
sin_source = torch.randn(1, 1, 8192, 64)
cos_lw = LazyWeight(source=cos_source)
sin_lw = LazyWeight(source=sin_source)
config = Rope1DConfig(
cos_matrix=cos_lw,
sin_matrix=sin_lw,
max_batch_size=1,
head_dim=64,
device=MagicMock(),
use_qk_fused=True,
datatype=ttnn.bfloat8_b,
)
assert config.head_dim == 64
assert config.use_qk_fused is True
assert config.datatype == ttnn.bfloat8_b
def test_rope_1d_from_model_args_rejects_galaxy(expect_error):
"""Test that from_model_args raises for Galaxy devices."""
from unittest.mock import MagicMock
args = MagicMock()
args.is_galaxy = True
with expect_error(ValueError, "Galaxy"):
RotarySetup1D.from_model_args(device=MagicMock(), args=args)
def test_compute_cos_sin_no_scaling():
"""Test cos/sin computation without scaling (Mistral/Qwen-style)."""
cos, sin = _rope_cos_sin(head_dim=128, max_seq_len=8192, theta=1000000.0)
assert cos.shape == (1, 1, 8192, 128)
assert sin.shape == (1, 1, 8192, 128)
# cos/sin values should be in [-1, 1]
assert cos.abs().max() <= 1.0 + 1e-6
assert sin.abs().max() <= 1.0 + 1e-6
def test_compute_cos_sin_llama3_scaling():
"""Test cos/sin computation with Llama-3.x scaling."""
cos, sin = _rope_cos_sin(head_dim=128, max_seq_len=8192, theta=500000.0, scaling=Llama3Scaling())
assert cos.shape == (1, 1, 8192, 128)
assert sin.shape == (1, 1, 8192, 128)
def test_compute_cos_sin_head_dim_64():
"""Test cos/sin computation with head_dim=64 (Llama-3.2-1B)."""
cos, sin = _rope_cos_sin(head_dim=64, max_seq_len=8192, theta=500000.0, scaling=Llama3Scaling())
assert cos.shape == (1, 1, 8192, 64)
assert sin.shape == (1, 1, 8192, 64)
# ============================================================================
# Integration Tests - Require device
# ============================================================================
# Collected from rope_1d_init_test_cases.csv (deduplicated)
# Format: (device_shape, batch_size, head_dim, max_seq_len, rope_theta, rope_scaling_str, use_qk_fused)
def _list_init_test_cases() -> list[pytest.param]:
# fmt: off
return [
# === Fast tests (one per unique model family) ===
# Llama-3.2-1B: head_dim=64, llama3 scaling, theta=500000
pytest.param((1, 1), 1, 64, 8192, 500000.0, "llama3", True, id="1x1-b1-hd64-llama3-fused"),
# Llama-3.1-8B: head_dim=128, llama3 scaling, theta=500000
pytest.param((1, 2), 1, 128, 8192, 500000.0, "llama3", True, id="1x2-b1-hd128-llama3-fused"),
# Mistral-7B: head_dim=128, no scaling, theta=1000000
pytest.param((1, 1), 1, 128, 8192, 1000000.0, "none", True, id="1x1-b1-hd128-none-fused"),
# Llama-3.2-11B: use_qk_fused=False
pytest.param((1, 2), 1, 128, 8192, 500000.0, "llama3", False, id="1x2-b1-hd128-llama3-nofused"),
# T3K Llama-3.3-70B
pytest.param((1, 8), 1, 128, 8192, 500000.0, "llama3", True, id="1x8-b1-hd128-llama3-fused"),
# T3K Qwen2.5-72B: no scaling, theta=1000000
pytest.param((1, 8), 1, 128, 8192, 1000000.0, "none", True, id="1x8-b1-hd128-none-fused"),
# Phi-4 (head_dim=128, no scaling, theta=250000) uses the exact same decode code path as the
# Mistral (1x1) and Qwen2.5-72B (1x8) "none"/hd128 rows above; theta is the only differing
# value and it does not change the code path, so no dedicated row is added here. Phi-4's RoPE
# is exercised end-to-end (real rope, not this reference harness) by the attention-1d module
# test (models/common/tests/modules/attention/test_attention_1d.py, the "*-Phi-4" cases).
# === Slow tests (remaining from CSV) ===
# (1,1) batch=32
pytest.param((1, 1), 32, 64, 2048, 500000.0, "llama3", True, id="1x1-b32-hd64-llama3-fused", marks=_slow),
pytest.param((1, 1), 32, 128, 2048, 500000.0, "llama3", True, id="1x1-b32-hd128-llama3-fused-8B", marks=_slow),
pytest.param((1, 1), 32, 128, 2048, 1000000.0, "none", True, id="1x1-b32-hd128-none-fused-Mistral", marks=_slow),
# (1,1) hd=128, llama3, batch=1 (Llama-3.2-3B, 3.1-8B on N150)
pytest.param((1, 1), 1, 128, 8192, 500000.0, "llama3", True, id="1x1-b1-hd128-llama3-fused-8B", marks=_slow),
pytest.param((1, 1), 1, 128, 1024, 500000.0, "llama3", True, id="1x1-b1-hd128-llama3-fused-seqlen1024", marks=_slow),
pytest.param((1, 1), 32, 128, 1024, 500000.0, "llama3", True, id="1x1-b32-hd128-llama3-fused-seqlen1024", marks=_slow),
pytest.param((1, 1), 1, 128, 32768, 500000.0, "llama3", True, id="1x1-b1-hd128-llama3-fused-seqlen32k", marks=_slow),
# (1,1) hd=128, none (Mistral), additional seq_len
pytest.param((1, 1), 1, 128, 1024, 1000000.0, "none", True, id="1x1-b1-hd128-none-fused-seqlen1024", marks=_slow),
pytest.param((1, 1), 32, 128, 1024, 1000000.0, "none", True, id="1x1-b32-hd128-none-fused-seqlen1024", marks=_slow),
pytest.param((1, 1), 1, 128, 32768, 1000000.0, "none", True, id="1x1-b1-hd128-none-fused-seqlen32k", marks=_slow),
# (1,1) hd=64 additional seq_len
pytest.param((1, 1), 1, 64, 1024, 500000.0, "llama3", True, id="1x1-b1-hd64-llama3-seqlen1024", marks=_slow),
pytest.param((1, 1), 32, 64, 1024, 500000.0, "llama3", True, id="1x1-b32-hd64-llama3-seqlen1024", marks=_slow),
pytest.param((1, 1), 1, 64, 32768, 500000.0, "llama3", True, id="1x1-b1-hd64-llama3-seqlen32k", marks=_slow),
# (1,2) variants
pytest.param((1, 2), 32, 64, 2048, 500000.0, "llama3", True, id="1x2-b32-hd64-llama3-fused", marks=_slow),
pytest.param((1, 2), 32, 128, 2048, 500000.0, "llama3", True, id="1x2-b32-hd128-llama3-fused", marks=_slow),
pytest.param((1, 2), 32, 128, 2048, 1000000.0, "none", True, id="1x2-b32-hd128-none-fused-Mistral", marks=_slow),
pytest.param((1, 2), 32, 128, 2048, 500000.0, "llama3", False, id="1x2-b32-hd128-llama3-nofused-11B", marks=_slow),
pytest.param((1, 2), 1, 128, 1024, 500000.0, "llama3", True, id="1x2-b1-hd128-llama3-fused-seqlen1024", marks=_slow),
pytest.param((1, 2), 32, 128, 1024, 500000.0, "llama3", True, id="1x2-b32-hd128-llama3-fused-seqlen1024", marks=_slow),
pytest.param((1, 2), 1, 128, 32768, 500000.0, "llama3", True, id="1x2-b1-hd128-llama3-fused-seqlen32k", marks=_slow),
pytest.param((1, 2), 1, 128, 8192, 1000000.0, "none", True, id="1x2-b1-hd128-none-fused-Qwen2", marks=_slow),
pytest.param((1, 2), 1, 64, 8192, 500000.0, "llama3", True, id="1x2-b1-hd64-llama3-fused", marks=_slow),
pytest.param((1, 2), 1, 64, 1024, 500000.0, "llama3", True, id="1x2-b1-hd64-llama3-fused-seqlen1024", marks=_slow),
pytest.param((1, 2), 32, 64, 1024, 500000.0, "llama3", True, id="1x2-b32-hd64-llama3-fused-seqlen1024", marks=_slow),
pytest.param((1, 2), 1, 64, 32768, 500000.0, "llama3", True, id="1x2-b1-hd64-llama3-fused-seqlen32k", marks=_slow),
pytest.param((1, 2), 1, 128, 1024, 500000.0, "llama3", False, id="1x2-b1-hd128-llama3-nofused-11B-seqlen1024", marks=_slow),
pytest.param((1, 2), 32, 128, 1024, 500000.0, "llama3", False, id="1x2-b32-hd128-llama3-nofused-11B-seqlen1024", marks=_slow),
pytest.param((1, 2), 1, 128, 32768, 500000.0, "llama3", False, id="1x2-b1-hd128-llama3-nofused-11B-seqlen32k", marks=_slow),
pytest.param((1, 2), 1, 128, 1024, 1000000.0, "none", True, id="1x2-b1-hd128-none-fused-seqlen1024", marks=_slow),
pytest.param((1, 2), 32, 128, 1024, 1000000.0, "none", True, id="1x2-b32-hd128-none-fused-seqlen1024", marks=_slow),
pytest.param((1, 2), 1, 128, 32768, 1000000.0, "none", True, id="1x2-b1-hd128-none-fused-seqlen32k", marks=_slow),
# (1,2) batch=4, 16 from Llama-3.2-11B (nofused)
pytest.param((1, 2), 16, 128, 512, 500000.0, "llama3", False, id="1x2-b16-hd128-llama3-nofused-11B", marks=_slow),
pytest.param((1, 2), 4, 128, 512, 500000.0, "llama3", False, id="1x2-b4-hd128-llama3-nofused-11B", marks=_slow),
# (1,8) variants — hd=64
pytest.param((1, 8), 1, 64, 8192, 500000.0, "llama3", True, id="1x8-b1-hd64-llama3-fused", marks=_slow),
pytest.param((1, 8), 32, 64, 2048, 500000.0, "llama3", True, id="1x8-b32-hd64-llama3-fused", marks=_slow),
pytest.param((1, 8), 1, 64, 1024, 500000.0, "llama3", True, id="1x8-b1-hd64-llama3-fused-seqlen1024", marks=_slow),
pytest.param((1, 8), 32, 64, 1024, 500000.0, "llama3", True, id="1x8-b32-hd64-llama3-fused-seqlen1024", marks=_slow),
pytest.param((1, 8), 1, 64, 32768, 500000.0, "llama3", True, id="1x8-b1-hd64-llama3-fused-seqlen32k", marks=_slow),
# (1,8) variants — hd=128, llama3, fused
pytest.param((1, 8), 32, 128, 2048, 500000.0, "llama3", True, id="1x8-b32-hd128-llama3-fused-8B", marks=_slow),
pytest.param((1, 8), 1, 128, 1024, 500000.0, "llama3", True, id="1x8-b1-hd128-llama3-fused-seqlen1024", marks=_slow),
pytest.param((1, 8), 32, 128, 1024, 500000.0, "llama3", True, id="1x8-b32-hd128-llama3-fused-seqlen1024", marks=_slow),
pytest.param((1, 8), 1, 128, 32768, 500000.0, "llama3", True, id="1x8-b1-hd128-llama3-fused-seqlen32k", marks=_slow),
# (1,8) variants — hd=128, llama3, nofused (11B)
pytest.param((1, 8), 1, 128, 8192, 500000.0, "llama3", False, id="1x8-b1-hd128-llama3-nofused-11B", marks=_slow),
pytest.param((1, 8), 32, 128, 2048, 500000.0, "llama3", False, id="1x8-b32-hd128-llama3-nofused-11B", marks=_slow),
pytest.param((1, 8), 1, 128, 1024, 500000.0, "llama3", False, id="1x8-b1-hd128-llama3-nofused-11B-seqlen1024", marks=_slow),
pytest.param((1, 8), 32, 128, 1024, 500000.0, "llama3", False, id="1x8-b32-hd128-llama3-nofused-11B-seqlen1024", marks=_slow),
pytest.param((1, 8), 1, 128, 32768, 500000.0, "llama3", False, id="1x8-b1-hd128-llama3-nofused-11B-seqlen32k", marks=_slow),
# (1,8) variants — hd=128, none (Qwen/Mistral/Mixtral)
pytest.param((1, 8), 32, 128, 2048, 1000000.0, "none", True, id="1x8-b32-hd128-none-fused-Qwen72B", marks=_slow),
pytest.param((1, 8), 1, 128, 1024, 1000000.0, "none", True, id="1x8-b1-hd128-none-fused-seqlen1024", marks=_slow),
pytest.param((1, 8), 32, 128, 1024, 1000000.0, "none", True, id="1x8-b32-hd128-none-fused-seqlen1024", marks=_slow),
pytest.param((1, 8), 1, 128, 32768, 1000000.0, "none", True, id="1x8-b1-hd128-none-fused-seqlen32k", marks=_slow),
# (1,8) batch=4 from Llama-3.2-11B (nofused)
pytest.param((1, 8), 4, 128, 512, 500000.0, "llama3", False, id="1x8-b4-hd128-llama3-nofused-11B", marks=_slow),
pytest.param((1, 8), 32, 128, 512, 500000.0, "llama3", False, id="1x8-b32-hd128-llama3-nofused-11B-seqlen512", marks=_slow),
# Llama-3.2-90B (from api CSV)
pytest.param((1, 8), 1, 128, 512, 500000.0, "llama3", False, id="1x8-b1-hd128-llama3-nofused-90B", marks=_slow),
]
# fmt: on
@pytest.mark.parametrize(
"ttnn_mesh_device",
[(1, 1), (1, 2), (1, 8)],
ids=["1x1", "1x2", "1x8"],
indirect=True,
)
@pytest.mark.parametrize(
"mesh_shape,batch_size,head_dim,max_seq_len,rope_theta,rope_scaling_str,use_qk_fused",
_list_init_test_cases(),
)
def test_rope_1d_decode_forward_vs_reference(
ttnn_mesh_device: ttnn.MeshDevice,
mesh_shape,
batch_size,
head_dim,
max_seq_len,
rope_theta,
rope_scaling_str,
use_qk_fused,
):
"""
Test RotarySetup1D initialization and all API methods.
Verifies:
1. Construction succeeds with given parameters
2. get_both_trans_mats() returns valid tensors with correct PCC
3. get_rot_idxs() produces correct shape
4. get_rot_mats() produces cos/sin with correct shapes and PCC vs reference
5. decode_forward() with ttnn.Tensor input matches torch-input path
"""
scaling = Llama3Scaling() if rope_scaling_str == "llama3" else None
cos_torch, sin_torch = _rope_cos_sin(head_dim=head_dim, max_seq_len=max_seq_len, theta=rope_theta, scaling=scaling)
scaling_tag = "llama3" if scaling else "none"
tag = f"theta{rope_theta}_{scaling_tag}"
cache_dir = Path(os.getenv("TT_CACHE_PATH", "model_cache/rope_1d"))
cos_lw = LazyWeight(source=cos_torch, device=ttnn_mesh_device, cache_dir_weight_name=(cache_dir, f"cos_{tag}"))
sin_lw = LazyWeight(source=sin_torch, device=ttnn_mesh_device, cache_dir_weight_name=(cache_dir, f"sin_{tag}"))
# Use __init__ (happy path) when defaults suffice, from_config (power path) otherwise.
# This exercises both constructors across the test matrix.
if not use_qk_fused:
rope = RotarySetup1D(cos_lw, sin_lw, max_batch_size=batch_size)
else:
config = Rope1DConfig(
cos_matrix=cos_lw,
sin_matrix=sin_lw,
max_batch_size=batch_size,
head_dim=head_dim,
device=ttnn_mesh_device,
use_qk_fused=use_qk_fused,
)
rope = RotarySetup1D.from_config(config)
# --- get_both_trans_mats ---
trans_mats = rope.get_both_trans_mats()
assert "decode" in trans_mats
assert "prefill" in trans_mats
# Prefill trans mat PCC: the rotary_embedding_llama op requires a single
# TILE_SIZE x TILE_SIZE tile (TT_FATAL on any other shape), so the module
# builds the prefill trans-mat at TILE_SIZE, not head_dim. See rope_1d.py.
prefill_ref = get_rot_transformation_mat(dhead=ttnn.TILE_SIZE) # [1, 1, 32, 32]
prefill_tt = to_torch_auto_compose(trans_mats["prefill"])
prefill_tt_trimmed = prefill_tt[:1, :1, : prefill_ref.shape[2], : prefill_ref.shape[3]]
pcc_prefill, msg_prefill = comp_pcc(prefill_ref.to(torch.bfloat16), prefill_tt_trimmed.to(torch.bfloat16), 0.9999)
assert pcc_prefill, f"prefill trans_mat PCC failed: {msg_prefill}"
# Decode trans mat PCC
effective_batch = batch_size * 2 if use_qk_fused else batch_size
decode_ref = get_rot_transformation_mat(dhead=ttnn.TILE_SIZE).repeat(1, 1, effective_batch, 1)
decode_tt = to_torch_auto_compose(trans_mats["decode"])
decode_tt_trimmed = decode_tt[:1, :1, : decode_ref.shape[2], : decode_ref.shape[3]]
pcc_decode, msg_decode = comp_pcc(decode_ref.to(torch.bfloat16), decode_tt_trimmed.to(torch.bfloat16), 0.9999)
assert pcc_decode, f"decode trans_mat PCC failed: {msg_decode}"
# --- PCC check: cos/sin vs torch reference ---
# Use non-zero positions to avoid sin(0)=0 (PCC undefined for all-zero tensors)
pcc_position_idxs = torch.arange(42, 42 + batch_size)
# TTTv2 API: prepare_rot_idxs → forward
rot_idxs = prepare_rot_idxs(rope.config, pcc_position_idxs, on_host=True)
pcc_rot_mats = rope.decode_forward(rot_idxs)
assert len(pcc_rot_mats) == 2
cos_torch_ref, sin_torch_ref = _rope_cos_sin(
head_dim=head_dim, max_seq_len=max_seq_len, theta=rope_theta, scaling=scaling
)
cos_tt = to_torch_auto_compose(pcc_rot_mats[0])
sin_tt = to_torch_auto_compose(pcc_rot_mats[1])
# Build expected cos/sin from torch reference for chosen positions
if use_qk_fused:
ref_positions = list(range(42, 42 + batch_size)) * 2
else:
ref_positions = list(range(42, 42 + batch_size))
expected_cos = cos_torch_ref[:, :, ref_positions, :] # [1, 1, effective_batch, head_dim]
expected_sin = sin_torch_ref[:, :, ref_positions, :]
# Reshape TT output: [1, batch, TILE_SIZE, head_dim] → [1, 1, batch*TILE_SIZE, head_dim]
cos_tt_flat = cos_tt.reshape(1, 1, -1, head_dim)
sin_tt_flat = sin_tt.reshape(1, 1, -1, head_dim)
# Compare first effective_batch rows (rest is padding from tile alignment)
cos_tt_trimmed = cos_tt_flat[:, :, :effective_batch, :]
sin_tt_trimmed = sin_tt_flat[:, :, :effective_batch, :]
pcc_cos, msg_cos = comp_pcc(expected_cos.to(torch.bfloat16), cos_tt_trimmed.to(torch.bfloat16), 0.999)
pcc_sin, msg_sin = comp_pcc(expected_sin.to(torch.bfloat16), sin_tt_trimmed.to(torch.bfloat16), 0.999)
logger.info(f"cos PCC: {msg_cos}")
logger.info(f"sin PCC: {msg_sin}")
assert pcc_cos, f"cos PCC failed: {msg_cos}"
assert pcc_sin, f"sin PCC failed: {msg_sin}"
logger.info(
f"RotarySetup1D: PASSED for mesh={mesh_shape}, batch={batch_size}, "
f"head_dim={head_dim}, max_seq_len={max_seq_len}, rope_theta={rope_theta}, "
f"scaling={rope_scaling_str}, fused={use_qk_fused}"
)
# ============================================================================
# Standalone helper test: prepare_rot_idxs
# ============================================================================
@pytest.mark.parametrize(
"ttnn_mesh_device",
[(1, 1), (1, 2)],
ids=["1x1", "1x2"],
indirect=True,
)
@pytest.mark.parametrize(
"batch_size,use_qk_fused",
[
pytest.param(1, False, id="b1-nofused"),
pytest.param(1, True, id="b1-fused"),
pytest.param(32, True, id="b32-fused"),
],
)
def test_prepare_rot_idxs(
ttnn_mesh_device: ttnn.MeshDevice,
batch_size,
use_qk_fused,
):
"""Test prepare_rot_idxs standalone helper produces correct ttnn tensor."""
cos_torch, sin_torch = _rope_cos_sin(head_dim=128, max_seq_len=8192, theta=500000.0, scaling=Llama3Scaling())
tag = "theta500000.0_llama3"
cache_dir = Path(os.getenv("TT_CACHE_PATH", "model_cache/rope_1d"))
cos_lw = LazyWeight(source=cos_torch, device=ttnn_mesh_device, cache_dir_weight_name=(cache_dir, f"cos_{tag}"))
sin_lw = LazyWeight(source=sin_torch, device=ttnn_mesh_device, cache_dir_weight_name=(cache_dir, f"sin_{tag}"))
config = Rope1DConfig(
cos_matrix=cos_lw,
sin_matrix=sin_lw,
max_batch_size=batch_size,
head_dim=128,
device=ttnn_mesh_device,
use_qk_fused=use_qk_fused,
)
rope = RotarySetup1D.from_config(config)
position_idxs = torch.arange(batch_size)
# Test on-device path
rot_idxs = prepare_rot_idxs(rope.config, position_idxs, on_host=False)
assert isinstance(rot_idxs, ttnn.Tensor)
# Test on-host path
rot_idxs_host = prepare_rot_idxs(rope.config, position_idxs, on_host=True)
assert isinstance(rot_idxs_host, ttnn.Tensor)
# Both paths should produce usable tensors for decode_forward()
cos_sin_device = rope.decode_forward(rot_idxs)
assert len(cos_sin_device) == 2
cos_sin_host = rope.get_rot_mats(rot_idxs_host)
assert len(cos_sin_host) == 2
# PCC: both paths should produce identical results
cos_d = to_torch_auto_compose(cos_sin_device[0])
cos_h = to_torch_auto_compose(cos_sin_host[0])
pcc_ok, msg = comp_pcc(cos_d, cos_h, 0.9999)
assert pcc_ok, f"on-device vs on-host cos mismatch: {msg}"
# ============================================================================
# forward() dispatcher tests
# ============================================================================
@pytest.mark.parametrize(
"ttnn_mesh_device",
[(1, 1)],
ids=["1x1"],
indirect=True,
)
def test_forward_dispatch_decode(ttnn_mesh_device: ttnn.MeshDevice):
"""Test that forward(mode='decode', ...) delegates to decode_forward."""
cos_torch, sin_torch = _rope_cos_sin(head_dim=128, max_seq_len=8192, theta=500000.0, scaling=Llama3Scaling())
cos_lw = LazyWeight(source=cos_torch, device=ttnn_mesh_device)
sin_lw = LazyWeight(source=sin_torch, device=ttnn_mesh_device)
rope = RotarySetup1D(cos_lw, sin_lw, max_batch_size=1)
position_idxs = torch.tensor([42])
rot_idxs = prepare_rot_idxs(rope.config, position_idxs)
via_forward = rope.forward(mode="decode", rot_idxs=rot_idxs)
via_direct = rope.decode_forward(rot_idxs)
cos_fwd = to_torch_auto_compose(via_forward[0])
cos_dir = to_torch_auto_compose(via_direct[0])
pcc_ok, msg = comp_pcc(cos_fwd, cos_dir, 0.9999)
assert pcc_ok, f"forward(decode) vs decode_forward mismatch: {msg}"
@pytest.mark.parametrize(
"ttnn_mesh_device",
[(1, 1)],
ids=["1x1"],
indirect=True,
)
def test_forward_dispatch_prefill(ttnn_mesh_device: ttnn.MeshDevice):
"""Test that forward(mode='prefill', ...) delegates to prefill_forward."""
cos_torch, sin_torch = _rope_cos_sin(head_dim=128, max_seq_len=8192, theta=500000.0, scaling=Llama3Scaling())
cos_lw = LazyWeight(source=cos_torch, device=ttnn_mesh_device)
sin_lw = LazyWeight(source=sin_torch, device=ttnn_mesh_device)
rope = RotarySetup1D(cos_lw, sin_lw, max_batch_size=1)
via_forward = rope.forward(mode="prefill", start_pos=0, seq_len=128)
via_direct = rope.prefill_forward(start_pos=0, seq_len=128)
cos_fwd = to_torch_auto_compose(via_forward[0])
cos_dir = to_torch_auto_compose(via_direct[0])
pcc_ok, msg = comp_pcc(cos_fwd, cos_dir, 0.9999)
assert pcc_ok, f"forward(prefill) vs prefill_forward mismatch: {msg}"
# ============================================================================
# prefill_forward tests
# ============================================================================
def _list_prefill_test_cases() -> list[pytest.param]:
# fmt: off
return [
# === Fast tests (one per model family + edge cases) ===
# Llama-3.2-1B: hd64, llama3
pytest.param(64, 8192, 500000.0, "llama3", 0, 128, None, id="hd64-llama3-seq8k-start0-S128"),
# Llama-3.2-3B / 3.1-8B: hd128, llama3
pytest.param(128, 8192, 500000.0, "llama3", 0, 128, None, id="hd128-llama3-seq8k-start0-S128"),
# Mistral / Qwen: hd128, no scaling
pytest.param(128, 8192, 1000000.0, "none", 0, 128, None, id="hd128-none-seq8k-start0-S128"),
# Chunked prefill (llama3): nonzero start_pos
pytest.param(128, 32768, 500000.0, "llama3", 4096, 4096, None, id="hd128-llama3-seq32k-start4k-S4k"),
# Chunked prefill (none): nonzero start_pos
pytest.param(128, 32768, 1000000.0, "none", 4096, 4096, None, id="hd128-none-seq32k-start4k-S4k"),
# SDPA padding (not collected — manual edge case)
pytest.param(128, 8192, 500000.0, "llama3", 0, 100, 128, id="hd128-llama3-seq8k-start0-S100-pad128"),
# === Slow tests (remaining from CSV) ===
# --- hd=64, llama3, theta=500k ---
pytest.param(64, 1024, 500000.0, "llama3", 0, 128, None, id="hd64-llama3-seq1k-start0-S128", marks=_slow),
pytest.param(64, 1024, 500000.0, "llama3", 0, 1024, None, id="hd64-llama3-seq1k-start0-S1024", marks=_slow),
pytest.param(64, 2048, 500000.0, "llama3", 0, 128, None, id="hd64-llama3-seq2k-start0-S128", marks=_slow),
pytest.param(64, 2048, 500000.0, "llama3", 0, 1024, None, id="hd64-llama3-seq2k-start0-S1024", marks=_slow),
pytest.param(64, 2048, 500000.0, "llama3", 0, 2048, None, id="hd64-llama3-seq2k-start0-S2048", marks=_slow),
pytest.param(64, 8192, 500000.0, "llama3", 0, 1024, None, id="hd64-llama3-seq8k-start0-S1024", marks=_slow),
pytest.param(64, 8192, 500000.0, "llama3", 0, 2048, None, id="hd64-llama3-seq8k-start0-S2048", marks=_slow),
pytest.param(64, 8192, 500000.0, "llama3", 0, 4096, None, id="hd64-llama3-seq8k-start0-S4096", marks=_slow),
pytest.param(64, 8192, 500000.0, "llama3", 0, 8192, None, id="hd64-llama3-seq8k-start0-S8192", marks=_slow),
pytest.param(64, 32768, 500000.0, "llama3", 0, 128, None, id="hd64-llama3-seq32k-start0-S128", marks=_slow),
pytest.param(64, 32768, 500000.0, "llama3", 0, 1024, None, id="hd64-llama3-seq32k-start0-S1024", marks=_slow),
pytest.param(64, 32768, 500000.0, "llama3", 0, 2048, None, id="hd64-llama3-seq32k-start0-S2048", marks=_slow),
pytest.param(64, 32768, 500000.0, "llama3", 0, 4096, None, id="hd64-llama3-seq32k-start0-S4096", marks=_slow),
pytest.param(64, 32768, 500000.0, "llama3", 0, 8192, None, id="hd64-llama3-seq32k-start0-S8192", marks=_slow),
pytest.param(64, 32768, 500000.0, "llama3", 0, 16384, None, id="hd64-llama3-seq32k-start0-S16384", marks=_slow),
pytest.param(64, 32768, 500000.0, "llama3", 0, 32768, None, id="hd64-llama3-seq32k-start0-S32768", marks=_slow),
# --- hd=128, llama3, theta=500k ---
pytest.param(128, 1024, 500000.0, "llama3", 0, 128, None, id="hd128-llama3-seq1k-start0-S128", marks=_slow),
pytest.param(128, 1024, 500000.0, "llama3", 0, 1024, None, id="hd128-llama3-seq1k-start0-S1024", marks=_slow),
pytest.param(128, 2048, 500000.0, "llama3", 0, 128, None, id="hd128-llama3-seq2k-start0-S128", marks=_slow),
pytest.param(128, 2048, 500000.0, "llama3", 0, 1024, None, id="hd128-llama3-seq2k-start0-S1024", marks=_slow),
pytest.param(128, 2048, 500000.0, "llama3", 0, 2048, None, id="hd128-llama3-seq2k-start0-S2048", marks=_slow),
pytest.param(128, 8192, 500000.0, "llama3", 0, 1024, None, id="hd128-llama3-seq8k-start0-S1024", marks=_slow),
pytest.param(128, 8192, 500000.0, "llama3", 0, 2048, None, id="hd128-llama3-seq8k-start0-S2048", marks=_slow),
pytest.param(128, 8192, 500000.0, "llama3", 0, 4096, None, id="hd128-llama3-seq8k-start0-S4096", marks=_slow),
pytest.param(128, 8192, 500000.0, "llama3", 0, 8192, None, id="hd128-llama3-seq8k-start0-S8192", marks=_slow),
pytest.param(128, 32768, 500000.0, "llama3", 0, 128, None, id="hd128-llama3-seq32k-start0-S128", marks=_slow),
pytest.param(128, 32768, 500000.0, "llama3", 0, 1024, None, id="hd128-llama3-seq32k-start0-S1024", marks=_slow),
pytest.param(128, 32768, 500000.0, "llama3", 0, 2048, None, id="hd128-llama3-seq32k-start0-S2048", marks=_slow),
pytest.param(128, 32768, 500000.0, "llama3", 0, 4096, None, id="hd128-llama3-seq32k-start0-S4096", marks=_slow),
pytest.param(128, 32768, 500000.0, "llama3", 0, 8192, None, id="hd128-llama3-seq32k-start0-S8192", marks=_slow),
pytest.param(128, 32768, 500000.0, "llama3", 0, 16384, None, id="hd128-llama3-seq32k-start0-S16384", marks=_slow),
pytest.param(128, 32768, 500000.0, "llama3", 0, 32768, None, id="hd128-llama3-seq32k-start0-S32768", marks=_slow),
# Chunked prefill (Llama-3.1-8B, 3.3-70B)
pytest.param(128, 32768, 500000.0, "llama3", 8192, 4096, None, id="hd128-llama3-seq32k-start8k-S4k", marks=_slow),
pytest.param(128, 32768, 500000.0, "llama3", 8192, 8192, None, id="hd128-llama3-seq32k-start8k-S8k", marks=_slow),
pytest.param(128, 32768, 500000.0, "llama3", 12288, 4096, None, id="hd128-llama3-seq32k-start12k-S4k", marks=_slow),
# --- hd=128, none, theta=1000k (Mistral / Qwen) ---
pytest.param(128, 1024, 1000000.0, "none", 0, 128, None, id="hd128-none-seq1k-start0-S128", marks=_slow),
pytest.param(128, 1024, 1000000.0, "none", 0, 1024, None, id="hd128-none-seq1k-start0-S1024", marks=_slow),
pytest.param(128, 2048, 1000000.0, "none", 0, 128, None, id="hd128-none-seq2k-start0-S128", marks=_slow),
pytest.param(128, 2048, 1000000.0, "none", 0, 1024, None, id="hd128-none-seq2k-start0-S1024", marks=_slow),
pytest.param(128, 2048, 1000000.0, "none", 0, 2048, None, id="hd128-none-seq2k-start0-S2048", marks=_slow),
pytest.param(128, 8192, 1000000.0, "none", 0, 1024, None, id="hd128-none-seq8k-start0-S1024", marks=_slow),
pytest.param(128, 8192, 1000000.0, "none", 0, 2048, None, id="hd128-none-seq8k-start0-S2048", marks=_slow),
pytest.param(128, 8192, 1000000.0, "none", 0, 4096, None, id="hd128-none-seq8k-start0-S4096", marks=_slow),
pytest.param(128, 8192, 1000000.0, "none", 0, 8192, None, id="hd128-none-seq8k-start0-S8192", marks=_slow),
pytest.param(128, 32768, 1000000.0, "none", 0, 128, None, id="hd128-none-seq32k-start0-S128", marks=_slow),
pytest.param(128, 32768, 1000000.0, "none", 0, 1024, None, id="hd128-none-seq32k-start0-S1024", marks=_slow),
pytest.param(128, 32768, 1000000.0, "none", 0, 2048, None, id="hd128-none-seq32k-start0-S2048", marks=_slow),
pytest.param(128, 32768, 1000000.0, "none", 0, 4096, None, id="hd128-none-seq32k-start0-S4096", marks=_slow),
pytest.param(128, 32768, 1000000.0, "none", 0, 8192, None, id="hd128-none-seq32k-start0-S8192", marks=_slow),
pytest.param(128, 32768, 1000000.0, "none", 0, 16384, None, id="hd128-none-seq32k-start0-S16384", marks=_slow),
pytest.param(128, 32768, 1000000.0, "none", 0, 32768, None, id="hd128-none-seq32k-start0-S32768", marks=_slow),
# Chunked prefill (Mistral-7B, Qwen2.5-Coder-32B)
pytest.param(128, 32768, 1000000.0, "none", 8192, 4096, None, id="hd128-none-seq32k-start8k-S4k", marks=_slow),
pytest.param(128, 32768, 1000000.0, "none", 12288, 4096, None, id="hd128-none-seq32k-start12k-S4k", marks=_slow),
pytest.param(128, 32768, 1000000.0, "none", 16384, 4096, None, id="hd128-none-seq32k-start16k-S4k", marks=_slow),
# --- SDPA padding edge cases (manual — not collected, but exercises pad_to path) ---
pytest.param(128, 8192, 500000.0, "llama3", 32, 64, 128, id="hd128-llama3-pad-start32-S64-pad128", marks=_slow),
pytest.param(128, 8192, 500000.0, "llama3", 0, 128, 128, id="hd128-llama3-pad-noop-S128-pad128", marks=_slow),
]
# fmt: on
@pytest.mark.parametrize(
"ttnn_mesh_device",
[(1, 1), (1, 2), (1, 8)],
ids=["1x1", "1x2", "1x8"],
indirect=True,
)
@pytest.mark.parametrize(
"head_dim,max_seq_len,rope_theta,rope_scaling_str,start_pos,prefill_seq_len,pad_to",
_list_prefill_test_cases(),
)
def test_rope_1d_prefill_forward_vs_reference(
ttnn_mesh_device: ttnn.MeshDevice,
head_dim,
max_seq_len,
rope_theta,
rope_scaling_str,
start_pos,
prefill_seq_len,
pad_to,
):
"""
Test RotarySetup1D.prefill_forward() returns correct cos/sin slices
by comparing against the torch reference tables.
"""
scaling = Llama3Scaling() if rope_scaling_str == "llama3" else None
cos_torch, sin_torch = _rope_cos_sin(head_dim=head_dim, max_seq_len=max_seq_len, theta=rope_theta, scaling=scaling)
scaling_tag = "llama3" if scaling else "none"
tag = f"theta{rope_theta}_{scaling_tag}"
cache_dir = Path(os.getenv("TT_CACHE_PATH", "model_cache/rope_1d"))
cos_lw = LazyWeight(source=cos_torch, device=ttnn_mesh_device, cache_dir_weight_name=(cache_dir, f"cos_{tag}"))
sin_lw = LazyWeight(source=sin_torch, device=ttnn_mesh_device, cache_dir_weight_name=(cache_dir, f"sin_{tag}"))
rope = RotarySetup1D(cos_lw, sin_lw, max_batch_size=1)
# Call prefill_forward
cos_sin = rope.prefill_forward(start_pos=start_pos, seq_len=prefill_seq_len, pad_to=pad_to)
assert len(cos_sin) == 2
cos_tt = to_torch_auto_compose(cos_sin[0])
sin_tt = to_torch_auto_compose(cos_sin[1])
# Expected output shape
expected_seq_dim = pad_to if (pad_to is not None and pad_to > prefill_seq_len) else prefill_seq_len
assert cos_tt.shape[2] >= expected_seq_dim, f"cos seq dim {cos_tt.shape[2]} < expected {expected_seq_dim}"
assert sin_tt.shape[2] >= expected_seq_dim, f"sin seq dim {sin_tt.shape[2]} < expected {expected_seq_dim}"
# PCC: compare the non-padded region against torch reference
end_pos = start_pos + prefill_seq_len
expected_cos = cos_torch[:, :, start_pos:end_pos, :]
expected_sin = sin_torch[:, :, start_pos:end_pos, :]
# Trim TT output to the non-padded region for comparison
cos_tt_trimmed = cos_tt[:1, :1, :prefill_seq_len, :head_dim]
sin_tt_trimmed = sin_tt[:1, :1, :prefill_seq_len, :head_dim]
pcc_cos, msg_cos = comp_pcc(expected_cos.to(torch.bfloat16), cos_tt_trimmed.to(torch.bfloat16), 0.999)
pcc_sin, msg_sin = comp_pcc(expected_sin.to(torch.bfloat16), sin_tt_trimmed.to(torch.bfloat16), 0.999)
logger.info(f"prefill_forward cos PCC: {msg_cos}")
logger.info(f"prefill_forward sin PCC: {msg_sin}")
assert pcc_cos, f"prefill cos PCC failed: {msg_cos}"
assert pcc_sin, f"prefill sin PCC failed: {msg_sin}"
# If padded, verify the padded region is zeros
if pad_to is not None and pad_to > prefill_seq_len:
cos_pad_region = (
cos_tt_trimmed[:1, :1, prefill_seq_len:pad_to, :head_dim] if cos_tt.shape[2] >= pad_to else None
)
if cos_pad_region is not None:
# Padded region is from the full output
cos_full_pad = cos_tt[:1, :1, prefill_seq_len:pad_to, :head_dim]
sin_full_pad = sin_tt[:1, :1, prefill_seq_len:pad_to, :head_dim]
assert torch.allclose(cos_full_pad, torch.zeros_like(cos_full_pad), atol=1e-3), "cos pad region not zero"
assert torch.allclose(sin_full_pad, torch.zeros_like(sin_full_pad), atol=1e-3), "sin pad region not zero"
logger.info(
f"prefill_forward: PASSED for head_dim={head_dim}, start_pos={start_pos}, "
f"seq_len={prefill_seq_len}, pad_to={pad_to}"
)
@pytest.mark.parametrize(
"ttnn_mesh_device",
[(1, 1)],
ids=["1x1"],
indirect=True,
)
def test_prefill_forward_bounds_check(ttnn_mesh_device: ttnn.MeshDevice, expect_error):
"""Test that prefill_forward raises when the requested range exceeds the table."""
cos_torch, sin_torch = _rope_cos_sin(head_dim=128, max_seq_len=256, theta=500000.0)
cos_lw = LazyWeight(source=cos_torch, device=ttnn_mesh_device)
sin_lw = LazyWeight(source=sin_torch, device=ttnn_mesh_device)
rope = RotarySetup1D(cos_lw, sin_lw, max_batch_size=1)
# Should work: exactly at the boundary
cos_sin = rope.prefill_forward(start_pos=0, seq_len=256)
assert len(cos_sin) == 2
# Should fail: exceeds table
with expect_error(AssertionError, "exceeds cos/sin table length"):
rope.prefill_forward(start_pos=0, seq_len=257)
with expect_error(AssertionError, "exceeds cos/sin table length"):
rope.prefill_forward(start_pos=200, seq_len=128)
# ============================================================================
# from_model_args backward compatibility test
# ============================================================================
@pytest.mark.parametrize(
"ttnn_mesh_device",
[(1, 1), (1, 2), (1, 8)],
ids=["1x1", "1x2", "1x8"],
indirect=True,
)
def test_rope_1d_vs_reference_from_model_args(ttnn_mesh_device: ttnn.MeshDevice):
"""
Test that RotarySetup1D.from_model_args produces numerically identical
rotation matrices compared to TTTv1 RotarySetup built with the same args.
"""
from models.tt_transformers.tt.model_config import ModelArgs
from models.tt_transformers.tt.rope import RotarySetup as TTTv1RotarySetup
model_args = ModelArgs(ttnn_mesh_device, max_batch_size=1, max_seq_len=128, cache_hf=True)
model_args.n_layers = 1
if model_args.is_galaxy:
pytest.skip("RotarySetup1D test only runs on non-TG devices")
# Build TTTv2 via from_model_args
rope_v2 = RotarySetup1D.from_model_args(
device=ttnn_mesh_device,
args=model_args,
model_name=model_args.model_name,
)
# Build TTTv1 reference with same params
from models.tt_transformers.tt.common import rope_scaling_model_factory
rope_scaling = None
if hasattr(model_args, "rope_scaling_params") and model_args.rope_scaling_params is not None:
rope_scaling = rope_scaling_model_factory(
model_args.rope_scaling_params, getattr(model_args, "original_max_context_len", None)
)
rope_v1 = TTTv1RotarySetup(
device=ttnn_mesh_device,
batch_size=model_args.max_batch_size,
head_dim=model_args.head_dim,
max_seq_len=model_args.max_seq_len,
rope_theta=model_args.rope_theta,
rope_scaling=rope_scaling,
use_qk_fused=getattr(model_args, "use_qk_fused", False),
datatype=ttnn.bfloat16,
)
# --- Backward-compat wrappers: get_rot_idxs, get_rot_mats, return_rot_idxs ---
position_idxs = torch.arange(42, 42 + model_args.max_batch_size)
# get_rot_idxs
rot_idxs = rope_v2.get_rot_idxs(position_idxs, on_host=True)
# get_rot_mats (torch input)
v2_cos_sin = rope_v2.get_rot_mats(position_idxs)
v1_cos_sin = rope_v1.get_rot_mats(position_idxs)
# get_rot_mats with return_rot_idxs
v2_mats_and_idxs = rope_v2.get_rot_mats(position_idxs, return_rot_idxs=True)
assert len(v2_mats_and_idxs) == 2
v2_rot_mats_2, v2_rot_idxs_2 = v2_mats_and_idxs
assert len(v2_rot_mats_2) == 2
# get_rot_mats via ttnn rot_idxs (production calling pattern)
v2_cos_sin_via_ttnn = rope_v2.get_rot_mats(rot_idxs)
# PCC: v2 vs v1
v2_cos_torch = to_torch_auto_compose(v2_cos_sin[0])
v1_cos_torch = to_torch_auto_compose(v1_cos_sin[0])
v2_sin_torch = to_torch_auto_compose(v2_cos_sin[1])
v1_sin_torch = to_torch_auto_compose(v1_cos_sin[1])
pcc_cos, msg_cos = comp_pcc(v1_cos_torch, v2_cos_torch, 0.9999)
pcc_sin, msg_sin = comp_pcc(v1_sin_torch, v2_sin_torch, 0.9999)
logger.info(f"from_model_args cos PCC: {msg_cos}")
logger.info(f"from_model_args sin PCC: {msg_sin}")
assert pcc_cos, f"from_model_args cos mismatch: {msg_cos}"
assert pcc_sin, f"from_model_args sin mismatch: {msg_sin}"
# PCC: torch-input vs ttnn-input paths should match
cos_via_ttnn = to_torch_auto_compose(v2_cos_sin_via_ttnn[0])
pcc_path, msg_path = comp_pcc(v2_cos_torch, cos_via_ttnn, 0.9999)
assert pcc_path, f"torch vs ttnn input path mismatch: {msg_path}"
# PCC check: decode transformation matrix
v2_trans = rope_v2.get_both_trans_mats()
v1_trans = rope_v1.get_both_trans_mats()
v2_decode = to_torch_auto_compose(v2_trans["decode"])
v1_decode = to_torch_auto_compose(v1_trans["decode"])
pcc_decode, msg_decode = comp_pcc(v1_decode, v2_decode, 0.9999)
logger.info(f"from_model_args trans_mat[decode] PCC: {msg_decode}")
assert pcc_decode, f"from_model_args trans_mat[decode] mismatch: {msg_decode}"
# PCC check: prefill transformation matrix (overlapping region)
v2_prefill = to_torch_auto_compose(v2_trans["prefill"])
v1_prefill = to_torch_auto_compose(v1_trans["prefill"])
min_h = min(v1_prefill.shape[-2], v2_prefill.shape[-2])
min_w = min(v1_prefill.shape[-1], v2_prefill.shape[-1])
v1_prefill_trimmed = v1_prefill[:1, :1, :min_h, :min_w]
v2_prefill_trimmed = v2_prefill[:1, :1, :min_h, :min_w]
pcc_prefill, msg_prefill = comp_pcc(v1_prefill_trimmed, v2_prefill_trimmed, 0.9999)
logger.info(f"from_model_args trans_mat[prefill] PCC (overlapping): {msg_prefill}")
assert pcc_prefill, f"from_model_args trans_mat[prefill] mismatch: {msg_prefill}"
logger.info(f"RotarySetup1D.from_model_args vs TTTv1: PASSED for {model_args.model_name}")
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