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# SPDX-License-Identifier: Apache-2.0
"""
Integration tests for the runtime-LoRA adapter loader.
Builds a tiny WanTransformer3DModel (1 block) with ``lora_enabled=True``,
writes a synthetic LoRA safetensors file, runs ``load_adapter_into``, and
checks that:
- every LoRA-aware Linear in the block got an adapter registered
- ``bind_active`` marks the right modules active across all paths
- ``unbind_active`` clears the active state
The forward math is covered by ``test_lora_variants.py``; this file
exercises the loader's key-mapping logic and the pipeline-mixin bind
flow against the default (``fuse``) mode.
Run with:
pytest -xvs models/tt_dit/experimental/tests/test_adapter_loader.py
"""
from __future__ import annotations
import tempfile
from pathlib import Path
import pytest
import torch
import ttnn
from models.tt_dit.experimental.lora.adapter_loader import load_adapter_into
from models.tt_dit.layers.lora import LoRAMixin
from models.tt_dit.models.transformers.wan2_2.transformer_wan import WanTransformer3DModel
from models.tt_dit.parallel.config import DiTParallelConfig, ParallelFactor
from models.tt_dit.parallel.manager import CCLManager
def _save_synthetic_adapter(
path: Path,
*,
num_blocks: int = 1,
dim: int = 128,
ffn_dim: int = 256,
rank: int = 8,
) -> None:
"""Write a fake LoRA safetensors that covers all 6 LoRA-targeted Linears
per block, using the lightx2v naming convention."""
from safetensors.torch import save_file
state: dict[str, torch.Tensor] = {}
dtype = torch.bfloat16
def add_pair(base: str, in_dim: int, out_dim: int):
state[f"{base}.lora_A.weight"] = torch.randn(rank, in_dim, dtype=dtype) * 0.02
state[f"{base}.lora_B.weight"] = torch.randn(out_dim, rank, dtype=dtype) * 0.02
for i in range(num_blocks):
# self-attn Q, K, V, O
add_pair(f"blocks.{i}.self_attn.q", dim, dim)
add_pair(f"blocks.{i}.self_attn.k", dim, dim)
add_pair(f"blocks.{i}.self_attn.v", dim, dim)
add_pair(f"blocks.{i}.self_attn.o", dim, dim)
# cross-attn Q, K, V, O
add_pair(f"blocks.{i}.cross_attn.q", dim, dim)
add_pair(f"blocks.{i}.cross_attn.k", dim, dim)
add_pair(f"blocks.{i}.cross_attn.v", dim, dim)
add_pair(f"blocks.{i}.cross_attn.o", dim, dim)
# ffn ff1, ff2 (lightx2v naming: ffn.0 = ff1, ffn.2 = ff2)
add_pair(f"blocks.{i}.ffn.0", dim, ffn_dim)
add_pair(f"blocks.{i}.ffn.2", ffn_dim, dim)
save_file(state, str(path))
def _count_registered(transformer) -> int:
"""Count LoRA modules with at least one entry in their bank."""
n = 0
for mod in _iter_lora_modules(transformer):
if any(a is not None for a in mod.lora_bank):
n += 1
return n
def _iter_lora_modules(module):
if isinstance(module, LoRAMixin):
yield module
for _, child in module.named_children():
yield from _iter_lora_modules(child)
@pytest.mark.parametrize("mesh_device", [(1, 1)], indirect=True)
def test_adapter_loader_registers_all_lora_linears(mesh_device: ttnn.MeshDevice) -> None:
"""Loader picks up Q/K/V/O for both attn1/attn2 plus ff1/ff2 per block."""
num_heads = 4
head_dim = 32
dim = num_heads * head_dim
ffn_dim = 2 * dim
rank = 8
num_blocks = 1
parallel_config = DiTParallelConfig(
cfg_parallel=ParallelFactor(factor=1, mesh_axis=0),
tensor_parallel=ParallelFactor(factor=1, mesh_axis=1),
sequence_parallel=ParallelFactor(factor=1, mesh_axis=0),
)
ccl_manager = CCLManager(mesh_device, topology=ttnn.Topology.Linear)
transformer = WanTransformer3DModel(
patch_size=(1, 2, 2),
num_heads=num_heads,
dim=dim,
in_channels=16,
out_channels=16,
text_dim=128,
freq_dim=64,
ffn_dim=ffn_dim,
num_layers=num_blocks,
mesh_device=mesh_device,
ccl_manager=ccl_manager,
parallel_config=parallel_config,
is_fsdp=False,
lora_enabled=True,
)
# Every block should have 5 LoRA-aware Linears wired in: to_qkv (self),
# to_q (cross), to_kv (cross), to_out for each attention (×2), ff1, ff2.
# That's 1 + 1 + 1 + 2 + 1 + 1 = 7 modules per block.
expected_per_block = 7
n_modules = sum(1 for _ in _iter_lora_modules(transformer))
assert (
n_modules == num_blocks * expected_per_block
), f"expected {num_blocks * expected_per_block} LoRA modules; got {n_modules}"
with tempfile.TemporaryDirectory() as tmp:
adapter_path = Path(tmp) / "synthetic.safetensors"
_save_synthetic_adapter(adapter_path, num_blocks=num_blocks, dim=dim, ffn_dim=ffn_dim, rank=rank)
handle = load_adapter_into(transformer, str(adapter_path), scale=1.0, name="synthetic")
# All 7 LoRA modules per block should have a registered adapter.
registered = _count_registered(transformer)
assert (
registered == num_blocks * expected_per_block
), f"expected {num_blocks * expected_per_block} registered adapters; got {registered}"
# Fused-QKV (to_qkv self-attn): rank should be 3*r. Fused KV (cross-attn
# to_kv): rank should be 2*r. Singletons: rank = r.
block0 = transformer.blocks[0]
assert block0.attn1.to_qkv.lora_bank[0].rank == 3 * rank
assert block0.attn2.to_kv.lora_bank[0].rank == 2 * rank
assert block0.attn2.to_q.lora_bank[0].rank == rank
assert block0.attn1.to_out.lora_bank[0].rank == rank
assert block0.attn2.to_out.lora_bank[0].rank == rank
assert block0.ffn.ff1.lora_bank[0].rank == rank
assert block0.ffn.ff2.lora_bank[0].rank == rank
# handle.rank tracks the max rank across targets (3*rank for the fused-QKV group).
assert handle.rank == 3 * rank
@pytest.mark.parametrize("mesh_device", [(1, 1)], indirect=True)
def test_pipeline_bind_unbind_walks_all_modules(mesh_device: ttnn.MeshDevice) -> None:
"""The pipeline mixin's bind/unbind walks every LoRA module on each set_active_lora."""
from models.tt_dit.experimental.pipelines.pipeline_wan_runtime_lora import _iter_lora_modules as pipeline_iter
num_heads = 4
head_dim = 32
dim = num_heads * head_dim
ffn_dim = 2 * dim
rank = 8
parallel_config = DiTParallelConfig(
cfg_parallel=ParallelFactor(factor=1, mesh_axis=0),
tensor_parallel=ParallelFactor(factor=1, mesh_axis=1),
sequence_parallel=ParallelFactor(factor=1, mesh_axis=0),
)
ccl_manager = CCLManager(mesh_device, topology=ttnn.Topology.Linear)
transformer = WanTransformer3DModel(
patch_size=(1, 2, 2),
num_heads=num_heads,
dim=dim,
in_channels=16,
out_channels=16,
text_dim=128,
freq_dim=64,
ffn_dim=ffn_dim,
num_layers=1,
mesh_device=mesh_device,
ccl_manager=ccl_manager,
parallel_config=parallel_config,
is_fsdp=False,
lora_enabled=True,
)
with tempfile.TemporaryDirectory() as tmp:
adapter_path = Path(tmp) / "synthetic.safetensors"
_save_synthetic_adapter(adapter_path, num_blocks=1, dim=dim, ffn_dim=ffn_dim, rank=rank)
handle = load_adapter_into(transformer, str(adapter_path), name="synthetic")
# Simulate the bind path the pipeline mixin runs.
for dotted, bank_idx in handle.target_indices.items():
cur = transformer
for part in dotted.split("."):
cur = cur[int(part)] if part.isdigit() else getattr(cur, part)
cur.bind_active(bank_idx)
# All modules covered by the handle should be active (delta merged).
for dotted in handle.target_indices:
cur = transformer
for part in dotted.split("."):
cur = cur[int(part)] if part.isdigit() else getattr(cur, part)
assert cur.is_lora_active, f"{dotted} not active after bind_active"
# Unbind everything → delta subtracted, is_lora_active False.
for mod in pipeline_iter(transformer):
if mod.is_lora_active:
mod.unbind_active()
for dotted in handle.target_indices:
cur = transformer
for part in dotted.split("."):
cur = cur[int(part)] if part.isdigit() else getattr(cur, part)
assert not cur.is_lora_active
assert cur.active_idx is None
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