ViperEkura
fc47319240
- Extract _resolve_base_type and _validate_component as module-level helpers - Replace ForwardRef._evaluate private API with eval in module namespace - Remove broad except Exception in __init_subclass__, _component_base always set - Replace direct _entries mutation in strategy.py with register() call form - Remove dead TOKENIZER_CLASSES registry from AutoTokenizer - Extract ModelFactory(BaseFactory[nn.Module]) as pure factory - AutoModel now inherits only nn.Module, no factory state - Move @AutoModel.register to @ModelFactory.register in transformer.py and encoder.py
107 lines
3.2 KiB
Python
107 lines
3.2 KiB
Python
import json
|
|
import os
|
|
import tempfile
|
|
|
|
import pytest
|
|
import safetensors.torch as st
|
|
import torch
|
|
|
|
from astrai.config.model_config import EncoderConfig
|
|
from astrai.model.automodel import ModelFactory
|
|
from astrai.model.encoder import EmbeddingEncoder
|
|
from tests.helpers import TINY_CONFIG, assert_state_dicts_equal
|
|
|
|
|
|
def _make_model(device, **kwargs):
|
|
config = EncoderConfig(**{**TINY_CONFIG, **kwargs})
|
|
return EmbeddingEncoder(config).to(device=device)
|
|
|
|
|
|
@pytest.mark.parametrize("pooling_type", ["mean", "cls", "last"])
|
|
def test_encoder_forward_pooling(pooling_type, device):
|
|
model = _make_model(device, pooling_type=pooling_type)
|
|
model.eval()
|
|
|
|
batch_size, seq_len = 2, 8
|
|
input_ids = torch.randint(
|
|
0, TINY_CONFIG["vocab_size"], (batch_size, seq_len), device=device
|
|
)
|
|
|
|
with torch.no_grad():
|
|
output = model(input_ids)
|
|
|
|
assert output.shape == (batch_size, TINY_CONFIG["hidden_size"])
|
|
assert not torch.isnan(output).any()
|
|
|
|
|
|
def test_encoder_forward_with_padding(device):
|
|
model = _make_model(device)
|
|
model.eval()
|
|
|
|
batch_size, seq_len = 2, 8
|
|
input_ids = torch.randint(
|
|
0, TINY_CONFIG["vocab_size"], (batch_size, seq_len), device=device
|
|
)
|
|
input_mask = torch.ones(batch_size, seq_len, dtype=torch.bool, device=device)
|
|
input_mask[:, 4:] = False
|
|
|
|
with torch.no_grad():
|
|
output = model(input_ids, input_mask=input_mask)
|
|
|
|
assert output.shape == (batch_size, TINY_CONFIG["hidden_size"])
|
|
assert not torch.isnan(output).any()
|
|
|
|
|
|
def test_encoder_normalize(device):
|
|
model = _make_model(device, pooling_type="mean", normalize_embeddings=True)
|
|
model.eval()
|
|
|
|
batch_size, seq_len = 2, 8
|
|
input_ids = torch.randint(
|
|
0, TINY_CONFIG["vocab_size"], (batch_size, seq_len), device=device
|
|
)
|
|
|
|
with torch.no_grad():
|
|
output = model(input_ids)
|
|
|
|
norms = output.norm(p=2, dim=-1)
|
|
assert torch.allclose(norms, torch.ones_like(norms), atol=1e-4)
|
|
|
|
|
|
def test_encoder_register():
|
|
assert ModelFactory.is_registered("embedding")
|
|
cls = ModelFactory.get_component_class("embedding")
|
|
assert cls is EmbeddingEncoder
|
|
|
|
|
|
def test_encoder_from_transformer_checkpoint(device):
|
|
model = _make_model(device)
|
|
state_dict = model.state_dict()
|
|
state_dict["lm_head.weight"] = torch.randn(
|
|
TINY_CONFIG["vocab_size"], TINY_CONFIG["hidden_size"], device=device
|
|
)
|
|
|
|
new_model = _make_model(device)
|
|
new_model.load_state_dict(state_dict, strict=True)
|
|
|
|
assert_state_dicts_equal(new_model.state_dict(), model.state_dict())
|
|
|
|
|
|
def test_encoder_save_load(device):
|
|
with tempfile.TemporaryDirectory(prefix="encoder_test_") as test_dir:
|
|
config_path = os.path.join(test_dir, "config.json")
|
|
weights_path = os.path.join(test_dir, "model.safetensors")
|
|
|
|
config_data = {**TINY_CONFIG, "pooling_type": "mean"}
|
|
with open(config_path, "w") as f:
|
|
json.dump(config_data, f)
|
|
|
|
config = EncoderConfig.from_file(config_path)
|
|
original = EmbeddingEncoder(config)
|
|
st.save_file(original.state_dict(), weights_path)
|
|
|
|
loaded = EmbeddingEncoder(config)
|
|
loaded.load_state_dict(st.load_file(weights_path))
|
|
|
|
assert_state_dicts_equal(original.state_dict(), loaded.state_dict())
|