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import torch
from lightning import seed_everything
from transformers_model.model import LitVanillaTransformer
# Loads a LitVanillaTransformer set for forward (product) prediction
def load_forward_model(
ckpt_path: str,
vocab_path: str,
num_beams: int,
topn: int,
device: Optional[str] = None,
) -> LitVanillaTransformer:
return load_lit_transformer_for_inference(
ckpt_path=ckpt_path,
vocab_path=vocab_path,
task="forward",
num_beams=num_beams,
topn=topn,
gen_max_length=256,
device=device,
)
# Loads a LitVanillaTransformer set for retrosynthesis prediction
def load_retro_model(
ckpt_path: str,
vocab_path: str,
num_beams: int,
topn: int,
device: Optional[str] = None,
) -> LitVanillaTransformer:
return load_lit_transformer_for_inference(
ckpt_path=ckpt_path,
vocab_path=vocab_path,
task="forward",
num_beams=num_beams,
topn=topn,
gen_max_length=256,
device=device,
)
# Loads the LitVanillaTransformer for prediction
def load_lit_transformer_for_inference(
ckpt_path: str,
vocab_path: str,
task: str = "forward",
num_beams: int = 3,
topn: int = 3,
gen_max_length: int = 256,
device: Optional[str] = None,
) -> LitVanillaTransformer:
"""
Load the trained LitVanillaTransformer exactly like LightningCLI would,
and prepare it for inference/generation.
"""
torch.set_float32_matmul_precision("high")
# Device handling when not set
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load from checkpoint while ensuring the same hyperparameters used at predict time.
lit = LitVanillaTransformer.load_from_checkpoint(
ckpt_path,
vocab_path=vocab_path,
task=task,
num_beams=num_beams,
topn=topn,
max_length=gen_max_length,
device=device,
)
lit.eval()
# Align internal config device with the actual target device to avoid mask/device mismatches
target_device = torch.device(device)
lit.to(target_device)
if hasattr(lit, "model") and hasattr(lit.model, "config"):
lit.model.config.device = target_device # your encoder/decoder build masks on config.device
# Return the LitVanillaTransformer
return lit
# Uses the transformers model to run predictions with the SMILES inputs
@torch.inference_mode()
def predict_smiles(
lit: LitVanillaTransformer, # Model used for inference
inputs: List[str], # List of SMILES for prediction
*,
batch_size: int = 512,
truncation: bool = True,
padding: str = "max_length",
data_max_length: int = 278,
seed: Optional[int] = 42,
) -> List[Dict]:
# Sets the seed for reproducibility
if seed is not None:
seed_everything(seed, workers=True)
# Gets the device in use
device = next(lit.parameters()).device
# Gets the tokenizer from the LitVanillaTransformer
tokenizer = lit.tokenizer
# List to hold prediction results
all_results: List[Dict] = []
# Prediction loop for batch prediction
for start in range(0, len(inputs), batch_size):
# Prepare the batch inputs according to batch size
batch_src = inputs[start : start + batch_size]
print(f"DEBUG - batch_src: {batch_src}")
# Prepare inputs by tokenizing exactly like in the DataModule
encoder_input_ids = tokenizer(
batch_src,
truncation=truncation,
padding=padding,
max_length=data_max_length,
return_token_type_ids=False,
return_tensors="pt",
)["input_ids"]
# Move inputs to device if one is set
if device is not None:
encoder_input_ids = encoder_input_ids.to(device)
# Generate the model predictions
outputs = lit.model.generate(
encoder_input_ids,
do_sample=False,
max_length=lit.hparams.max_length,
num_beams=lit.hparams.num_beams,
num_return_sequences=lit.hparams.topn,
return_dict_in_generate=True,
output_scores=True,
)
# Get predicted sequences and scores
sequences = outputs.sequences # shape [B*topn, T]
scores = outputs.sequences_scores # 1 score per sequence
# Decode and detokenize the sequences to obtain the predicted SMILES string
pred_texts = tokenizer.batch_decode(sequences, skip_special_tokens=True)
pred_texts = tokenizer.batch_detokenize_smiles_string(pred_texts)
# Also decode and detokenize the encoder inputs to a SMILES string
src_texts_dec = tokenizer.batch_decode(encoder_input_ids, skip_special_tokens=True)
src_texts_dec = tokenizer.batch_detokenize_smiles_string(src_texts_dec)
print(f"DEBUG - src_texts_dec: {src_texts_dec}")
# Get topn from the hyperparameters to know exactly how many results are grouped for the same source
topn = lit.hparams.topn
# List to hold each batch prediction results
batch_results: List[Dict] = []
# Loop through the inputs
for i, src in enumerate(src_texts_dec):
# Get starting index for each unique input
start_j = i * topn
# Loop through all predictions for a given input
for j in range(start_j, start_j + topn):
# Build each prediction result object
item = {
"source": src,
"predicted_target": pred_texts[j],
"confidence": scores[j].item(), # you keep raw score in your code
}
# Add the prediction result to the batch results
batch_results.append(item)
# Add this batch results to the total results list
all_results.extend(batch_results)
# Return the prediction results
return all_results
# Uses the transformers model to check the likelihood of a reaction using the input and output SMILES
@torch.inference_mode()
def reaction_likelihood(
lit: LitVanillaTransformer,
src_text: str,
tgt_text: str,
truncation: bool = True,
padding: str = "max_length",
data_max_length: int = 278,
) -> float:
# Get the model, tokenizer and device from LitVanillaTransformer
model = lit.model
tokenizer = lit.tokenizer
device = next(model.parameters()).device
# Tokenizes the SMILES sources
src_ids = tokenizer(
src_text,
truncation=truncation,
padding=padding,
max_length=data_max_length,
return_tensors="pt",
).input_ids.to(device)
# Tokenizes the SMILES targets
tgt_ids = tokenizer(
tgt_text,
truncation=truncation,
padding=padding,
max_length=data_max_length,
return_tensors="pt",
).input_ids.to(device)
# Gets the outputs from the model
outputs = model(
encoder_input_ids=src_ids,
decoder_input_ids=tgt_ids,
labels=tgt_ids,
return_dict=True,
)
# loss = mean NEGATIVE log-prob per token
loss = outputs.loss
# Number of actual tokens (excluding padding)
pad_id = tokenizer.pad_token_id
num_tokens = (tgt_ids != pad_id).sum()
# Calculates the log likelihood
log_likelihood = -loss * num_tokens
# Returns likelihood probability
return float(torch.exp(log_likelihood))
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