from transformers import AutoModelForCausalLM, AutoTokenizer import torch import torch.nn as nn from argparse import ArgumentParser from read import read_config from accelerate.utils import set_seed from data import load_data_for_inference from tqdm import tqdm from datasets import Dataset import torch.distributed as dist from transformers.trainer_pt_utils import nested_numpify from transformers import DefaultDataCollator from torch.utils.data import DataLoader, DistributedSampler import numpy as np import pyarrow as pa from pyarrow import compute as pc def calc_cross_entropy_no_reduction(lm_logits, labels): # calculate cross entropy across batch dim shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = nn.CrossEntropyLoss(reduction='none') loss = loss_fct(shift_logits.permute(0, 2, 1), shift_labels).mean(dim=1) return loss def rank0_print(msg): if dist.get_rank() == 0: print(msg) def inference(config): set_seed(config['seed']) rank0_print(f"World size: {dist.get_world_size()}") tokenizer = AutoTokenizer.from_pretrained(config['tokenizer_name'], model_max_length=config['max_length']) # llama has no pad token, set it to new token if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token train_dataset, val_dataset = load_data_for_inference(config, tokenizer) num_processes = dist.get_world_size() local_rank = dist.get_rank() train_sampler = DistributedSampler(train_dataset, shuffle=False, drop_last=True, num_replicas=num_processes, rank=local_rank) train_dataloader = DataLoader( train_dataset, collate_fn=DefaultDataCollator(), batch_size=config["batch_size"], sampler=train_sampler, drop_last=True ) val_sampler = DistributedSampler(val_dataset, shuffle=False, drop_last=True, num_replicas=num_processes, rank=local_rank) val_dataloader = DataLoader( val_dataset, collate_fn=DefaultDataCollator(), batch_size=config["batch_size"], sampler=val_sampler, drop_last=True ) model = AutoModelForCausalLM.from_pretrained(config["model_name"], trust_remote_code=True, torch_dtype=torch.bfloat16, ) model.to(f"cuda:{local_rank}") with torch.no_grad(): train_outputs = {"loss": [], "embeddings": [], "index": []} for batch in tqdm(train_dataloader, disable=local_rank != 0): batch["input_ids"] = batch["input_ids"].to(f"cuda:{local_rank}") batch["labels"] = batch["labels"].to(f"cuda:{local_rank}") outputs = model(input_ids=batch["input_ids"], labels=batch["labels"], output_hidden_states=True) loss = calc_cross_entropy_no_reduction(outputs.logits, batch["labels"]) train_outputs["loss"].extend(loss) embeddings = outputs.hidden_states[-1] batch_size = batch["input_ids"].shape[0] sequence_lengths = [] # since we use mutiturn with multiple <|endoftext|>, we need to find the place where # <|endoftext|> is repeated for item in batch["input_ids"]: indices = torch.where(item == tokenizer.pad_token_id)[0] found = False for index in indices: # case where sequence is less than max length if torch.all(item[index:] == tokenizer.pad_token_id): sequence_lengths.append(index) found = True break # case where sequence is >= max length if not found: sequence_lengths.append(len(item) - 1) sequence_lengths = torch.tensor(sequence_lengths) pooled_logits = embeddings[torch.arange(batch_size, device=embeddings.device), sequence_lengths] train_outputs["embeddings"].append(pooled_logits) train_outputs["index"].extend(batch["index"].to(model.device)) torch.cuda.empty_cache() train_outputs = nested_numpify(train_outputs) # stack since they're 0-dim arrays train_outputs["index"] = np.stack(train_outputs["index"]) train_outputs["loss"] = np.stack(train_outputs["loss"]) train_outputs["embeddings"] = np.concatenate(train_outputs["embeddings"]) df_train = Dataset.from_dict(train_outputs) curr_idx = df_train["index"] # compute mask in pyarrow since it's super fast # ty @bmschmidt for showing me this! table = train_dataset.data mask = pc.is_in(table['index'], value_set=pa.array(curr_idx, pa.int32())) filtered_table = table.filter(mask) # convert from pyarrow to Dataset filtered_train = Dataset.from_dict(filtered_table.to_pydict()) filtered_train = filtered_train.add_column("embeddings", df_train["embeddings"]) filtered_train = filtered_train.add_column("loss", df_train["loss"]) filtered_train = filtered_train.add_column("is_train", [True] * len(filtered_train)) filtered_train.to_json(f"inference/epoch_2_embeddings_train_shard_{local_rank}.jsonl", lines=True, orient="records", num_proc=64) val_outputs = {"loss": [], "embeddings": [], "index": []} for batch in tqdm(val_dataloader, disable=local_rank != 0): batch["input_ids"] = batch["input_ids"].to(f"cuda:{local_rank}") batch["labels"] = batch["labels"].to(f"cuda:{local_rank}") outputs = model(input_ids=batch["input_ids"], labels=batch["labels"], output_hidden_states=True) loss = calc_cross_entropy_no_reduction(outputs.logits, batch["labels"]) val_outputs["loss"].extend(loss) embeddings = outputs.hidden_states[-1] batch_size = batch["input_ids"].shape[0] sequence_lengths = [] # since we use mutiturn with multiple <|endoftext|>, we need to find the place where # <|endoftext|> is repeated for item in batch["input_ids"]: indices = torch.where(item == tokenizer.pad_token_id)[0] found = False for index in indices: # case where sequence is less than max length if torch.all(item[index:] == tokenizer.pad_token_id): sequence_lengths.append(index) found = True break # case where sequence is >= max length if not found: sequence_lengths.append(len(item) - 1) sequence_lengths = torch.tensor(sequence_lengths) pooled_logits = embeddings[torch.arange(batch_size, device=embeddings.device), sequence_lengths] val_outputs["embeddings"].append(pooled_logits) val_outputs["index"].extend(batch["index"].to(model.device)) torch.cuda.empty_cache() val_outputs = nested_numpify(val_outputs) val_outputs["index"] = np.stack(val_outputs["index"]) val_outputs["loss"] = np.stack(val_outputs["loss"]) val_outputs["embeddings"] = np.concatenate(val_outputs["embeddings"]) df_val = Dataset.from_dict(val_outputs) curr_idx = df_val["index"] # compute mask in pyarrow since it's super fast # ty @bmschmidt for showing me this! table = val_dataset.data mask = pc.is_in(table['index'], value_set=pa.array(curr_idx, pa.int32())) filtered_table = table.filter(mask) # convert from pyarrow to Dataset filtered_val = Dataset.from_dict(filtered_table.to_pydict()) filtered_val = filtered_val.add_column("embeddings", df_val["embeddings"]) filtered_val = filtered_val.add_column("loss", df_val["loss"]) filtered_val = filtered_val.add_column("is_train", [False] * len(filtered_val)) filtered_val.to_json(f"inference/epoch_2_embeddings_val_shard_{local_rank}.jsonl", lines=True, orient="records", num_proc=64) def main(): dist.init_process_group("nccl") parser = ArgumentParser() parser.add_argument("--config", type=str, default="config.yaml") args = parser.parse_args() config = read_config(args.config) inference(config) if __name__ == "__main__": # parse arguments by reading in a config main()