visualize_tokens.py

from collections import defaultdict
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from datasets import load_dataset
from rich.console import Console
from rich.table import Table
from transformers import (
    AutoTokenizer,
    PreTrainedModel,
    AutoModelForCausalLM,
    GenerationConfig,
)


######
# Utility functions
######

def generate(lm_backbone, queries, tokenizer, generation_config):
    """generate in a way that does not affect padding tokens"""
    context_length = queries.shape[1]
    attention_mask = queries != tokenizer.pad_token_id
    input_ids = torch.masked_fill(queries, ~attention_mask, 0)
    output = lm_backbone.generate(
        input_ids=input_ids,
        attention_mask=attention_mask,
        # position_ids=attention_mask.cumsum(1) - attention_mask.long(), # generation collapsed if this was turned on. TODO: why does generation collapse with this?
        generation_config=generation_config,
        return_dict_in_generate=True,
    )
    return torch.cat((queries, output.sequences[:, context_length:]), dim=1)

def forward(model, query_responses, tokenizer):
    attention_mask = query_responses != tokenizer.pad_token_id
    position_ids = attention_mask.cumsum(1) - attention_mask.long()
    input_ids = torch.masked_fill(query_responses, ~attention_mask, 0)
    return model(
        input_ids=input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
        return_dict=True,
        output_hidden_states=True,
    )

def print_rich_table(title: str, df: pd.DataFrame, console: Console) -> Table:
    table = Table(show_lines=True)
    for column in df.columns:
        table.add_column(column)
    for _, row in df.iterrows():
        table.add_row(*row.astype(str).tolist())
    console.rule(f"[bold red]{title}")
    console.print(table)


######
# Start
######

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-1b-deduped")
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
response_length = 80
validation_generation_config = GenerationConfig(
    max_new_tokens=response_length,
    temperature=(0.01 + 1e-7),
    top_k=0.0,
    top_p=1.0,
    do_sample=True,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
)

sft_dataset = load_dataset("vwxyzjn/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_1706381144")
base_model: PreTrainedModel = AutoModelForCausalLM.from_pretrained("EleutherAI/pythia-1b-deduped").to(device)

# # https://wandb.ai/costa-huang/tldr_summarize/runs/a0rutstb
# # https://huggingface.co/vwxyzjn/EleutherAI_pythia-1b-deduped__sft__tldr/tree/sft__55513__1706646024
# sft_model: PreTrainedModel = AutoModelForCausalLM.from_pretrained(
#     "vwxyzjn/EleutherAI_pythia-1b-deduped__sft__tldr",
#     revision="sft__55513__1706646024",
#     trust_remote_code=True,
# ).to(device)

# https://wandb.ai/costa-huang/tldr_summarize/runs/ulekmmac
# https://huggingface.co/vwxyzjn/EleutherAI_pythia-1b-deduped__ppo_left_padding__tldr/tree/ppo_left_padding__55513__1706746254
ppo_model: PreTrainedModel = AutoModelForCausalLM.from_pretrained(
    "vwxyzjn/EleutherAI_pythia-1b-deduped__ppo_left_padding__tldr",
    revision="ppo_left_padding__55513__1706746254",
    trust_remote_code=True,
).to(device)
compared_models = {
    "base_model": base_model,
    "aligned_model": ppo_model,
}

nchecks = 4
colors = {
    0: "on blue",
    1: "on yellow",
    2: "on yellow",
    3: "on red",
}
console = Console()
for i in range(len(sft_dataset["validation"])):
    table = defaultdict(list)
    query = torch.Tensor(sft_dataset["validation"][i:i+1]["query_token"]).to(device).long()
    query_reference_response = torch.Tensor(sft_dataset["validation"][i]["query_reference_response_token"]).to(device).long()
    with torch.no_grad():
        aligned_model = compared_models["aligned_model"]
        base_model = compared_models["base_model"]
        context_length = query.shape[1]
        aligned_model_query_response = generate(aligned_model, query, tokenizer, validation_generation_config)
        aligned_model_response = aligned_model_query_response[:, context_length:]
        base_model_query_response = generate(base_model, query, tokenizer, validation_generation_config)
        base_model_response = base_model_query_response[:, context_length:]
        aligned_model_output = forward(aligned_model, aligned_model_query_response, tokenizer)
        base_model_output = forward(base_model, aligned_model_query_response, tokenizer)
        aligned_model_logits = aligned_model_output.logits[:, context_length-1:-1]
        _, aligned_model_topk_indices = aligned_model_logits.topk(10)
        base_model_logits = base_model_output.logits[:, context_length-1:-1]
        _, base_model_topk_indices = base_model_logits.topk(10)

        aligned_model_topk_indices[:,:,0:1].expand(-1, -1, nchecks)
        matches = aligned_model_topk_indices[:,:,0:1].expand(-1, -1, nchecks) == base_model_topk_indices[:,:,0:nchecks]
        matched = matches.sum(2)
        match_idx = matches.float().argmax(2)
        final_matches = torch.where(matched > 0, match_idx, nchecks-1)
        stats = torch.stack([(final_matches == i).sum(1) for i in range(nchecks)]).T

    final_matches = final_matches.tolist()
    aligned_model_response = aligned_model_response.tolist()
    table["type"].append("Query")
    table["content"].append(tokenizer.decode(query[0], skip_special_tokens=True))
    table["type"].append("PPO Model Response")
    table["content"].append("".join([f"[{colors[jt]}]{tokenizer.decode(it)}[/{colors[jt]}]" for it, jt in zip(aligned_model_response[0], final_matches[0])]))
    table["type"].append("Matched Color Counts")
    table["content"].append(stats[0])
    table["type"].append("Base Model Response")
    table["content"].append(tokenizer.decode(base_model_response[0], skip_special_tokens=True))

    df = pd.DataFrame(table)
    print_rich_table("Results", df, console)
    if input("Continue? (press `n` to stop) ") == "n":
        break