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March 18, 2024 17:50
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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 ( | |
AutoConfig, | |
AutoModel, | |
AutoModelForCausalLM, | |
AutoTokenizer, | |
GenerationConfig, | |
PretrainedConfig, | |
PreTrainedModel, | |
) | |
###### | |
# RM model definition | |
###### | |
def layer_init(layer, std=np.sqrt(2), bias_const=0.0): | |
torch.nn.init.normal_(layer.weight, std=std) | |
torch.nn.init.constant_(layer.bias, val=bias_const) | |
return layer | |
class ScalarModelConfig(PretrainedConfig): | |
def __init__( | |
self, | |
base_model: str = "EleutherAI/pythia-160m", | |
base_config: PretrainedConfig = AutoConfig.from_pretrained("EleutherAI/pythia-160m"), | |
hidden_size: int = 768, | |
bias: float = 0.0, | |
**kwargs, | |
): | |
super().__init__(**kwargs) | |
self.base_model = base_model | |
self.base_config = base_config | |
self.hidden_size = hidden_size | |
self.bias = bias | |
class ScalarModel(PreTrainedModel): | |
config_class = ScalarModelConfig | |
def __init__(self, config: ScalarModelConfig): | |
super().__init__(config) | |
self.config = config | |
self.lm_backbone = AutoModel.from_pretrained( | |
config.base_model, | |
config=self.config.base_config, | |
trust_remote_code=True, | |
) | |
self.scalar_head = layer_init( | |
nn.Linear(self.config.hidden_size, 1), | |
std=1 / np.sqrt(self.config.hidden_size + 1), | |
) | |
def forward(self, **kwargs): | |
output = self.lm_backbone(**kwargs) | |
reward = self.scalar_head(output.hidden_states[-1]) - self.config.bias | |
return reward | |
###### | |
# 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 get_reward(model, query_responses, tokenizer): | |
attention_mask = query_responses != tokenizer.pad_token_id | |
input_ids = torch.masked_fill(query_responses, ~attention_mask, 0) | |
reward_logits = model( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
return_dict=True, | |
output_hidden_states=True, | |
) | |
sequence_lengths = (torch.eq(query_responses, tokenizer.pad_token_id).long().argmax(-1) - 1).to(query_responses.device) | |
# https://github.com/huggingface/transformers/blob/dc68a39c8111217683bf49a4912d0c9018bab33d/src/transformers/models/gpt2/modeling_gpt2.py#L1454 | |
return reward_logits[torch.arange(reward_logits.size(0), device=reward_logits.device), sequence_lengths], reward_logits | |
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) | |
# https://wandb.ai/costa-huang/tldr_summarize/runs/tewm564g | |
# https://huggingface.co/vwxyzjn/EleutherAI_pythia-1b-deduped__dpo__tldr/tree/dpo__55513__1707379566 | |
dpo_model: PreTrainedModel = AutoModelForCausalLM.from_pretrained( | |
"vwxyzjn/EleutherAI_pythia-1b-deduped__dpo__tldr", | |
revision="dpo__55513__1707379566", | |
trust_remote_code=True, | |
).to(device) | |
# # https://wandb.ai/costa-huang/tldr_summarize/runs/jsj57urt | |
# # https://huggingface.co/vwxyzjn/EleutherAI_pythia-1b-deduped__reward__tldr/tree/reward__55513__1706651113 | |
# scalar_model_config = ScalarModelConfig.from_pretrained( | |
# "vwxyzjn/EleutherAI_pythia-1b-deduped__reward__tldr", | |
# revision="reward__55513__1706651113", | |
# trust_remote_code=True, | |
# ) | |
# # hack to remove the path | |
# # models/EleutherAI/pythia-1b-deduped/sft_model_55513 -> EleutherAI/pythia-1b-deduped | |
# original_model = "/".join(scalar_model_config.base_config["_name_or_path"].split("/")[1:3]) | |
# scalar_model_config.base_config["_name_or_path"] = original_model | |
# scalar_model_config.base_model = original_model | |
# rm: PreTrainedModel = ScalarModel.from_pretrained( | |
# "vwxyzjn/EleutherAI_pythia-1b-deduped__reward__tldr", | |
# revision="reward__55513__1706651113", | |
# trust_remote_code=True, | |
# config=scalar_model_config, | |
# ).to(device) | |
# "Gold" RM (a much larger model) | |
# https://wandb.ai/costa-huang/tldr_summarize/runs/ddw0ixx9 | |
# https://huggingface.co/vwxyzjn/EleutherAI_pythia-6.9b-deduped__reward__tldr/tree/reward__55513__1706651113 | |
scalar_model_config = ScalarModelConfig.from_pretrained( | |
"vwxyzjn/EleutherAI_pythia-6.9b-deduped__reward__tldr", | |
revision="reward__55513__1706651113", | |
trust_remote_code=True, | |
) | |
# hack to remove the path | |
# models/EleutherAI/pythia-6.9b-deduped/sft_model_55513 -> EleutherAI/pythia-6.9b-deduped | |
original_model = "/".join(scalar_model_config.base_config["_name_or_path"].split("/")[1:3]) | |
scalar_model_config.base_config["_name_or_path"] = original_model | |
scalar_model_config.base_model = original_model | |
rm: PreTrainedModel = ScalarModel.from_pretrained( | |
"vwxyzjn/EleutherAI_pythia-6.9b-deduped__reward__tldr", | |
revision="reward__55513__1706651113", | |
trust_remote_code=True, | |
config=scalar_model_config, | |
).to(device) | |
nchecks = 4 | |
colors = { | |
0: "on blue", | |
1: "on yellow", | |
2: "on yellow", | |
3: "on red", | |
} | |
latex_colors = { | |
0: "\sethlcolor{LightBlue}", | |
1: "\sethlcolor{LightYellow}", | |
2: "\sethlcolor{LightYellow}", | |
3: "\sethlcolor{LightRed}", | |
} | |
include_logits = True | |
console = Console() | |
for i in range(len(sft_dataset["validation"])): | |
rich_table = defaultdict(list) | |
latex_table = defaultdict(list) | |
query = torch.LongTensor(sft_dataset["validation"][i : i + 1]["query_token"]).to(device) | |
context_length = query.shape[1] | |
query_reference_response = torch.cat((query, torch.LongTensor(tokenizer.encode(sft_dataset["validation"][i]["reference_response"])).to(device).unsqueeze(0)), dim=1) | |
for table in [rich_table, latex_table]: | |
table["Type"].append("Query") | |
table["Content"].append(tokenizer.decode(query[0], skip_special_tokens=True)) | |
table["Score (RM)"].append("N/A") | |
with torch.no_grad(): | |
model_stats = defaultdict(list) | |
for aligned_model, model_name in zip( | |
[sft_model, ppo_model, dpo_model], | |
["SFT Model Response", "PPO Model Response", "DPO Model Response"], | |
): | |
aligned_model_query_response = generate(aligned_model, query, tokenizer, validation_generation_config) | |
aligned_model_response = aligned_model_query_response[:, context_length:] | |
aligned_model_reward, aligned_model_reward_logits = get_reward(rm, aligned_model_query_response, tokenizer) | |
aligned_model_reward_logits = aligned_model_reward_logits.squeeze(-1)[:, context_length-1:] | |
# AI2 visualization https://allenai.github.io/re-align/tds.html | |
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() | |
for table in [rich_table, latex_table]: | |
table["Type"].append(model_name) | |
latex_table["Content"].append( | |
"".join( | |
[ | |
f"{latex_colors[jt]}" "\hl{" f"{tokenizer.decode(it)}" "}" | |
for it, jt in zip(aligned_model_response[0], final_matches[0]) | |
] | |
) | |
) | |
rich_table["Content"].append( | |
"".join( | |
[ | |
f"[{colors[jt]}]{tokenizer.decode(it)}[/{colors[jt]}]" | |
for it, jt in zip(aligned_model_response[0], final_matches[0]) | |
] | |
) | |
) | |
for table in [rich_table, latex_table]: | |
table["Score (RM)"].append(str(round(aligned_model_reward[0][0].item(), 4))) | |
if include_logits: | |
table["Type"].append(f"{model_name} Reward Logits") | |
table["Content"].append([round(logit, 4) for logit in aligned_model_reward_logits[0].tolist()]) | |
table["Score (RM)"].append(str(round(aligned_model_reward[0][0].item(), 4))) | |
# table["Type"].append("Matched Color Counts") | |
# table["Content"].append(stats[0]) | |
reference_reward, reference_reward_logits = get_reward(rm, query_reference_response, tokenizer) | |
reference_reward_logits = reference_reward_logits.squeeze(-1)[:, context_length-1:] | |
for table in [rich_table, latex_table]: | |
table["Type"].append("Reference response") | |
table["Content"].append(sft_dataset["validation"][i]["reference_response"]) | |
table["Score (RM)"].append(str(round(reference_reward[0][0].item(), 4))) | |
if include_logits: | |
table["Type"].append("Reference Reward Logits") | |
table["Content"].append([round(logit, 4) for logit in reference_reward_logits[0].tolist()]) | |
table["Score (RM)"].append(str(round(reference_reward[0][0].item(), 4))) | |
base_model_query_response = generate(base_model, query, tokenizer, validation_generation_config) | |
base_model_response = base_model_query_response[:, context_length:] | |
base_model_reward, base_model_reward_logits = get_reward(rm, base_model_query_response, tokenizer) | |
base_model_reward_logits = base_model_reward_logits.squeeze(-1)[:, context_length-1:] | |
for table in [rich_table, latex_table]: | |
table["Type"].append("Base Model Response") | |
table["Content"].append(tokenizer.decode(base_model_response[0], skip_special_tokens=True)) | |
table["Score (RM)"].append(str(round(base_model_reward[0][0].item(), 4))) | |
if include_logits: | |
table["Type"].append("Base Model Reward Logits") | |
table["Content"].append([round(logit, 4) for logit in base_model_reward_logits[0].tolist()]) | |
table["Score (RM)"].append(str(round(base_model_reward[0][0].item(), 4))) | |
rich_df = pd.DataFrame(rich_table) | |
latex_df = pd.DataFrame(latex_table) | |
print_rich_table("Results", rich_df, console) | |
# print(latex_df.to_latex(index=False)) | |
if input("Continue? (press `n` to stop) ") == "n": | |
break |
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