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decoding updates

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Asankhaya Sharma
2024-10-16 18:57:46 +08:00
parent 5d471d1c0a
commit 38830a7cd7
2 changed files with 251 additions and 1 deletions
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19
optillm/cot_decoding.py
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@@ -4,7 +4,9 @@ from typing import List, Tuple, Dict, Optional
import numpy as np
def get_device():
if torch.cuda.is_available():
if torch.backends.mps.is_available():
return torch.device("mps")
elif torch.cuda.is_available():
return torch.device("cuda")
else:
return torch.device("cpu")
@@ -143,3 +145,18 @@ def cot_decode(
else:
return max(paths, key=lambda x: x[1])
# Usage example
# from transformers import AutoModelForCausalLM, AutoTokenizer
# model_name = "Qwen/Qwen2.5-0.5B-Instruct"
# model = AutoModelForCausalLM.from_pretrained(model_name, attn_implementation="eager")
# tokenizer = AutoTokenizer.from_pretrained(model_name)
# messages = [
# {"role": "user", "content": "In a dance class of 20 students, 20% enrolled in contemporary dance, 25% of the remaining enrolled in jazz dance, and the rest enrolled in hip-hop dance. What percentage of the entire students enrolled in hip-hop dance?"}
# ]
# # Generate the response using CoT decoding
# print(f"Using device: {get_device()}")
# result, confidence = cot_decode(model, tokenizer, messages, aggregate_paths=True, max_new_tokens=512)
# print(f"CoT Decoding:\n {result}")

233
optillm/entropy_decoding.py Normal file
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@@ -0,0 +1,233 @@
import torch
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizer
from typing import List, Tuple, Dict, Optional
import logging
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# Device selection
if torch.backends.mps.is_available():
device = torch.device("mps")
elif torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
logging.info(f"Using device: {device}")
LN_2 = 0.69314718056 # ln(2)
def calculate_varentropy_logsoftmax(logits: torch.Tensor, axis: int = -1) -> Tuple[torch.Tensor, torch.Tensor]:
log_probs = F.log_softmax(logits, dim=axis)
probs = torch.exp(log_probs)
entropy = -torch.sum(probs * log_probs, dim=axis) / LN_2 # Convert to base-2
varentropy = torch.sum(probs * (log_probs / LN_2 + entropy.unsqueeze(-1))**2, dim=axis)
return entropy, varentropy
def calculate_attention_metrics(attention_scores: torch.Tensor) -> Dict[str, torch.Tensor]:
attention_probs = F.softmax(attention_scores, dim=-1)
attn_entropy = -torch.sum(attention_probs * torch.log2(torch.clamp(attention_probs, 1e-10, 1.0)), dim=-1)
attn_varentropy = torch.var(attn_entropy, dim=-1)
attn_varentropy = torch.where(torch.isnan(attn_varentropy), torch.zeros_like(attn_varentropy), attn_varentropy)
mean_attention = torch.mean(attention_probs, dim=1)
agreement = torch.mean(torch.abs(attention_probs - mean_attention.unsqueeze(1)), dim=(1, 2))
interaction_strength = torch.mean(torch.abs(attention_scores), dim=(1, 2, 3))
return {
"attn_entropy": torch.mean(attn_entropy),
"attn_varentropy": torch.mean(attn_varentropy),
"agreement": torch.mean(agreement),
"interaction_strength": interaction_strength
}
def _sample(logits: torch.Tensor, temperature=0.666, top_p=0.90, top_k=27, min_p: float = 0.0, generator: torch.Generator = None) -> torch.Tensor:
bsz = logits.shape[0]
logit = logits[:, -1]
probs = F.softmax(logit / temperature, dim=-1)
if min_p > 0.0:
p_max = torch.max(probs, dim=-1, keepdim=True).values
indices_to_remove = probs < (min_p * p_max)
logit = torch.where(indices_to_remove, torch.full_like(logit, float('-inf')), logit)
top_k_probs, top_k_indices = torch.topk(probs, k=min(top_k, probs.shape[-1]))
probs_sort = torch.flip(top_k_probs, dims=[-1])
probs_idx = torch.flip(top_k_indices, dims=[-1])
probs_sum = torch.cumsum(probs_sort, dim=-1)
mask = torch.where(probs_sum - probs_sort > top_p, torch.tensor(1.0, device=device), torch.tensor(0.0, device=device))
probs_sort = probs_sort * (1 - mask)
probs_sort = probs_sort / torch.sum(probs_sort, dim=-1, keepdim=True)
next_token = torch.multinomial(probs_sort, 1, generator=generator)
next_token_g = torch.gather(probs_idx, -1, next_token.reshape(bsz, 1).to(torch.int64))
return next_token_g.to(torch.int32)
def adaptive_sample(logits: torch.Tensor, metrics: Dict[str, torch.Tensor],
gen_tokens: torch.Tensor, n_samples: int,
base_temp: float = 0.666, base_top_p: float = 0.90, base_top_k: int = 40, base_min_p: float = 0.03,
generator: torch.Generator = None) -> torch.Tensor:
logits_uncertainty = metrics["logits_entropy"] + metrics["logits_varentropy"]
attn_uncertainty = metrics["attn_entropy"] + metrics["attn_varentropy"]
temperature = base_temp * (1 + 0.3 * logits_uncertainty + 0.2 * attn_uncertainty - 0.2 * metrics["agreement"])
top_p = torch.clamp(base_top_p * (1 + 0.1 * metrics["attn_varentropy"]), 0.1, 1.0)
top_k = int(torch.clamp(
torch.round(torch.tensor(base_top_k) * (1 + 0.3 * metrics["interaction_strength"].item() - 0.2 * metrics["agreement"].item())),
min=1,
max=100
).item())
min_p = torch.clamp(base_min_p * (1 - 0.5 * logits_uncertainty), 0.01, 0.5)
logging.debug(f"Adaptive sampling params: temp={temperature:.3f}, top_p={top_p:.3f}, top_k={top_k}, min_p={min_p:.3f}")
samples = []
for _ in range(n_samples):
sample = _sample(logits, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p, generator=generator)
samples.append(sample)
def score_sample(sample):
sample_flat = sample.flatten().to(torch.long)
one_hot = F.one_hot(sample_flat, logits.shape[-1])
log_probs = F.log_softmax(logits, dim=-1).view(-1, logits.shape[-1])
log_prob = torch.sum(log_probs * one_hot)
confidence_score = (
(1 - metrics["logits_entropy"]) * 0.1 +
(1 - metrics["attn_entropy"]) * 0.2 +
(1 - metrics["logits_varentropy"]) * 0.3 +
(1 - metrics["attn_varentropy"]) * 0.4 +
metrics["agreement"] * 0.5 +
metrics["interaction_strength"] * 0.6
)
return log_prob + confidence_score
sample_scores = torch.stack([score_sample(sample) for sample in samples])
best_sample_idx = torch.argmax(sample_scores)
return samples[best_sample_idx]
def entropy_decode(
model: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
messages: List[Dict[str, str]],
max_new_tokens: int = 512,
temperature: float = 0.666,
top_p: float = 0.90,
top_k: int = 27,
min_p: float = 0.03,
generator: torch.Generator = torch.Generator(device=device).manual_seed(1337)
) -> str:
model.to(device)
logging.info("Starting entropy decoding")
if hasattr(tokenizer, 'chat_template') and tokenizer.chat_template:
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
else:
input_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
input_text += "\nassistant:"
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)
attention_mask = torch.ones_like(input_ids).to(device)
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.eos_token_id
generated_tokens = []
gen_tokens = input_ids
past_key_values = None
stop = torch.tensor([tokenizer.eos_token_id], device=device, dtype=torch.int32)
for step in range(max_new_tokens):
logging.info(f"Generation step: {step + 1}")
with torch.no_grad():
outputs = model(
input_ids if past_key_values is None else input_ids[:, -1:],
attention_mask=attention_mask,
past_key_values=past_key_values,
use_cache=True,
output_attentions=True,
)
logits = outputs.logits[:, -1:, :]
attention_scores = outputs.attentions[-1]
past_key_values = outputs.past_key_values
entropy, varentropy = calculate_varentropy_logsoftmax(logits)
attention_metrics = calculate_attention_metrics(attention_scores)
metrics = {
"logits_entropy": entropy,
"logits_varentropy": varentropy,
**attention_metrics
}
logging.debug(f"Metrics: entropy={entropy.item():.3f}, varentropy={varentropy.item():.3f}")
if entropy < 0.1 and varentropy < 0.1:
next_token = torch.argmax(logits[:, -1], dim=-1, keepdim=True).to(torch.int32)
logging.debug("Using greedy sampling")
elif entropy > 3.0 and varentropy < 0.1:
if not torch.isin(gen_tokens[:,-1], torch.tensor([2564], device=device)).any():
next_token = torch.tensor([[2564]], dtype=torch.int32, device=device)
logging.debug("Inserting clarification token")
else:
temp_adj = 1.3 + 0.2 * attention_metrics["attn_entropy"]
next_token = _sample(logits, temperature=min(1.5, temperature * temp_adj), top_p=top_p, top_k=top_k, min_p=min_p, generator=generator)
logging.debug(f"Using adjusted temperature sampling: {temp_adj:.3f}")
elif entropy < 5.0 and varentropy > 5.0:
temp_adj = 1.2 + 0.3 * attention_metrics["interaction_strength"]
top_k_adj = max(5, int(top_k * (1 + 0.5 * (1 - attention_metrics["agreement"]))))
next_token = _sample(logits, temperature=min(1.5, temperature * temp_adj), top_p=top_p, top_k=top_k_adj, min_p=min_p, generator=generator)
logging.debug(f"Using exploration sampling: temp={temp_adj:.3f}, top_k={top_k_adj}")
elif entropy > 5.0 and varentropy > 5.0:
temp_adj = 2.0 + 0.5 * attention_metrics["attn_varentropy"]
top_p_adj = max(0.5, top_p - 0.2 * attention_metrics["attn_entropy"])
next_token = _sample(logits, temperature=max(2.0, temperature * temp_adj), top_p=top_p_adj, top_k=top_k, min_p=min_p, generator=generator)
logging.debug(f"Using high uncertainty sampling: temp={temp_adj:.3f}, top_p={top_p_adj:.3f}")
else:
next_token = adaptive_sample(
logits,
metrics,
gen_tokens,
n_samples=5,
base_temp=temperature,
base_top_p=top_p,
base_top_k=top_k,
base_min_p=min_p,
generator=generator
)
logging.debug("Using adaptive sampling")
generated_tokens.append(next_token.item())
gen_tokens = torch.cat((gen_tokens, next_token), dim=1)
input_ids = torch.cat([input_ids, next_token], dim=-1)
attention_mask = torch.cat([attention_mask, torch.ones((1, 1), device=device, dtype=torch.long)], dim=-1)
logging.debug(f"Generated token: {tokenizer.decode([next_token.item()])}")
if torch.isin(next_token, stop).any():
logging.info("Reached stop token. Ending generation.")
break
generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
logging.info("Finished entropy decoding")
logging.info(f"Generated text: {generated_text}")
return generated_text
# Usage example
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name, attn_implementation="eager")
tokenizer = AutoTokenizer.from_pretrained(model_name)
messages = [
{"role": "user", "content": "In a dance class of 20 students, 20% enrolled in contemporary dance, 25% of the remaining enrolled in jazz dance, and the rest enrolled in hip-hop dance. What percentage of the entire students enrolled in hip-hop dance?"}
]
logging.info("Starting entropy decoding process")
result = entropy_decode(model, tokenizer, messages)
print(f"Entropy Decoding Result:\n{result}")
logging.info("Entropy decoding process completed")