decoding updates

Author: codelion

optillm/cot_decoding.py

@@ -4,7 +4,9 @@
 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}")

optillm/entropy_decoding.py

@@ -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")