adding all files on benchmarks, data, new model tot scripts, new prompts - pt1

=0.26.0

@@ -0,0 +1,39 @@
+Collecting accelerate
+  Downloading accelerate-1.2.0-py3-none-any.whl.metadata (19 kB)
+Requirement already satisfied: numpy<3.0.0,>=1.17 in /opt/conda/lib/python3.10/site-packages (from accelerate) (1.25.2)
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+Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from accelerate) (5.9.3)
+Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from accelerate) (6.0.2)
+Requirement already satisfied: torch>=1.10.0 in /opt/conda/lib/python3.10/site-packages (from accelerate) (2.5.1)
+Requirement already satisfied: huggingface-hub>=0.21.0 in /opt/conda/lib/python3.10/site-packages (from accelerate) (0.26.3)
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+Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (12.4.127)
+Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (12.4.127)
+Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (12.4.127)
+Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (9.1.0.70)
+Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (12.4.5.8)
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+Requirement already satisfied: triton==3.1.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (3.1.0)
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+Downloading accelerate-1.2.0-py3-none-any.whl (336 kB)
+Installing collected packages: accelerate
+Successfully installed accelerate-1.2.0

run.py

@@ -4,7 +4,8 @@ import argparse
 import time
 
 from src.tot.tasks import get_task
-from src.tot.methods.bfs import solve, naive_solve
+from src.tot.methods.bfs import solve, naive_solve, solve_bench
+# from src.tot.methods.a_star import solve, naive_solve, solve_bench
 from src.tot.models import gpt_usage
 
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -16,19 +17,21 @@ def run(args):
     '''
     main run function
     '''
-    #bc of the way the original repo is structured, will need to load in llama models in run.py to avoid repeated loading 
+    #load in specific llama model, if applicable
+    #bc of the way the original repo is structured, will need to load in llama models in run.py to avoid repeated loading in models.py
     if args.backend == 'llama':
+        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
+
         if args.quantize and args.quantize=='ptq_int4':
-            model = AutoModelForCausalLM.from_pretrained("src/tot/quant/hf_quant_int4", device_map="cuda")
+            model = AutoModelForCausalLM.from_pretrained("src/tot/quant/hf_quant_int4", device_map="cuda", weights_only=False)
             model = torch.compile(model, mode="max-autotune")
-        if args.quantize and args.quantize=='ptq_int8':
+        elif args.quantize and args.quantize=='ptq_int8':
-            model = AutoModelForCausalLM.from_pretrained("src/tot/ptq_int8", device_map="cuda")
+            model = AutoModelForCausalLM.from_pretrained("src/tot/quant/ptq_int8", device_map="cuda")
             model = torch.compile(model, mode="max-autotune")
-        elif args.backend == 'qat':
+        elif args.quantize and args.quantize == 'qat':
-            model = AutoModelForCausalLM.from_pretrained("src/tot/qat_int8", device_map="cuda")
+            model = AutoModelForCausalLM.from_pretrained("src/tot/quant/qat_int8", device_map="cuda", weights_only=False)
             model = torch.compile(model, mode="max-autotune")
         else:
-            tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
             model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
 
     else: #gpt4 will be used later in this case
@@ -36,6 +39,44 @@ def run(args):
         tokenizer = None
 
     #set up
+    if args.task == 'comp_bench':
+        test_data = torch.load('src/tot/data/agg_dl_test.pt')
+
+        for sample in test_data:
+
+            start_timer = perf.counter()
+            
+            WHILE WE GO THROUGH THE TREE
+            #solve
+            ys, info = solve_bench(args, model, tokenizer)
+
+            runtime = time.perf_counter()-start_timer
+            print(f"""For iteration {i} -- 
+            Total Time to Solve: {runtime} seconds""")
+            
+            #evaluate/rate the proposals
+            
+
+            #compare the proposed final answer vs the ground truth
+            gt = tokenizer.decode(sample['label'][0], return_tensors='pt').replace("<|eot_id|>", "").replace("<|begin_of_text|>", "").strip()
+            # log
+            # infos = [task.test_output(i, y) for y in ys]
+            # info.update({'idx': i, 'ys': ys, 'infos': infos, 'usage_so_far (gpt only)': gpt_usage(args.backend), 'total_runtime': runtime})
+            # logs.append(info)
+            # with open(file, 'w') as f:
+            #     json.dump(logs, f, indent=4)
+            
+            # log main metric
+            # accs = [info['r'] for info in infos]
+            # cnt_avg += sum(accs) / len(accs)
+            # cnt_any += any(accs)
+            # print(i, 'sum(accs)', sum(accs), 'cnt_avg', cnt_avg, 'cnt_any', cnt_any, '\n')
+        
+        # n = args.task_end_index - args.task_start_index
+        # print(cnt_avg / n, cnt_any / n)
+        # print('usage_so_far', gpt_usage(args.backend))
+
+    else: #the original tot tasks from the paper
         task = get_task(args.task)
         logs, cnt_avg, cnt_any = [], 0, 0
         if args.naive_run:
@@ -82,25 +123,23 @@ def parse_args():
     '''
     args = argparse.ArgumentParser()
 
-    #what model to use
+    #the arguments to use for our purposes
     args.add_argument('--backend', type=str, choices=['gpt-4o', 'llama'], default='gpt-4o')
-    args.add_argument('--quantize', type=str, choices=['qat', 'ptq', 'spinquant'])
+    args.add_argument('--quantize', type=str, choices=['qat', 'ptq_int4', 'ptq_int8'])
-
-    #what temperature to use
     args.add_argument('--temperature', type=float, default=0.0)
+    args.add_argument('--method_generate', type=str, choices=['sample', 'propose'])
+    args.add_argument('--method_evaluate', type=str, choices=['value', 'vote'])
+    args.add_argument('--method_select', type=str, choices=['sample', 'greedy'], default='greedy')
 
-    #the problem task
+    #other args from the original repo 
-    args.add_argument('--task', type=str, required=True, choices=['game24', 'text', 'crosswords'])
+    args.add_argument('--task', type=str, required=True, choices=['game24', 'text', 'crosswords', 'comp_bench'])
 
-    #which tasks from the data file to solve
     args.add_argument('--task_start_index', type=int, default=900)
     args.add_argument('--task_end_index', type=int, default=1000)
     
     args.add_argument('--naive_run', action='store_true')
     args.add_argument('--prompt_sample', type=str, choices=['standard', 'cot'])  # only used when method_generate = sample, or naive_run
-    args.add_argument('--method_generate', type=str, choices=['sample', 'propose'])
+
-    args.add_argument('--method_evaluate', type=str, choices=['value', 'vote'])
-    args.add_argument('--method_select', type=str, choices=['sample', 'greedy'], default='greedy')
     args.add_argument('--n_generate_sample', type=int, default=1)  # only thing needed if naive_run
     args.add_argument('--n_evaluate_sample', type=int, default=1)
     args.add_argument('--n_select_sample', type=int, default=1)

run_bench.py

@@ -27,9 +27,6 @@ def load_llama(quant=None):
 
     return model, tokenizer
 
-def propose(problem, current_state, tokenizer, model, device):
-    pass
-
 def value_proposals(problem, current_state, proposals, tokenizer, model, device):
     '''
     Takes in string values of problem, current state, and proposals. 
@@ -38,7 +35,7 @@ def value_proposals(problem, current_state, proposals, tokenizer, model, device)
     valuations = []
     prompts = []
     for p in proposals:
-        prompts.append(value_prompt.format(problem=problem, current_state=current_state, proposal=proposal))
+        prompts.append(value_prompt.format(problem=problem, current_state=current_state, proposal=p))
     
     values = tokenizer(prompts, return_tensors='pt')
     value_inputs = values['input_ids'].to(device)
@@ -58,8 +55,19 @@ def value_proposals(problem, current_state, proposals, tokenizer, model, device)
 
     return valuations
 
-def final_eval():
+def final_eval(gt, final_prop):
-    pass
+    '''
+    compare the ground truth and final proposed solution by the model
+    '''
+    print("THIS IS THE FINAL PROP")
+    print(final_prop)
+    print("THIS IS THE GT")
+    print(gt)
+
+    if gt in final_prop:
+        return 1.0
+    else:
+        return 0.0
 
 
 def run(args):
@@ -83,6 +91,9 @@ def run(args):
     #set up
     test_data = torch.load('src/tot/data/agg_dl_test.pt')
 
+    total = 0
+    right = 0
+
     for samples in test_data:
         
         for sample in samples: #going to do this one problem at a time for now.
@@ -97,6 +108,7 @@ def run(args):
             #start solving via tot
             start_timer = perf.counter()
             
+            selected = ""
             for i in range(args.depth): #args.depth number of attempts to reach the solution
                 
                 #propose next step/solutions per node/prompt
@@ -105,7 +117,7 @@ def run(args):
                     input_ids,
                     attention_mask=mask,
                     temperature=args.temperature, 
-                    max_new_tokens=max_tokens, 
+                    max_new_tokens=args.max_tokens, 
                     num_return_sequences=args.breadth)
 
                 
@@ -123,21 +135,28 @@ def run(args):
                 if 100.0 in valuations:
                     break
                 
-                #select the proposals that act as input to the next iteration
+                #select the best proposal
                 val_props = list(zip(proposals, valuations))
-                valuations.sort(key = lambda ele: ele[1], descending=True)
+                val_props.sort(key = lambda ele: ele[1], descending=True)
-                selected = valuations[:args.greedy_n]
+                selected = val_props[:args.greedy_n]
 
-                inputs = tokenizer(valuations, return_tensors='pt')
+                #format the chosen proposal for the next iteration
+                next_prompt = propose_prompt.format(problem=problem, current_state=selected)
+                inputs = tokenizer(next_prompt, return_tensors='pt')
                 input_ids = inputs['input_ids'].to(device)
                 mask = inputs['attention_mask'].to(device)
 
 
             #compare the proposed final answer vs the ground truth
             gt = tokenizer.decode(label, skip_special_token=True)
+            judgement = final_eval(gt, selected)
 
-            judgement = final_eval(gt, final_proposal)
+            #keep track of the running totals
+            total += 1.0
+            right += judgement
+            print("Accuracy so far: ", total/right)
 
+    total_accuracy = right/total
 
 def parse_args():
     '''

src/tot/methods/bfs.py

@@ -36,7 +36,7 @@ def get_values(task, x, ys, n_evaluate_sample, cache_value=True):
     return values, times
 
 def get_votes(task, x, ys, n_evaluate_sample):
-    vote_prompt = task.vote_prompt_wrap(x, ys)
+    vote_prompt = task.cot_prompt_wrap(x, ys)
     vote_outputs = inference_model(vote_prompt, n=n_evaluate_sample, stop=None)
     values = task.vote_outputs_unwrap(vote_outputs, len(ys))
     return values

src/tot/models.py

@@ -1,7 +1,7 @@
 import os
 import openai
 from openai import OpenAI
-import backoff 
+# import backoff 
 import torch
 import transformers
 import time
@@ -41,7 +41,8 @@ def hf_model(model, tokenizer, prompt, temperature=0.7, max_tokens=1000, n=5, st
     """
     outputs = []
     all_times = []
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    # device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    device = 'cpu'
 
     #tokenize inputs
     inputs = tokenizer(prompt, return_tensors="pt")

src/tot/quant/hf_quant_int4/config.json

@@ -0,0 +1,48 @@
+{
+  "_name_or_path": "meta-llama/Llama-3.2-3B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "modules_to_not_convert": null,
+    "quant_method": "torchao",
+    "quant_type": "int4_weight_only",
+    "quant_type_kwargs": {
+      "group_size": 128
+    }
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

src/tot/quant/hf_quant_int4/generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.46.2"
+}

src/tot/quant/ptq_int8/config.json

@@ -0,0 +1,40 @@
+{
+  "_name_or_path": "meta-llama/Llama-3.2-3B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

src/tot/quant/ptq_int8/generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.46.2"
+}

src/tot/quant/qat_int8/config.json

@@ -0,0 +1,40 @@
+{
+  "_name_or_path": "meta-llama/Llama-3.2-3B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

src/tot/quant/qat_int8/generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.46.2"
+}

src/tot/tasks/__init__.py

@@ -8,5 +8,8 @@ def get_task(name):
     elif name == 'crosswords':
         from src.tot.tasks.crosswords import MiniCrosswordsTask
         return MiniCrosswordsTask()
+    elif name == 'comp_bench':
+        from src.tot.tasks.bench import Bench
+        return Bench()
     else:
         raise NotImplementedError

src/tot/tasks/base.py

@@ -5,11 +5,11 @@ class Task:
     def __init__(self):
         pass
 
-    def __len__(self) -> int:
+    # def __len__(self) -> int:
-        pass
+    #     pass
 
-    def get_input(self, idx: int) -> str:
+    # def get_input(self, idx: int) -> str:
-        pass
+    #     pass
 
     def test_output(self, idx: int, output: str):
         pass

src/tot/tasks/bench.py

@@ -0,0 +1,94 @@
+import re
+import os
+import sympy
+import pandas as pd
+from src.tot.tasks.base import Task, DATA_PATH
+from src.tot.prompts.game24 import * 
+
+
+def get_current_numbers(y: str) -> str:
+    last_line = y.strip().split('\n')[-1]
+    return last_line.split('left: ')[-1].split(')')[0]
+
+
+class Game24Task(Task):
+    """
+    Input (x)   : a string of 
+    Output (y)  : a trajectory of 3 steps to reach 24
+    Reward (r)  : 0 or 1, depending on whether the trajectory is correct
+    Input Example: 
+        1 2 3 4
+    Output Example: 
+        1 + 2 = 3 (left: 3 3 4)
+        3 + 3 = 6 (left: 4 6)
+        6 * 4 = 24 (left: 24)
+        (1 + 2 + 3) * 4 = 24
+    """
+    def __init__(self, file='24.csv', depth=5):
+        """
+        file: a csv file (fixed)
+        """
+        super().__init__()
+        self.data = torch.load('../data/agg_dl_test.pt')
+        self.value_cache = {}
+        self.steps = depth
+        self.stops = None
+
+    def __len__(self) -> int:
+        return len(self.data)
+    
+    def get_input(self, idx: int) -> str:
+        return self.data[idx]
+
+    def test_output(self, idx: int, output: str):
+        expression = output.strip().split('\n')[-1].lower().replace('answer: ', '').split('=')[0]
+        numbers = re.findall(r'\d+', expression)
+        problem_numbers = re.findall(r'\d+', self.data[idx])
+        if sorted(numbers) != sorted(problem_numbers):
+            print("sorted numbers length not equal to sorted problem numbers length")
+            return {'r': 0}
+        try:
+            # print(sympy.simplify(expression))
+            return {'r': int(sympy.simplify(expression) == 24)}
+        except Exception as e:
+            # print(e)
+            print("entered exception!!")
+            return {'r': 0}
+            
+    @staticmethod
+    def standard_prompt_wrap(x: str, y:str='') -> str:
+        return standard_prompt.format(input=x) + y
+
+    @staticmethod
+    def cot_prompt_wrap(x: str, y:str='') -> str:
+        return cot_prompt.format(input=x) + y
+    
+    @staticmethod
+    def propose_prompt_wrap(x: str, y: str='') -> str:
+        current_numbers = get_current_numbers(y if y else x)
+        if current_numbers == '24':
+            prompt = cot_prompt.format(input=x) + 'Steps:' + y
+            # print([prompt])
+        else:
+            prompt = propose_prompt.format(input=current_numbers)
+        return prompt
+    
+    @staticmethod
+    def value_prompt_wrap(x: str, y: str) -> str:
+        last_line = y.strip().split('\n')[-1]
+        if 'left: ' not in last_line:  # last step
+            ans = last_line.lower().replace('answer: ', '')
+            # print([value_last_step_prompt.format(input=x, answer=ans)])
+            return value_last_step_prompt.format(input=x, answer=ans)
+        current_numbers = get_current_numbers(y)
+        return value_prompt.format(input=current_numbers)
+    
+    @staticmethod
+    def value_outputs_unwrap(x: str, y: str, value_outputs: list) -> float:
+        if len(y.strip().split('\n')) == 4 and 'answer' not in y.lower():
+            return 0
+        value_names = [_.split('\n')[-1] for _ in value_outputs]
+        value_map = {'impossible': 0.001, 'likely': 1, 'sure': 20}  # TODO: ad hoc
+        value = sum(value * value_names.count(name) for name, value in value_map.items())
+        normalized_value = value / (len(value_names) * value_map['sure'])   # scale to [0, 1]
+        return normalized_value