vyraun · February 1, 2025 04:49 · Jan 29, 2025 · Jan 27, 2025 · Jan 27, 2025 · Jan 27, 2025
diff --git a/grpo_demo.py b/grpo_demo.py
@@ -1,7 +1,8 @@
 # train_grpo.py
 import re
+import torch
 from datasets import load_dataset, Dataset
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import LoraConfig
 from trl import GRPOConfig, GRPOTrainer
 
@@ -37,16 +38,17 @@ def extract_hash_answer(text: str) -> str | None:
         return None
     return text.split("####")[1].strip()
 
+# uncomment middle messages for 1-shot prompting
 def get_gsm8k_questions(split = "train") -> Dataset:
     data = load_dataset('openai/gsm8k', 'main')[split] # type: ignore
     data = data.map(lambda x: { # type: ignore
         'prompt': [
             {'role': 'system', 'content': SYSTEM_PROMPT},
-            {'role': 'user', 'content': 'What is the largest single-digit prime number?'},
-            {'role': 'assistant', 'content': XML_COT_FORMAT.format(
-                reasoning="9 is divisble by 3 and 8 is divisible by 2, but 7 is prime.",
-                answer="7"
-            )},
+            #{'role': 'user', 'content': 'What is the largest single-digit prime number?'},
+            #{'role': 'assistant', 'content': XML_COT_FORMAT.format(
+            #    reasoning="9 is divisble by 3 and 8 is divisible by 2, but 7 is prime.",
+            #    answer="7"
+            #)},
             {'role': 'user', 'content': x['question']}
         ],
         'answer': extract_hash_answer(x['answer'])
@@ -81,7 +83,7 @@ def soft_format_reward_func(completions, **kwargs) -> list[float]:
     responses = [completion[0]["content"] for completion in completions]
     matches = [re.match(pattern, r) for r in responses] 
     return [0.5 if match else 0.0 for match in matches]
-    
+
 def count_xml(text) -> float:
     count = 0.0
     if text.count("<reasoning>\n") == 1:
@@ -100,21 +102,35 @@ def xmlcount_reward_func(completions, **kwargs) -> list[float]:
     contents = [completion[0]["content"] for completion in completions]
     return [count_xml(c) for c in contents]
 
+#model_name = "meta-llama/Llama-3.2-1B-Instruct"
+model_name = "Qwen/Qwen2.5-1.5B-Instruct"
+
+if "Llama" in model_name:
+    output_dir = "outputs/Llama-1B-GRPO"
+    run_name = "Llama-1B-GRPO-gsm8k"
+else:
+    output_dir="outputs/Qwen-1.5B-GRPO"
+    run_name="Qwen-1.5B-GRPO-gsm8k"
+
 training_args = GRPOConfig(
-    output_dir="outputs/Llama-1B-base-GRPO",
-    run_name="Llama-1B-base-GRPO-gsm8k",
-    learning_rate=1e-6,
+    output_dir=output_dir,
+    run_name=run_name,
+    learning_rate=5e-6,
     adam_beta1 = 0.9,
-    adam_beta2 = 0.95,
+    adam_beta2 = 0.99,
     weight_decay = 0.1,
     warmup_ratio = 0.1,
     lr_scheduler_type='cosine',
     logging_steps=1,
+    bf16=True,
     per_device_train_batch_size=1,
-    gradient_accumulation_steps=6,
-    num_generations=12,
-    max_completion_length=512,
-    max_grad_norm=0.01,
+    gradient_accumulation_steps=4,
+    num_generations=16,
+    max_prompt_length=256,
+    max_completion_length=786,
+    num_train_epochs=1,
+    save_steps=100,
+    max_grad_norm=0.1,
     report_to="wandb",
     log_on_each_node=False,
 )
@@ -125,11 +141,19 @@ def xmlcount_reward_func(completions, **kwargs) -> list[float]:
     task_type="CAUSAL_LM",
     lora_dropout=0.05,
 )
-model_name = "meta-llama/Llama-3.2-1B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map=None
+).to("cuda")
+
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 tokenizer.pad_token = tokenizer.eos_token
+
+# use peft at your own risk; not working for me with multi-GPU training
 trainer = GRPOTrainer(
-    model=model_name,
+    model=model,
     processing_class=tokenizer,
     reward_funcs=[
         xmlcount_reward_func,

diff --git a/grpo_demo.py b/grpo_demo.py
@@ -125,8 +125,8 @@ def xmlcount_reward_func(completions, **kwargs) -> list[float]:
     task_type="CAUSAL_LM",
     lora_dropout=0.05,
 )
-model_name = "meta-llama/Llama-3.2-1B"
-tokenizer = AutoTokenizer.from_pretrained(model_name + "-Instruct")
+model_name = "meta-llama/Llama-3.2-1B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 tokenizer.pad_token = tokenizer.eos_token
 trainer = GRPOTrainer(
     model=model_name,

diff --git a/grpo_demo.py b/grpo_demo.py
@@ -61,7 +61,6 @@ def correctness_reward_func(prompts, completions, answer, **kwargs) -> list[floa
     q = prompts[0][-1]['content']
     extracted_responses = [extract_xml_answer(r) for r in responses]
     print('-'*20, f"Question:\n{q}", f"\nAnswer:\n{answer[0]}", f"\nResponse:\n{responses[0]}", f"\nExtracted:\n{extracted_responses[0]}")
-    responses = [extract_xml_answer(r) for r in responses]
     return [2.0 if r == a else 0.0 for r, a in zip(extracted_responses, answer)]
 
 def int_reward_func(completions, **kwargs) -> list[float]:

diff --git a/grpo_demo.py b/grpo_demo.py
@@ -8,7 +8,7 @@
 # Load and prep dataset
 
 SYSTEM_PROMPT = """
-Respond the in the following format:
+Respond in the following format:
 
 <reasoning>
 ...
@@ -18,6 +18,15 @@
 </answer>
 """
 
+XML_COT_FORMAT = """\
+<reasoning>
+{reasoning}
+</reasoning>
+<answer>
+{answer}
+</answer>
+"""
+
 def extract_xml_answer(text: str) -> str:
     answer = text.split("<answer>")[-1]
     answer = answer.split("</answer>")[0]
@@ -33,6 +42,11 @@ def get_gsm8k_questions(split = "train") -> Dataset:
     data = data.map(lambda x: { # type: ignore
         'prompt': [
             {'role': 'system', 'content': SYSTEM_PROMPT},
+            {'role': 'user', 'content': 'What is the largest single-digit prime number?'},
+            {'role': 'assistant', 'content': XML_COT_FORMAT.format(
+                reasoning="9 is divisble by 3 and 8 is divisible by 2, but 7 is prime.",
+                answer="7"
+            )},
             {'role': 'user', 'content': x['question']}
         ],
         'answer': extract_hash_answer(x['answer'])
@@ -77,24 +91,31 @@ def count_xml(text) -> float:
         count += 0.125
     if text.count("\n<answer>\n") == 1:
         count += 0.125
+        count -= len(text.split("\n</answer>\n")[-1])*0.001
     if text.count("\n</answer>") == 1:
         count += 0.125
+        count -= (len(text.split("\n</answer>")[-1]) - 1)*0.001
     return count
 
 def xmlcount_reward_func(completions, **kwargs) -> list[float]:
     contents = [completion[0]["content"] for completion in completions]
     return [count_xml(c) for c in contents]
 
 training_args = GRPOConfig(
-    output_dir="outputs/Llama-1B-GRPO",
-    run_name="Llama-1B-GRPO-gsm8k",
-    learning_rate=3e-6,
+    output_dir="outputs/Llama-1B-base-GRPO",
+    run_name="Llama-1B-base-GRPO-gsm8k",
+    learning_rate=1e-6,
+    adam_beta1 = 0.9,
+    adam_beta2 = 0.95,
+    weight_decay = 0.1,
+    warmup_ratio = 0.1,
+    lr_scheduler_type='cosine',
     logging_steps=1,
     per_device_train_batch_size=1,
     gradient_accumulation_steps=6,
     num_generations=12,
     max_completion_length=512,
-    max_grad_norm=0.001,
+    max_grad_norm=0.01,
     report_to="wandb",
     log_on_each_node=False,
 )
@@ -105,8 +126,8 @@ def xmlcount_reward_func(completions, **kwargs) -> list[float]:
     task_type="CAUSAL_LM",
     lora_dropout=0.05,
 )
-model_name = "meta-llama/Llama-3.2-1B-Instruct"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+model_name = "meta-llama/Llama-3.2-1B"
+tokenizer = AutoTokenizer.from_pretrained(model_name + "-Instruct")
 tokenizer.pad_token = tokenizer.eos_token
 trainer = GRPOTrainer(
     model=model_name,

diff --git a/grpo_demo.py b/grpo_demo.py
@@ -1,4 +1,4 @@
-# grpo_demo.py
+# train_grpo.py
 import re
 from datasets import load_dataset, Dataset
 from transformers import AutoTokenizer
@@ -94,6 +94,7 @@ def xmlcount_reward_func(completions, **kwargs) -> list[float]:
     gradient_accumulation_steps=6,
     num_generations=12,
     max_completion_length=512,
+    max_grad_norm=0.001,
     report_to="wandb",
     log_on_each_node=False,
 )

diff --git a/grpo_demo.py b/grpo_demo.py
@@ -0,0 +1,123 @@
+# grpo_demo.py
+import re
+from datasets import load_dataset, Dataset
+from transformers import AutoTokenizer
+from peft import LoraConfig
+from trl import GRPOConfig, GRPOTrainer
+
+# Load and prep dataset
+
+SYSTEM_PROMPT = """
+Respond the in the following format:
+
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>
+"""
+
+def extract_xml_answer(text: str) -> str:
+    answer = text.split("<answer>")[-1]
+    answer = answer.split("</answer>")[0]
+    return answer.strip()
+
+def extract_hash_answer(text: str) -> str | None:
+    if "####" not in text:
+        return None
+    return text.split("####")[1].strip()
+
+def get_gsm8k_questions(split = "train") -> Dataset:
+    data = load_dataset('openai/gsm8k', 'main')[split] # type: ignore
+    data = data.map(lambda x: { # type: ignore
+        'prompt': [
+            {'role': 'system', 'content': SYSTEM_PROMPT},
+            {'role': 'user', 'content': x['question']}
+        ],
+        'answer': extract_hash_answer(x['answer'])
+    }) # type: ignore
+    return data # type: ignore
+
+dataset = get_gsm8k_questions()
+
+# Reward functions
+def correctness_reward_func(prompts, completions, answer, **kwargs) -> list[float]:
+    responses = [completion[0]['content'] for completion in completions]
+    q = prompts[0][-1]['content']
+    extracted_responses = [extract_xml_answer(r) for r in responses]
+    print('-'*20, f"Question:\n{q}", f"\nAnswer:\n{answer[0]}", f"\nResponse:\n{responses[0]}", f"\nExtracted:\n{extracted_responses[0]}")
+    responses = [extract_xml_answer(r) for r in responses]
+    return [2.0 if r == a else 0.0 for r, a in zip(extracted_responses, answer)]
+
+def int_reward_func(completions, **kwargs) -> list[float]:
+    responses = [completion[0]['content'] for completion in completions]
+    extracted_responses = [extract_xml_answer(r) for r in responses]
+    return [0.5 if r.isdigit() else 0.0 for r in extracted_responses]
+
+def strict_format_reward_func(completions, **kwargs) -> list[float]:
+    """Reward function that checks if the completion has a specific format."""
+    pattern = r"^<reasoning>\n.*?\n</reasoning>\n<answer>\n.*?\n</answer>\n$"
+    responses = [completion[0]["content"] for completion in completions]
+    matches = [re.match(pattern, r) for r in responses] 
+    return [0.5 if match else 0.0 for match in matches]
+
+def soft_format_reward_func(completions, **kwargs) -> list[float]:
+    """Reward function that checks if the completion has a specific format."""
+    pattern = r"<reasoning>.*?</reasoning>\s*<answer>.*?</answer>"
+    responses = [completion[0]["content"] for completion in completions]
+    matches = [re.match(pattern, r) for r in responses] 
+    return [0.5 if match else 0.0 for match in matches]
+
+def count_xml(text) -> float:
+    count = 0.0
+    if text.count("<reasoning>\n") == 1:
+        count += 0.125
+    if text.count("\n</reasoning>\n") == 1:
+        count += 0.125
+    if text.count("\n<answer>\n") == 1:
+        count += 0.125
+    if text.count("\n</answer>") == 1:
+        count += 0.125
+    return count
+
+def xmlcount_reward_func(completions, **kwargs) -> list[float]:
+    contents = [completion[0]["content"] for completion in completions]
+    return [count_xml(c) for c in contents]
+
+training_args = GRPOConfig(
+    output_dir="outputs/Llama-1B-GRPO",
+    run_name="Llama-1B-GRPO-gsm8k",
+    learning_rate=3e-6,
+    logging_steps=1,
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=6,
+    num_generations=12,
+    max_completion_length=512,
+    report_to="wandb",
+    log_on_each_node=False,
+)
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=64,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "down_proj", "gate_proj"],
+    task_type="CAUSAL_LM",
+    lora_dropout=0.05,
+)
+model_name = "meta-llama/Llama-3.2-1B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+trainer = GRPOTrainer(
+    model=model_name,
+    processing_class=tokenizer,
+    reward_funcs=[
+        xmlcount_reward_func,
+        soft_format_reward_func,
+        strict_format_reward_func,
+        int_reward_func,
+        correctness_reward_func],
+    args=training_args,
+    train_dataset=dataset,
+    #peft_config=peft_config
+)
+trainer.train()