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from tqdm import tqdm
from datetime import datetime
import os
import json
from concurrent.futures import ThreadPoolExecutor, as_completed
from agents.reflexion_oneshot import Reflexion_Oneshot
from utils.utils import clear_code, extract_function_signatures, clear_json
from memories.Memory import MemoryClassMeta
from prompts import prompt_for_generation, prompt_for_reflection
from loguru import logger
from tenacity import RetryError
from dataloaders.ProblemState import tempCode
from typing import List, Optional
class GaAgent(Reflexion_Oneshot):
def __init__(self, model, dataset, corpus_path, max_perf_debug_num=5, mem_file=None, descendant_num=1):
super().__init__(model, dataset, corpus_path, mem_file, descendant_num)
self.max_perf_debug_num = max_perf_debug_num
def memory_init(self, mem_file=None, descendant_num=1):
"""
Args:
mem_file: previous stored memories, which can be loaded to continue run
"""
class Memory(metaclass=MemoryClassMeta, field_names=["ps",
"call_err_msg",
"exe_err_msg",
"reflection",
"function_signatures",
"oneshot",
"perf_candidates",
"perf_strategy",
"raw_codes",
"call_candidate",
"exe_candidate",
"temp_strategy",
"perf_debug_num",
"pass_call",
"pass_exe",
"pass_perf",
"history"]):
pass
if mem_file is not None:
assert mem_file.endswith(".json"), f"expect a json file, but got {mem_file} instead"
with open(mem_file, "r") as f:
input_mems = json.load(f)
assert len(input_mems) == len(self.dataset), f"expect {len(self.dataset)} samples, but got {len(input_mems)} instead"
for ps in self.dataset.problem_states:
if ps.label:
fs_mem = extract_function_signatures(ps.label)
else:
fs_mem = None
raw_codes =None
if mem_file is None:
os_mem = self.instruction_retriever.query(ps.instruction)[0]
tmp_mem = Memory(ps=ps,
call_err_msg=None,
exe_err_msg=None,
reflection=None,
function_signatures=fs_mem,
oneshot=os_mem["code"],
perf_candidates=[],
perf_strategy=None,
raw_codes=raw_codes,
call_candidate=None,
exe_candidate=None,
temp_strategy=None,
perf_debug_num=0,
pass_call=False,
pass_exe=False,
pass_perf=False,
history=[[] for _ in range(descendant_num)]
)
else:
input_mem = input_mems[ps.filename]
tmp_mem = Memory(
ps=ps,
call_err_msg=input_mem["call_err_msg"],
exe_err_msg=input_mem["exe_err_msg"],
reflection=input_mem["reflection"],
function_signatures=fs_mem,
oneshot=input_mem["oneshot"],
perf_candidates=input_mem["perf_candidates"],
perf_strategy=input_mem["perf_strategy"],
raw_codes=raw_codes,
call_candidate=input_mem["call_candidate"],
exe_candidate=input_mem["exe_candidate"],
temp_strategy=input_mem["temp_strategy"],
perf_debug_num=input_mem["perf_debug_num"],
pass_call=input_mem["pass_call"],
pass_exe=input_mem["pass_exe"],
pass_perf=input_mem["pass_perf"],
history=[[] for _ in range(descendant_num)]
)
self.memories.append(tmp_mem)
def write_memories(self, file_path):
output_dict = {}
with open(file_path, "w") as f:
for mem in self.memories:
output = {
"call_err_msg": str(mem.call_err_msg),
"exe_err_msg": str(mem.exe_err_msg),
"reflection": mem.reflection,
"oneshot": mem.oneshot,
"perf_candidates": [list(cand) for cand in mem.perf_candidates],
"perf_strategy": mem.perf_strategy,
"call_candidate": mem.call_candidate,
"exe_candidate": mem.exe_candidate,
"temp_strategy": mem.temp_strategy,
"perf_debug_num": mem.perf_debug_num,
"pass_call": mem.pass_call,
"pass_exe": mem.pass_exe,
"pass_perf": mem.pass_perf
}
output_dict[mem.ps.filename] = output
json.dump(output_dict, f)
def run(self, output_path=None, multi_thread=True, datalen=None, iteration_num=0, temperature=0, ancestor_num=5, descendant_num=1, mutation=False, start_idx=0, gpu_id=0, start_iter=0, descendant_debug=1, target_gpu='MI250', profiling=False, args={}):
"""
Args:
output_path: the folder to store the final result
multi_thread: whether use multithreading for generating
datalen: for debug, to specify how many data from the dataset you want to use
iteration_num: how many iterations you want to run
temperature: LLM temperature
ancestor_num: how many samples you want to add in the prompt when optimize the code
descendant_num: how many codes you want to generate in one try
start_idx: start idx of the data rows
gpu_id: which gpu you want to use when you test the scripts
start_iter: which iteration you want to start with. useful when you load previous result and memory
"""
assert ancestor_num >= 0, f"expect ancestor_num to be larger than 0, bug got {ancestor_num}"
assert descendant_num >= 0, f"expect descendant_num to be larger than 0, bug got {descendant_num}"
assert descendant_debug >= 0, f"expect descendant_debug to be larger than 0, bug got {descendant_debug}"
data_len = datalen if datalen else len(self.dataset)
try:
self.max_length = getattr(args, 'max_length', 30000)
except:
self.max_length = 30000
# import ipdb; ipdb.set_trace()
for iter in range(start_iter, iteration_num):
logger.info(f"\n=== Iteration {iter} ===")
if output_path is not None:
root, extension = os.path.splitext(output_path)
iter_path = f"{root}_{iter}{extension}"
mem_output_path = f"{root}_mem_{iter}.json"
if multi_thread:
thread_num = 64
# generate solution
logger.info(f"\ngenerate solution")
with tqdm(total=data_len) as pbar:
if multi_thread:
with ThreadPoolExecutor(max_workers=thread_num) as executor:
futures = {executor.submit(self.generate_solution, mem, temperature, descendant_num, mutation): mem for mem in self.memories[start_idx:(start_idx + data_len)]}
for future in as_completed(futures):
pbar.update(1)
else:
for mem in self.memories[start_idx:data_len]:
self.generate_solution(mem, temperature=temperature, descendant_num=descendant_num, mutation=mutation)
pbar.update(1)
# generate reflections
logger.info(f"\ngenerate LLM evaluation")
with tqdm(total=data_len) as pbar:
if multi_thread:
with ThreadPoolExecutor(max_workers=thread_num) as executor:
futures = {executor.submit(self.generate_llm_evaluate, mem, temperature): mem for mem in self.memories[start_idx:(start_idx + data_len)]}
for future in as_completed(futures):
pbar.update(1)
else:
for mem in self.memories[start_idx:data_len]:
self.generate_llm_evaluate(mem, temperature=temperature)
pbar.update(1)
# run scripts
logger.info(f"\nrun scripts on gpu")
if output_path is not None:
root, extension = os.path.splitext(output_path)
tmp_dir = f"{root}_tmp"
exe_dir = f"{root}_pass_exe"
perf_result_dir = f"{root}_perf_results"
perf_log_dir = f"{root}_perf_logs"
else:
tmp_dir = "tmp"
exe_dir = "pass_exe"
perf_result_dir = "perf_results"
perf_log_dir = "perf_logs"
for mem in tqdm(self.memories[start_idx:data_len]):
if mem.raw_codes:
for i in range(len(mem.raw_codes)):
raw_code = mem.raw_codes[i]
speedup = 0.0
if raw_code.pass_perf:
continue
try:
if raw_code.code :
pass_call, pass_exe, speedup, stdout, stderr = self.dataset.test_opt_correctness(raw_code.code, filename=mem.ps.filename, tmp_dir=tmp_dir, exe_dir=exe_dir, gpu_id=gpu_id)
else:
pass_call, pass_exe, speedup, stdout, stderr = False, False, 0.0, "", "Code is empty"
except Exception as e:
print(f"failed to test the code for {mem.ps.filename}")
raw_code.test_stdout = f"failed to test the code due to: {e}"
raw_code.test_stderr = f"failed to test the code due to: {e}"
continue
if not pass_call:
raw_code.test_stdout = stdout
raw_code.test_stderr = stderr
raw_code.profilig = None
elif pass_call and not pass_exe:
raw_code.pass_call = True
raw_code.test_stdout = stdout
raw_code.test_stderr = stderr if stderr else stdout
mem.call_candidate = raw_code.code
mem.temp_strategy = raw_code.strategy
mem.pass_call = True
raw_code.profilig= None
else:
raw_code.pass_call = True
raw_code.pass_exe = True
mem.pass_call = True
mem.exe_candidate = raw_code.code
mem.call_candidate = raw_code.code
mem.temp_strategy = raw_code.strategy
if profiling:
pass_prfiler, stdout_profile, stderr_profile, stdout_analyze = self.dataset.test_kernel_profiling(raw_code.code, mem.ps.filename, tmp_dir, exe_dir=exe_dir, target_gpu=target_gpu, timeout=30*60)
raw_code.profilig = stdout_analyze
mem.call_err_msg = raw_code.test_stdout
mem.exe_err_msg = raw_code.test_stderr
if speedup > 0.0 and pass_exe:
raw_code.pass_perf = True
mem.pass_perf = True
raw_code.latency = speedup
raw_code.eff = 0.0
descendant_debug = min(descendant_debug,len(mem.raw_codes))
if sum(rc.pass_exe for rc in mem.raw_codes)>=descendant_debug:
mem.pass_exe = True
# generate reflections
logger.info(f"\ngenerate reflections")
with tqdm(total=data_len) as pbar:
if multi_thread:
with ThreadPoolExecutor(max_workers=thread_num) as executor:
futures = {executor.submit(self.generate_reflexion, mem, temperature): mem for mem in self.memories[start_idx:(start_idx + data_len)]}
for future in as_completed(futures):
pbar.update(1)
else:
for mem in self.memories[start_idx:data_len]:
self.generate_reflexion(mem, temperature=temperature)
pbar.update(1)
# update perf_candidates
for mem in self.memories[start_idx:data_len]:
if mem.raw_codes:
for i in range(len(mem.raw_codes)):
raw_code = mem.raw_codes[i]
mem.history[i].append(raw_code)
codes_sorted = sorted(mem.history[i], key=lambda x: x.llm_metric, reverse=True)
mem.history[i] = codes_sorted[:5]
if raw_code.pass_perf and raw_code.strategy:
raw_code.strategy = None
self.update_perf_candidates(mem=mem, raw_code=raw_code, ancestor_num=ancestor_num)
if len(mem.perf_candidates) > 0:
mem.ps.solution = mem.perf_candidates[-1][0]
mem.ps.speedup = mem.perf_candidates[-1][1]
elif mem.exe_candidate:
mem.ps.solution = mem.exe_candidate
elif mem.call_candidate:
mem.ps.solution = mem.call_candidate
elif mem.raw_codes:
mem.ps.solution = mem.raw_codes[0].code
if output_path is not None:
self.dataset.write_file(iter_path)
self.write_memories(mem_output_path)
os.system(f'rm -rf {exe_dir}')
os.system(f'rm -rf {perf_result_dir}')
os.system(f'rm -rf {perf_log_dir}')
os.system(f'rm -rf {tmp_dir}')
def generate_solution(self, mem, temperature=0, descendant_num=1, mutation=False):
tab = "\n"
fss_text = "".join(f"* {sig}{tab}" for sig in mem.function_signatures)
text = prompt_for_generation.prompt.format(
instruction=mem.ps.instruction,
function_signatures=fss_text
)
# for the one that has perf_candidates, and the code generated in this round pass_exe, we need to generate a new code
# for the one that has perf_candidates, but the code generated in this round not pass_exe, if the debug_num has exceeds the man_debug_num, then generate a new code
# otherwise, go to debug
if (mem.perf_debug_num >= self.max_perf_debug_num) or mem.pass_exe:
mem.perf_debug_num = 0
mem.raw_codes =None
if len(mem.perf_candidates) > 0 and not mem.raw_codes:
text += """\nThere are some Optimized codes(NO.1, NO.2 and so on) to solve the Problem. The Optimized codes are arranged in ascending order based on their performance, where higher speedup indicates better performance. According to their performance(speedup is the latency compared with golden reference code) and the corresponding analysis, you need to generate a new code with better performance. You should maintain code correctness during optimization."""
text +="\nYou can use optimization strategies such as Memory access efficiency, Hardware resource utilization, IR analysis, Assembly analysis, Kernel occupancy, TorchInductor with Triton tuning knobs and Auto-tunable kernel configurations and environment variables."
for i, cand in enumerate(mem.perf_candidates):
text += f"\n### Reference {i+1}"
text += f"\nOptimized code: {cand[0]}"
text += f"\nOptimized speedup: {cand[1]}"
if cand[3]:
text += f"\nStrategy: {cand[3]}"
if cand[4]:
text += f"\nRocprof-compute profiling result:{cand[4]}"
if mutation:
text += "\nGenerate a better strategy completely different from Optimized Implementation. Based on the better strategy generate a better optimization code."
else:
text += "\nAnalyze and compare all optimization strategies based on Optimized Implementation codes and give a better strategy motivated by them. Based on the better strategy generate a better optimization code to get a higher speedup."
else:
if not mem.exe_candidate and not mem.call_candidate and not mem.raw_codes:
text += f"\nHere is an example snippet of code: {mem.oneshot}"
elif mem.raw_codes:
one_shot = self.code_retriever.query(mem.raw_codes[0].code)[0]["code"]
text += f"\nHere is an example snippet of code: {one_shot}"
if mem.raw_codes :
for i in range(len(mem.raw_codes)):
raw_code = mem.raw_codes[i]
if not raw_code.pass_perf:
history_text = self._build_history_prompt(mem.history[i])
text += f"\nPrevious attempt implementations:{history_text}"
text += prompt_for_generation.system_prompt
if raw_code.reflections:
raw_code.reflections = None
try:
raw_code.code, raw_code.strategy = self.call_llm_code(prompt=text, temperature=temperature)
except:
logger.info(f"failed to call LLM for {mem.ps.filename}")
mem.perf_debug_num +=1
return
gens_codes: List[tempCode] = []
for i in range(descendant_num):
gen_code = tempCode()
try:
text += prompt_for_generation.system_prompt
gen_code.code, gen_code.strategy = self.call_llm_code(prompt=text, temperature=temperature)
except:
logger.info(f"failed to call LLM for {mem.ps.filename}")
gens_codes.append(gen_code)
mem.raw_codes = gens_codes
mem.pass_exe = False
mem.pass_call = False
mem.pass_perf = False
return
def generate_reflexion(self, mem, temperature):
tab = "\n"
fss_text = "".join(f"* {sig}{tab}" for sig in mem.function_signatures)
m_info = """
- runnable test: test if the code can be successfully executed.
- correctness test: test if the output of the code is correct, i.e. if the code does implement the functionality required in the original problem.
- speedup: measures the total time from kernel launch to completion, reflecting the responsiveness and overhead of executing a single instance of the kernel on the GPU. And compare the time with golden reference code to get speedup.
"""
if mem.raw_codes :
for i in range(len(mem.raw_codes)):
raw_code = mem.raw_codes[i]
if raw_code.reflections:
continue
history_text = self._build_history_prompt(mem.history[i])
if raw_code.pass_exe:
result_txt = f"""
- runnable test: Succeed
- correctness test: Succeed
- speedup: {raw_code.latency}
"""
reflect_txt = prompt_for_reflection.prompt_evolve_strategy_optimize.format(
instruction=mem.ps.instruction,
function_signatures=fss_text,
metrics_info=m_info,
evolution_history=history_text,
current_program=raw_code.code,
test_result=result_txt,
reflection=raw_code.reflections
)
else:
if raw_code.pass_call:
result_txt = f"""
- runnable test: Succeed
- correctness test: Failed
Error Message: {raw_code.test_stderr}
"""
else:
result_txt = f"""
- runnable test: Failed
Error Message: {raw_code.test_stderr}
- correctness test: Failed
Error Message: {raw_code.test_stderr}
"""
reflect_txt = prompt_for_reflection.prompt_evolve_reflect.format(
instruction=mem.ps.instruction,
function_signatures=fss_text,
metrics_info=m_info,
evolution_history=history_text,
current_program=raw_code.code,
test_result=result_txt,
reflection=raw_code.reflections
)
reflect_msg = [
{
"role": "user",
"content": reflect_txt
}
]
raw_code.reflections = self.model.generate(reflect_msg, temperature=temperature)
def call_llm_code(self, prompt, temperature):
msg = [{"role": "user", "content": prompt}]
response = self.model.generate(msg, temperature=temperature, max_tokens=self.max_length)
try:
# response = self.model.generate(msg, temperature=temperature, max_tokens=30000)
opti = clear_json(response)
if 'code' in opti.keys() and 'strategy' in opti.keys():
code = clear_code(opti['code'])
strategy = opti['strategy']
logger.info(f"success to call LLM")
return code, strategy
except:
save_base_path = 'debug_result'
save_path = os.path.join(save_base_path, datetime.now().strftime("%Y%m%d_%H%M%S") + '.jsonl')
with open(save_path, "w", encoding="utf-8") as f:
json_line = json.dumps({'output': response, 'input': prompt}, ensure_ascii=False)
f.write(json_line + "\n")
logger.info(f"failed to call LLM")
return None
raise ValueError("failed to call LLM")
def call_llm_reflecion(self, prompt, temperature):
msg = [{"role": "user", "content": prompt}]
try:
response = self.model.generate(msg, temperature=temperature, max_tokens=30000)
opti = clear_json(response)
if 'reflection' in opti.keys():
reflection = opti['reflection']
return reflection
except:
logger.info(f"failed to call LLM")
raise ValueError("failed to call LLM")
def update_perf_candidates(self,mem, raw_code: tempCode, ancestor_num):
if len(mem.perf_candidates) < ancestor_num:
candidate = [raw_code.code, raw_code.latency, raw_code.eff, raw_code.reflections, raw_code.profilig]
mem.perf_candidates.append(tuple(candidate))
mem.perf_candidates = sorted(mem.perf_candidates, key=lambda x: x[1], reverse=False)
elif mem.perf_candidates[0][1] <= raw_code.latency:
candidate = [raw_code.code, raw_code.latency, raw_code.eff, raw_code.reflections, raw_code.profilig]
mem.perf_candidates[0] = tuple(candidate)
# order the candidates in ascending order with regard to speedups
mem.perf_candidates = sorted(mem.perf_candidates, key=lambda x: x[1], reverse=False)
def _build_history_prompt(self, history):
text = ""
history_template = """
### Attempt {attempt_number}
- Code:
```python
{code}
```
- Test Results:
{test_results}
- Analysis:
{reflection}
"""
for i, raw_code in enumerate(history):
if raw_code.pass_perf:
test_txt = """
runnable test: Succeed
correctness test: Succeed
speedup: {latency}
"""
test_txt = test_txt.format(
speedup=raw_code.latency
)
elif raw_code.pass_exe and not raw_code.pass_perf:
test_txt = """
runnable test: Succeed
correctness test: Succeed
"""
elif raw_code.pass_call and not raw_code.pass_exe:
test_txt = """
runnable test: Succeed
correctness test: {err_msg}
"""
test_txt = test_txt.format(
err_msg=raw_code.test_stderr
)
elif not raw_code.pass_call:
test_txt = """
runnable test: {err_msg}
"""
test_txt = test_txt.format(
err_msg=raw_code.test_stderr
)
text += history_template.format(
attempt_number=i+1,
code=raw_code.code,
test_results=test_txt,
reflection=raw_code.reflections
)
return text
def generate_llm_evaluate(self, mem, temperature=1.0):
if mem.raw_codes :
for i in range(len(mem.raw_codes)):
raw_code = mem.raw_codes[i]
if not raw_code.pass_perf:
text = ""
text += prompt_for_generation.llm_evaluate_prompt.format(current_program=raw_code.code)
msg = [{"role": "user", "content": text}]
try:
response = self.model.generate(msg, temperature=temperature, max_tokens=30000)
llm_eval = clear_json(response)
metric = 0.0
for k, v in llm_eval.items():
if k == "reasoning":
continue
if isinstance(v, float) or isinstance(v, int):
metric += float(v)
raw_code.llm_metric = metric
raw_code.llm_eval = llm_eval
except:
logger.info(f"failed to generate LLM evaluation")
raise ValueError("failed to generate LLM evaluation")
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