Agent Backend

graph_net/config/config_agent_backend.yaml

@@ -0,0 +1,24 @@
+llm_name: "gpt-5"
+temperature: 1.0
+top_p: 1.0
+max_tokens: 16384
+parallel_query_nums: 1
+iterative_query_nums: 2
+
+# support "CUDA", "Triton", future will support "TileLang", etc.
+DSL: "CUDA"
+
+# responses and logs will be saved in <top_save_dir>/<llm_name>
+top_save_dir: "./tmp/llm_cache" 
+
+# network settings
+timeout_seconds: 600.0
+max_retries: 3
+backoff_initial_seconds: 1.0
+backoff_max_seconds: 16.0
+
+# local Inference settings
+is_local_inference: False
+local_inference_vendor: "vllm"  # only for print logs
+local_inference_port: 30000
+local_inference_address: "localhost"

graph_net/config/fetch_agent_config.py

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+import os
+import yaml
+
+
+def get_llm_config() -> dict:
+    api_key = os.getenv("GRAPHNET_AGENT_API_KEY", None)
+    base_url = os.getenv("GRAPHNET_AGENT_BASE_URL", None)
+    config_dir = os.path.dirname(os.path.abspath(__file__))
+    llm_config_path = os.path.join(config_dir, "config_agent_backend.yaml")
+    with open(llm_config_path, "r") as file:
+        llm_config = yaml.safe_load(file)
+    llm_config["api_key"] = api_key
+    llm_config["api_url"] = base_url
+    return llm_config

graph_net/torch/backend/agent_backend.py

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+import torch
+from graph_net.torch.backend.graph_compiler_backend import GraphCompilerBackend
+from graph_net.torch.backend.agent_ncu import agent_compile
+
+
+class AgentCompiledModule(torch.nn.Module):
+    def __init__(self, module):
+        super().__init__()
+        self.module = module
+        self.count_compile = 0
+
+    def forward(self, *args, **kwargs):
+        self.module = self.compile(*args, **kwargs)
+        return self.module
+
+    def compile(self, *args, **kwargs):
+        dummy_input = tuple([*args, *kwargs.values()])
+        optimized_module = agent_compile.optimize(
+            self.module,
+            model_inputs=dummy_input,
+            task_name=f"default_task_{self.count_compile}",
+        )
+        self.count_compile += 1
+        return optimized_module
+
+
+class AgentBackend(GraphCompilerBackend):
+    def __call__(self, model):
+        return AgentCompiledModule(model)
+
+    def synchronize(self):
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()

graph_net/torch/backend/agent_ncu/agent_compile.py

@@ -0,0 +1,196 @@
+import os
+import torch
+import warnings
+from graph_net.config.fetch_agent_config import get_llm_config
+from graph_net.torch.backend.agent_utils.query_llm_utils import (
+    LLMQueryConfig,
+    query_llm_service,
+    add_token_usage,
+)
+from .prompt_cuda import (
+    generate_default_cuda_prompt,
+    judge_optimize_prompt,
+    judge_correct_prompt,
+    coder_optimize_prompt,
+    coder_correct_prompt,
+)
+from .kernel_text_util import (
+    extract_cuda_code,
+    remove_pybind_module,
+    compile_kernel,
+    exec_eval_cuda,
+    exec_eval_cuda_with_ncu,
+    format_with_kernelbench_style,
+)
+from .prompt_cuda import PROMPT_SYSTEM
+
+
+def generate(prompt, system_prompt, llm_query_config: LLMQueryConfig):
+    try:
+        query_result = query_llm_service(
+            prompt=prompt, system_prompt=system_prompt, query_config=llm_query_config
+        )
+        return query_result
+    except Exception as e:
+        raise RuntimeError(f"LLM query failed with exception: {e}")
+
+
+def optimize(module, model_inputs=None, task_name: str = "default_task"):
+    """Optimize the given PyTorch module using custom DSL operators."""
+
+    llm_config = get_llm_config()
+    llm_query_config = LLMQueryConfig(**llm_config)
+    traced_module = torch.fx.symbolic_trace(module)
+
+    if "cuda" == llm_config.dsl.lower():
+        return cuda_optimize(traced_module, model_inputs, task_name, llm_query_config)
+    elif "triton" == llm_config.dsl.lower():
+        return torch.compile(module)  # TODO add custom triton optimize
+    else:
+        raise NotImplementedError(f"Unsupported language: {llm_config.dsl}")
+
+    # return the best of optimized models
+
+
+def agent_fix_cuda_error(
+    error_info, torch_model_code, cuda_code, work_dir, llm_query_config
+):
+    """Fix CUDA code based on error information from compilation or execution."""
+
+    prompt = judge_correct_prompt(error_info, torch_model_code, cuda_code)
+    modify_text, tokens_judge_correct = generate(
+        prompt, PROMPT_SYSTEM, llm_query_config
+    )
+    prompt = coder_correct_prompt(error_info, cuda_code, modify_text)
+    with open(os.path.join(work_dir, "fix_cuda_error_prompt.txt"), "a") as f:
+        f.write(prompt)
+    cuda_code, tokens_cuda_code_fix = generate(prompt, PROMPT_SYSTEM, llm_query_config)
+    cuda_code = extract_cuda_code(cuda_code)
+    cuda_code = remove_pybind_module(cuda_code)
+    return cuda_code, add_token_usage(tokens_judge_correct, tokens_cuda_code_fix)
+
+
+def cuda_optimize(
+    gm,
+    model_inputs,
+    task_name: str = "default_task",
+    llm_query_config: LLMQueryConfig = None,
+):
+    best_model = gm
+    max_iters = llm_query_config.iterative_query_nums
+    store_dir = os.path.join(llm_query_config.top_save_dir, task_name)
+
+    torch_model_code = format_with_kernelbench_style(gm, model_inputs)
+
+    # iterative optimization
+    cur_iter_token_usage = None
+    for iter in range(max_iters):
+        print(f"=== Optimize {task_name}, Iteration {iter} ===", flush=True)
+
+        context_dir_path = os.path.join(store_dir, f"iter_{iter}")
+        already_done: bool = os.path.exists(
+            os.path.join(context_dir_path, "model_new.py")
+        )
+
+        # Skip already done iterations
+        if already_done:
+            print(f"Iteration {iter} already done, skipping...", flush=True)
+            with open(os.path.join(context_dir_path, "model_new.py"), "r") as fin:
+                cuda_code = fin.read()
+            continue
+        os.makedirs(context_dir_path, exist_ok=True)
+
+        is_success_compilable = False
+        is_success_functional = False
+
+        # Generate initial kernel code
+        if iter == 0:
+            prompt = generate_default_cuda_prompt(torch_model_code)
+            text_response, cur_iter_token_usage = generate(
+                prompt, PROMPT_SYSTEM, llm_query_config
+            )
+            raw_cuda_code = extract_cuda_code(text_response)
+            cuda_code = remove_pybind_module(raw_cuda_code)
+
+        # compile
+        try:
+            is_success_compilable, compile_info = compile_kernel(
+                cuda_code=cuda_code, work_dir=context_dir_path
+            )
+
+            with open(os.path.join(context_dir_path, "log.log"), "a") as f:
+                f.write(
+                    f"[Token Usage] Iteration {iter} cost: {cur_iter_token_usage}\n"
+                )
+            cur_iter_token_usage = None
+
+        except Exception as e:  # wrapper compile_kernel to catch all exceptions
+            print(f"Compilation failed with exception: {e}")
+            continue
+
+        # [Eval Result] compile failed
+        if not is_success_compilable:
+            cuda_code, fix_error_token = agent_fix_cuda_error(
+                compile_info["msg"][:4096],  # save tokens
+                torch_model_code,
+                cuda_code,
+                work_dir=context_dir_path,
+                llm_query_config=llm_query_config,
+            )
+            cur_iter_token_usage = add_token_usage(
+                cur_iter_token_usage, fix_error_token
+            )
+            continue
+
+        # [Result-Compile] compile success
+        try:
+            is_success_functional, eval_msg = exec_eval_cuda(
+                compile_info["exec_filename"],  # .so filename
+                compile_info["exec_content"],  # .so binary content
+                torch_model_code,
+                work_dir=context_dir_path,
+            )
+        except Exception as e:
+            print(f"Execution failed with exception: {e}", flush=True)
+            continue
+
+        # [Result-Execute] functional failed
+        if not is_success_functional:
+            cuda_code, fix_error_token = agent_fix_cuda_error(
+                eval_msg[:4096],
+                torch_model_code,
+                cuda_code,
+                work_dir=context_dir_path,
+                llm_query_config=llm_query_config,
+            )
+            cur_iter_token_usage = add_token_usage(
+                cur_iter_token_usage, fix_error_token
+            )
+            continue
+
+        # [Result-Execute] functional success: optimization with NCU analysis
+        else:
+            is_ncu_success, ncu_metric_info = exec_eval_cuda_with_ncu(
+                compile_info["exec_filename"],
+                compile_info["exec_content"],
+                work_dir=context_dir_path,
+            )
+            if not is_ncu_success:
+                warnings.warn("NCU analysis failed.", RuntimeWarning)
+                continue
+            optimize_prompt = judge_optimize_prompt(
+                torch_model_code, cuda_code, ncu_metric_info
+            )
+            optimize_strategy, strategy_token = generate(
+                optimize_prompt, PROMPT_SYSTEM, llm_query_config
+            )
+            optimize_prompt = coder_optimize_prompt(cuda_code, optimize_strategy)
+            cuda_code, cuda_gen_token = generate(
+                optimize_prompt, PROMPT_SYSTEM, llm_query_config
+            )
+            cuda_code = extract_cuda_code(cuda_code)
+            cuda_code = remove_pybind_module(cuda_code)
+            cur_iter_token_usage = add_token_usage(cur_iter_token_usage, strategy_token)
+            cur_iter_token_usage = add_token_usage(cur_iter_token_usage, cuda_gen_token)
+
+    return best_model