# NOTE: this only tests the speed of the LLaMA codegen, it doesn't actually run the net import unittest, time from examples.llama import Transformer, MODEL_PARAMS from tinygrad.tensor import Tensor from tinygrad import Device from tinygrad.nn.state import get_state_dict from tinygrad.device import Compiled, Allocator from tinygrad.helpers import Profiling class FakeProgram: def __init__(self, name:str, prg:bytes): pass def __call__(self, *bufs, global_size, local_size, wait=False): pass class FakeAllocator(Allocator): def _alloc(self, sz, dtype): return None def copyin(self, dest, src:memoryview): pass class TestLLaMASpeed(unittest.TestCase): @unittest.skipIf(not isinstance(Device[Device.DEFAULT], Compiled), "only test for compiled backends") def test_llama_compile(self): backup_program = Device[Device.DEFAULT].runtime backup_allocator = Device[Device.DEFAULT].allocator Device[Device.DEFAULT].runtime = FakeProgram Device[Device.DEFAULT].allocator = FakeAllocator() print("testing llama python run time") model = Transformer(**MODEL_PARAMS["1"]["7B"]["args"]) print("built model") # assign fake tensors to the values for v in get_state_dict(model).values(): v.assign(Tensor.empty(*v.shape, dtype=v.dtype)) print("assigned empty tensors, doing warmup") def run_llama(st, empty_method_cache=True): if empty_method_cache: Device[Device.DEFAULT].get_runner.cache_clear() tms = [time.perf_counter()] for i in range(10): model(Tensor([[1,2,3,4]]), i).realize() tms.append(time.perf_counter()) timings = [(tms[i+1]-tms[i])*1000 for i in range(len(tms)-1)] print(f"{st:15s} mean runtime: {sum(timings)/len(timings):7.2f}ms, runs: ", ", ".join(f'{x:7.2f}' for x in timings)) run_llama("codegen") run_llama("methodcache", False) with Profiling(sort='time', frac=0.1): run_llama("profile") Device[Device.DEFAULT].runtime = backup_program Device[Device.DEFAULT].allocator = backup_allocator if __name__ == '__main__': unittest.main()