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