efee1712aa
* fix.. speed_limit error... * draw tpms settings. * fix.. traffic light stopping only.. * fix.. waze cam * fix.. waze... * add setting (Enable comma connect ) * auto detect LFA2 * fix.. cruisespeed1 * vff2 driving model. * fix.. * agnos 12.3 * fix.. * ff * ff * test * ff * fix.. drawTurnInfo.. * Update drive_helpers.py * fix.. support eng voice eng sounds fix settings... english fix.. mph.. fix.. roadlimit speed bug.. * new vff model.. 250608 * fix soundd.. * fix safe exit speed.. * fix.. sounds. * fix.. radar timeStep.. * KerryGold model * Update drive_helpers.py * fix.. model. * fix.. * fix.. * Revert "fix.." This reverts commit b09ec459afb855c533d47fd7e8a1a6b1a09466e7. * Revert "fix.." This reverts commit 290bec6b83a4554ca232d531a911edccf94a2156. * fix esim * add more acc table. 10kph * kg update.. * fix cruisebutton mode3 * test atc..cond. * fix.. canfd * fix.. angle control limit
59 lines
2.3 KiB
Python
59 lines
2.3 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 Allocator, Compiled
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from tinygrad.engine.realize import method_cache
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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, vals=(), wait=False): pass
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class FakeAllocator(Allocator[Compiled]):
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def _alloc(self, sz, options): 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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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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backup_compiler = Device[Device.DEFAULT].compiler
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Device[Device.DEFAULT].runtime = FakeProgram
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Device[Device.DEFAULT].allocator = FakeAllocator(Device.default)
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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: method_cache.clear()
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tms = [time.perf_counter()]
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for i in range(5):
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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(0)")
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run_llama("codegen(1)")
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# test no compiler use for this
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Device[Device.DEFAULT].compiler = None
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run_llama("methodcache", False)
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with Profiling(sort='time', frac=0.1, fn="/tmp/llama.prof", ts=5):
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run_llama("profile", False)
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Device[Device.DEFAULT].runtime = backup_program
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Device[Device.DEFAULT].allocator = backup_allocator
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Device[Device.DEFAULT].compiler = backup_compiler
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if __name__ == '__main__':
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TestLLaMASpeed().test_llama_compile()
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#unittest.main()
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