mirror of https://github.com/commaai/tinygrad.git
68 lines
2.2 KiB
Python
Executable File
68 lines
2.2 KiB
Python
Executable File
#!/usr/bin/env python3
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import gc
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import time
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from tqdm import trange
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from extra.models.efficientnet import EfficientNet
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from tinygrad.nn.state import get_parameters
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from tinygrad.nn import optim
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import GlobalCounters
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from tinygrad.helpers import getenv
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from tinygrad.jit import CacheCollector
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def tensors_allocated():
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return sum(isinstance(x, Tensor) for x in gc.get_objects())
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NUM = getenv("NUM", 2)
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BS = getenv("BS", 8)
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CNT = getenv("CNT", 10)
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BACKWARD = getenv("BACKWARD", 0)
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TRAINING = getenv("TRAINING", 1)
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ADAM = getenv("ADAM", 0)
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CLCACHE = getenv("CLCACHE", 0)
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if __name__ == "__main__":
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print(f"NUM:{NUM} BS:{BS} CNT:{CNT}")
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model = EfficientNet(NUM, classes=1000, has_se=False, track_running_stats=False)
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parameters = get_parameters(model)
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for p in parameters: p.realize()
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if ADAM: optimizer = optim.Adam(parameters, lr=0.001)
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else: optimizer = optim.SGD(parameters, lr=0.001)
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Tensor.training = TRAINING
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Tensor.no_grad = not BACKWARD
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for i in trange(CNT):
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GlobalCounters.reset()
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cpy = time.monotonic()
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x_train = Tensor.randn(BS, 3, 224, 224, requires_grad=False).realize()
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y_train = Tensor.randn(BS, 1000, requires_grad=False).realize()
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# TODO: replace with TinyJit
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if i < 3 or not CLCACHE:
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st = time.monotonic()
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out = model.forward(x_train)
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loss = out.log_softmax().mul(y_train).mean()
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if i == 2 and CLCACHE: CacheCollector.start()
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if BACKWARD:
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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mt = time.monotonic()
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loss.realize()
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for p in parameters:
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p.realize()
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et = time.monotonic()
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else:
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st = mt = time.monotonic()
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for prg, args in cl_cache: prg(*args)
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et = time.monotonic()
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if i == 2 and CLCACHE:
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cl_cache = CacheCollector.finish()
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mem_used = GlobalCounters.mem_used
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loss_cpu = loss.detach().numpy()
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cl = time.monotonic()
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print(f"{(st-cpy)*1000.0:7.2f} ms cpy, {(cl-st)*1000.0:7.2f} ms run, {(mt-st)*1000.0:7.2f} ms build, {(et-mt)*1000.0:7.2f} ms realize, {(cl-et)*1000.0:7.2f} ms CL, {loss_cpu:7.2f} loss, {tensors_allocated():4d} tensors, {mem_used/1e9:.2f} GB used, {GlobalCounters.global_ops*1e-9/(cl-st):9.2f} GFLOPS")
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