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b14d31d6db
* simple convnext implementation * shorter function names * need to realize the random functions now * creating an optimizer realizes all params * assign contiguous * fix lazy lazy * why was i doing that...add convnext to tests * LazyNumpyArray * enable assert + comment * no two tiny
79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
import unittest
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import time
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import numpy as np
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from tinygrad.nn import optim
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from tinygrad.tensor import Device
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from tinygrad.helpers import getenv
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from extra.training import train
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from models.convnext import ConvNeXt
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from models.efficientnet import EfficientNet
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from models.transformer import Transformer
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from models.vit import ViT
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from models.resnet import ResNet18
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BS = getenv("BS", 2)
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def train_one_step(model,X,Y):
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params = optim.get_parameters(model)
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pcount = 0
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for p in params:
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pcount += np.prod(p.shape)
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optimizer = optim.SGD(params, lr=0.001)
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print("stepping %r with %.1fM params bs %d" % (type(model), pcount/1e6, BS))
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st = time.time()
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train(model, X, Y, optimizer, steps=1, BS=BS)
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et = time.time()-st
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print("done in %.2f ms" % (et*1000.))
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def check_gc():
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if Device.DEFAULT == "GPU":
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from extra.introspection import print_objects
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assert print_objects() == 0
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class TestTrain(unittest.TestCase):
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def test_convnext(self):
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model = ConvNeXt(depths=[1], dims=[16])
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X = np.zeros((BS,3,224,224), dtype=np.float32)
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Y = np.zeros((BS), dtype=np.int32)
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train_one_step(model,X,Y)
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check_gc()
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def test_efficientnet(self):
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model = EfficientNet(0)
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X = np.zeros((BS,3,224,224), dtype=np.float32)
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Y = np.zeros((BS), dtype=np.int32)
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train_one_step(model,X,Y)
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check_gc()
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def test_vit(self):
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model = ViT()
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X = np.zeros((BS,3,224,224), dtype=np.float32)
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Y = np.zeros((BS,), dtype=np.int32)
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train_one_step(model,X,Y)
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check_gc()
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def test_transformer(self):
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# this should be small GPT-2, but the param count is wrong
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# (real ff_dim is 768*4)
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model = Transformer(syms=10, maxlen=6, layers=12, embed_dim=768, num_heads=12, ff_dim=768//4)
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X = np.zeros((BS,6), dtype=np.float32)
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Y = np.zeros((BS,6), dtype=np.int32)
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train_one_step(model,X,Y)
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check_gc()
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def test_resnet(self):
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X = np.zeros((BS, 3, 224, 224), dtype=np.float32)
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Y = np.zeros((BS), dtype=np.int32)
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for resnet_v in [ResNet18]:
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model = resnet_v()
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model.load_from_pretrained()
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train_one_step(model, X, Y)
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check_gc()
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def test_bert(self):
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# TODO: write this
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pass
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if __name__ == '__main__':
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unittest.main()
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