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a9a1df785f
* initial commit * 81 passing * 105 passing tests * 148 passing * CI tests * install dep on ci * try opencl pkgs * try using vulkan * down to only 6 failing * refactor * cleaning up * another test skipped due to buffer limit * linter * segfault * indent fix * another segfault found * small touchups * Fix max and maxpool tests * Add constant folding * Add javascript export script * better asserts in codegen * manual upcasting * reverted token type change * skip safetensor test due to unsupported type * FIx efficientnet and all other model tests * Remove np copy * fixed indent and missing import * manually destroy the buffer * revert back to length * linter errors * removed extra val * skip broken tests * skipping more tests * Make the page pretty * Save model weights as safetensor * Fix imagenet to c test * Fix second imagenet to c bug * Async and paralel kernel compilation * workgroup support * reversed local size * fixed non local bug * correct local groups * ci experiment * removed typo * Fix define local by using shared memory * Refactor * try running on mac * match metal tests * add more workers * scope down tests * trying windows runner * fixed windows env * see how many it can do * merged master * refactor * missed refactor * increase test suite coverage * missing import * whitespace in test_efficientnet.py * getting there * fixed reset * fixed bufs * switched to cstyle * cleanup * min/max rename * one more linter issue * fixed demo * linter * testing ci chrome * add unsafe webgpu arg * add build step * remove WEBGPU from cmd line * use module * try forcing directx * trying forced metal backend * temp disable conv2d for CI * disable conv_trasnpose2d --------- Co-authored-by: 0x4d - Martin Loretz <20306567+martinloretzzz@users.noreply.github.com> Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
import unittest
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import time
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import numpy as np
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from tinygrad.state import get_parameters
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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 = 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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@unittest.skipIf(Device.DEFAULT == "WEBGPU", "too many buffers for webgpu")
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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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