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106 lines
4.6 KiB
Python
106 lines
4.6 KiB
Python
import unittest, time
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import numpy as np
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from tinygrad.tensor import Tensor
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from tinygrad.nn import optim
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from tinygrad.nn.state import get_parameters
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from tinygrad.jit import TinyJit
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from tinygrad import Device, GlobalCounters
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from tinygrad.helpers import CI, dtypes
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from tinygrad.shape.symbolic import Variable
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from test.helpers import derandomize_model
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from examples.gpt2 import Transformer as GPT2Transformer, MODEL_PARAMS as GPT2_MODEL_PARAMS
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from examples.hlb_cifar10 import SpeedyResNet
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from examples.llama import Transformer as LLaMaTransformer, MODEL_PARAMS as LLAMA_MODEL_PARAMS
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from examples.stable_diffusion import UNetModel
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def helper_test(nm, gen, train, max_memory_allowed, max_kernels_allowed, all_jitted=False):
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tms = []
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for _ in range(4):
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early_gen = [x.realize() if isinstance(x, Tensor) else x for x in gen()]
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GlobalCounters.reset()
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GlobalCounters.mem_used = 0
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Device[Device.DEFAULT].synchronize()
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st = time.perf_counter_ns()
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train(*early_gen)
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Device[Device.DEFAULT].synchronize()
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tms.append(time.perf_counter_ns() - st)
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# TODO: jit should expose this correctly with graph
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kernels_used = len(train.jit_cache) if hasattr(train, "jit_cache") else None
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print(f"{nm}: used {GlobalCounters.mem_used/1e9:.2f} GB and {kernels_used} kernels in {min(tms)/1e6:.2f} ms")
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assert GlobalCounters.mem_used/1e9 < max_memory_allowed, f"{nm} used more than {max_memory_allowed:.2f} GB"
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assert not kernels_used or kernels_used <= max_kernels_allowed, f"{nm} used more than {max_kernels_allowed} kernels"
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if all_jitted:
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assert kernels_used > 0 and kernels_used == GlobalCounters.kernel_count or (kernels_used == 1 and getattr(Device[Device.DEFAULT], "graph", None)), f"only {kernels_used} out of {GlobalCounters.kernel_count} were jitted"
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class TestRealWorld(unittest.TestCase):
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def setUp(self):
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self.old_type = Tensor.default_type
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np.random.seed(2002)
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def tearDown(self):
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Tensor.default_type = self.old_type
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@unittest.skipIf(Device.DEFAULT == "LLVM", "LLVM segmentation fault")
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@unittest.skipIf(CI, "too big for CI")
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def test_stable_diffusion(self):
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model = UNetModel()
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derandomize_model(model)
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@TinyJit
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def test(t, t2): return model(t, 801, t2).realize()
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helper_test("test_sd", lambda: (Tensor.randn(1, 4, 64, 64),Tensor.randn(1, 77, 768)), test, 18.0, 953)
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@unittest.skipIf(Device.DEFAULT == "LLVM", "LLVM segmentation fault")
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@unittest.skipIf(Device.DEFAULT in ["LLVM", "GPU"] and CI, "too long on CI LLVM, GPU requires cl_khr_fp1")
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def test_llama(self):
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Tensor.default_type = dtypes.float16
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args_tiny = {"dim": 1024, "hidden_dim": 2048, "n_heads": 8, "n_layers": 8, "norm_eps": 1e-05, "vocab_size": 1000}
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model = LLaMaTransformer(**(args_tiny if CI else LLAMA_MODEL_PARAMS["1"]["7B"]["args"]))
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derandomize_model(model)
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@TinyJit
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def test(t): return model(t, 0).realize()
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# TODO: test first token vs rest properly, also memory test is broken with CacheCollector
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helper_test("test_llama", lambda: (Tensor([[1,2,3,4]]),), test, 0.22 if CI else 13.5, 181 if CI else 685, all_jitted=True)
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@unittest.skipIf(Device.DEFAULT in ["LLVM", "GPU"] and CI, "too long on CI LLVM, GPU requires cl_khr_fp16")
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def test_gpt2(self):
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Tensor.default_type = dtypes.float16
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args_tiny = {"dim": 1024, "n_heads": 8, "n_layers": 8, "norm_eps": 1e-5, "vocab_size": 1000}
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model = GPT2Transformer(**(args_tiny if CI else GPT2_MODEL_PARAMS["gpt2-medium"]))
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derandomize_model(model)
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@TinyJit
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def test(t, v): return model(t, v).realize()
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helper_test("test_gpt2", lambda: (Tensor([[1,]]),Variable("pos", 1, 100).bind(1)), test, 0.21 if CI else 0.9, 164 if CI else 468, all_jitted=True)
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@unittest.skipIf(Device.DEFAULT == "LLVM", "LLVM segmentation fault")
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@unittest.skipIf(Device.DEFAULT in ["LLVM", "CLANG"] and CI, "too long on CI LLVM and CLANG")
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def test_train_cifar(self):
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# TODO: with default device
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#old_default = Device.DEFAULT
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#Device.DEFAULT = "FAKE"
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#Device['fake'].codegen = Device[old_default].codegen
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with Tensor.train():
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model = SpeedyResNet(Tensor.ones((12,3,2,2)))
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optimizer = optim.SGD(get_parameters(model), lr=0.01, momentum=0.8, nesterov=True, weight_decay=0.15)
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BS = 32 if CI else 512
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@TinyJit
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def train(X):
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out = model(X)
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loss = out.mean()
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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helper_test("train_cifar", lambda: (Tensor.randn(BS, 3, 32, 32),), train, (1.0/48)*BS, 142 if CI else 154) # it's 154 on metal
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# reset device
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#Device.DEFAULT = old_default
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if __name__ == '__main__':
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unittest.main() |