148 lines
6.2 KiB
Python
148 lines
6.2 KiB
Python
import unittest, time, gc
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import numpy as np
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from tinygrad.device import is_dtype_supported
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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.engine.jit import TinyJit
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from tinygrad import Tensor, Device, GlobalCounters, dtypes, Variable
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from tinygrad.helpers import CI, Context
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from extra.lr_scheduler import OneCycleLR
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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, hyp
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from examples.llama import Transformer as LLaMaTransformer
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from examples.stable_diffusion import UNetModel, unet_params
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from extra.models.unet import ResBlock
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global_mem_used = 0
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def helper_test(nm, gen, model, max_memory_allowed, max_kernels_allowed, all_jitted=False):
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with Context(JIT=2):
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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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Device[Device.DEFAULT].synchronize()
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st = time.perf_counter_ns()
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model(*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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mem_used = GlobalCounters.mem_used - global_mem_used
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# TODO: jit should expose this correctly with graph
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kernels_used = len(model.jit_cache) if hasattr(model, "jit_cache") else None
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print(f"{nm}: used {mem_used/1e9:.2f} GB and {kernels_used} kernels in {min(tms)/1e6:.2f} ms")
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assert mem_used/1e9 < max_memory_allowed, f"{nm} used more than {max_memory_allowed:.2f} GB - {mem_used/1e9:.2} GB used"
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assert not kernels_used or kernels_used <= max_kernels_allowed, f"{nm} used more than {max_kernels_allowed} kernels, it used {kernels_used}"
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if all_jitted:
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assert kernels_used > 0 and kernels_used == GlobalCounters.kernel_count or (kernels_used <= GlobalCounters.kernel_count and getattr(Device[Device.DEFAULT], "graph", None)), f"only {kernels_used} out of {GlobalCounters.kernel_count} were jitted" # noqa: E501
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class TestRealWorld(unittest.TestCase):
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def setUp(self):
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gc.collect()
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global global_mem_used
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global_mem_used = GlobalCounters.mem_used
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self.old_float = dtypes.default_float
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np.random.seed(2002)
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def tearDown(self):
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dtypes.default_float = self.old_float
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@unittest.skipIf(CI and Device.DEFAULT == "CPU", "slow, covered by METAL")
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@unittest.skipUnless(is_dtype_supported(dtypes.float16), "need dtypes.float16")
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def test_stable_diffusion(self):
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params = unet_params
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params["model_ch"] = 16
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params["ctx_dim"] = 16
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params["num_res_blocks"] = 1
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params["n_heads"] = 2
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model = UNetModel(**params)
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derandomize_model(model)
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@TinyJit
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def test(t, t2): return model(t, Tensor([801]), t2).realize()
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helper_test("test_sd", lambda: (Tensor.randn(1, 4, 64, 64),Tensor.randn(1, 77, params["ctx_dim"])), test, 18.0, 515)
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def test_unet_resblock(self):
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model = [ResBlock(16, 24, 16) for _ in range(4)]
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derandomize_model(model)
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@TinyJit
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def test(t, t2):
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for l in model: t = l(t, t2)
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return t.realize()
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helper_test("test_unet_resblock", lambda: (Tensor.empty(4, 16, 8, 8), Tensor.empty(1, 24)), test, 0.01, 37)
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@unittest.skipUnless(is_dtype_supported(dtypes.float16), "need dtypes.float16")
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def test_llama(self):
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dtypes.default_float = 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)
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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
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helper_test("test_llama", lambda: (Tensor([[1,2,3,4]]),), test, 0.27, 168, all_jitted=True)
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@unittest.skipUnless(is_dtype_supported(dtypes.float16), "need dtypes.float16")
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def test_gpt2(self):
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dtypes.default_float = 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):
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with Context(JIT=0): return model(t, v).realize()
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helper_test("test_gpt2", lambda: (Tensor([[1,]]),Variable("pos", 1, 100).bind(1)), test, 0.23 if CI else 0.9, 137 if CI else 396, all_jitted=True)
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@unittest.skipIf(CI and Device.DEFAULT == "CPU", "slow")
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def test_train_mnist(self):
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from examples.beautiful_mnist import Model
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with Tensor.train():
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model = Model()
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optimizer = optim.Adam(get_parameters(model))
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BS = 32
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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_mnist", lambda: (Tensor.randn(BS, 1, 28, 28),), train, 0.07, 92)
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@unittest.skipIf(CI and Device.DEFAULT in {"CPU", "GPU", "LLVM"}, "slow")
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def test_train_cifar(self):
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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
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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, 126)
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@unittest.skipUnless(is_dtype_supported(dtypes.float16), "need dtypes.float16")
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def test_train_cifar_hyp(self):
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dtypes.default_float = dtypes.float16
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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=hyp['opt']['momentum'], nesterov=True, weight_decay=hyp['opt']['bias_decay'])
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initial_div_factor = hyp['opt']['initial_div_factor']
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final_lr_ratio = hyp['opt']['final_lr_ratio']
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pct_start = hyp['opt']['percent_start']
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lr_scheduler = OneCycleLR(optimizer, max_lr=hyp['opt']['bias_lr'], pct_start=pct_start, div_factor=initial_div_factor,
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final_div_factor=1./(initial_div_factor*final_lr_ratio), total_steps=4)
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assert not np.isnan(lr_scheduler.min_lr), "lr too small or initial_div_facotr too big for half"
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if __name__ == '__main__':
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unittest.main()
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