forked from tinygrad/tinygrad
270 lines
12 KiB
Python
270 lines
12 KiB
Python
import unittest
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import numpy as np
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from tinygrad import Tensor, GlobalCounters, dtypes, nn, Device, Variable
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from tinygrad.helpers import Context, getenv, DEV
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from tinygrad.engine.realize import run_linear, estimate_uop, compile_linear
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from tinygrad.renderer.ptx import PTXRenderer
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from test.helpers import needs_second_gpu, check_schedule, assert_kernel_count, KernelCountException
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class TestArange(unittest.TestCase):
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def _get_flops(self, tensor, desired):
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GlobalCounters.reset()
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linear = compile_linear(tensor.schedule_linear())
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if len(linear.src) != 1: raise KernelCountException(1, len(linear.src))
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run_linear(linear)
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np.testing.assert_equal(tensor.numpy(), desired)
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return estimate_uop(linear.src[-1]).ops
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def test_arange_complexity(self):
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self.assertLess(self._get_flops(Tensor.arange(256).clone(), np.arange(256)), 256*4)
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self.assertLess(self._get_flops(Tensor.arange(2560).clone(), np.arange(2560)), 2560*4)
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def test_cat_complexity(self):
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x = Tensor.arange(2**10) + Tensor.empty((), dtype=dtypes.uint32)
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out = x.cat(x).cat(Tensor.empty(1, dtype=dtypes.uint32))
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linear = compile_linear(out.schedule_linear())
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self.assertLessEqual(estimate_uop(linear.src[-1]).ops, out.numel()*20)
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@unittest.skipIf(Device.DEFAULT == "CL", "flaky in CI")
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def test_arange_cumsum(self):
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np.testing.assert_equal(Tensor.arange(513).cumsum(0).numpy(), np.arange(513).cumsum())
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def test_arange_cat(self):
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t = Tensor.arange(2, dtype=dtypes.int)+Tensor([3])
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self.assertEqual(t.cat(t).tolist(), [3, 4, 3, 4])
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def test_eye_complexity(self):
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with Context(NOOPT=1):
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# NOTE: not every backend supports CMPEQ
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self.assertLessEqual(self._get_flops(Tensor.eye(2560).clone(), np.eye(2560)), 2*2560*2560)
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@unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX indexing is weird")
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def test_tri_complexity(self):
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with Context(NOOPT=1):
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t = Tensor.ones(256, 256).contiguous().realize()
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linear = compile_linear(t.triu().schedule_linear())
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self.assertLessEqual(estimate_uop(linear.src[-1]).ops, 4 * 256 * 256)
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DSET, DDIM = 2048, 32
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class TestIndexing(unittest.TestCase):
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def test_arange_2_reduce(self):
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needle = Tensor.zeros(16384, dtype=dtypes.int).contiguous()
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needle[1337] = 1
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needle.realize()
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with Context(NOOPT=1):
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GlobalCounters.reset()
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out = ((Tensor.arange(1,16385)-1)*needle).sum()
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linear, var_vals = check_schedule(out, 1)
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run_linear(linear, var_vals)
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self.assertEqual(out.item(), 1337)
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def test_manual_index(self):
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dataset = Tensor.rand(DSET, DDIM).realize()
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idxs = Tensor([0,3,5,6]).realize()
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real_index = dataset.numpy()[idxs.numpy()]
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print("*** indexing ***")
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with Context(NOOPT=1):
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GlobalCounters.reset()
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rng = Tensor.arange(DSET, dtype=dtypes.int).reshape(1, 1, DSET, 1).expand(4, DDIM, DSET, 1)
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idxs = idxs.reshape(4,1,1,1).expand(4, DDIM, DSET, 1)
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reshape_dataset = dataset.T.reshape(1, DDIM, DSET, 1).expand(4, DDIM, DSET, 1)
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full = (rng==idxs).where(reshape_dataset, Tensor.zeros(4, DDIM, DSET, 1, buffer=False))
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X = full.sum(axis=(2,3))
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linear, var_vals = check_schedule(X, 1)
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run_linear(linear, var_vals)
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assert GlobalCounters.global_ops < 4*DSET, f"too many ops {GlobalCounters.global_ops}"
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np.testing.assert_allclose(real_index, X.numpy())
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def test_index_variable(self):
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dataset = Tensor.rand(DSET, DDIM).realize()
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v = Variable("v", 0, DDIM-1)
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with Context(NOOPT=1):
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GlobalCounters.reset()
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vb = Tensor(v.bind(12))
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comp = dataset[vb].numpy()
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# no global ops because they are all indexing
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self.assertLess(GlobalCounters.global_ops, 1000)
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np.testing.assert_allclose(comp, dataset.numpy()[12])
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def test_index(self):
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dataset = Tensor.rand(DSET, DDIM).realize()
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idxs = Tensor([0,3,5,6]).realize()
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real_index = dataset.numpy()[idxs.numpy()]
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print("*** indexing ***")
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with Context(NOOPT=1):
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GlobalCounters.reset()
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X = dataset[idxs]
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assert X.shape == (4,DDIM)
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linear, var_vals = check_schedule(X, 1)
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run_linear(linear, var_vals)
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assert GlobalCounters.global_ops < 4*DSET, f"too many ops {GlobalCounters.global_ops}"
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np.testing.assert_allclose(real_index, X.numpy())
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def test_index_fused(self, noopt=1):
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dataset = Tensor.rand(DSET, DDIM).realize()
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idxs = Tensor([0,3,5,6]).realize()
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real_index = dataset.numpy()[idxs.numpy()]
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print("*** indexing ***")
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with Context(NOOPT=noopt):
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GlobalCounters.reset()
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X = dataset[idxs]
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assert X.shape == (4,DDIM)
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linear, var_vals = check_schedule(X, 1)
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run_linear(linear, var_vals)
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assert GlobalCounters.global_ops < 4*DSET, f"too many ops {GlobalCounters.global_ops} != {4*DSET}"
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np.testing.assert_allclose(real_index, X.numpy())
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@unittest.skip("not ready")
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def test_index_fused_opt(self): self.test_index_fused(0)
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@unittest.skipIf(Device.DEFAULT == "CL", "rusticl/llvmpipe bug: https://gitlab.freedesktop.org/mesa/mesa/-/work_items/15667")
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def test_index_fused_out_of_bounds(self):
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dataset = Tensor.rand(256, 256).realize()
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idxs = Tensor([-19238, -257, 256, 495, 10982377]).realize()
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with Context(NOOPT=1):
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X = dataset[idxs]
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np.testing.assert_equal(X.numpy(), 0)
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def test_index_mnist(self, noopt=1, op_limit=512*784*13, split_reduceop=0):
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# WEBGPU generates more ops due to bitpacking of < 4-byte dtypes
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if Device.DEFAULT == "WEBGPU": op_limit *= 15
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# from tinygrad.nn.datasets import mnist
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X_train, Y_train = Tensor.randint(DSET, 1, 28, 28, dtype='uchar').realize(), Tensor.randint(DSET, dtype='uchar').realize()
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with Context(NOOPT=noopt, SPLIT_REDUCEOP=split_reduceop):
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samples = Tensor.randint(getenv("BS", 512), high=X_train.shape[0]).realize()
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GlobalCounters.reset()
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x = X_train[samples].numpy()
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y = Y_train[samples].numpy()
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assert GlobalCounters.global_ops < op_limit, f"too many ops {GlobalCounters.global_ops} != {op_limit}"
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np.testing.assert_allclose(X_train.numpy()[samples.numpy()], x)
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np.testing.assert_allclose(Y_train.numpy()[samples.numpy()], y)
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def test_index_mnist_opt(self): self.test_index_mnist(0)
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def test_index_mnist_split(self): self.test_index_mnist(1, split_reduceop=1)
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def test_index_mnist_opt_split(self): self.test_index_mnist(0, split_reduceop=1)
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def test_llama_embedding(self, noopt=1, op_limit=65536):
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# llama3 is 128256
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vocab_size, embed_size = (10, 3)
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emb = nn.Embedding(vocab_size, embed_size)
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emb_w = emb.weight.numpy()
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x = Tensor([1,2,3,4])
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with Context(NOOPT=noopt):
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GlobalCounters.reset()
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z = emb(x).realize()
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self.assertLessEqual(GlobalCounters.global_ops, op_limit)
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assert_kernel_count(2)
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if getenv("CHECK", 1):
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import torch
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with torch.no_grad():
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torch_emb = torch.nn.Embedding(vocab_size, embed_size).eval()
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torch_emb.weight[:] = torch.tensor(emb_w, dtype=torch.float32)
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torch_z = torch_emb(torch.tensor(x.numpy()))
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# TODO: reshape to match torch, should we do this in nn?
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np.testing.assert_allclose(z.numpy().reshape(4, embed_size), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8)
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# at least the arange is being fused
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def test_llama_embedding_opt(self): self.test_llama_embedding(0, 1_736_704_000)
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# NOTE: call doesn't work with SPEC=2
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@unittest.skipIf(Device.DEFAULT not in ("CPU", "AMD"), "atomics only on AMD/CPU")
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@Context(USE_ATOMICS=1, SPEC=1)
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def test_llama_8b_embedding_backward(self):
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from tinygrad.renderer.cstyle import CStyleLanguage
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if Device.DEFAULT == "CPU" and not isinstance(Device["CPU"].renderer, CStyleLanguage): self.skipTest("CPU needs Clang renderer")
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vocab_size, embed_size = 1000, 128
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bs, seqlen = 4, 256
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idx = Tensor.randint(bs, seqlen, high=vocab_size)
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emb = nn.Embedding(vocab_size, embed_size)
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emb.weight = Tensor.ones(vocab_size, embed_size)
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gt = Tensor.zeros(bs, seqlen, embed_size)
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Tensor.realize(idx, emb.weight, gt)
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GlobalCounters.reset()
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loss = (emb(idx)-gt).square().sum()
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loss.backward()
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emb.weight.grad.realize()
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bwd_ops = GlobalCounters.global_ops
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print(f"embedding bwd: {GlobalCounters.kernel_count} kernels, {bwd_ops:,} ops")
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self.assertLess(bwd_ops, bs*seqlen*embed_size*20, f"backward ops {bwd_ops:,} should be less than 20 per with atomic scatter-add")
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# correctness check
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expected_grad = np.zeros((vocab_size, embed_size), dtype=np.float32)
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for i in idx.flatten().numpy(): expected_grad[i] += 2
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np.testing.assert_allclose(emb.weight.grad.numpy(), expected_grad, rtol=1e-5, atol=1e-5)
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@unittest.skipIf(Device.DEFAULT not in ("CPU", "AMD"), "atomics only on AMD/CPU")
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@Context(USE_ATOMICS=1, SPEC=1)
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def test_embedding_backward_padded_embed(self):
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from tinygrad.renderer.cstyle import CStyleLanguage
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if Device.DEFAULT == "CPU" and not isinstance(Device["CPU"].renderer, CStyleLanguage): self.skipTest("CPU needs Clang renderer")
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vocab_size, embed_size = 1000, 300
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bs, seqlen = 4, 256
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idx = Tensor.randint(bs, seqlen, high=vocab_size)
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emb = nn.Embedding(vocab_size, embed_size)
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emb.weight = Tensor.ones(vocab_size, embed_size)
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gt = Tensor.zeros(bs, seqlen, embed_size)
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Tensor.realize(idx, emb.weight, gt)
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loss = (emb(idx)-gt).square().sum()
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loss.backward()
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emb.weight.grad.realize()
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# correctness check
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expected_grad = np.zeros((vocab_size, embed_size), dtype=np.float32)
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for i in idx.flatten().numpy(): expected_grad[i] += 2
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np.testing.assert_allclose(emb.weight.grad.numpy(), expected_grad, rtol=1e-5, atol=1e-5)
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@needs_second_gpu
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@unittest.skipIf(Device.DEFAULT not in ("CPU", "AMD"), "atomics only on AMD/CPU")
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@Context(USE_ATOMICS=1, SPEC=1)
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def test_embedding_backward_vocab_sharded(self):
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from tinygrad.renderer.cstyle import CStyleLanguage
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if Device.DEFAULT == "CPU" and not isinstance(Device["CPU"].renderer, CStyleLanguage): self.skipTest("CPU needs Clang renderer")
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devices = (f"{Device.DEFAULT}:0", f"{Device.DEFAULT}:1")
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vocab_size, embed_size = 1000, 128
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bs, seqlen = 4, 256
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idx = Tensor.randint(bs, seqlen, high=vocab_size)
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emb = nn.Embedding(vocab_size, embed_size)
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emb.weight = Tensor.ones(vocab_size, embed_size)
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gt = Tensor.zeros(bs, seqlen, embed_size)
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Tensor.realize(idx, emb.weight, gt)
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# compute expected grad on single device
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expected_grad = np.zeros((vocab_size, embed_size), dtype=np.float32)
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for i in idx.flatten().numpy(): expected_grad[i] += 2
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# now shard the embedding weight on vocab axis and recompute
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emb.weight = Tensor.ones(vocab_size, embed_size)
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emb.weight.shard_(devices, axis=0)
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idx = idx.shard(devices, axis=None)
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gt = gt.shard(devices, axis=None)
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Tensor.realize(idx, emb.weight, gt)
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loss = (emb(idx)-gt).square().sum()
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loss.backward()
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np.testing.assert_allclose(emb.weight.grad.numpy(), expected_grad, rtol=1e-5, atol=1e-5)
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@unittest.skipUnless(Device.DEFAULT == "AMD" or (Device.DEFAULT == "NULL" and DEV.arch.startswith("gfx")), "tests AMD bf16 cast overhead")
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def base_test_llama_8b_rope_backward(self, dtype, ops_scale=1):
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from extra.models.llama import precompute_freqs_cis, apply_rotary_emb
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bs, seqlen, dim, n_heads = 1, 512, 256, 4
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head_dim = dim // n_heads
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x = Tensor.randn(bs, seqlen, dim, dtype=dtype)
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wq = Tensor.randn(dim, dim, dtype=dtype)
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freqs_cis = precompute_freqs_cis(head_dim, seqlen).cast(dtype)
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Tensor.realize(x, wq, freqs_cis)
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xq = (x @ wq.T)
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# main llama does not fuse it
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#xq = xq.contiguous_backward()
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xq = xq.reshape(bs, seqlen, n_heads, head_dim)
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xq_rope, _ = apply_rotary_emb(xq, xq, freqs_cis)
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xq_rope.sum().backward()
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linear = compile_linear(wq.grad.schedule_linear())
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if len(linear.src) != 1: raise KernelCountException(1, len(linear.src))
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bwd_ops = estimate_uop(linear.src[0]).ops
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expected_ops = bs*seqlen*dim*dim*ops_scale
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print(f"rope matmul bwd ({dtype}): {GlobalCounters.kernel_count} kernels, {bwd_ops:,} ops")
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self.assertLess(bwd_ops, expected_ops, f"rope bwd ops {bwd_ops:,} should be < {ops_scale} per (got {bwd_ops/(bs*seqlen*dim*dim):.1f})")
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def test_llama_8b_rope_backward_f16(self):
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self.base_test_llama_8b_rope_backward(dtypes.float16, ops_scale=2)
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# bfloat16 on non CDNA4 has ~10x ops overhead because of the software emulation
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def test_llama_8b_rope_backward_bf16(self):
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self.base_test_llama_8b_rope_backward(dtypes.bfloat16, ops_scale=2 if Device[Device.DEFAULT].renderer.target.arch.startswith("gfx950") else 25)
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if __name__ == "__main__":
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unittest.main()
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