45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
import unittest
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from tinygrad import Tensor, dtypes, TinyJit, UOp
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from tinygrad.apps.llm import apply_rope
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# TODO: test_scheduler, but just in uint
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class TestAttention(unittest.TestCase):
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def test_half_qkv_buffers(self):
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BS, seqlen, dim = 10, 4, 100
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q = Tensor.ones(BS, seqlen, dim, dtype=dtypes.half).contiguous().realize()
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k = Tensor.ones(BS, seqlen, dim, dtype=dtypes.half).contiguous().realize()
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v = Tensor.ones(BS, seqlen, dim, dtype=dtypes.half).contiguous().realize()
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attn = q.scaled_dot_product_attention(k, v)
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sched = attn.schedule()
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# attention has 5 kernels now
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self.assertEqual(len(sched), 5)
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softmax_inputs = sched[1:4]
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for si in softmax_inputs:
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assert all(b.dtype == dtypes.half for b in si.bufs), f"non half {si.bufs=}"
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def test_apply_rope(self):
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x = Tensor.randn(1, 2, 4, 8, dtype=dtypes.float32)
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result = apply_rope(x, 0)
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self.assertEqual(result.shape, x.shape)
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self.assertEqual(result.dtype, x.dtype)
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self.assertGreater((result - apply_rope(x, 5)).abs().max().item(), 1e-6)
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with self.assertRaises(AssertionError): apply_rope(Tensor.randn(1, 1, 4, 7, dtype=dtypes.float32), 0)
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def test_apply_rope_jit_prune(self):
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def rope_fn(x_in, pos): return apply_rope(x_in, pos)
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rope_noprune = TinyJit(rope_fn)
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rope_prune = TinyJit(rope_fn, prune=True)
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v_pos = UOp.variable("start_pos", 0, 100)
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for _ in range(3):
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rope_noprune(Tensor.randn(1, 2, 4, 8, dtype=dtypes.float32), v_pos.bind(1))
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rope_prune(Tensor.randn(1, 2, 4, 8, dtype=dtypes.float32), v_pos.bind(1))
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noprune_size = len(rope_noprune.captured.jit_cache)
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prune_size = len(rope_prune.captured.jit_cache)
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self.assertGreater(noprune_size, prune_size)
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self.assertGreaterEqual(noprune_size, 3)
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self.assertEqual(prune_size, 1)
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if __name__ == '__main__':
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unittest.main() |