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d59e6e7a37
* move more tests to test/null, split some existing ones * null work * null work * move more * fixes * move PIL * PIL in CLIP * don't move that
40 lines
1.5 KiB
Python
40 lines
1.5 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 as apply_rope_new, precompute_freqs_cis
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def apply_rope(x:Tensor, start_pos:int):
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B, H, T, Hd = x.shape
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precompute_freqs_cis.cache_clear()
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freqs_cis = precompute_freqs_cis(Hd, start_pos+T)[start_pos:start_pos+T]
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return apply_rope_new(x, freqs_cis)
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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 4 kernels now
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self.assertEqual(len(sched), 4)
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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, 2)
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self.assertEqual(prune_size, 1)
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if __name__ == '__main__':
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unittest.main()
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