Add tests for kernels

tests/kernels.py

+import random
+
+import torch
+
+from cacheflow.ops import reshape_and_cache
+
+
+def test_reshape_and_cache(
+    num_tokens: int,
+    num_heads: int,
+    head_size: int,
+    block_size: int,
+    num_blocks: int,
+    dtype: torch.dtype,
+) -> None:
+    num_slots = block_size * num_blocks
+    slot_mapping = random.sample(range(num_slots), num_tokens)
+    slot_mapping = torch.tensor(slot_mapping, dtype=torch.int, device='cuda')
+
+    kv_shape = (num_tokens, num_heads, head_size)
+    key = torch.randn(size=kv_shape, dtype=dtype, device='cuda')
+    value = torch.randn(size=kv_shape, dtype=dtype, device='cuda')
+    
+    x = 16 // torch.tensor([], dtype=dtype).element_size()
+    key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
+    key_cache = torch.randn(size=key_cache_shape, dtype=dtype, device='cuda')
+    cloned_key_cache = key_cache.clone()
+
+    value_cache_shape = (num_blocks, num_heads, block_size, head_size)
+    value_cache = torch.randn(
+        size=value_cache_shape, dtype=dtype, device='cuda')
+    cloned_value_cache = value_cache.clone()
+
+    reshape_and_cache(key, value, key_cache, value_cache, slot_mapping)
+
+    for i in range(num_tokens):
+        reshaped_key = key.reshape(num_tokens, num_heads, head_size // x, x)
+        block_idx = slot_mapping[i] // block_size
+        block_offset = slot_mapping[i] % block_size
+        cloned_key_cache[block_idx, :, :, block_offset, :] = reshaped_key[i]
+        cloned_value_cache[block_idx, :, block_offset, :] = value[i]
+
+    assert torch.allclose(key_cache, cloned_key_cache)
+    assert torch.allclose(value_cache, cloned_value_cache)
+
+
+@torch.no_grad()
+def test_kernels():
+    test_reshape_and_cache(
+        num_tokens=3, num_heads=2, head_size=16, block_size=2, num_blocks=2,
+        dtype=torch.half)
+
+
+if __name__ == '__main__':
+    test_kernels()