[Inference/Feat] Add convert_fp8 op for fp8 test in the future (#5706)

* add convert_fp8 op for fp8 test in the future

* rerun ci

tests/test_infer/test_kernels/cuda/test_convert_fp8.py

@@ -0,0 +1,57 @@
+import random
+
+import pytest
+import torch
+
+from colossalai.kernel.kernel_loader import InferenceOpsLoader
+from colossalai.utils import get_current_device
+
+inference_ops = InferenceOpsLoader().load()
+
+DTYPES = [torch.half, torch.bfloat16, torch.float]
+NUM_TOKENS = [42]  # Arbitrary values for testing
+NUM_LAYERS = [1]  # Arbitrary values for testing
+NUM_HEADS = [8]  # Arbitrary values for testing
+HEAD_SIZES = [64, 80, 96, 112, 128, 256]
+BLOCK_SIZES = [8, 16, 32]
+
+
+@pytest.mark.skipif(True, reason="FP8 conversion still needs improvement, now we skip it's relative test!")
+@pytest.mark.parametrize("num_heads", [8])
+@pytest.mark.parametrize("head_size", [64, 80, 96, 112, 128, 256])
+@pytest.mark.parametrize("block_size", [8, 16, 32])
+@pytest.mark.parametrize("num_blocks", [1024, 10000])
+@pytest.mark.parametrize("dtype", [torch.half, torch.bfloat16, torch.float])
+@pytest.mark.parametrize("seed", [0])
+@torch.inference_mode()
+def test_fp8_conversion(
+    num_heads: int,
+    head_size: int,
+    block_size: int,
+    num_blocks: int,
+    dtype: torch.dtype,
+    seed: int,
+) -> None:
+    random.seed(seed)
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+
+    device = get_current_device()
+
+    low = -224.0
+    high = 224.0
+    shape = (num_blocks, num_heads, head_size, block_size)
+    cache = torch.empty(shape, dtype=dtype, device=device)
+    cache.uniform_(low, high)
+
+    cache_fp8 = torch.empty_like(cache, dtype=torch.uint8)
+    inference_ops.convert_fp8(cache, cache_fp8)
+
+    converted_cache = torch.empty_like(cache)
+    inference_ops.convert_fp8(cache_fp8, converted_cache)
+
+    assert torch.allclose(cache, converted_cache, atol=0.001, rtol=0.1)
+
+
+if __name__ == "__main__":
+    test_fp8_conversion(8, 64, 8, 1024, torch.half, 0)