[Feature] llama shardformer fp8 support (#5938)

* add llama shardformer fp8

* Llama Shardformer Parity

* fix typo

* fix all reduce

* fix pytest failure

* fix reduce op and move function to fp8.py

* fix typo

tests/test_fp8/test_fp8_all_to_all.py

@@ -0,0 +1,39 @@
+import torch
+import torch.distributed as dist
+from torch.distributed.distributed_c10d import _get_default_group
+from torch.testing import assert_close
+
+from colossalai import launch
+from colossalai.accelerator import get_accelerator
+from colossalai.quantization.fp8 import all_to_all_fp8
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+
+
+@parameterize("shape", [(16, 8, 4)])
+@parameterize("scatter_dim", [0, 1, 2])
+@parameterize("dtype", [torch.bfloat16, torch.float16])
+@parameterize("fp8_format", ["e4m3", "e5m2"])
+def check_4gpu(shape, scatter_dim, dtype, fp8_format):
+    world_size = dist.get_world_size()
+    input_tensor = torch.rand(shape, dtype=dtype, device=get_accelerator().get_current_device())
+    input_tensor_list = list(torch.chunk(input_tensor, world_size, scatter_dim))
+    input_tensor_list = [x.contiguous() for x in input_tensor_list]
+    output_tensor_list_fp8 = [torch.empty_like(x) for x in input_tensor_list]
+    output_tensor_list = [torch.empty_like(x) for x in input_tensor_list]
+    all_to_all_fp8(output_tensor_list_fp8, input_tensor_list, group=_get_default_group(), fp8_format=fp8_format)
+    dist.all_to_all(output_tensor_list, input_tensor_list, group=_get_default_group())
+    assert_close(output_tensor_list_fp8, output_tensor_list, rtol=0.1, atol=0.1)
+
+
+def run_dist(rank, world_size, port):
+    launch(rank=rank, world_size=world_size, port=port, host="localhost")
+    check_4gpu()
+
+
+@rerun_if_address_is_in_use()
+def test_all_to_all():
+    spawn(run_dist, 4)
+
+
+if __name__ == "__main__":
+    test_all_to_all()