[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_reduce_scatter.py

@@ -0,0 +1,38 @@
+import torch
+from torch.distributed import reduce_scatter
+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 reduce_scatter_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):
+    x = torch.rand(shape, dtype=dtype, device=get_accelerator().get_current_device())
+    input_list = list(torch.chunk(x, dim=scatter_dim, chunks=4))
+    input_list = [t.contiguous() for t in input_list]
+    output_origin = torch.empty_like(input_list[0])
+    output_fp8 = torch.empty_like(input_list[0])
+    reduce_scatter(output_origin, input_list, group=_get_default_group())
+    reduce_scatter_fp8(output_fp8, input_list, group=_get_default_group(), fp8_format=fp8_format)
+    assert_close(output_origin, output_fp8, 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_reduce_scatter():
+    spawn(run_dist, 4)
+
+
+if __name__ == "__main__":
+    test_reduce_scatter()