Merge mscclpp-lang to mscclpp project (#442)

First step to merge msccl-tools into mscclpp repo. In this step will
move all msccl related code, pass the current tests and do some
necessary refactor.

Add `mscclpp.language` module
Add `_InstructionOptimizer` and `DagOptimizer` class to optimize the dag
Add `DagLower` to lower dag to intermediate representation 
Add documents for mscclpp.language
Remove msccl related code

python/examples/allreduce_nvls.py

@@ -0,0 +1,55 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import argparse
+from mscclpp.language import *
+from mscclpp.language.collectives import AllReduce
+from mscclpp.language.buffer import Buffer
+
+
+def allreduce_nvls(gpus, instances):
+    """
+    Allreduce via NVLS channel
+    Steps:
+    1. Sync all the ranks to make sure the data is ready.
+    2. Call group_load_reduce to reduce the data.
+    3. Call group_store to propagate the data to all the ranks.
+    """
+    size = gpus
+    chunksperloop = gpus
+    collective = AllReduce(size, chunksperloop, True)
+    with MSCCLPPProgram(
+        "allreduce_nvls",
+        collective,
+        size,
+        instances,
+    ):
+        # Each rank sends the nth chunk to the nth rank into scratch space
+        for rank in range(size):
+            index = rank
+            c = chunk(rank, Buffer.input, index)
+            reduce_chunks = []
+            # make sure the data is ready
+            for nghr in range(size):
+                if rank != nghr:
+                    c_peer = chunk(nghr, Buffer.input, index)
+                    reduce_chunks.append(c_peer)
+                    c.signal(nghr, Buffer.input, index, sendtb=0)
+            for nghr in range(size):
+                if rank != nghr:
+                    c.wait(nghr, Buffer.input, index, recvtb=0)
+            c = c.group_load_reduce(reduce_chunks, recvtb=0)
+            ngbrs = [nghr for nghr in range(size) if nghr != rank]
+            c.group_store(ngbrs, sendtb=0)
+
+        Json()
+        Check()
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("num_gpus", type=int, help="number of gpus")
+parser.add_argument("instances", type=int, help="number of instances")
+
+args = parser.parse_args()
+
+allreduce_nvls(args.num_gpus, args.instances)