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_allpairs_get.py

@@ -0,0 +1,78 @@
+# 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_allpairs(gpus, instances):
+    """
+    AllReduce with all pairs algorithm using get semantics.
+    Steps:
+    1. Sync all ranks to ensure the data is ready.
+    2. Each rank read chunks from all peers and reduces the data.
+    3. Signal all ranks to notify that the data is ready.
+    4. Wait for all chunks to be ready, then retrieve the chunks from all peers.
+    """
+    size = gpus
+    chunksperloop = gpus * gpus
+    collective = AllReduce(size, chunksperloop, True)
+    with MSCCLPPProgram(
+        "allreduce_pairs",
+        collective,
+        size,
+        instances,
+        protocol="Simple",
+    ):
+
+        # Each rank sends the nth chunk to the nth rank into scratch space
+        for rank in range(size):
+            for tb in range(size):
+                index = rank * size
+                c = chunk(rank, Buffer.input, index + tb)
+                # make sure the data is ready
+                for nghr in range(size):
+                    peer_index = nghr * size
+                    if rank != nghr:
+                        c_peer = chunk(rank, Buffer.input, peer_index + tb)
+                        c_peer.signal(nghr, Buffer.input, peer_index + tb, sendtb=tb)
+                for nghr in range(size):
+                    if rank != nghr:
+                        c.wait(nghr, Buffer.input, index + tb, recvtb=tb)
+                # reduce the chunks
+                for i in range(size):
+                    nghr = (rank + i) % size
+                    if rank != nghr:
+                        c.reduce(chunk(nghr, Buffer.input, index + tb), recvtb=tb)
+                for nghr in range(size):
+                    if rank != nghr:
+                        c.signal(nghr, Buffer.input, index + tb, sendtb=tb)
+
+        # wait for all the chunks is ready, then get the chunks
+        for rank in range(size):
+            for tb in range(size):
+                for nghr in range(size):
+                    if rank != nghr:
+                        index = nghr * size
+                        c = chunk(rank, Buffer.input, index + tb)
+                        c.wait(nghr, Buffer.input, index + tb, recvtb=tb)
+                for i in range(size):
+                    nghr = (rank + i) % size
+                    index = nghr * size
+                    if rank != nghr:
+                        c = chunk(rank, Buffer.input, index + tb)
+                        c.get(nghr, Buffer.input, index + tb, recvtb=tb)
+
+        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_allpairs(args.num_gpus, args.instances)