llama 70b model fusion and shardding (#18175)

### Description
Support llama-70b model fusion and shardding



### Motivation and Context
This change enables shard and export llama-70b model into Onnx as this
model is too large for single GPU.
This change also fuses llama-70b model with repeat_kv pattern different
with llama-7b and llama-13b.

onnxruntime/python/tools/symbolic_shape_infer.py

@@ -147,6 +147,7 @@ def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix=""):
             "GatherElements": self._infer_GatherElements,
             "GatherND": self._infer_GatherND,
             "Identity": self._pass_on_shape_and_type,
+            "AllReduce": self._pass_on_shape_and_type,
             "If": self._infer_If,
             "Loop": self._infer_Loop,
             "MatMul": self._infer_MatMul,

onnxruntime/python/tools/transformers/convert_generation.py

@@ -1272,7 +1272,7 @@ def find_past_seq_len_usage(subg: GraphProto):
     return tensor_names_to_rename, nodes_to_remove
 
 
-def replace_mha_with_gqa(model: OnnxModel, past_seq_len_input: str, kv_num_heads: int = 0):
+def replace_mha_with_gqa(model: OnnxModel, past_seq_len_input: str, kv_num_heads: int = 0, world_size: int = 1):
     past_seq_len = past_seq_len_input
     if past_seq_len not in model.get_graphs_input_names():
         # Add model input for past sequence length
@@ -1282,6 +1282,10 @@ def replace_mha_with_gqa(model: OnnxModel, past_seq_len_input: str, kv_num_heads
     # Replace MultiHeadAttention with GroupQueryAttention
     for node in model.model.graph.node:
         if node.op_type == "MultiHeadAttention":
+            num_heads_mha = 0
+            for att in node.attribute:
+                if att.name == "num_heads":
+                    num_heads_mha = att.i
             gqa_node = onnx.helper.make_node(
                 "GroupQueryAttention",
                 inputs=[
@@ -1295,8 +1299,8 @@ def replace_mha_with_gqa(model: OnnxModel, past_seq_len_input: str, kv_num_heads
                 outputs=node.output,
                 name=node.name.replace("MultiHeadAttention", "GroupQueryAttention"),
                 domain="com.microsoft",
-                num_heads=node.attribute[0].i,
-                kv_num_heads=node.attribute[0].i if kv_num_heads == 0 else kv_num_heads,
+                num_heads=num_heads_mha // world_size,
+                kv_num_heads=num_heads_mha // world_size if kv_num_heads == 0 else kv_num_heads // world_size,
                 is_past_bsnh=0,
             )
             model.model.graph.node.remove(node)

onnxruntime/python/tools/transformers/fusion_base.py

@@ -130,3 +130,8 @@ def add_nodes_to_remove(self, nodes: List[NodeProto]):
         for node in nodes:
             if node not in self.nodes_to_remove:
                 self.nodes_to_remove.append(node)
+
+    def add_nodes_to_remove_with_nodes_to_keep(self, nodes: List[NodeProto], nodes_to_keep: List[NodeProto]):
+        for node in nodes:
+            if node not in self.nodes_to_remove and node not in nodes_to_keep:
+                self.nodes_to_remove.append(node)