add large scale optimizer fuse

python/paddle/fluid/incubate/fleet/parameter_server/ir/pserver_pass.py

@@ -624,6 +624,7 @@ def large_scale_sparse_pass(program, main_program, config, is_startup=False):
         value_dims = []
         grad = None
         opt_idx = -1
+        fuse = False
 
         for op in block.ops:
             opt_idx += 1
@@ -631,6 +632,9 @@ def large_scale_sparse_pass(program, main_program, config, is_startup=False):
             if op.type not in opt_value_map.keys():
                 continue
 
+            if op.type in ["sgd", "adam"]:
+                fuse = True
+
             grad = main_program.global_block().vars[op.input("Grad")[0]]
 
             for value in opt_value_map[op.type]:
@@ -644,7 +648,68 @@ def large_scale_sparse_pass(program, main_program, config, is_startup=False):
 
             if value_names:
                 break
-        return grad, opt_idx, value_names, value_dims, acture_names
+        return grad, opt_idx, value_names, value_dims, acture_names, fuse
+
+    def add_fuse_large_scale_op(block, global_block, table_name, value_names,
+                                acture_names, grad, is_entry, opt_idx):
+
+        op = block.ops[opt_idx]
+
+        if op.type == "sgd":
+            grad = main_program.global_block().vars[op.input("Grad")[0]]
+            # remove origin optimzier op
+            block._remove_op(opt_idx)
+
+            block._insert_op(
+                opt_idx,
+                type="lookup_sparse_table_fuse_sgd",
+                inputs={"Grad": grad},
+                attrs={
+                    "is_entry": is_entry,
+                    "tablename": table_name,
+                    "value_names": value_names
+                })
+
+        elif op.type == "adam":
+            grad = main_program.global_block().vars[op.input("Grad")[0]]
+            beta1_pow = main_program.global_block().vars[op.input("Beta1Pow")[
+                0]]
+            beta2_pow = main_program.global_block().vars[op.input("Beta2Pow")[
+                0]]
+            beta1_pow_o = main_program.global_block().vars[op.input(
+                "Beta1PowOut")[0]]
+            beta2_pow_o = main_program.global_block().vars[op.input(
+                "Beta2PowOut")[0]]
+
+            beta1 = op.attr('shape')
+            beta2 = op.attr('beta2')
+            epsilon = op.attr('epsilon')
+
+            # remove origin optimzier op
+            block._remove_op(opt_idx)
+
+            block._insert_op(
+                opt_idx,
+                type="lookup_sparse_table_fuse_adam",
+                inputs={
+                    "Grad": grad,
+                    "Beta1Pow": beta1_pow,
+                    "Beta2Pow": beta2_pow
+                },
+                outputs={
+                    "Beta1PowOut": beta1_pow_o,
+                    "Beta2PowOut": beta2_pow_o
+                },
+                attrs={
+                    "beta1": beta1,
+                    "beta2": beta2,
+                    "epsilon": epsilon,
+                    "is_entry": is_entry,
+                    "tablename": table_name,
+                    "value_names": value_names
+                })
+        else:
+            raise ValueError("only support sgd/adam optimizer now")
 
     def add_large_scale_op(block, global_block, table_name, value_names,
                            acture_names, grad, is_entry, opt_idx):
@@ -711,20 +776,27 @@ def large_scale_sparse_pass(program, main_program, config, is_startup=False):
         for param, blockid in param_blockid_map.items():
             opt_block = program.block(blockid)
 
-            grad, opt_idx, value_names, value_dims, acture_names = \
+            grad, opt_idx, value_names, value_dims, acture_names, fuse = \
                 get_optimizer_values(opt_block)
 
             entry_attr = get_entry_attr(param)
             is_entry = False if entry_attr == "none" else True
+
+            if fuse:
+                add_fuse_large_scale_op(opt_block,
+                                        program.global_block(), param,
+                                        value_names, acture_names, grad,
+                                        is_entry, opt_idx)
+            else:
                 add_large_scale_op(opt_block,
                                    program.global_block(), param, value_names,
                                    acture_names, grad, is_entry, opt_idx)
-
     else:
         large_scale_kv_metas = []
         for param, blockid in param_blockid_map.items():
             opt_block = main_program.block(blockid)
-            grad, _, value_names, value_dims, acture_names = \
+
+            grad, _, value_names, value_dims, acture_names, fuse = \
                 get_optimizer_values(opt_block)
 
             entry_attr = get_entry_attr(param)