【pir】add fused_fused_gemm_epilogue_pass test (PaddlePaddle#58750)

* modify

* modify

* Update paddle/fluid/pir/transforms/fusion/fused_linear_param_grad_add_pass.cc

* Update paddle/fluid/pir/transforms/fusion/fused_linear_param_grad_add_pass.cc

* Update paddle/fluid/pir/transforms/fusion/fused_linear_param_grad_add_pass.cc

* code style

* delete flag

* modify

* add fused_linear test

* modify

* Update paddle/fluid/framework/new_executor/standalone_executor.cc

Author: xiaoguoguo626807

test/ir/new_ir/fused_pass/test_fused_gemm_epilogue_pass.py

@@ -0,0 +1,120 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle.autograd.ir_backward import grad as ir_grad
+from paddle.base import core
+
+np.random.seed(2013)
+
+import os
+import re
+
+
+def get_cuda_version():
+    result = os.popen("nvcc --version").read()
+    regex = r'release (\S+),'
+    match = re.search(regex, result)
+    if match:
+        num = str(match.group(1))
+        integer, decimal = num.split('.')
+        return int(integer) * 1000 + int(float(decimal) * 10)
+    else:
+        return -1
+
+
+@unittest.skipIf(
+    not core.is_compiled_with_cuda() or get_cuda_version() < 11060,
+    "core is not complied with CUDA or nvcc version is less than11.6",
+)
+class TestFusedgemm_epilogueAdd(unittest.TestCase):
+    def test_fused_gemm_epilogue_add(self):
+        with paddle.pir_utils.IrGuard():
+            x_np = np.random.normal(3, 2.5, size=(1024, 1024)).astype(
+                np.float32
+            )
+            y_np = x_np
+            z_np = np.random.normal(3, 2.5, size=(1024)).astype(np.float32)
+            main_program = paddle.static.Program()
+            with paddle.static.program_guard(main_program):
+                x_ = paddle.static.data(
+                    name="x", shape=[1024, 1024], dtype="float32"
+                )
+                y_ = paddle.static.data(
+                    name="y", shape=[1024, 1024], dtype="float32"
+                )
+                z_ = paddle.static.data(name="z", shape=[1024], dtype="float32")
+                x_.stop_gradient = False
+                y_.stop_gradient = False
+                z_.stop_gradient = False
+                x = paddle.assign(x_)
+                y = paddle.assign(y_)
+                z = paddle.assign(z_)
+                res1 = paddle.matmul(x=x, y=y)
+                res2 = paddle.add(res1, z)
+                res3 = paddle.assign(res2)
+
+                res4, res5, res6 = ir_grad(res3, [x, y, z])
+                res4_ = paddle.assign(res4)
+                res5_ = paddle.assign(res5)
+                res6_ = paddle.assign(res6)
+                op_names = [op.name() for op in main_program.global_block().ops]
+                self.assertTrue(
+                    'pd_op.matmul' in op_names and 'pd_op.add' in op_names
+                )
+                self.assertTrue(
+                    'pd_op.add_grad' in op_names
+                    and 'pd_op.matmul_grad' in op_names
+                )
+
+                with paddle.static.scope_guard(paddle.static.Scope()):
+                    exe = paddle.base.Executor(paddle.base.CUDAPlace(0))
+                    fetches0 = exe.run(
+                        main_program,
+                        feed={"x": x_np, "y": y_np, "z": z_np},
+                        fetch_list=[res3, res4_, res5_, res6_],
+                    )
+                # main_program = main_program.clone()
+
+                pm = paddle.pir.PassManager()
+                pm.add_pass(
+                    'fused_gemm_epilogue_pass'
+                )  # apply pass to elimitate dead code
+                pm.run(main_program)
+                op_names = [op.name() for op in main_program.global_block().ops]
+                self.assertTrue(
+                    'pd_op.fused_gemm_epilogue' in op_names
+                    and 'pd_op.fused_gemm_epilogue_grad' in op_names
+                )
+
+                with paddle.static.scope_guard(paddle.static.Scope()):
+                    exe = paddle.base.Executor(paddle.base.CUDAPlace(0))
+                    fetches1 = exe.run(
+                        main_program,
+                        feed={"x": x_np, "y": y_np, "z": z_np},
+                        fetch_list=[res3, res4_, res5_, res6_],
+                    )
+
+                np.array_equal(fetches0[0], fetches1[0])
+                np.array_equal(fetches0[1], fetches1[1])
+                np.array_equal(fetches0[2], fetches1[2])
+                np.array_equal(fetches0[3], fetches1[3])
+
+
+if __name__ == "__main__":
+    unittest.main()