add fc_elementwise_layernorm_fuse_pass (#37771)

* add fc_elementwise_layernorm_fuse_pass

* fix name conflictn

* rebuild CI

* fix Ran Programs=0 bug

paddle/fluid/framework/ir/CMakeLists.txt

@@ -156,7 +156,7 @@ cc_test(test_seqpool_cvm_concat_fuse_pass SRCS seqpool_cvm_concat_fuse_pass_test
 cc_test(test_repeated_fc_relu_fuse_pass_cc SRCS repeated_fc_relu_fuse_pass_tester.cc DEPS repeated_fc_relu_fuse_pass framework_proto)
 cc_test(test_is_test_pass SRCS is_test_pass_tester.cc DEPS is_test_pass)
 cc_test(test_simplify_with_basic_ops_pass SRCS simplify_with_basic_ops_pass_tester.cc DEPS simplify_with_basic_ops_pass)
-cc_test(test_fc_elementwise_layernorm_fuse_pass SRCS fc_elementwise_layernorm_fuse_pass_tester.cc DEPS fc_elementwise_layernorm_fuse_pass)
+cc_test(test_fc_elementwise_layernorm_fuse_pass_cc SRCS fc_elementwise_layernorm_fuse_pass_tester.cc DEPS fc_elementwise_layernorm_fuse_pass)
 cc_test(test_skip_layernorm_fuse_pass SRCS skip_layernorm_fuse_pass_tester.cc DEPS skip_layernorm_fuse_pass)
 cc_test(test_multihead_matmul_fuse_pass SRCS multihead_matmul_fuse_pass_tester.cc DEPS multihead_matmul_fuse_pass)
 cc_test(test_conv_bn_fuse_pass_cc SRCS conv_bn_fuse_pass_tester.cc DEPS conv_bn_fuse_pass)

paddle/fluid/framework/ir/fc_elementwise_layernorm_fuse_pass.cc

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <string>
 
 #include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -338,3 +339,9 @@ void FCElementwiseLayerNormFusePass::ApplyImpl(ir::Graph *graph) const {
 
 REGISTER_PASS(fc_elementwise_layernorm_fuse_pass,
               paddle::framework::ir::FCElementwiseLayerNormFusePass);
+REGISTER_PASS_CAPABILITY(fc_elementwise_layernorm_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("fc", 0)
+            .LE("elementwise_add", 1)
+            .EQ("layer_norm", 0));

python/paddle/fluid/tests/unittests/ir/inference/auto_scan_test.py

@@ -322,14 +322,14 @@ class PassAutoScanTest(AutoScanTest):
             "Expected operator list after fusion is {}, but now it's {}".format(
                 op_list_after_fusion, after_op_list), )
 
-    def run_and_statis(
-            self,
-            quant=False,
-            max_examples=100,
-            reproduce=None,
-            min_success_num=25,
-            max_duration=180,
-            passes=None, ):
+    def run_and_statis(self,
+                       quant=False,
+                       max_examples=100,
+                       reproduce=None,
+                       min_success_num=25,
+                       max_duration=180,
+                       passes=None,
+                       use_gpu_run_baseline=False):
         if os.getenv('HYPOTHESIS_TEST_PROFILE', 'ci') == "dev":
             max_examples *= 10
             min_success_num *= 10
@@ -354,7 +354,10 @@ class PassAutoScanTest(AutoScanTest):
             return self.sample_program_config(draw)
 
         def run_test(prog_config):
-            return self.run_test(quant=quant, prog_configs=[prog_config])
+            return self.run_test(
+                quant=quant,
+                prog_configs=[prog_config],
+                use_gpu_run_baseline=use_gpu_run_baseline)
 
         generator = st.composite(program_generator)
         loop_func = given(generator())(run_test)
@@ -371,8 +374,8 @@ class PassAutoScanTest(AutoScanTest):
         logging.info("Number of Ran Programs: {}".format(self.num_ran_programs))
         logging.info("Number of Ignore Tests: {}".format(self.num_ignore_tests))
         successful_ran_programs = int(self.num_ran_programs -
-                                      self.num_ignore_tests /
-                                      self.num_predictor_kinds)
+                                      self.num_ignore_tests / max(
+                                          self.num_predictor_kinds, 1))
         logging.info(
             "Number of successfully ran programs approximately equal to {}".
             format(successful_ran_programs))
@@ -391,7 +394,10 @@ class PassAutoScanTest(AutoScanTest):
                 format(max_duration))
             assert False
 
-    def run_test(self, quant=False, prog_configs=None):
+    def run_test(self,
+                 quant=False,
+                 prog_configs=None,
+                 use_gpu_run_baseline=False):
         status = True
 
         for prog_config in prog_configs:
@@ -413,7 +419,9 @@ class PassAutoScanTest(AutoScanTest):
             results: List[Dict[str, np.ndarray]] = []
 
             # baseline: cpu no ir_optim run
-            base_config = self.create_inference_config(ir_optim=False)
+
+            base_config = self.create_inference_config(
+                ir_optim=False, use_gpu=use_gpu_run_baseline)
             logging.info('RUN program_config: ' + str(prog_config))
             results.append(
                 self.run_test_config(model, params, prog_config, base_config,

python/paddle/fluid/tests/unittests/ir/inference/test_adaptive_pool2d_convert_global_pass_autoscan.py

@@ -109,7 +109,7 @@ class TestAdaptivePool2dConvertGlobalPass(PassAutoScanTest):
     def test(self):
         self.run_and_statis(
             quant=False,
-            max_examples=100,
+            max_examples=300,
             passes=["adaptive_pool2d_convert_global_pass"],
             min_success_num=40)
 

python/paddle/fluid/tests/unittests/ir/inference/test_fc_elementwise_layernorm_fuse_pass.py

+# Copyright (c) 2021 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.
+
+from auto_scan_test import PassAutoScanTest, IgnoreReasons
+from program_config import TensorConfig, ProgramConfig, OpConfig
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+import unittest
+
+import hypothesis
+from hypothesis import given, settings, seed, example, assume, reproduce_failure
+import hypothesis.strategies as st
+
+
+class TestFCElementwiseLayerNormFusePass(PassAutoScanTest):
+    """
+    x_var   w(persistable) bias_var(persistable)
+      \     |              /
+          fc     
+          |
+      fc_out_var  bias_var(persistable)
+            \        /
+          elementwise_add  bias_var(persistable)  scale_var(persistable)
+                  \            |                       /                    
+                           layer_norm
+                         /      |         \
+                        Y    mean_var  variance_var
+    """
+
+    def sample_predictor_configs(self, program_config):
+        # for gpu
+        config = self.create_inference_config(use_gpu=True)
+        yield config, ["fused_fc_elementwise_layernorm"], (1e-5, 1e-5)
+
+    def sample_program_config(self, draw):
+        # 1. Generate shape of input:X of fc
+        x_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=8), min_size=2, max_size=5))
+        x_shape = [2, 1]
+        x_rank = len(x_shape)
+        # 2. Generate attr:in_num_col_dims of fc
+        in_num_col_dims = draw(st.integers(min_value=1, max_value=x_rank - 1))
+        # 3. Generate legal shape of input:W/bias of fc
+        w_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=8), min_size=2, max_size=2))
+        w_shape[0] = int(np.prod(x_shape[in_num_col_dims:]))
+        w_shape = [1, 2]
+        fc_bias_shape = [w_shape[1], ]
+        if draw(st.booleans()):
+            fc_bias_shape.insert(0, 1)
+        fc_bias_shape = [2, ]
+        fc_out_shape = x_shape[:in_num_col_dims] + w_shape[1:]
+        # 4. Generate legal attr:axis/shape of elementwise_add
+        add_bias_shape = fc_out_shape[:]
+        axis = draw(st.integers(min_value=-1, max_value=0))
+        # 5. Generate legal shape of layer_norm
+        begin_norm_axis = draw(
+            st.integers(
+                min_value=1, max_value=len(fc_out_shape) - 1))
+        layer_norm_shape = [int(np.prod(fc_out_shape[begin_norm_axis:]))]
+        epsilon = 1e-5
+
+        fc_op = OpConfig(
+            "fc",
+            inputs={"Input": ["fc_x"],
+                    "W": ["fc_w"],
+                    "Bias": ["fc_bias"]},
+            outputs={"Out": ["fc_out"]},
+            in_num_col_dims=in_num_col_dims,
+            padding_weights=False,
+            activation_type="",
+            use_quantizer=False,
+            use_mkldnn=False, )
+        add_op = OpConfig(
+            "elementwise_add",
+            inputs={"X": ["fc_out"],
+                    "Y": ["add_bias"]},
+            outputs={"Out": ["add_out"]},
+            axis=axis, )
+        layer_norm_op = OpConfig(
+            "layer_norm",
+            inputs={
+                "X": ["add_out"],
+                "Scale": ["scale"],
+                "Bias": ["layer_norm_bias"]
+            },
+            outputs={
+                "Y": ["layer_norm_out"],
+                "Mean": ["layer_norm_mean"],
+                "Variance": ["layer_norm_var"]
+            },
+            begin_norm_axis=begin_norm_axis,
+            epsilon=epsilon)
+
+        ops = [fc_op, add_op, layer_norm_op]
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={
+                "fc_w": TensorConfig(shape=w_shape),
+                "fc_bias": TensorConfig(shape=fc_bias_shape),
+                "add_bias": TensorConfig(shape=add_bias_shape),
+                "scale": TensorConfig(shape=layer_norm_shape),
+                "layer_norm_bias": TensorConfig(shape=layer_norm_shape),
+            },
+            inputs={"fc_x": TensorConfig(shape=x_shape), },
+            outputs=ops[-1].outputs["Y"], )
+        return program_config
+
+    def test(self):
+        self.run_and_statis(
+            quant=False,
+            max_examples=300,
+            passes=["fc_elementwise_layernorm_fuse_pass"],
+            use_gpu_run_baseline=True)
+
+
+if __name__ == "__main__":
+    unittest.main()

tools/parallel_UT_rule.py

@@ -202,7 +202,7 @@ HIGH_PARALLEL_JOB_NEW = [
     'test_fleet_runtime',
     'test_rnn_cudnn_params_packing',
     'test_mkldnn_placement_pass',
-    'test_fc_elementwise_layernorm_fuse_pass',
+    'test_fc_elementwise_layernorm_fuse_pass_cc',
     'program_desc_test',
     'test_simplify_with_basic_ops_pass',
     'test_dygraph_mode_of_unittest',
@@ -1417,7 +1417,7 @@ CPU_PARALLEL_JOB = [
     'test_fc_mkldnn_op',
     'test_fc_lstm_fuse_pass',
     'test_fc_gru_fuse_pass',
-    'test_fc_elementwise_layernorm_fuse_pass',
+    'test_fc_elementwise_layernorm_fuse_pass_cc',
     'test_fc_bf16_mkldnn_op',
     'test_executor_feed_non_tensor',
     'test_executor_check_feed',