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update ut (#37089)
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@jiweibo
jiweibo authored Nov 11, 2021
1 parent 32c3e61 commit 6c183a8
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Showing 3 changed files with 61 additions and 93 deletions.
6 changes: 3 additions & 3 deletions python/paddle/fluid/tests/unittests/ir/inference/auto_scan_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -260,7 +260,7 @@ def run_test(self, quant=False, *args, **kwargs):

self.assertTrue(status)

def inference_config_str(self, config) -> bool:
def inference_config_str(self, config) -> str:
dic = {}
enable_mkldnn = config.mkldnn_enabled()
dic['use_mkldnn'] = enable_mkldnn
Expand Down Expand Up @@ -372,7 +372,7 @@ def run_test(self, quant=False, *args, **kwargs):
status = self.check_op_version() and status
self.assertTrue(status)

def inference_config_str(self, config) -> bool:
def inference_config_str(self, config) -> str:
dic = {}
enable_mkldnn = config.mkldnn_enabled()
dic['use_mkldnn'] = enable_mkldnn
Expand Down Expand Up @@ -491,7 +491,7 @@ def assert_op_size(self, trt_engine_num, paddle_op_num):
'paddle_op_num is {}, but got {}!'.format(
paddle_op_size, paddle_op_num))

def inference_config_str(self, config: paddle_infer.Config):
def inference_config_str(self, config: paddle_infer.Config) -> str:
dic = {}
enable_trt = config.tensorrt_engine_enabled()
trt_precison = config.tensorrt_precision_mode()
Expand Down
123 changes: 50 additions & 73 deletions python/paddle/fluid/tests/unittests/ir/inference/test_emb_eltwise_layernorm_fuse_pass.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
# limitations under the License.

from auto_scan_test import PassAutoScanTest, SkipReasons
from program_config import TensorConfig, ProgramConfig
from program_config import TensorConfig, ProgramConfig, OpConfig
import numpy as np
import paddle.inference as paddle_infer
from functools import partial
Expand Down Expand Up @@ -116,94 +116,71 @@ def generate_weight2(attrs):
'weight_size': kwargs['weight_size']
}]

ops_config = [{
"op_type": attrs[0]['op_type'],
"op_inputs": {
"Ids": ["input_data1"],
"W": ["embedding_weight1"]
},
"op_outputs": {
"Out": ["embedding_output1"]
},
"op_attrs": {
emb_op1 = OpConfig(
type=attrs[0]['op_type'],
inputs={"Ids": ["input_data1"],
"W": ["embedding_weight1"]},
outputs={"Out": ["embedding_output1"]},
attrs={
'is_sparse': attrs[0]['is_sparse'],
'is_distributed': attrs[0]['is_distributed'],
'padding_idx': attrs[0]['padding_idx'],
}
}, {
"op_type": attrs[0]['op_type'],
"op_inputs": {
"Ids": ["input_data2"],
"W": ["embedding_weight2"]
},
"op_outputs": {
"Out": ["embedding_output2"]
},
"op_attrs": {
'padding_idx': attrs[0]['padding_idx']
})
emb_op2 = OpConfig(
type=attrs[0]['op_type'],
inputs={"Ids": ["input_data2"],
"W": ["embedding_weight2"]},
outputs={"Out": ["embedding_output2"]},
attrs={
'is_sparse': attrs[0]['is_sparse'],
'is_distributed': attrs[0]['is_distributed'],
'padding_idx': attrs[0]['padding_idx'],
},
}, {
"op_type": attrs[0]['op_type'],
"op_inputs": {
"Ids": ["input_data3"],
"W": ["embedding_weight3"]
},
"op_outputs": {
"Out": ["embedding_output3"]
},
"op_attrs": {
'padding_idx': attrs[0]['padding_idx']
})
emb_op3 = OpConfig(
type=attrs[0]['op_type'],
inputs={"Ids": ["input_data3"],
"W": ["embedding_weight3"]},
outputs={"Out": ["embedding_output3"]},
attrs={
'is_sparse': attrs[0]['is_sparse'],
'is_distributed': attrs[0]['is_distributed'],
'padding_idx': attrs[0]['padding_idx'],
},
}, {
"op_type": "elementwise_add",
"op_inputs": {
"X": ["embedding_output2"],
"Y": ["embedding_output3"]
},
"op_outputs": {
"Out": ["elementwise_add_output1"]
},
"op_attrs": {
"axis": attrs[1]['axis'],
}
}, {
"op_type": "elementwise_add",
"op_inputs": {
"X": ["elementwise_add_output1"],
"Y": ["embedding_output1"]
'padding_idx': attrs[0]['padding_idx']
})
add_op1 = OpConfig(
type='elementwise_add',
inputs={
"X": [emb_op2.outputs["Out"][0]],
"Y": [emb_op3.outputs["Out"][0]],
},
"op_outputs": {
"Out": ["elementwise_add_output2"]
outputs={"Out": ["elementwise_add_output1"]},
attrs={"axis": attrs[1]['axis']})
add_op2 = OpConfig(
type='elementwise_add',
inputs={
"X": [add_op1.outputs["Out"][0]],
"Y": [emb_op1.outputs["Out"][0]],
},
"op_attrs": {
"axis": attrs[1]['axis'],
}
}, {
"op_type": "layer_norm",
"op_inputs": {
"X": ["elementwise_add_output2"],
outputs={"Out": ["elementwise_add_output2"]},
attrs={"axis": attrs[1]['axis']})
layer_norm_op = OpConfig(
type='layer_norm',
inputs={
"X": [add_op2.outputs["Out"][0]],
"Bias": ["layer_norm_bias"],
"Scale": ["layer_norm_scale"]
},
"op_outputs": {
outputs={
"Y": ["layer_norm_output1"],
"Mean": ["layer_norm_output2"],
"Variance": ["layer_norm_output3"]
},
"op_attrs": {
attrs={
'begin_norm_axis': attrs[2]['begin_norm_axis'],
'epsilon': attrs[2]['epsilon'],
}
}]

ops = self.generate_op_config(ops_config)
'epsilon': attrs[2]['epsilon']
})

program_config = ProgramConfig(
ops=ops,
ops=[emb_op1, emb_op2, emb_op3, add_op1, add_op2, layer_norm_op],
weights={
"embedding_weight1":
TensorConfig(data_gen=partial(generate_weight1, attrs[3])),
Expand Down Expand Up @@ -242,7 +219,7 @@ def sample_predictor_configs(self, program_config):
precision_mode=paddle_infer.PrecisionType.Float32,
use_static=False,
use_calib_mode=False)
yield config, (10, 3), (1e-5, 1e-5)
yield config, (10, 5), (1e-5, 1e-5)
# trt dynamic_shape
config = self.create_trt_inference_config()
config.enable_tensorrt_engine(
Expand Down Expand Up @@ -280,7 +257,7 @@ def sample_predictor_configs(self, program_config):
"input_data2": [2, 128],
"input_data3": [2, 128]
})
yield config, (10, 3), (1e-5, 1e-5)
yield config, (10, 5), (1e-5, 1e-5)

def add_skip_pass_case(self):
def teller1(program_config, predictor_config):
Expand Down
25 changes: 8 additions & 17 deletions python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_prelu_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
# limitations under the License.

from auto_scan_test import MkldnnAutoScanTest, SkipReasons
from program_config import TensorConfig, ProgramConfig
from program_config import TensorConfig, ProgramConfig, OpConfig
import numpy as np
import paddle.inference as paddle_infer
from functools import partial
Expand Down Expand Up @@ -52,24 +52,15 @@ def generate_alpha(*args, **kwargs):
return np.zeros((1)).astype(np.float32)
return np.random.random(kwargs['in_shape']).astype(np.float32)

ops_config = [{
"op_type": "prelu",
"op_inputs": {
"X": ["input_data"],
"Alpha": ["alpha_weight"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": {
"mode": kwargs['mode']
}
}]

ops = self.generate_op_config(ops_config)
prelu_op = OpConfig(
type="prelu",
inputs={"X": ["input_data"],
"Alpha": ["alpha_weight"]},
outputs={"Out": ["output_data"]},
attrs={"mode": kwargs['mode']})

program_config = ProgramConfig(
ops=ops,
ops=[prelu_op],
weights={
"alpha_weight":
TensorConfig(data_gen=partial(generate_alpha, *args, **kwargs))
Expand Down

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