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c96ae6e6
编写于
7月 14, 2020
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提交者:
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7月 14, 2020
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!51 Add fused_op testcases to CI
Merge pull request !51 from looop5/fused_op_testcases
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tests/operators/composite/need_adapt/Fused_LambNextMV_10229161408386697243.info
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tests/operators/composite/need_adapt/Fused_LambNextMV_10637164683062061938.info
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tests/operators/composite/need_adapt/Fused_LambNextMV_12035260795432113340.info
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tests/operators/composite/need_adapt/Fused_LambNextMV_13257561028613500504.info
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tests/operators/composite/need_adapt/Fused_LambNextMV_13769955845847610041.info
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tests/operators/composite/need_adapt/Fused_LambNextMV_14492938012907533443.info
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tests/operators/composite/need_adapt/Fused_LambNextMV_17212422678037132886.info
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tests/operators/composite/need_adapt/Fused_LambNextMV_18170962302098161342.info
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tests/operators/composite/need_adapt/Fused_LambNextMV_1932853756890330796.info
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tests/operators/composite/need_adapt/Fused_LambNextMV_3976511398164728650.info
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tests/operators/composite/need_adapt/Fused_LambUpdateWithLR_12073466097680829202.info
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tests/operators/composite/need_adapt/Fused_LambUpdateWithLR_1445905573061742177.info
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tests/operators/composite/need_adapt/Fused_LambUpdateWithLR_15600956116817642484.info
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tests/operators/composite/need_adapt/Fused_LambUpdateWithLR_15689878575778426853.info
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tests/operators/composite/need_adapt/Fused_LambUpdateWithLR_3830386909471115343.info
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tests/operators/composite/need_adapt/Fused_LambUpdateWithLR_8598988701258930330.info
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tests/operators/cube/test_matmul_001.py
tests/operators/cube/test_matmul_001.py
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tests/operators/composite/need_adapt/Fused_LambNextMV_10229161408386697243.info
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浏览文件 @
c96ae6e6
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tests/operators/composite/need_adapt/Fused_LambNextMV_10637164683062061938.info
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浏览文件 @
c96ae6e6
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tests/operators/composite/need_adapt/Fused_LambNextMV_12035260795432113340.info
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浏览文件 @
c96ae6e6
{"composite":true,"composite_graph":"1961_sp_1901_1841_1782_1722_1698_1674_1650_sp_706_construct","input_desc":[[{"data_type":"float32","shape":[],"tensor_name":"input_2"}],[{"data_type":"float32","shape":[],"tensor_name":"input_0"}],[{"data_type":"float32","shape":[],"tensor_name":"input_1"}],[{"data_type":"float32","shape":[1],"tensor_name":"input_14"}],[{"data_type":"float32","shape":[30522,1024],"tensor_name":"input_16"}],[{"data_type":"float32","shape":[30522,1024],"tensor_name":"input_17"}]],"op":"Fused_LambUpdateWithLR_8598988701258930330","op_desc":[{"attr":null,"impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[1],"tensor_name":"input_0"}],[{"data_type":"float32","name":"y","shape":[1],"tensor_name":"input_1"}]],"name":"RealDiv","output_desc":[{"data_type":"float32","name":"output","shape":[],"tensor_name":"output_0_0"}],"process":"aicore"},{"attr":null,"fusion":"SelectGT_0010","impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[1],"tensor_name":"input_2"}],[{"data_type":"float32","name":"y","shape":[1],"tensor_name":"input_3","value":0.0}]],"name":"Greater","output_desc":[{"data_type":"bool","name":"output","shape":[],"tensor_name":"output_0_1"}],"process":"aicore"},{"attr":null,"fusion":"SelectGT_0010_end","impl_path":"","input_desc":[[{"data_type":"bool","name":"condition","shape":[1],"tensor_name":"output_0_1"}],[{"data_type":"float32","name":"x","shape":[1],"tensor_name":"output_0_0"}],[{"data_type":"float32","name":"y","shape":[1],"tensor_name":"input_6","value":1.0}]],"name":"Select","output_desc":[{"data_type":"float32","name":"output","shape":[],"tensor_name":"output_0_2"}],"process":"aicore"},{"attr":null,"fusion":"SelectGT_0010","impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[1],"tensor_name":"input_0"}],[{"data_type":"float32","name":"y","shape":[1],"tensor_name":"input_3","value":0.0}]],"name":"Greater","output_desc":[{"data_type":"bool","name":"output","shape":[],"tensor_name":"output_0_3"}],"process":"aicore"},{"attr":null,"fusion":"SelectGT_0000_end","impl_path":"","input_desc":[[{"data_type":"bool","name":"condition","shape":[1],"tensor_name":"output_0_3"}],[{"data_type":"float32","name":"x","shape":[1],"tensor_name":"output_0_2"}],[{"data_type":"float32","name":"y","shape":[1],"tensor_name":"input_9","value":1.0}]],"name":"Select","output_desc":[{"data_type":"float32","name":"output","shape":[],"tensor_name":"output_0_4"}],"process":"aicore"},{"attr":null,"impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[1],"tensor_name":"output_0_4"}],[{"data_type":"float32","name":"y","shape":[1],"tensor_name":"input_11","value":10.0}]],"name":"Minimum","output_desc":[{"data_type":"float32","name":"output","shape":[],"tensor_name":"output_0_5"}],"process":"aicore"},{"attr":null,"impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[1],"tensor_name":"output_0_5"}],[{"data_type":"float32","name":"y","shape":[1],"tensor_name":"input_3","value":0.0}]],"name":"Maximum","output_desc":[{"data_type":"float32","name":"output","shape":[],"tensor_name":"output_0_6"}],"process":"aicore"},{"attr":[{"name":"x_shape","value":[]},{"name":"y_shape","value":[1]},{"name":"data_format","value":["DefaultFormat","DefaultFormat"]}],"impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[1],"tensor_name":"output_0_6"}],[{"data_type":"float32","name":"y","shape":[1],"tensor_name":"input_14"}]],"name":"Mul","output_desc":[{"data_type":"float32","name":"output","shape":[1],"tensor_name":"output_0_7"}],"process":"aicore"},{"attr":[{"name":"x_shape","value":[1]},{"name":"y_shape","value":[30522,1024]},{"name":"data_format","value":["DefaultFormat","DefaultFormat"]}],"impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[1],"tensor_name":"output_0_7"}],[{"data_type":"float32","name":"y","shape":[30522,1024],"tensor_name":"input_16"}]],"name":"Mul","output_desc":[{"data_type":"float32","name":"output","shape":[30522,1024],"tensor_name":"output_0_8"}],"process":"aicore"},{"attr":null,"impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[30522,1024],"tensor_name":"input_17"}],[{"data_type":"float32","name":"y","shape":[30522,1024],"tensor_name":"output_0_8"}]],"name":"Sub","output_desc":[{"data_type":"float32","name":"output","shape":[30522,1024],"tensor_name":"output_0_9"}],"process":"aicore"},{"attr":[{"name":"fake_output","value":true}],"impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[30522,1024],"tensor_name":"input_17"}],[{"data_type":"float32","name":"y","shape":[30522,1024],"tensor_name":"output_0_9"}],[{"data_type":"float32","name":"z","shape":[30522,1024],"tensor_name":"output_0_9"}]],"name":"InplaceAssign","output_desc":[{"data_type":"float32","name":"output","shape":[30522,1024],"tensor_name":"output_0_10"}],"process":"aicore"}],"output_desc":[{"data_type":"float32","shape":[30522,1024],"tensor_name":"output_0_10"}],"platform":"AKG",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tests/operators/cube/test_matmul_001.py
浏览文件 @
c96ae6e6
...
@@ -33,6 +33,16 @@ class TestCase(TestBase):
...
@@ -33,6 +33,16 @@ class TestCase(TestBase):
# caseflag,opfuncname,testRunArgs, dimArgs
# caseflag,opfuncname,testRunArgs, dimArgs
# shape_x, shape_y, bias, left_format, right_format, output_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs
# shape_x, shape_y, bias, left_format, right_format, output_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs
# bert shape with batch
# [16, 16, 32, 32, 16, 16] * [16, 16, 4, 32, 16, 16] -> [16, 16, 4, 32, 16, 16]
(
"batchmatmul_run_bert_00"
,
"matmul_run"
,
((
16
,
16
,
512
,
512
),
(
16
,
16
,
512
,
64
),
0
,
"zN"
,
"zN"
,
"zN"
,
False
,
False
,
"float16"
,
"float16"
,
"batchmatmul_cce"
)),
# [16, 16, 32, 32, 16, 16] * [16, 16, 4, 32, 16, 16] -> [16, 16, 4, 32, 16, 16]
(
"batchmatmul_run_bert_01"
,
"matmul_run"
,
((
16
,
16
,
512
,
512
),
(
16
,
16
,
512
,
64
),
0
,
"zN"
,
"zN"
,
"zN"
,
True
,
False
,
"float16"
,
"float16"
,
"batchmatmul_cce"
)),
# [16, 16, 4, 32, 16, 16] * [16, 16, 4, 32, 16, 16] -> [16, 16, 32, 32, 16, 16]
(
"batchmatmul_run_bert_02"
,
"matmul_run"
,
((
16
,
16
,
512
,
64
),
(
16
,
16
,
512
,
64
),
0
,
"zN"
,
"zN"
,
"zN"
,
False
,
True
,
"float16"
,
"float16"
,
"batchmatmul_cce"
)),
# bert shape
# bert shape
(
"matmul_run_bert_00"
,
"matmul_run"
,
((
16
,
1024
),
(
16
,
1024
),
0
,
"zN"
,
"zN"
,
"zN"
,
False
,
True
,
"float16"
,
None
,
"float16"
,
"matmul_cce"
)),
(
"matmul_run_bert_00"
,
"matmul_run"
,
((
16
,
1024
),
(
16
,
1024
),
0
,
"zN"
,
"zN"
,
"zN"
,
False
,
True
,
"float16"
,
None
,
"float16"
,
"matmul_cce"
)),
(
"matmul_run_bert_01"
,
"matmul_run"
,
((
8192
,
4096
),
(
8192
,
1024
),
0
,
"zN"
,
"zN"
,
"zN"
,
True
,
False
,
"float16"
,
None
,
"float32"
,
"matmul_cce"
)),
(
"matmul_run_bert_01"
,
"matmul_run"
,
((
8192
,
4096
),
(
8192
,
1024
),
0
,
"zN"
,
"zN"
,
"zN"
,
True
,
False
,
"float16"
,
None
,
"float32"
,
"matmul_cce"
)),
...
...
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