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提交 4200d597 编写于 作者: M mindspore-ci-bot 提交者： Gitee
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!14 add composite benchmark in akg 

Merge pull request !14 from dylangeng/benchmark
上级 34d89438 0d495b6a
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tests/perf_benchmark/benchmark/json_benchmark/case1.json 0 → 100644
浏览文件 @ 4200d597
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tests/perf_benchmark/benchmark/json_benchmark/case10.json 0 → 100644
浏览文件 @ 4200d597
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tests/perf_benchmark/benchmark/json_benchmark/case3.json 0 → 100644
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}],
"platform": "AKG",
"process": "aicore"
}
\ No newline at end of file
tests/perf_benchmark/benchmark/json_benchmark/case4_logsoftmax.json 0 → 100644
浏览文件 @ 4200d597
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tests/perf_benchmark/benchmark/json_benchmark/case5_reciprocal.json 0 → 100644
浏览文件 @ 4200d597
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tests/perf_benchmark/benchmark/json_benchmark/case6_mul_mul.json 0 → 100644
浏览文件 @ 4200d597
{"composite":true,"composite_graph":"10313","input_desc":[[{"data_type":"float16","shape":[16,1,1,512],"tensor_name":"input_2"}],[{"data_type":"float16","shape":[16,16,512,512],"tensor_name":"input_1"}]],"op":"Fused_Mul_Mul_TensorAdd__4400644352246048056","op_desc":[{"attr":[{"name":"x_shape","value":[1]},{"name":"y_shape","value":[16,16,512,512]},{"name":"data_format","value":["DefaultFormat","DefaultFormat"]}],"impl_path":"","input_desc":[[{"data_type":"float16","name":"x","shape":[1],"tensor_name":"input_0","value":0.125}],[{"data_type":"float16","name":"y","shape":[16,16,512,512],"tensor_name":"input_1"}]],"name":"Mul","output_desc":[{"data_type":"float16","name":"output","shape":[16,16,512,512],"tensor_name":"output_0_0"}]},{"attr":[{"name":"x_shape","value":[16,1,1,512]},{"name":"y_shape","value":[1]},{"name":"data_format","value":["DefaultFormat","DefaultFormat"]}],"impl_path":"","input_desc":[[{"data_type":"float16","name":"x","shape":[16,1,1,512],"tensor_name":"input_2"}],[{"data_type":"float16","name":"y","shape":[1],"tensor_name":"input_3","value":-10000.0}]],"name":"Mul","output_desc":[{"data_type":"float16","name":"output","shape":[16,1,1,512],"tensor_name":"output_0_1"}]},{"attr":null,"impl_path":"","input_desc":[[{"data_type":"float16","name":"x","shape":[16,1,1,512],"tensor_name":"output_0_1"}],[{"data_type":"float16","name":"y","shape":[16,16,512,512],"tensor_name":"output_0_0"}]],"name":"TensorAdd","output_desc":[{"data_type":"float16","name":"output","shape":[16,16,512,512],"tensor_name":"output_0_2"}]}],"output_desc":[{"data_type":"float16","shape":[16,1,1,512],"tensor_name":"output_0_1"},{"data_type":"float16","shape":[16,16,512,512],"tensor_name":"output_0_2"}],"platform":"AKG","process":"aicore"}
tests/perf_benchmark/benchmark/json_benchmark/case7_mul_mul.json 0 → 100644
浏览文件 @ 4200d597
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tests/perf_benchmark/benchmark/json_benchmark/case8_cast_cast.json 0 → 100644
浏览文件 @ 4200d597
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tests/perf_benchmark/benchmark/json_benchmark/case9.json 0 → 100644
浏览文件 @ 4200d597
{"composite":true,"composite_graph":"11288","input_desc":[[{"data_type":"float32","shape":[1024],"tensor_name":"input_1"}],[{"data_type":"float32","shape":[1024],"tensor_name":"input_0"}],[{"data_type":"float16","shape":[8192,1024],"tensor_name":"input_2"}]],"op":"Fused_Cast_Cast_Mul_TensorAdd___12292245117929986167","op_desc":[{"attr":[{"name":"dst_type","value":"float16"}],"impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[1024],"tensor_name":"input_0"}]],"name":"Cast","output_desc":[{"data_type":"float16","name":"output","shape":[1024],"tensor_name":"output_0_0"}]},{"attr":[{"name":"dst_type","value":"float16"}],"impl_path":"","input_desc":[[{"data_type":"float32","name":"x","shape":[1024],"tensor_name":"input_1"}]],"name":"Cast","output_desc":[{"data_type":"float16","name":"output","shape":[1024],"tensor_name":"output_0_1"}]},{"attr":[{"name":"x_shape","value":[8192,1024]},{"name":"y_shape","value":[1024]},{"name":"data_format","value":["DefaultFormat","DefaultFormat"]}],"impl_path":"","input_desc":[[{"data_type":"float16","name":"x","shape":[8192,1024],"tensor_name":"input_2"}],[{"data_type":"float16","name":"y","shape":[1024],"tensor_name":"output_0_1"}]],"name":"Mul","output_desc":[{"data_type":"float16","name":"output","shape":[8192,1024],"tensor_name":"output_0_2"}]},{"attr":null,"impl_path":"","input_desc":[[{"data_type":"float16","name":"x","shape":[8192,1024],"tensor_name":"output_0_2"}],[{"data_type":"float16","name":"y","shape":[1024],"tensor_name":"output_0_0"}]],"name":"TensorAdd","output_desc":[{"data_type":"float16","name":"output","shape":[8192,1024],"tensor_name":"output_0_3"}]}],"output_desc":[{"data_type":"float16","shape":[1024],"tensor_name":"output_0_1"},{"data_type":"float16","shape":[8192,1024],"tensor_name":"output_0_3"}],"platform":"AKG","process":"aicore"}
tests/perf_benchmark/benchmark/network_benchmark_op.py 0 → 100644
浏览文件 @ 4200d597
import boot
def test_compile_too_long():
boot.run("conv_backprop_filter_run_019", "conv_filter_ad_run", ((32, 128, 56, 56), (128, 128, 3, 3), (0, 1, 0, 1), (2, 2), (1, 1))),
boot.run("conv_backprop_filter_run_010", "conv_backprop_filter_run", ((1, 3, 224, 224),(64, 3, 7, 7),(3, 3, 3, 3),(2, 2),(1, 1))),
def test_resnet_benchmark():
boot.run("resnet50_maxpool_with_argmax_000", "maxpool_with_argmax_run", ((32, 4, 112, 112, 16), (3, 3), (2, 2), 'SAME', True, "float16")),
boot.run("resnet50_bn_split_005", "bn_split_run", ((32, 4, 112, 112, 16), "float32", 0.1, 1e-4, "resnet50_bn_split")),
boot.run("resnet50_conv_bn1_026", "conv_bn1_run", ((32, 3, 224, 224), (64, 3, 7, 7), (2, 3, 2, 3), (2, 2), (1, 1), False)),
boot.run("resnet50_four2five_003", "four2five_run", ([32, 3, 224, 224], "float16", "NCHW", "float16")),
boot.run("resnet50_softmax_004", "softmax_run", ((32, 1001), "float32", -1, "softmax_32")),
boot.run("resnet50_apply_momentum_002", "apply_momentum_run", ((128, 32, 16, 16), "float32", False)),
boot.run("resnet50_mean_000", "mean_run", ((32, 128, 7, 7, 16), "float32", (2, 3), True, "cce_mean")),
def test_bert_benchmark():
boot.run("bert_batch_matmul_003_242", "batchmatmul_run", ((), 4096, 3072, 768, (3072, ), "float32", False, True, "batch_matmul_output")),
boot.run("fused_layernorm_002_1280_1024", "fused_layernorm_run", ((1280, 1024), 1, -1, 'float16')),
boot.run("logsoftmax_grad_002", "logsoftmax_grad_run", ((160, 30522), "float32", -1, "cce_logsoftmax_fp16")),
boot.run("unsortedsegmentsum_002", "unsortedsegmentsum_run", ([1280, 1024], [1280], 8192, "float32")),
boot.run("transpose_002", "transpose_run", ((8, 16, 128, 64), (0, 2, 1, 3), "float32")),
boot.run("fused_layer_norm_grad_01", "fused_layer_norm_grad_run", ((8192, 1024), -1, -1, "float16")),
boot.run("logsoftmax_002_fp32", "logsoftmax_run", ((160, 30522), "float32", -1, "cce_logsoftmax_fp32")),
boot.run("strided_slice_grad_002", "strided_slice_grad_run",((128, 128, 768), [0, 0, 0], [128, 1, 768], [1, 1, 1], 0, 0, 0, 0, 0, (128, 1, 768), "int32"))
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