- 03 8月, 2023 1 次提交
-
-
由 Wang Xin 提交于
-
- 04 4月, 2023 1 次提交
-
-
由 huangjiyi 提交于
* update * fix bug * update * fix bug
-
- 17 1月, 2023 1 次提交
-
-
由 YuanRisheng 提交于
* change feed_op to phi kernel * fix ci bugs * fix build bugs * fix ci bugs * fix compile bugs * fix ci bugs * perfect code * perfect comment code * fix install bugs * modify code according comment * remove visitor in feed_op * modify according comment * perfect code according comment * add infershape * fix py3 bugs * fix getexpected kernel type * fix getexpected kernel type * fix ci bugs * add registry for custom device * fix py3 bugs * fix floating point error * fix py3 test bugs
-
- 04 1月, 2023 1 次提交
-
-
由 HongyuJia 提交于
* execute use kernel_key first * change OpKernelType->KernelKey * fix py3 compile error, remove redundant header files * fix build_strategy_test * fix DataType::RAW * fix custom_type test: operator_test.cc * fix transform place * fix backends_are_same_class * try fix place TransDataDevice * support all KernelKey * fix TransformData * fix place_are_same_class * fix merge * fix test_params_no_grad * fix specific place of GetExpectedKernelType * fix specific place of GetExpectedKernelType * fix GetKernelTypeForVar * fix dtype error * fix fetch_v2 * change GetKernelTypeForVar * fix interpreter * fix typo error * polish codes * polish codes * polish codes * fix conflict
-
- 28 9月, 2022 1 次提交
-
-
由 Chen Weihang 提交于
* remove needless using tensor * remove needless using tensor * resolve conflict * replace tensor using * fix format error * revert needless changing * fix rocm and npu compile error * fix cinn compile error * fix format error * fix mkldnn format error * fix mkldnn format error * fix cinn compile error * fix cinn compile error * fix cinn compile error * resolve conflict
-
- 15 7月, 2022 1 次提交
-
-
由 Ruibiao Chen 提交于
-
- 26 6月, 2022 1 次提交
-
-
由 Sing_chan 提交于
-
- 05 6月, 2022 1 次提交
-
-
由 Sing_chan 提交于
-
- 26 10月, 2021 1 次提交
-
-
由 Jack Zhou 提交于
* optimize fast tokenizer
-
- 20 10月, 2021 1 次提交
-
-
由 Steffy-zxf 提交于
Add Tokenizer related functionalities for Transformer model in order that the process of training and predicting is consistent. * support the text string as an input Tensor * support the "VOCAB"unordered_map<wstring, int> as an input Tensor to lookup tokens * Tokenizer used for BERT. This tokenizer applies an end-to-end, text string to wordpiece tokenization. * It first applies basic tokenization, followed by wordpiece tokenization.
-