更新 pyo3 到 0.18

HIT-SCIR · Feb 7, 2023 · 25d353a · 25d353a
1 parent acd3ed1
commit 25d353a
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Showing 9 changed files with 13 additions and 28 deletions.
diff --git a/python/extension/Cargo.toml b/python/extension/Cargo.toml
@@ -21,7 +21,7 @@ rayon = { version = "1.5" }
 rayon-cond = { version = "0.2" }
 anyhow = { version = "1.0" }
 serde = { version = "1.0", features = ["derive"] }
-pyo3 = { version = "0.17", features = ["extension-module", "anyhow", "serde"] }
+pyo3 = { version = "0.18", features = ["extension-module", "anyhow", "serde"] }
 mimalloc = { version = "0.1", default-features = false, optional = true }
 
 [dependencies.ltp]

diff --git a/python/extension/src/hook.rs b/python/extension/src/hook.rs
@@ -20,7 +20,6 @@ impl PyHook {
     }
 
     /// add words to the hook, the freq can be zero
-    #[args(freq = "None")]
     #[pyo3(text_signature = "(self, word, freq = None)")]
     pub fn add_word(&mut self, word: &str, freq: Option<usize>) -> usize {
         self.hook.add_word(word, freq)

diff --git a/python/extension/src/perceptron/model.rs b/python/extension/src/perceptron/model.rs
@@ -20,7 +20,6 @@ pub enum ModelType {
 #[pymethods]
 impl ModelType {
     #[new]
-    #[args(model_type = "None")]
     pub fn new(model_type: Option<&str>) -> PyResult<Self> {
         Ok(match model_type {
             Some("cws") => ModelType::CWS,
@@ -80,15 +79,13 @@ pub struct PyModel {
 #[pymethods]
 impl PyModel {
     #[new]
-    #[args(model_type = "ModelType::Auto")]
     pub fn new(path: &str, model_type: ModelType) -> PyResult<Self> {
         Self::load(path, model_type)
     }
 
     /// Load Model from a path
     #[staticmethod]
     #[pyo3(text_signature = "(path, model_type=ModelType.Auto)")]
-    #[args(model_type = "ModelType::Auto")]
     pub fn load(path: &str, model_type: ModelType) -> PyResult<Self> {
         let file = std::fs::File::open(path)?;
         let format = if path.ends_with(".json") {
@@ -139,15 +136,15 @@ impl PyModel {
             EnumModel::CWS(model) => Ok(crate::perceptron::specialization::PyCWSModel {
                 model: model.clone(),
             }
-            .into_py(py)),
+                .into_py(py)),
             EnumModel::POS(model) => Ok(crate::perceptron::specialization::PyPOSModel {
                 model: model.clone(),
             }
-            .into_py(py)),
+                .into_py(py)),
             EnumModel::NER(model) => Ok(crate::perceptron::specialization::PyNERModel {
                 model: model.clone(),
             }
-            .into_py(py)),
+                .into_py(py)),
         }
     }
 
@@ -168,7 +165,6 @@ impl PyModel {
         Ok(())
     }
 
-    #[args(args = "*", parallelism = true)]
     pub fn __call__(&self, py: Python, args: &PyTuple, parallelism: bool) -> PyResult<PyObject> {
         let first = args.get_item(0)?;
         let is_single = match &self.model {
@@ -210,7 +206,6 @@ impl PyModel {
 
     /// Predict a sentence
     #[pyo3(text_signature = "(self, *args)")]
-    #[args(args = "*")]
     pub fn predict(&self, py: Python, args: &PyTuple) -> PyResult<PyObject> {
         Ok(match &self.model {
             EnumModel::CWS(model) => {
@@ -222,7 +217,7 @@ impl PyModel {
                         .into_iter()
                         .map(|s| PyString::new(py, s)),
                 )
-                .into()
+                    .into()
             }
             EnumModel::POS(model) => {
                 let words: Vec<&str> = args.get_item(0)?.extract()?;
@@ -233,7 +228,7 @@ impl PyModel {
                         .into_iter()
                         .map(|s| PyString::new(py, s)),
                 )
-                .into()
+                    .into()
             }
             EnumModel::NER(model) => {
                 let words: Vec<&str> = args.get_item(0)?.extract()?;
@@ -245,14 +240,13 @@ impl PyModel {
                         .into_iter()
                         .map(|s| PyString::new(py, s)),
                 )
-                .into()
+                    .into()
             }
         })
     }
 
     /// Predict batched sentences
     #[pyo3(text_signature = "(self, *args, parallelism = True)")]
-    #[args(args = "*", parallelism = true)]
     pub fn batch_predict(
         &self,
         py: Python,

diff --git a/python/extension/src/perceptron/specialization/cws.rs b/python/extension/src/perceptron/specialization/cws.rs
@@ -123,7 +123,6 @@ impl PyCWSModel {
         Ok(())
     }
 
-    #[args(args = "*", parallelism = true)]
     pub fn __call__(&self, py: Python, args: &PyTuple, parallelism: bool) -> PyResult<PyObject> {
         let first = args.get_item(0)?;
         let is_single = match first.get_type().name()? {
@@ -153,11 +152,10 @@ impl PyCWSModel {
                 .into_iter()
                 .map(|s| PyString::new(py, s)),
         )
-        .into())
+            .into())
     }
 
     /// Predict batched sentences
-    #[args(parallelism = true)]
     #[pyo3(text_signature = "(self, batch_text, parallelism=True)")]
     pub fn batch_predict(
         &self,

diff --git a/python/extension/src/perceptron/specialization/ner.rs b/python/extension/src/perceptron/specialization/ner.rs
@@ -24,7 +24,6 @@ impl PyNERModel {
         Ok(Self::inner_load(path)?)
     }
 
-    #[args(args = "*", parallelism = true)]
     pub fn __call__(&self, py: Python, args: &PyTuple, parallelism: bool) -> PyResult<PyObject> {
         let first = args.get_item(0)?;
         let is_single = match first.get_type().name()? {
@@ -71,11 +70,10 @@ impl PyNERModel {
                 .into_iter()
                 .map(|s| PyString::new(py, s)),
         )
-        .into())
+            .into())
     }
 
     /// Predict batched sentences
-    #[args(parallelism = true)]
     #[pyo3(text_signature = "(self, batch_words, batch_pos , parallelism=True)")]
     pub fn batch_predict(
         &self,

diff --git a/python/extension/src/perceptron/specialization/pos.rs b/python/extension/src/perceptron/specialization/pos.rs
@@ -24,7 +24,6 @@ impl PyPOSModel {
         Ok(Self::inner_load(path)?)
     }
 
-    #[args(args = "*", parallelism = true)]
     pub fn __call__(&self, py: Python, args: &PyTuple, parallelism: bool) -> PyResult<PyObject> {
         let first = args.get_item(0)?;
         let is_single = match first.get_type().name()? {
@@ -62,11 +61,10 @@ impl PyPOSModel {
                 .into_iter()
                 .map(|s| PyString::new(py, s)),
         )
-        .into())
+            .into())
     }
 
     /// Predict batched sentences
-    #[args(parallelism = true)]
     #[pyo3(text_signature = "(self, batch_words, parallelism=True)")]
     pub fn batch_predict(
         &self,

diff --git a/python/extension/src/perceptron/trainer.rs b/python/extension/src/perceptron/trainer.rs
@@ -39,7 +39,6 @@ pub struct PyTrainer {
 #[pymethods]
 impl PyTrainer {
     #[new]
-    #[args(model_type = "ModelType::Auto", labels = "None")]
     pub fn new(model_type: ModelType, labels: Option<Vec<String>>) -> PyResult<Self> {
         let trainer = match (model_type, labels) {
             (ModelType::CWS, _) => EnumTrainer::CWS(Default::default()),

diff --git a/python/extension/src/stnsplit.rs b/python/extension/src/stnsplit.rs
@@ -39,7 +39,6 @@ impl StnSplit {
     }
 
     /// batch split to sentences
-    #[args(threads = "8")]
     #[pyo3(text_signature = "(self, batch_text, threads=8)")]
     pub fn batch_split(
         &self,

diff --git a/rust/ltp-cffi/examples/example.c b/rust/ltp-cffi/examples/example.c
@@ -33,11 +33,11 @@ int main() {
   const char *pos_model_path = "data/legacy-models/pos_model.bin";
   const char *ner_model_path = "data/legacy-models/ner_model.bin";
   Model *cws_model = NULL;
-  cws_model = model_load(cws_model_path, strlen(cws_model_path));
+  cws_model = model_load(cws_model_path);
   Model *pos_model = NULL;
-  pos_model = model_load(pos_model_path, strlen(pos_model_path));
+  pos_model = model_load(pos_model_path);
   Model *ner_model = NULL;
-  ner_model = model_load(ner_model_path, strlen(ner_model_path));
+  ner_model = model_load(ner_model_path);
 
   const char *sentence = "他叫汤姆去拿外衣";
   size_t word_length[MAX_WORD_LEN] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0};