chore: Use comparison requirement for mlx-sys version when used as dep (#208)

* fix mlx-sys dep patch version

* restrict hf-hub dep to be <0.4.2

* fix tokenizers version

* clippy & fmt

Author: minghuaw

Cargo.toml

@@ -1,7 +1,7 @@
 [workspace.package]
 # All but mlx-sys should follow the same version. mlx-sys should follow 
 # the version of mlx-c.
-version = "0.21.0"
+version = "0.21.1"
 edition = "2021"
 authors = [
     "Minghua Wu <michael.wu1107@gmail.com>",
@@ -28,7 +28,7 @@ resolver = "2"
 
 [workspace.dependencies]
 # workspace local dependencies
-mlx-sys = { version = "0.1.0", path = "mlx-sys" }
+mlx-sys = { version = "=0.1.0", path = "mlx-sys" }
 mlx-macros = { version = "0.21.0", path = "mlx-macros" }
 mlx-internal-macros = { version = "0.21.0", path = "mlx-internal-macros" }
 mlx-rs = { version = "0.21.0", path = "mlx-rs" }

examples/mistral/Cargo.toml

@@ -9,10 +9,10 @@ authors.workspace = true
 mlx-rs.workspace = true
 
 # External dependencies
-tokenizers = "0.21"
+tokenizers = "=0.21.0" # 0.21.1 uses features that went stable in 1.82 while our MSRV is 1.81
 thiserror = "1.0"
 anyhow = "1.0"
-hf-hub = "0.4"
+hf-hub = "=0.4.1" # 0.4.1 use features that went stable in 1.82 while our MSRV is 1.81
 dotenv = "0.15"
 serde = { version = "1", features = ["derive"] }
 serde_json = "1"

mlx-rs/CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## 0.21.1
+
+- Fix `mlx-sys` dependency to patch version in workspace
+
 ## 0.21.0
 
 - Initial feature-complete release

mlx-rs/src/nn/activation.rs

@@ -285,7 +285,7 @@ impl Module<&Array> for Glu {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        glu(x, self.axis).map_err(Into::into)
+        glu(x, self.axis)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -336,7 +336,7 @@ impl Module<&Array> for Mish {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        mish(x).map_err(Into::into)
+        mish(x)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -358,7 +358,7 @@ impl Module<&Array> for Relu {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        relu(x).map_err(Into::into)
+        relu(x)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -393,7 +393,7 @@ impl Module<&Array> for LeakyRelu {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        leaky_relu(x, self.neg_slope).map_err(Into::into)
+        leaky_relu(x, self.neg_slope)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -415,7 +415,7 @@ impl Module<&Array> for Relu6 {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        relu6(x).map_err(Into::into)
+        relu6(x)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -472,7 +472,7 @@ impl Module<&Array> for Softplus {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        softplus(x).map_err(Into::into)
+        softplus(x)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -494,7 +494,7 @@ impl Module<&Array> for Softsign {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        softsign(x).map_err(Into::into)
+        softsign(x)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -530,7 +530,7 @@ impl Module<&Array> for Celu {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        celu(x, self.alpha).map_err(Into::into)
+        celu(x, self.alpha)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -552,7 +552,7 @@ impl Module<&Array> for Silu {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        silu(x).map_err(Into::into)
+        silu(x)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -587,7 +587,7 @@ impl Module<&Array> for LogSoftmax {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        log_softmax(x, self.axis).map_err(Into::into)
+        log_softmax(x, self.axis)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -609,7 +609,7 @@ impl Module<&Array> for LogSigmoid {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        log_sigmoid(x).map_err(Into::into)
+        log_sigmoid(x)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -671,7 +671,7 @@ impl Module<&Array> for Prelu {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        prelu(x, &self.weight).map_err(Into::into)
+        prelu(x, &self.weight)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -716,9 +716,9 @@ impl Module<&Array> for Gelu {
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
         match self.approximate {
-            GeluApprox::None => gelu(x).map_err(Into::into),
-            GeluApprox::Precise => gelu_approximate(x).map_err(Into::into),
-            GeluApprox::Fast => gelu_fast_approximate(x).map_err(Into::into),
+            GeluApprox::None => gelu(x),
+            GeluApprox::Precise => gelu_approximate(x),
+            GeluApprox::Fast => gelu_fast_approximate(x),
         }
     }
 
@@ -757,7 +757,7 @@ impl Module<&Array> for HardSwish {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        hard_swish(x).map_err(Into::into)
+        hard_swish(x)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -795,7 +795,7 @@ impl Module<&Array> for Step {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        step(x, self.threshold).map_err(Into::into)
+        step(x, self.threshold)
     }
 
     fn training_mode(&mut self, _: bool) {}
@@ -817,7 +817,7 @@ impl Module<&Array> for Selu {
     type Output = Array;
 
     fn forward(&mut self, x: &Array) -> Result<Array> {
-        selu(x).map_err(Into::into)
+        selu(x)
     }
 
     fn training_mode(&mut self, _: bool) {}

mlx-rs/src/nn/dropout.rs

@@ -70,7 +70,7 @@ impl Module<&Array> for Dropout {
 
         let p1 = array!(self.one_minus_p);
         let mask = bernoulli(&p1, x.shape(), None)?;
-        multiply(multiply(array!(1.0 / self.one_minus_p), mask)?, x).map_err(Into::into)
+        multiply(multiply(array!(1.0 / self.one_minus_p), mask)?, x)
     }
 
     fn training_mode(&mut self, mode: bool) {
@@ -170,7 +170,7 @@ impl Module<&Array> for Dropout2d {
         let p1 = array!(self.one_minus_p);
         let mask = bernoulli(&p1, &mask_shape, None)?;
 
-        multiply(multiply(array!(1.0 / self.one_minus_p), mask)?, x).map_err(Into::into)
+        multiply(multiply(array!(1.0 / self.one_minus_p), mask)?, x)
     }
 
     fn training_mode(&mut self, mode: bool) {
@@ -267,7 +267,7 @@ impl Module<&Array> for Dropout3d {
         let p1 = array!(self.one_minus_p);
         let mask = bernoulli(&p1, &mask_shape, None)?;
 
-        multiply(multiply(array!(1.0 / self.one_minus_p), mask)?, x).map_err(Into::into)
+        multiply(multiply(array!(1.0 / self.one_minus_p), mask)?, x)
     }
 
     fn training_mode(&mut self, mode: bool) {