make cargo build happy

outbrain-inc · Jan 2, 2023 · 4426515 · 4426515
1 parent 5f085f4
commit 4426515
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Showing 5 changed files with 38 additions and 70 deletions.
diff --git a/src/block_leaky_relu.rs b/src/block_leaky_relu.rs
@@ -1,22 +1,13 @@
 use std::any::Any;
-use std::io;
-use merand48::*;
-use core::arch::x86_64::*;
 use std::error::Error;
-use std::mem::{self, MaybeUninit};
 
-
-use crate::optimizer;
 use crate::regressor;
 use crate::model_instance;
 use crate::feature_buffer;
 use crate::port_buffer;
-use crate::consts;
 use crate::block_helpers;
 use crate::graph;
-use optimizer::OptimizerTrait;
 use regressor::BlockTrait;
-use block_helpers::{Weight, WeightAndOptimizerData};
 use crate::graph::{BlockGraph};
 
 
@@ -29,12 +20,11 @@ pub struct BlockLeakyRELU {
 
 
 pub fn new_leaky_relu_block(bg: &mut BlockGraph,
-                            mi: &model_instance::ModelInstance,
                             input: graph::BlockPtrOutput,
 ) -> Result<graph::BlockPtrOutput, Box<dyn Error>> {
     let num_inputs = bg.get_num_output_values(vec![&input]);
     assert!(num_inputs != 0);
-    let mut block = Box::new(BlockLeakyRELU {
+    let block = Box::new(BlockLeakyRELU {
         output_offset: usize::MAX,
         input_offset: usize::MAX,
         num_inputs: num_inputs,
@@ -110,7 +100,7 @@ impl BlockTrait for BlockLeakyRELU
     }
 
 
-    fn allocate_and_init_weights(&mut self, mi: &model_instance::ModelInstance) {}
+    fn allocate_and_init_weights(&mut self, _mi: &model_instance::ModelInstance) {}
 
     fn get_num_output_values(&self, output: graph::OutputSlot) -> usize {
         assert!(output.get_output_index() == 0);
@@ -136,10 +126,8 @@ mod tests {
     use super::*;
     use crate::block_misc;
     use crate::feature_buffer;
-    use crate::feature_buffer::HashAndValueAndSeq;
-    use crate::vwmap;
-    use block_helpers::{slearn2, spredict2};
-    use block_misc::{Observe};
+    use block_helpers::slearn2;
+    use block_misc::Observe;
     use crate::assert_epsilon;
 
     fn fb_vec() -> feature_buffer::FeatureBuffer {
@@ -159,8 +147,8 @@ mod tests {
         let mut mi = model_instance::ModelInstance::new_empty().unwrap();
         let mut bg = BlockGraph::new();
         let input_block = block_misc::new_const_block(&mut bg, vec![2.0]).unwrap();
-        let leaky_relu_block = new_leaky_relu_block(&mut bg, &mi, input_block).unwrap();
-        let observe_block = block_misc::new_observe_block(&mut bg, leaky_relu_block, Observe::Forward, Some(1.0)).unwrap();
+        let leaky_relu_block = new_leaky_relu_block(&mut bg, input_block).unwrap();
+        block_misc::new_observe_block(&mut bg, leaky_relu_block, Observe::Forward, Some(1.0)).unwrap();
         bg.finalize();
         bg.allocate_and_init_weights(&mi);
 
@@ -172,11 +160,11 @@ mod tests {
     }
 
     fn test_simple_negative() {
-        let mut mi = model_instance::ModelInstance::new_empty().unwrap();
+        let mi = model_instance::ModelInstance::new_empty().unwrap();
         let mut bg = BlockGraph::new();
         let input_block = block_misc::new_const_block(&mut bg, vec![-2.0]).unwrap();
-        let leaky_relu_block = new_leaky_relu_block(&mut bg, &mi, input_block).unwrap();
-        let observe_block = block_misc::new_observe_block(&mut bg, leaky_relu_block, Observe::Forward, Some(1.0)).unwrap();
+        let leaky_relu_block = new_leaky_relu_block(&mut bg, input_block).unwrap();
+        block_misc::new_observe_block(&mut bg, leaky_relu_block, Observe::Forward, Some(1.0)).unwrap();
         bg.finalize();
         bg.allocate_and_init_weights(&mi);
 

diff --git a/src/block_relu.rs b/src/block_relu.rs
@@ -151,7 +151,7 @@ mod tests {
     use crate::feature_buffer::HashAndValueAndSeq;
     use crate::vwmap;
     use block_helpers::{slearn2, spredict2};
-    use block_misc::{Observe};
+    use block_misc::Observe;
     use crate::assert_epsilon;
 
     fn fb_vec() -> feature_buffer::FeatureBuffer {

diff --git a/src/block_sigmoid.rs b/src/block_sigmoid.rs
@@ -1,21 +1,14 @@
 use std::any::Any;
-use std::io;
-use merand48::*;
-use core::arch::x86_64::*;
 use std::error::Error;
-use std::mem::{self, MaybeUninit};
+use libm::expf;
 
-use crate::optimizer;
 use crate::regressor;
 use crate::model_instance;
 use crate::feature_buffer;
 use crate::port_buffer;
-use crate::consts;
 use crate::block_helpers;
 use crate::graph;
-use optimizer::OptimizerTrait;
 use regressor::BlockTrait;
-use block_helpers::{Weight, WeightAndOptimizerData};
 use crate::graph::{BlockGraph};
 
 pub struct BlockSigmoid {
@@ -25,12 +18,11 @@ pub struct BlockSigmoid {
 }
 
 pub fn new_sigmoid_block(bg: &mut graph::BlockGraph,
-                         mi: &model_instance::ModelInstance,
                          input: graph::BlockPtrOutput,
 ) -> Result<graph::BlockPtrOutput, Box<dyn Error>> {
     let num_inputs = bg.get_num_output_values(vec![&input]);
     assert!(num_inputs != 0);
-    let mut block = Box::new(BlockSigmoid {
+    let block = Box::new(BlockSigmoid {
         output_offset: usize::MAX,
         input_offset: usize::MAX,
         num_inputs: num_inputs,
@@ -46,7 +38,7 @@ impl BlockTrait for BlockSigmoid
         self
     }
 
-    fn allocate_and_init_weights(&mut self, mi: &model_instance::ModelInstance) {}
+    fn allocate_and_init_weights(&mut self, _mi: &model_instance::ModelInstance) {}
 
     fn get_num_output_slots(&self) -> usize { 1 }
 
@@ -82,13 +74,13 @@ impl BlockTrait for BlockSigmoid
 
                 // for now doing the actual slow sigmoid computation. once we establish a baseline,
                 // we can replace with a fast approximation or a lookup table
-                if x  < 0. {
-                    let epx = f32::powf(e, x);
+                if x  < 0.0 {
+                    let epx = expf(x);
                     let s = epx / (1.0 + epx);
                     *pb.tape.get_unchecked_mut(self.output_offset + i) = s;
                     *pb.tape.get_unchecked_mut(self.input_offset + i) = s * (1.0 - s);
                 } else {
-                    let s = 1.0 / (1.0 + f32::powf(e, -x));
+                    let s = 1.0 / (1.0 + expf(-x));
                     *pb.tape.get_unchecked_mut(self.output_offset + i) = s;
                     *pb.tape.get_unchecked_mut(self.input_offset + i) = s * (1.0 - s);
                 }
@@ -116,7 +108,7 @@ impl BlockTrait for BlockSigmoid
         unsafe {
             for i in 0..self.num_inputs as usize {
                 let x = *pb.tape.get_unchecked_mut(self.input_offset + i);
-                *pb.tape.get_unchecked_mut(self.output_offset + i) = 1.0 / (1.0 + f32::powf(e, -x));
+                *pb.tape.get_unchecked_mut(self.output_offset + i) = 1.0 / (1.0 + expf(-x));
             }
             block_helpers::forward(further_blocks, fb, pb);
         } // unsafe end
@@ -129,10 +121,8 @@ mod tests {
     use super::*;
     use crate::block_misc;
     use crate::feature_buffer;
-    use crate::feature_buffer::HashAndValueAndSeq;
-    use crate::vwmap;
-    use block_helpers::{slearn2, spredict2};
-    use block_misc::{Observe};
+    use block_helpers::slearn2;
+    use block_misc::Observe;
     use crate::assert_epsilon;
 
     fn fb_vec() -> feature_buffer::FeatureBuffer {
@@ -152,8 +142,8 @@ mod tests {
         let mut mi = model_instance::ModelInstance::new_empty().unwrap();
         let mut bg = BlockGraph::new();
         let input_block = block_misc::new_const_block(&mut bg, vec![2.0]).unwrap();
-        let sigmoid_block = new_sigmoid_block(&mut bg, &mi, input_block).unwrap();
-        let observe_block = block_misc::new_observe_block(&mut bg, sigmoid_block, Observe::Forward, Some(1.0)).unwrap();
+        let sigmoid_block = new_sigmoid_block(&mut bg, input_block).unwrap();
+        block_misc::new_observe_block(&mut bg, sigmoid_block, Observe::Forward, Some(1.0)).unwrap();
         bg.finalize();
         bg.allocate_and_init_weights(&mi);
 
@@ -165,11 +155,11 @@ mod tests {
     }
 
     fn test_simple_negative() {
-        let mut mi = model_instance::ModelInstance::new_empty().unwrap();
+        let mi = model_instance::ModelInstance::new_empty().unwrap();
         let mut bg = BlockGraph::new();
         let input_block = block_misc::new_const_block(&mut bg, vec![-2.0]).unwrap();
-        let sigmoid_block = new_sigmoid_block(&mut bg, &mi, input_block).unwrap();
-        let observe_block = block_misc::new_observe_block(&mut bg, sigmoid_block, Observe::Forward, Some(1.0)).unwrap();
+        let sigmoid_block = new_sigmoid_block(&mut bg, input_block).unwrap();
+        block_misc::new_observe_block(&mut bg, sigmoid_block, Observe::Forward, Some(1.0)).unwrap();
         bg.finalize();
         bg.allocate_and_init_weights(&mi);
 

diff --git a/src/block_tanh.rs b/src/block_tanh.rs
@@ -1,23 +1,15 @@
 use std::any::Any;
-use std::io;
-use merand48::*;
-use core::arch::x86_64::*;
 use std::error::Error;
-use std::mem::{self, MaybeUninit};
+use libm::tanhf;
 
-use crate::optimizer;
 use crate::regressor;
 use crate::model_instance;
 use crate::feature_buffer;
 use crate::port_buffer;
-use crate::consts;
 use crate::block_helpers;
 use crate::graph;
-use optimizer::OptimizerTrait;
 use regressor::BlockTrait;
-use block_helpers::{Weight, WeightAndOptimizerData};
 use crate::graph::{BlockGraph};
-use crate::libm::tanhf;
 
 
 pub struct BlockTanh {
@@ -28,12 +20,11 @@ pub struct BlockTanh {
 
 
 pub fn new_tanh_block(  bg: &mut graph::BlockGraph,
-                        mi: &model_instance::ModelInstance,
                         input: graph::BlockPtrOutput
                         ) -> Result<graph::BlockPtrOutput, Box<dyn Error>> {    
     let num_inputs = bg.get_num_output_values(vec![&input]);
     assert!(num_inputs != 0);
-    let mut block = Box::new(BlockTanh {
+    let block = Box::new(BlockTanh {
         output_offset: usize::MAX,
         input_offset: usize::MAX,
         num_inputs: num_inputs,
@@ -50,7 +41,7 @@ impl BlockTrait for BlockTanh
         self
     }
 
-    fn allocate_and_init_weights(&mut self, mi: &model_instance::ModelInstance) {
+    fn allocate_and_init_weights(&mut self, _mi: &model_instance::ModelInstance) {
     }
 
     fn get_num_output_slots(&self) -> usize {1}   
@@ -129,10 +120,8 @@ mod tests {
     use super::*;
     use crate::block_misc;
     use crate::feature_buffer;
-    use crate::feature_buffer::HashAndValueAndSeq;
-    use crate::vwmap;
-    use block_helpers::{slearn2, spredict2};
-    use block_misc::{Observe};
+    use block_helpers::slearn2;
+    use block_misc::Observe;
     use crate::assert_epsilon;
 
     fn fb_vec() -> feature_buffer::FeatureBuffer {
@@ -152,8 +141,8 @@ mod tests {
         let mut mi = model_instance::ModelInstance::new_empty().unwrap();        
         let mut bg = BlockGraph::new();
         let input_block = block_misc::new_const_block(&mut bg, vec![2.0]).unwrap();
-        let tanh_block = new_tanh_block(&mut bg, &mi, input_block).unwrap();
-        let observe_block = block_misc::new_observe_block(&mut bg, tanh_block, Observe::Forward, Some(1.0)).unwrap();
+        let tanh_block = new_tanh_block(&mut bg, input_block).unwrap();
+        block_misc::new_observe_block(&mut bg, tanh_block, Observe::Forward, Some(1.0)).unwrap();
         bg.finalize();
         bg.allocate_and_init_weights(&mi);
 
@@ -164,11 +153,11 @@ mod tests {
         assert_epsilon!(slearn2  (&mut bg, &fb, &mut pb, true), 2.0); // tanh desnt learn
     }
     fn test_simple_negative() {
-        let mut mi = model_instance::ModelInstance::new_empty().unwrap();        
+        let mi = model_instance::ModelInstance::new_empty().unwrap();
         let mut bg = BlockGraph::new();
         let input_block = block_misc::new_const_block(&mut bg, vec![-2.0]).unwrap();
-        let tanh_block = new_tanh_block(&mut bg, &mi, input_block).unwrap();
-        let observe_block = block_misc::new_observe_block(&mut bg, tanh_block, Observe::Forward, Some(1.0)).unwrap();
+        let tanh_block = new_tanh_block(&mut bg, input_block).unwrap();
+        block_misc::new_observe_block(&mut bg, tanh_block, Observe::Forward, Some(1.0)).unwrap();
         bg.finalize();
         bg.allocate_and_init_weights(&mi);
 

diff --git a/src/regressor.rs b/src/regressor.rs
@@ -210,20 +210,21 @@ impl Regressor  {
                 }
 
                 match activation {
+                    NNActivation::None => { },
                     NNActivation::Relu => {
                         output = block_relu::new_relu_block(&mut bg, &mi, output).unwrap();
                         println!("Relu layer");
                     },
                     NNActivation::LeakyRelu => {
-                        output = block_leaky_relu::new_leaky_rely_block(&mut bg, &mi, output).unwrap();
+                        output = block_leaky_relu::new_leaky_relu_block(&mut bg, output).unwrap();
                         println!("LeakyRelu layer");
                     },
                     NNActivation::Tanh => {
-                        output = block_tanh::new_tanh_block(&mut bg, &mi, output).unwrap();
+                        output = block_tanh::new_tanh_block(&mut bg, output).unwrap();
                         println!("Tanh layer");
                     },
                     NNActivation::Sigmoid => {
-                        output = block_sigmoid::new_sigmoid_block(&mut bg, &mi, output).unwrap();
+                        output = block_sigmoid::new_sigmoid_block(&mut bg, output).unwrap();
                         println!("Sigmoid layer");
                     }
                 }