Clippy fixes for onnx + fix a broken test. (#2510)

author: Laurent Mazare <laurent.mazare@gmail.com> 2024-09-26 23:37:59 +0200
committer: GitHub <noreply@github.com> 2024-09-26 23:37:59 +0200
commit: 2c25754281fb6672b9ebf84f6f6f5a5b12efe10d (patch)
tree: cd42c6cb6755015356797f723ab969d4b23cab29
parent: ed48f54b542bd3090032c516a50ee4d7067384a9 (diff)
download: candle-2c25754281fb6672b9ebf84f6f6f5a5b12efe10d.tar.gz
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2 files changed, 273 insertions, 281 deletions
diff --git a/candle-onnx/src/eval.rs b/candle-onnx/src/eval.rs
index 9c22eeab..de3e1010 100644
--- a/candle-onnx/src/eval.rs
+++ b/candle-onnx/src/eval.rs
@@ -2,7 +2,7 @@ use crate::onnx::attribute_proto::AttributeType;
 use crate::onnx::tensor_proto::DataType;
 use crate::onnx::{self, GraphProto};
 use candle::{bail, DType, Device, Result, Tensor};
-use std::{collections::HashMap, usize};
+use std::collections::HashMap;
 
 pub type Value = Tensor;
 
@@ -321,7 +321,7 @@ fn simple_eval_(
     for node in graph.node.iter() {
         let get = |input_name: &str| match values.get(input_name) {
             Some(value) => Ok(value),
-            None => bail!("cannot find {input_name} for op {}", node.name),
+            None => bail!("cannot find {input_name} for op '{}'", node.name),
         };
         let get_opt = |i: usize| {
             node.input
@@ -362,7 +362,7 @@ fn simple_eval_(
                 // HACK: current implementation of broadcast_pow cannot handle negative base,
                 // so we use powf where we can, which *does* correctly handle negative base.
                 if let Ok(exp) = (|| input1.to_dtype(DType::F64)?.to_scalar::<f64>())() {
-                    let output = input0.powf(exp as f64)?;
+                    let output = input0.powf(exp)?;
                     values.insert(node.output[0].clone(), output);
                 } else {
                     let output = input0.broadcast_pow(input1)?;
@@ -643,7 +643,7 @@ fn simple_eval_(
                     let mask = indices.lt(&zeros)?;
                     mask.to_dtype(indices.dtype())?
                         .broadcast_mul(&max)?
-                        .add(&indices)?
+                        .add(indices)?
                 };
 
                 // In Pytorch or Numpy this can be done by indexing the xs tensor using the indices
@@ -767,7 +767,7 @@ fn simple_eval_(
 
                 // where_cond requires that all inputs are the same shape.
                 // In contrast, the Where op in ONNX only requires that they are broadcastable.
-                let shape = broadcast_shape_from_many(&[&cond.dims(), &a.dims(), &b.dims()])?;
+                let shape = broadcast_shape_from_many(&[cond.dims(), a.dims(), b.dims()])?;
                 let cond = cond.broadcast_as(shape.clone())?;
                 let a = a.broadcast_as(shape.clone())?;
                 let b = b.broadcast_as(shape)?;
@@ -1283,8 +1283,7 @@ fn simple_eval_(
                     .map(|x| x as usize)
                     .collect::<Vec<_>>();
 
-                let target_shape =
-                    broadcast_shape(&input_tensor_dims, input_shape_dims.as_slice())?;
+                let target_shape = broadcast_shape(input_tensor_dims, input_shape_dims.as_slice())?;
 
                 let expanded_tensor = input_tensor.broadcast_as(target_shape)?;
 
@@ -1301,12 +1300,12 @@ fn simple_eval_(
                     .unwrap_or(0);
 
                 let axes = match axes {
-                    Some(axes) => axes?
+                    Some(Ok(axes)) => axes
                         .to_vec1::<i64>()?
                         .into_iter()
                         .map(|x| x as usize)
                         .collect::<Vec<_>>(),
-                    None => {
+                    Some(Err(_)) | None => {
                         if noop_with_empty_axes == 1 {
                             vec![]
                         } else {
@@ -1640,7 +1639,7 @@ fn simple_eval_(
                 let w = w.get(0)?; // w[iofc] has shape [4*hidden_size, input_size]
                 let r = r.get(0)?; // r[iofc] has shape [4*hidden_size, hidden_size]
                 let b = b.get(0)?; // concat of [wb[iofc],rb[iofc]] has shape [8*hidden_size]
-                let idx_wb = Tensor::arange(0 * hidden_size, 4 * hidden_size, x.device())?;
+                let idx_wb = Tensor::arange(0, 4 * hidden_size, x.device())?;
                 let idx_rb = Tensor::arange(4 * hidden_size, 8 * hidden_size, x.device())?;
                 let wb = b.index_select(&idx_wb, 0)?;
                 let rb = b.index_select(&idx_rb, 0)?;
@@ -1649,8 +1648,8 @@ fn simple_eval_(
 
                 // w, r, wb, rb are all iofc but lstm expects ifco
                 // so we need to move some stuff around
-                let idx_i = Tensor::arange(0 * hidden_size, 1 * hidden_size, x.device())?;
-                let idx_o = Tensor::arange(1 * hidden_size, 2 * hidden_size, x.device())?;
+                let idx_i = Tensor::arange(0, hidden_size, x.device())?;
+                let idx_o = Tensor::arange(hidden_size, 2 * hidden_size, x.device())?;
                 let idx_f = Tensor::arange(2 * hidden_size, 3 * hidden_size, x.device())?;
                 let idx_c = Tensor::arange(3 * hidden_size, 4 * hidden_size, x.device())?;
                 let idx_ifco = Tensor::cat(&[&idx_i, &idx_f, &idx_c, &idx_o], 0)?;
@@ -1674,7 +1673,7 @@ fn simple_eval_(
                 )?;
 
                 let mut lstm_state = candle_nn::rnn::LSTMState::new(h, c);
-                let mut h_acc = if node.output.get(0).map(String::as_str).unwrap_or("") != "" {
+                let mut h_acc = if node.output.first().map(String::as_str).unwrap_or("") != "" {
                     Some(vec![])
                 } else {
                     None
@@ -1688,7 +1687,7 @@ fn simple_eval_(
                 }
 
                 assert_eq!(num_directions, 1, "if support for bidirectional is ever added, outputs will have to be concatenated, not simply reshaped");
-                if let Some(name) = node.output.get(0) {
+                if let Some(name) = node.output.first() {
                     let h_acc = h_acc.as_ref().unwrap();
                     let h_acc = lstm.states_to_tensor(h_acc)?;
                     let h_acc = h_acc.reshape((
diff --git a/candle-onnx/tests/ops.rs b/candle-onnx/tests/ops.rs
index 55d6fb86..2a138131 100644
--- a/candle-onnx/tests/ops.rs
+++ b/candle-onnx/tests/ops.rs
@@ -1,12 +1,5 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
 use candle::test_utils::to_vec2_round;
 use candle::{DType, Device, NdArray, Result, Tensor};
-use candle_onnx::eval::Value;
 use candle_onnx::onnx::attribute_proto::AttributeType;
 use candle_onnx::onnx::tensor_proto::DataType;
 use candle_onnx::onnx::tensor_shape_proto::{dimension, Dimension};
@@ -3574,312 +3567,312 @@ fn test_lstm() -> Result<()> {
     let number_directions = 1;
     let weight_ih_l0 = Tensor::from_vec::<_, f32>(
         vec![
-            -1.5255959033966064,
-            -0.7502318024635315,
-            -0.6539809107780457,
-            -1.6094847917556763,
-            -0.1001671776175499,
-            -0.6091889142990112,
-            -0.9797722697257996,
-            -1.6090962886810303,
-            -0.7121446132659912,
-            0.30372199416160583,
-            -0.777314305305481,
-            -0.25145524740219116,
-            -0.22227048873901367,
-            1.6871134042739868,
-            0.22842517495155334,
-            0.46763551235198975,
-            -0.6969724297523499,
-            -1.1607614755630493,
-            0.6995424032211304,
-            0.1990816295146942,
-            0.8656923770904541,
-            0.2444038987159729,
-            -0.6629113554954529,
-            0.8073082566261292,
-            1.1016806364059448,
-            -0.1759360432624817,
-            -2.2455577850341797,
-            -1.4464579820632935,
-            0.0611552819609642,
-            -0.6177444458007812,
-            -0.7980698347091675,
-            -0.13162320852279663,
-            1.8793457746505737,
-            -0.07213178277015686,
-            0.15777060389518738,
-            -0.7734549045562744,
-            0.1990565061569214,
-            0.04570277780294418,
-            0.15295691788196564,
-            -0.47567880153656006,
-            -0.11101982742547989,
-            0.2927352488040924,
-            -0.1578451544046402,
-            -0.028787139803171158,
-            0.4532545804977417,
-            1.1421611309051514,
-            0.2486107051372528,
-            -1.7754007577896118,
-            -0.025502461940050125,
-            -1.023330569267273,
-            -0.5961851477622986,
-            -1.0055307149887085,
-            0.42854228615760803,
-            1.4760777950286865,
-            -1.7868678569793701,
-            1.610317587852478,
-            -0.703956663608551,
-            -0.18526579439640045,
-            -0.9962350726127625,
-            -0.8312552571296692,
+            -1.525_595_9,
+            -0.750_231_8,
+            -0.653_980_9,
+            -1.609_484_8,
+            -0.100_167_18,
+            -0.609_188_9,
+            -0.979_772_27,
+            -1.609_096_3,
+            -0.712_144_6,
+            0.303_722,
+            -0.777_314_3,
+            -0.251_455_25,
+            -0.222_270_49,
+            1.687_113_4,
+            0.228_425_17,
+            0.467_635_5,
+            -0.696_972_4,
+            -1.160_761_5,
+            0.699_542_4,
+            0.199_081_63,
+            0.865_692_4,
+            0.244_403_9,
+            -0.662_911_36,
+            0.807_308_26,
+            1.101_680_6,
+            -0.175_936_04,
+            -2.245_557_8,
+            -1.446_458,
+            0.061_155_282,
+            -0.617_744_45,
+            -0.798_069_83,
+            -0.131_623_21,
+            1.879_345_8,
+            -0.072_131_78,
+            0.157_770_6,
+            -0.773_454_9,
+            0.199_056_5,
+            0.045_702_778,
+            0.152_956_92,
+            -0.475_678_8,
+            -0.111_019_83,
+            0.292_735_25,
+            -0.157_845_15,
+            -0.028_787_14,
+            0.453_254_58,
+            1.142_161_1,
+            0.248_610_7,
+            -1.775_400_8,
+            -0.025_502_462,
+            -1.023_330_6,
+            -0.596_185_15,
+            -1.005_530_7,
+            0.428_542_3,
+            1.476_077_8,
+            -1.786_867_9,
+            1.610_317_6,
+            -0.703_956_66,
+            -0.185_265_8,
+            -0.996_235_1,
+            -0.831_255_26,
         ],
         (20, 3),
         &Device::Cpu,
     )?;
     let weight_hh_l0 = Tensor::from_vec::<_, f32>(
         vec![
-            0.4099724292755127,
-            0.4084506630897522,
-            0.25786539912223816,
-            1.095021367073059,
-            -0.5064865946769714,
-            0.09977540373802185,
-            -0.653973400592804,
-            0.731693685054779,
-            -1.456732988357544,
-            1.6089353561401367,
-            0.09376997500658035,
-            -1.2597490549087524,
-            0.25463348627090454,
-            -0.5019572973251343,
-            -1.041200041770935,
-            0.7322672009468079,
-            1.3075355291366577,
-            -1.1627987623214722,
-            0.11963611096143723,
-            -0.1631353348493576,
-            0.6614453196525574,
-            1.1899205446243286,
-            0.8165339231491089,
-            -0.9135236144065857,
-            -0.3538065254688263,
-            0.7639270424842834,
-            -0.5889506936073303,
-            -0.7635973691940308,
-            1.3352056741714478,
-            0.6042736172676086,
-            -0.10344208031892776,
-            -0.15121692419052124,
-            1.2465683221817017,
-            0.505721390247345,
-            0.9505112171173096,
-            1.2966482639312744,
-            0.873796284198761,
-            -0.5602594017982483,
-            1.2857844829559326,
-            0.8168238401412964,
-            -1.464799404144287,
-            -1.2629283666610718,
-            1.122018814086914,
-            1.5663341283798218,
-            2.558138370513916,
-            -0.23336388170719147,
-            -0.013472129590809345,
-            1.8606348037719727,
-            1.549620509147644,
-            0.34762924909591675,
-            0.09300802648067474,
-            0.6147403120994568,
-            0.7123645544052124,
-            -1.7765072584152222,
-            0.3538645803928375,
-            1.1996132135391235,
-            -0.7122589349746704,
-            -0.620034396648407,
-            -0.22813494503498077,
-            -0.7892746329307556,
-            -1.6111117601394653,
-            -1.8716129064559937,
-            0.5430836081504822,
-            0.6606786251068115,
-            0.270527720451355,
-            0.5596919655799866,
-            -0.31839630007743835,
-            1.5117206573486328,
-            -1.363267183303833,
-            -0.9832196235656738,
-            1.5112667083740234,
-            0.6418707370758057,
-            -0.7474458813667297,
-            -0.923438549041748,
-            0.5733984112739563,
-            -0.10929951071739197,
-            0.5181121230125427,
-            0.10653535276651382,
-            0.26924076676368713,
-            1.3247679471969604,
-            0.037456899881362915,
-            -0.6378393173217773,
-            -0.8147554397583008,
-            -0.6895065307617188,
-            0.8436542749404907,
-            1.1657012701034546,
-            0.5269321799278259,
-            1.6192532777786255,
-            -0.963976263999939,
-            0.14152038097381592,
-            -0.1636609584093094,
-            -0.3582225739955902,
-            1.7222793102264404,
-            -0.3035756051540375,
-            0.23887419700622559,
-            1.3440011739730835,
-            0.1032256931066513,
-            1.1003541946411133,
-            -0.3416801989078522,
-            0.947338879108429,
+            0.409_972_43,
+            0.408_450_66,
+            0.257_865_4,
+            1.095_021_4,
+            -0.506_486_6,
+            0.099_775_404,
+            -0.653_973_4,
+            0.731_693_7,
+            -1.456_733,
+            1.608_935_4,
+            0.093_769_975,
+            -1.259_749,
+            0.254_633_5,
+            -0.501_957_3,
+            -1.041_2,
+            0.732_267_2,
+            1.307_535_5,
+            -1.162_798_8,
+            0.119_636_11,
+            -0.163_135_33,
+            0.661_445_3,
+            1.189_920_5,
+            0.816_533_9,
+            -0.913_523_6,
+            -0.353_806_53,
+            0.763_927_04,
+            -0.588_950_7,
+            -0.763_597_37,
+            1.335_205_7,
+            0.604_273_6,
+            -0.103_442_08,
+            -0.151_216_92,
+            1.246_568_3,
+            0.505_721_4,
+            0.950_511_2,
+            1.296_648_3,
+            0.873_796_3,
+            -0.560_259_4,
+            1.285_784_5,
+            0.816_823_84,
+            -1.464_799_4,
+            -1.262_928_4,
+            1.122_018_8,
+            1.566_334_1,
+            2.558_138_4,
+            -0.233_363_88,
+            -0.013_472_13,
+            1.860_634_8,
+            1.549_620_5,
+            0.347_629_25,
+            0.093_008_03,
+            0.614_740_3,
+            0.712_364_55,
+            -1.776_507_3,
+            0.353_864_58,
+            1.199_613_2,
+            -0.712_258_93,
+            -0.620_034_4,
+            -0.228_134_95,
+            -0.789_274_63,
+            -1.611_111_8,
+            -1.871_612_9,
+            0.543_083_6,
+            0.660_678_6,
+            0.270_527_72,
+            0.559_691_97,
+            -0.318_396_3,
+            1.511_720_7,
+            -1.363_267_2,
+            -0.983_219_6,
+            1.511_266_7,
+            0.641_870_74,
+            -0.747_445_9,
+            -0.923_438_55,
+            0.573_398_4,
+            -0.109_299_51,
+            0.518_112_1,
+            0.106_535_35,
+            0.269_240_77,
+            1.324_768,
+            0.037_456_9,
+            -0.637_839_3,
+            -0.814_755_44,
+            -0.689_506_53,
+            0.843_654_3,
+            1.165_701_3,
+            0.526_932_2,
+            1.619_253_3,
+            -0.963_976_26,
+            0.141_520_38,
+            -0.163_660_96,
+            -0.358_222_57,
+            1.722_279_3,
+            -0.303_575_6,
+            0.238_874_2,
+            1.344_001_2,
+            0.103_225_69,
+            1.100_354_2,
+            -0.341_680_2,
+            0.947_338_9,
         ],
         (20, 5),
         &Device::Cpu,
     )?;
     let bias_ih_l0 = Tensor::from_vec::<_, f32>(
         vec![
-            -0.568515956401825,
-            0.8375961780548096,
-            1.783660650253296,
-            -0.1954246610403061,
-            0.235193133354187,
-            1.9142433404922485,
-            1.8364111185073853,
-            1.324532389640808,
-            -0.07051458209753036,
-            0.34697940945625305,
-            -0.653679609298706,
-            1.5586202144622803,
-            0.2185661494731903,
-            -0.5743072628974915,
-            1.4571250677108765,
-            1.7709556818008423,
-            -2.0172998905181885,
-            0.42350319027900696,
-            0.5730220079421997,
-            -1.7962429523468018,
+            -0.568_515_96,
+            0.837_596_2,
+            1.783_660_7,
+            -0.195_424_66,
+            0.235_193_13,
+            1.914_243_3,
+            1.836_411_1,
+            1.324_532_4,
+            -0.070_514_58,
+            0.346_979_4,
+            -0.653_679_6,
+            1.558_620_2,
+            0.218_566_15,
+            -0.574_307_26,
+            1.457_125_1,
+            1.770_955_7,
+            -2.017_3,
+            0.423_503_2,
+            0.573_022,
+            -1.796_243,
         ],
         (20,),
         &Device::Cpu,
     )?;
     let bias_hh_l0 = Tensor::from_vec::<_, f32>(
         vec![
-            1.2470403909683228,
-            1.2738511562347412,
-            0.3909492492675781,
-            0.387210488319397,
-            0.14440394937992096,
-            0.7771684527397156,
-            -2.3381125926971436,
-            -0.829120397567749,
-            1.1661391258239746,
-            1.4786574840545654,
-            0.26760873198509216,
-            0.7561198472976685,
-            -0.5873361229896545,
-            -2.061920642852783,
-            0.4304734766483307,
-            0.3376566171646118,
-            -0.3437853455543518,
-            -0.6172260642051697,
-            1.2529692649841309,
-            -0.05141742154955864,
+            1.247_040_4,
+            1.273_851_2,
+            0.390_949_25,
+            0.387_210_5,
+            0.144_403_95,
+            0.777_168_45,
+            -2.338_112_6,
+            -0.829_120_4,
+            1.166_139_1,
+            1.478_657_5,
+            0.267_608_73,
+            0.756_119_85,
+            -0.587_336_1,
+            -2.061_920_6,
+            0.430_473_48,
+            0.337_656_62,
+            -0.343_785_35,
+            -0.617_226_06,
+            1.252_969_3,
+            -0.051_417_42,
         ],
         (20,),
         &Device::Cpu,
     )?;
     let input = Tensor::from_vec::<_, f32>(
         vec![
-            0.6472128033638,
-            -0.04116716980934143,
-            -0.17749308049678802,
-            -0.500039279460907,
-            0.8672749400138855,
-            -0.27319222688674927,
-            -0.4607681334018707,
-            -0.0990937128663063,
-            0.47284480929374695,
-            1.0049484968185425,
-            -0.2871420383453369,
-            -1.1618621349334717,
+            0.647_212_8,
+            -0.041_167_17,
+            -0.177_493_08,
+            -0.500_039_3,
+            0.867_274_94,
+            -0.273_192_23,
+            -0.460_768_13,
+            -0.099_093_71,
+            0.472_844_8,
+            1.004_948_5,
+            -0.287_142_04,
+            -1.161_862_1,
         ],
         (4, 1, 3),
         &Device::Cpu,
     )?;
     let h0 = Tensor::from_vec::<_, f32>(
         vec![
-            0.02758178487420082,
-            0.5652382373809814,
-            -0.011487378738820553,
-            0.6706400513648987,
-            -0.4929250478744507,
+            0.027_581_785,
+            0.565_238_24,
+            -0.011_487_379,
+            0.670_640_05,
+            -0.492_925_05,
         ],
         (1, 1, 5),
         &Device::Cpu,
     )?;
     let c0 = Tensor::from_vec::<_, f32>(
         vec![
-            1.505028486251831,
-            -2.32635498046875,
-            1.6168899536132812,
-            -0.9026237726211548,
-            0.17366823554039001,
+            1.505_028_5,
+            -2.326_355,
+            1.616_89,
+            -0.902_623_8,
+            0.173_668_24,
         ],
         (1, 1, 5),
         &Device::Cpu,
     )?;
     let output = Tensor::from_vec::<_, f32>(
         vec![
-            0.5956016778945923,
-            -0.01723279245197773,
-            0.11035571992397308,
-            -0.49323174357414246,
-            0.047632161527872086,
-            0.6358451843261719,
-            0.040328118950128555,
-            -0.3788611590862274,
-            -0.7464339733123779,
-            0.20080909132957458,
-            0.5840265154838562,
-            0.1453288197517395,
-            -0.7345298528671265,
-            -0.5214304327964783,
-            0.21903817355632782,
-            0.7420451641082764,
-            0.31943878531455994,
-            -0.04726646468043327,
-            -0.2823849618434906,
-            0.2713133990764618,
+            0.595_601_7,
+            -0.017_232_792,
+            0.110_355_72,
+            -0.493_231_74,
+            0.047_632_16,
+            0.635_845_2,
+            0.040_328_12,
+            -0.378_861_16,
+            -0.746_434,
+            0.200_809_09,
+            0.584_026_5,
+            0.145_328_82,
+            -0.734_529_85,
+            -0.521_430_43,
+            0.219_038_17,
+            0.742_045_16,
+            0.319_438_8,
+            -0.047_266_465,
+            -0.282_384_96,
+            0.271_313_4,
         ],
         (4, 1, 5),
         &Device::Cpu,
     )?;
     let hn = Tensor::from_vec::<_, f32>(
         vec![
-            0.7420451641082764,
-            0.31943878531455994,
-            -0.04726646468043327,
-            -0.2823849618434906,
-            0.2713133990764618,
+            0.742_045_16,
+            0.319_438_8,
+            -0.047_266_465,
+            -0.282_384_96,
+            0.271_313_4,
         ],
         (1, 1, 5),
         &Device::Cpu,
     )?;
     let cn = Tensor::from_vec::<_, f32>(
         vec![
-            0.9630558490753174,
-            1.0033069849014282,
-            -1.754899024963379,
-            -1.5967122316360474,
-            0.8252924680709839,
+            0.963_055_85,
+            1.003_307,
+            -1.754_899,
+            -1.596_712_2,
+            0.825_292_47,
         ],
         (1, 1, 5),
         &Device::Cpu,
@@ -3929,8 +3922,8 @@ fn test_lstm() -> Result<()> {
     let idx_iofc = {
         let stride = hidden_size as i64;
         let dev = weight_ih_l0.device();
-        let idx_i = Tensor::arange(0 * stride, 1 * stride, dev)?;
-        let idx_f = Tensor::arange(1 * stride, 2 * stride, dev)?;
+        let idx_i = Tensor::arange(0, stride, dev)?;
+        let idx_f = Tensor::arange(stride, 2 * stride, dev)?;
         let idx_g = Tensor::arange(2 * stride, 3 * stride, dev)?;
         let idx_o = Tensor::arange(3 * stride, 4 * stride, dev)?;
 
@@ -3966,15 +3959,15 @@ fn test_lstm() -> Result<()> {
         Ok(diffs.iter().all(|f| f.abs() < 0.0001))
     };
     assert!(
-        diff_close_enough(&output, &actual_output)?,
+        diff_close_enough(&output, actual_output)?,
         "output did not match expected\n{actual_output}\n{output}",
     );
     assert!(
-        diff_close_enough(&hn, &actual_hn)?,
+        diff_close_enough(&hn, actual_hn)?,
         "hn did not match expected\n{actual_hn}\n{hn}",
     );
     assert!(
-        diff_close_enough(&cn, &actual_cn)?,
+        diff_close_enough(&cn, actual_cn)?,
         "cn did not match expected\n{actual_cn}\n{cn}",
     );
 
@@ -4064,14 +4057,14 @@ fn make_graph_helper(
             doc_string: "".to_string(),
         }],
         input: inputs
-            .into_iter()
+            .iter()
             .map(|name| ValueInfoProto {
                 name: name.to_string(),
                 ..ValueInfoProto::default()
             })
             .collect(),
         output: outputs
-            .into_iter()
+            .iter()
             .map(|name| ValueInfoProto {
                 name: name.to_string(),
                 ..ValueInfoProto::default()
@@ -4282,7 +4275,7 @@ fn test_reduce_sum_do_not_keep_dims() -> Result<()> {
 
     // Test with random data
     {
-        let shape = (3, 2, 2);
+        let _shape = (3, 2, 2);
         let data = Tensor::from_vec(
             vec![
                 -5.2f32, 7.8, -3.1, 9.4, 2.6, -8.7, 4.3, -1.9, 6.5, -0.8, -7.2, 3.6,
author	Laurent Mazare <laurent.mazare@gmail.com>	2024-09-26 23:37:59 +0200
committer	GitHub <noreply@github.com>	2024-09-26 23:37:59 +0200
commit	2c25754281fb6672b9ebf84f6f6f5a5b12efe10d (patch)
tree	cd42c6cb6755015356797f723ab969d4b23cab29
parent	ed48f54b542bd3090032c516a50ee4d7067384a9 (diff)
download	candle-2c25754281fb6672b9ebf84f6f6f5a5b12efe10d.tar.gz candle-2c25754281fb6672b9ebf84f6f6f5a5b12efe10d.tar.bz2 candle-2c25754281fb6672b9ebf84f6f6f5a5b12efe10d.zip