Merge pull request #20 from LaurentMazare/tensor-display

Add some pretty print display to Tensors

candle-core/src/display.rs

@@ -0,0 +1,455 @@
+/// Pretty printing of tensors
+/// This implementation should be in line with the PyTorch version.
+/// https://github.com/pytorch/pytorch/blob/7b419e8513a024e172eae767e24ec1b849976b13/torch/_tensor_str.py
+use crate::{DType, Result, Tensor, WithDType};
+use half::{bf16, f16};
+
+impl Tensor {
+    fn fmt_dt<T: WithDType + std::fmt::Display>(
+        &self,
+        f: &mut std::fmt::Formatter,
+    ) -> std::fmt::Result {
+        write!(f, "Tensor[")?;
+        match self.dims() {
+            [] => {
+                if let Ok(v) = self.to_scalar::<T>() {
+                    write!(f, "{v}")?
+                }
+            }
+            [s] if *s < 10 => {
+                if let Ok(vs) = self.to_vec1::<T>() {
+                    for (i, v) in vs.iter().enumerate() {
+                        if i > 0 {
+                            write!(f, ", ")?;
+                        }
+                        write!(f, "{v}")?;
+                    }
+                }
+            }
+            dims => {
+                write!(f, "dims ")?;
+                for (i, d) in dims.iter().enumerate() {
+                    if i > 0 {
+                        write!(f, ", ")?;
+                    }
+                    write!(f, "{d}")?;
+                }
+            }
+        }
+        write!(f, "; {}]", self.dtype().as_str())
+    }
+}
+
+impl std::fmt::Debug for Tensor {
+    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
+        match self.dtype() {
+            DType::U32 => self.fmt_dt::<u32>(f),
+            DType::BF16 => self.fmt_dt::<bf16>(f),
+            DType::F16 => self.fmt_dt::<f16>(f),
+            DType::F32 => self.fmt_dt::<f32>(f),
+            DType::F64 => self.fmt_dt::<f64>(f),
+        }
+    }
+}
+
+#[allow(dead_code)]
+/// Options for Tensor pretty printing
+pub struct PrinterOptions {
+    precision: usize,
+    threshold: usize,
+    edge_items: usize,
+    line_width: usize,
+    sci_mode: Option<bool>,
+}
+
+static PRINT_OPTS: std::sync::Mutex<PrinterOptions> =
+    std::sync::Mutex::new(PrinterOptions::const_default());
+
+impl PrinterOptions {
+    // We cannot use the default trait as it's not const.
+    const fn const_default() -> Self {
+        Self {
+            precision: 4,
+            threshold: 1000,
+            edge_items: 3,
+            line_width: 80,
+            sci_mode: None,
+        }
+    }
+}
+
+pub fn set_print_options(options: PrinterOptions) {
+    *PRINT_OPTS.lock().unwrap() = options
+}
+
+pub fn set_print_options_default() {
+    *PRINT_OPTS.lock().unwrap() = PrinterOptions::const_default()
+}
+
+pub fn set_print_options_short() {
+    *PRINT_OPTS.lock().unwrap() = PrinterOptions {
+        precision: 2,
+        threshold: 1000,
+        edge_items: 2,
+        line_width: 80,
+        sci_mode: None,
+    }
+}
+
+pub fn set_print_options_full() {
+    *PRINT_OPTS.lock().unwrap() = PrinterOptions {
+        precision: 4,
+        threshold: usize::MAX,
+        edge_items: 3,
+        line_width: 80,
+        sci_mode: None,
+    }
+}
+
+struct FmtSize {
+    current_size: usize,
+}
+
+impl FmtSize {
+    fn new() -> Self {
+        Self { current_size: 0 }
+    }
+
+    fn final_size(self) -> usize {
+        self.current_size
+    }
+}
+
+impl std::fmt::Write for FmtSize {
+    fn write_str(&mut self, s: &str) -> std::fmt::Result {
+        self.current_size += s.len();
+        Ok(())
+    }
+}
+
+trait TensorFormatter {
+    type Elem: WithDType;
+
+    fn fmt<T: std::fmt::Write>(&self, v: Self::Elem, max_w: usize, f: &mut T) -> std::fmt::Result;
+
+    fn max_width(&self, to_display: &Tensor) -> usize {
+        let mut max_width = 1;
+        if let Ok(vs) = to_display.flatten_all().and_then(|t| t.to_vec1()) {
+            for &v in vs.iter() {
+                let mut fmt_size = FmtSize::new();
+                let _res = self.fmt(v, 1, &mut fmt_size);
+                max_width = usize::max(max_width, fmt_size.final_size())
+            }
+        }
+        max_width
+    }
+
+    fn write_newline_indent(i: usize, f: &mut std::fmt::Formatter) -> std::fmt::Result {
+        writeln!(f)?;
+        for _ in 0..i {
+            write!(f, " ")?
+        }
+        Ok(())
+    }
+
+    fn fmt_tensor(
+        &self,
+        t: &Tensor,
+        indent: usize,
+        max_w: usize,
+        summarize: bool,
+        po: &PrinterOptions,
+        f: &mut std::fmt::Formatter,
+    ) -> std::fmt::Result {
+        let dims = t.dims();
+        let edge_items = po.edge_items;
+        write!(f, "[")?;
+        match dims {
+            [] => {
+                if let Ok(v) = t.to_scalar::<Self::Elem>() {
+                    self.fmt(v, max_w, f)?
+                }
+            }
+            [v] if summarize && *v > 2 * edge_items => {
+                if let Ok(vs) = t
+                    .narrow(0, 0, edge_items)
+                    .and_then(|t| t.to_vec1::<Self::Elem>())
+                {
+                    for v in vs.into_iter() {
+                        self.fmt(v, max_w, f)?;
+                        write!(f, ", ")?;
+                    }
+                }
+                write!(f, "...")?;
+                if let Ok(vs) = t
+                    .narrow(0, v - edge_items, edge_items)
+                    .and_then(|t| t.to_vec1::<Self::Elem>())
+                {
+                    for v in vs.into_iter() {
+                        write!(f, ", ")?;
+                        self.fmt(v, max_w, f)?;
+                    }
+                }
+            }
+            [_] => {
+                let elements_per_line = usize::max(1, po.line_width / (max_w + 2));
+                if let Ok(vs) = t.to_vec1::<Self::Elem>() {
+                    for (i, v) in vs.into_iter().enumerate() {
+                        if i > 0 {
+                            if i % elements_per_line == 0 {
+                                write!(f, ",")?;
+                                Self::write_newline_indent(indent, f)?
+                            } else {
+                                write!(f, ", ")?;
+                            }
+                        }
+                        self.fmt(v, max_w, f)?
+                    }
+                }
+            }
+            _ => {
+                if summarize && dims[0] > 2 * edge_items {
+                    for i in 0..edge_items {
+                        match t.get(i) {
+                            Ok(t) => self.fmt_tensor(&t, indent + 1, max_w, summarize, po, f)?,
+                            Err(e) => write!(f, "{e:?}")?,
+                        }
+                        write!(f, ",")?;
+                        Self::write_newline_indent(indent, f)?
+                    }
+                    write!(f, "...")?;
+                    Self::write_newline_indent(indent, f)?;
+                    for i in dims[0] - edge_items..dims[0] {
+                        match t.get(i) {
+                            Ok(t) => self.fmt_tensor(&t, indent + 1, max_w, summarize, po, f)?,
+                            Err(e) => write!(f, "{e:?}")?,
+                        }
+                        if i + 1 != dims[0] {
+                            write!(f, ",")?;
+                            Self::write_newline_indent(indent, f)?
+                        }
+                    }
+                } else {
+                    for i in 0..dims[0] {
+                        match t.get(i) {
+                            Ok(t) => self.fmt_tensor(&t, indent + 1, max_w, summarize, po, f)?,
+                            Err(e) => write!(f, "{e:?}")?,
+                        }
+                        if i + 1 != dims[0] {
+                            write!(f, ",")?;
+                            Self::write_newline_indent(indent, f)?
+                        }
+                    }
+                }
+            }
+        }
+        write!(f, "]")?;
+        Ok(())
+    }
+}
+
+struct FloatFormatter<S: WithDType> {
+    int_mode: bool,
+    sci_mode: bool,
+    precision: usize,
+    _phantom: std::marker::PhantomData<S>,
+}
+
+impl<S> FloatFormatter<S>
+where
+    S: WithDType + num_traits::Float + std::fmt::Display,
+{
+    fn new(t: &Tensor, po: &PrinterOptions) -> Result<Self> {
+        let mut int_mode = true;
+        let mut sci_mode = false;
+
+        // Rather than containing all values, this should only include
+        // values that end up being displayed according to [threshold].
+        let values = t
+            .flatten_all()?
+            .to_vec1()?
+            .into_iter()
+            .filter(|v: &S| v.is_finite() && !v.is_zero())
+            .collect::<Vec<_>>();
+        if !values.is_empty() {
+            let mut nonzero_finite_min = S::max_value();
+            let mut nonzero_finite_max = S::min_value();
+            for &v in values.iter() {
+                let v = v.abs();
+                if v < nonzero_finite_min {
+                    nonzero_finite_min = v
+                }
+                if v > nonzero_finite_max {
+                    nonzero_finite_max = v
+                }
+            }
+
+            for &value in values.iter() {
+                if value.ceil() != value {
+                    int_mode = false;
+                    break;
+                }
+            }
+            if let Some(v1) = S::from(1000.) {
+                if let Some(v2) = S::from(1e8) {
+                    if let Some(v3) = S::from(1e-4) {
+                        sci_mode = nonzero_finite_max / nonzero_finite_min > v1
+                            || nonzero_finite_max > v2
+                            || nonzero_finite_min < v3
+                    }
+                }
+            }
+        }
+
+        match po.sci_mode {
+            None => {}
+            Some(v) => sci_mode = v,
+        }
+        Ok(Self {
+            int_mode,
+            sci_mode,
+            precision: po.precision,
+            _phantom: std::marker::PhantomData,
+        })
+    }
+}
+
+impl<S> TensorFormatter for FloatFormatter<S>
+where
+    S: WithDType + num_traits::Float + std::fmt::Display + std::fmt::LowerExp,
+{
+    type Elem = S;
+
+    fn fmt<T: std::fmt::Write>(&self, v: Self::Elem, max_w: usize, f: &mut T) -> std::fmt::Result {
+        if self.sci_mode {
+            write!(
+                f,
+                "{v:width$.prec$e}",
+                v = v,
+                width = max_w,
+                prec = self.precision
+            )
+        } else if self.int_mode {
+            if v.is_finite() {
+                write!(f, "{v:width$.0}.", v = v, width = max_w - 1)
+            } else {
+                write!(f, "{v:max_w$.0}")
+            }
+        } else {
+            write!(
+                f,
+                "{v:width$.prec$}",
+                v = v,
+                width = max_w,
+                prec = self.precision
+            )
+        }
+    }
+}
+
+struct IntFormatter<S: WithDType> {
+    _phantom: std::marker::PhantomData<S>,
+}
+
+impl<S: WithDType> IntFormatter<S> {
+    fn new() -> Self {
+        Self {
+            _phantom: std::marker::PhantomData,
+        }
+    }
+}
+
+impl<S> TensorFormatter for IntFormatter<S>
+where
+    S: WithDType + std::fmt::Display,
+{
+    type Elem = S;
+
+    fn fmt<T: std::fmt::Write>(&self, v: Self::Elem, max_w: usize, f: &mut T) -> std::fmt::Result {
+        write!(f, "{v:max_w$}")
+    }
+}
+
+fn get_summarized_data(t: &Tensor, edge_items: usize) -> Result<Tensor> {
+    let dims = t.dims();
+    if dims.is_empty() {
+        Ok(t.clone())
+    } else if dims.len() == 1 {
+        if dims[0] > 2 * edge_items {
+            Tensor::cat(
+                &[
+                    t.narrow(0, 0, edge_items)?,
+                    t.narrow(0, dims[0] - edge_items, edge_items)?,
+                ],
+                0,
+            )
+        } else {
+            Ok(t.clone())
+        }
+    } else if dims[0] > 2 * edge_items {
+        let mut vs: Vec<_> = (0..edge_items)
+            .map(|i| get_summarized_data(&t.get(i)?, edge_items))
+            .collect::<Result<Vec<_>>>()?;
+        for i in (dims[0] - edge_items)..dims[0] {
+            vs.push(get_summarized_data(&t.get(i)?, edge_items)?)
+        }
+        Tensor::cat(&vs, 0)
+    } else {
+        let vs: Vec<_> = (0..dims[0])
+            .map(|i| get_summarized_data(&t.get(i)?, edge_items))
+            .collect::<Result<Vec<_>>>()?;
+        Tensor::cat(&vs, 0)
+    }
+}
+
+impl std::fmt::Display for Tensor {
+    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
+        let po = PRINT_OPTS.lock().unwrap();
+        let summarize = self.elem_count() > po.threshold;
+        let to_display = if summarize {
+            match get_summarized_data(self, po.edge_items) {
+                Ok(v) => v,
+                Err(err) => return write!(f, "{err:?}"),
+            }
+        } else {
+            self.clone()
+        };
+        match self.dtype() {
+            DType::U32 => {
+                let tf: IntFormatter<u32> = IntFormatter::new();
+                let max_w = tf.max_width(&to_display);
+                tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
+                writeln!(f)?;
+            }
+            DType::BF16 => {
+                if let Ok(tf) = FloatFormatter::<bf16>::new(&to_display, &po) {
+                    let max_w = tf.max_width(&to_display);
+                    tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
+                    writeln!(f)?;
+                }
+            }
+            DType::F16 => {
+                if let Ok(tf) = FloatFormatter::<f16>::new(&to_display, &po) {
+                    let max_w = tf.max_width(&to_display);
+                    tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
+                    writeln!(f)?;
+                }
+            }
+            DType::F64 => {
+                if let Ok(tf) = FloatFormatter::<f64>::new(&to_display, &po) {
+                    let max_w = tf.max_width(&to_display);
+                    tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
+                    writeln!(f)?;
+                }
+            }
+            DType::F32 => {
+                if let Ok(tf) = FloatFormatter::<f32>::new(&to_display, &po) {
+                    let max_w = tf.max_width(&to_display);
+                    tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
+                    writeln!(f)?;
+                }
+            }
+        };
+        write!(f, "Tensor[{:?}, {}]", self.dims(), self.dtype().as_str())
+    }
+}

candle-core/src/lib.rs

@@ -3,6 +3,7 @@ mod cpu_backend;
 #[cfg(feature = "cuda")]
 mod cuda_backend;
 mod device;
+pub mod display;
 mod dtype;
 mod dummy_cuda_backend;
 mod error;
@@ -12,7 +13,7 @@ mod shape;
 mod storage;
 mod strided_index;
 mod tensor;
-mod utils;
+pub mod utils;
 
 pub use cpu_backend::CpuStorage;
 pub use device::{Device, DeviceLocation};

candle-core/src/tensor.rs

@@ -44,12 +44,6 @@ impl std::ops::Deref for Tensor {
     }
 }
 
-impl std::fmt::Debug for Tensor {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "[{:?}, {:?}]", &self.shape().dims(), self.device())
-    }
-}
-
 macro_rules! unary_op {
     ($fn_name:ident, $op_name:ident) => {
         pub fn $fn_name(&self) -> Result<Self> {
@@ -658,6 +652,9 @@ impl Tensor {
     }
 
     pub fn flatten(&self, start_dim: Option<usize>, end_dim: Option<usize>) -> Result<Tensor> {
+        if self.rank() == 0 {
+            self.reshape(1)
+        } else {
             let start_dim = start_dim.unwrap_or(0);
             let end_dim = end_dim.unwrap_or_else(|| self.rank() - 1);
             if start_dim < end_dim {
@@ -672,6 +669,7 @@ impl Tensor {
                 Ok(self.clone())
             }
         }
+    }
 
     pub fn flatten_all(&self) -> Result<Tensor> {
         self.flatten(None, None)
@@ -930,6 +928,36 @@ impl Tensor {
         }
     }
 
+    pub fn squeeze(&self, index: usize) -> Result<Self> {
+        // The PyTorch semantics are to return the same tensor if the target dimension
+        // does not have a size of 1.
+        let dims = self.dims();
+        if dims[index] == 1 {
+            let mut dims = dims.to_vec();
+            dims.remove(index);
+            self.reshape(dims)
+        } else {
+            Ok(self.clone())
+        }
+    }
+
+    pub fn unsqueeze(&self, index: usize) -> Result<Self> {
+        let mut dims = self.dims().to_vec();
+        dims.insert(index, 1);
+        self.reshape(dims)
+    }
+
+    pub fn stack<A: AsRef<Tensor>>(args: &[A], dim: usize) -> Result<Self> {
+        if args.is_empty() {
+            return Err(Error::OpRequiresAtLeastOneTensor { op: "stack" });
+        }
+        let args = args
+            .iter()
+            .map(|t| t.as_ref().unsqueeze(dim))
+            .collect::<Result<Vec<_>>>()?;
+        Self::cat(&args, dim)
+    }
+
     pub fn cat<A: AsRef<Tensor>>(args: &[A], dim: usize) -> Result<Self> {
         if args.is_empty() {
             return Err(Error::OpRequiresAtLeastOneTensor { op: "cat" });

candle-core/src/utils.rs

@@ -1,6 +1,6 @@
 use std::str::FromStr;
 
-pub(crate) fn get_num_threads() -> usize {
+pub fn get_num_threads() -> usize {
     // Respond to the same environment variable as rayon.
     match std::env::var("RAYON_NUM_THREADS")
         .ok()

candle-core/tests/display_tests.rs

@@ -0,0 +1,84 @@
+use anyhow::Result;
+use candle::{DType, Device::Cpu, Tensor};
+
+#[test]
+fn display_scalar() -> Result<()> {
+    let t = Tensor::new(1234u32, &Cpu)?;
+    let s = format!("{t}");
+    assert_eq!(&s, "[1234]\nTensor[[], u32]");
+    let t = t.to_dtype(DType::F32)?.neg()?;
+    let s = format!("{}", (&t / 10.0)?);
+    assert_eq!(&s, "[-123.4000]\nTensor[[], f32]");
+    let s = format!("{}", (&t / 1e8)?);
+    assert_eq!(&s, "[-1.2340e-5]\nTensor[[], f32]");
+    let s = format!("{}", (&t * 1e8)?);
+    assert_eq!(&s, "[-1.2340e11]\nTensor[[], f32]");
+    let s = format!("{}", (&t * 0.)?);
+    assert_eq!(&s, "[0.]\nTensor[[], f32]");
+    Ok(())
+}
+
+#[test]
+fn display_vector() -> Result<()> {
+    let t = Tensor::new::<&[u32; 0]>(&[], &Cpu)?;
+    let s = format!("{t}");
+    assert_eq!(&s, "[]\nTensor[[0], u32]");
+    let t = Tensor::new(&[0.1234567, 1.0, -1.2, 4.1, f64::NAN], &Cpu)?;
+    let s = format!("{t}");
+    assert_eq!(
+        &s,
+        "[ 0.1235,  1.0000, -1.2000,  4.1000,     NaN]\nTensor[[5], f64]"
+    );
+    let t = (Tensor::ones(50, DType::F32, &Cpu)? * 42.)?;
+    let s = format!("\n{t}");
+    let expected = r#"
+[42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42.,
+ 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42.,
+ 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42., 42.,
+ 42., 42.]
+Tensor[[50], f32]"#;
+    assert_eq!(&s, expected);
+    let t = (Tensor::ones(11000, DType::F32, &Cpu)? * 42.)?;
+    let s = format!("{t}");
+    assert_eq!(
+        &s,
+        "[42., 42., 42., ..., 42., 42., 42.]\nTensor[[11000], f32]"
+    );
+    Ok(())
+}
+
+#[test]
+fn display_multi_dim() -> Result<()> {
+    let t = (Tensor::ones((200, 100), DType::F32, &Cpu)? * 42.)?;
+    let s = format!("\n{t}");
+    let expected = r#"
+[[42., 42., 42., ..., 42., 42., 42.],
+ [42., 42., 42., ..., 42., 42., 42.],
+ [42., 42., 42., ..., 42., 42., 42.],
+ ...
+ [42., 42., 42., ..., 42., 42., 42.],
+ [42., 42., 42., ..., 42., 42., 42.],
+ [42., 42., 42., ..., 42., 42., 42.]]
+Tensor[[200, 100], f32]"#;
+    assert_eq!(&s, expected);
+    let t = t.reshape(&[2, 1, 1, 100, 100])?;
+    let t = format!("\n{t}");
+    let expected = r#"
+[[[[[42., 42., 42., ..., 42., 42., 42.],
+    [42., 42., 42., ..., 42., 42., 42.],
+    [42., 42., 42., ..., 42., 42., 42.],
+    ...
+    [42., 42., 42., ..., 42., 42., 42.],
+    [42., 42., 42., ..., 42., 42., 42.],
+    [42., 42., 42., ..., 42., 42., 42.]]]],
+ [[[[42., 42., 42., ..., 42., 42., 42.],
+    [42., 42., 42., ..., 42., 42., 42.],
+    [42., 42., 42., ..., 42., 42., 42.],
+    ...
+    [42., 42., 42., ..., 42., 42., 42.],
+    [42., 42., 42., ..., 42., 42., 42.],
+    [42., 42., 42., ..., 42., 42., 42.]]]]]
+Tensor[[2, 1, 1, 100, 100], f32]"#;
+    assert_eq!(&t, expected);
+    Ok(())
+}