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//! Display formatting for Tensor, matching PyTorch's output style.
//!
//! ## REQ status (per `.design/ferrotorch-core/display.md`)
//!
//! | REQ | Status | Evidence |
//! |---|---|---|
//! | REQ-1 | SHIPPED | 0-D branch at `display.rs:18-30`; consumer: every `format!("{t}")` call site |
//! | REQ-2 | SHIPPED | 1-D branch at `display.rs:33-60`; consumer: production `format!` machinery |
//! | REQ-3 | SHIPPED | 2-D branch at `display.rs:62-116`; consumer: production `format!` machinery |
//! | REQ-4 | SHIPPED | 3-D+ summary branch at `display.rs:118-128`; consumer: production `format!` (R-DEV-7 deviation: torch recursively renders all dims) |
//! | REQ-5 | SHIPPED | suffix logic at `display.rs:24-28,131-135`; consumer: every autograd-graph debug print |
//! | REQ-6 | SHIPPED | `self.data_vec()` at `display.rs:12`; consumer: every CUDA / non-contiguous tensor printed |
//! | REQ-7 | SHIPPED | `Err` arm at `display.rs:14`; consumer: every meta-tensor printed in shape-inference |
use std::fmt;
use crate::dtype::Float;
use crate::tensor::Tensor;
impl<T: Float> fmt::Display for Tensor<T> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
let shape = self.shape();
// Use data_vec() so non-contiguous and GPU tensors display correctly.
let data = match self.data_vec() {
Ok(d) => d,
Err(_) => return write!(f, "tensor(<inaccessible>, shape={shape:?})"),
};
// Scalar
if shape.is_empty() {
if data.is_empty() {
return write!(f, "tensor([], shape=[])");
}
let val = data[0];
write!(f, "tensor({val}")?;
if self.grad_fn().is_some() {
write!(f, ", grad_fn=<{}>", self.grad_fn().unwrap().name())?;
} else if self.requires_grad() {
write!(f, ", requires_grad=true")?;
}
return write!(f, ")");
}
// 1-D
if shape.len() == 1 {
write!(f, "tensor([")?;
let max_show = 6;
let len = shape[0];
if len <= max_show {
for (i, &v) in data.iter().enumerate() {
if i > 0 {
write!(f, ", ")?;
}
write!(f, "{v:.4}")?;
}
} else {
for (i, val) in data.iter().enumerate().take(3) {
if i > 0 {
write!(f, ", ")?;
}
write!(f, "{val:.4}")?;
}
write!(f, ", ..., ")?;
for (i, val) in data.iter().enumerate().skip(len - 3) {
if i > len - 3 {
write!(f, ", ")?;
}
write!(f, "{val:.4}")?;
}
}
write!(f, "]")?;
}
// 2-D
else if shape.len() == 2 {
let rows = shape[0];
let cols = shape[1];
let max_rows = 6;
write!(f, "tensor([")?;
let display_row = |f: &mut fmt::Formatter<'_>, row: usize| -> fmt::Result {
write!(f, "[")?;
let max_cols = 6;
if cols <= max_cols {
for c in 0..cols {
if c > 0 {
write!(f, ", ")?;
}
write!(f, "{:.4}", data[row * cols + c])?;
}
} else {
for c in 0..3 {
if c > 0 {
write!(f, ", ")?;
}
write!(f, "{:.4}", data[row * cols + c])?;
}
write!(f, ", ..., ")?;
for c in (cols - 3)..cols {
if c > cols - 3 {
write!(f, ", ")?;
}
write!(f, "{:.4}", data[row * cols + c])?;
}
}
write!(f, "]")
};
if rows <= max_rows {
for r in 0..rows {
if r > 0 {
write!(f, ",\n ")?;
}
display_row(f, r)?;
}
} else {
for r in 0..3 {
if r > 0 {
write!(f, ",\n ")?;
}
display_row(f, r)?;
}
write!(f, ",\n ...")?;
for r in (rows - 3)..rows {
write!(f, ",\n ")?;
display_row(f, r)?;
}
}
write!(f, "]")?;
}
// 3-D+: summary
else {
let numel = self.numel();
write!(f, "tensor(<{numel} elements>, shape={shape:?}")?;
if self.grad_fn().is_some() {
write!(f, ", grad_fn=<{}>", self.grad_fn().unwrap().name())?;
} else if self.requires_grad() {
write!(f, ", requires_grad=true")?;
}
return write!(f, ")");
}
// Suffix metadata for 1D/2D
if self.grad_fn().is_some() {
write!(f, ", grad_fn=<{}>", self.grad_fn().unwrap().name())?;
} else if self.requires_grad() {
write!(f, ", requires_grad=true")?;
}
write!(f, ")")
}
}
#[cfg(test)]
mod tests {
use crate::*;
#[test]
#[allow(clippy::approx_constant)] // 3.14 is an arbitrary test display value, not π.
fn test_display_scalar() {
let t = scalar(3.14f32).unwrap();
let s = format!("{t}");
assert!(s.contains("3.14"));
assert!(s.starts_with("tensor("));
}
#[test]
fn test_display_1d() {
let t = tensor(&[1.0f32, 2.0, 3.0]).unwrap();
let s = format!("{t}");
assert!(s.contains("1.0000"));
assert!(s.contains("3.0000"));
}
#[test]
fn test_display_2d() {
let t = from_slice(&[1.0f32, 2.0, 3.0, 4.0], &[2, 2]).unwrap();
let s = format!("{t}");
assert!(s.contains("[1.0000, 2.0000]"));
}
#[test]
fn test_display_with_grad_fn() {
let a = scalar(2.0f32).unwrap().requires_grad_(true);
let b = scalar(3.0f32).unwrap().requires_grad_(true);
let c = (&a + &b).unwrap();
let s = format!("{c}");
assert!(s.contains("grad_fn=<AddBackward>"));
}
#[test]
fn test_display_requires_grad() {
let t = scalar(1.0f32).unwrap().requires_grad_(true);
let s = format!("{t}");
assert!(s.contains("requires_grad=true"));
}
#[test]
fn test_display_large_1d_truncated() {
let data: Vec<f32> = (0..100).map(|i| i as f32).collect();
let t = from_vec(data, &[100]).unwrap();
let s = format!("{t}");
assert!(s.contains("..."));
}
#[test]
fn test_display_3d_summary() {
let t = zeros::<f32>(&[2, 3, 4]).unwrap();
let s = format!("{t}");
assert!(s.contains("24 elements"));
assert!(s.contains("shape=[2, 3, 4]"));
}
}