burn-tensor 0.22.0

pub use burn_std::{
    DeviceError, DeviceSettings, ExecutionError, backtrace::BackTrace, device::DeviceId,
};

#[cfg(feature = "cubecl")]
pub use burn_backend::cubecl::{
    AdapterLuid, DeviceIdentity, MemoryAccess, PciAddress, PciVendor, PhysicalDevice,
    ThroughputError, ThroughputKey, ThroughputMode, ThroughputValue,
};
use burn_backend::{Backend, DeviceOps};
pub use burn_backend::{
    MemoryPoolUsage, ProfileDuration, ProfileOptions, ProfileTicks, SlicedPoolReport, TimingMethod,
};
#[allow(unused)]
use burn_dispatch::DispatchDeviceId;
#[cfg(feature = "autodiff")]
use burn_dispatch::GradientCheckpointingStrategy;
#[cfg(any(feature = "cpu", feature = "cuda", feature = "rocm", feature = "wgpu"))]
use burn_dispatch::devices::RuntimeId;
#[cfg(any(feature = "metal", feature = "vulkan", feature = "webgpu"))]
use burn_dispatch::devices::WgpuBackend;
use burn_dispatch::{Dispatch, DispatchDevice};
use burn_std::{BoolDType, FloatDType, IntDType, TensorData};

#[cfg(feature = "capture")]
pub use burn_dispatch::backends::capture::{
    CaptureError, CaptureScope, CapturedGraph, CompletedCaptureScope, TensorId,
};

#[cfg(any(feature = "cpu", feature = "cuda", feature = "rocm", feature = "wgpu"))]
use alloc::string::String;
use alloc::vec;
use alloc::vec::Vec;

/// A high-level device handle for tensor operations.
///
/// [`Device`] provides a unified interface to interact with the underlying compute backend.
///
/// Autodiff support is configured on the device rather than through a separate type parameter.
/// Tensors inherit that context when created and can later change it independently.
#[cfg_attr(
    feature = "autodiff",
    doc = "Wrap a device with [`.autodiff()`](Device::autodiff) to enable automatic differentiation with the device."
)]
#[cfg_attr(
    not(feature = "autodiff"),
    doc = "Enable the `autodiff` feature to add automatic differentiation support to devices."
)]
///
/// # Backend selection
///
/// Default Cargo features do not enable an execution backend. Select one explicitly,
/// for example with the `wgpu` or `flex` feature; there is no implicit CPU fallback.
/// Backend-free builds can expose tensor/model APIs, but cannot create an execution device.
///
/// [`Device::default()`] selects the first enabled backend in this order:
/// CUDA, Metal, ROCm, Vulkan, WebGPU, wgpu, CPU, LibTorch, Flex, Remote, NdArray.
/// Use an explicit factory method when the choice must be independent of Cargo feature unification.
/// Without an execution backend, `Device::default()` panics with configuration guidance.
///
/// Enable the desired backend via Cargo feature flags, then call the
/// corresponding factory method:
///
/// ```rust,ignore
/// // Default CUDA device (requires the `cuda` feature).
/// let device = Device::cuda(DeviceIndex::Default);
///
/// // CUDA device at hardware index 1.
/// let device = Device::cuda(1);
///
/// // WGPU with explicit selector (requires `wgpu`/`vulkan`/`metal`/`webgpu`).
/// let device = Device::wgpu(DeviceKind::DiscreteGpu(0));
///
/// // Default device for whichever backend is enabled.
/// let device = Default::default();
/// ```
///
/// Available factory methods (each gated by its matching Cargo feature):
/// `Device::cpu`, `Device::cuda` / `Device::rocm` / `Device::libtorch_cuda`
/// (take an integer index or a [`DeviceIndex`]), `Device::wgpu` /
/// `Device::vulkan` / `Device::metal` / `Device::webgpu` (take a
/// [`DeviceKind`]), `Device::flex`, `Device::ndarray`, `Device::libtorch`,
/// `Device::libtorch_mps`, `Device::libtorch_vulkan`, `Device::capture`.
///
/// # Autodiff
///
/// Requires `autodiff` feature.
///
/// Gradient computation is opt-in for a device:
///
/// ```rust,ignore
/// let device = Device::default().autodiff();
///
/// // Tensors created on this device can participate in an autodiff graph.
/// let x = Tensor::<1>::from_floats([1.0, 2.0, 3.0], &device).require_grad();
/// ```
pub struct Device {
    blob: device_opaque::Opaque,
}

// Aligned, type-erased storage for `DispatchDevice`. See `crate::macros` for
// why this indirection exists (it keeps the dispatch type tree out of
// downstream MIR).
burn_std::obfuscate!(
    type: DispatchDevice,
    module: device_opaque,
    derives: [Send, Sync]
);

impl Clone for Device {
    fn clone(&self) -> Self {
        Self::new(self.as_dispatch().clone())
    }
}

impl Default for Device {
    fn default() -> Self {
        Self::new(DispatchDevice::default())
    }
}

impl core::fmt::Debug for Device {
    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
        write!(f, "Device<{:?}>", self.as_dispatch())
    }
}

// Manually implement both `eq` and `ne` to add documentation on equality.
#[allow(clippy::partialeq_ne_impl)]
impl PartialEq for Device {
    /// Compares devices based on hardware identity.
    ///
    /// Returns `true` if both devices represent the same compute resource.
    /// Note that this comparison ignores autodiff and checkpointing settings. Inspect
    /// [`Device::is_autodiff`] and `Device::gradient_checkpointing_strategy()` when execution
    /// context also matters.
    fn eq(&self, other: &Self) -> bool {
        self.as_dispatch() == other.as_dispatch()
    }

    /// Compares devices based on hardware identity.
    ///
    /// Returns `false` if both devices represent the same compute resource,
    /// even if one has autodiff enabled and the other does not.
    fn ne(&self, other: &Self) -> bool {
        !self.eq(other)
    }
}

impl Eq for Device {}

impl Device {
    /// Wrap a backend-specific device in a unified [`Device`].
    ///
    /// Used by:
    /// - the backend-specific factory methods below (`Device::cuda`, etc.)
    ///   — these are the recommended entry points for downstream code;
    /// - burn-tensor's bridge ops, which already hold a [`DispatchDevice`]
    ///   and just need to wrap it;
    /// - direct callers (tests, type-erased helpers) that have a concrete
    ///   backend device type at hand.
    ///
    /// Anything convertible into [`DispatchDevice`] is accepted, including
    /// `DispatchDevice` itself.
    pub fn new(device: impl Into<DispatchDevice>) -> Self {
        Self {
            blob: device_opaque::Opaque::new(device.into()),
        }
    }

    /// Borrow the underlying [`DispatchDevice`].
    ///
    /// The inverse of [`Device::new`]. Useful to backend-extension authors who need to dispatch on
    /// the concrete backend variant (e.g. matching `DispatchDevice::Remote(_)`).
    pub fn as_dispatch(&self) -> &DispatchDevice {
        self.blob.as_ref()
    }

    /// Crate-internal owning extraction of the underlying dispatch device.
    pub(crate) fn into_dispatch(self) -> DispatchDevice {
        self.blob.into_inner()
    }
}

impl<D: Into<DispatchDevice>> From<D> for Device {
    fn from(device: D) -> Self {
        Self::new(device)
    }
}

/// Selector for the hardware index of a backend whose devices are simply
/// indexed (e.g. CUDA, ROCm).
///
/// Backend factory methods that take an index (`Device::cuda`, `Device::rocm`,
/// `Device::libtorch_cuda`) accept `impl Into<DeviceIndex>`, so the common
/// shorthand is to pass a plain integer literal:
///
/// ```rust,ignore
/// Device::cuda(0);                    // hardware index 0
/// Device::cuda(DeviceIndex::Default); // backend-chosen default
/// ```
#[derive(Clone, Copy, Debug, Hash, PartialEq, Eq, Default)]
pub enum DeviceIndex {
    /// Target a specific hardware device by its index.
    Specified(usize),
    /// Let the backend pick its default device (typically index `0`).
    #[default]
    Default,
}

impl DeviceIndex {
    /// Construct a [`DeviceIndex::Specified`] from anything convertible into
    /// a `usize`.
    pub fn new(index: impl Into<usize>) -> Self {
        Self::Specified(index.into())
    }

    /// Resolve to a concrete hardware index, defaulting to `0` for
    /// [`DeviceIndex::Default`]. Backend factory methods are each gated by a
    /// Cargo feature, so this looks dead when none of them are enabled.
    #[allow(dead_code)]
    pub(crate) fn resolve(self) -> usize {
        match self {
            DeviceIndex::Specified(i) => i,
            DeviceIndex::Default => 0,
        }
    }
}

impl From<usize> for DeviceIndex {
    fn from(i: usize) -> Self {
        Self::Specified(i)
    }
}

impl From<u32> for DeviceIndex {
    fn from(i: u32) -> Self {
        Self::Specified(i as usize)
    }
}

impl From<u64> for DeviceIndex {
    fn from(i: u64) -> Self {
        Self::Specified(i as usize)
    }
}

impl From<i32> for DeviceIndex {
    fn from(i: i32) -> Self {
        Self::Specified(usize::try_from(i).expect("device index must be non-negative"))
    }
}

impl From<i64> for DeviceIndex {
    fn from(i: i64) -> Self {
        Self::Specified(usize::try_from(i).expect("device index must be non-negative"))
    }
}

/// Selector for the more flexible backends whose device handle is a tagged
/// enum (e.g. WGPU, which can target a discrete/integrated/virtual GPU, a CPU
/// adapter, an externally-created wgpu setup, or just "best available").
///
/// The variants mirror `WgpuDeviceKind` from cubecl so the mapping is direct, but
/// it is kept as a burn-owned enum so callers don't have to depend on cubecl.
#[derive(Clone, Debug, Hash, PartialEq, Eq, Default)]
pub enum DeviceKind {
    /// Discrete GPU with the given index. The index is the index of the discrete GPU in the list
    /// of all discrete GPUs found on the system.
    DiscreteGpu(usize),

    /// Integrated GPU with the given index. The index is the index of the integrated GPU in the
    /// list of all integrated GPUs found on the system.
    IntegratedGpu(usize),

    /// Virtual GPU with the given index. The index is the index of the virtual GPU in the list of
    /// all virtual GPUs found on the system.
    VirtualGpu(usize),

    /// CPU.
    Cpu,

    /// The best available device found with the current graphics API.
    ///
    /// This will prioritize GPUs wgpu recognizes as "high power". Additionally, you can override this using
    /// the `CUBECL_WGPU_DEFAULT_DEVICE` environment variable. This variable is spelled as if i was a `WgpuDevice`,
    /// so for example `CUBECL_WGPU_DEFAULT_DEVICE=IntegratedGpu(1)` or `CUBECL_WGPU_DEFAULT_DEVICE=Cpu`
    #[default]
    DefaultDevice,

    /// Use an externally created, existing, wgpu setup. This is helpful when using `CubeCL` in conjunction
    /// with some existing wgpu setup (eg. egui or bevy), as resources can be transferred in & out of `CubeCL`.
    ///
    /// # Notes
    ///
    /// This identifies an already registered runtime. To register an application's
    /// setup, use `Device::wgpu_options().setup(setup).init()` and retain the
    /// returned device. Clone that device to share it; do not invent an existing ID.
    Existing(u32),
}

impl Device {
    /// Create a reusable graph-capture device.
    ///
    /// Operations on tensors moved to this device are recorded rather than executed. Use
    /// [`Device::capture_scope`] to delimit each capture and declare its graph boundaries.
    #[cfg(feature = "capture")]
    pub fn capture() -> Self {
        Self::new(DispatchDevice::capture())
    }

    /// Capture the operations performed by `capture` on this device.
    ///
    /// The closure receives a [`CaptureScope`] and must return the token produced by
    /// [`CaptureScope::complete`], containing the ordered runtime input and output tensor IDs.
    /// Requiring this return value prevents a capture from being finalized without an explicit
    /// boundary declaration. Completing the scope immediately rejects further tensor operations;
    /// the device can then be reused for later, independent scopes after the closure returns.
    ///
    /// Returns [`CaptureError::InvalidDevice`] if this is not a capture device, and
    /// [`CaptureError::AlreadyActive`] if another scope is active on the same device.
    #[cfg(feature = "capture")]
    pub fn capture_scope(
        &self,
        capture: impl FnOnce(CaptureScope) -> CompletedCaptureScope,
    ) -> Result<CapturedGraph, CaptureError> {
        match self.as_dispatch() {
            DispatchDevice::Capture(device) => device.capture_scope(capture),
            #[allow(unreachable_patterns)] // Capture can be the only enabled backend.
            _ => Err(CaptureError::InvalidDevice),
        }
    }

    /// Default CPU device backed by CubeCL's CPU backend.
    #[cfg(feature = "cpu")]
    pub fn cpu() -> Self {
        Self::new(burn_dispatch::devices::CpuDevice::default())
    }

    /// CUDA device at the given hardware index.
    ///
    /// Accepts a plain integer (e.g. `Device::cuda(0)`) or a
    /// [`DeviceIndex`] — use [`DeviceIndex::Default`] to let the backend
    /// pick.
    #[cfg(feature = "cuda")]
    pub fn cuda(index: impl Into<DeviceIndex>) -> Self {
        Self::new(burn_dispatch::devices::CudaDevice::new(
            index.into().resolve(),
        ))
    }

    /// ROCm/HIP device at the given hardware index.
    ///
    /// Same selector semantics as [`Device::cuda`].
    #[cfg(feature = "rocm")]
    pub fn rocm(index: impl Into<DeviceIndex>) -> Self {
        Self::new(burn_dispatch::devices::RocmDevice::new(
            index.into().resolve(),
        ))
    }

    /// Flex backend device.
    #[cfg(feature = "flex")]
    pub fn flex() -> Self {
        Self::new(burn_dispatch::devices::FlexDevice)
    }

    /// Default NdArray (CPU) device.
    #[cfg(feature = "ndarray")]
    #[deprecated(
        since = "0.22.0",
        note = "burn-ndarray is deprecated and will be removed in a future release. Use `Device::flex()` for pure-Rust CPU execution instead."
    )]
    #[allow(deprecated)] // constructing the deprecated device is this constructor's job
    pub fn ndarray() -> Self {
        Self::new(burn_dispatch::devices::NdArrayDevice::default())
    }

    /// LibTorch CPU device.
    #[cfg(feature = "tch")]
    #[deprecated(
        since = "0.22.0",
        note = "burn-tch is deprecated and will be removed in a future release. Use a CubeCL backend (`Device::cuda`, `Device::rocm`, `Device::metal`, `Device::vulkan`, `Device::cpu`) or `Device::flex()` instead."
    )]
    #[allow(deprecated)] // constructing the deprecated device is this constructor's job
    pub fn libtorch() -> Self {
        Self::new(burn_dispatch::devices::LibTorchDevice::Cpu)
    }

    /// LibTorch CUDA device at the given hardware index.
    #[cfg(feature = "tch")]
    #[deprecated(
        since = "0.22.0",
        note = "burn-tch is deprecated and will be removed in a future release. Use a CubeCL backend (`Device::cuda`, `Device::rocm`, `Device::metal`, `Device::vulkan`, `Device::cpu`) or `Device::flex()` instead."
    )]
    #[allow(deprecated)] // constructing the deprecated device is this constructor's job
    pub fn libtorch_cuda(index: impl Into<DeviceIndex>) -> Self {
        Self::new(burn_dispatch::devices::LibTorchDevice::Cuda(
            index.into().resolve(),
        ))
    }

    /// LibTorch Metal Performance Shaders (MPS) device.
    #[cfg(feature = "tch")]
    #[deprecated(
        since = "0.22.0",
        note = "burn-tch is deprecated and will be removed in a future release. Use a CubeCL backend (`Device::cuda`, `Device::rocm`, `Device::metal`, `Device::vulkan`, `Device::cpu`) or `Device::flex()` instead."
    )]
    #[allow(deprecated)] // constructing the deprecated device is this constructor's job
    pub fn libtorch_mps() -> Self {
        Self::new(burn_dispatch::devices::LibTorchDevice::Mps)
    }

    /// LibTorch Vulkan device.
    #[cfg(feature = "tch")]
    #[deprecated(
        since = "0.22.0",
        note = "burn-tch is deprecated and will be removed in a future release. Use a CubeCL backend (`Device::cuda`, `Device::rocm`, `Device::metal`, `Device::vulkan`, `Device::cpu`) or `Device::flex()` instead."
    )]
    #[allow(deprecated)] // constructing the deprecated device is this constructor's job
    pub fn libtorch_vulkan() -> Self {
        Self::new(burn_dispatch::devices::LibTorchDevice::Vulkan)
    }

    /// WGPU device, selected via [`DeviceKind`].
    ///
    /// [`AutoCompiler`](burn_dispatch::devices::AutoCompiler) dispatches to the most
    /// appropriate shader language (WGSL, SPIR-V, or MSL) based on the enabled features.
    ///
    /// For [`DeviceKind::DefaultDevice`], the adapter is picked by `wgpu`'s
    /// selection heuristics (high-power GPU preferred, or overridden by
    /// `CUBECL_WGPU_DEFAULT_DEVICE`).
    ///
    /// The graphics API — and so the shader compiler — is the runtime's to settle, from the
    /// enabled features and what the machine offers. `Device::vulkan`, `Device::metal` and
    /// `Device::webgpu` pin one instead: the same adapter on two APIs is two devices.
    ///
    /// This constructor is lazy. Use [`Device::wgpu_options`] to initialize with runtime
    /// options or share an existing wgpu setup. In a browser, initialize asynchronously
    /// with [`init_async`](crate::wgpu::WgpuOptions::init_async) before creating tensors.
    #[cfg(feature = "wgpu")]
    pub fn wgpu(device_kind: DeviceKind) -> Self {
        Self::new(wgpu_device(
            device_kind,
            burn_dispatch::devices::WgpuBackend::Auto,
        ))
    }

    /// Vulkan-backed WGPU device, selected via [`DeviceKind`] — and so `SPIR-V`, where the
    /// build and the adapter allow it.
    ///
    /// Pinned to Vulkan: the device comes up there or not at all, rather than quietly on
    /// whichever API [`Device::wgpu`] would settle on.
    #[cfg(feature = "vulkan")]
    pub fn vulkan(device_kind: DeviceKind) -> Self {
        Self::new(wgpu_device(
            device_kind,
            burn_dispatch::devices::WgpuBackend::Vulkan,
        ))
    }

    /// Metal-backed WGPU device, selected via [`DeviceKind`], requiring native MSL support.
    ///
    /// Pinned to Metal, as `Device::vulkan` is to Vulkan. Runtime initialization panics if
    /// native MSL is unavailable on the selected device. Use [`Device::wgpu`] for automatic
    /// compiler selection with WGSL fallback.
    #[cfg(feature = "metal")]
    pub fn metal(device_kind: DeviceKind) -> Self {
        Self::new(wgpu_device(
            device_kind,
            burn_dispatch::devices::WgpuBackend::Metal,
        ))
    }

    /// WebGPU-backed device, selected via [`DeviceKind`] — the browser's own. Pinned to
    /// WebGPU, as `Device::vulkan` is to Vulkan.
    #[cfg(feature = "webgpu")]
    pub fn webgpu(device_kind: DeviceKind) -> Self {
        Self::new(wgpu_device(
            device_kind,
            burn_dispatch::devices::WgpuBackend::WebGpu,
        ))
    }

    /// Enables autodiff on this device.
    ///
    /// Tensors created on the returned device inherit its autodiff context. A tensor can later
    /// change that context independently with [`Tensor::autodiff`](crate::Tensor::autodiff) or
    /// [`Tensor::without_autodiff`](crate::Tensor::without_autodiff).
    ///
    /// Calling this method on a device that already has autodiff enabled returns it unchanged,
    /// preserving its gradient-checkpointing strategy. This operation is idempotent.
    /// Calling it repeatedly doesn't enable higher-order differentiation; only first-order
    /// autodiff is supported.
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// let device = Device::default().autodiff();
    /// let x = Tensor::<1>::from_floats([1.0, 2.0, 3.0], &device).require_grad();
    /// let gradients = x.backward();
    /// ```
    ///
    #[cfg(feature = "autodiff")]
    #[must_use]
    pub fn autodiff(self) -> Self {
        match self.into_dispatch() {
            device @ DispatchDevice::Autodiff(_) => Self::new(device),
            other => Self::new(DispatchDevice::autodiff(other)),
        }
    }

    /// Returns this device's gradient-checkpointing strategy when autodiff is enabled.
    #[cfg(feature = "autodiff")]
    pub fn gradient_checkpointing_strategy(&self) -> Option<GradientCheckpointingStrategy> {
        match self.as_dispatch() {
            DispatchDevice::Autodiff(device) => Some(device.gradient_checkpointing_strategy()),
            _ => None,
        }
    }

    /// Enables gradient checkpointing on the autodiff device.
    ///
    /// Gradient checkpointing recomputes activations during backpropagation for operations
    /// marked as memory-bound, while compute-bound operations still cache their
    /// output. This reduces peak memory usage at the cost of additional computation
    /// for memory-bound ops.
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// let device = Device::default().autodiff().gradient_checkpointing();
    /// ```
    ///
    /// # Panics
    ///
    /// Panics if autodiff is not enabled on this device.
    #[cfg(feature = "autodiff")]
    #[must_use]
    pub fn gradient_checkpointing(self) -> Self {
        self.with_gradient_checkpointing_strategy(GradientCheckpointingStrategy::Balanced)
    }

    /// Sets the gradient-checkpointing strategy on this autodiff device.
    #[cfg(feature = "autodiff")]
    #[must_use]
    pub(crate) fn with_gradient_checkpointing_strategy(
        self,
        strategy: GradientCheckpointingStrategy,
    ) -> Self {
        match self.into_dispatch() {
            DispatchDevice::Autodiff(device) => Self::new(
                DispatchDevice::autodiff_with_gradient_checkpointing(device.inner(), strategy),
            ),
            _ => panic!(
                "Gradient checkpointing requires autodiff; use Device::autodiff().gradient_checkpointing()"
            ),
        }
    }

    /// Returns this device without its autodiff association.
    ///
    /// If autodiff is not enabled, the device is returned unchanged. This operation is idempotent.
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// let device = Device::default().autodiff();
    /// let inference_device = device.without_autodiff();
    ///
    /// assert!(!inference_device.is_autodiff());
    /// ```
    #[must_use]
    pub fn without_autodiff(self) -> Self {
        if self.is_autodiff() {
            Self::new(self.into_dispatch().inner())
        } else {
            self
        }
    }

    /// Applies `source`'s autodiff association and gradient-checkpointing strategy to this device,
    /// discarding this device's own.
    #[must_use]
    #[doc(hidden)]
    pub fn with_autodiff_context_from(self, source: &Self) -> Self {
        #[cfg(feature = "autodiff")]
        {
            match source.gradient_checkpointing_strategy() {
                Some(strategy) => self
                    .autodiff()
                    .with_gradient_checkpointing_strategy(strategy),
                None => self.without_autodiff(),
            }
        }

        #[cfg(not(feature = "autodiff"))]
        {
            let _ = source;
            self
        }
    }

    /// Returns this device without its autodiff association.
    ///
    /// This is equivalent to [`without_autodiff`](Self::without_autodiff). `inner` reflects the
    /// historical backend-decorator model, while `without_autodiff` describes the device's runtime
    /// property directly.
    #[must_use]
    pub fn inner(self) -> Self {
        self.without_autodiff()
    }

    /// Synchronize the device, waiting for all pending operations to complete.
    ///
    /// # Errors
    ///
    /// Returns an [`ExecutionError`] if an operation failed to execute.
    pub fn sync(&self) -> Result<(), ExecutionError> {
        Dispatch::sync(self.as_dispatch())
    }

    /// Flush the device's pending operations, handing them off for execution without waiting
    /// for them to complete.
    ///
    /// Backends that buffer work hold registered operations in a local queue until enough
    /// accumulate: the fusion backend batches ops to build optimizations, and the remote backend
    /// batches them before sending them over the network. `flush` forces that queue out now — the
    /// fusion backend processes its pending optimizations and the remote backend sends its batch to
    /// the server.
    ///
    /// Unlike [`sync`](Self::sync), this does not block on results — it only ensures buffered
    /// operations are dispatched instead of sitting idle. Eager backends, which execute each
    /// operation as it is registered, have nothing buffered and treat this as a no-op.
    ///
    /// # Errors
    ///
    /// Returns an [`ExecutionError`] when the buffered operations cannot be dispatched, e.g. on a
    /// device that is poisoned.
    pub fn flush(&self) -> Result<(), ExecutionError> {
        Dispatch::flush(self.as_dispatch())
    }

    /// Measure how long this device spends on the work `func` puts on it, in
    /// device time.
    ///
    /// The measurement is a [`ProfileDuration`]: a future the device answers
    /// once it has run both ends of the window, so nothing here waits on the
    /// device, and windows nest — an inner `profile` costs the outer one
    /// nothing. Collect them and [`resolve`](ProfileDuration::resolve) once
    /// the run is over.
    ///
    /// The window spans the stream from the call to `func`'s return. Work the
    /// stream still owed from before falls in; work a backend queues past the
    /// end falls out — the fusion backend holds a closure's last operations
    /// back to batch them, so a window over lazy work alone can read as
    /// empty. Ending the closure with a read, or
    /// [`profile_with`](Self::profile_with) and
    /// [`ProfileOptions::flush`], closes the window over all of it. A window
    /// that nothing ran in reads as no time.
    ///
    /// A backend with no device clock (ndarray, LibTorch, a remote device
    /// whose server has none) measures wall-clock time between two syncs
    /// instead: that one waits, and an inner window's syncs are charged to
    /// the outer.
    ///
    /// ```rust,ignore
    /// let (output, duration) = device.profile(|| model.forward(input))?;
    /// // Later, once the run is over:
    /// let ticks = duration.resolve().await.expect("the window carried work");
    /// println!("forward: {:?}", ticks.duration());
    /// ```
    ///
    /// # Errors
    ///
    /// Returns an [`ExecutionError`] when the device refuses to open or close
    /// the window — a remote device does from a browser thread, which cannot
    /// wait on the server.
    pub fn profile<O: Send + 'static>(
        &self,
        func: impl FnOnce() -> O + Send,
    ) -> Result<(O, ProfileDuration), ExecutionError> {
        self.profile_with(ProfileOptions::default(), func)
    }

    /// [`profile`](Self::profile) with [`ProfileOptions`].
    pub fn profile_with<O: Send + 'static>(
        &self,
        options: ProfileOptions,
        func: impl FnOnce() -> O + Send,
    ) -> Result<(O, ProfileDuration), ExecutionError> {
        Dispatch::profile(self.as_dispatch(), options, func)
    }

    /// Seeds the random number generator for this device.
    ///
    /// Seeding before tensor operations that involve randomness (e.g. [`Tensor::random`](crate::Tensor::random))
    /// makes those operations reproducible in a single-threaded program.
    ///
    /// # Note
    ///
    /// Depending on the backend, the seed may be applied globally rather than scoped
    /// to this specific device. It is guaranteed that at least this device will be seeded.
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// let device = Default::default();
    /// device.seed(42);
    /// let t = Tensor::<1>::random([8], Distribution::Default, &device);
    /// ```
    pub fn seed(&self, seed: u64) {
        Dispatch::seed(self.as_dispatch(), seed)
    }

    /// Returns whether this device is associated with autodiff.
    ///
    /// This is device context inherited by newly created tensors, not a statement that any tensor
    /// participates in a graph or retains gradients.
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// let device = Default::default();
    /// assert!(!device.is_autodiff());
    ///
    /// let ad_device = device.autodiff();
    /// assert!(ad_device.is_autodiff());
    /// ```
    pub fn is_autodiff(&self) -> bool {
        Dispatch::ad_enabled(self.as_dispatch())
    }

    /// Returns `true` if this device supports `dtype` for general computation:
    /// storage, conversion, *and* arithmetic.
    ///
    /// A type can be less than generally supported — bf16 on a Vulkan device,
    /// for example, is often storable and convertible but has no arithmetic
    /// (SPIR-V's `SPV_KHR_bfloat16` permits only conversions, dot products,
    /// and cooperative-matrix use). Computing in such a type produces
    /// backend-dependent garbage, so check before selecting a reduced
    /// precision:
    ///
    /// ```rust,ignore
    /// let dtype = if device.supports_dtype(FloatDType::BF16) {
    ///     FloatDType::BF16
    /// } else {
    ///     FloatDType::F32
    /// };
    /// ```
    pub fn supports_dtype(&self, dtype: impl Into<burn_std::DType>) -> bool {
        Dispatch::supports_dtype(self.as_dispatch(), dtype.into())
    }

    /// Sets the current allocation mode to persistent.
    pub fn memory_persistent_allocations<
        Output: Send,
        Input: Send,
        Func: Fn(Input) -> Output + Send,
    >(
        &self,
        input: Input,
        func: Func,
    ) -> Output {
        Dispatch::memory_persistent_allocations(self.as_dispatch(), input, func)
    }

    /// Triggers a memory cleanup on this device.
    ///
    /// The amount of memory reclaimed depends on the allocator implementation.
    /// Calling this method does not guarantee that any memory will be freed.
    pub fn memory_cleanup(&self) {
        Dispatch::memory_cleanup(self.as_dispatch());
    }

    /// This device's dynamic pools, in the order they were installed. `None` on
    /// a backend that does not report them.
    pub fn memory_pool_report(&self) -> Option<Vec<SlicedPoolReport>> {
        Dispatch::memory_pool_report(self.as_dispatch())
    }

    /// What this device's allocator currently holds. `None` on a backend that
    /// does not report it.
    pub fn memory_pool_usage(&self) -> Option<MemoryPoolUsage> {
        Dispatch::memory_pool_usage(self.as_dispatch())
    }

    /// Prepares the given data for transfer between the CPU and accelerator devices such as GPUs.
    ///
    /// Depending on the backend, the data may be transferred to pinned memory
    /// or another transfer-optimized format to improve transfer performance.
    pub fn staging<'a, Iter>(&self, data: Iter)
    where
        Iter: Iterator<Item = &'a mut TensorData>,
    {
        Dispatch::staging(data, self.as_dispatch());
    }

    /// Returns the [`DeviceSettings`] for this device.
    ///
    /// Settings include the default float, integer, and boolean data types used when creating
    /// tensors on this device.
    ///
    /// Before initialization, returns a snapshot of the backend defaults without locking them.
    /// Another thread may configure the device afterward, so subsequent tensor operations may use
    /// different settings. [Configure the device](Device::configure) before relying on its defaults.
    pub fn settings(&self) -> DeviceSettings {
        burn_backend::get_device_settings::<Dispatch>(self.as_dispatch())
    }

    /// Returns settings for tensor operations, initializing them to defaults if unset.
    pub(crate) fn get_or_init_settings(&self) -> DeviceSettings {
        burn_backend::get_or_init_device_settings::<Dispatch>(self.as_dispatch())
    }

    /// Configures the [settings](DeviceSettings) for this device.
    ///
    /// This configures the dtype used when no explicit type is specified at tensor
    /// creation time.
    ///
    /// Settings can only be initialized once per device. Configure defaults before creating
    /// tensors or initializing model parameters: tensor creation locks them to the backend's
    /// defaults, even with an explicit dtype, and any later call returns
    /// [`DeviceError::AlreadyInitialized`]. Querying [`settings`](Device::settings) does not lock them.
    ///
    /// Individual tensors can still use an explicit supported dtype at creation or be converted
    /// with [`Tensor::cast`](crate::Tensor::cast); neither changes the defaults.
    ///
    /// # Errors
    ///
    /// Returns [`DeviceError::UnsupportedDType`] if a requested dtype is unsupported.
    /// Returns [`DeviceError::AlreadyInitialized`] if settings have already been initialized
    /// for this device, either by a prior call or by a tensor operation.
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// use burn_tensor::{Device, FloatDType, Int, IntDType, Tensor};
    ///
    /// let mut device = Device::cuda(0);
    /// device.configure((FloatDType::F16, IntDType::I32))?;
    ///
    /// // Float tensors will now use F16
    /// let floats = Tensor::<2>::zeros([2, 3], &device);
    /// // Int tensors will now use I32
    /// let ints = Tensor::<2, Int>::zeros([2, 3], &device);
    /// ```
    pub fn configure(&mut self, config: impl Into<DeviceConfig>) -> Result<(), DeviceError> {
        let mut config = config.into();

        let defaults = self.as_dispatch().defaults();

        let float_dtype = config.float_dtype.take().unwrap_or(defaults.float_dtype);
        let int_dtype = config.int_dtype.take().unwrap_or(defaults.int_dtype);
        let bool_dtype = config.bool_dtype.take().unwrap_or(defaults.bool_dtype);

        burn_backend::set_default_dtypes::<Dispatch>(
            self.as_dispatch(),
            float_dtype,
            int_dtype,
            bool_dtype,
        )
    }

    /// Retrieves all available [`Device`]s that match the given [`DeviceType`] filter.
    ///
    /// Backends enumerate the hardware found on this machine, and `DeviceType::Remote` every
    /// device a remote server hosts:
    ///
    /// ```rust,ignore
    /// // Every CUDA device on this machine.
    /// let local = Device::enumerate(DeviceType::Cuda);
    ///
    /// // Filters combine with `|`.
    /// let both = Device::enumerate(DeviceType::Cuda | DeviceType::Remote(host));
    /// ```
    ///
    /// # Panics
    ///
    /// Where `RemoteHost::devices` returns an error for a `DeviceType::Remote`, and on wasm for any
    /// `DeviceType::Remote`, which a browser can only list with `RemoteHost::devices_async`.
    pub fn enumerate(filter: impl Into<DeviceFilter>) -> Devices {
        #[allow(unused)]
        let mut devices = Vec::new();

        #[allow(clippy::never_loop)] // at least one backend is expected to be enabled.
        for device_type in filter.into() {
            #[allow(unused)]
            let type_id = match device_type {
                // The cubecl runtimes are one dispatch backend, so `DispatchDeviceId::Cube`
                // would list every runtime's devices at once. These name the runtime the
                // caller asked for instead. `Metal`, `Vulkan` and `WebGpu` pin wgpu to the
                // requested graphics API; `Wgpu` leaves the API to auto-selection.
                #[cfg(feature = "cpu")]
                DeviceType::Cpu => {
                    push_cube(&mut devices, RuntimeId::Cpu);
                    continue;
                }
                #[cfg(feature = "cuda")]
                DeviceType::Cuda => {
                    push_cube(&mut devices, RuntimeId::Cuda);
                    continue;
                }
                #[cfg(feature = "rocm")]
                DeviceType::Rocm => {
                    push_cube(&mut devices, RuntimeId::Hip);
                    continue;
                }
                #[cfg(feature = "wgpu")]
                DeviceType::Wgpu => {
                    push_cube(&mut devices, RuntimeId::Wgpu);
                    continue;
                }
                #[cfg(feature = "metal")]
                DeviceType::Metal => {
                    push_wgpu(&mut devices, WgpuBackend::Metal);
                    continue;
                }
                #[cfg(feature = "vulkan")]
                DeviceType::Vulkan => {
                    push_wgpu(&mut devices, WgpuBackend::Vulkan);
                    continue;
                }
                #[cfg(feature = "webgpu")]
                DeviceType::WebGpu => {
                    push_wgpu(&mut devices, WgpuBackend::WebGpu);
                    continue;
                }
                #[cfg(feature = "flex")]
                DeviceType::Flex => DispatchDeviceId::Flex,
                #[cfg(feature = "ndarray")]
                DeviceType::NdArray => DispatchDeviceId::NdArray,
                #[cfg(feature = "tch")]
                DeviceType::LibTorch => DispatchDeviceId::LibTorch,
                #[cfg(feature = "remote")]
                DeviceType::Remote(host) => {
                    devices.extend(host.enumerate());
                    continue;
                }
            };

            #[allow(unreachable_code)] // need to have one backend enabled, so it is reachable
            for device in Dispatch::enumerate(type_id) {
                devices.push(Device::new(device))
            }
        }

        Devices(devices)
    }

    /// Who this device is: the part's name, what its kernels are compiled for and, for a card
    /// on a bus, the card itself. `None` for a backend that does not report one. Opens the
    /// device.
    #[cfg(feature = "cubecl")]
    pub fn identity(&self) -> Option<DeviceIdentity> {
        self.as_dispatch().identity()
    }

    /// Every card this build can reach, once each, with the devices that reach it.
    ///
    /// A card visible to two runtimes (an NVIDIA GPU under CUDA and under Vulkan) is one entry
    /// holding both devices, so a caller placing work on cards never puts two stages on one
    /// card by another name. Opens every device of every runtime, so call it once and keep
    /// the answer.
    #[cfg(any(feature = "cpu", feature = "cuda", feature = "rocm", feature = "wgpu"))]
    pub fn enumerate_physical() -> Vec<PhysicalGpu> {
        let mut gpus: Vec<PhysicalGpu> = Vec::new();
        for device in Dispatch::enumerate(DispatchDeviceId::Cube) {
            let device = Device::new(device);
            let Some(DeviceIdentity {
                name,
                physical: Some(physical),
                ..
            }) = device.identity()
            else {
                continue;
            };
            match gpus
                .iter_mut()
                .find(|gpu| gpu.physical.is_same_card(&physical))
            {
                Some(gpu) => {
                    gpu.physical.fill_from(&physical);
                    gpu.devices.0.push(device);
                }
                None => gpus.push(PhysicalGpu {
                    physical,
                    name,
                    devices: Devices(vec![device]),
                }),
            }
        }
        gpus
    }

    /// Measure peak compute and memory throughput for this device.
    ///
    /// Runs cubecl-std's throughput benchmarks for each [`ThroughputKey`],
    /// returning one [`ThroughputStat`] per key (in the same order). A key the
    /// device has no peak for keeps its place, carrying the [`ThroughputError`]
    /// saying why. Only cubecl-backed devices (cuda, wgpu, ...) report
    /// measurements; other backends return an empty vector.
    #[cfg(feature = "cubecl")]
    pub fn performance_stats(&self, keys: &[ThroughputKey]) -> Vec<ThroughputStat> {
        self.as_dispatch()
            .performance_stats(keys)
            .into_iter()
            .zip(keys.iter().copied())
            .map(|(value, key)| ThroughputStat { key, value })
            .collect()
    }
}

/// One card and every device that reaches it, from [`Device::enumerate_physical`].
#[cfg(any(feature = "cpu", feature = "cuda", feature = "rocm", feature = "wgpu"))]
#[derive(Debug, Clone)]
pub struct PhysicalGpu {
    /// The card's PCI address, Windows LUID and vendor, from whichever of its runtimes reported
    /// each.
    pub physical: PhysicalDevice,
    /// The part as the first runtime that reached it names it.
    pub name: String,
    /// Every device that runs on this card, one per runtime that reaches it.
    pub devices: Devices,
}

/// A single peak-throughput measurement produced by [`Device::performance_stats`].
#[cfg(feature = "cubecl")]
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct ThroughputStat {
    /// The measurement key (mode + dtype) that was benchmarked.
    pub key: ThroughputKey,
    /// The measured throughput for that key, or why the device has none.
    pub value: Result<ThroughputValue, ThroughputError>,
}

/// Short, column-friendly name for a throughput mode.
#[cfg(feature = "cubecl")]
fn mode_label(mode: &ThroughputMode) -> &'static str {
    match mode {
        ThroughputMode::ComputeDirect { .. } => "compute-direct",
        ThroughputMode::ComputeCmma { .. } => "compute-cmma",
        ThroughputMode::Memory(spec) => match spec.access {
            MemoryAccess::Copy => "memory",
            MemoryAccess::Read => "memory-read",
            MemoryAccess::Write => "memory-write",
        },
        ThroughputMode::Launch => "launch",
    }
}

#[cfg(feature = "cubecl")]
impl core::fmt::Display for ThroughputStat {
    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
        // Width/alignment flags are ignored on `ThroughputMode`/`ElemType` directly
        // (their fmt impls don't call `f.pad`), so render them to `String`s first —
        // `str`'s `Display` honors padding. The mode is labelled by hand rather than
        // derived through `Debug`: its variants carry payloads that would blow out the column.
        let mode = mode_label(&self.key.mode);

        // `ThroughputKey::dtype()` reports f32 for the modes that don't compute with an
        // element type, so blank the column there rather than print a misleading type.
        let dtype = match self.key.mode {
            ThroughputMode::ComputeDirect { dtype } | ThroughputMode::ComputeCmma { dtype, .. } => {
                alloc::format!("{dtype}")
            }
            ThroughputMode::Memory(_) | ThroughputMode::Launch => alloc::string::String::new(),
        };

        let value = match &self.value {
            Ok(value) => value.format(&self.key),
            Err(error) => alloc::format!("{error}"),
        };

        write!(f, "{mode:<14} {dtype:<5} {value}")
    }
}

/// Append every device of the cubecl `runtime` to `devices`, skipping any already listed.
///
/// A filter can name the same runtime twice (e.g. two `DeviceType::Cuda` entries),
/// and a caller enumerating hardware should see each device once.
#[cfg(any(feature = "cpu", feature = "cuda", feature = "rocm", feature = "wgpu"))]
fn push_cube(devices: &mut Vec<Device>, runtime: RuntimeId) {
    for device in Dispatch::enumerate_cube(runtime) {
        let device = Device::new(device);
        if !devices.contains(&device) {
            devices.push(device);
        }
    }
}

/// Append every wgpu device on `backend`, skipping any already listed.
#[cfg(any(feature = "metal", feature = "vulkan", feature = "webgpu"))]
fn push_wgpu(devices: &mut Vec<Device>, backend: WgpuBackend) {
    push_wgpu_with(
        devices,
        backend,
        burn_dispatch::devices::CubeDevice::enumerate,
    );
}

#[cfg(any(feature = "metal", feature = "vulkan", feature = "webgpu"))]
fn push_wgpu_with(
    devices: &mut Vec<Device>,
    backend: WgpuBackend,
    mut enumerate: impl FnMut(DeviceId) -> Vec<DeviceId>,
) {
    use burn_dispatch::devices::{CubeDevice, WgpuDevice, WgpuDeviceKind};

    // Enumeration preserves the seed's kind and graphics API. Ask for each concrete
    // kind on the requested API: Auto may expose different kinds or fewer adapters.
    // DefaultDevice is an alias, and Existing devices cannot be discovered.
    for kind in [
        WgpuDeviceKind::DiscreteGpu(0),
        WgpuDeviceKind::IntegratedGpu(0),
        WgpuDeviceKind::VirtualGpu(0),
        WgpuDeviceKind::Cpu,
        WgpuDeviceKind::Other(0),
    ] {
        let seed = CubeDevice::Wgpu(WgpuDevice::new(kind).on(backend));
        for id in enumerate(seed.to_id()) {
            let device = Device::new(CubeDevice::from_id(id));
            if !devices.contains(&device) {
                devices.push(device);
            }
        }
    }
}

/// Map our backend-agnostic [`DeviceKind`] onto cubecl's `WgpuDevice`, on `backend`.
///
/// Shared by [`Device::wgpu`], which leaves the graphics API to the runtime, and
/// [`Device::vulkan`], [`Device::metal`] and [`Device::webgpu`], which each pin theirs.
#[cfg(feature = "wgpu")]
pub(crate) fn wgpu_device(
    device_kind: DeviceKind,
    backend: burn_dispatch::devices::WgpuBackend,
) -> burn_dispatch::devices::WgpuDevice {
    use burn_dispatch::devices::{WgpuDevice, WgpuDeviceKind};

    let kind = match device_kind {
        DeviceKind::DiscreteGpu(i) => WgpuDeviceKind::DiscreteGpu(i),
        DeviceKind::IntegratedGpu(i) => WgpuDeviceKind::IntegratedGpu(i),
        DeviceKind::VirtualGpu(i) => WgpuDeviceKind::VirtualGpu(i),
        DeviceKind::Cpu => WgpuDeviceKind::Cpu,
        DeviceKind::DefaultDevice => WgpuDeviceKind::DefaultDevice,
        DeviceKind::Existing(id) => WgpuDeviceKind::Existing(id),
    };

    WgpuDevice::new(kind).on(backend)
}

/// Represents the devices that can be used.
///
/// `DeviceType` is used to filter the available device types for [`Device::enumerate`]. Each
/// variant selects a backend's hardware on this machine, except `Remote`, which selects a remote
/// server's devices.
///
/// Variants combine into a [`DeviceFilter`] with the `|` operator, so a single
/// [`Device::enumerate`] call can span several backends and remote servers.
#[allow(missing_docs)]
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DeviceType {
    #[cfg(feature = "cpu")]
    Cpu,
    #[cfg(feature = "cuda")]
    Cuda,
    #[cfg(feature = "rocm")]
    Rocm,
    #[cfg(feature = "wgpu")]
    Wgpu,
    #[cfg(feature = "metal")]
    Metal,
    #[cfg(feature = "vulkan")]
    Vulkan,
    #[cfg(feature = "webgpu")]
    WebGpu,
    #[cfg(feature = "flex")]
    Flex,
    #[cfg(feature = "ndarray")]
    NdArray,
    #[cfg(feature = "tch")]
    LibTorch,
    /// Every device the remote server `host` hosts, connected on first use like any listed device.
    /// [`RemoteHost::devices`](crate::remote::RemoteHost::devices) does the same and returns an
    /// error where `enumerate` panics.
    #[cfg(feature = "remote")]
    Remote(crate::remote::RemoteHost),
}

/// A set of [`DeviceType`]s passed to [`Device::enumerate`].
///
/// Built from a single [`DeviceType`], a `Vec<DeviceType>`, or by combining variants with the
/// `|` operator (`DeviceType::Cuda | DeviceType::Cpu`).
#[derive(Debug, Clone, Default)]
pub struct DeviceFilter(Vec<DeviceType>);

impl DeviceFilter {
    /// Create an empty filter.
    pub fn new() -> Self {
        Self::default()
    }

    /// Add a [`DeviceType`] to the filter.
    pub fn with(mut self, device_type: DeviceType) -> Self {
        self.0.push(device_type);
        self
    }
}

impl From<DeviceType> for DeviceFilter {
    fn from(value: DeviceType) -> Self {
        DeviceFilter(vec![value])
    }
}

impl From<Vec<DeviceType>> for DeviceFilter {
    fn from(value: Vec<DeviceType>) -> Self {
        DeviceFilter(value)
    }
}

impl IntoIterator for DeviceFilter {
    type Item = DeviceType;
    type IntoIter = alloc::vec::IntoIter<DeviceType>;
    fn into_iter(self) -> Self::IntoIter {
        self.0.into_iter()
    }
}

impl core::ops::BitOr for DeviceType {
    type Output = DeviceFilter;
    fn bitor(self, rhs: Self) -> DeviceFilter {
        DeviceFilter(vec![self, rhs])
    }
}

impl core::ops::BitOr<DeviceType> for DeviceFilter {
    type Output = DeviceFilter;
    fn bitor(mut self, rhs: DeviceType) -> DeviceFilter {
        self.0.push(rhs);
        self
    }
}

/// Configuration options used to initialize a device.
///
/// Unlike [`DeviceSettings`], this type represents partial user-provided
/// configuration and does not require all settings to be specified.
///
/// Any unspecified options will be resolved to device-specific defaults
/// when the device is initialized.
///
/// Use [`Device::configure`] to apply this configuration to a device.
#[derive(new, Debug, Clone, Default)]
pub struct DeviceConfig {
    /// Default floating-point data type.
    pub float_dtype: Option<FloatDType>,

    /// Default integer data type.
    pub int_dtype: Option<IntDType>,

    /// Default boolean data type.
    pub bool_dtype: Option<BoolDType>,
    // TODO: maybe quantization, but for now we keep this as device defaults
}

impl DeviceConfig {
    /// Sets the default floating-point data type for tensors created on the device.
    pub fn float_dtype(mut self, dtype: impl Into<FloatDType>) -> Self {
        self.float_dtype = Some(dtype.into());
        self
    }

    /// Sets the default integer data type for tensors created on the device.
    pub fn int_dtype(mut self, dtype: impl Into<IntDType>) -> Self {
        self.int_dtype = Some(dtype.into());
        self
    }

    /// Sets the default boolean data type storage precision for tensors created on the device.
    pub fn bool_dtype(mut self, dtype: impl Into<BoolDType>) -> Self {
        self.bool_dtype = Some(dtype.into());
        self
    }
}

impl From<FloatDType> for DeviceConfig {
    fn from(value: FloatDType) -> Self {
        DeviceConfig::new(Some(value), None, None)
    }
}

impl From<IntDType> for DeviceConfig {
    fn from(value: IntDType) -> Self {
        DeviceConfig::new(None, Some(value), None)
    }
}

impl From<BoolDType> for DeviceConfig {
    fn from(value: BoolDType) -> Self {
        DeviceConfig::new(None, None, Some(value))
    }
}

impl From<(FloatDType, IntDType)> for DeviceConfig {
    fn from(value: (FloatDType, IntDType)) -> Self {
        DeviceConfig::new(Some(value.0), Some(value.1), None)
    }
}

/// A collection of [`Device`]s returned by [`Device::enumerate`].
///
/// This type provides bulk operations and transformations over multiple
/// devices, such as enabling autodiff or configuring the device settings.
///
/// # Example
///
/// ```rust,ignore
/// let mut devices = Device::enumerate(DeviceType::Cuda)
///     .autodiff();
///
/// devices.configure(
///     DeviceConfig::default().float_dtype(FloatDType::F16),
/// )?;
/// ```
///
/// `Devices` dereferences to a slice of [`Device`], so it can be iterated,
/// indexed, and passed anywhere a `&[Device]` is expected.
#[derive(Debug, Clone)]
pub struct Devices(Vec<Device>);

impl Devices {
    /// Enables autodiff across all contained devices.
    ///
    /// Devices that already have autodiff enabled are left unchanged, preserving their
    /// gradient-checkpointing strategy.
    ///
    /// See [`Device::autodiff`].
    #[cfg(feature = "autodiff")]
    #[must_use]
    pub fn autodiff(mut self) -> Self {
        for device in &mut self.0 {
            *device = core::mem::take(device).autodiff();
        }

        self
    }

    /// Removes the autodiff association from every contained device.
    ///
    /// See [`Device::without_autodiff`].
    #[must_use]
    pub fn without_autodiff(mut self) -> Self {
        for device in &mut self.0 {
            *device = core::mem::take(device).without_autodiff();
        }

        self
    }

    /// Configures the [settings](DeviceSettings) for all devices.
    ///
    /// This configures the dtype used when no explicit type is specified at tensor
    /// creation time.
    ///
    /// Settings can only be initialized once per device. Configure defaults before creating
    /// tensors or initializing model parameters; tensor creation locks them, even with an explicit
    /// dtype. Querying [`Device::settings`] does not lock them.
    ///
    /// Stops at the first error; devices configured before it keep their settings.
    ///
    /// See [`Device::configure`].
    pub fn configure(&mut self, config: impl Into<DeviceConfig>) -> Result<(), DeviceError> {
        let config = config.into();
        for device in &mut self.0 {
            device.configure(config.clone())?;
        }
        Ok(())
    }

    /// Returns the `Vec` of [`Device`]s.
    pub fn into_vec(self) -> Vec<Device> {
        self.0
    }
}

// Loop over `&Devices` or `Devices` seamlessly
impl FromIterator<Device> for Devices {
    fn from_iter<I: IntoIterator<Item = Device>>(devices: I) -> Self {
        Self(devices.into_iter().collect())
    }
}

impl IntoIterator for Devices {
    type Item = Device;
    type IntoIter = alloc::vec::IntoIter<Device>;
    fn into_iter(self) -> Self::IntoIter {
        self.0.into_iter()
    }
}

impl core::ops::Deref for Devices {
    type Target = [Device];
    fn deref(&self) -> &Self::Target {
        &self.0
    }
}

#[cfg(all(test, feature = "capture"))]
mod capture_tests {
    use super::*;

    #[cfg(all(feature = "flex", feature = "autodiff"))]
    #[test]
    #[should_panic(expected = "Cannot move a tensor to a capture device with autodiff enabled")]
    fn capture_transfer_rejects_autodiff_context() {
        let tensor = crate::Tensor::<1, crate::Int>::from_ints([1, 2], &Device::flex().autodiff());
        let capture = Device::capture();
        let _ = capture.capture_scope(|scope| {
            let _ = tensor.to_device(&capture);
            scope.complete([], [])
        });
    }

    #[cfg(all(feature = "flex", feature = "autodiff"))]
    #[test]
    #[should_panic(expected = "Cannot move a tensor to a capture device with autodiff enabled")]
    fn capture_float_transfer_rejects_autodiff_context() {
        let tensor =
            crate::Tensor::<1>::from_floats([1.0, 2.0], &Device::flex().autodiff()).require_grad();
        let capture = Device::capture();
        let _ = capture.capture_scope(|scope| {
            let _ = tensor.to_device(&capture);
            scope.complete([], [])
        });
    }

    #[test]
    fn user_facing_capture_device_supports_repeated_scopes() {
        let device = Device::capture();

        let first = device
            .capture_scope(|scope| scope.complete([], []))
            .unwrap();
        let second = device
            .capture_scope(|scope| scope.complete([], []))
            .unwrap();

        assert!(first.graph.operations.is_empty());
        assert!(second.graph.operations.is_empty());
    }

    #[test]
    fn capture_scope_rejects_a_non_capture_device() {
        let device = Device::default();

        let result = device.capture_scope(|scope| scope.complete([], []));

        assert!(matches!(result, Err(CaptureError::InvalidDevice)));
    }

    #[test]
    fn capture_device_reports_recordable_dtype_support() {
        let device = Device::capture();

        let captured = device.capture_scope(|scope| {
            assert!(device.supports_dtype(FloatDType::F32));
            assert!(device.supports_dtype(FloatDType::F64));
            assert!(device.supports_dtype(FloatDType::BF16));
            assert!(device.supports_dtype(IntDType::I32));
            assert!(device.supports_dtype(BoolDType::Native));
            scope.complete([], [])
        });

        assert!(captured.is_ok());
    }
}

#[cfg(all(test, feature = "flex", feature = "autodiff"))]
mod autodiff_move_tests {
    use crate::{Bool, Device, GradientCheckpointingStrategy, Int, Tensor, TensorData};

    // A tensor without autodiff (e.g. a gradient) can be moved onto an autodiff device; it lands
    // on the underlying hardware and stays outside autodiff.
    #[test]
    fn move_non_autodiff_float_tensor_to_autodiff_device() {
        let device = Device::default();
        let ad_device = device.clone().autodiff();

        let t = Tensor::<2>::from_floats([[1.0, 2.0], [3.0, 4.0]], &device);
        let moved = t.to_device(&ad_device);

        assert!(!moved.is_autodiff());
        assert!(!moved.is_tracked());
        assert_eq!(
            moved.try_into_vec_as::<f32>().unwrap(),
            vec![1.0, 2.0, 3.0, 4.0]
        );
    }

    #[test]
    fn move_autodiff_tensor_preserves_association_strategy_and_graph() {
        let device = Device::default();
        let ad_device = device.clone().autodiff().gradient_checkpointing();

        let tensor = Tensor::<1>::from_floats([1.0, 2.0], &ad_device).require_grad();
        let moved = tensor.clone().to_device(&device);

        assert!(moved.is_autodiff());
        assert!(moved.is_tracked());
        // The transfer is a differentiable operation, so its result is a derived tensor whose own
        // gradient isn't retained by default.
        assert!(!moved.is_require_grad());
        assert_eq!(
            moved.gradient_checkpointing_strategy(),
            Some(GradientCheckpointingStrategy::Balanced)
        );

        let grads = moved.sum().backward();
        tensor
            .grad(&grads)
            .expect("the transfer should preserve the graph")
            .into_data()
            .assert_eq(&TensorData::from([1.0f32, 1.0]), true);
    }

    #[test]
    fn move_non_float_tensors_preserves_context() {
        let device = Device::default();
        let ad_device = device.clone().autodiff().gradient_checkpointing();

        let plain_int = Tensor::<1, Int>::from_ints([1, 2], &device).to_device(&ad_device);
        assert!(!plain_int.is_autodiff());

        let autodiff_bool =
            Tensor::<1, Bool>::from_bool([true, false], &ad_device).to_device(&device);
        assert!(autodiff_bool.is_autodiff());
        assert_eq!(
            autodiff_bool.gradient_checkpointing_strategy(),
            Some(GradientCheckpointingStrategy::Balanced)
        );
    }

    #[test]
    #[should_panic(
        expected = "Gradient checkpointing requires autodiff; use Device::autodiff().gradient_checkpointing()"
    )]
    fn gradient_checkpointing_requires_autodiff() {
        let _ = Device::default().gradient_checkpointing();
    }
}

#[cfg(all(test, feature = "cuda", feature = "wgpu"))]
mod tests {
    use super::*;

    /// `cargo test -p burn-tensor --features cuda,wgpu a_card_reached_by_two_runtimes -- --ignored`
    #[test]
    #[ignore = "needs an NVIDIA card reachable through CUDA and Vulkan"]
    fn a_card_reached_by_two_runtimes_is_listed_once() {
        let gpus = Device::enumerate_physical();
        let nvidia: Vec<_> = gpus
            .iter()
            .filter(|gpu| gpu.physical.vendor == Some(PciVendor::Nvidia))
            .collect();
        assert!(!nvidia.is_empty(), "no NVIDIA card in {gpus:?}");
        for gpu in &nvidia {
            assert!(
                gpu.physical.pci_address.is_some(),
                "{} has no PCI address",
                gpu.name
            );
            assert!(
                gpu.devices.len() >= 2,
                "{} is reached by {:?} only",
                gpu.name,
                gpu.devices
            );
        }
        for (i, gpu) in gpus.iter().enumerate() {
            for other in &gpus[i + 1..] {
                assert!(
                    !gpu.physical.is_same_card(&other.physical),
                    "one card listed twice: {gpu:?} and {other:?}"
                );
            }
        }
    }
}

#[cfg(all(test, any(feature = "metal", feature = "vulkan", feature = "webgpu")))]
mod enumerate_wgpu_tests {
    use super::*;
    use burn_dispatch::devices::{CubeDevice, WgpuDevice, WgpuDeviceKind};

    fn cube_device(kind: WgpuDeviceKind, backend: WgpuBackend) -> CubeDevice {
        CubeDevice::Wgpu(WgpuDevice::new(kind).on(backend))
    }

    #[test]
    fn enumerate_requested_api_independently_of_auto() {
        for backend in [WgpuBackend::Metal, WgpuBackend::Vulkan, WgpuBackend::WebGpu] {
            // An existing Auto device must not limit discovery on the requested API
            // or be deduplicated with a device pinned to that API.
            let auto = cube_device(WgpuDeviceKind::DiscreteGpu(0), WgpuBackend::Auto);
            let expected = [
                WgpuDeviceKind::DiscreteGpu(0),
                WgpuDeviceKind::DiscreteGpu(1),
                WgpuDeviceKind::IntegratedGpu(0),
                WgpuDeviceKind::VirtualGpu(0),
                WgpuDeviceKind::Cpu,
                WgpuDeviceKind::Other(0),
            ]
            .map(|kind| cube_device(kind, backend));
            let enumerate = |seed: DeviceId| {
                let CubeDevice::Wgpu(seed_device) = CubeDevice::from_id(seed) else {
                    panic!("expected a wgpu seed");
                };
                assert_eq!(seed_device.backend, backend);
                expected
                    .iter()
                    .filter(|device| device.to_id().type_id == seed.type_id)
                    .map(CubeDevice::to_id)
                    .collect()
            };

            let mut devices = vec![Device::new(auto.clone())];
            push_wgpu_with(&mut devices, backend, enumerate);
            let expected_devices: Vec<_> = core::iter::once(&auto)
                .chain(expected.iter())
                .cloned()
                .map(Device::new)
                .collect();
            assert_eq!(devices, expected_devices);

            push_wgpu_with(&mut devices, backend, enumerate);
            assert_eq!(
                devices, expected_devices,
                "repeated filters must not duplicate devices"
            );
        }
    }

    #[test]
    fn enumerate_unavailable_api_is_empty() {
        let mut devices = Vec::new();
        push_wgpu_with(&mut devices, WgpuBackend::Vulkan, |_| Vec::new());
        assert!(devices.is_empty());
    }

    #[cfg(not(target_family = "wasm"))]
    #[test]
    fn enumerate_pins_the_graphics_api() {
        let cases = [
            #[cfg(feature = "metal")]
            (DeviceType::Metal, WgpuBackend::Metal),
            #[cfg(feature = "vulkan")]
            (DeviceType::Vulkan, WgpuBackend::Vulkan),
            #[cfg(feature = "webgpu")]
            (DeviceType::WebGpu, WgpuBackend::WebGpu),
        ];
        for (device_type, backend) in cases {
            for device in Device::enumerate(device_type).iter() {
                match device.as_dispatch() {
                    DispatchDevice::Cube(CubeDevice::Wgpu(wgpu)) => {
                        assert_eq!(wgpu.backend, backend);
                    }
                    other => panic!("expected a wgpu device, got {other:?}"),
                }
            }
        }
    }
}