scirs2_core/array_protocol/
gpu_impl.rs

1// Copyright (c) 2025, `SciRS2` Team
2//
3// Licensed under either of
4//
5// * Apache License, Version 2.0
6//   (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
7// * MIT license
8//   (LICENSE-MIT or http://opensource.org/licenses/MIT)
9//
10// at your option.
11//
12
13//! GPU array implementation using the array protocol.
14//!
15//! This module provides an implementation of GPU arrays that leverage
16//! the array protocol for delegating operations to GPU-specific code.
17
18use std::any::{Any, TypeId};
19use std::collections::HashMap;
20use std::fmt::Debug;
21
22use crate::array_protocol::{ArrayFunction, ArrayProtocol, GPUArray, NotImplemented};
23use crate::error::{CoreError, CoreResult, ErrorContext};
24use ndarray::{Array, Dimension};
25
26/// GPU backends that can be used
27#[derive(Debug, Clone, Copy, PartialEq, Eq)]
28pub enum GPUBackend {
29    /// CUDA (NVIDIA GPUs)
30    CUDA,
31
32    /// `ROCm` (AMD GPUs)
33    ROCm,
34
35    /// Metal (Apple GPUs)
36    Metal,
37
38    /// WebGPU (cross-platform)
39    WebGPU,
40
41    /// `OpenCL` (cross-platform)
42    OpenCL,
43}
44
45impl Default for GPUBackend {
46    fn default() -> Self {
47        Self::CUDA
48    }
49}
50
51/// Configuration for GPU operations
52#[derive(Debug, Clone)]
53pub struct GPUConfig {
54    /// The GPU backend to use
55    pub backend: GPUBackend,
56
57    /// The device ID to use
58    pub device_id: usize,
59
60    /// Whether to use asynchronous operations
61    pub async_ops: bool,
62
63    /// Whether to use mixed precision
64    pub mixed_precision: bool,
65
66    /// The fraction of GPU memory to use for the operation
67    pub memory_fraction: f32,
68}
69
70impl Default for GPUConfig {
71    fn default() -> Self {
72        Self {
73            backend: GPUBackend::default(),
74            device_id: 0,
75            async_ops: true,
76            mixed_precision: false,
77            memory_fraction: 0.9,
78        }
79    }
80}
81
82/// A mock implementation of a GPU array
83pub struct GPUNdarray<T, D: Dimension>
84where
85    T: Clone + Send + Sync + 'static + num_traits::Zero,
86    T: std::ops::Div<f64, Output = T>,
87    D: Clone + Send + Sync + 'static + ndarray::RemoveAxis,
88{
89    /// The host-side copy of the data (in a real implementation, this would be on the GPU)
90    host_data: Array<T, D>,
91
92    /// Configuration for GPU operations
93    config: GPUConfig,
94
95    /// Whether the data is currently on the GPU
96    on_gpu: bool,
97
98    /// Unique ID for this GPU array
99    id: String,
100}
101
102impl<T, D> Debug for GPUNdarray<T, D>
103where
104    T: Debug + Clone + Send + Sync + 'static + num_traits::Zero + std::ops::Div<f64, Output = T>,
105    D: Dimension + Debug + Clone + Send + Sync + 'static + ndarray::RemoveAxis,
106{
107    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
108        f.debug_struct("GPUNdarray")
109            .field("config", &self.config)
110            .field("on_gpu", &self.on_gpu)
111            .field("id", &self.id)
112            .field("shape", &self.host_data.shape())
113            .finish()
114    }
115}
116
117impl<T, D> GPUNdarray<T, D>
118where
119    T: Clone + Send + Sync + 'static + num_traits::Zero + std::ops::Div<f64, Output = T>,
120    D: Dimension + Clone + Send + Sync + 'static + ndarray::RemoveAxis,
121{
122    /// Create a new GPU array from a host array.
123    #[must_use]
124    pub fn new(hostdata: Array<T, D>, config: GPUConfig) -> Self {
125        let uuid = uuid::Uuid::new_v4();
126        let id = format!("uuid{uuid}");
127        let mut array = Self {
128            host_data: hostdata,
129            config,
130            on_gpu: false,
131            id,
132        };
133
134        // In a real implementation, this would allocate GPU memory
135        // and copy the host data to the GPU
136        array.on_gpu = true;
137
138        array
139    }
140
141    /// Get the shape of the array.
142    #[must_use]
143    pub fn shape(&self) -> &[usize] {
144        self.host_data.shape()
145    }
146
147    /// Get a reference to the host data.
148    #[must_use]
149    pub const fn host_data(&self) -> &Array<T, D> {
150        &self.host_data
151    }
152
153    /// Get a mutable reference to the host data.
154    pub fn host_data_mut(&mut self) -> &mut Array<T, D> {
155        // In a real implementation, this would sync from GPU to host
156        &mut self.host_data
157    }
158
159    /// Get a reference to the GPU configuration.
160    #[must_use]
161    pub const fn config(&self) -> &GPUConfig {
162        &self.config
163    }
164
165    /// Execute a GPU kernel on this array.
166    ///
167    /// # Errors
168    /// Returns `CoreError` if kernel execution fails.
169    pub fn execute_kernel<F, R>(&self, kernel: F) -> CoreResult<R>
170    where
171        F: FnOnce(&Array<T, D>) -> CoreResult<R>,
172    {
173        // In a real implementation, this would execute a GPU kernel
174        // For now, we just call the function on the host data
175        kernel(&self.host_data)
176    }
177
178    /// Synchronize data from GPU to host.
179    ///
180    /// # Errors
181    /// Returns `CoreError` if synchronization fails.
182    pub fn sync_to_host(&mut self) -> CoreResult<()> {
183        // In a real implementation, this would copy data from GPU to host
184        // For now, we just set a flag
185        Ok(())
186    }
187
188    /// Synchronize data from host to GPU.
189    ///
190    /// # Errors
191    /// Returns `CoreError` if synchronization fails.
192    pub fn sync_to_gpu(&mut self) -> CoreResult<()> {
193        // In a real implementation, this would copy data from host to GPU
194        // For now, we just set a flag
195        self.on_gpu = true;
196        Ok(())
197    }
198}
199
200impl<T, D> ArrayProtocol for GPUNdarray<T, D>
201where
202    T: Clone + Send + Sync + 'static + num_traits::Zero,
203    T: std::ops::Div<f64, Output = T> + std::ops::Mul<Output = T> + std::ops::Add<Output = T>,
204    D: Dimension + Clone + Send + Sync + 'static + ndarray::RemoveAxis,
205{
206    fn array_function(
207        &self,
208        func: &ArrayFunction,
209        _types: &[TypeId],
210        args: &[Box<dyn Any>],
211        kwargs: &HashMap<String, Box<dyn Any>>,
212    ) -> Result<Box<dyn Any>, NotImplemented> {
213        match func.name {
214            "scirs2::array_protocol::operations::sum" => {
215                // Example implementation of sum for a GPU array
216                // In a real implementation, this would use GPU-accelerated reduction
217                let axis = kwargs.get("axis").and_then(|a| a.downcast_ref::<usize>());
218
219                if let Some(&_ax) = axis {
220                    // Sum along a specific axis - this would use GPU kernel in real implementation
221                    // But we can't use sum_axis without RemoveAxis trait
222                    // Just return the full sum for simplicity
223                    let sum = self.host_data.sum();
224                    Ok(Box::new(sum))
225                } else {
226                    // Sum all elements
227                    let sum = self.host_data.sum();
228                    Ok(Box::new(sum))
229                }
230            }
231            "scirs2::array_protocol::operations::mean" => {
232                // Example implementation of mean for a GPU array
233                let sum = self.host_data.sum();
234                let count = self.host_data.len();
235                #[allow(clippy::cast_precision_loss)]
236                let mean = sum / count as f64;
237
238                Ok(Box::new(mean))
239            }
240            "scirs2::array_protocol::operations::add" => {
241                // Element-wise addition
242                if args.len() < 2 {
243                    return Err(NotImplemented);
244                }
245
246                // Try to get the second argument as a GPU array first
247                if let Some(other) = args[1].downcast_ref::<Self>() {
248                    // Check shapes match
249                    if self.shape() != other.shape() {
250                        return Err(NotImplemented);
251                    }
252
253                    // Use GPU kernel for addition (in this case simulated)
254                    let Ok(result) = kernels::add(self, other) else {
255                        return Err(NotImplemented);
256                    };
257
258                    return Ok(Box::new(result));
259                }
260
261                // If the other array is not a GPU array, we could potentially handle
262                // other array types, but for simplicity, we'll just return NotImplemented
263                Err(NotImplemented)
264            }
265            "scirs2::array_protocol::operations::multiply" => {
266                // Element-wise multiplication
267                if args.len() < 2 {
268                    return Err(NotImplemented);
269                }
270
271                // Try to get the second argument as a GPU array
272                if let Some(other) = args[1].downcast_ref::<Self>() {
273                    // Check shapes match
274                    if self.shape() != other.shape() {
275                        return Err(NotImplemented);
276                    }
277
278                    // Use GPU kernel for multiplication (in this case simulated)
279                    let Ok(result) = kernels::multiply(self, other) else {
280                        return Err(NotImplemented);
281                    };
282
283                    return Ok(Box::new(result));
284                }
285
286                // If the other array is not a GPU array, we could potentially handle
287                // other array types, but for simplicity, we'll just return NotImplemented
288                Err(NotImplemented)
289            }
290            "scirs2::array_protocol::operations::matmul" => {
291                // Matrix multiplication
292                if args.len() < 2 {
293                    return Err(NotImplemented);
294                }
295
296                // We can only handle matrix multiplication for 2D arrays
297                // Note: For Dimension trait, checking ndim would need more complex logic
298                // For simplicity, we'll just check if this is specifically an Ix2 array
299                if TypeId::of::<D>() != TypeId::of::<ndarray::Ix2>() {
300                    return Err(NotImplemented);
301                }
302
303                // Try to get the second argument as a GPU array with the same type
304                if let Some(other) = args[1].downcast_ref::<Self>() {
305                    // For simplicity, we'll use the existing kernel function for the specific case
306                    // of f64 arrays with 2 dimensions
307                    if TypeId::of::<T>() == TypeId::of::<f64>()
308                        && TypeId::of::<D>() == TypeId::of::<ndarray::Ix2>()
309                    {
310                        let self_f64 = unsafe {
311                            &*std::ptr::from_ref(self).cast::<GPUNdarray<f64, ndarray::Ix2>>()
312                        };
313                        let other_f64 = unsafe {
314                            &*std::ptr::from_ref(other).cast::<GPUNdarray<f64, ndarray::Ix2>>()
315                        };
316
317                        match kernels::matmul(self_f64, other_f64) {
318                            Ok(result) => {
319                                // We can't safely transmute between _types with different sizes
320                                // Since we're in a specific case where we know T is f64 and D is Ix2,
321                                // we can just return the f64 result directly
322                                return Ok(Box::new(result));
323                            }
324                            Err(_) => return Err(NotImplemented),
325                        }
326                    }
327                    // For other types, create a placeholder result for demonstration
328                    // In a real implementation, we would support more _types and dimensions
329                    let result = Self::new(self.host_data.clone(), self.config.clone());
330                    return Ok(Box::new(result));
331                }
332
333                Err(NotImplemented)
334            }
335            "scirs2::array_protocol::operations::transpose" => {
336                // Transpose operation
337                // Check for 2D array using TypeId
338                if TypeId::of::<D>() != TypeId::of::<ndarray::Ix2>() {
339                    return Err(NotImplemented);
340                }
341
342                // In a real implementation, this would use a GPU kernel
343                // For now, we'll simulate by cloning to CPU, transposing, and creating a new GPU array
344                let transposed = self.host_data.t().to_owned();
345                let result = Self::new(transposed, self.config.clone());
346
347                Ok(Box::new(result))
348            }
349            "scirs2::array_protocol::operations::reshape" => {
350                // Reshape operation
351                if let Some(shape) = kwargs
352                    .get("shape")
353                    .and_then(|s| s.downcast_ref::<Vec<usize>>())
354                {
355                    match self.host_data.clone().into_shape_with_order(shape.clone()) {
356                        Ok(reshaped) => {
357                            let result = GPUNdarray::new(reshaped, self.config.clone());
358                            return Ok(Box::new(result));
359                        }
360                        Err(_) => return Err(NotImplemented),
361                    }
362                }
363
364                Err(NotImplemented)
365            }
366            _ => Err(NotImplemented),
367        }
368    }
369
370    fn as_any(&self) -> &dyn Any {
371        self
372    }
373
374    fn shape(&self) -> &[usize] {
375        self.host_data.shape()
376    }
377
378    fn box_clone(&self) -> Box<dyn ArrayProtocol> {
379        Box::new(self.clone())
380    }
381}
382
383impl<T, D> GPUArray for GPUNdarray<T, D>
384where
385    T: Clone + Send + Sync + 'static + num_traits::Zero,
386    T: std::ops::Div<f64, Output = T> + std::ops::Mul<Output = T> + std::ops::Add<Output = T>,
387    D: Dimension + Clone + Send + Sync + 'static + ndarray::RemoveAxis,
388{
389    /// # Errors
390    /// Returns `CoreError` if GPU transfer fails.
391    fn to_gpu(&self) -> CoreResult<Box<dyn GPUArray>> {
392        // Already on GPU
393        Ok(Box::new(self.clone()))
394    }
395
396    /// # Errors
397    /// Returns `CoreError` if CPU transfer fails.
398    fn to_cpu(&self) -> CoreResult<Box<dyn ArrayProtocol>> {
399        // Create a regular ndarray from the host data
400        let array = super::NdarrayWrapper::new(self.host_data.clone());
401
402        Ok(Box::new(array) as Box<dyn ArrayProtocol>)
403    }
404
405    fn is_on_gpu(&self) -> bool {
406        self.on_gpu
407    }
408
409    fn device_info(&self) -> HashMap<String, String> {
410        let mut info = HashMap::new();
411        info.insert("backend".to_string(), format!("{:?}", self.config.backend));
412        info.insert("device_id".to_string(), self.config.device_id.to_string());
413        info.insert("on_gpu".to_string(), self.on_gpu.to_string());
414        info.insert("id".to_string(), self.id.clone());
415        info
416    }
417}
418
419impl<T, D> Clone for GPUNdarray<T, D>
420where
421    T: Clone + Send + Sync + 'static + num_traits::Zero,
422    T: std::ops::Div<f64, Output = T>,
423    D: Dimension + Clone + Send + Sync + 'static + ndarray::RemoveAxis,
424{
425    fn clone(&self) -> Self {
426        Self {
427            host_data: self.host_data.clone(),
428            config: self.config.clone(),
429            on_gpu: self.on_gpu,
430            id: self.id.clone(),
431        }
432    }
433}
434
435/// A builder for GPU arrays
436pub struct GPUArrayBuilder {
437    config: GPUConfig,
438}
439
440impl Default for GPUArrayBuilder {
441    fn default() -> Self {
442        Self::new()
443    }
444}
445
446impl GPUArrayBuilder {
447    /// Create a new GPU array builder with default settings.
448    #[must_use]
449    pub fn new() -> Self {
450        Self {
451            config: GPUConfig::default(),
452        }
453    }
454
455    /// Set the GPU backend to use.
456    #[must_use]
457    pub const fn backend(mut self, backend: GPUBackend) -> Self {
458        self.config.backend = backend;
459        self
460    }
461
462    /// Set the device ID to use.
463    #[must_use]
464    pub const fn device_id(mut self, device_id: usize) -> Self {
465        self.config.device_id = device_id;
466        self
467    }
468
469    /// Set whether to use asynchronous operations.
470    #[must_use]
471    pub const fn async_ops(mut self, asyncops: bool) -> Self {
472        self.config.async_ops = asyncops;
473        self
474    }
475
476    /// Set whether to use mixed precision.
477    #[must_use]
478    pub const fn mixed_precision(mut self, mixedprecision: bool) -> Self {
479        self.config.mixed_precision = mixedprecision;
480        self
481    }
482
483    /// Set the fraction of GPU memory to use.
484    #[must_use]
485    pub const fn memory_fraction(mut self, memoryfraction: f32) -> Self {
486        self.config.memory_fraction = memoryfraction;
487        self
488    }
489
490    /// Build a GPU array from a host array.
491    #[must_use]
492    pub fn build<T, D>(self, hostdata: Array<T, D>) -> GPUNdarray<T, D>
493    where
494        T: Clone + Send + Sync + 'static + num_traits::Zero + std::ops::Div<f64, Output = T>,
495        D: Dimension + Clone + Send + Sync + 'static + ndarray::RemoveAxis,
496    {
497        GPUNdarray::new(hostdata, self.config)
498    }
499}
500
501/// A collection of GPU kernels for common operations
502pub mod kernels {
503    use super::*;
504    use ndarray::{Array, Dimension};
505
506    /// Add two arrays element-wise.
507    ///
508    /// # Errors
509    /// Returns `CoreError::ShapeError` if arrays have different shapes.
510    pub fn add<T, D>(a: &GPUNdarray<T, D>, b: &GPUNdarray<T, D>) -> CoreResult<GPUNdarray<T, D>>
511    where
512        T: Clone
513            + std::ops::Add<Output = T>
514            + Send
515            + Sync
516            + 'static
517            + num_traits::Zero
518            + std::ops::Div<f64, Output = T>,
519        D: Dimension + Clone + Send + Sync + 'static + ndarray::RemoveAxis,
520    {
521        // In a real implementation, this would use a GPU kernel
522        // For now, we just add the arrays on the CPU
523
524        // Check that the shapes match
525        if a.shape() != b.shape() {
526            return Err(CoreError::ShapeError(ErrorContext::new(format!(
527                "Shape mismatch: {:?} vs {:?}",
528                a.shape(),
529                b.shape()
530            ))));
531        }
532
533        // Perform the addition
534        let result_data = a.host_data().clone() + b.host_data().clone();
535
536        // Create a new GPU array from the result
537        Ok(GPUNdarray::<T, D>::new(result_data, a.config.clone()))
538    }
539
540    /// Multiply two arrays element-wise.
541    ///
542    /// # Errors
543    /// Returns `CoreError::ShapeError` if arrays have different shapes.
544    pub fn multiply<T, D>(
545        a: &GPUNdarray<T, D>,
546        b: &GPUNdarray<T, D>,
547    ) -> CoreResult<GPUNdarray<T, D>>
548    where
549        T: Clone
550            + std::ops::Mul<Output = T>
551            + Send
552            + Sync
553            + 'static
554            + num_traits::Zero
555            + std::ops::Div<f64, Output = T>,
556        D: Dimension + Clone + Send + Sync + 'static + ndarray::RemoveAxis,
557    {
558        // In a real implementation, this would use a GPU kernel
559        // For now, we just multiply the arrays on the CPU
560
561        // Check that the shapes match
562        if a.shape() != b.shape() {
563            return Err(CoreError::ShapeError(ErrorContext::new(format!(
564                "Shape mismatch: {:?} vs {:?}",
565                a.shape(),
566                b.shape()
567            ))));
568        }
569
570        // Perform the multiplication
571        let result_data = a.host_data().clone() * b.host_data().clone();
572
573        // Create a new GPU array from the result
574        Ok(GPUNdarray::<T, D>::new(result_data, a.config.clone()))
575    }
576
577    /// Matrix multiplication.
578    ///
579    /// # Errors
580    /// Returns `CoreError::ShapeError` if arrays are not compatible for matrix multiplication.
581    pub fn matmul<T>(
582        a: &GPUNdarray<T, ndarray::Ix2>,
583        b: &GPUNdarray<T, ndarray::Ix2>,
584    ) -> CoreResult<GPUNdarray<T, ndarray::Ix2>>
585    where
586        T: Clone
587            + std::ops::Mul<Output = T>
588            + std::ops::Add<Output = T>
589            + Default
590            + Send
591            + Sync
592            + 'static
593            + num_traits::Zero
594            + std::ops::Div<f64, Output = T>,
595    {
596        // In a real implementation, this would use cuBLAS or similar
597        // For now, we just perform matrix multiplication on the CPU
598
599        // Check that the shapes are compatible for matrix multiplication
600        let ashape = a.shape();
601        let bshape = b.shape();
602
603        if ashape.len() != 2 || bshape.len() != 2 || ashape[1] != bshape[0] {
604            return Err(CoreError::ShapeError(ErrorContext::new(format!(
605                "Incompatible shapes for matmul: {ashape:?} vs {bshape:?}"
606            ))));
607        }
608
609        // This is a simplified implementation for a GPU array
610        // In a real implementation, this would use GPU-accelerated matrix multiplication
611        let m = ashape[0];
612        let p = bshape[1];
613
614        // Just create a default result (all zeros) for demonstration purposes
615        let result_data = Array::default((m, p));
616
617        // Create a new GPU array from the result - with explicit type
618        Ok(GPUNdarray::<T, ndarray::Ix2>::new(
619            result_data,
620            a.config.clone(),
621        ))
622    }
623}
624
625#[cfg(test)]
626mod tests {
627    use super::*;
628    use ndarray::{arr2, Array2};
629
630    #[test]
631    fn test_gpu_ndarray_creation() {
632        let array = Array2::<f64>::ones((10, 5));
633        let config = GPUConfig::default();
634
635        let gpu_array = GPUNdarray::new(array.clone(), config);
636
637        // Check that the array was created correctly
638        assert_eq!(gpu_array.shape(), &[10, 5]);
639        assert!(gpu_array.is_on_gpu());
640
641        // Check device info
642        let info = gpu_array.device_info();
643        assert_eq!(info.get("backend").unwrap(), "CUDA");
644        assert_eq!(info.get("device_id").unwrap(), "0");
645        assert_eq!(info.get("on_gpu").unwrap(), "true");
646    }
647
648    #[test]
649    fn test_gpu_array_builder() {
650        let array = Array2::<f64>::ones((10, 5));
651
652        let gpu_array = GPUArrayBuilder::new()
653            .backend(GPUBackend::CUDA)
654            .device_id(1)
655            .async_ops(true)
656            .mixed_precision(true)
657            .memory_fraction(0.8)
658            .build(array.clone());
659
660        // Check that the configuration was set correctly
661        assert_eq!(gpu_array.config.backend, GPUBackend::CUDA);
662        assert_eq!(gpu_array.config.device_id, 1);
663        assert!(gpu_array.config.async_ops);
664        assert!(gpu_array.config.mixed_precision);
665        assert_eq!(gpu_array.config.memory_fraction, 0.8);
666    }
667
668    #[test]
669    fn test_gpu_array_kernels() {
670        let a = arr2(&[[1.0, 2.0], [3.0, 4.0]]);
671        let b = arr2(&[[5.0, 6.0], [7.0, 8.0]]);
672
673        let gpu_a = GPUNdarray::new(a.clone(), GPUConfig::default());
674        let gpu_b = GPUNdarray::new(b.clone(), GPUConfig::default());
675
676        // Test addition
677        let result = kernels::add(&gpu_a, &gpu_b).unwrap();
678        let expected = a + b;
679        assert_eq!(result.host_data(), &expected);
680
681        // Test multiplication
682        let a = arr2(&[[1.0, 2.0], [3.0, 4.0]]);
683        let b = arr2(&[[5.0, 6.0], [7.0, 8.0]]);
684
685        let gpu_a = GPUNdarray::new(a.clone(), GPUConfig::default());
686        let gpu_b = GPUNdarray::new(b.clone(), GPUConfig::default());
687
688        let result = kernels::multiply(&gpu_a, &gpu_b).unwrap();
689        let expected = a * b;
690        assert_eq!(result.host_data(), &expected);
691    }
692}
scirs2_core/array_protocol/gpu_impl.rs

scirs2_core/array_protocol/
gpu_impl.rs
