cuda_rust_wasm/neural_integration/
bridge.rs

1//! Bridge implementation connecting CUDA-WASM with ruv-FANN
2//!
3//! This module provides the core bridge functionality that enables
4//! seamless integration between CUDA kernels and ruv-FANN neural networks.
5
6use super::{
7    BridgeConfig, BufferHandle, CompiledKernel, DeviceInfo, GpuBackendTrait, GpuDevice,
8    NeuralIntegrationError, NeuralOperation, NeuralResult, Precision, BindingType,
9};
10use crate::backend::backend_trait::BackendTrait;
11use crate::runtime::Runtime;
12use crate::transpiler::Transpiler;
13use std::collections::HashMap;
14use std::sync::{Arc, Mutex, RwLock};
15
16/// WebGPU backend implementation for neural operations
17pub struct WebGpuBackend {
18    device: Option<wgpu::Device>,
19    queue: Option<wgpu::Queue>,
20    adapter_info: Option<wgpu::AdapterInfo>,
21    runtime: Arc<Runtime>,
22    kernel_cache: Arc<RwLock<HashMap<String, CompiledKernel>>>,
23    buffer_pool: Arc<Mutex<BufferPool>>,
24    config: BridgeConfig,
25}
26
27/// Buffer pool for efficient memory management
28struct BufferPool {
29    buffers: HashMap<BufferHandle, wgpu::Buffer>,
30    free_buffers: Vec<(usize, BufferHandle)>, // size, handle
31    next_handle: u64,
32}
33
34impl BufferPool {
35    fn new() -> Self {
36        Self {
37            buffers: HashMap::new(),
38            free_buffers: Vec::new(),
39            next_handle: 1,
40        }
41    }
42    
43    fn get_or_create(&mut self, device: &wgpu::Device, size: usize, usage: wgpu::BufferUsages) -> BufferHandle {
44        // Try to reuse existing buffer
45        if let Some(pos) = self.free_buffers.iter().position(|(s, _)| *s >= size) {
46            let (_, handle) = self.free_buffers.remove(pos);
47            return handle;
48        }
49        
50        // Create new buffer
51        let buffer = device.create_buffer(&wgpu::BufferDescriptor {
52            label: Some("Neural operation buffer"),
53            size: size as u64,
54            usage,
55            mapped_at_creation: false,
56        });
57        
58        let handle = BufferHandle(self.next_handle);
59        self.next_handle += 1;
60        
61        self.buffers.insert(handle, buffer);
62        handle
63    }
64    
65    fn return_buffer(&mut self, handle: BufferHandle, size: usize) {
66        self.free_buffers.push((size, handle));
67    }
68    
69    fn get_buffer(&self, handle: BufferHandle) -> Option<&wgpu::Buffer> {
70        self.buffers.get(&handle)
71    }
72}
73
74impl WebGpuBackend {
75    /// Create a new WebGPU backend
76    pub fn new(config: &BridgeConfig) -> NeuralResult<Self> {
77        let runtime = Arc::new(Runtime::new().map_err(|e| {
78            NeuralIntegrationError::GpuInitError(format!("Failed to create runtime: {e}"))
79        })?);
80        
81        let mut backend = Self {
82            device: None,
83            queue: None,
84            adapter_info: None,
85            runtime,
86            kernel_cache: Arc::new(RwLock::new(HashMap::new())),
87            buffer_pool: Arc::new(Mutex::new(BufferPool::new())),
88            config: config.clone(),
89        };
90        
91        // Initialize WebGPU if possible
92        if let Err(e) = backend.init_webgpu() {
93            log::warn!("WebGPU initialization failed: {e}");
94            if !config.auto_fallback {
95                return Err(e);
96            }
97        }
98        
99        Ok(backend)
100    }
101    
102    /// Initialize WebGPU device and queue
103    #[cfg(not(target_arch = "wasm32"))]
104    fn init_webgpu(&mut self) -> NeuralResult<()> {
105        use pollster::FutureExt;
106        
107        let instance = wgpu::Instance::new(wgpu::InstanceDescriptor {
108            backends: wgpu::Backends::all(),
109            dx12_shader_compiler: Default::default(),
110            flags: wgpu::InstanceFlags::default(),
111            gles_minor_version: wgpu::Gles3MinorVersion::default(),
112        });
113        
114        let adapter = instance.request_adapter(&wgpu::RequestAdapterOptions {
115            power_preference: match self.config.gpu_device {
116                GpuDevice::HighPerformance => wgpu::PowerPreference::HighPerformance,
117                GpuDevice::LowPower => wgpu::PowerPreference::LowPower,
118                _ => wgpu::PowerPreference::default(),
119            },
120            compatible_surface: None,
121            force_fallback_adapter: false,
122        }).block_on().ok_or_else(|| {
123            NeuralIntegrationError::GpuInitError("No suitable GPU adapter found".to_string())
124        })?;
125        
126        self.adapter_info = Some(adapter.get_info());
127        
128        let (device, queue) = adapter.request_device(
129            &wgpu::DeviceDescriptor {
130                required_features: wgpu::Features::empty(),
131                required_limits: wgpu::Limits::default(),
132                label: Some("Neural Bridge Device"),
133            },
134            None,
135        ).block_on().map_err(|e| {
136            NeuralIntegrationError::GpuInitError(format!("Failed to create device: {e}"))
137        })?;
138        
139        self.device = Some(device);
140        self.queue = Some(queue);
141        
142        log::info!("WebGPU initialized successfully");
143        Ok(())
144    }
145    
146    #[cfg(target_arch = "wasm32")]
147    fn init_webgpu(&mut self) -> NeuralResult<()> {
148        // WASM initialization will be handled differently
149        log::info!("WASM WebGPU initialization deferred to runtime");
150        Ok(())
151    }
152    
153    /// Compile a CUDA kernel to WGSL
154    fn compile_kernel(&self, cuda_source: &str, name: &str) -> NeuralResult<CompiledKernel> {
155        // Check cache first
156        if let Ok(cache) = self.kernel_cache.read() {
157            if let Some(kernel) = cache.get(name) {
158                return Ok(kernel.clone());
159            }
160        }
161        
162        // Transpile CUDA to WGSL using our transpiler
163        let wgsl_source = self.transpile_cuda_to_wgsl(cuda_source)?;
164        
165        let kernel = CompiledKernel {
166            name: name.to_string(),
167            wgsl_source,
168            entry_point: "main".to_string(),
169            workgroup_size: [64, 1, 1], // Default workgroup size
170            bind_group_layout: vec![
171                BindingType::Buffer { read_only: true },  // Input buffer
172                BindingType::Buffer { read_only: false }, // Output buffer
173            ],
174        };
175        
176        // Cache the kernel
177        if let Ok(mut cache) = self.kernel_cache.write() {
178            cache.insert(name.to_string(), kernel.clone());
179        }
180        
181        Ok(kernel)
182    }
183    
184    /// Transpile CUDA source to WGSL
185    fn transpile_cuda_to_wgsl(&self, cuda_source: &str) -> NeuralResult<String> {
186        // Create a transpiler instance
187        let transpiler = Transpiler::new();
188        
189        // Parse the CUDA source
190        let ast = crate::parser::CudaParser::new()
191            .parse(cuda_source)
192            .map_err(|e| NeuralIntegrationError::TranspilationError(e.to_string()))?;
193        
194        // Transpile to WGSL
195        let wgsl = transpiler
196            .to_wgsl(ast)
197            .map_err(|e| NeuralIntegrationError::TranspilationError(e.to_string()))?;
198        
199        Ok(wgsl)
200    }
201}
202
203impl GpuBackendTrait for WebGpuBackend {
204    fn initialize(&self) -> NeuralResult<()> {
205        if self.device.is_some() && self.queue.is_some() {
206            Ok(())
207        } else {
208            Err(NeuralIntegrationError::GpuInitError("Device not initialized".to_string()))
209        }
210    }
211    
212    fn is_available(&self) -> bool {
213        self.device.is_some() && self.queue.is_some()
214    }
215    
216    fn get_device_info(&self) -> DeviceInfo {
217        if let Some(ref info) = self.adapter_info {
218            DeviceInfo {
219                name: info.name.clone(),
220                vendor: format!("{:?}", info.vendor),
221                device_type: format!("{:?}", info.device_type),
222                memory_size: 0, // WebGPU doesn't expose this directly
223                compute_units: 0, // WebGPU doesn't expose this directly
224                max_workgroup_size: 256, // Common default
225                supports_f16: false, // Conservative default
226                supports_f64: false, // WebGPU doesn't support f64 in shaders
227            }
228        } else {
229            DeviceInfo {
230                name: "Unknown".to_string(),
231                vendor: "Unknown".to_string(),
232                device_type: "Unknown".to_string(),
233                memory_size: 0,
234                compute_units: 0,
235                max_workgroup_size: 64,
236                supports_f16: false,
237                supports_f64: false,
238            }
239        }
240    }
241    
242    fn create_buffer(&self, size: usize) -> NeuralResult<BufferHandle> {
243        let device = self.device.as_ref().ok_or_else(|| {
244            NeuralIntegrationError::GpuInitError("Device not initialized".to_string())
245        })?;
246        
247        let mut pool = self.buffer_pool.lock().unwrap();
248        let handle = pool.get_or_create(
249            device,
250            size,
251            wgpu::BufferUsages::STORAGE | wgpu::BufferUsages::COPY_SRC | wgpu::BufferUsages::COPY_DST,
252        );
253        
254        Ok(handle)
255    }
256    
257    fn execute_kernel(&self, kernel: &CompiledKernel, inputs: &[BufferHandle]) -> NeuralResult<BufferHandle> {
258        let device = self.device.as_ref().ok_or_else(|| {
259            NeuralIntegrationError::GpuInitError("Device not initialized".to_string())
260        })?;
261        
262        let queue = self.queue.as_ref().ok_or_else(|| {
263            NeuralIntegrationError::GpuInitError("Queue not initialized".to_string())
264        })?;
265        
266        // Create compute shader
267        let shader_module = device.create_shader_module(wgpu::ShaderModuleDescriptor {
268            label: Some(&format!("{} shader", kernel.name)),
269            source: wgpu::ShaderSource::Wgsl(kernel.wgsl_source.as_str().into()),
270        });
271        
272        // Create bind group layout
273        let bind_group_layout = device.create_bind_group_layout(&wgpu::BindGroupLayoutDescriptor {
274            label: Some(&format!("{} bind group layout", kernel.name)),
275            entries: &kernel.bind_group_layout.iter().enumerate().map(|(i, binding_type)| {
276                wgpu::BindGroupLayoutEntry {
277                    binding: i as u32,
278                    visibility: wgpu::ShaderStages::COMPUTE,
279                    ty: match binding_type {
280                        BindingType::Buffer { read_only } => wgpu::BindingType::Buffer {
281                            ty: wgpu::BufferBindingType::Storage { read_only: *read_only },
282                            has_dynamic_offset: false,
283                            min_binding_size: None,
284                        },
285                        BindingType::UniformBuffer => wgpu::BindingType::Buffer {
286                            ty: wgpu::BufferBindingType::Uniform,
287                            has_dynamic_offset: false,
288                            min_binding_size: None,
289                        },
290                        BindingType::StorageTexture => wgpu::BindingType::StorageTexture {
291                            access: wgpu::StorageTextureAccess::WriteOnly,
292                            format: wgpu::TextureFormat::Rgba8Unorm,
293                            view_dimension: wgpu::TextureViewDimension::D2,
294                        },
295                    },
296                    count: None,
297                }
298            }).collect::<Vec<_>>(),
299        });
300        
301        // Create compute pipeline
302        let compute_pipeline_layout = device.create_pipeline_layout(&wgpu::PipelineLayoutDescriptor {
303            label: Some(&format!("{} pipeline layout", kernel.name)),
304            bind_group_layouts: &[&bind_group_layout],
305            push_constant_ranges: &[],
306        });
307        
308        let compute_pipeline = device.create_compute_pipeline(&wgpu::ComputePipelineDescriptor {
309            label: Some(&format!("{} pipeline", kernel.name)),
310            layout: Some(&compute_pipeline_layout),
311            module: &shader_module,
312            entry_point: &kernel.entry_point,
313        });
314        
315        // Get input buffers
316        let pool = self.buffer_pool.lock().unwrap();
317        let input_buffers: Vec<&wgpu::Buffer> = inputs.iter()
318            .map(|handle| pool.get_buffer(*handle))
319            .collect::<Option<Vec<_>>>()
320            .ok_or_else(|| NeuralIntegrationError::OperationError("Invalid buffer handle".to_string()))?;
321        
322        // Create output buffer (same size as first input for simplicity)
323        let output_buffer = device.create_buffer(&wgpu::BufferDescriptor {
324            label: Some("Output buffer"),
325            size: input_buffers[0].size(),
326            usage: wgpu::BufferUsages::STORAGE | wgpu::BufferUsages::COPY_SRC,
327            mapped_at_creation: false,
328        });
329        
330        // Create bind group
331        let mut bind_group_entries = Vec::new();
332        for (i, buffer) in input_buffers.iter().enumerate() {
333            bind_group_entries.push(wgpu::BindGroupEntry {
334                binding: i as u32,
335                resource: buffer.as_entire_binding(),
336            });
337        }
338        bind_group_entries.push(wgpu::BindGroupEntry {
339            binding: input_buffers.len() as u32,
340            resource: output_buffer.as_entire_binding(),
341        });
342        
343        let bind_group = device.create_bind_group(&wgpu::BindGroupDescriptor {
344            label: Some(&format!("{} bind group", kernel.name)),
345            layout: &bind_group_layout,
346            entries: &bind_group_entries,
347        });
348        
349        // Execute the compute pass
350        let mut encoder = device.create_command_encoder(&wgpu::CommandEncoderDescriptor {
351            label: Some(&format!("{} encoder", kernel.name)),
352        });
353        
354        {
355            let mut compute_pass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
356                label: Some(&format!("{} pass", kernel.name)),
357                timestamp_writes: None,
358            });
359            
360            compute_pass.set_pipeline(&compute_pipeline);
361            compute_pass.set_bind_group(0, &bind_group, &[]);
362            
363            // Dispatch with appropriate workgroup count
364            let workgroup_count = (input_buffers[0].size() as u32 / 4) / kernel.workgroup_size[0] + 1;
365            compute_pass.dispatch_workgroups(workgroup_count, 1, 1);
366        }
367        
368        queue.submit(std::iter::once(encoder.finish()));
369        
370        // Return handle to output buffer
371        // Note: In a real implementation, we'd need to properly manage the output buffer
372        // For now, we'll create a new handle
373        drop(pool);
374        let mut pool = self.buffer_pool.lock().unwrap();
375        let handle = BufferHandle(pool.next_handle);
376        pool.next_handle += 1;
377        pool.buffers.insert(handle, output_buffer);
378        
379        Ok(handle)
380    }
381}
382
383/// Extract WGSL from transpiled Rust code
384pub fn extract_wgsl_from_rust(rust_code: &str) -> NeuralResult<CompiledKernel> {
385    // This is a simplified implementation
386    // In a real implementation, we would parse the Rust code and extract WGSL
387    
388    // For now, we'll generate basic WGSL for common operations
389    let wgsl_source = generate_basic_wgsl(rust_code)?;
390    
391    Ok(CompiledKernel {
392        name: "extracted_kernel".to_string(),
393        wgsl_source,
394        entry_point: "main".to_string(),
395        workgroup_size: [64, 1, 1],
396        bind_group_layout: vec![
397            BindingType::Buffer { read_only: true },
398            BindingType::Buffer { read_only: false },
399        ],
400    })
401}
402
403/// Generate basic WGSL for common operations
404fn generate_basic_wgsl(rust_code: &str) -> NeuralResult<String> {
405    // Analyze the Rust code to determine the operation type
406    if rust_code.contains("matrix_multiply") || rust_code.contains("matmul") {
407        Ok(include_str!("../webgpu/shaders/matrix_vector_multiply.wgsl").to_string())
408    } else if rust_code.contains("vector_add") || rust_code.contains("add") {
409        Ok(r#"
410@group(0) @binding(0) var<storage, read> input_a: array<f32>;
411@group(0) @binding(1) var<storage, read> input_b: array<f32>;
412@group(0) @binding(2) var<storage, read_write> output: array<f32>;
413
414@compute @workgroup_size(64)
415fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
416    let index = global_id.x;
417    if (index >= arrayLength(&input_a)) {
418        return;
419    }
420    output[index] = input_a[index] + input_b[index];
421}
422"#.to_string())
423    } else if rust_code.contains("sigmoid") {
424        Ok(r#"
425@group(0) @binding(0) var<storage, read> input: array<f32>;
426@group(0) @binding(1) var<storage, read_write> output: array<f32>;
427
428@compute @workgroup_size(64)
429fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
430    let index = global_id.x;
431    if (index >= arrayLength(&input)) {
432        return;
433    }
434    output[index] = 1.0 / (1.0 + exp(-input[index]));
435}
436"#.to_string())
437    } else {
438        // Default: copy operation
439        Ok(r#"
440@group(0) @binding(0) var<storage, read> input: array<f32>;
441@group(0) @binding(1) var<storage, read_write> output: array<f32>;
442
443@compute @workgroup_size(64)
444fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
445    let index = global_id.x;
446    if (index >= arrayLength(&input)) {
447        return;
448    }
449    output[index] = input[index];
450}
451"#.to_string())
452    }
453}
454
455/// Execute operation on CPU as fallback
456pub fn execute_cpu_fallback<T>(operation: NeuralOperation<T>, inputs: &[T]) -> NeuralResult<Vec<T>>
457where
458    T: Clone + Send + Sync + 'static + num_traits::Float,
459{
460    match operation {
461        NeuralOperation::VectorAdd { size, _phantom } => {
462            if inputs.len() < size * 2 {
463                return Err(NeuralIntegrationError::OperationError("Insufficient input data".to_string()));
464            }
465            
466            let mut result = Vec::with_capacity(size);
467            for i in 0..size {
468                result.push(inputs[i] + inputs[i + size]);
469            }
470            Ok(result)
471        }
472        
473        NeuralOperation::ActivationFunction { function, size, _phantom } => {
474            if inputs.len() < size {
475                return Err(NeuralIntegrationError::OperationError("Insufficient input data".to_string()));
476            }
477            
478            let mut result = Vec::with_capacity(size);
479            for i in 0..size {
480                let value = match function {
481                    super::ActivationFunction::Sigmoid => {
482                        T::one() / (T::one() + (-inputs[i]).exp())
483                    }
484                    super::ActivationFunction::ReLU => {
485                        if inputs[i] > T::zero() { inputs[i] } else { T::zero() }
486                    }
487                    super::ActivationFunction::Tanh => inputs[i].tanh(),
488                    super::ActivationFunction::LeakyReLU => {
489                        if inputs[i] > T::zero() { 
490                            inputs[i] 
491                        } else { 
492                            inputs[i] * T::from(0.01).unwrap_or(T::zero())
493                        }
494                    }
495                    super::ActivationFunction::Swish => {
496                        inputs[i] * (T::one() / (T::one() + (-inputs[i]).exp()))
497                    }
498                    super::ActivationFunction::GELU => {
499                        // Approximation of GELU
500                        let sqrt_2_pi = T::from(0.7978845608).unwrap_or(T::one());
501                        let x = inputs[i];
502                        x * T::from(0.5).unwrap_or(T::one()) * 
503                        (T::one() + (sqrt_2_pi * (x + T::from(0.044715).unwrap_or(T::zero()) * x * x * x)).tanh())
504                    }
505                };
506                result.push(value);
507            }
508            Ok(result)
509        }
510        
511        NeuralOperation::MatrixMultiply { a_rows, a_cols, b_cols, _phantom } => {
512            if inputs.len() < a_rows * a_cols + a_cols * b_cols {
513                return Err(NeuralIntegrationError::OperationError("Insufficient input data for matrix multiplication".to_string()));
514            }
515            
516            let mut result = Vec::with_capacity(a_rows * b_cols);
517            let matrix_a = &inputs[0..a_rows * a_cols];
518            let matrix_b = &inputs[a_rows * a_cols..];
519            
520            for i in 0..a_rows {
521                for j in 0..b_cols {
522                    let mut sum = T::zero();
523                    for k in 0..a_cols {
524                        sum = sum + matrix_a[i * a_cols + k] * matrix_b[k * b_cols + j];
525                    }
526                    result.push(sum);
527                }
528            }
529            Ok(result)
530        }
531        
532        _ => {
533            Err(NeuralIntegrationError::OperationError(format!("CPU fallback not implemented for operation: {}", operation.name())))
534        }
535    }
536}
537
538#[cfg(test)]
539mod tests {
540    use super::*;
541    
542    #[test]
543    fn test_cpu_vector_add() {
544        let operation = NeuralOperation::VectorAdd { size: 3, _phantom: std::marker::PhantomData };
545        let inputs = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
546        let result = execute_cpu_fallback(operation, &inputs).unwrap();
547        assert_eq!(result, vec![5.0, 7.0, 9.0]);
548    }
549    
550    #[test]
551    fn test_cpu_sigmoid() {
552        let operation = NeuralOperation::ActivationFunction { 
553            function: super::super::ActivationFunction::Sigmoid, 
554            size: 3,
555            _phantom: std::marker::PhantomData 
556        };
557        let inputs = vec![0.0f32, 1.0, -1.0];
558        let result = execute_cpu_fallback(operation, &inputs).unwrap();
559        
560        // Check that sigmoid(0) ≈ 0.5
561        assert!((result[0] - 0.5).abs() < 1e-6);
562        // Check that sigmoid(1) > 0.5
563        assert!(result[1] > 0.5);
564        // Check that sigmoid(-1) < 0.5
565        assert!(result[2] < 0.5);
566    }
567    
568    #[test]
569    fn test_wgsl_generation() {
570        let rust_code = "fn vector_add(a: &[f32], b: &[f32]) -> Vec<f32> { ... }";
571        let wgsl = generate_basic_wgsl(rust_code).unwrap();
572        assert!(wgsl.contains("vector_add") || wgsl.contains("input_a"));
573        assert!(wgsl.contains("@compute"));
574    }
575}
cuda_rust_wasm/neural_integration/bridge.rs

cuda_rust_wasm/neural_integration/
bridge.rs
