trustformers_core/
gpu_accelerated.rs

1#![allow(unused_variables)] // Multi-backend GPU implementation with feature gates
2
3use crate::errors::{Result, TrustformersError};
4use crate::gpu::GpuBackend;
5#[cfg(feature = "cuda")]
6use crate::kernels::cuda_kernels::CudaKernel;
7#[cfg(feature = "intel")]
8use crate::kernels::intel_kernels::{IntelKernel, IntelKernelConfig};
9#[cfg(feature = "rocm")]
10use crate::kernels::rocm_kernels::RocmKernel;
11#[cfg(feature = "vulkan")]
12use crate::kernels::vulkan_kernels::VulkanKernel;
13use crate::tensor::Tensor;
14#[cfg(any(
15    feature = "cuda",
16    feature = "rocm",
17    feature = "intel",
18    feature = "vulkan"
19))]
20use std::sync::{Arc, Mutex};
21
22/// GPU-accelerated operations manager
23///
24/// This provides a high-level interface for GPU-accelerated tensor operations,
25/// automatically selecting the best implementation based on available hardware.
26pub struct GpuAcceleratedOps {
27    backend: GpuBackend,
28    #[cfg(feature = "cuda")]
29    cuda_kernel: Option<Arc<Mutex<CudaKernel>>>,
30    #[cfg(feature = "rocm")]
31    rocm_kernel: Option<Arc<Mutex<RocmKernel>>>,
32    #[cfg(feature = "intel")]
33    intel_kernel: Option<Arc<Mutex<IntelKernel>>>,
34    #[cfg(feature = "vulkan")]
35    vulkan_kernel: Option<Arc<Mutex<VulkanKernel>>>,
36    #[allow(dead_code)]
37    device_id: usize,
38    #[allow(dead_code)]
39    enable_async: bool,
40}
41
42/// Configuration for GPU operations
43#[derive(Debug, Clone)]
44pub struct GpuOpsConfig {
45    pub device_id: usize,
46    pub enable_async: bool,
47    pub memory_pool_size: u64,
48    pub kernel_cache_size: usize,
49    pub precision: GpuPrecision,
50}
51
52/// Supported precision types for GPU operations
53#[derive(Debug, Clone, Copy, PartialEq)]
54pub enum GpuPrecision {
55    FP32,
56    FP16,
57    BF16,
58    INT8,
59    INT4,
60}
61
62impl Default for GpuOpsConfig {
63    fn default() -> Self {
64        Self {
65            device_id: 0,
66            enable_async: true,
67            memory_pool_size: 2 * 1024 * 1024 * 1024, // 2GB
68            kernel_cache_size: 1000,
69            precision: GpuPrecision::FP32,
70        }
71    }
72}
73
74impl GpuAcceleratedOps {
75    /// Create new GPU-accelerated operations manager
76    pub fn new(config: GpuOpsConfig) -> Result<Self> {
77        let backend = GpuBackend::default();
78
79        #[cfg(feature = "cuda")]
80        let cuda_kernel = if backend == GpuBackend::Cuda {
81            Some(Arc::new(Mutex::new(CudaKernel::new()?)))
82        } else {
83            None
84        };
85
86        #[cfg(feature = "rocm")]
87        let rocm_kernel = if backend == GpuBackend::Rocm {
88            Some(Arc::new(Mutex::new(RocmKernel::new()?)))
89        } else {
90            None
91        };
92
93        #[cfg(feature = "intel")]
94        let intel_kernel = if backend == GpuBackend::Intel {
95            let intel_config = IntelKernelConfig {
96                device_id: config.device_id,
97                workgroup_size: 256,
98                ..Default::default()
99            };
100            Some(Arc::new(Mutex::new(IntelKernel::new(intel_config)?)))
101        } else {
102            None
103        };
104
105        #[cfg(feature = "vulkan")]
106        let vulkan_kernel = if backend == GpuBackend::Vulkan {
107            let mut kernel = VulkanKernel::new()?;
108            kernel.initialize(config.device_id)?;
109            Some(Arc::new(Mutex::new(kernel)))
110        } else {
111            None
112        };
113
114        Ok(Self {
115            backend,
116            #[cfg(feature = "cuda")]
117            cuda_kernel,
118            #[cfg(feature = "rocm")]
119            rocm_kernel,
120            #[cfg(feature = "intel")]
121            intel_kernel,
122            #[cfg(feature = "vulkan")]
123            vulkan_kernel,
124            device_id: config.device_id,
125            enable_async: config.enable_async,
126        })
127    }
128
129    /// Matrix multiplication with GPU acceleration
130    pub fn matmul(&self, a: &Tensor, b: &Tensor) -> Result<Tensor> {
131        let a_shape = a.shape();
132        let b_shape = b.shape();
133
134        if a_shape.len() != 2 || b_shape.len() != 2 {
135            return Err(TrustformersError::tensor_op_error(
136                "Matrix multiplication requires 2D tensors",
137                "matmul",
138            ));
139        }
140
141        if a_shape[1] != b_shape[0] {
142            return Err(TrustformersError::tensor_op_error(
143                "Matrix dimensions incompatible for multiplication",
144                "matmul",
145            ));
146        }
147
148        let mut result = Tensor::zeros(&[a_shape[0], b_shape[1]])?;
149
150        match self.backend {
151            GpuBackend::Cuda => {
152                #[cfg(feature = "cuda")]
153                if let Some(ref cuda_kernel) = self.cuda_kernel {
154                    let mut kernel = cuda_kernel.lock().expect("lock should not be poisoned");
155                    kernel.matmul(a, b, &mut result, None)?;
156                } else {
157                    return Err(TrustformersError::tensor_op_error(
158                        "CUDA kernel not available",
159                        "matmul",
160                    ));
161                }
162                #[cfg(not(feature = "cuda"))]
163                return Err(TrustformersError::tensor_op_error(
164                    "CUDA support not enabled",
165                    "matmul",
166                ));
167            },
168            GpuBackend::Rocm => {
169                #[cfg(feature = "rocm")]
170                if let Some(ref rocm_kernel) = self.rocm_kernel {
171                    let mut kernel = rocm_kernel.lock().expect("lock should not be poisoned");
172                    kernel.matmul(a, b, &mut result, None)?;
173                } else {
174                    return Err(TrustformersError::tensor_op_error(
175                        "ROCm kernel not available",
176                        "matmul",
177                    ));
178                }
179                #[cfg(not(feature = "rocm"))]
180                return Err(TrustformersError::tensor_op_error(
181                    "ROCm support not enabled",
182                    "matmul",
183                ));
184            },
185            GpuBackend::Intel => {
186                #[cfg(feature = "intel")]
187                if let Some(ref intel_kernel) = self.intel_kernel {
188                    let mut kernel = intel_kernel.lock().expect("lock should not be poisoned");
189                    kernel.gemm(
190                        a,
191                        b,
192                        &mut result,
193                        1.0,
194                        0.0,
195                        crate::kernels::intel_kernels::IntelPrecision::FP32,
196                    )?;
197                } else {
198                    return Err(TrustformersError::tensor_op_error(
199                        "Intel kernel not available",
200                        "matmul",
201                    ));
202                }
203                #[cfg(not(feature = "intel"))]
204                return Err(TrustformersError::tensor_op_error(
205                    "Intel oneAPI support not enabled",
206                    "matmul",
207                ));
208            },
209            GpuBackend::Vulkan => {
210                #[cfg(feature = "vulkan")]
211                if let Some(ref vulkan_kernel) = self.vulkan_kernel {
212                    let mut kernel = vulkan_kernel.lock().expect("lock should not be poisoned");
213                    kernel.matmul(a, b, &mut result, None)?;
214                } else {
215                    return Err(TrustformersError::tensor_op_error(
216                        "Vulkan kernel not available",
217                        "matmul",
218                    ));
219                }
220                #[cfg(not(feature = "vulkan"))]
221                return Err(TrustformersError::tensor_op_error(
222                    "Vulkan support not enabled",
223                    "matmul",
224                ));
225            },
226            _ => {
227                // Fallback to CPU implementation
228                self.cpu_matmul(a, b, &mut result)?;
229            },
230        }
231
232        Ok(result)
233    }
234
235    /// Batch matrix multiplication
236    pub fn batch_matmul(&self, a: &Tensor, b: &Tensor) -> Result<Tensor> {
237        let a_shape = a.shape();
238        let b_shape = b.shape();
239
240        if a_shape.len() != 3 || b_shape.len() != 3 {
241            return Err(TrustformersError::tensor_op_error(
242                "Batch matrix multiplication requires 3D tensors",
243                "batch_matmul",
244            ));
245        }
246
247        if a_shape[0] != b_shape[0] || a_shape[2] != b_shape[1] {
248            return Err(TrustformersError::tensor_op_error(
249                "Batch matrix dimensions incompatible",
250                "batch_matmul",
251            ));
252        }
253
254        let result = Tensor::zeros(&[a_shape[0], a_shape[1], b_shape[2]])?;
255
256        // For batch operations, we can parallelize across the batch dimension
257        for batch in 0..a_shape[0] {
258            let a_slice = a.slice(0, batch, batch + 1)?;
259            let b_slice = b.slice(0, batch, batch + 1)?;
260            let mut result_slice = result.slice(0, batch, batch + 1)?;
261
262            match self.backend {
263                GpuBackend::Cuda => {
264                    #[cfg(feature = "cuda")]
265                    if let Some(ref cuda_kernel) = self.cuda_kernel {
266                        let mut kernel = cuda_kernel.lock().expect("lock should not be poisoned");
267                        kernel.matmul(&a_slice, &b_slice, &mut result_slice, None)?;
268                    } else {
269                        self.cpu_matmul(&a_slice, &b_slice, &mut result_slice)?;
270                    }
271                    #[cfg(not(feature = "cuda"))]
272                    self.cpu_matmul(&a_slice, &b_slice, &mut result_slice)?;
273                },
274                GpuBackend::Rocm => {
275                    #[cfg(feature = "rocm")]
276                    if let Some(ref rocm_kernel) = self.rocm_kernel {
277                        let mut kernel = rocm_kernel.lock().expect("lock should not be poisoned");
278                        kernel.matmul(&a_slice, &b_slice, &mut result_slice, None)?;
279                    } else {
280                        self.cpu_matmul(&a_slice, &b_slice, &mut result_slice)?;
281                    }
282                    #[cfg(not(feature = "rocm"))]
283                    self.cpu_matmul(&a_slice, &b_slice, &mut result_slice)?;
284                },
285                _ => {
286                    self.cpu_matmul(&a_slice, &b_slice, &mut result_slice)?;
287                },
288            }
289        }
290
291        Ok(result)
292    }
293
294    /// Flash attention implementation
295    pub fn flash_attention(
296        &self,
297        query: &Tensor,
298        key: &Tensor,
299        value: &Tensor,
300        scale: f32,
301        mask: Option<&Tensor>,
302    ) -> Result<Tensor> {
303        let q_shape = query.shape();
304        let k_shape = key.shape();
305        let v_shape = value.shape();
306
307        if q_shape.len() != 3 || k_shape.len() != 3 || v_shape.len() != 3 {
308            return Err(TrustformersError::tensor_op_error(
309                "Attention requires 3D tensors [batch, seq_len, hidden_dim]",
310                "flash_attention",
311            ));
312        }
313
314        if q_shape[0] != k_shape[0] || q_shape[0] != v_shape[0] {
315            return Err(TrustformersError::tensor_op_error(
316                "Batch dimensions must match for attention",
317                "flash_attention",
318            ));
319        }
320
321        let mut output = Tensor::zeros(&q_shape)?;
322
323        match self.backend {
324            GpuBackend::Cuda => {
325                #[cfg(feature = "cuda")]
326                if let Some(ref cuda_kernel) = self.cuda_kernel {
327                    let mut kernel = cuda_kernel.lock().expect("lock should not be poisoned");
328                    kernel.flash_attention(query, key, value, &mut output, None)?;
329                } else {
330                    self.cpu_attention(query, key, value, &mut output, scale, mask)?;
331                }
332                #[cfg(not(feature = "cuda"))]
333                self.cpu_attention(query, key, value, &mut output, scale, mask)?;
334            },
335            GpuBackend::Rocm => {
336                #[cfg(feature = "rocm")]
337                if let Some(ref rocm_kernel) = self.rocm_kernel {
338                    let mut kernel = rocm_kernel.lock().expect("lock should not be poisoned");
339                    kernel.flash_attention(query, key, value, &mut output, None)?;
340                } else {
341                    self.cpu_attention(query, key, value, &mut output, scale, mask)?;
342                }
343                #[cfg(not(feature = "rocm"))]
344                self.cpu_attention(query, key, value, &mut output, scale, mask)?;
345            },
346            GpuBackend::Intel => {
347                #[cfg(feature = "intel")]
348                if let Some(ref intel_kernel) = self.intel_kernel {
349                    let mut kernel = intel_kernel.lock().expect("lock should not be poisoned");
350                    kernel.attention(
351                        query,
352                        key,
353                        value,
354                        &mut output,
355                        scale,
356                        crate::kernels::intel_kernels::IntelPrecision::FP32,
357                    )?;
358                } else {
359                    self.cpu_attention(query, key, value, &mut output, scale, mask)?;
360                }
361                #[cfg(not(feature = "intel"))]
362                self.cpu_attention(query, key, value, &mut output, scale, mask)?;
363            },
364            GpuBackend::Vulkan => {
365                #[cfg(feature = "vulkan")]
366                if let Some(ref vulkan_kernel) = self.vulkan_kernel {
367                    let mut kernel = vulkan_kernel.lock().expect("lock should not be poisoned");
368                    kernel.flash_attention(query, key, value, &mut output, None)?;
369                } else {
370                    self.cpu_attention(query, key, value, &mut output, scale, mask)?;
371                }
372                #[cfg(not(feature = "vulkan"))]
373                self.cpu_attention(query, key, value, &mut output, scale, mask)?;
374            },
375            _ => {
376                self.cpu_attention(query, key, value, &mut output, scale, mask)?;
377            },
378        }
379
380        Ok(output)
381    }
382
383    /// Layer normalization with GPU acceleration
384    pub fn layer_norm(
385        &self,
386        input: &Tensor,
387        gamma: &Tensor,
388        beta: &Tensor,
389        epsilon: f32,
390    ) -> Result<Tensor> {
391        let input_shape = input.shape();
392        let mut output = Tensor::zeros(&input_shape)?;
393
394        match self.backend {
395            GpuBackend::Cuda => {
396                #[cfg(feature = "cuda")]
397                if let Some(ref cuda_kernel) = self.cuda_kernel {
398                    let mut kernel = cuda_kernel.lock().expect("lock should not be poisoned");
399                    kernel.layer_norm(input, gamma, beta, &mut output, epsilon, None)?;
400                } else {
401                    self.cpu_layer_norm(input, gamma, beta, &mut output, epsilon)?;
402                }
403                #[cfg(not(feature = "cuda"))]
404                self.cpu_layer_norm(input, gamma, beta, &mut output, epsilon)?;
405            },
406            GpuBackend::Rocm => {
407                #[cfg(feature = "rocm")]
408                if let Some(ref rocm_kernel) = self.rocm_kernel {
409                    let mut kernel = rocm_kernel.lock().expect("lock should not be poisoned");
410                    kernel.layer_norm(input, gamma, beta, &mut output, epsilon, None)?;
411                } else {
412                    self.cpu_layer_norm(input, gamma, beta, &mut output, epsilon)?;
413                }
414                #[cfg(not(feature = "rocm"))]
415                self.cpu_layer_norm(input, gamma, beta, &mut output, epsilon)?;
416            },
417            GpuBackend::Intel => {
418                #[cfg(feature = "intel")]
419                if let Some(ref intel_kernel) = self.intel_kernel {
420                    let mut kernel = intel_kernel.lock().expect("lock should not be poisoned");
421                    kernel.layer_norm(
422                        input,
423                        gamma,
424                        Some(beta),
425                        &mut output,
426                        epsilon,
427                        crate::kernels::intel_kernels::IntelPrecision::FP32,
428                    )?;
429                } else {
430                    self.cpu_layer_norm(input, gamma, beta, &mut output, epsilon)?;
431                }
432                #[cfg(not(feature = "intel"))]
433                self.cpu_layer_norm(input, gamma, beta, &mut output, epsilon)?;
434            },
435            GpuBackend::Vulkan => {
436                #[cfg(feature = "vulkan")]
437                if let Some(ref vulkan_kernel) = self.vulkan_kernel {
438                    let mut kernel = vulkan_kernel.lock().expect("lock should not be poisoned");
439                    kernel.layer_norm(
440                        input,
441                        gamma,
442                        Some(beta),
443                        &mut output,
444                        epsilon,
445                        crate::kernels::vulkan_kernels::VulkanPrecision::FP32,
446                    )?;
447                } else {
448                    self.cpu_layer_norm(input, gamma, beta, &mut output, epsilon)?;
449                }
450                #[cfg(not(feature = "vulkan"))]
451                self.cpu_layer_norm(input, gamma, beta, &mut output, epsilon)?;
452            },
453            _ => {
454                self.cpu_layer_norm(input, gamma, beta, &mut output, epsilon)?;
455            },
456        }
457
458        Ok(output)
459    }
460
461    /// GELU activation with GPU acceleration
462    pub fn gelu(&self, input: &Tensor) -> Result<Tensor> {
463        let input_shape = input.shape();
464        let mut output = Tensor::zeros(&input_shape)?;
465
466        match self.backend {
467            GpuBackend::Cuda => {
468                #[cfg(feature = "cuda")]
469                if let Some(ref cuda_kernel) = self.cuda_kernel {
470                    let mut kernel = cuda_kernel.lock().expect("lock should not be poisoned");
471                    kernel.fused_gelu(input, &mut output, None)?;
472                } else {
473                    self.cpu_gelu(input, &mut output)?;
474                }
475                #[cfg(not(feature = "cuda"))]
476                self.cpu_gelu(input, &mut output)?;
477            },
478            GpuBackend::Rocm => {
479                #[cfg(feature = "rocm")]
480                if let Some(ref rocm_kernel) = self.rocm_kernel {
481                    let mut kernel = rocm_kernel.lock().expect("lock should not be poisoned");
482                    kernel.fused_gelu(input, &mut output, None)?;
483                } else {
484                    self.cpu_gelu(input, &mut output)?;
485                }
486                #[cfg(not(feature = "rocm"))]
487                self.cpu_gelu(input, &mut output)?;
488            },
489            GpuBackend::Vulkan => {
490                #[cfg(feature = "vulkan")]
491                if let Some(ref vulkan_kernel) = self.vulkan_kernel {
492                    let mut kernel = vulkan_kernel.lock().expect("lock should not be poisoned");
493                    kernel.gelu(input, &mut output, None)?;
494                } else {
495                    self.cpu_gelu(input, &mut output)?;
496                }
497                #[cfg(not(feature = "vulkan"))]
498                self.cpu_gelu(input, &mut output)?;
499            },
500            _ => {
501                self.cpu_gelu(input, &mut output)?;
502            },
503        }
504
505        Ok(output)
506    }
507
508    /// Reduce sum with GPU acceleration
509    pub fn reduce_sum(&self, input: &Tensor, dim: usize) -> Result<Tensor> {
510        let input_shape = input.shape();
511
512        if dim >= input_shape.len() {
513            return Err(TrustformersError::tensor_op_error(
514                "Reduction dimension out of bounds",
515                "reduce_sum",
516            ));
517        }
518
519        let mut output_shape = input_shape.clone();
520        output_shape.remove(dim);
521        let mut output = Tensor::zeros(&output_shape)?;
522
523        match self.backend {
524            GpuBackend::Cuda => {
525                #[cfg(feature = "cuda")]
526                if let Some(ref cuda_kernel) = self.cuda_kernel {
527                    let mut kernel = cuda_kernel.lock().expect("lock should not be poisoned");
528                    kernel.reduce_sum(input, &mut output, dim, None)?;
529                } else {
530                    self.cpu_reduce_sum(input, &mut output, dim)?;
531                }
532                #[cfg(not(feature = "cuda"))]
533                self.cpu_reduce_sum(input, &mut output, dim)?;
534            },
535            GpuBackend::Rocm => {
536                #[cfg(feature = "rocm")]
537                if let Some(ref rocm_kernel) = self.rocm_kernel {
538                    let mut kernel = rocm_kernel.lock().expect("lock should not be poisoned");
539                    kernel.reduce_sum(input, &mut output, dim, None)?;
540                } else {
541                    self.cpu_reduce_sum(input, &mut output, dim)?;
542                }
543                #[cfg(not(feature = "rocm"))]
544                self.cpu_reduce_sum(input, &mut output, dim)?;
545            },
546            GpuBackend::Vulkan => {
547                #[cfg(feature = "vulkan")]
548                if let Some(ref vulkan_kernel) = self.vulkan_kernel {
549                    let mut kernel = vulkan_kernel.lock().expect("lock should not be poisoned");
550                    kernel.reduce_sum(input, &mut output, dim, None)?;
551                } else {
552                    self.cpu_reduce_sum(input, &mut output, dim)?;
553                }
554                #[cfg(not(feature = "vulkan"))]
555                self.cpu_reduce_sum(input, &mut output, dim)?;
556            },
557            _ => {
558                self.cpu_reduce_sum(input, &mut output, dim)?;
559            },
560        }
561
562        Ok(output)
563    }
564
565    /// Softmax with GPU acceleration
566    pub fn softmax(&self, input: &Tensor, dim: usize) -> Result<Tensor> {
567        let input_shape = input.shape();
568        let output = Tensor::zeros(&input_shape)?;
569
570        // Softmax: exp(x - max(x)) / sum(exp(x - max(x)))
571        let max_vals = self.reduce_max(input, dim)?;
572        let shifted = self.subtract_broadcast(input, &max_vals, dim)?;
573        let exp_vals = self.exp(&shifted)?;
574        let sum_exp = self.reduce_sum(&exp_vals, dim)?;
575        let result = self.divide_broadcast(&exp_vals, &sum_exp, dim)?;
576
577        Ok(result)
578    }
579
580    /// Check if GPU acceleration is available
581    pub fn is_gpu_available(&self) -> bool {
582        match self.backend {
583            GpuBackend::Cuda => {
584                #[cfg(feature = "cuda")]
585                return self.cuda_kernel.is_some();
586                #[cfg(not(feature = "cuda"))]
587                return false;
588            },
589            GpuBackend::Rocm => {
590                #[cfg(feature = "rocm")]
591                return self.rocm_kernel.is_some();
592                #[cfg(not(feature = "rocm"))]
593                return false;
594            },
595            GpuBackend::Vulkan => {
596                #[cfg(feature = "vulkan")]
597                return self.vulkan_kernel.is_some();
598                #[cfg(not(feature = "vulkan"))]
599                return false;
600            },
601            GpuBackend::Cpu => false,
602            _ => false, // Other backends not implemented yet
603        }
604    }
605
606    /// Get current GPU backend
607    pub fn get_backend(&self) -> GpuBackend {
608        self.backend
609    }
610
611    /// Get GPU memory usage
612    pub fn get_memory_usage(&self) -> Result<(u64, u64, u64)> {
613        match self.backend {
614            GpuBackend::Cuda => {
615                #[cfg(feature = "cuda")]
616                if let Some(ref cuda_kernel) = self.cuda_kernel {
617                    let kernel = cuda_kernel.lock().expect("lock should not be poisoned");
618                    kernel.get_memory_stats(self.device_id)
619                } else {
620                    Ok((0, 0, 0))
621                }
622                #[cfg(not(feature = "cuda"))]
623                Ok((0, 0, 0))
624            },
625            GpuBackend::Rocm => {
626                #[cfg(feature = "rocm")]
627                if let Some(ref rocm_kernel) = self.rocm_kernel {
628                    let kernel = rocm_kernel.lock().expect("lock should not be poisoned");
629                    kernel.get_memory_stats(self.device_id)
630                } else {
631                    Ok((0, 0, 0))
632                }
633                #[cfg(not(feature = "rocm"))]
634                Ok((0, 0, 0))
635            },
636            GpuBackend::Vulkan => {
637                #[cfg(feature = "vulkan")]
638                if let Some(ref vulkan_kernel) = self.vulkan_kernel {
639                    let kernel = vulkan_kernel.lock().expect("lock should not be poisoned");
640                    kernel.get_memory_stats(self.device_id)
641                } else {
642                    Ok((0, 0, 0))
643                }
644                #[cfg(not(feature = "vulkan"))]
645                Ok((0, 0, 0))
646            },
647            _ => Ok((0, 0, 0)),
648        }
649    }
650
651    /// Synchronize GPU operations
652    pub fn synchronize(&self) -> Result<()> {
653        // In a real implementation, this would call cudaDeviceSynchronize()
654        Ok(())
655    }
656
657    // CPU fallback implementations
658    fn cpu_matmul(&self, a: &Tensor, b: &Tensor, result: &mut Tensor) -> Result<()> {
659        let a_data = a.data()?;
660        let b_data = b.data()?;
661        let result_data = result.data_mut()?;
662
663        let a_shape = a.shape();
664        let b_shape = b.shape();
665
666        // Implement CPU matrix multiplication
667        for i in 0..a_shape[0] {
668            for j in 0..b_shape[1] {
669                let mut sum = 0.0;
670                for k in 0..a_shape[1] {
671                    sum += a_data[i * a_shape[1] + k] * b_data[k * b_shape[1] + j];
672                }
673                result_data[i * b_shape[1] + j] = sum;
674            }
675        }
676
677        Ok(())
678    }
679
680    fn cpu_attention(
681        &self,
682        query: &Tensor,
683        key: &Tensor,
684        value: &Tensor,
685        output: &mut Tensor,
686        scale: f32,
687        _mask: Option<&Tensor>,
688    ) -> Result<()> {
689        let q_shape = query.shape();
690        let q_data = query.data()?;
691        let k_data = key.data()?;
692        let v_data = value.data()?;
693        let o_data = output.data_mut()?;
694
695        let batch_size = q_shape[0];
696        let seq_len = q_shape[1];
697        let hidden_dim = q_shape[2];
698
699        for batch in 0..batch_size {
700            for i in 0..seq_len {
701                // Compute attention scores
702                let mut scores = vec![0.0; seq_len];
703                for (j, score_ref) in scores.iter_mut().enumerate() {
704                    let mut score = 0.0;
705                    for d in 0..hidden_dim {
706                        let q_idx = batch * seq_len * hidden_dim + i * hidden_dim + d;
707                        let k_idx = batch * seq_len * hidden_dim + j * hidden_dim + d;
708                        score += q_data[q_idx] * k_data[k_idx];
709                    }
710                    *score_ref = score * scale;
711                }
712
713                // Apply softmax
714                let max_score = scores.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
715                let mut exp_sum = 0.0;
716                for score in &mut scores {
717                    *score = (*score - max_score).exp();
718                    exp_sum += *score;
719                }
720                for score in &mut scores {
721                    *score /= exp_sum;
722                }
723
724                // Compute weighted sum
725                for d in 0..hidden_dim {
726                    let mut output_val = 0.0;
727                    for (j, &score) in scores.iter().enumerate() {
728                        let v_idx = batch * seq_len * hidden_dim + j * hidden_dim + d;
729                        output_val += score * v_data[v_idx];
730                    }
731                    let o_idx = batch * seq_len * hidden_dim + i * hidden_dim + d;
732                    o_data[o_idx] = output_val;
733                }
734            }
735        }
736
737        Ok(())
738    }
739
740    fn cpu_layer_norm(
741        &self,
742        input: &Tensor,
743        gamma: &Tensor,
744        beta: &Tensor,
745        output: &mut Tensor,
746        epsilon: f32,
747    ) -> Result<()> {
748        let input_data = input.data()?;
749        let gamma_data = gamma.data()?;
750        let beta_data = beta.data()?;
751        let output_data = output.data_mut()?;
752
753        let input_shape = input.shape();
754        let last_dim = input_shape[input_shape.len() - 1];
755        let num_elements = input_data.len() / last_dim;
756
757        for i in 0..num_elements {
758            let start = i * last_dim;
759            let end = start + last_dim;
760            let slice = &input_data[start..end];
761
762            // Compute mean
763            let mean = slice.iter().sum::<f32>() / last_dim as f32;
764
765            // Compute variance
766            let variance = slice.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / last_dim as f32;
767            let std_dev = (variance + epsilon).sqrt();
768
769            // Normalize
770            for j in 0..last_dim {
771                let normalized = (slice[j] - mean) / std_dev;
772                output_data[start + j] = normalized * gamma_data[j] + beta_data[j];
773            }
774        }
775
776        Ok(())
777    }
778
779    fn cpu_gelu(&self, input: &Tensor, output: &mut Tensor) -> Result<()> {
780        let input_data = input.data()?;
781        let output_data = output.data_mut()?;
782
783        for i in 0..input_data.len() {
784            let x = input_data[i];
785            // GELU approximation: 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))
786            let x_cubed = x * x * x;
787            let tanh_arg = 0.797885 * (x + 0.044715 * x_cubed);
788            let tanh_val = tanh_arg.tanh();
789            output_data[i] = 0.5 * x * (1.0 + tanh_val);
790        }
791
792        Ok(())
793    }
794
795    fn cpu_reduce_sum(&self, input: &Tensor, output: &mut Tensor, dim: usize) -> Result<()> {
796        let input_data = input.data()?;
797        let output_data = output.data_mut()?;
798        let input_shape = input.shape();
799
800        let reduce_size = input_shape[dim];
801        let outer_size = input_shape[..dim].iter().product::<usize>();
802        let inner_size = input_shape[dim + 1..].iter().product::<usize>();
803
804        for outer in 0..outer_size {
805            for inner in 0..inner_size {
806                let mut sum = 0.0;
807                for reduce_idx in 0..reduce_size {
808                    let input_idx =
809                        outer * reduce_size * inner_size + reduce_idx * inner_size + inner;
810                    sum += input_data[input_idx];
811                }
812                let output_idx = outer * inner_size + inner;
813                output_data[output_idx] = sum;
814            }
815        }
816
817        Ok(())
818    }
819
820    // Helper methods for complex operations
821    fn reduce_max(&self, input: &Tensor, dim: usize) -> Result<Tensor> {
822        // Similar to reduce_sum but with max operation
823        let input_shape = input.shape();
824        let mut output_shape = input_shape.clone();
825        output_shape.remove(dim);
826
827        // Implementation would be similar to reduce_sum
828        Tensor::zeros(&output_shape)
829    }
830
831    fn subtract_broadcast(&self, a: &Tensor, b: &Tensor, dim: usize) -> Result<Tensor> {
832        // Broadcast subtraction
833        let a_shape = a.shape();
834        Tensor::zeros(&a_shape)
835    }
836
837    fn exp(&self, input: &Tensor) -> Result<Tensor> {
838        // Element-wise exponential
839        let input_shape = input.shape();
840        Tensor::zeros(&input_shape)
841    }
842
843    fn divide_broadcast(&self, a: &Tensor, b: &Tensor, dim: usize) -> Result<Tensor> {
844        // Broadcast division
845        let a_shape = a.shape();
846        Tensor::zeros(&a_shape)
847    }
848}
849
850#[cfg(test)]
851mod tests {
852    use super::*;
853
854    #[test]
855    fn test_gpu_accelerated_ops_creation() {
856        let config = GpuOpsConfig::default();
857        let ops = GpuAcceleratedOps::new(config);
858        assert!(ops.is_ok());
859    }
860
861    #[test]
862    fn test_gpu_ops_config_default() {
863        let config = GpuOpsConfig::default();
864        assert_eq!(config.device_id, 0);
865        assert!(config.enable_async);
866        assert_eq!(config.precision, GpuPrecision::FP32);
867    }
868
869    #[test]
870    fn test_backend_detection() {
871        let config = GpuOpsConfig::default();
872        let ops = GpuAcceleratedOps::new(config).expect("operation failed in test");
873
874        // Backend should be detected automatically
875        let backend = ops.get_backend();
876        assert!(matches!(
877            backend,
878            GpuBackend::Cuda | GpuBackend::Rocm | GpuBackend::Cpu | GpuBackend::Metal
879        ));
880    }
881
882    #[test]
883    fn test_matmul_dimensions() {
884        let config = GpuOpsConfig::default();
885        let ops = GpuAcceleratedOps::new(config).expect("operation failed in test");
886
887        let a = Tensor::ones(&[2, 3]).expect("Failed to create ones tensor");
888        let b = Tensor::ones(&[3, 4]).expect("Failed to create ones tensor");
889
890        let result = ops.matmul(&a, &b);
891        assert!(result.is_ok());
892
893        let result_tensor = result.expect("tensor operation failed");
894        assert_eq!(result_tensor.shape(), &[2, 4]);
895    }
896
897    #[test]
898    fn test_batch_matmul_dimensions() {
899        let config = GpuOpsConfig::default();
900        let ops = GpuAcceleratedOps::new(config).expect("operation failed in test");
901
902        let a = Tensor::ones(&[2, 3, 4]).expect("Failed to create ones tensor");
903        let b = Tensor::ones(&[2, 4, 5]).expect("Failed to create ones tensor");
904
905        let result = ops.batch_matmul(&a, &b);
906        assert!(result.is_ok());
907
908        let result_tensor = result.expect("tensor operation failed");
909        assert_eq!(result_tensor.shape(), &[2, 3, 5]);
910    }
911
912    #[test]
913    fn test_flash_attention_dimensions() {
914        let config = GpuOpsConfig::default();
915        let ops = GpuAcceleratedOps::new(config).expect("operation failed in test");
916
917        let batch_size = 2;
918        let seq_len = 10;
919        let hidden_dim = 64;
920
921        let query =
922            Tensor::ones(&[batch_size, seq_len, hidden_dim]).expect("Failed to create ones tensor");
923        let key =
924            Tensor::ones(&[batch_size, seq_len, hidden_dim]).expect("Failed to create ones tensor");
925        let value =
926            Tensor::ones(&[batch_size, seq_len, hidden_dim]).expect("Failed to create ones tensor");
927
928        let result = ops.flash_attention(&query, &key, &value, 0.125, None);
929        assert!(result.is_ok());
930
931        let result_tensor = result.expect("tensor operation failed");
932        assert_eq!(result_tensor.shape(), &[batch_size, seq_len, hidden_dim]);
933    }
934
935    #[test]
936    fn test_layer_norm_dimensions() {
937        let config = GpuOpsConfig::default();
938        let ops = GpuAcceleratedOps::new(config).expect("operation failed in test");
939
940        let input = Tensor::ones(&[2, 10, 64]).expect("Failed to create ones tensor");
941        let gamma = Tensor::ones(&[64]).expect("Failed to create ones tensor");
942        let beta = Tensor::zeros(&[64]).expect("Failed to create zero tensor");
943
944        let result = ops.layer_norm(&input, &gamma, &beta, 1e-5);
945        assert!(result.is_ok());
946
947        let result_tensor = result.expect("tensor operation failed");
948        assert_eq!(result_tensor.shape(), &[2, 10, 64]);
949    }
950
951    #[test]
952    fn test_gelu_dimensions() {
953        let config = GpuOpsConfig::default();
954        let ops = GpuAcceleratedOps::new(config).expect("operation failed in test");
955
956        let input = Tensor::ones(&[2, 10, 64]).expect("Failed to create ones tensor");
957
958        let result = ops.gelu(&input);
959        assert!(result.is_ok());
960
961        let result_tensor = result.expect("tensor operation failed");
962        assert_eq!(result_tensor.shape(), &[2, 10, 64]);
963    }
964
965    #[test]
966    fn test_memory_usage() {
967        let config = GpuOpsConfig::default();
968        let ops = GpuAcceleratedOps::new(config).expect("operation failed in test");
969
970        let (_total, _peak, _free) = ops.get_memory_usage().expect("operation failed in test");
971        // Should return some values (may be 0 if no GPU available)
972        // Note: No need to assert >= 0 for u64 values, they're always non-negative
973    }
974
975    #[test]
976    fn test_synchronize() {
977        let config = GpuOpsConfig::default();
978        let ops = GpuAcceleratedOps::new(config).expect("operation failed in test");
979
980        let result = ops.synchronize();
981        assert!(result.is_ok());
982    }
983}
trustformers_core/gpu_accelerated.rs

trustformers_core/
gpu_accelerated.rs
