sklears_simd/
tpu.rs

1//! TPU (Tensor Processing Unit) acceleration support for SIMD operations
2//!
3//! This module provides Google TPU interfaces for machine learning operations
4//! with fallback to CPU SIMD implementations.
5
6use crate::traits::SimdError;
7
8#[cfg(feature = "no-std")]
9use alloc::{
10    boxed::Box,
11    string::{String, ToString},
12    vec,
13    vec::Vec,
14};
15#[cfg(feature = "no-std")]
16use core::{any, mem};
17#[cfg(not(feature = "no-std"))]
18use std::{any, mem, string::ToString};
19
20/// TPU computation types
21#[derive(Debug, Clone, Copy, PartialEq, Eq)]
22pub enum TpuVersion {
23    V1,
24    V2,
25    V3,
26    V4,
27    V5e,
28    V5p,
29}
30
31/// TPU device information
32#[derive(Debug, Clone)]
33pub struct TpuDevice {
34    pub id: u32,
35    pub name: String,
36    pub version: TpuVersion,
37    pub cores: u32,
38    pub memory_gb: u64,
39    pub peak_flops: u64,
40    pub matrix_unit_count: u32,
41    pub vector_unit_count: u32,
42}
43
44/// TPU memory buffer wrapper
45#[derive(Debug)]
46pub struct TpuBuffer<T> {
47    pub ptr: *mut T,
48    pub size: usize,
49    pub device: TpuDevice,
50    pub shape: Vec<usize>,
51    #[allow(dead_code)] // Reserved for native TPU buffer handle (Google XLA / libtpu)
52    backend_handle: Option<Box<dyn any::Any + Send + Sync>>,
53}
54
55unsafe impl<T: Send> Send for TpuBuffer<T> {}
56unsafe impl<T: Sync> Sync for TpuBuffer<T> {}
57
58impl<T> Drop for TpuBuffer<T> {
59    fn drop(&mut self) {
60        // Free TPU memory when buffer is dropped
61        // Implementation depends on TPU runtime
62    }
63}
64
65/// TPU context for managing resources
66pub struct TpuContext {
67    pub device: TpuDevice,
68    pub runtime_version: String,
69    #[allow(dead_code)] // Reserved for native TPU context (Google XLA / libtpu runtime handle)
70    backend_context: Option<Box<dyn any::Any + Send + Sync>>,
71}
72
73/// TPU computation configuration
74#[derive(Debug, Clone)]
75pub struct TpuConfig {
76    pub precision: TpuPrecision,
77    pub batch_size: usize,
78    pub pipeline_depth: u32,
79    pub memory_optimization: bool,
80    pub auto_sharding: bool,
81}
82
83/// TPU precision modes
84#[derive(Debug, Clone, Copy, PartialEq, Eq)]
85pub enum TpuPrecision {
86    BFloat16,
87    Float32,
88    Int8,
89    Int16,
90    Int32,
91}
92
93impl Default for TpuConfig {
94    fn default() -> Self {
95        Self {
96            precision: TpuPrecision::BFloat16,
97            batch_size: 1,
98            pipeline_depth: 1,
99            memory_optimization: true,
100            auto_sharding: false,
101        }
102    }
103}
104
105/// TPU operations interface
106pub trait TpuOperations {
107    /// Allocate TPU memory
108    fn allocate<T>(&self, shape: &[usize]) -> Result<TpuBuffer<T>, SimdError>;
109
110    /// Copy data from host to TPU
111    fn copy_to_tpu<T>(
112        &self,
113        host_data: &[T],
114        tpu_buffer: &mut TpuBuffer<T>,
115    ) -> Result<(), SimdError>;
116
117    /// Copy data from TPU to host
118    fn copy_to_host<T>(
119        &self,
120        tpu_buffer: &TpuBuffer<T>,
121        host_data: &mut [T],
122    ) -> Result<(), SimdError>;
123
124    /// Execute matrix multiplication on TPU
125    fn matmul(
126        &self,
127        a: &TpuBuffer<f32>,
128        b: &TpuBuffer<f32>,
129        c: &mut TpuBuffer<f32>,
130        config: &TpuConfig,
131    ) -> Result<(), SimdError>;
132
133    /// Execute convolution on TPU
134    fn conv2d(
135        &self,
136        input: &TpuBuffer<f32>,
137        kernel: &TpuBuffer<f32>,
138        output: &mut TpuBuffer<f32>,
139        config: &TpuConfig,
140    ) -> Result<(), SimdError>;
141
142    /// Execute batch normalization on TPU
143    fn batch_norm(
144        &self,
145        input: &TpuBuffer<f32>,
146        scale: &TpuBuffer<f32>,
147        bias: &TpuBuffer<f32>,
148        output: &mut TpuBuffer<f32>,
149        config: &TpuConfig,
150    ) -> Result<(), SimdError>;
151
152    /// Execute activation function on TPU
153    fn activation(
154        &self,
155        input: &TpuBuffer<f32>,
156        output: &mut TpuBuffer<f32>,
157        activation_type: TpuActivation,
158        config: &TpuConfig,
159    ) -> Result<(), SimdError>;
160
161    /// Execute reduction operation on TPU
162    fn reduce(
163        &self,
164        input: &TpuBuffer<f32>,
165        output: &mut TpuBuffer<f32>,
166        reduction_type: TpuReduction,
167        axes: &[usize],
168        config: &TpuConfig,
169    ) -> Result<(), SimdError>;
170
171    /// Synchronize TPU operations
172    fn synchronize(&self) -> Result<(), SimdError>;
173}
174
175/// TPU activation functions
176#[derive(Debug, Clone, Copy, PartialEq, Eq)]
177pub enum TpuActivation {
178    ReLU,
179    Tanh,
180    Sigmoid,
181    Swish,
182    Gelu,
183    Softmax,
184}
185
186/// TPU reduction operations
187#[derive(Debug, Clone, Copy, PartialEq, Eq)]
188pub enum TpuReduction {
189    Sum,
190    Mean,
191    Max,
192    Min,
193    Prod,
194    All,
195    Any,
196}
197
198/// TPU runtime implementation
199pub struct TpuRuntime {
200    devices: Vec<TpuDevice>,
201    contexts: Vec<TpuContext>,
202}
203
204impl TpuRuntime {
205    /// Create new TPU runtime
206    pub fn new() -> Result<Self, SimdError> {
207        let devices = Self::discover_devices()?;
208        let contexts = Vec::new();
209        Ok(Self { devices, contexts })
210    }
211
212    /// Discover available TPU devices
213    fn discover_devices() -> Result<Vec<TpuDevice>, SimdError> {
214        // In a real implementation, this would interface with TPU runtime
215        // For now, return empty list or simulated devices
216        Ok(vec![])
217    }
218
219    /// Get available TPU devices
220    pub fn devices(&self) -> &[TpuDevice] {
221        &self.devices
222    }
223
224    /// Create context for TPU device
225    pub fn create_context(&mut self, device_id: u32) -> Result<&TpuContext, SimdError> {
226        let device = self
227            .devices
228            .get(device_id as usize)
229            .ok_or_else(|| SimdError::InvalidArgument("Invalid TPU device ID".to_string()))?;
230
231        let context = TpuContext {
232            device: device.clone(),
233            runtime_version: "2.0.0".to_string(),
234            backend_context: None,
235        };
236
237        self.contexts.push(context);
238        Ok(self
239            .contexts
240            .last()
241            .expect("collection should not be empty"))
242    }
243
244    /// Check if TPU is available
245    pub fn is_available() -> bool {
246        // In a real implementation, this would check for TPU runtime
247        false
248    }
249
250    /// Get TPU compute capability
251    pub fn get_compute_capability(
252        &self,
253        device_id: u32,
254    ) -> Result<TpuComputeCapability, SimdError> {
255        let device = self
256            .devices
257            .get(device_id as usize)
258            .ok_or_else(|| SimdError::InvalidArgument("Invalid TPU device ID".to_string()))?;
259
260        Ok(TpuComputeCapability::from_device(device))
261    }
262}
263
264/// TPU compute capability information
265#[derive(Debug, Clone)]
266pub struct TpuComputeCapability {
267    pub version: TpuVersion,
268    pub matrix_unit_dim: usize,
269    pub vector_unit_width: usize,
270    pub max_matrix_size: usize,
271    pub memory_bandwidth_gbps: f64,
272    pub supported_precisions: Vec<TpuPrecision>,
273}
274
275impl TpuComputeCapability {
276    fn from_device(device: &TpuDevice) -> Self {
277        let (matrix_unit_dim, vector_unit_width, max_matrix_size, memory_bandwidth_gbps) =
278            match device.version {
279                TpuVersion::V1 => (256, 128, 1024, 600.0),
280                TpuVersion::V2 => (256, 128, 1024, 700.0),
281                TpuVersion::V3 => (256, 128, 1024, 900.0),
282                TpuVersion::V4 => (256, 128, 1024, 1200.0),
283                TpuVersion::V5e => (256, 128, 1024, 1600.0),
284                TpuVersion::V5p => (256, 128, 1024, 2400.0),
285            };
286
287        Self {
288            version: device.version,
289            matrix_unit_dim,
290            vector_unit_width,
291            max_matrix_size,
292            memory_bandwidth_gbps,
293            supported_precisions: vec![
294                TpuPrecision::BFloat16,
295                TpuPrecision::Float32,
296                TpuPrecision::Int8,
297                TpuPrecision::Int16,
298                TpuPrecision::Int32,
299            ],
300        }
301    }
302}
303
304/// TPU-optimized matrix multiplication
305pub fn tpu_matmul(
306    a: &[f32],
307    b: &[f32],
308    c: &mut [f32],
309    m: usize,
310    n: usize,
311    k: usize,
312    _config: &TpuConfig,
313) -> Result<(), SimdError> {
314    // Fallback to CPU SIMD implementation
315    matrix_multiply_fallback(a, b, c, m, n, k)
316}
317
318/// TPU-optimized convolution
319pub fn tpu_conv2d(
320    _input: &[f32],
321    _kernel: &[f32],
322    _output: &mut [f32],
323    _input_shape: &[usize],
324    _kernel_shape: &[usize],
325    _config: &TpuConfig,
326) -> Result<(), SimdError> {
327    // Fallback to CPU SIMD implementation
328    // This would need to be implemented in the image processing module
329    Err(SimdError::NotImplemented(
330        "TPU conv2d not implemented".to_string(),
331    ))
332}
333
334/// TPU batch processing utilities
335pub mod batch {
336    use super::*;
337
338    /// Process batch of operations on TPU
339    pub fn process_batch<T, F>(
340        inputs: &[&[T]],
341        outputs: &mut [&mut [T]],
342        _batch_size: usize,
343        op: F,
344    ) -> Result<(), SimdError>
345    where
346        T: Clone + Send + Sync,
347        F: Fn(&[T], &mut [T]) -> Result<(), SimdError> + Send + Sync,
348    {
349        if inputs.len() != outputs.len() {
350            return Err(SimdError::InvalidArgument(
351                "Input and output batch sizes must match".to_string(),
352            ));
353        }
354
355        for (input, output) in inputs.iter().zip(outputs.iter_mut()) {
356            op(input, output)?;
357        }
358
359        Ok(())
360    }
361
362    /// Optimal batch size calculation
363    pub fn optimal_batch_size(data_size: usize, memory_limit: usize, compute_units: u32) -> usize {
364        let memory_per_item = data_size * mem::size_of::<f32>();
365        let memory_based_batch = memory_limit / memory_per_item;
366        let compute_based_batch = compute_units as usize * 8; // Heuristic
367
368        memory_based_batch.min(compute_based_batch).max(1)
369    }
370}
371
372/// Simple fallback implementations for missing functions
373fn matrix_multiply_fallback(
374    a: &[f32],
375    b: &[f32],
376    c: &mut [f32],
377    m: usize,
378    n: usize,
379    k: usize,
380) -> Result<(), SimdError> {
381    if a.len() != m * k || b.len() != k * n || c.len() != m * n {
382        return Err(SimdError::DimensionMismatch {
383            expected: m * n,
384            actual: c.len(),
385        });
386    }
387
388    for i in 0..m {
389        for j in 0..n {
390            let mut sum = 0.0;
391            for ki in 0..k {
392                sum += a[i * k + ki] * b[ki * n + j];
393            }
394            c[i * n + j] = sum;
395        }
396    }
397    Ok(())
398}
399
400#[allow(non_snake_case)]
401#[cfg(all(test, not(feature = "no-std")))]
402mod tests {
403    use super::*;
404
405    #[cfg(feature = "no-std")]
406    use alloc::{
407        string::{String, ToString},
408        vec,
409        vec::Vec,
410    };
411
412    #[test]
413    fn test_tpu_runtime_creation() {
414        let runtime = TpuRuntime::new();
415        assert!(runtime.is_ok());
416    }
417
418    #[test]
419    fn test_tpu_availability() {
420        // TPU should not be available in test environment
421        assert!(!TpuRuntime::is_available());
422    }
423
424    #[test]
425    fn test_tpu_config_default() {
426        let config = TpuConfig::default();
427        assert_eq!(config.precision, TpuPrecision::BFloat16);
428        assert_eq!(config.batch_size, 1);
429        assert!(config.memory_optimization);
430    }
431
432    #[test]
433    fn test_tpu_matmul_fallback() {
434        let a = vec![1.0, 2.0, 3.0, 4.0];
435        let b = vec![5.0, 6.0, 7.0, 8.0];
436        let mut c = vec![0.0; 4];
437        let config = TpuConfig::default();
438
439        let result = tpu_matmul(&a, &b, &mut c, 2, 2, 2, &config);
440        assert!(result.is_ok());
441    }
442
443    #[test]
444    fn test_batch_processing() {
445        let input1 = vec![1.0, 2.0, 3.0];
446        let input2 = vec![4.0, 5.0, 6.0];
447        let inputs = vec![input1.as_slice(), input2.as_slice()];
448
449        let mut output1 = vec![0.0; 3];
450        let mut output2 = vec![0.0; 3];
451        let mut outputs = vec![output1.as_mut_slice(), output2.as_mut_slice()];
452
453        let result = batch::process_batch(&inputs, &mut outputs, 2, |input, output| {
454            for (i, o) in input.iter().zip(output.iter_mut()) {
455                *o = *i * 2.0;
456            }
457            Ok(())
458        });
459
460        assert!(result.is_ok());
461        assert_eq!(outputs[0], &[2.0, 4.0, 6.0]);
462        assert_eq!(outputs[1], &[8.0, 10.0, 12.0]);
463    }
464
465    #[test]
466    fn test_optimal_batch_size() {
467        let batch_size = batch::optimal_batch_size(1000, 1000000, 16);
468        assert!(batch_size > 0);
469        assert!(batch_size <= 1000000 / (1000 * 4)); // 4 bytes per f32
470    }
471}
sklears_simd/tpu.rs

sklears_simd/
tpu.rs
