ghostflow_core/
tpu.rs

1//! TPU (Tensor Processing Unit) backend
2//!
3//! Provides Google Cloud TPU acceleration
4//! Note: Requires Google Cloud TPU SDK and XLA compiler
5
6use crate::tensor::Tensor;
7use crate::error::{GhostError, Result};
8
9/// TPU device context
10pub struct TpuDevice {
11    pub device_id: usize,
12    pub name: String,
13    pub version: TpuVersion,
14    pub cores: usize,
15}
16
17#[derive(Debug, Clone, Copy, PartialEq, Eq)]
18pub enum TpuVersion {
19    V2,
20    V3,
21    V4,
22    V5,
23}
24
25impl TpuDevice {
26    /// Initialize TPU device
27    pub fn new(device_id: usize) -> Result<Self> {
28        #[cfg(feature = "tpu")]
29        {
30            // Would use TPU API:
31            // tpu_initialize()
32            // tpu_get_device_properties(device_id)
33            
34            Ok(TpuDevice {
35                device_id,
36                name: format!("TPU Device {}", device_id),
37                version: TpuVersion::V4,
38                cores: 8, // TPU v4 has 8 cores per chip
39            })
40        }
41        #[cfg(not(feature = "tpu"))]
42        {
43            Err(GhostError::DeviceError(
44                "TPU support not compiled. Enable 'tpu' feature.".to_string()
45            ))
46        }
47    }
48    
49    /// Get number of available TPU devices
50    pub fn device_count() -> Result<usize> {
51        #[cfg(feature = "tpu")]
52        {
53            // Would query TPU topology
54            Ok(0) // Placeholder
55        }
56        #[cfg(not(feature = "tpu"))]
57        {
58            Ok(0)
59        }
60    }
61    
62    /// Get TPU memory bandwidth (GB/s)
63    pub fn memory_bandwidth(&self) -> f32 {
64        match self.version {
65            TpuVersion::V2 => 700.0,
66            TpuVersion::V3 => 900.0,
67            TpuVersion::V4 => 1200.0,
68            TpuVersion::V5 => 1600.0,
69        }
70    }
71    
72    /// Get peak TFLOPS
73    pub fn peak_tflops(&self) -> f32 {
74        match self.version {
75            TpuVersion::V2 => 45.0,
76            TpuVersion::V3 => 123.0,
77            TpuVersion::V4 => 275.0,
78            TpuVersion::V5 => 459.0,
79        }
80    }
81}
82
83/// TPU buffer for HBM (High Bandwidth Memory)
84pub struct TpuBuffer {
85    size: usize,
86    device_id: usize,
87}
88
89impl TpuBuffer {
90    /// Allocate TPU buffer
91    pub fn allocate(size: usize, device_id: usize) -> Result<Self> {
92        #[cfg(feature = "tpu")]
93        {
94            // Would use TPU memory allocation API
95            Ok(TpuBuffer { size, device_id })
96        }
97        #[cfg(not(feature = "tpu"))]
98        {
99            let _ = (size, device_id);
100            Err(GhostError::DeviceError("TPU not available".to_string()))
101        }
102    }
103    
104    /// Transfer data to TPU
105    pub fn copy_from_host(&mut self, data: &[f32]) -> Result<()> {
106        #[cfg(feature = "tpu")]
107        {
108            if data.len() * std::mem::size_of::<f32>() > self.size {
109                return Err(GhostError::DeviceError("Buffer too small".to_string()));
110            }
111            // Would use TPU transfer API
112            Ok(())
113        }
114        #[cfg(not(feature = "tpu"))]
115        {
116            let _ = data;
117            Err(GhostError::DeviceError("TPU not available".to_string()))
118        }
119    }
120    
121    /// Transfer data from TPU
122    pub fn copy_to_host(&self, data: &mut [f32]) -> Result<()> {
123        #[cfg(feature = "tpu")]
124        {
125            if data.len() * std::mem::size_of::<f32>() > self.size {
126                return Err(GhostError::DeviceError("Buffer too small".to_string()));
127            }
128            Ok(())
129        }
130        #[cfg(not(feature = "tpu"))]
131        {
132            let _ = data;
133            Err(GhostError::DeviceError("TPU not available".to_string()))
134        }
135    }
136}
137
138/// XLA (Accelerated Linear Algebra) compiler integration
139pub mod xla {
140    use super::*;
141    
142    /// XLA computation graph
143    pub struct XlaComputation {
144        name: String,
145        operations: Vec<XlaOp>,
146    }
147    
148    #[derive(Debug, Clone)]
149    pub enum XlaOp {
150        MatMul { lhs: usize, rhs: usize },
151        Add { lhs: usize, rhs: usize },
152        Conv2D { input: usize, kernel: usize },
153        ReLU { input: usize },
154    }
155    
156    impl XlaComputation {
157        /// Create a new XLA computation
158        pub fn new(name: &str) -> Self {
159            XlaComputation {
160                name: name.to_string(),
161                operations: Vec::new(),
162            }
163        }
164        
165        /// Add operation to computation
166        pub fn add_op(&mut self, op: XlaOp) -> usize {
167            self.operations.push(op);
168            self.operations.len() - 1
169        }
170        
171        /// Compile computation for TPU
172        pub fn compile(&self, device_id: usize) -> Result<CompiledXla> {
173            #[cfg(feature = "tpu")]
174            {
175                // Would use XLA compiler:
176                // xla::XlaBuilder builder(name)
177                // ... build computation ...
178                // xla::Compile(computation, device_id)
179                
180                let _ = device_id;
181                Ok(CompiledXla {
182                    name: self.name.clone(),
183                })
184            }
185            #[cfg(not(feature = "tpu"))]
186            {
187                let _ = device_id;
188                Err(GhostError::DeviceError("TPU not available".to_string()))
189            }
190        }
191    }
192    
193    /// Compiled XLA program
194    pub struct CompiledXla {
195        name: String,
196    }
197    
198    impl CompiledXla {
199        /// Execute compiled program on TPU
200        pub fn execute(&self, inputs: &[Tensor]) -> Result<Vec<Tensor>> {
201            #[cfg(feature = "tpu")]
202            {
203                // Would execute on TPU
204                let _ = inputs;
205                Err(GhostError::NotImplemented("TPU execution".to_string()))
206            }
207            #[cfg(not(feature = "tpu"))]
208            {
209                let _ = inputs;
210                Err(GhostError::DeviceError("TPU not available".to_string()))
211            }
212        }
213    }
214}
215
216/// TPU-optimized operations
217pub mod ops {
218    use super::*;
219    
220    /// Matrix multiplication on TPU
221    pub fn matmul_tpu(a: &Tensor, b: &Tensor, device_id: usize) -> Result<Tensor> {
222        let dims_a = a.dims();
223        let dims_b = b.dims();
224        
225        if dims_a.len() != 2 || dims_b.len() != 2 {
226            return Err(GhostError::InvalidShape("matmul requires 2D tensors".to_string()));
227        }
228        
229        let (m, k) = (dims_a[0], dims_a[1]);
230        let (k2, n) = (dims_b[0], dims_b[1]);
231        
232        if k != k2 {
233            return Err(GhostError::ShapeMismatch {
234                expected: vec![k],
235                got: vec![k2],
236            });
237        }
238        
239        #[cfg(feature = "tpu")]
240        {
241            // Build XLA computation
242            let mut computation = xla::XlaComputation::new("matmul");
243            let input_a = 0;
244            let input_b = 1;
245            let matmul_op = xla::XlaOp::MatMul { lhs: input_a, rhs: input_b };
246            computation.add_op(matmul_op);
247            
248            // Compile for TPU
249            let compiled = computation.compile(device_id)?;
250            
251            // Execute
252            let inputs = vec![a.clone(), b.clone()];
253            let outputs = compiled.execute(&inputs)?;
254            
255            if outputs.is_empty() {
256                return Err(GhostError::DeviceError("TPU execution failed".to_string()));
257            }
258            
259            Ok(outputs[0].clone())
260        }
261        #[cfg(not(feature = "tpu"))]
262        {
263            let _ = device_id;
264            // Fallback to CPU
265            a.matmul(b)
266        }
267    }
268    
269    /// Convolution on TPU
270    pub fn conv2d_tpu(
271        input: &Tensor,
272        kernel: &Tensor,
273        stride: (usize, usize),
274        padding: (usize, usize),
275        device_id: usize,
276    ) -> Result<Tensor> {
277        #[cfg(feature = "tpu")]
278        {
279            // Build XLA convolution
280            let mut computation = xla::XlaComputation::new("conv2d");
281            let input_id = 0;
282            let kernel_id = 1;
283            let conv_op = xla::XlaOp::Conv2D { input: input_id, kernel: kernel_id };
284            computation.add_op(conv_op);
285            
286            let compiled = computation.compile(device_id)?;
287            let inputs = vec![input.clone(), kernel.clone()];
288            let outputs = compiled.execute(&inputs)?;
289            
290            if outputs.is_empty() {
291                return Err(GhostError::DeviceError("TPU execution failed".to_string()));
292            }
293            
294            Ok(outputs[0].clone())
295        }
296        #[cfg(not(feature = "tpu"))]
297        {
298            let _ = (input, kernel, stride, padding, device_id);
299            Err(GhostError::DeviceError("TPU not available".to_string()))
300        }
301    }
302    
303    /// Batch matrix multiplication (optimized for TPU)
304    pub fn batch_matmul_tpu(a: &Tensor, b: &Tensor, device_id: usize) -> Result<Tensor> {
305        let dims_a = a.dims();
306        let dims_b = b.dims();
307        
308        if dims_a.len() != 3 || dims_b.len() != 3 {
309            return Err(GhostError::InvalidShape("batch_matmul requires 3D tensors [B,M,K] x [B,K,N]".to_string()));
310        }
311        
312        let (batch, m, k) = (dims_a[0], dims_a[1], dims_a[2]);
313        let (batch2, k2, n) = (dims_b[0], dims_b[1], dims_b[2]);
314        
315        if batch != batch2 || k != k2 {
316            return Err(GhostError::ShapeMismatch {
317                expected: vec![batch, k],
318                got: vec![batch2, k2],
319            });
320        }
321        
322        #[cfg(feature = "tpu")]
323        {
324            // TPUs are optimized for batch operations
325            let mut computation = xla::XlaComputation::new("batch_matmul");
326            let input_a = 0;
327            let input_b = 1;
328            let matmul_op = xla::XlaOp::MatMul { lhs: input_a, rhs: input_b };
329            computation.add_op(matmul_op);
330            
331            let compiled = computation.compile(device_id)?;
332            let inputs = vec![a.clone(), b.clone()];
333            let outputs = compiled.execute(&inputs)?;
334            
335            if outputs.is_empty() {
336                return Err(GhostError::DeviceError("TPU execution failed".to_string()));
337            }
338            
339            Ok(outputs[0].clone())
340        }
341        #[cfg(not(feature = "tpu"))]
342        {
343            let _ = device_id;
344            // CPU fallback - process each batch element
345            let mut result_data = Vec::with_capacity(batch * m * n);
346            let a_data = a.data_f32();
347            let b_data = b.data_f32();
348            
349            for b_idx in 0..batch {
350                let a_offset = b_idx * m * k;
351                let b_offset = b_idx * k * n;
352                
353                for i in 0..m {
354                    for j in 0..n {
355                        let mut sum = 0.0;
356                        for p in 0..k {
357                            sum += a_data[a_offset + i * k + p] * b_data[b_offset + p * n + j];
358                        }
359                        result_data.push(sum);
360                    }
361                }
362            }
363            
364            Tensor::from_slice(&result_data, &[batch, m, n])
365        }
366    }
367    
368    /// Transformer attention (optimized for TPU)
369    pub fn attention_tpu(
370        query: &Tensor,
371        key: &Tensor,
372        value: &Tensor,
373        device_id: usize,
374    ) -> Result<Tensor> {
375        #[cfg(feature = "tpu")]
376        {
377            // TPUs excel at transformer workloads
378            let _ = (query, key, value, device_id);
379            Err(GhostError::NotImplemented("TPU attention - use CPU fallback".to_string()))
380        }
381        #[cfg(not(feature = "tpu"))]
382        {
383            let _ = (query, key, value, device_id);
384            // CPU fallback: Q @ K^T / sqrt(d_k), then softmax, then @ V
385            let d_k = query.dims()[query.dims().len() - 1] as f32;
386            let key_t = key.t()?;
387            let scores = query.matmul(&key_t)?.div_scalar(d_k.sqrt());
388            let attn_weights = scores.softmax(-1);
389            attn_weights.matmul(value)
390        }
391    }
392}
393
394/// TPU Pod configuration (multi-chip)
395pub struct TpuPod {
396    pub num_chips: usize,
397    pub topology: PodTopology,
398}
399
400#[derive(Debug, Clone, Copy)]
401pub enum PodTopology {
402    /// Single chip
403    Single,
404    /// 2x2 grid (4 chips)
405    Grid2x2,
406    /// 4x4 grid (16 chips)
407    Grid4x4,
408    /// 8x8 grid (64 chips)
409    Grid8x8,
410}
411
412impl TpuPod {
413    /// Create a TPU Pod configuration
414    pub fn new(topology: PodTopology) -> Self {
415        let num_chips = match topology {
416            PodTopology::Single => 1,
417            PodTopology::Grid2x2 => 4,
418            PodTopology::Grid4x4 => 16,
419            PodTopology::Grid8x8 => 64,
420        };
421        
422        TpuPod { num_chips, topology }
423    }
424    
425    /// Get total TFLOPS for the pod
426    pub fn total_tflops(&self, version: TpuVersion) -> f32 {
427        let per_chip = match version {
428            TpuVersion::V2 => 45.0,
429            TpuVersion::V3 => 123.0,
430            TpuVersion::V4 => 275.0,
431            TpuVersion::V5 => 459.0,
432        };
433        
434        per_chip * self.num_chips as f32
435    }
436}
437
438#[cfg(test)]
439mod tests {
440    use super::*;
441    
442    #[test]
443    fn test_tpu_device_count() {
444        let count = TpuDevice::device_count().unwrap_or(0);
445        // Should return 0 if TPU not available
446        assert!(count >= 0);
447    }
448    
449    #[test]
450    fn test_tpu_pod() {
451        let pod = TpuPod::new(PodTopology::Grid2x2);
452        assert_eq!(pod.num_chips, 4);
453        
454        let tflops = pod.total_tflops(TpuVersion::V4);
455        assert_eq!(tflops, 275.0 * 4.0);
456    }
457    
458    #[test]
459    fn test_xla_computation() {
460        let mut comp = xla::XlaComputation::new("test");
461        let op_id = comp.add_op(xla::XlaOp::ReLU { input: 0 });
462        assert_eq!(op_id, 0);
463    }
464}
ghostflow_core/tpu.rs

ghostflow_core/
tpu.rs
