torsh_cli/commands/model/
tensor_integration.rs

1//! Real ToRSh tensor integration for model operations
2//!
3//! This module provides integration with torsh-tensor for real model operations,
4//! replacing mock implementations with actual tensor serialization and operations.
5
6// Infrastructure module - functions designed for CLI command integration
7#![allow(dead_code)]
8
9use anyhow::{Context, Result};
10use std::collections::HashMap;
11use tracing::{debug, info};
12
13// ✅ SciRS2 POLICY COMPLIANT: Use scirs2-core unified access patterns
14use scirs2_core::random::{thread_rng, Distribution, Normal};
15
16// ToRSh tensor integration
17use torsh::core::device::DeviceType;
18use torsh::tensor::Tensor;
19
20use super::types::{DType, Device, LayerInfo, ModelMetadata, TensorInfo, TorshModel};
21
22/// Real tensor wrapper for model weights
23#[derive(Debug, Clone)]
24pub struct ModelTensor {
25    /// Tensor name
26    pub name: String,
27    /// Actual tensor data (f32 for simplicity, can be extended)
28    pub data: Tensor<f32>,
29    /// Whether gradients are required
30    pub requires_grad: bool,
31}
32
33impl ModelTensor {
34    /// Create a new model tensor with random initialization
35    pub fn new_random(
36        name: String,
37        shape: Vec<usize>,
38        requires_grad: bool,
39        device: DeviceType,
40    ) -> Result<Self> {
41        // Use SciRS2 for random initialization
42        let mut rng = thread_rng();
43        let normal = Normal::new(0.0, 0.1).context("Failed to create normal distribution")?;
44
45        let num_elements: usize = shape.iter().product();
46        let data: Vec<f32> = (0..num_elements)
47            .map(|_| normal.sample(&mut rng) as f32)
48            .collect();
49
50        let tensor = Tensor::from_data(data, shape, device)?;
51
52        Ok(Self {
53            name,
54            data: tensor,
55            requires_grad,
56        })
57    }
58
59    /// Create a new model tensor from existing data
60    pub fn from_data(
61        name: String,
62        data: Vec<f32>,
63        shape: Vec<usize>,
64        requires_grad: bool,
65        device: DeviceType,
66    ) -> Result<Self> {
67        let tensor = Tensor::from_data(data, shape, device)?;
68
69        Ok(Self {
70            name,
71            data: tensor,
72            requires_grad,
73        })
74    }
75
76    /// Get the shape of the tensor
77    pub fn shape(&self) -> Vec<usize> {
78        self.data.shape().dims().to_vec()
79    }
80
81    /// Get the number of elements
82    pub fn numel(&self) -> usize {
83        self.shape().iter().product()
84    }
85
86    /// Convert to bytes for serialization
87    pub fn to_bytes(&self) -> Result<Vec<u8>> {
88        // Use torsh-tensor's built-in serialization when available
89        // For now, convert to raw bytes
90        let data_vec: Vec<f32> = self.data.to_vec()?;
91        let mut bytes = Vec::with_capacity(data_vec.len() * 4);
92
93        for value in data_vec {
94            bytes.extend_from_slice(&value.to_le_bytes());
95        }
96
97        Ok(bytes)
98    }
99
100    /// Create from bytes
101    pub fn from_bytes(
102        name: String,
103        bytes: &[u8],
104        shape: Vec<usize>,
105        requires_grad: bool,
106        device: DeviceType,
107    ) -> Result<Self> {
108        let num_elements: usize = shape.iter().product();
109        let expected_bytes = num_elements * 4; // f32 = 4 bytes
110
111        if bytes.len() != expected_bytes {
112            anyhow::bail!(
113                "Byte length mismatch: expected {}, got {}",
114                expected_bytes,
115                bytes.len()
116            );
117        }
118
119        let mut data = Vec::with_capacity(num_elements);
120        for chunk in bytes.chunks_exact(4) {
121            let value = f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]);
122            data.push(value);
123        }
124
125        Self::from_data(name, data, shape, requires_grad, device)
126    }
127}
128
129/// Create a realistic model with actual tensor operations
130pub fn create_real_model(name: &str, num_layers: usize, device: DeviceType) -> Result<TorshModel> {
131    info!("Creating real model '{}' with {} layers", name, num_layers);
132
133    let mut layers = Vec::new();
134    let mut weights = HashMap::new();
135
136    let mut input_dim = 784; // MNIST-like input
137    let mut output_dim = 512;
138
139    for i in 0..num_layers {
140        let layer_name = format!("layer_{}", i);
141        let is_last = i == num_layers - 1;
142
143        if is_last {
144            output_dim = 10; // Classification output
145        }
146
147        // Create layer info
148        let layer = LayerInfo {
149            name: layer_name.clone(),
150            layer_type: "Linear".to_string(),
151            input_shape: vec![input_dim],
152            output_shape: vec![output_dim],
153            parameters: (input_dim * output_dim + output_dim) as u64,
154            trainable: true,
155            config: HashMap::new(),
156        };
157
158        // Create real weight tensor using torsh-tensor
159        let weight_name = format!("{}.weight", layer_name);
160        let weight_tensor = ModelTensor::new_random(
161            weight_name.clone(),
162            vec![output_dim, input_dim],
163            true,
164            device,
165        )?;
166
167        // Create real bias tensor
168        let bias_name = format!("{}.bias", layer_name);
169        let bias_tensor =
170            ModelTensor::new_random(bias_name.clone(), vec![output_dim], true, device)?;
171
172        // Convert to TensorInfo for storage
173        let weight_info = TensorInfo {
174            name: weight_name.clone(),
175            shape: weight_tensor.shape(),
176            dtype: DType::F32,
177            requires_grad: weight_tensor.requires_grad,
178            device: Device::Cpu, // Map DeviceType to Device
179        };
180
181        let bias_info = TensorInfo {
182            name: bias_name.clone(),
183            shape: bias_tensor.shape(),
184            dtype: DType::F32,
185            requires_grad: bias_tensor.requires_grad,
186            device: Device::Cpu,
187        };
188
189        layers.push(layer);
190        weights.insert(weight_name, weight_info);
191        weights.insert(bias_name, bias_info);
192
193        input_dim = output_dim;
194        output_dim = if is_last { 10 } else { output_dim / 2 };
195    }
196
197    let mut metadata = ModelMetadata::default();
198    metadata.format = "torsh".to_string();
199    metadata.version = "0.1.0".to_string();
200    metadata.description = Some(format!("Real {} layer model with torsh-tensor", num_layers));
201    metadata.tags = vec!["real".to_string(), "torsh-tensor".to_string()];
202
203    Ok(TorshModel {
204        layers,
205        weights,
206        metadata,
207    })
208}
209
210/// Perform real tensor operations for model inference
211pub fn forward_pass(model: &TorshModel, _input: &Tensor<f32>) -> Result<Tensor<f32>> {
212    debug!("Performing forward pass through model");
213
214    // For now, return a simple placeholder
215    // In real implementation, this would iterate through layers and apply operations
216    let output_shape = model
217        .layers
218        .last()
219        .map(|l| l.output_shape.clone())
220        .unwrap_or_else(|| vec![10]);
221
222    Ok(Tensor::zeros(output_shape.as_slice(), DeviceType::Cpu)?)
223}
224
225/// Calculate real memory usage of model tensors
226pub fn calculate_real_memory_usage(tensors: &[ModelTensor]) -> usize {
227    tensors.iter().map(|t| t.numel() * 4).sum() // f32 = 4 bytes
228}
229
230/// Validate tensor shapes match layer configurations
231pub fn validate_tensor_shapes(model: &TorshModel) -> Result<()> {
232    for layer in &model.layers {
233        let weight_name = format!("{}.weight", layer.name);
234
235        if let Some(weight_info) = model.weights.get(&weight_name) {
236            // Validate weight shape matches layer configuration
237            if !layer.output_shape.is_empty() && !weight_info.shape.is_empty() {
238                let expected_output = layer.output_shape[0];
239                let actual_output = weight_info.shape[0];
240
241                if expected_output != actual_output {
242                    anyhow::bail!(
243                        "Layer {} weight shape mismatch: expected output {}, got {}",
244                        layer.name,
245                        expected_output,
246                        actual_output
247                    );
248                }
249            }
250        }
251    }
252
253    Ok(())
254}
255
256/// Initialize layer weights with Xavier/Glorot initialization
257pub fn xavier_init(input_dim: usize, output_dim: usize, device: DeviceType) -> Result<Tensor<f32>> {
258    let mut rng = thread_rng();
259
260    // Xavier initialization: scale = sqrt(2 / (input_dim + output_dim))
261    let scale = (2.0 / (input_dim + output_dim) as f64).sqrt();
262    let normal = Normal::new(0.0, scale)?;
263
264    let num_elements = input_dim * output_dim;
265    let data: Vec<f32> = (0..num_elements)
266        .map(|_| normal.sample(&mut rng) as f32)
267        .collect();
268
269    Ok(Tensor::from_data(
270        data,
271        vec![output_dim, input_dim],
272        device,
273    )?)
274}
275
276/// Initialize layer bias with zeros
277pub fn zero_bias_init(output_dim: usize, device: DeviceType) -> Result<Tensor<f32>> {
278    Ok(Tensor::zeros(&[output_dim], device)?)
279}
280
281/// Estimate FLOPs for a tensor operation
282pub fn estimate_tensor_flops(
283    operation: &str,
284    input_shape: &[usize],
285    output_shape: &[usize],
286) -> u64 {
287    match operation {
288        "linear" | "matmul" => {
289            // Matrix multiplication: 2 * M * N * K (M = batch, N = output, K = input)
290            let input_size: u64 = input_shape.iter().map(|&x| x as u64).product();
291            let output_size: u64 = output_shape.iter().map(|&x| x as u64).product();
292            2 * input_size * output_size
293        }
294        "relu" | "sigmoid" | "tanh" => {
295            // Activation: 1 op per element
296            output_shape.iter().map(|&x| x as u64).product()
297        }
298        "conv2d" => {
299            // Simplified convolution estimate
300            let output_size: u64 = output_shape.iter().map(|&x| x as u64).product();
301            output_size * 9 // Assuming 3x3 kernel
302        }
303        _ => {
304            // Default: assume element-wise operation
305            output_shape.iter().map(|&x| x as u64).product()
306        }
307    }
308}
309
310/// Perform numerical gradient checking
311pub fn gradient_check(_model: &TorshModel, _input: &Tensor<f32>, epsilon: f32) -> Result<bool> {
312    debug!("Performing gradient check with epsilon = {}", epsilon);
313
314    // Simplified gradient checking
315    // In real implementation, would compute numerical gradients and compare with autograd
316
317    // For now, always return true (placeholder)
318    Ok(true)
319}
320
321/// Calculate model statistics using real tensors
322pub fn calculate_tensor_statistics(tensors: &[ModelTensor]) -> HashMap<String, f64> {
323    let mut stats = HashMap::new();
324
325    let total_params: usize = tensors.iter().map(|t| t.numel()).sum();
326    let memory_mb = total_params as f64 * 4.0 / (1024.0 * 1024.0);
327
328    stats.insert("total_parameters".to_string(), total_params as f64);
329    stats.insert("memory_mb".to_string(), memory_mb);
330    stats.insert("num_tensors".to_string(), tensors.len() as f64);
331
332    stats
333}
334
335#[cfg(test)]
336mod tests {
337    use super::*;
338
339    #[test]
340    fn test_model_tensor_creation() {
341        let tensor =
342            ModelTensor::new_random("test".to_string(), vec![10, 20], true, DeviceType::Cpu)
343                .expect("operation should succeed");
344
345        assert_eq!(tensor.shape(), vec![10, 20]);
346        assert_eq!(tensor.numel(), 200);
347        assert!(tensor.requires_grad);
348    }
349
350    #[test]
351    fn test_real_model_creation() {
352        let model = create_real_model("test_model", 3, DeviceType::Cpu)
353            .expect("create real model should succeed");
354
355        assert_eq!(model.layers.len(), 3);
356        assert!(model.weights.len() >= 6); // At least 3 layers * 2 (weight + bias)
357    }
358
359    #[test]
360    fn test_tensor_serialization() {
361        let tensor = ModelTensor::new_random("test".to_string(), vec![5, 5], true, DeviceType::Cpu)
362            .expect("operation should succeed");
363
364        let bytes = tensor.to_bytes().expect("byte conversion should succeed");
365        assert_eq!(bytes.len(), 25 * 4); // 25 elements * 4 bytes per f32
366
367        let reconstructed = ModelTensor::from_bytes(
368            "test".to_string(),
369            &bytes,
370            vec![5, 5],
371            true,
372            DeviceType::Cpu,
373        )
374        .expect("operation should succeed");
375
376        assert_eq!(reconstructed.shape(), tensor.shape());
377    }
378
379    #[test]
380    fn test_xavier_initialization() {
381        let tensor = xavier_init(100, 50, DeviceType::Cpu).expect("xavier init should succeed");
382        assert_eq!(tensor.shape().dims(), &[50, 100]);
383    }
384
385    #[test]
386    fn test_flops_estimation() {
387        let input_shape = vec![128, 784];
388        let output_shape = vec![128, 512];
389
390        let flops = estimate_tensor_flops("linear", &input_shape, &output_shape);
391        assert!(flops > 0);
392    }
393}
torsh_cli/commands/model/tensor_integration.rs

torsh_cli/commands/model/
tensor_integration.rs
