ghostflow_cuda/
ops.rs

1//! CUDA tensor operations
2
3#![cfg_attr(not(feature = "cuda"), allow(unused_variables))]
4
5use crate::error::{CudaError, CudaResult};
6use crate::tensor::CudaTensor;
7use crate::stream::CudaStream;
8
9/// Element-wise operations
10pub mod elementwise {
11    use super::*;
12
13    /// Add two tensors
14    pub fn add(a: &CudaTensor, b: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
15        #[cfg(feature = "cuda")]
16        {
17            // Use CUDA kernel for element-wise addition
18            // In real implementation: launch optimized CUDA kernel
19            // For now, fall back to CPU for safety
20            // TODO: Integrate with optimized_kernels.cu
21        }
22        
23        // CPU fallback
24        let a_cpu = a.to_tensor()?;
25        let b_cpu = b.to_tensor()?;
26        let result = a_cpu.add(&b_cpu)
27            .map_err(|e| CudaError::InvalidValue(e.to_string()))?;
28        CudaTensor::from_tensor(&result, a.device_id())
29    }
30
31    /// Subtract two tensors
32    pub fn sub(a: &CudaTensor, b: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
33        let a_cpu = a.to_tensor()?;
34        let b_cpu = b.to_tensor()?;
35        let result = a_cpu.sub(&b_cpu)
36            .map_err(|e| CudaError::InvalidValue(e.to_string()))?;
37        CudaTensor::from_tensor(&result, a.device_id())
38    }
39
40    /// Multiply two tensors
41    pub fn mul(a: &CudaTensor, b: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
42        let a_cpu = a.to_tensor()?;
43        let b_cpu = b.to_tensor()?;
44        let result = a_cpu.mul(&b_cpu)
45            .map_err(|e| CudaError::InvalidValue(e.to_string()))?;
46        CudaTensor::from_tensor(&result, a.device_id())
47    }
48
49    /// Divide two tensors
50    pub fn div(a: &CudaTensor, b: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
51        let a_cpu = a.to_tensor()?;
52        let b_cpu = b.to_tensor()?;
53        let result = a_cpu.div(&b_cpu)
54            .map_err(|e| CudaError::InvalidValue(e.to_string()))?;
55        CudaTensor::from_tensor(&result, a.device_id())
56    }
57
58    /// ReLU activation
59    pub fn relu(x: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
60        let x_cpu = x.to_tensor()?;
61        let result = x_cpu.relu();
62        CudaTensor::from_tensor(&result, x.device_id())
63    }
64
65    /// Sigmoid activation
66    pub fn sigmoid(x: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
67        let x_cpu = x.to_tensor()?;
68        let result = x_cpu.sigmoid();
69        CudaTensor::from_tensor(&result, x.device_id())
70    }
71
72    /// GELU activation
73    pub fn gelu(x: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
74        let x_cpu = x.to_tensor()?;
75        let result = x_cpu.gelu();
76        CudaTensor::from_tensor(&result, x.device_id())
77    }
78
79    /// Softmax
80    pub fn softmax(x: &CudaTensor, dim: i32, stream: &CudaStream) -> CudaResult<CudaTensor> {
81        let x_cpu = x.to_tensor()?;
82        let result = x_cpu.softmax(dim);
83        CudaTensor::from_tensor(&result, x.device_id())
84    }
85}
86
87/// Matrix operations
88pub mod matmul {
89    use super::*;
90
91    /// Matrix multiplication using cuBLAS
92    pub fn matmul(a: &CudaTensor, b: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
93        // In real implementation: use cublasSgemm or cublasGemmEx
94        // For now, fall back to CPU
95        let a_cpu = a.to_tensor()?;
96        let b_cpu = b.to_tensor()?;
97        let result = a_cpu.matmul(&b_cpu)
98            .map_err(|e| CudaError::InvalidValue(e.to_string()))?;
99        CudaTensor::from_tensor(&result, a.device_id())
100    }
101
102    /// Batched matrix multiplication
103    pub fn bmm(a: &CudaTensor, b: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
104        // In real implementation: use cublasSgemmBatched
105        let a_cpu = a.to_tensor()?;
106        let b_cpu = b.to_tensor()?;
107        let result = a_cpu.matmul(&b_cpu)
108            .map_err(|e| CudaError::InvalidValue(e.to_string()))?;
109        CudaTensor::from_tensor(&result, a.device_id())
110    }
111}
112
113/// Reduction operations
114pub mod reduction {
115    use super::*;
116
117    /// Sum all elements
118    pub fn sum(x: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
119        let x_cpu = x.to_tensor()?;
120        let result = x_cpu.sum();
121        CudaTensor::from_tensor(&result, x.device_id())
122    }
123
124    /// Mean of all elements
125    pub fn mean(x: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
126        let x_cpu = x.to_tensor()?;
127        let result = x_cpu.mean();
128        CudaTensor::from_tensor(&result, x.device_id())
129    }
130
131    /// Max element
132    pub fn max(x: &CudaTensor, stream: &CudaStream) -> CudaResult<CudaTensor> {
133        let x_cpu = x.to_tensor()?;
134        let result = x_cpu.max();
135        CudaTensor::from_tensor(&result, x.device_id())
136    }
137}
138
139/// Convolution operations
140pub mod conv {
141    use super::*;
142
143    /// 2D convolution using cuDNN
144    pub fn conv2d(
145        input: &CudaTensor,
146        weight: &CudaTensor,
147        bias: Option<&CudaTensor>,
148        stride: (usize, usize),
149        padding: (usize, usize),
150        stream: &CudaStream,
151    ) -> CudaResult<CudaTensor> {
152        // In real implementation: use cudnnConvolutionForward
153        // This is a placeholder
154        Err(CudaError::InvalidValue("Conv2d not yet implemented".into()))
155    }
156}
157
158/// Attention operations (Flash Attention)
159pub mod attention {
160    use super::*;
161
162    /// Flash Attention forward pass
163    /// 
164    /// Implements the memory-efficient attention algorithm from
165    /// "FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness"
166    pub fn flash_attention(
167        q: &CudaTensor,  // [batch, heads, seq_len, head_dim]
168        k: &CudaTensor,
169        v: &CudaTensor,
170        scale: f32,
171        causal: bool,
172        stream: &CudaStream,
173    ) -> CudaResult<CudaTensor> {
174        // In real implementation: custom CUDA kernel with tiling
175        // For now, fall back to standard attention on CPU
176        
177        let q_cpu = q.to_tensor()?;
178        let k_cpu = k.to_tensor()?;
179        let v_cpu = v.to_tensor()?;
180        
181        // Standard attention: softmax(QK^T / sqrt(d)) * V
182        let k_t = k_cpu.transpose(2, 3)
183            .map_err(|e| CudaError::InvalidValue(e.to_string()))?;
184        
185        let scores = q_cpu.matmul(&k_t)
186            .map_err(|e| CudaError::InvalidValue(e.to_string()))?;
187        
188        let scaled = scores.mul_scalar(scale);
189        
190        // Apply causal mask if needed
191        let masked = if causal {
192            apply_causal_mask(&scaled)?
193        } else {
194            scaled
195        };
196        
197        let attn_weights = masked.softmax(-1);
198        
199        let output = attn_weights.matmul(&v_cpu)
200            .map_err(|e| CudaError::InvalidValue(e.to_string()))?;
201        
202        CudaTensor::from_tensor(&output, q.device_id())
203    }
204
205    fn apply_causal_mask(scores: &ghostflow_core::Tensor) -> CudaResult<ghostflow_core::Tensor> {
206        let dims = scores.dims();
207        let seq_len = dims[dims.len() - 1];
208        let mut data = scores.data_f32();
209        
210        // Apply -inf to upper triangle
211        let batch_size: usize = dims[..dims.len()-2].iter().product();
212        let matrix_size = seq_len * seq_len;
213        
214        for b in 0..batch_size {
215            for i in 0..seq_len {
216                for j in (i + 1)..seq_len {
217                    data[b * matrix_size + i * seq_len + j] = f32::NEG_INFINITY;
218                }
219            }
220        }
221        
222        ghostflow_core::Tensor::from_slice(&data, dims)
223            .map_err(|e| CudaError::InvalidValue(e.to_string()))
224    }
225}
ghostflow_cuda/ops.rs

ghostflow_cuda/
ops.rs
