tenflowers-core 0.1.1

Core tensor operations and execution engine for TenfloweRS
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166

pub mod activation;
pub mod advanced_math;
pub mod async_binary;
pub mod basic;
pub mod benchmark;
pub mod binary;
pub mod comparison;
pub mod conv;
pub mod einsum;
pub mod fft;
pub mod framework_comparison;
pub mod fusion;
#[cfg(feature = "gpu")]
pub mod gpu_tensorflow_benchmark;
#[cfg(feature = "gpu")]
pub mod hybrid_scheduler;
pub mod lapack;
#[cfg(feature = "blas")]
pub mod lapack_f32;
#[cfg(feature = "blas")]
pub mod lapack_f64;
pub mod linalg;
pub mod logical;
pub mod manipulation;
pub mod matmul;
pub mod normalization;
pub mod numpy_array_creation;
pub mod numpy_compat;
pub mod optimized_binary;
pub mod optimized_matmul_v2;
pub mod performance_benchmark;
pub mod pooling;
pub mod random;
pub mod reduction;
pub mod registry;
pub mod registry_extensions;
pub mod shape_inference;
pub mod shape_inference_registry;
pub mod special;
pub mod stats;
pub mod ultra_matmul_v3;
pub mod ultra_performance_matmul;
pub mod unary;
pub mod unified_dispatch;

#[cfg(all(test, feature = "gpu"))]
mod cpu_gpu_overlap_test;

pub use activation::{
    elu, gelu, hard_swish, hardswish, leaky_relu, log_softmax, mish, prelu, relu, relu6, sigmoid,
    softmax, swish, tanh,
};
pub use advanced_math::{
    expit, gelu_tanh, hard_sigmoid, log_sigmoid, logit, logsumexp, selu, softplus, softsign,
};
pub use async_binary::{
    add_async, add_async_priority, batch_add_async, batch_mul_async, div_async,
    is_async_operations_idle, mul_async, mul_async_priority, pow_async, prelu_async, sub_async,
    synchronize_async_operations,
};
pub use basic::{add, clamp, div, mul, scalar_add, sub};
pub use binary::pow;
pub use comparison::{eq, ge, gt, le, lt, ne};
pub use conv::{conv1d, conv2d, conv3d, depthwise_conv2d};
pub use einsum::einsum;
pub use fft::{
    fft, fft2, fft2_bf16, fft2_f16, fft2_inplace, fft3, fft3_inplace, fft_bf16, fft_f16,
    fft_inplace, ifft, ifft2, ifft2_bf16, ifft2_f16, ifft2_inplace, ifft3, ifft3_inplace,
    ifft_bf16, ifft_f16, ifft_inplace, rfft,
};
pub use framework_comparison::{
    print_framework_comparison_results, run_framework_comparison_benchmark,
    FrameworkBenchmarkConfig, FrameworkComparisonResult,
};
pub use fusion::{
    execute_fused_graph, get_fusion_stats, print_fusion_report, record_fusion_opportunity,
    reset_fusion_stats, ElementwiseOpType, FusionGraph, FusionNode, FusionPassBuilder, FusionStats,
};
#[cfg(feature = "gpu")]
pub use gpu_tensorflow_benchmark::{
    run_quick_gpu_tensorflow_benchmark, GpuBenchmarkConfig, GpuBenchmarkResult,
    GpuTensorFlowBenchmark,
};
pub use lapack::{
    cholesky_lapack, determinant_lapack, eigenvalues_lapack, inverse_lapack, is_lapack_available,
    lapack_provider, lstsq_lapack, lu_decompose_lapack, matmul_blas, qr_lapack, solve_lapack,
    svd_lapack,
};
pub use linalg::{cholesky, det, eig, inv, lu, svd};
pub use logical::{logical_and, logical_not, logical_or, logical_xor};
pub use manipulation::{
    // Shape operations
    broadcast_to,
    // Utility operations (extracted to utilities.rs)
    cast,
    // Concatenation operations (extracted to concatenation.rs)
    concat,
    expand_as,
    expand_dims,
    flatten,
    // Transposition operations (extracted to transpose.rs)
    flip,
    // Indexing operations (extracted to indexing.rs)
    gather,
    identity,
    one_hot,
    pad,
    repeat,
    reshape,
    roll,
    scatter,
    select,
    slice,
    slice_with_stride,
    split,
    squeeze,
    stack,
    tile,
    transpose,
    unsqueeze,
    where_op,
};
pub use matmul::{batch_matmul, dot, matmul, outer};
pub use normalization::{batch_norm, group_norm, layer_norm};
pub use numpy_array_creation::{
    arange, diag, diagonal, eye, from_range, fromfunction, full, geomspace,
    identity as np_identity, linspace, logspace, meshgrid, ones, tri, tril, triu, zeros,
};
pub use numpy_compat::{
    absolute, apply_ufunc, arccos, arccosh, arcsin, arcsinh, arctan, arctan2, arctanh, ceil, cos,
    cosh, create_f32_ufunc_registry, exp, exp2, expm1, fix, floor, fmax, fmin, fmod, isfinite,
    isinf, isnan, list_ufuncs, log, log10, log1p, log2, maximum, minimum, modulo, negative,
    numpy_broadcast_arrays, reciprocal, remainder, rint, sign, signbit, sin, sinh, sqrt, square,
    tan, tanh as np_tanh, trunc, UfuncRegistry,
};
pub use optimized_matmul_v2::ultra_matmul_v2;
pub use pooling::{
    adaptive_avg_pool2d, adaptive_max_pool2d, avg_pool2d, avg_pool3d, fractional_avg_pool2d,
    fractional_max_pool2d, global_avg_pool2d, global_avg_pool3d, global_max_pool2d,
    global_max_pool3d, max_pool2d, max_pool3d, roi_align2d, roi_pool2d,
};
pub use random::{
    multinomial_f32, rand_f32, rand_f64, randn_f32, randn_f64, random_normal_f32,
    random_normal_f32_device, random_normal_f64, random_uniform_f32, random_uniform_f32_device,
    random_uniform_f64, random_uniform_int,
};
pub use reduction::{argmax, argmin, cumprod, cumsum, max, mean, min, sum, topk};
pub use shape_inference::{
    infer_binary_elementwise, infer_binary_elementwise_validated, infer_concat, infer_conv2d,
    infer_matmul, infer_reduction, infer_reshape, BroadcastableConstraint, ExactShapeConstraint,
    MatMulCompatibleConstraint, MinRankConstraint, RankConstraint, ShapeConstraint, ShapeContext,
    ShapeValidator,
};
pub use special::{
    bessel_j0, bessel_j1, bessel_y0, bessel_y1, digamma, erf, erfc, gamma, lgamma, smooth_l1_loss,
};
pub use stats::{
    correlation, covariance, histogram, kurtosis, median, moment, percentile, quantile, range,
    skewness,
};
pub use ultra_matmul_v3::{
    clear_performance_analytics, configure_ultra_performance, get_performance_analytics,
    ultra_matmul_v3, UltraPerformanceConfig,
};
pub use ultra_performance_matmul::ultra_matmul;
pub use unary::{abs, neg};