TensorLogic CLI

Command-line interface for TensorLogic compilation

A comprehensive command-line tool for compiling logical expressions to tensor graphs using TensorLogic.

Features

• 🚀 Multiple Modes: Interactive REPL, batch processing, watch mode
• 📝 Rich Input Formats: Expression strings, JSON, YAML, stdin
• 📊 Multiple Output Formats: Graph, DOT, JSON, statistics
• ⚙️ 6 Compilation Strategies: Differentiable, Boolean, fuzzy logic variants
• 🎨 Colored Output: Beautiful terminal output with status indicators
• 🔍 Graph Analysis: Complexity metrics, FLOP estimation, memory analysis
• 📁 Configuration Files: Persistent settings via .tensorlogicrc
• 🔄 File Watching: Auto-recompilation on file changes
• 📦 Batch Processing: Process multiple expressions with progress bars
• 🐚 Shell Completion: Bash, Zsh, Fish, PowerShell support
• 🎯 Enhanced Parser: Arithmetic, comparisons, conditionals, Unicode operators

Installation

From crates.io

cargo install tensorlogic-cli

From source

git clone https://github.com/cool-japan/tensorlogic.git
cd tensorlogic
cargo install --path crates/tensorlogic-cli

Development build

cargo build -p tensorlogic-cli --release
# Binary at target/release/tensorlogic

Quick Start

Basic Compilation

tensorlogic "knows(x, y)"

Interactive REPL

tensorlogic repl

TensorLogic Interactive REPL
Type '.help' for available commands, '.exit' to quit

tensorlogic> knows(x, y) AND likes(y, z)
✓ Compilation successful
  3 tensors, 3 nodes, depth 2

Batch Processing

# expressions.txt contains one expression per line
tensorlogic batch expressions.txt

Watch Mode

tensorlogic watch my_expression.tl

Usage

Command Structure

tensorlogic [OPTIONS] <INPUT>
tensorlogic <SUBCOMMAND>

Main Options

Subcommands

repl - Interactive REPL

Start an interactive Read-Eval-Print Loop for exploring TensorLogic.

tensorlogic repl

REPL Commands:

• .help - Show help
• .exit - Exit REPL
• .clear - Clear screen
• .context - Show compiler context
• .domain <name> <size> - Add domain
• .strategy [name] - Show/set strategy
• .validate - Toggle validation
• .debug - Toggle debug mode
• .history - Show command history

batch - Batch Processing

Process multiple expressions from files (one expression per line).

tensorlogic batch file1.txt file2.txt

Features:

• Progress bar with elapsed time
• Summary statistics (successes/failures)
• Line-by-line error reporting
• Comments support (# prefix)

watch - File Watching

Watch a file and recompile on changes.

tensorlogic watch expression.tl

Features:

• Auto-recompilation on file save
• Debounced updates (configurable)
• Clear screen on reload (configurable)
• Timestamp display

completion - Shell Completion

Generate shell completion scripts.

# Bash
tensorlogic completion bash > /etc/bash_completion.d/tensorlogic

# Zsh
tensorlogic completion zsh > ~/.zsh/completion/_tensorlogic

# Fish
tensorlogic completion fish > ~/.config/fish/completions/tensorlogic.fish

# PowerShell
tensorlogic completion powershell > tensorlogic.ps1

config - Configuration Management

Manage configuration files.

# Show current configuration
tensorlogic config show

# Show config file path
tensorlogic config path

# Initialize default config
tensorlogic config init

# Edit configuration
tensorlogic config edit

Configuration File

TensorLogic CLI supports persistent configuration via .tensorlogicrc (TOML format).

Search order:

1. TENSORLOGIC_CONFIG environment variable
2. .tensorlogicrc in current directory
3. .tensorlogicrc in home directory

Example configuration:

# Default compilation strategy
strategy = "soft_differentiable"

# Enable colored output
colored = true

# Enable validation by default
validate = false

# Default domains
[domains]
Person = 100
City = 50

# REPL settings
[repl]
prompt = "tensorlogic> "
history_file = ".tensorlogic_history"
max_history = 1000
auto_save = true

# Watch mode settings
[watch]
debounce_ms = 500
clear_screen = true
show_timestamps = true

Initialize Configuration

tensorlogic config init

Input Formats

1. Expression String (Default)

Direct expression input with enhanced syntax:

tensorlogic "knows(x, y) AND likes(y, z)"

Supported syntax:

• Predicates: pred(x, y, ...)
• Logical: AND (&, &&, ∧), OR (|, ||, ∨), NOT (~, !, ¬), IMPLIES (->, =>, →)
• Quantifiers: EXISTS x IN Domain. expr (∃), FORALL x IN Domain. expr (∀)
• Arithmetic: +, -, * (×), / (÷)
• Comparisons: = (==), <, >, <= (≤), >= (≥), != (≠)
• Conditional: IF cond THEN x ELSE y
• Parentheses: (...) for grouping

Examples:

# Basic predicate
tensorlogic "person(x)"

# Logical operations
tensorlogic "p(x) AND q(y) OR r(z)"

# Quantifiers
tensorlogic "EXISTS x IN Person. knows(x, alice)"
tensorlogic "FORALL x IN Person. likes(x, pizza)"

# Arithmetic
tensorlogic "age(x) + 10"

# Comparisons
tensorlogic "age(x) > 18"

# Conditional
tensorlogic "IF age(x) >= 18 THEN adult(x) ELSE child(x)"

# Complex expression
tensorlogic "(p(x) OR q(y)) AND (r(z) -> s(w))"

2. JSON Input

tensorlogic --input-format json expression.json

{
  "And": {
    "left": {
      "Pred": {
        "name": "knows",
        "args": [{"Var": "x"}, {"Var": "y"}]
      }
    },
    "right": {
      "Pred": {
        "name": "likes",
        "args": [{"Var": "y"}, {"Var": "z"}]
      }
    }
  }
}

3. YAML Input

tensorlogic --input-format yaml expression.yaml

And:
  left:
    Pred:
      name: knows
      args:
        - Var: x
        - Var: y
  right:
    Pred:
      name: likes
      args:
        - Var: y
        - Var: z

4. Stdin Input

echo '{"Pred": {"name": "test", "args": []}}' | tensorlogic --input-format json -

Output Formats

1. Graph (Default)

Human-readable graph structure:

tensorlogic "knows(x, y)" --output-format graph

2. DOT Format

Generate Graphviz DOT for visualization:

tensorlogic "knows(x, y)" --output-format dot > graph.dot
dot -Tpng graph.dot -o graph.png

3. JSON

Machine-readable JSON output:

tensorlogic "knows(x, y)" --output-format json

4. Statistics

Graph statistics and metrics:

tensorlogic "knows(x, y) AND likes(y, z)" --output-format stats

Output:

Graph Statistics:
  Tensors: 3
  Nodes: 3
  Inputs: 2
  Outputs: 1
  Depth: 2
  Avg Fanout: 1.00

Operation Breakdown:
  Einsum: 2
  ElemBinary: 1

Estimated Complexity:
  FLOPs: 6000
  Memory: 24000 bytes

Compilation Strategies

Choose from 6 preset strategies:

1. Soft Differentiable (Default)

For neural network training with smooth gradients:

tensorlogic --strategy soft_differentiable "p AND q"

• AND: Element-wise product
• OR: Probabilistic sum
• NOT: Complement (1 - x)

2. Hard Boolean

For discrete Boolean logic:

tensorlogic --strategy hard_boolean "p AND q"

• AND: Minimum
• OR: Maximum
• NOT: Complement

3. Fuzzy Gödel

Gödel fuzzy logic (min/max operations):

tensorlogic --strategy fuzzy_godel "p AND q"

4. Fuzzy Product

Product fuzzy logic (probabilistic):

tensorlogic --strategy fuzzy_product "p AND q"

5. Fuzzy Łukasiewicz

Łukasiewicz fuzzy logic (bounded):

tensorlogic --strategy fuzzy_lukasiewicz "p AND q"

6. Probabilistic

Probabilistic interpretation:

tensorlogic --strategy probabilistic "p AND q"

Domain Definitions

Define domains for variables:

tensorlogic --domain Person:100 --domain City:50 "lives_in(x, c)"

Multiple domains:

tensorlogic \
  --domain Person:100 \
  --domain Location:50 \
  --domain Event:20 \
  "attends(p, e) AND located_at(e, l)"

Graph Analysis

Enable detailed analysis with --analyze:

tensorlogic "complex(expression)" --analyze

Output includes:

• Tensor and node counts
• Graph depth (longest path)
• Average fanout (outputs per node)
• Operation breakdown (Einsum, element-wise, reduction)
• Estimated FLOPs (computational cost)
• Estimated memory usage

Validation

Enable graph validation:

tensorlogic "knows(x, y)" --validate

Checks:

• Free variable consistency
• Arity validation
• Type checking
• Graph structure validity

Debug Mode

Enable detailed debug output:

tensorlogic "knows(x, y)" --debug

Shows:

• Parsed expression (AST)
• Compiler context (domains, strategies)
• Intermediate compilation steps
• Final graph structure
• Validation results

Examples

Example 1: Simple Predicate

tensorlogic "knows(alice, bob)"

Example 2: Logical Conjunction

tensorlogic "knows(x, y) AND likes(y, z)"

Example 3: Quantifier

tensorlogic --domain Person:100 "EXISTS x IN Person. knows(x, bob)"

Example 4: Implication

tensorlogic "knows(x, y) -> likes(x, y)"

Example 5: Arithmetic and Comparison

tensorlogic "age(x) + 10 > 30"

Example 6: Conditional Expression

tensorlogic "IF age(x) >= 18 THEN adult(x) ELSE child(x)"

Example 7: Visualization

tensorlogic "knows(x, y) AND likes(y, z)" \
  --output-format dot \
  --validate > graph.dot
dot -Tpng graph.dot -o graph.png

Example 8: Complex Expression with Analysis

tensorlogic \
  "FORALL x IN Person. (knows(x, y) -> likes(x, y))" \
  --domain Person:100 \
  --strategy fuzzy_godel \
  --output-format stats \
  --analyze \
  --validate

Example 9: Batch Processing with Progress

cat > expressions.txt << EOF
knows(x, y)
likes(y, z)
knows(x, y) AND likes(y, z)
EXISTS x. knows(x, bob)
FORALL x. person(x) -> mortal(x)
EOF

tensorlogic batch expressions.txt

Example 10: Interactive REPL Session

tensorlogic repl

tensorlogic> .domain Person 100
✓ Added domain 'Person' with size 100

tensorlogic> .strategy fuzzy_godel
✓ Strategy set to: fuzzy_godel

tensorlogic> EXISTS x IN Person. knows(x, alice)
✓ Compilation successful
  2 tensors, 2 nodes, depth 2

tensorlogic> .history
   1: .domain Person 100
   2: .strategy fuzzy_godel
   3: EXISTS x IN Person. knows(x, alice)

tensorlogic> .exit

Integration

With Graphviz

Generate PNG visualization:

tensorlogic "knows(x, y)" --output-format dot | dot -Tpng -o graph.png

With jq (JSON Processing)

Extract tensor count:

tensorlogic "knows(x, y)" --output-format json | jq '.tensors | length'

In Scripts

#!/bin/bash
EXPR="knows(x, y) AND likes(y, z)"

if tensorlogic "$EXPR" --output-format stats --validate --quiet; then
    echo "✓ Compilation successful"
else
    echo "✗ Compilation failed"
    exit 1
fi

With Make

.PHONY: compile watch

compile:
	tensorlogic expression.tl --validate --analyze

watch:
	tensorlogic watch expression.tl

Troubleshooting

Command Not Found

Make sure ~/.cargo/bin is in your PATH:

export PATH="$HOME/.cargo/bin:$PATH"

Parsing Errors

Use --debug to see detailed parsing information:

tensorlogic "your expression" --debug

Validation Failures

Check free variables and domains:

tensorlogic "EXISTS x. p(x, y)" \
  --domain Domain:10 \
  --debug \
  --validate

Configuration Issues

Show current configuration:

tensorlogic config show

Show config file path:

tensorlogic config path

Reinitialize configuration:

tensorlogic config init

Environment Variables

Example:

export TENSORLOGIC_CONFIG=~/.config/tensorlogic.toml
export EDITOR=nano
tensorlogic config edit

Performance Tips

1. Use batch mode for multiple expressions
2. Enable validation only when needed
3. Use quiet mode (-q) in scripts
4. Disable colors (--no-color) for log files
5. Use analysis (--analyze) to identify bottlenecks

Development

Building

cargo build -p tensorlogic-cli

Testing

cargo test -p tensorlogic-cli

Running from Source

cargo run -p tensorlogic-cli -- --help

Code Structure

src/
├── main.rs       - Main entry point and command routing
├── cli.rs        - Clap CLI definitions
├── config.rs     - Configuration file support
├── parser.rs     - Enhanced expression parser
├── output.rs     - Colored output formatting
├── analysis.rs   - Graph metrics and analysis
├── repl.rs       - Interactive REPL mode
├── batch.rs      - Batch processing
├── watch.rs      - File watching
└── completion.rs - Shell completion generation

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

Licensed under Apache 2.0 License. See LICENSE for details.

See Also

• Main README: README.md
• Project Guide: CLAUDE.md
• Meta Crate: tensorlogic
• Compiler Documentation: tensorlogic-compiler
• IR Documentation: tensorlogic-ir

Resources

• Repository: https://github.com/cool-japan/tensorlogic
• Documentation: https://docs.rs/tensorlogic-cli
• Issues: https://github.com/cool-japan/tensorlogic/issues

Part of the COOLJAPAN Ecosystem

For questions and support, please open an issue on GitHub.