tt-perf-report 1.1.12

This tool analyzes performance traces from TT-Metal operations, providing insights into throughput, bottlenecks, and optimization opportunities.

Performance Report Analysis Tool

This tool analyzes performance traces from Metal operations, providing insights into throughput, bottlenecks, and optimization opportunities.

Installation

This tool can be installed from PyPI:

pipx install tt-perf-report

Installing with pipx will automatically create a virtual environment and make the tt-perf-report command available.

Generating Performance Traces

1. Build Metal with performance tracing (enabled in default build):

./build_metal

2. Run your test in TT-Metal with the tracy module to capture traces:

python -m tracy -r -p -v -m pytest path/to/test.py

This generates a CSV file containing operation timing data.

Using Tracy Signposts

Tracy signposts mark specific sections of code for analysis. Add signposts to your Python code:

import tracy

# Mark different sections of your code
tracy.signpost("Compilation pass")
model(input_data)

tracy.signpost("Performance pass")
for _ in range(10):
    model(input_data)

The tool uses the last signpost by default, which is typically the most relevant section for a performance test(e.g., the final iteration after compilation / warmup).

Common signpost usage:

• --start-signpost NAME: Analyze ops after the specified signpost
• --end-signpost NAME: Analyze ops before the specified signpost
• --ignore-signposts: Analyze the entire trace

Filtering Operations

The output of the performance report is a table of operations. Each operation is assigned a unique ID starting from 1. You can re-run the tool with different IDs to focus on specific sections of the trace.

Use --id-range to analyze specific sections:

# Analyze ops 5 through 10
tt-perf-report trace.csv --id-range 5-10

# Analyze from op 31 onwards
tt-perf-report trace.csv --id-range 31-

# Analyze up to op 12
tt-perf-report trace.csv --id-range -12

This is particularly useful for:

• Isolating decode pass in prefill+decode LLM inference
• Analyzing single transformer layers without embeddings/projections
• Focusing on specific model components

Output Options

• --min-percentage value: Hide ops below specified % of total time (default: 0.5)
• --color/--no-color: Force colored/plain output
• --csv FILENAME: Output the table to CSV format for further analysis or inclusion into automated reporting pipelines
• --no-advice: Show only performance table, skip optimization advice

Understanding the Performance Report

The performance report provides several key metrics for analyzing operation performance:

Core Metrics

• Device Time: Time spent executing the operation on device (in microseconds)
• Op-to-op Gap: Time between operations, including host overhead and kernel dispatch (in microseconds)
• Total %: Percentage of total execution time spent on this operation
• Cores: Number of cores used by the operation (max 64 on Wormhole)

Performance Metrics

• DRAM: Memory bandwidth achieved (in GB/s)
• DRAM %: Percentage of theoretical peak DRAM bandwidth (288 GB/s on Wormhole)
• FLOPs: Compute throughput achieved (in TFLOPs)
• FLOPs %: Percentage of theoretical peak compute for the given math fidelity
• Bound: Performance classification of the operation:
  • DRAM: Memory bandwidth bound (>65% of peak DRAM)
  • FLOP: Compute bound (>65% of peak FLOPs)
  • BOTH: Both memory and compute bound
  • SLOW: Neither memory nor compute bound
  • HOST: Operation running on host CPU

Additional Fields

• Math Fidelity: Precision configuration used for matrix operations:
  • HiFi4: Highest precision (74 TFLOPs/core)
  • HiFi2: Medium precision (148 TFLOPs/core)
  • LoFi: Lowest precision (262 TFLOPs/core)

The tool automatically highlights potential optimization opportunities:

• Red op-to-op times indicate high host or kernel launch overhead (>6.5μs)
• Red core counts indicate underutilization (<10 cores)
• Green metrics indicate good utilization of available resources
• Yellow metrics indicate room for optimization

Examples

Note:
trace.csv in the examples below refers to your input CSV file (the performance trace you want to analyze).

Typical use:

tt-perf-report trace.csv

Merge traces captured on multiple machines from the same workload run:

tt-perf-report trace_host0.csv trace_host1.csv trace_host2.csv

Build a table of all ops with no advice:

tt-perf-report trace.csv --no-advice

View ops 100-200 with advice:

tt-perf-report trace.csv --id-range 100-200

Export the table of ops and columns as a CSV file:

tt-perf-report trace.csv --csv my_report.csv