# PyTorch: [MPS] [Inductor] Compute in float32 after casting to fp16/bf16 (#200559)

> **Key Architectural Takeaway:** Fixes #200545 Inductor for MPS kept fp16/bf16 cast results in half precision.

**Published:** 2026-10-12T13:36:45+00:00  
**Source:** PyTorch GitHub Releases  
**Category:** bigtech-foss  
**Canonical URL:** https://fosswire.org/news/pytorch-mps-inductor-compute-in-float32-after-casting-to-fp1.html  

## Executive Summary
Fixes #200545 Inductor for MPS kept fp16/bf16 cast results in half precision. With bf16 it fails to compile because metal abs has no bfloat overload (pretty weird ) Fix also includes one for fp16 x.half().log1p() returns 0 for small x , so we match CPU and CUDA and use float32 for fp16 as well. Pull Request resolved: #200559 Approved by: https://github.com/malfet

## Architectural & Systems Analysis
From an infrastructure engineering, cloud-native scale, and hyperscale governance standpoint:

- **Hyperscale Provenance:** Open-sourcing internal frameworks subjects proprietary systems to rigorous public analysis.
- **De-facto Standard Cohesion:** Publishing enterprise tooling establishes interoperable specifications across distributed execution.
- **Vendor Decoupling:** Platform engineers can inspect underlying telemetry and memory layouts without black-box vendor lock-in.

## Impact on the Open Ecosystem
Bridges enterprise engineering scale with independent, reproducible open-source software stacks.