![trunk/594e2876180688bb525c499cdb5d428d67524809: [Inductor] Fix bias_addmm for amd (#178929)](https://avatars.githubusercontent.com/u/28608314?s=60&v=4)
trunk/594e2876180688bb525c499cdb5d428d67524809: [Inductor] Fix bias_addmm for amd (#178929)
Differential Revision: D98773652 Pull Request resolved: #178929 Approved by: https://github.com/jananisriram
Differential Revision: D98773652
Pull Request resolved: https://github.com/pytorch/pytorch/pull/178929 Approved by: https://github.com/jananisriram`
Assets 2
PyTorch Releases
https://github.com/pytorch/pytorch/releases/tag/trunk%2F594e2876180688bb525c499cdb5d428d67524809Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
github
Running Llama2 Models in Vanilla Minecraft With Pure Commands
I made a program that converts any llama2 large language model into a minecraft datapack, and you can run inference right inside the game. It's still semi-finished, Currently I've only implemented argmax sampling, so the output tends to stuck in loops sometimes. Adding top-p sampling will probably improve this a lot. The tokenizer is also missing for now, it can only generate text from scratch. Inference speed is...quite slow. With a 15M parameter model, it takes roughly 20 minutes to produce a single token. If you want to try it out yourself, you can download "stories15M.bin" and "tokenizer.bin" from llama2.c , and follow the instructions in my repository down below. I will keep working on this project, hopefully one day I will be able to bring a usable chat model in Minecraft. Github Rep
![trunk/5d6292dfff853cd0559300c88d7330752c185e40: [Native DSL] Add torch.backends.python_native (#178902)](https://avatars.githubusercontent.com/u/4992598?s=60&v=4)
trunk/5d6292dfff853cd0559300c88d7330752c185e40: [Native DSL] Add torch.backends.python_native (#178902)
Summary: Adds user-facing control of python_native op overrides defined in torch._native . Allows for: Per-DSL control and information via. torch.backends.python_native.$dsl .name # (property) .available # (property) .enabled # (property, settable) .version # (property) .disable() # (method) .enable() # (method) .disabled() # (context manager) And module-level control via. torch.backends.python_native .available_dsls (property) .all_dsls (property) .get_dsl_operations() (method) .disable_operations() (method) .enable_operations() (method) .disable_dispatch_keys() (method) .enable_dispatch_keys() (method) .operations_disabled() (context) .operations_disabled() (context manager) Tests and docs for this functionality are also added. Test Plan: pytest -sv test/python_native/test_torch_backends
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
Railway Environments Explained: Branch Deployments, Staging, and Zero-Config Databases
Originally published on NextFuture Railway Environments Explained: Branch Deployments, Staging, and Zero-Config Databases Most developers only scratch the surface of Railway. They push a repo, Railway builds it, it runs. That's nice. But the real productivity gain comes from Railway Environments — a feature that lets you clone your entire infrastructure per branch, spin up isolated databases in seconds, and tear it all down when the PR merges. No YAML, no cloud console, no ops ticket. This guide goes deep on environments, branch deployments, and zero-config databases so you can use Railway the way it was designed to be used. What Are Railway Environments? A Railway Environment is a full copy of your project's services and configuration — isolated from every other environment. By default Ra
Building Google Docs-style Real-Time Dashboards in Laravel (Reverb) & React ⚡
The Problem: Stale Data in B2B Decision Making In the high-stakes world of B2B SaaS and industrial management platforms, stale data is more than an inconvenience; it is a critical bottleneck. When multiple stakeholders are viewing the same analytical dashboard, decisions must be made based on live, concurrent state. If user A updates a critical inventory metric, user B must see that change reflected instantly without a manual page refresh. Traditional polling methods (repeatedly hitting an API endpoint) are resource-intensive, introduce unacceptable latency, and fail to scale under the load required by modern enterprise applications built at Smart Tech Devs. The Solution: Event-Driven Real-Time Architecture To build truly interactive, collaborative environments, we must shift from a Reques
Stop Writing Raw Python - Let C Handle It
April 2026 · 6 min read* A .NET developer's guide to Python performance — and why the rules are different I've spent most of my career in .NET. C#, the CLR, JIT compilation — these are things I know deeply. I'm proficient in TypeScript and JavaScript for full-stack and mobile work. Python, though, has always been at arm's length. I never really needed to cross over into that world. That changed recently. And the first real thing it taught me was something I hadn't expected: don't write your own raw code if you can help it. That's strange advice if you're coming from C#. In .NET, hand-crafted code and library code run through the same JIT compiler. Performance is often comparable, so you usually optimise based on other considerations — readability, semantics, maintainability. Python, it tur
Struggling with DSA Preparation? Evaluate the Right Sheet for Placement Success
Introduction Problem Statement You’re staring down the barrel of placement season, 4-5 months away, and your DSA prep feels like a house of cards. Random Leetcode grinding hasn’t cut it – you hit a wall when hints disappear, logic crumbles, and code refuses to materialize. Now, you’re eyeing a pattern-wise DSA sheet as a potential lifeline. But will it actually pull you out of this coding quicksand, or is it just another mirage? Here’s the crux: Your current approach – unstructured, unfocused problem-solving – is a feedback loop of inefficiency . You’re not building conceptual scaffolding ; you’re stacking random bricks. This sheet promises a pattern-based system , but it’s not a magic wand. Its effectiveness hinges on three critical factors : Your current skill level: Pattern recognition
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!